{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-two-tower-base",
"attemptId": "gemini-3-8-flash-high-base-r05-b9049e57",
"taskChecksum": "e928e704530bf16ac0f0121f705b84764ea215dfeb4b911933a12ea25c6e2fc5",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "a666d72d-5c39-4e8f-8a1d-19dfce207292",
"steps": [
{
"id": 1,
"role": "user",
"timestamp": "2026-09-29T02:12:14.462Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T02:12:14.462Z",
"text": "\nThis is the Gemini CLI. We are setting up the context for our chat.\nToday's date is Tuesday, September 29, 2026 (formatted according to the user's locale).\nMy operating system is: linux\nThe project's temporary directory is: /home/agent/.gemini/tmp/app\n- **Workspace Directories:**\n - /app\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\nโโโโsolution/\n\n\n"
}
]
},
{
"id": 2,
"role": "user",
"timestamp": "2026-09-29T02:12:14.504Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T02:12:14.504Z",
"text": "# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n"
}
]
},
{
"id": 3,
"role": "agent",
"timestamp": "2026-09-29T02:12:24.740Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:24.740Z",
"text": "Reviewing Instructions\n\nI'm currently focusing on meticulously reviewing the prompt, mandates, and all associated instructions. My primary objective is to ensure complete adherence to the stated requirements, particularly regarding the \"update_topic\" exception."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:24.740Z",
"text": "Clarifying Task Parameters\n\nI'm now focused on the specific parameters of the task. I need to build a latency-efficient PyTorch two-tower ranker for a music streaming service, ensuring it operates at production scale without constructing a global user-item interaction matrix. Candidates will be precomputed offline."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:24.740Z",
"text": "Designing Two-Tower Architecture\n\nI'm actively designing the two-tower architecture. The core idea is to decompose the relevance score into user and item vectors via separate PyTorch networks. User vectors will be derived from interaction history, not just IDs, to capture richer user preferences. I'm also considering how to efficiently handle the 30,000 candidate tracks for MIPS."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:24.740Z",
"text": "Processing Data Sources\n\nI am now examining the provided data files. My focus is on understanding the structure of `validation_preference_state.parquet`, `test_preference_state.parquet`, and the `.npz` files. I'm also detailing the column types and the specific metrics for evaluation, including graded NDCG@10."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:24.740Z",
"callId": "update_topic__call_105930",
"name": "update_topic",
"input": {
"strategic_intent": "Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.",
"summary": "Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.",
"title": "Initial Research & Environment Setup"
},
"recorded": {
"id": "update_topic__call_105930",
"name": "update_topic",
"args": {
"strategic_intent": "Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.",
"summary": "Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.",
"title": "Initial Research & Environment Setup"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_105930",
"name": "update_topic",
"response": {
"output": "Current topic: \"Initial Research & Environment Setup\"\nTopic summary: Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.\n\nStrategic Intent: Explore the environment, inspect the evaluation scripts, data shapes, and directory structure."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:24.781Z",
"resultDisplay": "## ๐ Topic: **Initial Research & Environment Setup**\n\n**Summary:**\nStarting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.\n\n> [!STRATEGY]\n> **Intent:** Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.",
"description": "Update topic to: \"Initial Research & Environment Setup\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:12:24.783Z",
"content": "Current topic: \"Initial Research & Environment Setup\"\nTopic summary: Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.\n\nStrategic Intent: Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.",
"isError": false
},
"responseMessageId": "4fc101c1-bdab-4e5a-992c-6e8ac77a0f5f"
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:24.740Z",
"callId": "run_shell_command__call_105939",
"name": "run_shell_command",
"input": {
"description": "Inspect the contents of /task and /task/data",
"command": "ls -la /task; ls -la /task/data"
},
"recorded": {
"id": "run_shell_command__call_105939",
"name": "run_shell_command",
"args": {
"description": "Inspect the contents of /task and /task/data",
"command": "ls -la /task; ls -la /task/data"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_105939",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:12 ..\n-r--r--r-- 1 root root 0 Sep 29 01:55 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 data\n-r--r--r-- 1 root root 5774 Sep 29 01:55 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:55 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 01:55 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:55 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:55 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:55 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:56 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:55 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:55 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:55 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:55 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:55 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:55 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:55 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:55 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:55 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:55 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:55 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:56 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:24.781Z",
"resultDisplay": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:12 ..\n-r--r--r-- 1 root root 0 Sep 29 01:55 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 data\n-r--r--r-- 1 root root 5774 Sep 29 01:55 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:55 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 01:55 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:55 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:55 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:55 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:56 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:55 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:55 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:55 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:55 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:55 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:55 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:55 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:55 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:55 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:55 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:55 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:56 validation_preference_state.parquet",
"description": "ls -la /task; ls -la /task/data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:24.783Z",
"content": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:12 ..\n-r--r--r-- 1 root root 0 Sep 29 01:55 .keep\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 data\n-r--r--r-- 1 root root 5774 Sep 29 01:55 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:55 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 29 01:55 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:56 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:09 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:55 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:55 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:55 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:56 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:55 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:55 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:55 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:55 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:55 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:55 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:55 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:55 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:55 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:55 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:55 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:56 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
"isError": false
},
"responseMessageId": "4fc101c1-bdab-4e5a-992c-6e8ac77a0f5f"
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:24.740Z",
"callId": "read_file__call_105945",
"name": "read_file",
"input": {
"end_line": 100,
"file_path": "/task/evaluate_public.py",
"start_line": 1
},
"recorded": {
"id": "read_file__call_105945",
"name": "read_file",
"args": {
"end_line": 100,
"file_path": "/task/evaluate_public.py",
"start_line": 1
},
"result": [
{
"functionResponse": {
"id": "read_file__call_105945",
"name": "read_file",
"response": {
"error": "Path not in workspace: Attempted path \"/task/evaluate_public.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app"
}
}
}
],
"status": "error",
"timestamp": "2026-09-29T02:12:24.781Z",
"resultDisplay": "Path not in workspace: Attempted path \"/task/evaluate_public.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"description": "Reads and returns the content of a specified file. If the file is large, the content will be truncated. The tool's response will clearly indicate if truncation has occurred and will provide details on how to read more of the file using the 'start_line' and 'end_line' parameters. Handles text, images (PNG, JPG, GIF, WEBP, SVG, BMP), audio files (MP3, WAV, AIFF, AAC, OGG, FLAC), and PDF files. For text files, it can read specific line ranges.",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:12:24.783Z",
"content": "Path not in workspace: Attempted path \"/task/evaluate_public.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"isError": true
},
"responseMessageId": "4fc101c1-bdab-4e5a-992c-6e8ac77a0f5f"
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:24.740Z",
"callId": "read_file__call_105951",
"name": "read_file",
"input": {
"start_line": 1,
"file_path": "/task/ranking_evaluation.py",
"end_line": 100
},
"recorded": {
"id": "read_file__call_105951",
"name": "read_file",
"args": {
"start_line": 1,
"file_path": "/task/ranking_evaluation.py",
"end_line": 100
},
"result": [
{
"functionResponse": {
"id": "read_file__call_105951",
"name": "read_file",
"response": {
"error": "Path not in workspace: Attempted path \"/task/ranking_evaluation.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app"
}
}
}
],
"status": "error",
"timestamp": "2026-09-29T02:12:24.781Z",
"resultDisplay": "Path not in workspace: Attempted path \"/task/ranking_evaluation.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"description": "Reads and returns the content of a specified file. If the file is large, the content will be truncated. The tool's response will clearly indicate if truncation has occurred and will provide details on how to read more of the file using the 'start_line' and 'end_line' parameters. Handles text, images (PNG, JPG, GIF, WEBP, SVG, BMP), audio files (MP3, WAV, AIFF, AAC, OGG, FLAC), and PDF files. For text files, it can read specific line ranges.",
"displayName": "ReadFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:12:24.783Z",
"content": "Path not in workspace: Attempted path \"/task/ranking_evaluation.py\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"isError": true
},
"responseMessageId": "4fc101c1-bdab-4e5a-992c-6e8ac77a0f5f"
}
]
},
{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T02:12:27.570Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:27.570Z",
"text": "Exploring File Access\n\nI've identified a key constraint: `read_file` is restricted to specific directories. To circumvent this, I'm now considering leveraging `run_shell_command` with utilities like `cat` and `head` to access files outside these designated areas. This approach will allow me to inspect files such as `evaluate_public.py`, `ranking_evaluation.py`, and `requirements.txt`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:27.570Z",
"callId": "run_shell_command__call_40209",
"name": "run_shell_command",
"input": {
"description": "View evaluate_public.py to see evaluation logic and options",
"command": "cat /task/evaluate_public.py"
},
"recorded": {
"id": "run_shell_command__call_40209",
"name": "run_shell_command",
"args": {
"description": "View evaluate_public.py to see evaluation logic and options",
"command": "cat /task/evaluate_public.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_40209",
"name": "run_shell_command",
"response": {
"output": "\nOutput: #!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nProcess Group PGID: 1616\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:27.599Z",
"resultDisplay": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()",
"description": "cat /task/evaluate_public.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:27.601Z",
"content": "\nOutput: #!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\nProcess Group PGID: 1616\n",
"isError": false
},
"responseMessageId": "cd43fc2a-b430-4fb5-8117-a56de204f6fb"
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:27.570Z",
"callId": "run_shell_command__call_40215",
"name": "run_shell_command",
"input": {
"command": "cat /task/ranking_evaluation.py",
"description": "View ranking_evaluation.py to understand evaluation metrics and data structures"
},
"recorded": {
"id": "run_shell_command__call_40215",
"name": "run_shell_command",
"args": {
"command": "cat /task/ranking_evaluation.py",
"description": "View ranking_evaluation.py to understand evaluation metrics and data structures"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_40215",
"name": "run_shell_command",
"response": {
"output": "\nOutput: \"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nProcess Group PGID: 1619\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:27.599Z",
"resultDisplay": "\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }",
"description": "cat /task/ranking_evaluation.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:27.601Z",
"content": "\nOutput: \"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nProcess Group PGID: 1619\n",
"isError": false
},
"responseMessageId": "cd43fc2a-b430-4fb5-8117-a56de204f6fb"
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:27.570Z",
"callId": "run_shell_command__call_40218",
"name": "run_shell_command",
"input": {
"description": "View requirements.txt",
"command": "cat /task/requirements.txt"
},
"recorded": {
"id": "run_shell_command__call_40218",
"name": "run_shell_command",
"args": {
"description": "View requirements.txt",
"command": "cat /task/requirements.txt"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_40218",
"name": "run_shell_command",
"response": {
"output": "\nOutput: # Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\nProcess Group PGID: 1622\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:27.599Z",
"resultDisplay": "# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1",
"description": "cat /task/requirements.txt",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:27.601Z",
"content": "\nOutput: # Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\nProcess Group PGID: 1622\n",
"isError": false
},
"responseMessageId": "cd43fc2a-b430-4fb5-8117-a56de204f6fb"
}
]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T02:12:30.019Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:30.019Z",
"callId": "run_shell_command__call_5350260",
"name": "run_shell_command",
"input": {
"command": "nproc; free -h; df -h",
"description": "Check system CPU, memory, and disk space"
},
"recorded": {
"id": "run_shell_command__call_5350260",
"name": "run_shell_command",
"args": {
"command": "nproc; free -h; df -h",
"description": "Check system CPU, memory, and disk space"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5350260",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 8\n/usr/bin/bash: line 4: free: command not found\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware\nProcess Group PGID: 1625\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:30.036Z",
"resultDisplay": "8\n/usr/bin/bash: line 4: free: command not found\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware",
"description": "nproc; free -h; df -h",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:30.037Z",
"content": "\nOutput: 8\n/usr/bin/bash: line 4: free: command not found\nFilesystem Size Used Avail Use% Mounted on\noverlay 96G 14G 83G 15% /\ntmpfs 64M 0 64M 0% /dev\nshm 64M 0 64M 0% /dev/shm\n/dev/root 96G 14G 83G 15% /etc/hosts\ntmpfs 7.7G 0 7.7G 0% /proc/acpi\ntmpfs 7.7G 0 7.7G 0% /proc/scsi\ntmpfs 7.7G 0 7.7G 0% /sys/firmware\nProcess Group PGID: 1625\n",
"isError": false
},
"responseMessageId": "ffa020df-6892-4666-966c-cabae5934bb2"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T02:12:34.240Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:34.240Z",
"callId": "run_shell_command__call_5880073",
"name": "run_shell_command",
"input": {
"command": "cat /proc/meminfo | head -n 5",
"description": "Check RAM from /proc/meminfo"
},
"recorded": {
"id": "run_shell_command__call_5880073",
"name": "run_shell_command",
"args": {
"command": "cat /proc/meminfo | head -n 5",
"description": "Check RAM from /proc/meminfo"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5880073",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988968 kB\nMemFree: 1284324 kB\nMemAvailable: 14408784 kB\nBuffers: 248460 kB\nCached: 12547192 kB\nProcess Group PGID: 1630\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:34.256Z",
"resultDisplay": "MemTotal: 15988968 kB\nMemFree: 1284324 kB\nMemAvailable: 14408784 kB\nBuffers: 248460 kB\nCached: 12547192 kB",
"description": "cat /proc/meminfo | head -n 5",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:34.257Z",
"content": "\nOutput: MemTotal: 15988968 kB\nMemFree: 1284324 kB\nMemAvailable: 14408784 kB\nBuffers: 248460 kB\nCached: 12547192 kB\nProcess Group PGID: 1630\n",
"isError": false
},
"responseMessageId": "f25b916b-b247-4bc3-855b-0c5ccc4184b8"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T02:12:38.807Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:38.807Z",
"text": "Analyzing Datasets\n\nI'm currently focused on inspecting the `candidate_catalog.parquet` and `target_users.parquet` datasets. My plan is to write a Python script to determine their column structures, row counts, and the ID ranges within each, leveraging our available CPU, RAM, and disk space."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:38.807Z",
"callId": "run_shell_command__call_49370",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\ndef info(name):\n df = pd.read_parquet(DATA / f'{name}.parquet')\n print(f'{name}: shape={df.shape}, cols={list(df.columns)}')\n for col in df.columns:\n if df[col].dtype in ['int64', 'float64', 'int32']:\n print(f' {col}: min={df[col].min()}, max={df[col].max()}')\n return df\n\nprint('--- TARGET USERS ---')\ntu = info('target_users')\nprint('--- CANDIDATE CATALOG ---')\ncc = info('candidate_catalog')\nprint('--- PREFERENCE STATES ---')\nvps = info('validation_preference_state')\ntps = info('test_preference_state')\nprint('--- TRAIN EVENTS ---')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'train_{fam}.parquet')\n print(f'train_{fam}: len={len(df)}')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'val_{fam}.parquet')\n print(f'val_{fam}: len={len(df)}')\n\"",
"description": "Explore metadata and sizes of parquet files"
},
"recorded": {
"id": "run_shell_command__call_49370",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\ndef info(name):\n df = pd.read_parquet(DATA / f'{name}.parquet')\n print(f'{name}: shape={df.shape}, cols={list(df.columns)}')\n for col in df.columns:\n if df[col].dtype in ['int64', 'float64', 'int32']:\n print(f' {col}: min={df[col].min()}, max={df[col].max()}')\n return df\n\nprint('--- TARGET USERS ---')\ntu = info('target_users')\nprint('--- CANDIDATE CATALOG ---')\ncc = info('candidate_catalog')\nprint('--- PREFERENCE STATES ---')\nvps = info('validation_preference_state')\ntps = info('test_preference_state')\nprint('--- TRAIN EVENTS ---')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'train_{fam}.parquet')\n print(f'train_{fam}: len={len(df)}')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'val_{fam}.parquet')\n print(f'val_{fam}: len={len(df)}')\n\"",
"description": "Explore metadata and sizes of parquet files"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_49370",
"name": "run_shell_command",
"response": {
"output": "\nOutput: --- TARGET USERS ---\ntarget_users: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\n recent_positive_listens: min=20.0, max=19592.0\n history_positive_listens: min=20, max=26597\n--- CANDIDATE CATALOG ---\ncandidate_catalog: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n positive_listens: min=163, max=26615\n popularity_rank: min=1, max=30000\n--- PREFERENCE STATES ---\nvalidation_preference_state: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_preference_state: shape=(484776, 3), cols=['uid', 'item_id', 'state']\n--- TRAIN EVENTS ---\ntrain_likes: len=814831\ntrain_dislikes: len=101075\ntrain_unlikes: len=292665\ntrain_undislikes: len=19960\nval_likes: len=37795\nval_dislikes: len=3563\nval_unlikes: len=9346\nval_undislikes: len=606\nProcess Group PGID: 1634\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:40.514Z",
"resultDisplay": "--- TARGET USERS ---\ntarget_users: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\n recent_positive_listens: min=20.0, max=19592.0\n history_positive_listens: min=20, max=26597\n--- CANDIDATE CATALOG ---\ncandidate_catalog: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n positive_listens: min=163, max=26615\n popularity_rank: min=1, max=30000\n--- PREFERENCE STATES ---\nvalidation_preference_state: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_preference_state: shape=(484776, 3), cols=['uid', 'item_id', 'state']\n--- TRAIN EVENTS ---\ntrain_likes: len=814831\ntrain_dislikes: len=101075\ntrain_unlikes: len=292665\ntrain_undislikes: len=19960\nval_likes: len=37795\nval_dislikes: len=3563\nval_unlikes: len=9346\nval_undislikes: len=606",
"description": "Explore metadata and sizes of parquet files",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:40.515Z",
"content": "\nOutput: --- TARGET USERS ---\ntarget_users: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']\n recent_positive_listens: min=20.0, max=19592.0\n history_positive_listens: min=20, max=26597\n--- CANDIDATE CATALOG ---\ncandidate_catalog: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\n positive_listens: min=163, max=26615\n popularity_rank: min=1, max=30000\n--- PREFERENCE STATES ---\nvalidation_preference_state: shape=(469124, 3), cols=['uid', 'item_id', 'state']\ntest_preference_state: shape=(484776, 3), cols=['uid', 'item_id', 'state']\n--- TRAIN EVENTS ---\ntrain_likes: len=814831\ntrain_dislikes: len=101075\ntrain_unlikes: len=292665\ntrain_undislikes: len=19960\nval_likes: len=37795\nval_dislikes: len=3563\nval_unlikes: len=9346\nval_undislikes: len=606\nProcess Group PGID: 1634\n",
"isError": false
},
"responseMessageId": "8ca64a93-a4da-41f0-94d0-6496be8f4a44"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T02:12:43.858Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:43.858Z",
"callId": "run_shell_command__call_41016",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nprint('Reading val_listens...')\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens:', vl.shape, vl.columns)\nprint(vl.head(3))\n\nprint('Reading train_listens metadata...')\nimport pyarrow.parquet as pq\ntable_meta = pq.read_metadata(DATA / 'train_listens.parquet')\nprint('train_listens num_rows:', table_meta.num_rows, 'num_row_groups:', table_meta.num_row_groups)\nschema = table_meta.schema\nprint('schema:', schema)\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nprint('validation_eligible_candidates:', list(vec.keys()))\nprint('uids:', vec['uids'].shape, 'item_ids:', vec['item_ids'].shape, 'packed:', vec['packed_eligible'].shape)\n\ntec = np.load(DATA / 'test_eligible_candidates.npz')\nprint('test_eligible_candidates:', list(tec.keys()))\nprint('uids:', tec['uids'].shape, 'item_ids:', tec['item_ids'].shape, 'packed:', tec['packed_eligible'].shape)\n\"",
"description": "Inspect listens datasets and eligible candidates npz files"
},
"recorded": {
"id": "run_shell_command__call_41016",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nprint('Reading val_listens...')\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens:', vl.shape, vl.columns)\nprint(vl.head(3))\n\nprint('Reading train_listens metadata...')\nimport pyarrow.parquet as pq\ntable_meta = pq.read_metadata(DATA / 'train_listens.parquet')\nprint('train_listens num_rows:', table_meta.num_rows, 'num_row_groups:', table_meta.num_row_groups)\nschema = table_meta.schema\nprint('schema:', schema)\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nprint('validation_eligible_candidates:', list(vec.keys()))\nprint('uids:', vec['uids'].shape, 'item_ids:', vec['item_ids'].shape, 'packed:', vec['packed_eligible'].shape)\n\ntec = np.load(DATA / 'test_eligible_candidates.npz')\nprint('test_eligible_candidates:', list(tec.keys()))\nprint('uids:', tec['uids'].shape, 'item_ids:', tec['item_ids'].shape, 'packed:', tec['packed_eligible'].shape)\n\"",
"description": "Inspect listens datasets and eligible candidates npz files"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_41016",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Reading val_listens...\nval_listens: (1501697, 6) Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\nReading train_listens metadata...\ntrain_listens num_rows: 43484159 num_row_groups: 42\nschema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\nvalidation_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)\ntest_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)\nProcess Group PGID: 1651\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:45.657Z",
"resultDisplay": "Reading val_listens...\nval_listens: (1501697, 6) Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\nReading train_listens metadata...\ntrain_listens num_rows: 43484159 num_row_groups: 42\nschema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\nvalidation_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)\ntest_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)",
"description": "Inspect listens datasets and eligible candidates npz files",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:45.657Z",
"content": "\nOutput: Reading val_listens...\nval_listens: (1501697, 6) Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 24943430 9321269 0 100 195\n1 100 24943605 5452713 0 100 175\n2 100 24943765 6448777 0 100 160\nReading train_listens metadata...\ntrain_listens num_rows: 43484159 num_row_groups: 42\nschema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\nvalidation_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)\ntest_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']\nuids: (8456,) item_ids: (30000,) packed: (8456, 3750)\nProcess Group PGID: 1651\n",
"isError": false
},
"responseMessageId": "a0ab43ac-a788-4a39-b367-453c614530e3"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T02:12:57.533Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:12:57.533Z",
"text": "Analyzing Data Dimensions\n\nMy current focus is on understanding the scale of the data. The training set is substantial at over 43 million entries, while the validation set is considerably smaller. We're dealing with a user base of 8,456 individuals and a candidate catalog of 30,000 items. The validation candidate file confirms the 8,456 user IDs."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:57.533Z",
"callId": "run_shell_command__call_5225314",
"name": "run_shell_command",
"input": {
"description": "Check candidate catalog matching and candidate count distributions",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\ntec = np.load(DATA / 'test_eligible_candidates.npz')\n\nprint('cc item_id equals vec item_ids?', np.array_equal(cc['item_id'].values, vec['item_ids']))\nprint('vec item_ids equals tec item_ids?', np.array_equal(vec['item_ids'], tec['item_ids']))\nprint('vec uids equals tec uids?', np.array_equal(vec['uids'], tec['uids']))\n\n# check eligible counts per user\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_val = unpacked.sum(axis=1)\nprint(f'Val eligible counts per user: min={counts_val.min()}, max={counts_val.max()}, mean={counts_val.mean():.1f}, median={np.median(counts_val)}')\n\nunpacked_test = np.unpackbits(tec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_test = unpacked_test.sum(axis=1)\nprint(f'Test eligible counts per user: min={counts_test.min()}, max={counts_test.max()}, mean={counts_test.mean():.1f}, median={np.median(counts_test)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_5225314",
"name": "run_shell_command",
"args": {
"description": "Check candidate catalog matching and candidate count distributions",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\ntec = np.load(DATA / 'test_eligible_candidates.npz')\n\nprint('cc item_id equals vec item_ids?', np.array_equal(cc['item_id'].values, vec['item_ids']))\nprint('vec item_ids equals tec item_ids?', np.array_equal(vec['item_ids'], tec['item_ids']))\nprint('vec uids equals tec uids?', np.array_equal(vec['uids'], tec['uids']))\n\n# check eligible counts per user\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_val = unpacked.sum(axis=1)\nprint(f'Val eligible counts per user: min={counts_val.min()}, max={counts_val.max()}, mean={counts_val.mean():.1f}, median={np.median(counts_val)}')\n\nunpacked_test = np.unpackbits(tec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_test = unpacked_test.sum(axis=1)\nprint(f'Test eligible counts per user: min={counts_test.min()}, max={counts_test.max()}, mean={counts_test.mean():.1f}, median={np.median(counts_test)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5225314",
"name": "run_shell_command",
"response": {
"output": "\nOutput: cc item_id equals vec item_ids? True\nvec item_ids equals tec item_ids? True\nvec uids equals tec uids? True\nVal eligible counts per user: min=24657, max=30000, mean=29067.3, median=29242.0\nTest eligible counts per user: min=24552, max=30000, mean=29041.3, median=29220.0\nProcess Group PGID: 1671\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:12:59.459Z",
"resultDisplay": "cc item_id equals vec item_ids? True\nvec item_ids equals tec item_ids? True\nvec uids equals tec uids? True\nVal eligible counts per user: min=24657, max=30000, mean=29067.3, median=29242.0\nTest eligible counts per user: min=24552, max=30000, mean=29041.3, median=29220.0",
"description": "Check candidate catalog matching and candidate count distributions",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:12:59.459Z",
"content": "\nOutput: cc item_id equals vec item_ids? True\nvec item_ids equals tec item_ids? True\nvec uids equals tec uids? True\nVal eligible counts per user: min=24657, max=30000, mean=29067.3, median=29242.0\nTest eligible counts per user: min=24552, max=30000, mean=29041.3, median=29220.0\nProcess Group PGID: 1671\n",
"isError": false
},
"responseMessageId": "be4c84e6-24d8-4c06-a429-4c477cd8ed6d"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T02:13:01.921Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:01.921Z",
"text": "Exploring Eligibility Parameters\n\nI'm currently examining the `validation_preference_state` and `test_preference_state` to understand the nuances of item eligibility. The vast majority of the 30,000 items are indeed accessible, with only a small fraction excluded per user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:01.921Z",
"callId": "run_shell_command__call_47198",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state values:')\nprint(vps['state'].value_counts())\nprint(vps.head(10))\n\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state values:')\nprint(tps['state'].value_counts())\n\"",
"description": "Inspect preference states"
},
"recorded": {
"id": "run_shell_command__call_47198",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state values:')\nprint(vps['state'].value_counts())\nprint(vps.head(10))\n\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state values:')\nprint(tps['state'].value_counts())\n\"",
"description": "Inspect preference states"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_47198",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Validation preference state values:\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nTest preference state values:\nstate\nliked 432659\ndisliked 52117\nName: count, dtype: int64\nProcess Group PGID: 1688\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:03.548Z",
"resultDisplay": "Validation preference state values:\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nTest preference state values:\nstate\nliked 432659\ndisliked 52117\nName: count, dtype: int64",
"description": "Inspect preference states",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:03.549Z",
"content": "\nOutput: Validation preference state values:\nstate\nliked 418435\ndisliked 50689\nName: count, dtype: int64\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nTest preference state values:\nstate\nliked 432659\ndisliked 52117\nName: count, dtype: int64\nProcess Group PGID: 1688\n",
"isError": false
},
"responseMessageId": "b111fe9e-5b88-4145-b6f2-57d35c565ea3"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T02:13:07.105Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:07.105Z",
"text": "Investigating data structure\n\nI'm currently examining the timestamps within the training data, specifically focusing on the end point and the subsequent validation and test windows. This is to understand the temporal organization of the dataset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:07.105Z",
"callId": "run_shell_command__call_810595",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens ts min:', vl.timestamp.min(), 'max:', vl.timestamp.max(), 'delta:', vl.timestamp.max() - vl.timestamp.min())\nvlk = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('val_likes ts min:', vlk.timestamp.min(), 'max:', vlk.timestamp.max())\n\ntlk = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('train_likes ts min:', tlk.timestamp.min(), 'max:', tlk.timestamp.max())\n\n# check how many uids in train_listens vs target_users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint('target_users count:', len(tu))\n\"",
"description": "Check timestamps across sets"
},
"recorded": {
"id": "run_shell_command__call_810595",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens ts min:', vl.timestamp.min(), 'max:', vl.timestamp.max(), 'delta:', vl.timestamp.max() - vl.timestamp.min())\nvlk = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('val_likes ts min:', vlk.timestamp.min(), 'max:', vlk.timestamp.max())\n\ntlk = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('train_likes ts min:', tlk.timestamp.min(), 'max:', tlk.timestamp.max())\n\n# check how many uids in train_listens vs target_users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint('target_users count:', len(tu))\n\"",
"description": "Check timestamps across sets"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_810595",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val_listens ts min: 24788600 max: 25393395 delta: 604795\nval_likes ts min: 24788815 max: 25393390\ntrain_likes ts min: 80 max: 24786790\ntarget_users count: 8456\nProcess Group PGID: 1706\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:08.718Z",
"resultDisplay": "val_listens ts min: 24788600 max: 25393395 delta: 604795\nval_likes ts min: 24788815 max: 25393390\ntrain_likes ts min: 80 max: 24786790\ntarget_users count: 8456",
"description": "Check timestamps across sets",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:08.718Z",
"content": "\nOutput: val_listens ts min: 24788600 max: 25393395 delta: 604795\nval_likes ts min: 24788815 max: 25393390\ntrain_likes ts min: 80 max: 24786790\ntarget_users count: 8456\nProcess Group PGID: 1706\n",
"isError": false
},
"responseMessageId": "582dcd6f-72fb-46ca-a03b-51167e6bfdd9"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T02:13:19.924Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:19.924Z",
"text": "Confirming Interval Logic\n\nI'm verifying the exact duration of a week and a half-hour gap. It seems the intervals are precisely 604,800 seconds and 1800 seconds, respectively, which aligns with the upcoming hidden test's timing."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:19.924Z",
"text": "Analyzing Validation Scripts\n\nI'm now examining the `evaluate_public.py` script, specifically the `validation_truth` function. I need to understand its expectations for listens and likes data, and how it defines \"strong\" listens and organic likes to derive the ground truth for validation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:19.924Z",
"callId": "run_shell_command__call_2814366",
"name": "run_shell_command",
"input": {
"description": "Analyze validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
},
"recorded": {
"id": "run_shell_command__call_2814366",
"name": "run_shell_command",
"args": {
"description": "Analyze validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2814366",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1726\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:21.427Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Analyze validation ground truth statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:21.428Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1726\n",
"isError": false
},
"responseMessageId": "0e04c743-057f-4796-ac47-c0713e0874e4"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T02:13:29.069Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:29.069Z",
"callId": "run_shell_command__call_3095981",
"name": "run_shell_command",
"input": {
"description": "Run validation ground truth analysis with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
},
"recorded": {
"id": "run_shell_command__call_3095981",
"name": "run_shell_command",
"args": {
"description": "Run validation ground truth analysis with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3095981",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Number of users with at least 1 relevant item in val: 4263\nRelevant items per user: min, median, mean, max: 1 4.0 8.859723199624677 442\nTotal positives: 37769, grade 3 (likes): 4417, grade 1 (strong listens): 33352\nProcess Group PGID: 1737\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:31.164Z",
"resultDisplay": "Number of users with at least 1 relevant item in val: 4263\nRelevant items per user: min, median, mean, max: 1 4.0 8.859723199624677 442\nTotal positives: 37769, grade 3 (likes): 4417, grade 1 (strong listens): 33352",
"description": "Run validation ground truth analysis with PYTHONPATH=/task",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:31.165Z",
"content": "\nOutput: Number of users with at least 1 relevant item in val: 4263\nRelevant items per user: min, median, mean, max: 1 4.0 8.859723199624677 442\nTotal positives: 37769, grade 3 (likes): 4417, grade 1 (strong listens): 33352\nProcess Group PGID: 1737\n",
"isError": false
},
"responseMessageId": "206d2eab-581b-4013-b7d6-2da742a94abd"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T02:13:37.345Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:37.345Z",
"text": "Assessing Baseline Popularity\n\nI'm now investigating a baseline strategy, specifically ranking items by their catalog popularity. This will serve as a crucial point of comparison as I evaluate the effectiveness of other approaches."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:37.345Z",
"callId": "run_shell_command__call_5523555",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# For each user, get top 100 eligible items by catalog popularity\ncatalog_items = cc['item_id'].values # already sorted by popularity rank 1..30000\n\n# Let's vectorize candidate filtering\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=30000, bitorder='little')\n# unpacked is shape (8456, 30000), where col c corresponds to item_ids[c]\nitem_ids = eligibility.item_ids # array of item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n # eligible item indices\n mask = unpacked[i] == 1\n # top 100 eligible items\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nimport evaluate_public\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\nvalidated = validate_ranking(df, targets_set)\nranked = ranked_items(validated)\nprint(score_rankings(ranked, graded))\n\"",
"description": "Evaluate popularity baseline on validation set"
},
"recorded": {
"id": "run_shell_command__call_5523555",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# For each user, get top 100 eligible items by catalog popularity\ncatalog_items = cc['item_id'].values # already sorted by popularity rank 1..30000\n\n# Let's vectorize candidate filtering\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=30000, bitorder='little')\n# unpacked is shape (8456, 30000), where col c corresponds to item_ids[c]\nitem_ids = eligibility.item_ids # array of item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n # eligible item indices\n mask = unpacked[i] == 1\n # top 100 eligible items\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nimport evaluate_public\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\nvalidated = validate_ranking(df, targets_set)\nranked = ranked_items(validated)\nprint(score_rankings(ranked, graded))\n\"",
"description": "Evaluate popularity baseline on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5523555",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1757\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:41.458Z",
"resultDisplay": "{'quality_ndcg@10': 0.01071, 'quality_users': 4263}",
"description": "Evaluate popularity baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:41.458Z",
"content": "\nOutput: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1757\n",
"isError": false
},
"responseMessageId": "a5e0ced7-ae68-4ade-9f78-3deef72cb33b"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T02:13:51.721Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:51.721Z",
"text": "Refining Model Architecture\n\nMy focus is now on meticulously dissecting the prompt's requirements, specifically examining each sentence. I'm also noting that the current popularity baseline stands at `quality_ndcg@10`: 0.010710, and I am starting to build a two-tower neural network in CPU PyTorch."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:51.721Z",
"text": "Developing Two-Tower Model\n\nI'm focusing on how to ensure my model can handle millions of users and items without building a global matrix. The architecture will necessitate separate user and item towers for efficient, offline computation and strict request-time latency. I'm implementing this in PyTorch, prioritizing interaction history for user representation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:51.721Z",
"text": "Integrating Temporal Data\n\nI'm now incorporating the specific data structures provided, focusing on how to represent cutoff-specific preferences and temporal dynamics within my two-tower architecture. The training data's end timestamp and validation/test windows are crucial for the temporal backtest I'm setting up in PyTorch."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:51.721Z",
"text": "Validating Approach Scenarios\n\nI'm now planning the validation strategy, distinguishing between building a ranker with training history and validation outcomes, and then refitting it for the final submission. My focus is on ensuring validation events are not used as features when measuring performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:51.721Z",
"callId": "run_shell_command__call_30661",
"name": "run_shell_command",
"input": {
"description": "Investigate preference state and eligibility",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vps.head(10))\nprint(vps.groupby('state').size())\n# Are pairs with state='disliked' in eligible candidates?\nvec = np.load = __import__('numpy').load(DATA / 'validation_eligible_candidates.npz')\nfrom ranking_evaluation import CandidateSet\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# Check if disliked items are eligible\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\""
},
"recorded": {
"id": "run_shell_command__call_30661",
"name": "run_shell_command",
"args": {
"description": "Investigate preference state and eligibility",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vps.head(10))\nprint(vps.groupby('state').size())\n# Are pairs with state='disliked' in eligible candidates?\nvec = np.load = __import__('numpy').load(DATA / 'validation_eligible_candidates.npz')\nfrom ranking_evaluation import CandidateSet\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# Check if disliked items are eligible\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30661",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 11, in \nNameError: name 'np' is not defined\nValidation preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nstate\ndisliked 50689\nliked 418435\ndtype: int64\nExit Code: 1\nProcess Group PGID: 1777\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:53.330Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 11, in \nNameError: name 'np' is not defined\nValidation preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nstate\ndisliked 50689\nliked 418435\ndtype: int64",
"description": "Investigate preference state and eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:53.331Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 11, in \nNameError: name 'np' is not defined\nValidation preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\nstate\ndisliked 50689\nliked 418435\ndtype: int64\nExit Code: 1\nProcess Group PGID: 1777\n",
"isError": false
},
"responseMessageId": "fcfb0776-e6ab-4719-92a5-1b5f398aec60"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T02:14:11.470Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:11.470Z",
"callId": "run_shell_command__call_4125680",
"name": "run_shell_command",
"input": {
"description": "Check disliked and liked items against candidate eligibility",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\nfrom ranking_evaluation import CandidateSet\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\n# Check whole dataset\ndisliked_uids = disliked.uid.values\ndisliked_items = disliked.item_id.values\ndisliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(disliked_uids, disliked_items))\nprint(f'Total disliked items eligible: {disliked_el_all} out of {len(disliked)}')\n\nliked_uids = liked.uid.values\nliked_items = liked.item_id.values\nliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(liked_uids[:5000], liked_items[:5000]))\nprint(f'Liked sample (5000) eligible: {liked_el_all} out of 5000')\n\""
},
"recorded": {
"id": "run_shell_command__call_4125680",
"name": "run_shell_command",
"args": {
"description": "Check disliked and liked items against candidate eligibility",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\nfrom ranking_evaluation import CandidateSet\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\n# Check whole dataset\ndisliked_uids = disliked.uid.values\ndisliked_items = disliked.item_id.values\ndisliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(disliked_uids, disliked_items))\nprint(f'Total disliked items eligible: {disliked_el_all} out of {len(disliked)}')\n\nliked_uids = liked.uid.values\nliked_items = liked.item_id.values\nliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(liked_uids[:5000], liked_items[:5000]))\nprint(f'Liked sample (5000) eligible: {liked_el_all} out of 5000')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4125680",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Are disliked items eligible?: 0 out of 100\nAre liked items eligible?: 0 out of 100\nTotal disliked items eligible: 0 out of 50689\nLiked sample (5000) eligible: 0 out of 5000\nProcess Group PGID: 1794\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:13.281Z",
"resultDisplay": "Are disliked items eligible?: 0 out of 100\nAre liked items eligible?: 0 out of 100\nTotal disliked items eligible: 0 out of 50689\nLiked sample (5000) eligible: 0 out of 5000",
"description": "Check disliked and liked items against candidate eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:13.282Z",
"content": "\nOutput: Are disliked items eligible?: 0 out of 100\nAre liked items eligible?: 0 out of 100\nTotal disliked items eligible: 0 out of 50689\nLiked sample (5000) eligible: 0 out of 5000\nProcess Group PGID: 1794\n",
"isError": false
},
"responseMessageId": "93395231-2964-428d-a858-319d4125f532"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T02:14:16.584Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:16.584Z",
"text": "Filtering out liked items\n\nI'm realizing that items already present in the `validation_preference_state`, meaning they've been liked or disliked, are automatically excluded from `validation_eligible_candidates`. This clarifies the \"discovery shelf\" concept โ the platform proactively removes previously interacted-with items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:16.584Z",
"callId": "run_shell_command__call_5881467",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncc_items = set(cc.item_id)\nprint('VPS items in candidate catalog:', vps.item_id.isin(cc_items).sum(), 'out of', len(vps))\n\"",
"description": "Check VPS items in candidate catalog"
},
"recorded": {
"id": "run_shell_command__call_5881467",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncc_items = set(cc.item_id)\nprint('VPS items in candidate catalog:', vps.item_id.isin(cc_items).sum(), 'out of', len(vps))\n\"",
"description": "Check VPS items in candidate catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5881467",
"name": "run_shell_command",
"response": {
"output": "\nOutput: VPS items in candidate catalog: 469124 out of 469124\nProcess Group PGID: 1811\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:18.222Z",
"resultDisplay": "VPS items in candidate catalog: 469124 out of 469124",
"description": "Check VPS items in candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:18.222Z",
"content": "\nOutput: VPS items in candidate catalog: 469124 out of 469124\nProcess Group PGID: 1811\n",
"isError": false
},
"responseMessageId": "d9904586-b118-4be3-b4f1-c3ef63e941e3"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T02:14:22.257Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:22.257Z",
"text": "Verifying Candidate Pool\n\nI've confirmed that every pair within `validation_preference_state` is indeed present in `candidate_catalog`. Furthermore, I've established that each user's existing liked and disliked items are successfully excluded from their candidate pool, which precisely aligns with the observed preference state counts."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:22.257Z",
"callId": "run_shell_command__call_27932",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_counts = 30000 - unpacked.sum(axis=1)\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_counts = vps.groupby('uid').size()\nuids = vec['uids']\nvps_c = [vps_counts.get(uid, 0) for uid in uids]\nprint('Ineligible count stats: min, mean, max:', ineligible_counts.min(), ineligible_counts.mean(), ineligible_counts.max())\nprint('VPS count stats: min, mean, max:', np.min(vps_c), np.mean(vps_c), np.max(vps_c))\n\"",
"description": "Compare ineligible counts and VPS counts"
},
"recorded": {
"id": "run_shell_command__call_27932",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_counts = 30000 - unpacked.sum(axis=1)\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_counts = vps.groupby('uid').size()\nuids = vec['uids']\nvps_c = [vps_counts.get(uid, 0) for uid in uids]\nprint('Ineligible count stats: min, mean, max:', ineligible_counts.min(), ineligible_counts.mean(), ineligible_counts.max())\nprint('VPS count stats: min, mean, max:', np.min(vps_c), np.mean(vps_c), np.max(vps_c))\n\"",
"description": "Compare ineligible counts and VPS counts"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_27932",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Ineligible count stats: min, mean, max: 0 932.6937086092715 5343\nVPS count stats: min, mean, max: 0 55.478240302743615 1847\nProcess Group PGID: 1828\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:24.066Z",
"resultDisplay": "Ineligible count stats: min, mean, max: 0 932.6937086092715 5343\nVPS count stats: min, mean, max: 0 55.478240302743615 1847",
"description": "Compare ineligible counts and VPS counts",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:24.067Z",
"content": "\nOutput: Ineligible count stats: min, mean, max: 0 932.6937086092715 5343\nVPS count stats: min, mean, max: 0 55.478240302743615 1847\nProcess Group PGID: 1828\n",
"isError": false
},
"responseMessageId": "c1b56ccc-5f10-4624-bb82-606d3eacdda7"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T02:14:36.470Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:36.470Z",
"text": "Refining Candidate Selection\n\nMy recent focus has been on a more precise approach to candidate selection. I've confirmed that the platform provides explicit boolean masks, eliminating any need for inference regarding candidate eligibility. This direct data, found in `validation_eligible_candidates.npz` and `test_eligible`, simplifies the process significantly."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:36.470Z",
"text": "Analyzing Production Constraints\n\nI'm now thoroughly analyzing the architectural constraints and production setting requirements. The system must handle millions of users and items without a global user-item matrix. Candidate embeddings are precomputed offline for fast retrieval, requiring an exact model decomposition into independent user and item vectors."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:36.470Z",
"text": "Developing User Tower Logic\n\nI'm concentrating on the user tower's functionality, specifically its reliance on interaction history over simple user-ID lookups. This dynamic approach is crucial for handling cold-start users and evolving preferences in a streaming environment, ensuring better generalization and real-time adaptability for relevance scoring."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:36.470Z",
"text": "Implementing Item Tower\n\nI've clarified the item tower's role: taking item IDs/features to output a d-dimensional embedding. Precomputing these offline is key for fast inference. I'll then compute scores using dot products with user embeddings, mask ineligible items, and select the top candidates."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:36.470Z",
"text": "Exploring Score Function\n\nMy current thinking is focused on the scoring mechanism. I've noted the requirement for a dot product between user and item embeddings, suggesting a maximum inner product search scenario, possibly with cosine similarity if embeddings are normalized. I'm now investigating appropriate training objectives for this two-tower architecture, considering options like InfoNCE loss."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:36.470Z",
"callId": "run_shell_command__call_5227126",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid.values)\nprint('Target users count:', len(target_uids))\n\n# Let's see how many listens are for target users vs other users\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid'])\nall_listens_uids = table['uid'].to_numpy()\nprint('Total rows in train_listens:', len(all_listens_uids))\nimport numpy as np\nunique_uids = np.unique(all_listens_uids)\nprint('Unique uids in train_listens:', len(unique_uids))\nis_target = np.isin(unique_uids, list(target_uids))\nprint('Target uids present in train_listens:', is_target.sum())\n\"",
"description": "Check unique users in train_listens"
},
"recorded": {
"id": "run_shell_command__call_5227126",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid.values)\nprint('Target users count:', len(target_uids))\n\n# Let's see how many listens are for target users vs other users\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid'])\nall_listens_uids = table['uid'].to_numpy()\nprint('Total rows in train_listens:', len(all_listens_uids))\nimport numpy as np\nunique_uids = np.unique(all_listens_uids)\nprint('Unique uids in train_listens:', len(unique_uids))\nis_target = np.isin(unique_uids, list(target_uids))\nprint('Target uids present in train_listens:', is_target.sum())\n\"",
"description": "Check unique users in train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5227126",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users count: 8456\nTotal rows in train_listens: 43484159\nUnique uids in train_listens: 9238\nTarget uids present in train_listens: 8456\nProcess Group PGID: 1845\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:38.481Z",
"resultDisplay": "Target users count: 8456\nTotal rows in train_listens: 43484159\nUnique uids in train_listens: 9238\nTarget uids present in train_listens: 8456",
"description": "Check unique users in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:38.482Z",
"content": "\nOutput: Target users count: 8456\nTotal rows in train_listens: 43484159\nUnique uids in train_listens: 9238\nTarget uids present in train_listens: 8456\nProcess Group PGID: 1845\n",
"isError": false
},
"responseMessageId": "46d547bf-7d03-4059-8431-6c81e3a565fa"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T02:14:50.737Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:50.737Z",
"text": "Analyzing User Data\n\nI'm currently investigating the overlap between the unique users in `train_listens` and our `target_users`. It appears only a fraction of the total listens are coming from these key individuals, and I'm now digging into the timestamps to understand usage patterns."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:50.737Z",
"callId": "run_shell_command__call_7137792",
"name": "run_shell_command",
"input": {
"description": "Check timestamps distribution in train_listens",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['timestamp'])\nts = table['timestamp'].to_numpy()\nprint('Timestamp min:', ts.min(), 'max:', ts.max())\nprint('Max ts in days:', ts.max() / 86400, 'Min ts in days:', ts.min() / 86400)\ncutoff = 24786800\nprint('Distance from max to cutoff:', cutoff - ts.max())\n# 90 days before cutoff:\nday90 = cutoff - 90 * 86400\nprint('90 days before cutoff timestamp:', day90)\nrecent_count = (ts >= day90).sum()\nprint(f'Rows in last 90 days: {recent_count} ({recent_count / len(ts) * 100:.1f}%)')\nday30 = cutoff - 30 * 86400\nrecent_30 = (ts >= day30).sum()\nprint(f'Rows in last 30 days: {recent_30} ({recent_30 / len(ts) * 100:.1f}%)')\nday14 = cutoff - 14 * 86400\nrecent_14 = (ts >= day14).sum()\nprint(f'Rows in last 14 days: {recent_14} ({recent_14 / len(ts) * 100:.1f}%)')\n\""
},
"recorded": {
"id": "run_shell_command__call_7137792",
"name": "run_shell_command",
"args": {
"description": "Check timestamps distribution in train_listens",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['timestamp'])\nts = table['timestamp'].to_numpy()\nprint('Timestamp min:', ts.min(), 'max:', ts.max())\nprint('Max ts in days:', ts.max() / 86400, 'Min ts in days:', ts.min() / 86400)\ncutoff = 24786800\nprint('Distance from max to cutoff:', cutoff - ts.max())\n# 90 days before cutoff:\nday90 = cutoff - 90 * 86400\nprint('90 days before cutoff timestamp:', day90)\nrecent_count = (ts >= day90).sum()\nprint(f'Rows in last 90 days: {recent_count} ({recent_count / len(ts) * 100:.1f}%)')\nday30 = cutoff - 30 * 86400\nrecent_30 = (ts >= day30).sum()\nprint(f'Rows in last 30 days: {recent_30} ({recent_30 / len(ts) * 100:.1f}%)')\nday14 = cutoff - 14 * 86400\nrecent_14 = (ts >= day14).sum()\nprint(f'Rows in last 14 days: {recent_14} ({recent_14 / len(ts) * 100:.1f}%)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_7137792",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Timestamp min: 0 max: 24786795\nMax ts in days: 286.88420138888887 Min ts in days: 0.0\nDistance from max to cutoff: 5\n90 days before cutoff timestamp: 17010800\nRows in last 90 days: 16769274 (38.6%)\nRows in last 30 days: 6126103 (14.1%)\nRows in last 14 days: 2920227 (6.7%)\nProcess Group PGID: 1866\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:52.785Z",
"resultDisplay": "Timestamp min: 0 max: 24786795\nMax ts in days: 286.88420138888887 Min ts in days: 0.0\nDistance from max to cutoff: 5\n90 days before cutoff timestamp: 17010800\nRows in last 90 days: 16769274 (38.6%)\nRows in last 30 days: 6126103 (14.1%)\nRows in last 14 days: 2920227 (6.7%)",
"description": "Check timestamps distribution in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:52.786Z",
"content": "\nOutput: Timestamp min: 0 max: 24786795\nMax ts in days: 286.88420138888887 Min ts in days: 0.0\nDistance from max to cutoff: 5\n90 days before cutoff timestamp: 17010800\nRows in last 90 days: 16769274 (38.6%)\nRows in last 30 days: 6126103 (14.1%)\nRows in last 14 days: 2920227 (6.7%)\nProcess Group PGID: 1866\n",
"isError": false
},
"responseMessageId": "238ed1e5-3dd9-4853-b25d-ed09ccb95e98"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T02:14:56.553Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:14:56.553Z",
"text": "Analyzing Listen Data\n\nI'm currently reviewing the temporal distribution of our training dataset, noting the significant volume of listens within the last 90, 30, and 14 days. My next step is to examine the specific items present within the `train_listens` dataset to understand their composition relative to these recent listen volumes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:56.553Z",
"callId": "run_shell_command__call_5795184",
"name": "run_shell_command",
"input": {
"description": "Check item overlap between train_listens and candidate_catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\nprint('Catalog items count:', len(catalog_items))\n\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nunique_items = np.unique(items)\nprint('Unique items in train_listens:', len(unique_items))\nin_catalog = np.isin(unique_items, list(catalog_items))\nprint(f'Items in catalog: {in_catalog.sum()} ({in_catalog.sum() / len(unique_items) * 100:.1f}%)')\n\n# Fraction of listens that are catalog items:\nlistens_in_catalog = np.isin(items, list(catalog_items)).mean()\nprint(f'Fraction of all listens in catalog: {listens_in_catalog * 100:.2f}%')\n\""
},
"recorded": {
"id": "run_shell_command__call_5795184",
"name": "run_shell_command",
"args": {
"description": "Check item overlap between train_listens and candidate_catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\nprint('Catalog items count:', len(catalog_items))\n\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nunique_items = np.unique(items)\nprint('Unique items in train_listens:', len(unique_items))\nin_catalog = np.isin(unique_items, list(catalog_items))\nprint(f'Items in catalog: {in_catalog.sum()} ({in_catalog.sum() / len(unique_items) * 100:.1f}%)')\n\n# Fraction of listens that are catalog items:\nlistens_in_catalog = np.isin(items, list(catalog_items)).mean()\nprint(f'Fraction of all listens in catalog: {listens_in_catalog * 100:.2f}%')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5795184",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog items count: 30000\nUnique items in train_listens: 845395\nItems in catalog: 30000 (3.5%)\nFraction of all listens in catalog: 71.86%\nProcess Group PGID: 1886\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:59.101Z",
"resultDisplay": "Catalog items count: 30000\nUnique items in train_listens: 845395\nItems in catalog: 30000 (3.5%)\nFraction of all listens in catalog: 71.86%",
"description": "Check item overlap between train_listens and candidate_catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:59.102Z",
"content": "\nOutput: Catalog items count: 30000\nUnique items in train_listens: 845395\nItems in catalog: 30000 (3.5%)\nFraction of all listens in catalog: 71.86%\nProcess Group PGID: 1886\n",
"isError": false
},
"responseMessageId": "e21763fb-92c1-4e4f-99b3-d4f62e83f9ed"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T02:15:06.693Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:06.693Z",
"text": "Confirming Candidate Scope\n\nI've confirmed that our candidate catalog of 30,000 items is fully represented within the training listens, with these candidates accounting for a substantial 71.86% of all listens. This indicates strong alignment with the overall music streaming universe."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:06.693Z",
"text": "Refining Candidate Scope\n\nI've clarified that the 30,000 items in our candidate catalog are the critical focus, distinct from the broader log history. My current effort centers on ensuring this specific set is fully accounted for, as per the prompt's explicit definition of `candidate_catalog.parquet`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:06.693Z",
"text": "Verifying Candidate Item Set\n\nI've confirmed that the `validation_eligible_candidates.npz` and `test_eligible_candidates.npz` files both contain precisely the 30,000 items from the `candidate_catalog`. This means the items I need to consider for predictions are consistent and complete across these datasets."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:06.693Z",
"callId": "run_shell_command__call_62899",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('Train likes in catalog:', likes.item_id.isin(catalog_items).mean())\nval_likes = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('Val likes in catalog:', val_likes.item_id.isin(catalog_items).mean())\n\nval_listens = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('Val listens in catalog:', val_listens.item_id.isin(catalog_items).mean())\n\"",
"description": "Check catalog coverage on likes and val listens"
},
"recorded": {
"id": "run_shell_command__call_62899",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('Train likes in catalog:', likes.item_id.isin(catalog_items).mean())\nval_likes = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('Val likes in catalog:', val_likes.item_id.isin(catalog_items).mean())\n\nval_listens = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('Val listens in catalog:', val_listens.item_id.isin(catalog_items).mean())\n\"",
"description": "Check catalog coverage on likes and val listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_62899",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Train likes in catalog: 0.6809068383505291\nVal likes in catalog: 0.5570842704061384\nVal listens in catalog: 0.6737517621730615\nProcess Group PGID: 1908\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:15:08.523Z",
"resultDisplay": "Train likes in catalog: 0.6809068383505291\nVal likes in catalog: 0.5570842704061384\nVal listens in catalog: 0.6737517621730615",
"description": "Check catalog coverage on likes and val listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:15:08.524Z",
"content": "\nOutput: Train likes in catalog: 0.6809068383505291\nVal likes in catalog: 0.5570842704061384\nVal listens in catalog: 0.6737517621730615\nProcess Group PGID: 1908\n",
"isError": false
},
"responseMessageId": "8ca7cf18-3835-4684-9e58-f76108fac262"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T02:15:14.230Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:14.230Z",
"text": "Refining Validation Logic\n\nI'm currently zeroing in on the `validation_truth` function. My focus is on precisely defining the eligibility criteria within the validation evaluation, specifically by refining the `strong` condition to accurately capture organic plays against target IDs."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:14.230Z",
"text": "Sharpening Eligibility Criteria\n\nI'm currently focused on ensuring only items within the defined candidate set are considered eligible. This means rigorously checking the `eligibility.is_eligible` function, particularly its interaction with user and item arrays, to prevent any out-of-set items from erroneously passing validation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:14.230Z",
"callId": "run_shell_command__call_5882477",
"name": "run_shell_command",
"input": {
"description": "Verify all gold items are subset of candidate catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\n\nall_gold_items = set()\nfor items in graded.values():\n all_gold_items.update(items.keys())\nprint('Total unique gold items in val:', len(all_gold_items))\nprint('Are all gold items in candidate catalog?:', all_gold_items.issubset(cc_set))\n\""
},
"recorded": {
"id": "run_shell_command__call_5882477",
"name": "run_shell_command",
"args": {
"description": "Verify all gold items are subset of candidate catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\n\nall_gold_items = set()\nfor items in graded.values():\n all_gold_items.update(items.keys())\nprint('Total unique gold items in val:', len(all_gold_items))\nprint('Are all gold items in candidate catalog?:', all_gold_items.issubset(cc_set))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5882477",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total unique gold items in val: 14124\nAre all gold items in candidate catalog?: True\nProcess Group PGID: 1928\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:15:16.314Z",
"resultDisplay": "Total unique gold items in val: 14124\nAre all gold items in candidate catalog?: True",
"description": "Verify all gold items are subset of candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:15:16.315Z",
"content": "\nOutput: Total unique gold items in val: 14124\nAre all gold items in candidate catalog?: True\nProcess Group PGID: 1928\n",
"isError": false
},
"responseMessageId": "802d18a7-5d5c-486b-8820-254091189ed3"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T02:15:32.521Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Analyzing Production Constraints\n\nI'm currently delving into the two-tower model's architectural constraints, particularly focusing on the strict request-time latency requirements for the production shelf. Understanding these limitations is key to ensuring our validated gold items can be efficiently cataloged."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Refining Two-Tower Model\n\nI'm refining the two-tower architecture, ensuring user towers use interaction history, not just IDs. I'm also detailing the offline computation of item embeddings and their storage in a nearest-neighbor index for efficient retrieval at request time."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Optimizing Inference Path\n\nMy focus is now on optimizing the inference path, specifically ensuring our user and item towers can deliver embeddings within the strict production latency budgets. I'm also verifying that all submitted items are indeed from the eligible candidate sets and that the output format adheres precisely to the submission guidelines."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Exploring Tower Inputs\n\nI am exploring the specific features each tower can accept, meticulously examining the candidate catalog data. My current focus is on how to best leverage `item_id`, `positive_listens`, `organic_positive_listens`, and `popularity_rank` for the item tower's embedding generation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Processing User Interaction Data\n\nI'm now analyzing the rich user interaction data, considering features like normalized popularity and listen ratios for the item tower. For the user tower, I'm investigating how to effectively process recent listens, likes, dislikes, and the significant historical preference states to generate meaningful user embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Synthesizing User Representations\n\nI'm now synthesizing user representations by pooling embeddings from recent listens (weighted by play ratio and time decay), likes, and dislikes. I'm also incorporating summary statistics like listen counts, which will be concatenated to form the final user tower input."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:32.521Z",
"text": "Validating Production Readiness\n\nI am now verifying that the entire architecture adheres to all production constraints, ensuring our CPU PyTorch neural network for user and item towers, with offline item embedding precomputation and request-time retrieval via nearest-neighbor search, meets all performance targets. The model correctly decomposes into independent user and item vectors."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:32.521Z",
"callId": "run_shell_command__call_107238",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nDATA = '/task/data'\nt0 = time.time()\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read 43M rows in {time.time() - t0:.2f} seconds')\n\"",
"description": "Benchmark parquet reading time for train_listens"
},
"recorded": {
"id": "run_shell_command__call_107238",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nDATA = '/task/data'\nt0 = time.time()\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read 43M rows in {time.time() - t0:.2f} seconds')\n\"",
"description": "Benchmark parquet reading time for train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_107238",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read 43M rows in 1.37 seconds\nProcess Group PGID: 1948\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:15:34.476Z",
"resultDisplay": "Read 43M rows in 1.37 seconds",
"description": "Benchmark parquet reading time for train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:15:34.477Z",
"content": "\nOutput: Read 43M rows in 1.37 seconds\nProcess Group PGID: 1948\n",
"isError": false
},
"responseMessageId": "45adc0a6-eade-4bd3-bb9b-0033db6d1ec4"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T02:15:40.604Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:40.604Z",
"text": "Exploring Data Deeply\n\nI'm impressed by PyArrow's speed, processing 43 million rows in under two seconds. My focus now shifts to a more granular exploration, specifically defining positive events within the training window. I'm also examining how positive labels are determined in validation, based on organic behavior in the subsequent window."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:40.604Z",
"text": "Defining Positive Events\n\nMy current focus is on precisely defining what constitutes a \"positive event.\" I'm differentiating between an organic listener liking a track (grade 3) and them listening to 80% or more (grade 1), noting grade 3 takes precedence. I'm also scrutinizing the validation window's construction and how it relates to the training cutoff, ensuring a clear separation between feature generation and outcome assessment for the ranking evaluation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:40.604Z",
"callId": "run_shell_command__call_2510234",
"name": "run_shell_command",
"input": {
"description": "Check target users coverage in preference states",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state count:', len(vps))\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state count:', len(tps))\n\nprint('Target users count in VPS:', vps.uid.nunique(), 'in TPS:', tps.uid.nunique())\n\""
},
"recorded": {
"id": "run_shell_command__call_2510234",
"name": "run_shell_command",
"args": {
"description": "Check target users coverage in preference states",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state count:', len(vps))\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state count:', len(tps))\n\nprint('Target users count in VPS:', vps.uid.nunique(), 'in TPS:', tps.uid.nunique())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2510234",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Validation preference state count: 469124\nTest preference state count: 484776\nTarget users count in VPS: 7038 in TPS: 7092\nProcess Group PGID: 1970\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:15:42.211Z",
"resultDisplay": "Validation preference state count: 469124\nTest preference state count: 484776\nTarget users count in VPS: 7038 in TPS: 7092",
"description": "Check target users coverage in preference states",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:15:42.212Z",
"content": "\nOutput: Validation preference state count: 469124\nTest preference state count: 484776\nTarget users count in VPS: 7038 in TPS: 7092\nProcess Group PGID: 1970\n",
"isError": false
},
"responseMessageId": "ae8db860-8b23-48a8-956c-4c8a10cbf4fe"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T02:15:44.531Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:44.531Z",
"text": "Analyzing User Data\n\nI'm focusing on the discrepancy between `target_users` and VPS users, noting that many users lack explicit preference data but do have listening history. I'll explore this further to understand its implications for my analysis."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:44.531Z",
"callId": "run_shell_command__call_114764",
"name": "run_shell_command",
"input": {
"description": "Inspect target_users.parquet",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint(tu.head(10))\nprint(tu.describe())\n\""
},
"recorded": {
"id": "run_shell_command__call_114764",
"name": "run_shell_command",
"args": {
"description": "Inspect target_users.parquet",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint(tu.head(10))\nprint(tu.describe())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_114764",
"name": "run_shell_command",
"response": {
"output": "\nOutput: uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n5 800 1703.0 7281\n6 900 42.0 136\n7 1000 525.0 597\n8 1100 1976.0 3653\n9 1400 782.0 10713\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nProcess Group PGID: 1988\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:15:46.099Z",
"resultDisplay": "uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n5 800 1703.0 7281\n6 900 42.0 136\n7 1000 525.0 597\n8 1100 1976.0 3653\n9 1400 782.0 10713\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000",
"description": "Inspect target_users.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:15:46.101Z",
"content": "\nOutput: uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\n5 800 1703.0 7281\n6 900 42.0 136\n7 1000 525.0 597\n8 1100 1976.0 3653\n9 1400 782.0 10713\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\nProcess Group PGID: 1988\n",
"isError": false
},
"responseMessageId": "ceee2c26-ffc7-4536-af52-6ca2fe04a145"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T02:15:51.573Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:15:51.573Z",
"text": "Analyzing User Interaction\n\nMy current focus is on understanding the richness of user interaction data. It's clear all target users possess a significant number of recent and historical positive listens, indicating a strong engagement history. I'm now examining the columns within `train_listens` to further analyze these patterns."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:15:51.573Z",
"callId": "run_shell_command__call_68105",
"name": "run_shell_command",
"input": {
"description": "Check whether validation target items are repeat listens or discovery",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# For each user, let's see their past listens in train_listens\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas()\ndf_train = df_train[df_train.uid.isin(targets)]\ntrain_user_items = df_train.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Check overlap of graded items with train items\ntotal_graded = 0\nin_train_count = 0\nfor uid, items in graded.items():\n past = train_user_items.get(uid, set())\n for it in items:\n total_graded += 1\n if it in past:\n in_train_count += 1\nprint(f'Total graded items in val: {total_graded}, previously listened in train: {in_train_count} ({in_train_count / total_graded * 100:.1f}%)')\nprint(f'Never listened before (true discovery): {total_graded - in_train_count} ({(total_graded - in_train_count) / total_graded * 100:.1f}%)')\n\""
},
"recorded": {
"id": "run_shell_command__call_68105",
"name": "run_shell_command",
"args": {
"description": "Check whether validation target items are repeat listens or discovery",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# For each user, let's see their past listens in train_listens\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas()\ndf_train = df_train[df_train.uid.isin(targets)]\ntrain_user_items = df_train.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Check overlap of graded items with train items\ntotal_graded = 0\nin_train_count = 0\nfor uid, items in graded.items():\n past = train_user_items.get(uid, set())\n for it in items:\n total_graded += 1\n if it in past:\n in_train_count += 1\nprint(f'Total graded items in val: {total_graded}, previously listened in train: {in_train_count} ({in_train_count / total_graded * 100:.1f}%)')\nprint(f'Never listened before (true discovery): {total_graded - in_train_count} ({(total_graded - in_train_count) / total_graded * 100:.1f}%)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_68105",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total graded items in val: 37769, previously listened in train: 0 (0.0%)\nNever listened before (true discovery): 37769 (100.0%)\nProcess Group PGID: 2004\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:16:04.347Z",
"resultDisplay": "Total graded items in val: 37769, previously listened in train: 0 (0.0%)\nNever listened before (true discovery): 37769 (100.0%)",
"description": "Check whether validation target items are repeat listens or discovery",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:16:04.348Z",
"content": "\nOutput: Total graded items in val: 37769, previously listened in train: 0 (0.0%)\nNever listened before (true discovery): 37769 (100.0%)\nProcess Group PGID: 2004\n",
"isError": false
},
"responseMessageId": "856a2fc2-c4a6-46bf-b767-bdb67b389b3c"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T02:16:07.611Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:07.611Z",
"text": "Discovering true novelties\n\nIt appears my current candidate eligibility filters are too stringent, as they're excluding all tracks from consideration in the validation set. This is leading to a perceived 100% discovery rate, which is not a true reflection of the data. I need to re-evaluate these filters."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:16:07.611Z",
"callId": "run_shell_command__call_99715",
"name": "run_shell_command",
"input": {
"description": "Check if past listens are eligible in validation candidate set",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas().drop_duplicates()\n# Check 10,000 pairs from train_listens\nsample = df_train.head(10000)\neligible_count = sum(eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in sample.itertuples())\nprint(f'Train listens eligible: {eligible_count} out of {len(sample)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_99715",
"name": "run_shell_command",
"args": {
"description": "Check if past listens are eligible in validation candidate set",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas().drop_duplicates()\n# Check 10,000 pairs from train_listens\nsample = df_train.head(10000)\neligible_count = sum(eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in sample.itertuples())\nprint(f'Train listens eligible: {eligible_count} out of {len(sample)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_99715",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Train listens eligible: 0 out of 10000\nProcess Group PGID: 2028\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:16:12.689Z",
"resultDisplay": "Train listens eligible: 0 out of 10000",
"description": "Check if past listens are eligible in validation candidate set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:16:12.690Z",
"content": "\nOutput: Train listens eligible: 0 out of 10000\nProcess Group PGID: 2028\n",
"isError": false
},
"responseMessageId": "4754ad65-fd91-4aaf-877a-fc10132fca29"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T02:16:16.591Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:16.591Z",
"text": "Discovering Novel Candidates\n\nI've realized a crucial detail: candidate eligibility for the \"Made for You\" shelf is strictly about discovery. The system is designed to only present items the user has *never* interacted with, aiming for novel recommendations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:16:16.591Z",
"callId": "enter_plan_mode__call_13125",
"name": "enter_plan_mode",
"input": {
"reason": "Plan the two-tower neural network architecture, data pipeline, loss function, validation workflow, and final submission pipeline."
},
"recorded": {
"id": "enter_plan_mode__call_13125",
"name": "enter_plan_mode",
"args": {
"reason": "Plan the two-tower neural network architecture, data pipeline, loss function, validation workflow, and final submission pipeline."
},
"result": [
{
"functionResponse": {
"id": "enter_plan_mode__call_13125",
"name": "enter_plan_mode",
"response": {
"output": "Switching to Plan mode."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:16:16.597Z",
"resultDisplay": "Switching to Plan mode: Plan the two-tower neural network architecture, data pipeline, loss function, validation workflow, and final submission pipeline.",
"description": "Plan the two-tower neural network architecture, data pipeline, loss function, validation workflow, and final submission pipeline.",
"displayName": "enter_plan_mode",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:16:16.599Z",
"content": "Switching to Plan mode.",
"isError": false
},
"responseMessageId": "f82d2c10-3190-495d-a015-b1a7fd01329a"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T02:16:57.900Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Exploring Plan Mode\n\nI've entered Plan Mode and am exploring its limitations. I understand I can only utilize read-only tools and write to a specific directory for plans. I've noted that the `replace` tool is available."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Confirming Plan Constraints\n\nI've confirmed that `run_shell_command` is unavailable in Plan Mode, which aligns with the exploration phase's focus on read-only tools and specific plan writing. My prior comprehensive data analysis should suffice for this stage."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Evaluating Task Protocols\n\nI've been reviewing the protocols for handling different task complexities. It appears I must strictly adhere to presenting strategies for simple tasks, outlining multiple approaches for standard tasks, and detailing trade-offs for complex tasks, all without direct user interaction."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Confirming Non-Interactive Protocols\n\nI've thoroughly reviewed the system prompt and available tools. It's clear that direct user interaction is impossible in this headless environment, and attempting to use `ask_user` or similar functions would terminate the session. My strategy will focus on generating the plan file at `/home/agent/.gem` using the available `write_file` tool."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Reviewing Architecture Details\n\nI am meticulously reviewing the system's architecture and technical specifications. My focus is on understanding the constraints for user and item towers, precomputed embeddings, and the request-time latency budget to ensure the plan accurately reflects these requirements."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Analyzing Task Flow\n\nI am currently processing the detailed task steps, focusing on the training, evaluation, and submission phases. I've noted the specific scripts and data artifacts involved, and I'm preparing to outline the required code for tower definition, training, and application within the given constraints."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Processing Item/User Tower Structures\n\nI'm now deeply analyzing the defined structures for both the item and user towers. Specifically, I'm focusing on how item embeddings will be integrated with feature vectors in the item tower and how the user tower will leverage interaction history through listen sequences and weighted engagement."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Synthesizing Tower Inputs\n\nI'm now integrating the weighted pooled listens, liked items, and disliked items into a unified representation. This is being concatenated with aggregated summary features before being passed through the User Tower MLP for final processing."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Refining Score Calculations\n\nI am now focusing on the mechanics of the scoring function, specifically how the user and item tower outputs, $u$ and $v$ respectively, are combined. I've noted that the dot product $u \\cdot v$ is used, and that the resulting output is $L_2$ normalized to the unit sphere, ensuring the dot product remains within $[-1, 1]$."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Calculating Loss Weights\n\nI am now calculating the loss weights based on positive pair importance, applying higher values for organic likes and strong listens, and lower values for regular listens. I'm also incorporating explicit hard negative sampling from disliked items to prevent recommending undesirable genres or artists."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Defining Training Strategies\n\nI am now defining the training strategies, specifically exploring temporal splits for backtesting and creating training pairs from recent history using rolling window masking. This data augmentation aims to generate diverse training pairs while preventing leakage and overfitting."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:16:57.900Z",
"text": "Developing Training Approach\n\nI am now focused on the computational aspects of training. I've noted the plan to utilize PyTorch on CPU with fast batching, employing `Dataset` and `DataLoader` for efficient data handling, and incorporating vectorized operations for rapid iteration."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:16:57.900Z",
"callId": "write_file__call_93849",
"name": "write_file",
"input": {
"file_path": "/home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"content": "# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n"
},
"recorded": {
"id": "write_file__call_93849",
"name": "write_file",
"args": {
"file_path": "/home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"content": "# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_93849",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md. Here is the updated code:\n# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:16:57.913Z",
"resultDisplay": {
"fileDiff": "Index: plan.md\n===================================================================\n--- plan.md\tOriginal\n+++ plan.md\tWritten\n@@ -0,0 +1,124 @@\n+# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n+\n+## Objective\n+Design, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n+\n+## Key Architectural Invariants & Constraints\n+1. **Strict Two-Tower Decomposition**:\n+ - `ItemTower(item_id, item_features) -> v \\in R^d`\n+ - `UserTower(user_interaction_history) -> u \\in R^d`\n+ - Relevance score: `s(u, v) = u^T v` (dot product)\n+ - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n+ - User embedding $u \\in R^d$ is computed online from interaction history.\n+ - User tower does NOT use user ID lookup (must derive representation from interaction history).\n+ - No construction or decomposition of a global user-item interaction matrix.\n+2. **Platform Candidate Boundary**:\n+ - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n+ - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n+3. **Temporal Split & Retraining**:\n+ - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n+ - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n+\n+---\n+\n+## Technical Design\n+\n+### 1. Item Tower Architecture\n+- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n+- **Inputs**:\n+ - `item_id`: mapped to item index in `[0, 30000)`.\n+ - Item metadata features from `candidate_catalog.parquet`:\n+ - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n+ - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n+ - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n+ - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n+- **Layers**:\n+ - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n+ - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n+ - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n+ - $L_2$ Normalization to ensure stable dot product space.\n+\n+### 2. User Tower Architecture (History-Derived)\n+- **Inputs**:\n+ - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n+ - Set of explicit liked items (from likes & preference state).\n+ - Set of explicit disliked items (from dislikes & preference state).\n+ - User activity features (listen count, organic listen fraction, like count, dislike count).\n+- **Pooling & Layers**:\n+ - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n+ - Likes pooling: Mean pooling of liked item embeddings.\n+ - Dislikes pooling: Mean pooling of disliked item embeddings.\n+ - User MLP:\n+ - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n+ - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n+ - $L_2$ Normalization to unit sphere.\n+\n+### 3. Training Scheme & Loss Function\n+- **Dataset Construction**:\n+ - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n+ - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n+- **Loss Function**:\n+ - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n+ $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n+ $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n+ - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n+ $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n+ - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n+\n+---\n+\n+## Phased Implementation Plan\n+\n+### Phase 1: Data Preprocessing & Feature Extraction\n+- Build fast extraction scripts in `/app/solution/`:\n+ - `data_prep.py`:\n+ - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n+ - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n+ - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n+ - Extract item metadata features for the 30,000 catalog tracks.\n+\n+### Phase 2: PyTorch Model Definition\n+- `model.py`:\n+ - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n+ - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n+ - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n+\n+### Phase 3: Training & Validation Loop\n+- `train_val.py`:\n+ - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n+ - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n+ - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n+ - Compute user embeddings for all 8,456 target users at the validation cutoff.\n+ - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n+ - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n+\n+### Phase 4: Tuning & Optimization\n+- Experiment with:\n+ - Embedding dimensions ($d=64, 128$)\n+ - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n+ - Recency half-life (14 days vs 30 days vs 60 days)\n+ - Incorporating dislike embeddings into user representation\n+ - Validate score progression against the popularity baseline (`0.01071`).\n+\n+### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n+- `refit_submission.py`:\n+ - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n+ - Update user histories up to validation cutoff with all observed events.\n+ - Use `test_preference_state.parquet` for active explicit preferences.\n+ - Train final model on full combined dataset.\n+ - Precompute item embeddings for all 30,000 catalog items.\n+ - Compute user embeddings for all 8,456 target users.\n+ - Apply `test_eligible_candidates.npz` mask.\n+ - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n+ - Validate submission structure with `ranking_evaluation.validate_ranking`.\n+\n+### Phase 6: Documentation & Deliverables Verification\n+- Ensure `/app/solution/README.md` documents:\n+ - Model architecture (User Tower, Item Tower, History aggregation)\n+ - Training objective (InfoNCE with popularity correction and dislike penalty)\n+ - Public validation NDCG@10 score\n+ - Final submission command\n+- Verify `/app/solution/submission.parquet` meets all schema requirements:\n+ - Columns: `uid`, `item_id`, `rank` (1..100)\n+ - Exactly 8,456 * 100 = 845,600 rows\n+ - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n",
"fileName": "plan.md",
"filePath": "/home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"originalContent": "",
"newContent": "# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n",
"diffStat": {
"model_added_lines": 124,
"model_removed_lines": 0,
"model_added_chars": 7264,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to ../.../plans/plan.md",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:16:57.914Z",
"content": "Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md. Here is the updated code:\n# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n",
"isError": false
},
"responseMessageId": "d772dd5b-c099-4a89-ad0d-0cac4019762b"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T02:17:05.982Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:05.982Z",
"callId": "exit_plan_mode__call_161629",
"name": "exit_plan_mode",
"input": {
"plan_filename": "plan.md"
},
"recorded": {
"id": "exit_plan_mode__call_161629",
"name": "exit_plan_mode",
"args": {
"plan_filename": "plan.md"
},
"result": [
{
"functionResponse": {
"id": "exit_plan_mode__call_161629",
"name": "exit_plan_mode",
"response": {
"output": "Plan approved. Switching to YOLO mode (all tool calls auto-approved).\n\nThe approved implementation plan is stored at: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md\nRead and follow the plan strictly during implementation."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:05.987Z",
"resultDisplay": "Plan approved: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"description": "Requesting plan approval for: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"displayName": "exit_plan_mode",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:17:07.686Z",
"content": "Plan approved. Switching to YOLO mode (all tool calls auto-approved).\n\nThe approved implementation plan is stored at: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md\nRead and follow the plan strictly during implementation.",
"isError": false
},
"responseMessageId": "e261a3a6-8be2-4dea-966e-7ae072bb4214"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T02:17:12.428Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:12.428Z",
"callId": "update_topic__call_4580874",
"name": "update_topic",
"input": {
"strategic_intent": "Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.",
"title": "Data Preprocessing & Feature Extraction",
"summary": "Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages."
},
"recorded": {
"id": "update_topic__call_4580874",
"name": "update_topic",
"args": {
"strategic_intent": "Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.",
"title": "Data Preprocessing & Feature Extraction",
"summary": "Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_4580874",
"name": "update_topic",
"response": {
"output": "Current topic: \"Data Preprocessing & Feature Extraction\"\nTopic summary: Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages.\n\nStrategic Intent: Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:12.432Z",
"resultDisplay": "## ๐ Topic: **Data Preprocessing & Feature Extraction**\n\n**Summary:**\nTransitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages.\n\n> [!STRATEGY]\n> **Intent:** Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.",
"description": "Update topic to: \"Data Preprocessing & Feature Extraction\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:17:12.432Z",
"content": "Current topic: \"Data Preprocessing & Feature Extraction\"\nTopic summary: Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages.\n\nStrategic Intent: Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.",
"isError": false
},
"responseMessageId": "604a4c52-3513-47f7-88e6-c4795670a4fb"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T02:17:19.815Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:19.815Z",
"text": "Exploring Data Structures\n\nI'm currently evaluating the most efficient data structures for both training and inference, focusing on optimizing the mapping of up to 30,000 candidate items to a compact index range. This initial step is crucial for performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:19.815Z",
"text": "Confirming Catalog Consistency\n\nI've confirmed that the `item_id` order in `validation_eligible_candidates.npz` perfectly matches `candidate_catalog.parquet`. This means indices directly correspond across `item_id`, catalog rows, and the `packed_eligible` bits, which is excellent for platform consistency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:19.815Z",
"callId": "run_shell_command__call_5378438",
"name": "run_shell_command",
"input": {
"description": "Check target users catalog listens",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\n# Let's read train_listens for target users\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_tu = df[df.uid.isin(target_uids)]\nprint('Total listens for target users in train:', len(df_tu))\nin_cc = df_tu.item_id.isin(cc_set)\nprint(f'Catalog listens for target users: {in_cc.sum()} ({in_cc.mean() * 100:.1f}%)')\npos = df_tu[df_tu.played_ratio_pct >= 50]\nprint(f'Positive listens (>=50%): {len(pos)}, in catalog: {pos.item_id.isin(cc_set).sum()} ({pos.item_id.isin(cc_set).mean() * 100:.1f}%)')\n\""
},
"recorded": {
"id": "run_shell_command__call_5378438",
"name": "run_shell_command",
"args": {
"description": "Check target users catalog listens",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\n# Let's read train_listens for target users\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_tu = df[df.uid.isin(target_uids)]\nprint('Total listens for target users in train:', len(df_tu))\nin_cc = df_tu.item_id.isin(cc_set)\nprint(f'Catalog listens for target users: {in_cc.sum()} ({in_cc.mean() * 100:.1f}%)')\npos = df_tu[df_tu.played_ratio_pct >= 50]\nprint(f'Positive listens (>=50%): {len(pos)}, in catalog: {pos.item_id.isin(cc_set).sum()} ({pos.item_id.isin(cc_set).mean() * 100:.1f}%)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5378438",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total listens for target users in train: 43222297\nCatalog listens for target users: 31062540 (71.9%)\nPositive listens (>=50%): 27367343, in catalog: 19959853 (72.9%)\nProcess Group PGID: 2051\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:32.742Z",
"resultDisplay": "Total listens for target users in train: 43222297\nCatalog listens for target users: 31062540 (71.9%)\nPositive listens (>=50%): 27367343, in catalog: 19959853 (72.9%)",
"description": "Check target users catalog listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:17:32.743Z",
"content": "\nOutput: Total listens for target users in train: 43222297\nCatalog listens for target users: 31062540 (71.9%)\nPositive listens (>=50%): 27367343, in catalog: 19959853 (72.9%)\nProcess Group PGID: 2051\n",
"isError": false
},
"responseMessageId": "a7a9abb9-cd03-4485-b9e2-45acd49c0d43"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T02:17:38.933Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:38.933Z",
"text": "Analyzing Target User Engagement\n\nI've confirmed that nearly all of the 43.48 million listens in `train_listens` belong to our target users. Within this group, I've identified an impressive 19.96 million positive catalog listens. I'm now proceeding to the next analytical step."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:38.933Z",
"callId": "run_shell_command__call_4297143",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_liked = set(zip(vps[vps.state == 'liked'].uid, vps[vps.state == 'liked'].item_id))\nvps_disliked = set(zip(vps[vps.state == 'disliked'].uid, vps[vps.state == 'disliked'].item_id))\n\ntl = pd.read_parquet(DATA / 'train_likes.parquet')\ntl_set = set(zip(tl.uid, tl.item_id))\ntu = pd.read_parquet(DATA / 'train_unlikes.parquet')\ntu_set = set(zip(tu.uid, tu.item_id))\n\nprint('VPS liked in train_likes:', len(vps_liked.intersection(tl_set)), 'out of', len(vps_liked))\nprint('VPS liked in train_unlikes:', len(vps_liked.intersection(tu_set)), 'out of', len(vps_liked))\n\"",
"description": "Check consistency of VPS liked with train_likes and train_unlikes"
},
"recorded": {
"id": "run_shell_command__call_4297143",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_liked = set(zip(vps[vps.state == 'liked'].uid, vps[vps.state == 'liked'].item_id))\nvps_disliked = set(zip(vps[vps.state == 'disliked'].uid, vps[vps.state == 'disliked'].item_id))\n\ntl = pd.read_parquet(DATA / 'train_likes.parquet')\ntl_set = set(zip(tl.uid, tl.item_id))\ntu = pd.read_parquet(DATA / 'train_unlikes.parquet')\ntu_set = set(zip(tu.uid, tu.item_id))\n\nprint('VPS liked in train_likes:', len(vps_liked.intersection(tl_set)), 'out of', len(vps_liked))\nprint('VPS liked in train_unlikes:', len(vps_liked.intersection(tu_set)), 'out of', len(vps_liked))\n\"",
"description": "Check consistency of VPS liked with train_likes and train_unlikes"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4297143",
"name": "run_shell_command",
"response": {
"output": "\nOutput: VPS liked in train_likes: 418435 out of 418435\nVPS liked in train_unlikes: 5417 out of 418435\nProcess Group PGID: 2073\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:42.003Z",
"resultDisplay": "VPS liked in train_likes: 418435 out of 418435\nVPS liked in train_unlikes: 5417 out of 418435",
"description": "Check consistency of VPS liked with train_likes and train_unlikes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:17:42.004Z",
"content": "\nOutput: VPS liked in train_likes: 418435 out of 418435\nVPS liked in train_unlikes: 5417 out of 418435\nProcess Group PGID: 2073\n",
"isError": false
},
"responseMessageId": "22e17ab6-397b-4fd4-b968-99a65c8c2587"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T02:17:53.594Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:53.593Z",
"text": "Validating Preference States\n\nMy current focus is on understanding how the explicit preference state (`validation_preference_state`) aligns with the state at the test cutoff (`test_preference_state`), accounting for all interactions. This comparison is crucial for the next steps."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:53.593Z",
"text": "Developing Training Targets\n\nI am now focusing on how to construct effective training targets for the two-tower model. Specifically, I'm exploring how to leverage historical user behavior within defined windows before a validation cutoff to identify positive interactions, such as organic likes and high listen percentages."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:53.594Z",
"callId": "run_shell_command__call_579647",
"name": "run_shell_command",
"input": {
"description": "Check event counts across 7-day windows in training data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\n# Let's inspect organic likes in train\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\norg_likes = likes[likes.is_organic == 1]\nprint('Total organic likes in train:', len(org_likes))\n\n# Read listens with is_organic == 1 and played_ratio_pct >= 80\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nstrong_listens = table.to_pandas()\nprint('Strong organic listens in last 35 days of train:', len(strong_listens))\n\nfor w in range(4):\n w_end = cutoff - w * (week + gap)\n w_start = w_end - week\n w_likes = org_likes[(org_likes.timestamp >= w_start) & (org_likes.timestamp <= w_end)]\n w_strong = strong_listens[(strong_listens.timestamp >= w_start) & (strong_listens.timestamp <= w_end)]\n print(f'Window {w} [{w_start}..{w_end}]: organic likes={len(w_likes)}, strong listens={len(w_strong)}, unique users with targets={len(set(w_likes.uid).union(set(w_strong.uid)))}')\n\""
},
"recorded": {
"id": "run_shell_command__call_579647",
"name": "run_shell_command",
"args": {
"description": "Check event counts across 7-day windows in training data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\n# Let's inspect organic likes in train\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\norg_likes = likes[likes.is_organic == 1]\nprint('Total organic likes in train:', len(org_likes))\n\n# Read listens with is_organic == 1 and played_ratio_pct >= 80\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nstrong_listens = table.to_pandas()\nprint('Strong organic listens in last 35 days of train:', len(strong_listens))\n\nfor w in range(4):\n w_end = cutoff - w * (week + gap)\n w_start = w_end - week\n w_likes = org_likes[(org_likes.timestamp >= w_start) & (org_likes.timestamp <= w_end)]\n w_strong = strong_listens[(strong_listens.timestamp >= w_start) & (strong_listens.timestamp <= w_end)]\n print(f'Window {w} [{w_start}..{w_end}]: organic likes={len(w_likes)}, strong listens={len(w_strong)}, unique users with targets={len(set(w_likes.uid).union(set(w_strong.uid)))}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_579647",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total organic likes in train: 461839\nStrong organic listens in last 35 days of train: 2024728\nWindow 0 [24182000..24786800]: organic likes=17201, strong listens=428612, unique users with targets=6853\nWindow 1 [23575400..24180200]: organic likes=14981, strong listens=406037, unique users with targets=6657\nWindow 2 [22968800..23573600]: organic likes=14715, strong listens=400103, unique users with targets=6545\nWindow 3 [22362200..22967000]: organic likes=16990, strong listens=399933, unique users with targets=6412\nProcess Group PGID: 2091\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:55.737Z",
"resultDisplay": "Total organic likes in train: 461839\nStrong organic listens in last 35 days of train: 2024728\nWindow 0 [24182000..24786800]: organic likes=17201, strong listens=428612, unique users with targets=6853\nWindow 1 [23575400..24180200]: organic likes=14981, strong listens=406037, unique users with targets=6657\nWindow 2 [22968800..23573600]: organic likes=14715, strong listens=400103, unique users with targets=6545\nWindow 3 [22362200..22967000]: organic likes=16990, strong listens=399933, unique users with targets=6412",
"description": "Check event counts across 7-day windows in training data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:17:55.738Z",
"content": "\nOutput: Total organic likes in train: 461839\nStrong organic listens in last 35 days of train: 2024728\nWindow 0 [24182000..24786800]: organic likes=17201, strong listens=428612, unique users with targets=6853\nWindow 1 [23575400..24180200]: organic likes=14981, strong listens=406037, unique users with targets=6657\nWindow 2 [22968800..23573600]: organic likes=14715, strong listens=400103, unique users with targets=6545\nWindow 3 [22362200..22967000]: organic likes=16990, strong listens=399933, unique users with targets=6412\nProcess Group PGID: 2091\n",
"isError": false
},
"responseMessageId": "17c7fa82-a280-4e7d-a049-675c1e3ffdae"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T02:18:02.557Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:02.557Z",
"text": "Observing Performance Metrics\n\nI'm seeing a remarkably consistent pattern across all 7-day intervals. Each week delivers approximately 15,000 to 17,000 organic likes and around 400,000 strong organic listens. Crucially, I'm also consistently reaching about 6,500 target users who have set specific targets. This stability is quite promising."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:18:02.557Z",
"callId": "run_shell_command__call_3100109",
"name": "run_shell_command",
"input": {
"description": "Check discovery fraction in training window",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nw_end = cutoff\nw_start = w_end - week\nc_cut = w_start - gap # history cutoff\n\n# Load candidate catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nw_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp <= w_end) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', w_start), ('timestamp', '<=', w_end), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nw_listens = table.to_pandas()\nw_listens = w_listens[w_listens.item_id.isin(cc_items)]\n\n# Check history before c_cut\nhist_table = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', c_cut)]\n)\nhist_df = hist_table.to_pandas()\nhist_pairs = set(zip(hist_df.uid, hist_df.item_id))\n\ndiscovery_likes = [pair not in hist_pairs for pair in zip(w_likes.uid, w_likes.item_id)]\ndiscovery_listens = [pair not in hist_pairs for pair in zip(w_listens.uid, w_listens.item_id)]\n\nprint('Catalog organic likes in window:', len(w_likes), 'discovery fraction:', np.mean(discovery_likes))\nprint('Catalog strong listens in window:', len(w_listens), 'discovery fraction:', np.mean(discovery_listens))\nprint('Discovery organic likes count:', sum(discovery_likes))\nprint('Discovery strong listens count:', sum(discovery_listens))\n\""
},
"recorded": {
"id": "run_shell_command__call_3100109",
"name": "run_shell_command",
"args": {
"description": "Check discovery fraction in training window",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nw_end = cutoff\nw_start = w_end - week\nc_cut = w_start - gap # history cutoff\n\n# Load candidate catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nw_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp <= w_end) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', w_start), ('timestamp', '<=', w_end), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nw_listens = table.to_pandas()\nw_listens = w_listens[w_listens.item_id.isin(cc_items)]\n\n# Check history before c_cut\nhist_table = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', c_cut)]\n)\nhist_df = hist_table.to_pandas()\nhist_pairs = set(zip(hist_df.uid, hist_df.item_id))\n\ndiscovery_likes = [pair not in hist_pairs for pair in zip(w_likes.uid, w_likes.item_id)]\ndiscovery_listens = [pair not in hist_pairs for pair in zip(w_listens.uid, w_listens.item_id)]\n\nprint('Catalog organic likes in window:', len(w_likes), 'discovery fraction:', np.mean(discovery_likes))\nprint('Catalog strong listens in window:', len(w_listens), 'discovery fraction:', np.mean(discovery_listens))\nprint('Discovery organic likes count:', sum(discovery_likes))\nprint('Discovery strong listens count:', sum(discovery_listens))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3100109",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog organic likes in window: 11210 discovery fraction: 0.7895628902765388\nCatalog strong listens in window: 305188 discovery fraction: 0.2222695518827739\nDiscovery organic likes count: 8851\nDiscovery strong listens count: 67834\nProcess Group PGID: 2113\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:18:24.523Z",
"resultDisplay": "Catalog organic likes in window: 11210 discovery fraction: 0.7895628902765388\nCatalog strong listens in window: 305188 discovery fraction: 0.2222695518827739\nDiscovery organic likes count: 8851\nDiscovery strong listens count: 67834",
"description": "Check discovery fraction in training window",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:18:24.524Z",
"content": "\nOutput: Catalog organic likes in window: 11210 discovery fraction: 0.7895628902765388\nCatalog strong listens in window: 305188 discovery fraction: 0.2222695518827739\nDiscovery organic likes count: 8851\nDiscovery strong listens count: 67834\nProcess Group PGID: 2113\n",
"isError": false
},
"responseMessageId": "a273a5e3-f18c-40b5-a90c-e0d606acb149"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T02:18:35.715Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:35.715Z",
"text": "Analyzing Discovery Performance\n\nMy current focus is on the substantial volume of organic likes and strong listens within a 7-day period, revealing hundreds of thousands of valuable discovery training examples. I'm also noting how filtering prior listening behavior precisely mirrors the validation process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:35.715Z",
"text": "Refining Input Features\n\nI'm now considering how to best represent user taste, exploring the impact of recent listens versus longer-term preferences, and how to weight them. I'm also investigating the utility of item embeddings, with a focus on differentiable pooling of these embeddings based on recent activity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:35.715Z",
"text": "Incorporating Explicit Feedback\n\nI'm now integrating explicit user feedback like track likes and dislikes. For likes, I'm calculating a mean embedding of liked tracks, and similarly for dislikes. These, along with aggregated user features, will form the input to the user tower network."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:35.715Z",
"text": "Precomputing Item Embeddings\n\nI've finalized the item tower architecture, incorporating catalog features and projecting them with a linear layer, layer normalization, and GELU activation. The item embedding and projected features are concatenated, then passed through an MLP for normalization. Crucially, these item embeddings can be precomputed offline. This allows for efficient scoring of user embeddings against all candidate items at request time, effectively performing Maximum Inner Product Search."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:18:35.715Z",
"callId": "run_shell_command__call_91948",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport torch\nimport time\n\nB = 8456\nD = 128\nN = 30000\n\nu = torch.randn(B, D)\nu = u / u.norm(dim=1, keepdim=True)\nv = torch.randn(N, D)\nv = v / v.norm(dim=1, keepdim=True)\n\nt0 = time.time()\nscores = torch.matmul(u, v.t())\ntop100 = torch.topk(scores, 100, dim=1)\nt1 = time.time()\nprint(f'MIPS for all {B} target users over {N} candidate items took: {t1 - t0:.3f} seconds!')\n\"",
"description": "Benchmark MIPS retrieval latency for all 8456 target users"
},
"recorded": {
"id": "run_shell_command__call_91948",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport torch\nimport time\n\nB = 8456\nD = 128\nN = 30000\n\nu = torch.randn(B, D)\nu = u / u.norm(dim=1, keepdim=True)\nv = torch.randn(N, D)\nv = v / v.norm(dim=1, keepdim=True)\n\nt0 = time.time()\nscores = torch.matmul(u, v.t())\ntop100 = torch.topk(scores, 100, dim=1)\nt1 = time.time()\nprint(f'MIPS for all {B} target users over {N} candidate items took: {t1 - t0:.3f} seconds!')\n\"",
"description": "Benchmark MIPS retrieval latency for all 8456 target users"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_91948",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MIPS for all 8456 target users over 30000 candidate items took: 0.495 seconds!\nProcess Group PGID: 2135\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:18:39.298Z",
"resultDisplay": "MIPS for all 8456 target users over 30000 candidate items took: 0.495 seconds!",
"description": "Benchmark MIPS retrieval latency for all 8456 target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:18:39.299Z",
"content": "\nOutput: MIPS for all 8456 target users over 30000 candidate items took: 0.495 seconds!\nProcess Group PGID: 2135\n",
"isError": false
},
"responseMessageId": "bf2b748b-9f71-483a-82ba-f2a96c68ae1d"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T02:18:47.235Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:47.235Z",
"text": "Optimizing Candidate Generation\n\nI've just confirmed a significant speedup in candidate generation, processing all users and items in under a second. The next critical question is whether the item embedding table can be shared to further streamline this process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:47.235Z",
"text": "Evaluating Embedding Sharing\n\nI'm currently exploring the implications of sharing the item embedding table between the user and item towers. My current focus is on how gradients would flow and impact item representations if the user tower directly pools item embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:47.235Z",
"text": "Analyzing Embedding Sharing Implications\n\nMy current thoughts are focused on the practicalities of sharing the item embedding table. I'm examining how user interaction history is extracted and stored for both training and inference, ensuring it aligns with the model's architecture requirements. This involves checking available data files for the `candidate_catalog.parquet`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:18:47.235Z",
"callId": "run_shell_command__call_60857",
"name": "run_shell_command",
"input": {
"description": "Check contents of /app/solution",
"command": "ls -la /app/solution"
},
"recorded": {
"id": "run_shell_command__call_60857",
"name": "run_shell_command",
"args": {
"description": "Check contents of /app/solution",
"command": "ls -la /app/solution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_60857",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:09 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:09 ..\nProcess Group PGID: 2148\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:18:47.251Z",
"resultDisplay": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:09 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:09 ..",
"description": "ls -la /app/solution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:18:47.252Z",
"content": "\nOutput: total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:09 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:09 ..\nProcess Group PGID: 2148\n",
"isError": false
},
"responseMessageId": "6266890d-7353-4bfc-a827-808c01a51aaa"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T02:18:52.434Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:18:52.434Z",
"text": "Establishing a Baseline\n\nI'm focusing on creating an initial, valid submission right away. This serves as a crucial starting point before diving into complex model development, ensuring I have a functional baseline that can be refined."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:18:52.434Z",
"callId": "run_shell_command__call_1022",
"name": "run_shell_command",
"input": {
"description": "Create and verify initial valid baseline submission",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ntest_eligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\n# Unpack eligible bits\nunpacked = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder='little')\nitem_ids = test_eligibility.item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n mask = unpacked[i] == 1\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\nsub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub = validate_ranking(sub, targets_set)\n# Check all items eligible\nall_el = all(test_eligibility.is_eligible(uid, itm) for uid, itm in zip(val_sub.uid, val_sub.item_id))\nprint('All items eligible in test candidate set?:', all_el)\nprint('Shape:', val_sub.shape)\nval_sub.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Successfully saved initial baseline submission to /app/solution/submission.parquet!')\n\""
},
"recorded": {
"id": "run_shell_command__call_1022",
"name": "run_shell_command",
"args": {
"description": "Create and verify initial valid baseline submission",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ntest_eligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\n# Unpack eligible bits\nunpacked = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder='little')\nitem_ids = test_eligibility.item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n mask = unpacked[i] == 1\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\nsub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub = validate_ranking(sub, targets_set)\n# Check all items eligible\nall_el = all(test_eligibility.is_eligible(uid, itm) for uid, itm in zip(val_sub.uid, val_sub.item_id))\nprint('All items eligible in test candidate set?:', all_el)\nprint('Shape:', val_sub.shape)\nval_sub.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Successfully saved initial baseline submission to /app/solution/submission.parquet!')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1022",
"name": "run_shell_command",
"response": {
"output": "\nOutput: All items eligible in test candidate set?: True\nShape: (845600, 3)\nSuccessfully saved initial baseline submission to /app/solution/submission.parquet!\nProcess Group PGID: 2151\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:18:56.300Z",
"resultDisplay": "All items eligible in test candidate set?: True\nShape: (845600, 3)\nSuccessfully saved initial baseline submission to /app/solution/submission.parquet!",
"description": "Create and verify initial valid baseline submission",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:18:56.302Z",
"content": "\nOutput: All items eligible in test candidate set?: True\nShape: (845600, 3)\nSuccessfully saved initial baseline submission to /app/solution/submission.parquet!\nProcess Group PGID: 2151\n",
"isError": false
},
"responseMessageId": "383245ae-21e8-4dbb-a657-c562c5dcf4c1"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T02:18:58.595Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:18:58.595Z",
"callId": "run_shell_command__call_100173",
"name": "run_shell_command",
"input": {
"command": "ls -lh /app/solution/submission.parquet",
"description": "Check size of baseline submission"
},
"recorded": {
"id": "run_shell_command__call_100173",
"name": "run_shell_command",
"args": {
"command": "ls -lh /app/solution/submission.parquet",
"description": "Check size of baseline submission"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_100173",
"name": "run_shell_command",
"response": {
"output": "\nOutput: -rw-r--r-- 1 agent agent 719K Sep 29 02:18 /app/solution/submission.parquet\nProcess Group PGID: 2168\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:18:58.612Z",
"resultDisplay": "-rw-r--r-- 1 agent agent 719K Sep 29 02:18 /app/solution/submission.parquet",
"description": "ls -lh /app/solution/submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:18:58.613Z",
"content": "\nOutput: -rw-r--r-- 1 agent agent 719K Sep 29 02:18 /app/solution/submission.parquet\nProcess Group PGID: 2168\n",
"isError": false
},
"responseMessageId": "348de7e1-0c1d-4bc3-8bb9-8b4d60dfa82f"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T02:19:10.877Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:10.877Z",
"text": "Constructing Two-Tower Model\n\nI'm now focused on setting up the two-tower model pipeline. My immediate steps involve creating the `/app/solution/` directory and populating it with essential modules: `data_prep.py` for all data handling and `model.py` for defining the PyTorch UserTower, ItemTower, and the overarching TwoTowerModel."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:10.877Z",
"text": "Developing Evaluation & Refitting Scripts\n\nI'm now outlining the training and validation loop, ensuring it accurately evaluates against the public benchmark. Following that, I'll refine the model on combined training and validation data for final submission, ensuring predictions are generated for the hidden test set and saved as per the specified format. Documentation will also be finalized."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:10.877Z",
"text": "Generating Training Samples\n\nI am currently exploring methods for generating training samples, specifically focusing on creating $(u, v^+, \\text{grade})$ tuples. My current thinking involves leveraging historical data prior to $T_{train\\_cut}$ and considering both multiple temporal backtest slices and continuous history slices as potential approaches."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:19:10.877Z",
"callId": "run_shell_command__call_4992151",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(tu.uid)\n\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nlistens = table.to_pandas()\nlistens = listens[listens.uid.isin(targets) & (listens.item_id.isin(cc_items))]\n\ntotal_pairs = 0\nfor w in range(4):\n c_cut = cutoff - (w + 1) * (week + gap)\n target_start = c_cut + gap\n target_end = target_start + week\n \n w_likes = likes[(likes.timestamp >= target_start) & (likes.timestamp <= target_end)]\n w_listens = listens[(listens.timestamp >= target_start) & (listens.timestamp <= target_end)]\n \n # User history items before c_cut\n # Let's see how many pairs\n u_likes = len(w_likes)\n u_listens = len(w_listens)\n print(f'Window {w}: c_cut={c_cut}, target_likes={u_likes}, target_listens={u_listens}')\n total_pairs += u_likes + u_listens\n\nprint('Total potential target pairs across 4 windows:', total_pairs)\n\"",
"description": "Count target pairs across 4 windows"
},
"recorded": {
"id": "run_shell_command__call_4992151",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(tu.uid)\n\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nlistens = table.to_pandas()\nlistens = listens[listens.uid.isin(targets) & (listens.item_id.isin(cc_items))]\n\ntotal_pairs = 0\nfor w in range(4):\n c_cut = cutoff - (w + 1) * (week + gap)\n target_start = c_cut + gap\n target_end = target_start + week\n \n w_likes = likes[(likes.timestamp >= target_start) & (likes.timestamp <= target_end)]\n w_listens = listens[(listens.timestamp >= target_start) & (listens.timestamp <= target_end)]\n \n # User history items before c_cut\n # Let's see how many pairs\n u_likes = len(w_likes)\n u_listens = len(w_listens)\n print(f'Window {w}: c_cut={c_cut}, target_likes={u_likes}, target_listens={u_listens}')\n total_pairs += u_likes + u_listens\n\nprint('Total potential target pairs across 4 windows:', total_pairs)\n\"",
"description": "Count target pairs across 4 windows"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4992151",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Window 0: c_cut=24180200, target_likes=7774, target_listens=304885\nWindow 1: c_cut=23573600, target_likes=7063, target_listens=289032\nWindow 2: c_cut=22967000, target_likes=7518, target_listens=287032\nWindow 3: c_cut=22360400, target_likes=7572, target_listens=289103\nTotal potential target pairs across 4 windows: 1199979\nProcess Group PGID: 2171\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:19:13.317Z",
"resultDisplay": "Window 0: c_cut=24180200, target_likes=7774, target_listens=304885\nWindow 1: c_cut=23573600, target_likes=7063, target_listens=289032\nWindow 2: c_cut=22967000, target_likes=7518, target_listens=287032\nWindow 3: c_cut=22360400, target_likes=7572, target_listens=289103\nTotal potential target pairs across 4 windows: 1199979",
"description": "Count target pairs across 4 windows",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:19:13.318Z",
"content": "\nOutput: Window 0: c_cut=24180200, target_likes=7774, target_listens=304885\nWindow 1: c_cut=23573600, target_likes=7063, target_listens=289032\nWindow 2: c_cut=22967000, target_likes=7518, target_listens=287032\nWindow 3: c_cut=22360400, target_likes=7572, target_listens=289103\nTotal potential target pairs across 4 windows: 1199979\nProcess Group PGID: 2171\n",
"isError": false
},
"responseMessageId": "8c2b17e2-f32b-4667-a88e-e84661eb04b6"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T02:19:31.743Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Analyzing User History\n\nI'm currently processing a massive dataset of 1.2 million pairs across four windows, with a special focus on the week preceding the cutoff in Window 0. This period saw a significant volume of engagement, with 7,774 likes and 304,885 strong listens. My immediate task is to determine the optimal representation for user history at the cutoff point."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Defining User History\n\nI'm refining how I represent recent user interactions. My focus is on incorporating weighted recent listens, with a 14-day half-life and adjusted for play ratio and organic engagement. I'm also considering explicit likes for a more comprehensive user profile."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Incorporating Explicit Interactions\n\nI'm now integrating explicit user feedback. This involves creating embeddings for both liked and disliked tracks, weighting them by recent activity and play ratio. I'm also developing a feature vector to capture overall user engagement, which will be fed into a user tower MLP for representation learning."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Refining Item Representation\n\nI'm now solidifying item feature embeddings. This involves projecting catalog features like normalized listens and organic ratios, then combining them with the base item embedding. A subsequent MLP transforms this into a dense, L2-normalized representation for similarity calculations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Correcting for Popularity Bias\n\nI'm now exploring techniques to mitigate popularity bias. Specifically, I'm implementing LogQ correction to prevent over-recommending hyper-popular items and am investigating its impact on discovery. I'm also incorporating sample weighting based on engagement grade to refine the training process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:31.743Z",
"text": "Implementing Dislike Penalties\n\nI'm now incorporating explicit negative feedback by penalizing user embeddings when they are too close to disliked items. This aims to push my learned user representations away from content they actively dislike, thereby improving recommendation accuracy. I'll be testing this approach in Python to assess its efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:19:31.743Z",
"callId": "run_shell_command__call_1622401",
"name": "run_shell_command",
"input": {
"description": "Test catalog and user mapping initialization",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\n\n# 1. Catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\nprint(f'Catalog items: {len(item_to_idx)}')\n\n# 2. Target users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = list(tu.uid.values)\nuid_to_idx = {int(u): i for i, u in enumerate(target_uids)}\nprint(f'Target users: {len(uid_to_idx)}')\n\nprint(f'Init maps in {time.time() - t0:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_1622401",
"name": "run_shell_command",
"args": {
"description": "Test catalog and user mapping initialization",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\n\n# 1. Catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\nprint(f'Catalog items: {len(item_to_idx)}')\n\n# 2. Target users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = list(tu.uid.values)\nuid_to_idx = {int(u): i for i, u in enumerate(target_uids)}\nprint(f'Target users: {len(uid_to_idx)}')\n\nprint(f'Init maps in {time.time() - t0:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1622401",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog items: 30000\nTarget users: 8456\nInit maps in 0.04s\nProcess Group PGID: 2193\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:19:33.312Z",
"resultDisplay": "Catalog items: 30000\nTarget users: 8456\nInit maps in 0.04s",
"description": "Test catalog and user mapping initialization",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:19:33.313Z",
"content": "\nOutput: Catalog items: 30000\nTarget users: 8456\nInit maps in 0.04s\nProcess Group PGID: 2193\n",
"isError": false
},
"responseMessageId": "737cb766-4f2a-4180-b706-c134dca30f7c"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T02:19:45.569Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:45.569Z",
"text": "Processing Train Listens\n\nI'm currently focusing on optimizing the extraction of the last 100 listens for 8,456 users before a specified cutoff. I'll be testing this approach in Python to assess its efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:19:45.569Z",
"callId": "run_shell_command__call_47202",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 45 * 86400), ('played_ratio_pct', '>=', 50)]\n)\nprint(f'Read filtered pyarrow table in {time.time() - t0:.2f}s, rows={table.num_rows}')\ndf = table.to_pandas()\nprint(f'Converted to pandas in {time.time() - t0:.2f}s')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ndf = df[df.uid.isin(target_uids)]\nprint(f'Filtered to target users in {time.time() - t0:.2f}s, rows={len(df)}')\nuser_counts = df.groupby('uid').size()\nprint('Target users covered in last 45d:', len(user_counts), 'out of 8456')\nprint('Min listens in last 45d:', user_counts.min(), 'median:', user_counts.median(), 'max:', user_counts.max())\n\"",
"description": "Benchmark reading filtered train_listens for target users"
},
"recorded": {
"id": "run_shell_command__call_47202",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 45 * 86400), ('played_ratio_pct', '>=', 50)]\n)\nprint(f'Read filtered pyarrow table in {time.time() - t0:.2f}s, rows={table.num_rows}')\ndf = table.to_pandas()\nprint(f'Converted to pandas in {time.time() - t0:.2f}s')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ndf = df[df.uid.isin(target_uids)]\nprint(f'Filtered to target users in {time.time() - t0:.2f}s, rows={len(df)}')\nuser_counts = df.groupby('uid').size()\nprint('Target users covered in last 45d:', len(user_counts), 'out of 8456')\nprint('Min listens in last 45d:', user_counts.min(), 'median:', user_counts.median(), 'max:', user_counts.max())\n\"",
"description": "Benchmark reading filtered train_listens for target users"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_47202",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read filtered pyarrow table in 0.35s, rows=5683546\nConverted to pandas in 0.36s\nFiltered to target users in 0.81s, rows=5681249\nTarget users covered in last 45d: 8330 out of 8456\nMin listens in last 45d: 1 median: 425.0 max: 11435\nProcess Group PGID: 2210\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:19:48.012Z",
"resultDisplay": "Read filtered pyarrow table in 0.35s, rows=5683546\nConverted to pandas in 0.36s\nFiltered to target users in 0.81s, rows=5681249\nTarget users covered in last 45d: 8330 out of 8456\nMin listens in last 45d: 1 median: 425.0 max: 11435",
"description": "Benchmark reading filtered train_listens for target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:19:48.014Z",
"content": "\nOutput: Read filtered pyarrow table in 0.35s, rows=5683546\nConverted to pandas in 0.36s\nFiltered to target users in 0.81s, rows=5681249\nTarget users covered in last 45d: 8330 out of 8456\nMin listens in last 45d: 1 median: 425.0 max: 11435\nProcess Group PGID: 2210\n",
"isError": false
},
"responseMessageId": "0adabd71-a3d1-4ce4-ab7e-e0bdffb6bc0e"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T02:19:51.457Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:51.457Z",
"text": "Confirming Coverage Metrics\n\nI'm reviewing the recent coverage data. It appears that over the last 45 days, 8,330 users have been covered. Looking back 90 days, our entire target user base of 8,456 has been accounted for, aligning with our definition of \"target_users.\""
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:19:51.457Z",
"callId": "run_shell_command__call_5380946",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\nuids_in_90d = set(table['uid'].to_numpy())\nprint('Target users in last 90d:', len(target_uids.intersection(uids_in_90d)), 'out of 8456')\n\"",
"description": "Verify 90d coverage of all target users"
},
"recorded": {
"id": "run_shell_command__call_5380946",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\nuids_in_90d = set(table['uid'].to_numpy())\nprint('Target users in last 90d:', len(target_uids.intersection(uids_in_90d)), 'out of 8456')\n\"",
"description": "Verify 90d coverage of all target users"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5380946",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users in last 90d: 8456 out of 8456\nProcess Group PGID: 2232\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:19:53.859Z",
"resultDisplay": "Target users in last 90d: 8456 out of 8456",
"description": "Verify 90d coverage of all target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:19:53.861Z",
"content": "\nOutput: Target users in last 90d: 8456 out of 8456\nProcess Group PGID: 2232\n",
"isError": false
},
"responseMessageId": "3cf95ee3-c9e2-4b61-bb30-3d156d14138b"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T02:19:58.675Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:19:58.675Z",
"text": "Covering User Needs\n\nI've confirmed that all target users are accounted for within the 90-day window prior to the cutoff. My current focus is on efficiently retrieving the most recent K listens for each of these users. I'm exploring pandas and numpy for optimized sorting and extraction."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:19:58.675Z",
"callId": "run_shell_command__call_2514140",
"name": "run_shell_command",
"input": {
"description": "Benchmark sorting and taking tail(100) listens per user",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ndf = df[df.uid.isin(set(tu.uid))]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, len={len(df)}')\n\nt1 = time.time()\ndf = df.sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\nprint(f'Sorted and took tail(100) in {time.time() - t1:.2f}s, len={len(recent)}')\nprint(f'Users in recent: {recent.uid.nunique()}')\n\""
},
"recorded": {
"id": "run_shell_command__call_2514140",
"name": "run_shell_command",
"args": {
"description": "Benchmark sorting and taking tail(100) listens per user",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ndf = df[df.uid.isin(set(tu.uid))]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, len={len(df)}')\n\nt1 = time.time()\ndf = df.sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\nprint(f'Sorted and took tail(100) in {time.time() - t1:.2f}s, len={len(recent)}')\nprint(f'Users in recent: {recent.uid.nunique()}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2514140",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read and filtered in 1.22s, len=10579105\nSorted and took tail(100) in 1.54s, len=808180\nUsers in recent: 8456\nProcess Group PGID: 2256\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:20:03.009Z",
"resultDisplay": "Read and filtered in 1.22s, len=10579105\nSorted and took tail(100) in 1.54s, len=808180\nUsers in recent: 8456",
"description": "Benchmark sorting and taking tail(100) listens per user",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:20:03.010Z",
"content": "\nOutput: Read and filtered in 1.22s, len=10579105\nSorted and took tail(100) in 1.54s, len=808180\nUsers in recent: 8456\nProcess Group PGID: 2256\n",
"isError": false
},
"responseMessageId": "8f1f1049-53c0-46c9-b1bc-04a9f61a4aa0"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T02:20:11.163Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:20:11.163Z",
"text": "Processing Recent Listens\n\nI've successfully retrieved the most recent 100 positive listens for all target users, noting the scale of this data. My current focus is on efficiently converting this `recent` list into a more performant lookup structure or a NumPy array for faster processing."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:20:11.163Z",
"callId": "run_shell_command__call_37035",
"name": "run_shell_command",
"input": {
"description": "Benchmark vectorized listen tensor population",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(set(uids))].sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\n\nt0 = time.time()\nN = len(uids)\nK = 100\nlisten_items = np.full((N, K), 30000, dtype=np.int64)\nlisten_weights = np.zeros((N, K), dtype=np.float32)\n\n# Vectorized weight calculation\ndecay_lambda = np.log(2) / (14 * 86400)\ntime_diff = (cutoff - recent['timestamp'].values).astype(np.float32)\nrec_weights = np.exp(-decay_lambda * time_diff) * (recent['played_ratio_pct'].values / 100.0) * (1.0 + 0.5 * recent['is_organic'].values)\n\n# Map items and uids\nitem_col = [item_to_idx.get(int(x), 30000) for x in recent['item_id'].values]\nuid_rows = [uid_to_idx[int(x)] for x in recent['uid'].values]\n\n# Position within user\nrecent['pos'] = recent.groupby('uid').cumcount()\npositions = recent['pos'].values\n\nlisten_items[uid_rows, positions] = item_col\nlisten_weights[uid_rows, positions] = rec_weights\n\n# Normalize weights\nsum_w = listen_weights.sum(axis=1, keepdims=True) + 1e-6\nlisten_weights /= sum_w\n\nprint(f'Populated listen arrays in {time.time() - t0:.2f}s!')\nprint('Shape items:', listen_items.shape, 'Shape weights:', listen_weights.shape)\nprint('Non-zero weights per user min:', (listen_weights > 0).sum(axis=1).min())\n\""
},
"recorded": {
"id": "run_shell_command__call_37035",
"name": "run_shell_command",
"args": {
"description": "Benchmark vectorized listen tensor population",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(set(uids))].sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\n\nt0 = time.time()\nN = len(uids)\nK = 100\nlisten_items = np.full((N, K), 30000, dtype=np.int64)\nlisten_weights = np.zeros((N, K), dtype=np.float32)\n\n# Vectorized weight calculation\ndecay_lambda = np.log(2) / (14 * 86400)\ntime_diff = (cutoff - recent['timestamp'].values).astype(np.float32)\nrec_weights = np.exp(-decay_lambda * time_diff) * (recent['played_ratio_pct'].values / 100.0) * (1.0 + 0.5 * recent['is_organic'].values)\n\n# Map items and uids\nitem_col = [item_to_idx.get(int(x), 30000) for x in recent['item_id'].values]\nuid_rows = [uid_to_idx[int(x)] for x in recent['uid'].values]\n\n# Position within user\nrecent['pos'] = recent.groupby('uid').cumcount()\npositions = recent['pos'].values\n\nlisten_items[uid_rows, positions] = item_col\nlisten_weights[uid_rows, positions] = rec_weights\n\n# Normalize weights\nsum_w = listen_weights.sum(axis=1, keepdims=True) + 1e-6\nlisten_weights /= sum_w\n\nprint(f'Populated listen arrays in {time.time() - t0:.2f}s!')\nprint('Shape items:', listen_items.shape, 'Shape weights:', listen_weights.shape)\nprint('Non-zero weights per user min:', (listen_weights > 0).sum(axis=1).min())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_37035",
"name": "run_shell_command",
"response": {
"output": "\nOutput: :43: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\nPopulated listen arrays in 0.45s!\nShape items: (8456, 100) Shape weights: (8456, 100)\nNon-zero weights per user min: 20\nProcess Group PGID: 2278\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:20:16.506Z",
"resultDisplay": ":43: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\nPopulated listen arrays in 0.45s!\nShape items: (8456, 100) Shape weights: (8456, 100)\nNon-zero weights per user min: 20",
"description": "Benchmark vectorized listen tensor population",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:20:18.264Z",
"content": "\nOutput: :43: SettingWithCopyWarning: \nA value is trying to be set on a copy of a slice from a DataFrame.\nTry using .loc[row_indexer,col_indexer] = value instead\n\nSee the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\nPopulated listen arrays in 0.45s!\nShape items: (8456, 100) Shape weights: (8456, 100)\nNon-zero weights per user min: 20\nProcess Group PGID: 2278\n",
"isError": false
},
"responseMessageId": "940d678a-137e-40d5-8ad4-521e13f639d4"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T02:20:52.895Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:20:52.895Z",
"text": "Populating Tensors Swiftly\n\nI've successfully populated the complete listen tensor for all 8,456 users, with each user having between 20 and 100 items. My next immediate task is to replicate this efficient tensor population process for both explicit likes and dislikes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:20:52.895Z",
"callId": "run_shell_command__call_167931",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nliked = vps[vps.state == 'liked'].groupby('uid').size()\ndisliked = vps[vps.state == 'disliked'].groupby('uid').size()\n\nprint('Liked counts: min, median, 90th percentile, max:', liked.min(), liked.median(), liked.quantile(0.9), liked.max())\nprint('Disliked counts: min, median, 90th percentile, max:', disliked.min(), disliked.median(), disliked.quantile(0.9), disliked.max())\n\"",
"description": "Check liked and disliked counts distribution"
},
"recorded": {
"id": "run_shell_command__call_167931",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nliked = vps[vps.state == 'liked'].groupby('uid').size()\ndisliked = vps[vps.state == 'disliked'].groupby('uid').size()\n\nprint('Liked counts: min, median, 90th percentile, max:', liked.min(), liked.median(), liked.quantile(0.9), liked.max())\nprint('Disliked counts: min, median, 90th percentile, max:', disliked.min(), disliked.median(), disliked.quantile(0.9), disliked.max())\n\"",
"description": "Check liked and disliked counts distribution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_167931",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Liked counts: min, median, 90th percentile, max: 1 28.0 158.0 1327\nDisliked counts: min, median, 90th percentile, max: 1 3.0 20.0 1703\nProcess Group PGID: 2300\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:20:54.543Z",
"resultDisplay": "Liked counts: min, median, 90th percentile, max: 1 28.0 158.0 1327\nDisliked counts: min, median, 90th percentile, max: 1 3.0 20.0 1703",
"description": "Check liked and disliked counts distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:20:54.544Z",
"content": "\nOutput: Liked counts: min, median, 90th percentile, max: 1 28.0 158.0 1327\nDisliked counts: min, median, 90th percentile, max: 1 3.0 20.0 1703\nProcess Group PGID: 2300\n",
"isError": false
},
"responseMessageId": "635f8678-0762-4525-af8b-17ce2848d6d9"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T02:21:07.459Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:21:07.459Z",
"text": "Analyzing Engagement Metrics\n\nMy current focus is on interpreting engagement data. I've observed that the median 'like' count is 28, with the 90th percentile at 158. Conversely, 'dislikes' are significantly lower, with a median of 3 and a 90th percentile of 20. This suggests a robust strategy for retaining a substantial number of positively engaged items, potentially up to 50 or 100, prioritizing recency or sampling for those exceeding this threshold."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:21:07.459Z",
"text": "Refining User Features\n\nI'm now incorporating new user summary features, including log-transformed total and recent listens, organic listen ratio, and average played ratio. These, alongside existing like and dislike counts, form a 6-dimensional, standardized feature vector for each user. I've also finalized the logic for handling users with no likes or dislikes, ensuring their masks are correctly set to zero."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:21:07.459Z",
"text": "Defining Training Windows\n\nI'm exploring the creation of multiple training windows prior to validation. My current thinking involves setting three distinct cutoffs, each separated by a week plus a buffer. I'm also considering an additional window that includes recent positive interactions, focusing on organic discovery events within specific timeframes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:21:07.459Z",
"text": "Evaluating Discovery Pairs\n\nI've confirmed approximately 220,000 discovery pairs across the three training windows, each mirroring the evaluation task. This dataset size, with a batch size of 512, allows for rapid training iterations, potentially completing multiple epochs in under a minute on CPU. I'm also exploring incorporating general positive listens with reduced weighting."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:21:07.459Z",
"callId": "run_shell_command__call_4982727",
"name": "run_shell_command",
"input": {
"description": "Check active likes at historical cutoffs",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes = pd.read_parquet(DATA / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(DATA / 'train_undislikes.parquet')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\nfor w in range(1, 4):\n c_cut = cutoff - w * (week + gap)\n w_likes = likes[(likes.timestamp < c_cut) & (likes.uid.isin(target_uids))]\n w_unlikes = unlikes[(unlikes.timestamp < c_cut) & (unlikes.uid.isin(target_uids))]\n \n # Active likes\n active_likes = set(zip(w_likes.uid, w_likes.item_id)) - set(zip(w_unlikes.uid, w_unlikes.item_id))\n print(f'Cutoff w={w} ({c_cut}): active likes={len(active_likes)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_4982727",
"name": "run_shell_command",
"args": {
"description": "Check active likes at historical cutoffs",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes = pd.read_parquet(DATA / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(DATA / 'train_undislikes.parquet')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\nfor w in range(1, 4):\n c_cut = cutoff - w * (week + gap)\n w_likes = likes[(likes.timestamp < c_cut) & (likes.uid.isin(target_uids))]\n w_unlikes = unlikes[(unlikes.timestamp < c_cut) & (unlikes.uid.isin(target_uids))]\n \n # Active likes\n active_likes = set(zip(w_likes.uid, w_likes.item_id)) - set(zip(w_unlikes.uid, w_unlikes.item_id))\n print(f'Cutoff w={w} ({c_cut}): active likes={len(active_likes)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4982727",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Cutoff w=1 (24180200): active likes=583825\nCutoff w=2 (23573600): active likes=563988\nCutoff w=3 (22967000): active likes=543365\nProcess Group PGID: 2318\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:21:11.584Z",
"resultDisplay": "Cutoff w=1 (24180200): active likes=583825\nCutoff w=2 (23573600): active likes=563988\nCutoff w=3 (22967000): active likes=543365",
"description": "Check active likes at historical cutoffs",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:21:11.585Z",
"content": "\nOutput: Cutoff w=1 (24180200): active likes=583825\nCutoff w=2 (23573600): active likes=563988\nCutoff w=3 (22967000): active likes=543365\nProcess Group PGID: 2318\n",
"isError": false
},
"responseMessageId": "6badcc1c-51f5-499f-992f-21944e02893a"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T02:22:33.834Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:22:33.834Z",
"text": "Calculating Preferences\n\nI've realized that active likes and dislikes can be determined at any specific point in time, $C$, by applying simple filters to past data. I can isolate likes before $C$ and subtract unlikes before $C$, and similarly for dislikes and undislikes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:22:33.834Z",
"text": "Structuring Data Loading\n\nI'm now focusing on organizing the data loading process. I will implement functions within `data_prep.py` to handle catalog features and construct detailed user histories. This includes parsing listen and preference data up to specific cutoffs."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:22:33.834Z",
"callId": "write_file__call_4045786",
"name": "write_file",
"input": {
"file_path": "/app/solution/data_prep.py",
"content": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n"
},
"recorded": {
"id": "write_file__call_4045786",
"name": "write_file",
"args": {
"file_path": "/app/solution/data_prep.py",
"content": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_4045786",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/data_prep.py. Here is the updated code:\n\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:22:33.846Z",
"resultDisplay": {
"fileDiff": "Index: data_prep.py\n===================================================================\n--- data_prep.py\tOriginal\n+++ data_prep.py\tWritten\n@@ -0,0 +1,354 @@\n+\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\n+from __future__ import annotations\n+\n+import time\n+from pathlib import Path\n+from typing import Dict, List, Optional, Set, Tuple\n+\n+import numpy as np\n+import pandas as pd\n+import pyarrow.parquet as pq\n+import torch\n+\n+\n+def load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n+ \"\"\"Load candidate catalog items, normalized features, and popularity.\n+\n+ Returns:\n+ catalog_features: FloatTensor of shape (30000, 4).\n+ item_to_idx: mapping from catalog item_id to index [0, 29999].\n+ catalog_item_ids: int64 ndarray of shape (30000,).\n+ item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n+ \"\"\"\n+ catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n+ catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n+ item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n+\n+ pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n+ org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n+ pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n+\n+ log_pos = np.log1p(pos_listens)\n+ log_org = np.log1p(org_listens)\n+ org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n+ norm_rank = pop_rank / float(len(catalog_df))\n+\n+ # Standardize features\n+ features = np.column_stack([\n+ (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n+ (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n+ org_ratio,\n+ norm_rank,\n+ ]).astype(np.float32)\n+\n+ # LogQ for popularity debiasing\n+ prob = pos_listens / pos_listens.sum()\n+ item_logq = np.log(prob + 1e-12).astype(np.float32)\n+\n+ return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n+\n+\n+def extract_user_histories(\n+ data_dir: Path,\n+ cutoff: int,\n+ target_uids: np.ndarray,\n+ item_to_idx: Dict[int, int],\n+ pref_state_path: Optional[Path] = None,\n+ lookback_days: int = 90,\n+ max_listens: int = 100,\n+ max_likes: int = 60,\n+ max_dislikes: int = 30,\n+ decay_half_life_days: float = 14.0,\n+ include_val: bool = False,\n+) -> Dict[str, torch.Tensor]:\n+ \"\"\"Extract user history tensors at a given cutoff timestamp.\n+\n+ Args:\n+ data_dir: Path to data directory.\n+ cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n+ target_uids: Ordered array of target user IDs.\n+ item_to_idx: Catalog mapping.\n+ pref_state_path: Optional path to preference state parquet.\n+ lookback_days: Max days to look back for listens.\n+ max_listens: Max recent listens per user.\n+ max_likes: Max liked items per user.\n+ max_dislikes: Max disliked items per user.\n+ decay_half_life_days: Exponential decay half life.\n+ include_val: Whether to include val_* files in history (for final test refit).\n+\n+ Returns:\n+ Dictionary of tensors:\n+ - listen_items: LongTensor (N, max_listens)\n+ - listen_weights: FloatTensor (N, max_listens)\n+ - like_items: LongTensor (N, max_likes)\n+ - like_mask: FloatTensor (N, max_likes)\n+ - dislike_items: LongTensor (N, max_dislikes)\n+ - dislike_mask: FloatTensor (N, max_dislikes)\n+ - user_features: FloatTensor (N, 6)\n+ \"\"\"\n+ n_users = len(target_uids)\n+ uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n+ target_uids_set = set(uid_to_row.keys())\n+\n+ # --- 1. Process Listens ---\n+ lookback_ts = cutoff - int(lookback_days * 86400)\n+ filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n+ \n+ train_tbl = pq.read_table(\n+ data_dir / \"train_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+ filters=filters,\n+ )\n+ listens_df = train_tbl.to_pandas()\n+\n+ if include_val and (data_dir / \"val_listens.parquet\").exists():\n+ val_tbl = pq.read_table(\n+ data_dir / \"val_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+ filters=filters,\n+ )\n+ val_df = val_tbl.to_pandas()\n+ listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n+\n+ listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n+ recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n+\n+ listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n+ listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n+\n+ decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n+ time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n+ raw_weights = (\n+ np.exp(-decay_lambda * time_diff)\n+ * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n+ * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n+ ).astype(np.float32)\n+\n+ recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n+ recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n+ recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n+ positions = recent_listens[\"pos\"].to_numpy()\n+\n+ row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n+ mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n+\n+ listen_items[row_indices, positions] = mapped_item_indices\n+ listen_weights[row_indices, positions] = raw_weights\n+\n+ # Normalize weights row-wise\n+ w_sum = listen_weights.sum(axis=1, keepdims=True)\n+ mask = w_sum > 0\n+ listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n+\n+ # --- 2. Process Preference State (Likes & Dislikes) ---\n+ like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n+ like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n+ dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n+ dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n+\n+ if pref_state_path and pref_state_path.exists():\n+ pref_df = pd.read_parquet(pref_state_path)\n+ pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n+ liked_df = pref_df[pref_df.state == \"liked\"]\n+ disliked_df = pref_df[pref_df.state == \"disliked\"]\n+ else:\n+ # Reconstruct active preferences from events\n+ likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n+ unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n+ dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n+ undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n+\n+ if include_val and (data_dir / \"val_likes.parquet\").exists():\n+ likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n+ unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n+ dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n+ undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n+\n+ likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n+ unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n+ dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n+ undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n+\n+ # Set difference for active likes\n+ active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n+ liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n+\n+ active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n+ disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n+\n+ # Populate likes\n+ if len(liked_df) > 0:\n+ liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n+ liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n+ l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n+ l_pos = liked_tail[\"pos\"].values\n+ l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n+ like_items[l_rows, l_pos] = l_items\n+ like_mask[l_rows, l_pos] = 1.0\n+\n+ # Populate dislikes\n+ if len(disliked_df) > 0:\n+ disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n+ disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n+ d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n+ d_pos = disliked_tail[\"pos\"].values\n+ d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n+ dislike_items[d_rows, d_pos] = d_items\n+ dislike_mask[d_rows, d_pos] = 1.0\n+\n+ # --- 3. User Summary Features ---\n+ tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n+ tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n+\n+ user_feats = np.zeros((n_users, 6), dtype=np.float32)\n+ listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n+ organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n+ like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n+ dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n+\n+ for uid, r in uid_to_row.items():\n+ rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n+ n_rec = float(listen_counts.get(uid, 0))\n+ n_org = float(organic_counts.get(uid, 0))\n+ n_like = float(like_counts.get(uid, 0))\n+ n_dis = float(dislike_counts.get(uid, 0))\n+\n+ user_feats[r, 0] = np.log1p(hist_listens)\n+ user_feats[r, 1] = np.log1p(rec_listens)\n+ user_feats[r, 2] = np.log1p(n_rec)\n+ user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n+ user_feats[r, 4] = np.log1p(n_like)\n+ user_feats[r, 5] = np.log1p(n_dis)\n+\n+ # Standardize user features\n+ feat_mean = user_feats.mean(axis=0, keepdims=True)\n+ feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n+ user_feats = (user_feats - feat_mean) / feat_std\n+\n+ return {\n+ \"listen_items\": torch.from_numpy(listen_items),\n+ \"listen_weights\": torch.from_numpy(listen_weights),\n+ \"like_items\": torch.from_numpy(like_items),\n+ \"like_mask\": torch.from_numpy(like_mask),\n+ \"dislike_items\": torch.from_numpy(dislike_items),\n+ \"dislike_mask\": torch.from_numpy(dislike_mask),\n+ \"user_features\": torch.from_numpy(user_feats),\n+ }\n+\n+\n+def build_training_pairs(\n+ data_dir: Path,\n+ cutoffs: List[int],\n+ item_to_idx: Dict[int, int],\n+ target_uids: np.ndarray,\n+ week_seconds: int = 7 * 86400,\n+ gap_seconds: int = 1800,\n+ listen_strong_threshold: int = 80,\n+ sample_listens_per_user: int = 30,\n+ include_val: bool = False,\n+) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n+ \"\"\"Build multi-window training samples for the two-tower model.\n+\n+ For each cutoff C:\n+ - History extracted up to C\n+ - Targets extracted from [C + gap, C + gap + week]\n+ - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n+\n+ Returns:\n+ histories_list: list of user history dicts (one per cutoff).\n+ user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n+ target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n+ pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n+ \"\"\"\n+ uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n+ target_uids_set = set(uid_to_row.keys())\n+\n+ # Load likes\n+ likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n+ if include_val and (data_dir / \"val_likes.parquet\").exists():\n+ likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n+ org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n+\n+ # Load strong organic listens\n+ min_cutoff = min(cutoffs)\n+ table = pq.read_table(\n+ data_dir / \"train_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+ filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n+ )\n+ listens_df = table.to_pandas()\n+ if include_val and (data_dir / \"val_listens.parquet\").exists():\n+ val_tbl = pq.read_table(\n+ data_dir / \"val_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n+ filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n+ )\n+ listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n+ strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n+\n+ histories_list: List[Dict[str, torch.Tensor]] = []\n+ pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n+\n+ for w_idx, cutoff in enumerate(cutoffs):\n+ t_start = cutoff + gap_seconds\n+ t_end = t_start + week_seconds\n+\n+ # 1. Extract history up to cutoff\n+ print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n+ hist = extract_user_histories(\n+ data_dir=data_dir,\n+ cutoff=cutoff,\n+ target_uids=target_uids,\n+ item_to_idx=item_to_idx,\n+ include_val=include_val,\n+ )\n+ histories_list.append(hist)\n+\n+ # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n+ # Read past items from the history listen_items and likes\n+ past_items_per_user: Dict[int, Set[int]] = {}\n+ for r, uid in enumerate(target_uids):\n+ # Items in listens and likes\n+ l_itms = set(hist[\"listen_items\"][r].numpy())\n+ lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n+ past = l_itms.union(lk_itms)\n+ past.discard(30000)\n+ past_items_per_user[r] = past\n+\n+ # 3. Targets in [t_start, t_end]\n+ w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n+ w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n+\n+ # Map and filter discovery likes (grade 3, weight 3.0)\n+ user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n+\n+ for uid, item in zip(w_likes.uid, w_likes.item_id):\n+ r = uid_to_row[int(uid)]\n+ item_idx = item_to_idx.get(int(item))\n+ if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n+ pairs_list.append((w_idx, r, item_idx, 3.0))\n+ user_seen_targets[r].add(item_idx)\n+\n+ # Map and filter discovery listens (grade 1, weight 1.0)\n+ # To balance, subsample if a user has too many listens\n+ grouped_listens = w_listens.groupby(\"uid\")\n+ for uid, group in grouped_listens:\n+ r = uid_to_row[int(uid)]\n+ items = group[\"item_id\"].to_numpy()\n+ if len(items) > sample_listens_per_user:\n+ items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n+ for itm in items:\n+ item_idx = item_to_idx.get(int(itm))\n+ if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n+ pairs_list.append((w_idx, r, item_idx, 1.0))\n+ user_seen_targets[r].add(item_idx)\n+\n+ np_pairs = np.array(pairs_list, dtype=np.float32)\n+ np.random.shuffle(np_pairs)\n+\n+ user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n+ target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n+ weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n+\n+ print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n+ return histories_list, user_win, target_items, weights\n",
"fileName": "data_prep.py",
"filePath": "/app/solution/data_prep.py",
"originalContent": "",
"newContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"diffStat": {
"model_added_lines": 354,
"model_removed_lines": 0,
"model_added_chars": 16458,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/data_prep.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:22:33.847Z",
"content": "Successfully created and wrote to new file: /app/solution/data_prep.py. Here is the updated code:\n\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"isError": false
},
"responseMessageId": "4c5580c0-d1f4-466a-a256-fe0ee67ebdf0"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T02:23:21.439Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:23:21.439Z",
"callId": "run_shell_command__call_4302037",
"name": "run_shell_command",
"input": {
"description": "Test load_catalog_features and extract_user_histories",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, extract_user_histories\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\nprint(f'Catalog loaded in {time.time() - t0:.2f}s: feats shape={cat_feats.shape}, logq shape={item_logq.shape}')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nt1 = time.time()\nhist = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\nprint(f'Extracted validation user histories in {time.time() - t1:.2f}s:')\nfor k, v in hist.items():\n print(f' {k}: shape={v.shape}, dtype={v.dtype}')\n\""
},
"recorded": {
"id": "run_shell_command__call_4302037",
"name": "run_shell_command",
"args": {
"description": "Test load_catalog_features and extract_user_histories",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, extract_user_histories\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\nprint(f'Catalog loaded in {time.time() - t0:.2f}s: feats shape={cat_feats.shape}, logq shape={item_logq.shape}')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nt1 = time.time()\nhist = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\nprint(f'Extracted validation user histories in {time.time() - t1:.2f}s:')\nfor k, v in hist.items():\n print(f' {k}: shape={v.shape}, dtype={v.dtype}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4302037",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog loaded in 0.03s: feats shape=torch.Size([30000, 4]), logq shape=(30000,)\nExtracted validation user histories in 3.42s:\n listen_items: shape=torch.Size([8456, 100]), dtype=torch.int64\n listen_weights: shape=torch.Size([8456, 100]), dtype=torch.float32\n like_items: shape=torch.Size([8456, 60]), dtype=torch.int64\n like_mask: shape=torch.Size([8456, 60]), dtype=torch.float32\n dislike_items: shape=torch.Size([8456, 30]), dtype=torch.int64\n dislike_mask: shape=torch.Size([8456, 30]), dtype=torch.float32\n user_features: shape=torch.Size([8456, 6]), dtype=torch.float32\nProcess Group PGID: 2337\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:23:28.923Z",
"resultDisplay": "Catalog loaded in 0.03s: feats shape=torch.Size([30000, 4]), logq shape=(30000,)\nExtracted validation user histories in 3.42s:\n listen_items: shape=torch.Size([8456, 100]), dtype=torch.int64\n listen_weights: shape=torch.Size([8456, 100]), dtype=torch.float32\n like_items: shape=torch.Size([8456, 60]), dtype=torch.int64\n like_mask: shape=torch.Size([8456, 60]), dtype=torch.float32\n dislike_items: shape=torch.Size([8456, 30]), dtype=torch.int64\n dislike_mask: shape=torch.Size([8456, 30]), dtype=torch.float32\n user_features: shape=torch.Size([8456, 6]), dtype=torch.float32",
"description": "Test load_catalog_features and extract_user_histories",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:23:28.924Z",
"content": "\nOutput: Catalog loaded in 0.03s: feats shape=torch.Size([30000, 4]), logq shape=(30000,)\nExtracted validation user histories in 3.42s:\n listen_items: shape=torch.Size([8456, 100]), dtype=torch.int64\n listen_weights: shape=torch.Size([8456, 100]), dtype=torch.float32\n like_items: shape=torch.Size([8456, 60]), dtype=torch.int64\n like_mask: shape=torch.Size([8456, 60]), dtype=torch.float32\n dislike_items: shape=torch.Size([8456, 30]), dtype=torch.int64\n dislike_mask: shape=torch.Size([8456, 30]), dtype=torch.float32\n user_features: shape=torch.Size([8456, 6]), dtype=torch.float32\nProcess Group PGID: 2337\n",
"isError": false
},
"responseMessageId": "87425cfc-ce7b-4474-8f5b-13017f201401"
}
]
},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T02:23:45.552Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:23:45.552Z",
"text": "Refining training pair construction\n\nMy current focus is on optimizing `build_training_pairs`. I've successfully extracted user histories in 3.42 seconds and am now calculating the necessary training window boundaries, denoted as $C_1$ and $C_2$, with values 24180200 and 23573600 respectively, to ensure robust training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:23:45.552Z",
"callId": "run_shell_command__call_16444",
"name": "run_shell_command",
"input": {
"description": "Test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
},
"recorded": {
"id": "run_shell_command__call_16444",
"name": "run_shell_command",
"args": {
"description": "Test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_16444",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 17, in \n File \"/app/solution/data_prep.py\", line 263, in build_training_pairs\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n ^\nNameError: name 'u' is not defined\nExit Code: 1\nProcess Group PGID: 2359\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:23:49.549Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 17, in \n File \"/app/solution/data_prep.py\", line 263, in build_training_pairs\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n ^\nNameError: name 'u' is not defined",
"description": "Test build_training_pairs",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:23:49.550Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 17, in \n File \"/app/solution/data_prep.py\", line 263, in build_training_pairs\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n ^\nNameError: name 'u' is not defined\nExit Code: 1\nProcess Group PGID: 2359\n",
"isError": false
},
"responseMessageId": "5c503a27-4fe7-476d-aac9-4b5c42e2e8ff"
}
]
},
{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T02:23:52.137Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:23:52.137Z",
"callId": "replace__call_55922",
"name": "replace",
"input": {
"old_string": " uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}",
"instruction": "Fix variable name in uid_to_row comprehension",
"file_path": "/app/solution/data_prep.py",
"allow_multiple": false,
"new_string": " uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}"
},
"recorded": {
"id": "replace__call_55922",
"name": "replace",
"args": {
"old_string": " uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}",
"instruction": "Fix variable name in uid_to_row comprehension",
"file_path": "/app/solution/data_prep.py",
"allow_multiple": false,
"new_string": " uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}"
},
"result": [
{
"functionResponse": {
"id": "replace__call_55922",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:23:52.149Z",
"resultDisplay": {
"fileDiff": "Index: data_prep.py\n===================================================================\n--- data_prep.py\tCurrent\n+++ data_prep.py\tProposed\n@@ -260,7 +260,7 @@\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n- uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n+ uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n \n # Load likes\n",
"fileName": "data_prep.py",
"filePath": "/app/solution/data_prep.py",
"originalContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"newContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"diffStat": {
"model_added_lines": 1,
"model_removed_lines": 1,
"model_added_chars": 67,
"model_removed_chars": 65,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/data_prep.py: uid_to_row = {int(u): r fo... => uid_to_row = {int(uid): r ...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:23:52.150Z",
"content": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n...",
"isError": false
},
"responseMessageId": "069a6d63-b953-42fb-b83c-622d1f3d41d4"
}
]
},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T02:23:55.581Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:23:55.581Z",
"callId": "run_shell_command__call_3380262",
"name": "run_shell_command",
"input": {
"description": "Re-run test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
},
"recorded": {
"id": "run_shell_command__call_3380262",
"name": "run_shell_command",
"args": {
"description": "Re-run test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3380262",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Extracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76517 discovery training pairs across 2 windows.\nTotal time: 11.34s\nPairs: user_win shape=torch.Size([76517, 2]), target_items shape=torch.Size([76517]), weights=torch.Size([76517])\nProcess Group PGID: 2376\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:24:11.005Z",
"resultDisplay": "Extracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76517 discovery training pairs across 2 windows.\nTotal time: 11.34s\nPairs: user_win shape=torch.Size([76517, 2]), target_items shape=torch.Size([76517]), weights=torch.Size([76517])",
"description": "Re-run test build_training_pairs",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:24:11.006Z",
"content": "\nOutput: Extracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76517 discovery training pairs across 2 windows.\nTotal time: 11.34s\nPairs: user_win shape=torch.Size([76517, 2]), target_items shape=torch.Size([76517]), weights=torch.Size([76517])\nProcess Group PGID: 2376\n",
"isError": false
},
"responseMessageId": "a40a1dbb-aa9e-438d-981c-7cd250deaddc"
}
]
},
{
"id": 54,
"role": "agent",
"timestamp": "2026-09-29T02:24:19.727Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:19.727Z",
"text": "Generating Discovery Pairs\n\nI've successfully generated over 76,000 discovery training pairs within just 11 seconds using two windows. I'm now exploring the impact of increasing the number of windows to three or raising `sample_listens_per_user` to 40-50, aiming for over 150,000 pairs in approximately 16 seconds. The cleanliness of the `user_win` output is also a positive observation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:19.727Z",
"text": "Optimizing History Indexing\n\nI'm considering how to best index user history features for training efficiency. Instead of storing duplicate tensors, I can pre-index them by stacking user histories across windows. This would allow for a single tensor indexing operation, potentially streamlining the batching process and reducing memory overhead."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:24:19.727Z",
"callId": "update_topic__call_109998",
"name": "update_topic",
"input": {
"title": "PyTorch Two-Tower Architecture",
"strategic_intent": "Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.",
"summary": "Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval."
},
"recorded": {
"id": "update_topic__call_109998",
"name": "update_topic",
"args": {
"title": "PyTorch Two-Tower Architecture",
"strategic_intent": "Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.",
"summary": "Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_109998",
"name": "update_topic",
"response": {
"output": "Current topic: \"PyTorch Two-Tower Architecture\"\nTopic summary: Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval.\n\nStrategic Intent: Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:24:19.729Z",
"resultDisplay": "## ๐ Topic: **PyTorch Two-Tower Architecture**\n\n**Summary:**\nCompleted Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval.\n\n> [!STRATEGY]\n> **Intent:** Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.",
"description": "Update topic to: \"PyTorch Two-Tower Architecture\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:24:19.730Z",
"content": "Current topic: \"PyTorch Two-Tower Architecture\"\nTopic summary: Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval.\n\nStrategic Intent: Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.",
"isError": false
},
"responseMessageId": "972cf14e-c6cc-419d-a3cf-6bb4e4ef1f1e"
}
]
},
{
"id": 55,
"role": "agent",
"timestamp": "2026-09-29T02:24:51.379Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Designing Model Architecture\n\nI am now focusing on the precise design of the model architecture within `solution/model.py`. My current consideration revolves around fulfilling the requirement for an exact decomposition into user vectors and independently precomputable components."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Developing Item Tower\n\nI'm actively building the `ItemTower` in PyTorch. The design focuses on embedding items and projecting catalog features using an MLP. This is a key step towards generating the item vectors needed for our model."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Refining Item Projection\n\nI'm refining the `ItemTower`'s output projection. The logic now concatenates item embeddings with projected catalog features before passing them through a final MLP. This ensures normalized item vectors suitable for downstream computations.\n\n**Integrating User Tower Design**\n\nI am now considering the `UserTower` design. I am debating whether to share the `item_embed` layer from the `ItemTower` or create a separate one. Sharing is standard and beneficial for two-tower models, maintaining a consistent representation space."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Considering Embedding Sharing\n\nI am weighing the benefits of sharing the item embedding table between the `UserTower` and `ItemTower`. While a separate table is technically feasible, sharing it could reduce parameter count and ensure a more consistent representation space for items across both towers."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Processing User Embeddings\n\nI'm finalizing the `UserTower`. It now processes listen, like, and dislike histories, pooling embeddings and incorporating projected user features. The combined representation is then passed through a multi-layer perceptron, finally normalizing to produce the user vector."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Validating Decomposition\n\nI have confirmed that the model architecture precisely decomposes into a user vector and independently precomputable item vectors. Candidate item embeddings can indeed be computed offline, and user vectors will be generated at request time, allowing for efficient scoring via dot product."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:51.379Z",
"text": "Implementing Loss Function\n\nI am now implementing the InfoNCE loss with sampled softmax and popularity debiasing. This involves calculating dot products between user and item embeddings, applying temperature scaling and logit debiasing, and computing the loss for positive items. Additionally, I am incorporating a hard negative dislike penalty to further refine the model."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:24:51.379Z",
"callId": "write_file__call_107140",
"name": "write_file",
"input": {
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
},
"recorded": {
"id": "write_file__call_107140",
"name": "write_file",
"args": {
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_107140",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:\n\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:24:51.387Z",
"resultDisplay": {
"fileDiff": "Index: model.py\n===================================================================\n--- model.py\tOriginal\n+++ model.py\tWritten\n@@ -0,0 +1,291 @@\n+\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\n+from __future__ import annotations\n+\n+from typing import Dict, Optional, Tuple\n+\n+import torch\n+import torch.nn as nn\n+import torch.nn.functional as F\n+\n+\n+class ItemTower(nn.Module):\n+ \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n+\n+ def __init__(\n+ self,\n+ item_embed: nn.Embedding,\n+ feat_dim: int = 4,\n+ embed_dim: int = 64,\n+ hidden_dim: int = 64,\n+ out_dim: int = 64,\n+ ):\n+ super().__init__()\n+ self.item_embed = item_embed\n+ self.feat_mlp = nn.Sequential(\n+ nn.Linear(feat_dim, 32),\n+ nn.LayerNorm(32),\n+ nn.GELU(),\n+ )\n+ self.out_mlp = nn.Sequential(\n+ nn.Linear(embed_dim + 32, hidden_dim),\n+ nn.LayerNorm(hidden_dim),\n+ nn.GELU(),\n+ nn.Linear(hidden_dim, out_dim),\n+ )\n+\n+ def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n+ \"\"\"Compute normalized item embeddings.\n+\n+ Args:\n+ item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n+ item_features: FloatTensor of shape (B, 4).\n+\n+ Returns:\n+ L2-normalized FloatTensor of shape (B, out_dim).\n+ \"\"\"\n+ emb = self.item_embed(item_indices)\n+ feat = self.feat_mlp(item_features)\n+ combined = torch.cat([emb, feat], dim=-1)\n+ out = self.out_mlp(combined)\n+ return F.normalize(out, p=2, dim=-1)\n+\n+\n+class UserTower(nn.Module):\n+ \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n+\n+ def __init__(\n+ self,\n+ item_embed: nn.Embedding,\n+ embed_dim: int = 64,\n+ user_feat_dim: int = 6,\n+ hidden_dim: int = 128,\n+ out_dim: int = 64,\n+ dropout: float = 0.1,\n+ ):\n+ super().__init__()\n+ self.item_embed = item_embed\n+ self.user_feat_mlp = nn.Sequential(\n+ nn.Linear(user_feat_dim, 32),\n+ nn.LayerNorm(32),\n+ nn.GELU(),\n+ )\n+ in_dim = embed_dim * 3 + 32\n+ self.mlp = nn.Sequential(\n+ nn.Linear(in_dim, hidden_dim),\n+ nn.LayerNorm(hidden_dim),\n+ nn.GELU(),\n+ nn.Dropout(dropout),\n+ nn.Linear(hidden_dim, hidden_dim),\n+ nn.LayerNorm(hidden_dim),\n+ nn.GELU(),\n+ nn.Linear(hidden_dim, out_dim),\n+ )\n+\n+ def forward(\n+ self,\n+ listen_items: torch.Tensor,\n+ listen_weights: torch.Tensor,\n+ like_items: torch.Tensor,\n+ like_mask: torch.Tensor,\n+ dislike_items: torch.Tensor,\n+ dislike_mask: torch.Tensor,\n+ user_features: torch.Tensor,\n+ ) -> torch.Tensor:\n+ \"\"\"Compute normalized user embeddings from interaction history.\n+\n+ Args:\n+ listen_items: LongTensor (B, max_listens)\n+ listen_weights: FloatTensor (B, max_listens)\n+ like_items: LongTensor (B, max_likes)\n+ like_mask: FloatTensor (B, max_likes)\n+ dislike_items: LongTensor (B, max_dislikes)\n+ dislike_mask: FloatTensor (B, max_dislikes)\n+ user_features: FloatTensor (B, user_feat_dim)\n+\n+ Returns:\n+ L2-normalized FloatTensor of shape (B, out_dim).\n+ \"\"\"\n+ # 1. Weighted listens pooling\n+ l_emb = self.item_embed(listen_items) # (B, K, D)\n+ pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n+\n+ # 2. Likes pooling\n+ lk_emb = self.item_embed(like_items) # (B, L, D)\n+ lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n+ pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n+ has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n+ pooled_likes = pooled_likes * has_likes\n+\n+ # 3. Dislikes pooling\n+ dk_emb = self.item_embed(dislike_items) # (B, M, D)\n+ dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n+ pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n+ has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n+ pooled_dislikes = pooled_dislikes * has_dislikes\n+\n+ # 4. User features projection\n+ user_feat_proj = self.user_feat_mlp(user_features)\n+\n+ # 5. Combined MLP\n+ combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n+ out = self.mlp(combined)\n+ return F.normalize(out, p=2, dim=-1)\n+\n+\n+class TwoTowerRanker(nn.Module):\n+ \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n+\n+ def __init__(\n+ self,\n+ num_catalog_items: int = 30000,\n+ embed_dim: int = 64,\n+ hidden_dim: int = 128,\n+ out_dim: int = 64,\n+ dropout: float = 0.1,\n+ temperature: float = 0.07,\n+ ):\n+ super().__init__()\n+ # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n+ self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n+ nn.init.normal_(self.item_embed.weight, std=0.05)\n+ with torch.no_grad():\n+ self.item_embed.weight[num_catalog_items].fill_(0.0)\n+\n+ self.item_tower = ItemTower(\n+ item_embed=self.item_embed,\n+ feat_dim=4,\n+ embed_dim=embed_dim,\n+ hidden_dim=hidden_dim,\n+ out_dim=out_dim,\n+ )\n+ self.user_tower = UserTower(\n+ item_embed=self.item_embed,\n+ embed_dim=embed_dim,\n+ user_feat_dim=6,\n+ hidden_dim=hidden_dim,\n+ out_dim=out_dim,\n+ dropout=dropout,\n+ )\n+ self.temperature = temperature\n+\n+ def precompute_candidate_embeddings(\n+ self,\n+ catalog_features: torch.Tensor,\n+ batch_size: int = 4096,\n+ ) -> torch.Tensor:\n+ \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n+\n+ Args:\n+ catalog_features: FloatTensor of shape (30000, 4).\n+\n+ Returns:\n+ FloatTensor of shape (30000, out_dim).\n+ \"\"\"\n+ self.eval()\n+ n_items = len(catalog_features)\n+ embeddings = []\n+ with torch.no_grad():\n+ for i in range(0, n_items, batch_size):\n+ end = min(i + batch_size, n_items)\n+ idx = torch.arange(i, end, dtype=torch.int64)\n+ feat = catalog_features[i:end]\n+ emb = self.item_tower(idx, feat)\n+ embeddings.append(emb)\n+ return torch.cat(embeddings, dim=0)\n+\n+ def compute_user_embeddings(\n+ self,\n+ user_history: Dict[str, torch.Tensor],\n+ batch_size: int = 1024,\n+ ) -> torch.Tensor:\n+ \"\"\"Compute user embeddings from interaction history.\n+\n+ Args:\n+ user_history: Dictionary containing history tensors.\n+\n+ Returns:\n+ FloatTensor of shape (N_users, out_dim).\n+ \"\"\"\n+ self.eval()\n+ n_users = len(user_history[\"listen_items\"])\n+ user_embeddings = []\n+ with torch.no_grad():\n+ for i in range(0, n_users, batch_size):\n+ end = min(i + batch_size, n_users)\n+ u_emb = self.user_tower(\n+ listen_items=user_history[\"listen_items\"][i:end],\n+ listen_weights=user_history[\"listen_weights\"][i:end],\n+ like_items=user_history[\"like_items\"][i:end],\n+ like_mask=user_history[\"like_mask\"][i:end],\n+ dislike_items=user_history[\"dislike_items\"][i:end],\n+ dislike_mask=user_history[\"dislike_mask\"][i:end],\n+ user_features=user_history[\"user_features\"][i:end],\n+ )\n+ user_embeddings.append(u_emb)\n+ return torch.cat(user_embeddings, dim=0)\n+\n+ def forward_loss(\n+ self,\n+ user_history_batch: Dict[str, torch.Tensor],\n+ target_item_indices: torch.Tensor,\n+ target_item_features: torch.Tensor,\n+ target_item_logq: torch.Tensor,\n+ sample_weights: torch.Tensor,\n+ dislike_item_indices: Optional[torch.Tensor] = None,\n+ dislike_item_features: Optional[torch.Tensor] = None,\n+ dislike_mask: Optional[torch.Tensor] = None,\n+ ) -> torch.Tensor:\n+ \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n+\n+ Args:\n+ user_history_batch: Dict of user history tensors of batch size B.\n+ target_item_indices: LongTensor of positive item indices (B,).\n+ target_item_features: FloatTensor of positive item features (B, 4).\n+ target_item_logq: FloatTensor of positive item logq (B,).\n+ sample_weights: FloatTensor of positive sample weights (B,).\n+ dislike_item_indices: Optional LongTensor of hard negative items (B,).\n+ dislike_item_features: Optional FloatTensor (B, 4).\n+ dislike_mask: Optional FloatTensor (B,).\n+\n+ Returns:\n+ Scalar loss tensor.\n+ \"\"\"\n+ # User vectors (B, D)\n+ u = self.user_tower(\n+ listen_items=user_history_batch[\"listen_items\"],\n+ listen_weights=user_history_batch[\"listen_weights\"],\n+ like_items=user_history_batch[\"like_items\"],\n+ like_mask=user_history_batch[\"like_mask\"],\n+ dislike_items=user_history_batch[\"dislike_items\"],\n+ dislike_mask=user_history_batch[\"dislike_mask\"],\n+ user_features=user_history_batch[\"user_features\"],\n+ )\n+\n+ # Positive candidate vectors (B, D)\n+ v_pos = self.item_tower(target_item_indices, target_item_features)\n+\n+ # Dot product matrix (B, B)\n+ sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n+\n+ # Popularity correction and temperature scaling\n+ # sim / tau - logq\n+ logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n+\n+ # Cross entropy with in-batch negatives\n+ batch_size = len(target_item_indices)\n+ targets = torch.arange(batch_size, device=target_item_indices.device)\n+ per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n+\n+ # Weight by grade importance (likes 3.0, listens 1.0)\n+ norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n+ infonce_loss = (per_sample_loss * norm_weights).mean()\n+\n+ # Dislike penalty\n+ if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n+ v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n+ dislike_sim = (u * v_neg).sum(dim=-1)\n+ # Penalize similarity to disliked tracks\n+ dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n+ return infonce_loss + 0.2 * dislike_loss\n+\n+ return infonce_loss\n",
"fileName": "model.py",
"filePath": "/app/solution/model.py",
"originalContent": "",
"newContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"diffStat": {
"model_added_lines": 291,
"model_removed_lines": 0,
"model_added_chars": 10598,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/model.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:24:51.389Z",
"content": "Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:\n\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"isError": false
},
"responseMessageId": "e5f09136-36d4-43b7-9249-76e6c7bb81f7"
}
]
},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-29T02:25:08.070Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:25:08.070Z",
"callId": "run_shell_command__call_2287786",
"name": "run_shell_command",
"input": {
"description": "Test model forward, backward, precompute and retrieval",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nfrom model import TwoTowerRanker\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\ncat_feats = torch.randn(30000, 4)\nv = model.precompute_candidate_embeddings(cat_feats)\nprint('Precomputed candidate embeddings shape:', v.shape, 'norms:', v.norm(dim=1)[:5])\n\nB = 16\nu_hist = {\n 'listen_items': torch.randint(0, 30000, (B, 100)),\n 'listen_weights': torch.ones(B, 100) / 100.0,\n 'like_items': torch.randint(0, 30000, (B, 60)),\n 'like_mask': torch.ones(B, 60),\n 'dislike_items': torch.randint(0, 30000, (B, 30)),\n 'dislike_mask': torch.ones(B, 30),\n 'user_features': torch.randn(B, 6),\n}\nu = model.compute_user_embeddings(u_hist)\nprint('User embeddings shape:', u.shape, 'norms:', u.norm(dim=1)[:5])\n\nscores = torch.matmul(u, v.t())\nprint('Scores shape:', scores.shape, 'min:', scores.min().item(), 'max:', scores.max().item())\n\n# Test loss\nloss = model.forward_loss(\n user_history_batch=u_hist,\n target_item_indices=torch.randint(0, 30000, (B,)),\n target_item_features=torch.randn(B, 4),\n target_item_logq=torch.zeros(B),\n sample_weights=torch.ones(B),\n)\nprint('Loss:', loss.item())\nloss.backward()\nprint('Backward succeeded!')\n\""
},
"recorded": {
"id": "run_shell_command__call_2287786",
"name": "run_shell_command",
"args": {
"description": "Test model forward, backward, precompute and retrieval",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nfrom model import TwoTowerRanker\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\ncat_feats = torch.randn(30000, 4)\nv = model.precompute_candidate_embeddings(cat_feats)\nprint('Precomputed candidate embeddings shape:', v.shape, 'norms:', v.norm(dim=1)[:5])\n\nB = 16\nu_hist = {\n 'listen_items': torch.randint(0, 30000, (B, 100)),\n 'listen_weights': torch.ones(B, 100) / 100.0,\n 'like_items': torch.randint(0, 30000, (B, 60)),\n 'like_mask': torch.ones(B, 60),\n 'dislike_items': torch.randint(0, 30000, (B, 30)),\n 'dislike_mask': torch.ones(B, 30),\n 'user_features': torch.randn(B, 6),\n}\nu = model.compute_user_embeddings(u_hist)\nprint('User embeddings shape:', u.shape, 'norms:', u.norm(dim=1)[:5])\n\nscores = torch.matmul(u, v.t())\nprint('Scores shape:', scores.shape, 'min:', scores.min().item(), 'max:', scores.max().item())\n\n# Test loss\nloss = model.forward_loss(\n user_history_batch=u_hist,\n target_item_indices=torch.randint(0, 30000, (B,)),\n target_item_features=torch.randn(B, 4),\n target_item_logq=torch.zeros(B),\n sample_weights=torch.ones(B),\n)\nprint('Loss:', loss.item())\nloss.backward()\nprint('Backward succeeded!')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2287786",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Precomputed candidate embeddings shape: torch.Size([30000, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nUser embeddings shape: torch.Size([16, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nScores shape: torch.Size([16, 30000]) min: -0.33630895614624023 max: 0.42073172330856323\nLoss: 3.4881701469421387\nBackward succeeded!\nProcess Group PGID: 2398\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:25:11.106Z",
"resultDisplay": "Precomputed candidate embeddings shape: torch.Size([30000, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nUser embeddings shape: torch.Size([16, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nScores shape: torch.Size([16, 30000]) min: -0.33630895614624023 max: 0.42073172330856323\nLoss: 3.4881701469421387\nBackward succeeded!",
"description": "Test model forward, backward, precompute and retrieval",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:25:11.108Z",
"content": "\nOutput: Precomputed candidate embeddings shape: torch.Size([30000, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nUser embeddings shape: torch.Size([16, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])\nScores shape: torch.Size([16, 30000]) min: -0.33630895614624023 max: 0.42073172330856323\nLoss: 3.4881701469421387\nBackward succeeded!\nProcess Group PGID: 2398\n",
"isError": false
},
"responseMessageId": "03bc0c52-bdda-4a15-9568-bae9c05ca79e"
}
]
},
{
"id": 57,
"role": "agent",
"timestamp": "2026-09-29T02:25:29.805Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:25:29.805Z",
"text": "Implementing Training Loop\n\nMy focus has shifted to the training and validation phase. I'm now working on integrating the `update_topic` function to manage progression through training epochs and ensure smooth validation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:25:29.805Z",
"callId": "update_topic__call_5806596",
"name": "update_topic",
"input": {
"title": "Training & Public Validation",
"strategic_intent": "Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.",
"summary": "Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py."
},
"recorded": {
"id": "update_topic__call_5806596",
"name": "update_topic",
"args": {
"title": "Training & Public Validation",
"strategic_intent": "Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.",
"summary": "Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_5806596",
"name": "update_topic",
"response": {
"output": "Current topic: \"Training & Public Validation\"\nTopic summary: Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py.\n\nStrategic Intent: Implement training and validation script, evaluate against public evaluator, and measure NDCG@10."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:25:29.808Z",
"resultDisplay": "## ๐ Topic: **Training & Public Validation**\n\n**Summary:**\nCompleted Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py.\n\n> [!STRATEGY]\n> **Intent:** Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.",
"description": "Update topic to: \"Training & Public Validation\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:25:29.809Z",
"content": "Current topic: \"Training & Public Validation\"\nTopic summary: Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py.\n\nStrategic Intent: Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.",
"isError": false
},
"responseMessageId": "95523d7a-6ec8-459b-a66b-71b9333dd625"
}
]
},
{
"id": 58,
"role": "agent",
"timestamp": "2026-09-29T02:25:53.212Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:25:53.212Z",
"text": "Designing `train_val.py`\n\nI'm focusing on the initial steps for `solution/train_val.py`. My immediate concern is implementing the functionality to load catalog features, including extracting `catalog_features`, `item_to_idx`, `catalog_item_ids`, and `item_logq` from the specified data directory."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:25:53.212Z",
"text": "Implementing Data Loading\n\nI've begun implementing the data loading logic for `solution/train_val.py`. My current focus is on accurately extracting catalog features and preparing the user histories based on historical cutoffs for PyTorch Dataset construction."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:25:53.212Z",
"text": "Calculating Validation Scores\n\nI'm now focused on the validation phase. This involves extracting user histories at the validation cutoff, precomputing candidate and user embeddings, and then efficiently calculating MIPS scores using a matrix multiplication. I'll also apply the validation candidate eligibility mask before retrieving the top 100 items per user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:25:53.212Z",
"callId": "write_file__call_121574",
"name": "write_file",
"input": {
"content": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/train_val.py"
},
"recorded": {
"id": "write_file__call_121574",
"name": "write_file",
"args": {
"content": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/train_val.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_121574",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/train_val.py. Here is the updated code:\n\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:25:53.221Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tOriginal\n+++ train_val.py\tWritten\n@@ -0,0 +1,244 @@\n+\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import json\n+import subprocess\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+from torch.utils.data import DataLoader, TensorDataset\n+\n+from data_prep import build_training_pairs, extract_user_histories, load_catalog_features\n+from model import TwoTowerRanker\n+\n+\n+def train_epoch(\n+ model: TwoTowerRanker,\n+ optimizer: torch.optim.Optimizer,\n+ scheduler: torch.optim.lr_scheduler.LRScheduler,\n+ histories_stacked: dict[str, torch.Tensor],\n+ global_uids: torch.Tensor,\n+ target_items: torch.Tensor,\n+ sample_weights: torch.Tensor,\n+ catalog_features: torch.Tensor,\n+ item_logq: torch.Tensor,\n+ batch_size: int = 512,\n+) -> float:\n+ model.train()\n+ total_loss = 0.0\n+ num_batches = 0\n+\n+ n_samples = len(target_items)\n+ perm = torch.randperm(n_samples)\n+\n+ for i in range(0, n_samples, batch_size):\n+ idx = perm[i : min(i + batch_size, n_samples)]\n+ b_uids = global_uids[idx]\n+ b_items = target_items[idx]\n+ b_weights = sample_weights[idx]\n+\n+ b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n+ b_feats = catalog_features[b_items]\n+ b_logq = item_logq[b_items]\n+\n+ optimizer.zero_grad()\n+ loss = model.forward_loss(\n+ user_history_batch=b_hist,\n+ target_item_indices=b_items,\n+ target_item_features=b_feats,\n+ target_item_logq=b_logq,\n+ sample_weights=b_weights,\n+ )\n+ loss.backward()\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n+ optimizer.step()\n+ scheduler.step()\n+\n+ total_loss += loss.item()\n+ num_batches += 1\n+\n+ return total_loss / max(num_batches, 1)\n+\n+\n+def evaluate_on_validation(\n+ model: TwoTowerRanker,\n+ val_user_history: dict[str, torch.Tensor],\n+ catalog_features: torch.Tensor,\n+ catalog_item_ids: np.ndarray,\n+ target_uids: np.ndarray,\n+ data_dir: Path,\n+ output_path: Path,\n+) -> float:\n+ print(\"Precomputing candidate item embeddings offline...\")\n+ t0 = time.time()\n+ cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n+ print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n+\n+ print(\"Computing user embeddings for validation targets...\")\n+ t0 = time.time()\n+ user_embeddings = model.compute_user_embeddings(val_user_history)\n+ print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n+\n+ print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n+ t0 = time.time()\n+ scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n+\n+ # Load validation eligibility bitmask\n+ vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n+ unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n+ ineligible_mask = unpacked_eligible == 0\n+ scores[torch.from_numpy(ineligible_mask)] = -1e9\n+\n+ # Top 100 retrieval\n+ top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n+ print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n+\n+ # Build validation dataframe\n+ rows = []\n+ for u_idx, uid in enumerate(target_uids):\n+ top_items = catalog_item_ids[top100[u_idx]]\n+ for r, item_id in enumerate(top_items, 1):\n+ rows.append((int(uid), int(item_id), r))\n+\n+ val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n+ output_path.parent.mkdir(parents=True, exist_ok=True)\n+ val_df.to_parquet(output_path, index=False)\n+ print(f\"Saved validation ranking to {output_path}\")\n+\n+ # Run public evaluator\n+ cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n+ env = {\"PYTHONPATH\": \"/task\"}\n+ result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n+ print(\"Evaluator Output:\\n\", result.stdout)\n+ if result.returncode != 0:\n+ print(\"Evaluator Error:\\n\", result.stderr)\n+ return 0.0\n+\n+ try:\n+ report = json.loads(result.stdout)\n+ score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n+ return score\n+ except Exception as e:\n+ print(f\"Failed to parse evaluator JSON: {e}\")\n+ return 0.0\n+\n+\n+def main():\n+ parser = argparse.ArgumentParser()\n+ parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n+ parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n+ parser.add_argument(\"--epochs\", type=int, default=5)\n+ parser.add_argument(\"--batch_size\", type=int, default=512)\n+ parser.add_argument(\"--lr\", type=float, default=1e-3)\n+ parser.add_argument(\"--embed_dim\", type=int, default=64)\n+ parser.add_argument(\"--hidden_dim\", type=int, default=128)\n+ parser.add_argument(\"--out_dim\", type=int, default=64)\n+ parser.add_argument(\"--temperature\", type=float, default=0.07)\n+ parser.add_argument(\"--num_windows\", type=int, default=3)\n+ parser.add_argument(\"--sample_listens\", type=int, default=30)\n+ args = parser.parse_args()\n+\n+ data_dir = Path(args.data_dir)\n+ torch.manual_seed(42)\n+ np.random.seed(42)\n+\n+ # Set intraop parallelism\n+ torch.set_num_threads(8)\n+\n+ print(\"=== Loading Catalog and Users ===\")\n+ cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n+ target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n+ target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n+\n+ val_cutoff = 24786800\n+ week_sec = 7 * 86400\n+ gap_sec = 1800\n+\n+ cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n+ print(f\"Training cutoffs: {cutoffs}\")\n+\n+ print(\"=== Building Training Pairs ===\")\n+ t0 = time.time()\n+ histories_list, user_win, target_items, sample_weights = build_training_pairs(\n+ data_dir=data_dir,\n+ cutoffs=cutoffs,\n+ item_to_idx=item_to_idx,\n+ target_uids=target_uids,\n+ sample_listens_per_user=args.sample_listens_per_user,\n+ )\n+ print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n+\n+ # Stack user histories across windows\n+ keys = list(histories_list[0].keys())\n+ histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n+ n_users = len(target_uids)\n+ global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n+ item_logq_t = torch.from_numpy(item_logq)\n+\n+ print(\"=== Extracting Validation Cutoff History ===\")\n+ t0 = time.time()\n+ val_history = extract_user_histories(\n+ data_dir=data_dir,\n+ cutoff=val_cutoff,\n+ target_uids=target_uids,\n+ item_to_idx=item_to_idx,\n+ pref_state_path=data_dir / \"validation_preference_state.parquet\",\n+ )\n+ print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n+\n+ print(\"=== Initializing Two-Tower Ranker ===\")\n+ model = TwoTowerRanker(\n+ num_catalog_items=30000,\n+ embed_dim=args.embed_dim,\n+ hidden_dim=args.hidden_dim,\n+ out_dim=args.out_dim,\n+ temperature=args.temperature,\n+ )\n+\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n+ total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n+\n+ print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n+ best_score = 0.0\n+ for epoch in range(1, args.epochs + 1):\n+ t_ep = time.time()\n+ avg_loss = train_epoch(\n+ model=model,\n+ optimizer=optimizer,\n+ scheduler=scheduler,\n+ histories_stacked=histories_stacked,\n+ global_uids=global_uids,\n+ target_items=target_items,\n+ sample_weights=sample_weights,\n+ catalog_features=cat_feats,\n+ item_logq=item_logq_t,\n+ batch_size=args.batch_size,\n+ )\n+ ep_duration = time.time() - t_ep\n+ print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n+\n+ # Validate\n+ score = evaluate_on_validation(\n+ model=model,\n+ val_user_history=val_history,\n+ catalog_features=cat_feats,\n+ catalog_item_ids=cat_items,\n+ target_uids=target_uids,\n+ data_dir=data_dir,\n+ output_path=Path(args.output_path),\n+ )\n+ print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n+ if score > best_score:\n+ best_score = score\n+ torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n+\n+ print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n+\n+\n+if __name__ == \"__main__\":\n+ main()\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 244,
"model_removed_lines": 0,
"model_added_chars": 8484,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/train_val.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:25:53.223Z",
"content": "Successfully created and wrote to new file: /app/solution/train_val.py. Here is the updated code:\n\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"isError": false
},
"responseMessageId": "d5e57f0e-61da-463a-9f12-e255a4d683cb"
}
]
},
{
"id": 59,
"role": "agent",
"timestamp": "2026-09-29T02:26:07.771Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:07.771Z",
"callId": "run_shell_command__call_4998913",
"name": "run_shell_command",
"input": {
"description": "Run initial train_val test with 2 windows and 3 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
},
"recorded": {
"id": "run_shell_command__call_4998913",
"name": "run_shell_command",
"args": {
"description": "Run initial train_val test with 2 windows and 3 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4998913",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 171, in main\n sample_listens_per_user=args.sample_listens_per_user,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Namespace' object has no attribute 'sample_listens_per_user'\nExit Code: 1\nProcess Group PGID: 2411\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:26:11.784Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 171, in main\n sample_listens_per_user=args.sample_listens_per_user,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Namespace' object has no attribute 'sample_listens_per_user'",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:26:11.786Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 171, in main\n sample_listens_per_user=args.sample_listens_per_user,\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Namespace' object has no attribute 'sample_listens_per_user'\nExit Code: 1\nProcess Group PGID: 2411\n",
"isError": false
},
"responseMessageId": "1c8527a0-7f79-4dfc-b1ec-d8ae9ca5567b"
}
]
},
{
"id": 60,
"role": "agent",
"timestamp": "2026-09-29T02:26:17.307Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:17.307Z",
"callId": "replace__call_4588944",
"name": "replace",
"input": {
"old_string": " sample_listens_per_user=args.sample_listens_per_user,",
"new_string": " sample_listens_per_user=args.sample_listens,",
"instruction": "Fix argument name for sample_listens",
"file_path": "/app/solution/train_val.py",
"allow_multiple": false
},
"recorded": {
"id": "replace__call_4588944",
"name": "replace",
"args": {
"old_string": " sample_listens_per_user=args.sample_listens_per_user,",
"new_string": " sample_listens_per_user=args.sample_listens,",
"instruction": "Fix argument name for sample_listens",
"file_path": "/app/solution/train_val.py",
"allow_multiple": false
},
"result": [
{
"functionResponse": {
"id": "replace__call_4588944",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:26:17.320Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -168,7 +168,7 @@\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n- sample_listens_per_user=args.sample_listens_per_user,\n+ sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n \n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 1,
"model_removed_lines": 1,
"model_added_chars": 52,
"model_removed_chars": 61,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: sample_listens_per_use... => sample_listens_per_use...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:26:17.322Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n...",
"isError": false
},
"responseMessageId": "7f81c70c-5684-4a5a-b5d6-fefcf9fdca73"
}
]
},
{
"id": 61,
"role": "agent",
"timestamp": "2026-09-29T02:26:19.480Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:19.480Z",
"callId": "run_shell_command__call_4444180",
"name": "run_shell_command",
"input": {
"description": "Run train_val with fixed sample_listens argument",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
},
"recorded": {
"id": "run_shell_command__call_4444180",
"name": "run_shell_command",
"args": {
"description": "Run train_val with fixed sample_listens argument",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4444180",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76229 discovery training pairs across 2 windows.\nData preparation completed in 11.35s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.32s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 3 Epochs ===\nEpoch 1/3: Loss=6.2061 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 226, in main\n score = evaluate_on_validation(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_val.py\", line 115, in evaluate_on_validation\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 548, in run\n with Popen(*popenargs, **kwargs) as process:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1026, in __init__\n self._execute_child(args, executable, preexec_fn, close_fds,\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1955, in _execute_child\n raise child_exception_type(errno_num, err_msg, err_filename)\nFileNotFoundError: [Errno 2] No such file or directory: 'python3'\nExit Code: 1\nProcess Group PGID: 2435\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:26:45.687Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76229 discovery training pairs across 2 windows.\nData preparation completed in 11.35s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.32s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 3 Epochs ===\nEpoch 1/3: Loss=6.2061 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 226, in main\n score = evaluate_on_validation(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_val.py\", line 115, in evaluate_on_validation\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 548, in run\n with Popen(*popenargs, **kwargs) as process:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1026, in __init__\n self._execute_child(args, executable, preexec_fn, close_fds,\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1955, in _execute_child\n raise child_exception_type(errno_num, err_msg, err_filename)\nFileNotFoundError: [Errno 2] No such file or directory: 'python3'",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:26:47.417Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nGenerated 76229 discovery training pairs across 2 windows.\nData preparation completed in 11.35s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.32s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 3 Epochs ===\nEpoch 1/3: Loss=6.2061 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nTraceback (most recent call last):\n File \"/app/solution/train_val.py\", line 244, in \n main()\n File \"/app/solution/train_val.py\", line 226, in main\n score = evaluate_on_validation(\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/app/solution/train_val.py\", line 115, in evaluate_on_validation\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 548, in run\n with Popen(*popenargs, **kwargs) as process:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1026, in __init__\n self._execute_child(args, executable, preexec_fn, close_fds,\n File \"/usr/local/lib/python3.12/subprocess.py\", line 1955, in _execute_child\n raise child_exception_type(errno_num, err_msg, err_filename)\nFileNotFoundError: [Errno 2] No such file or directory: 'python3'\nExit Code: 1\nProcess Group PGID: 2435\n",
"isError": false
},
"responseMessageId": "428a3a2a-8cc1-4630-b863-afae7fbdf824"
}
]
},
{
"id": 62,
"role": "agent",
"timestamp": "2026-09-29T02:26:52.042Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:26:52.042Z",
"text": "Investigating Runtime Errors\n\nI'm currently pinpointing the cause of a `FileNotFoundError` related to 'python3'. It appears the issue stems from how the `PATH` environment variable is being handled when `PYTHONPATH` is explicitly set."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:52.042Z",
"callId": "run_shell_command__call_5769250",
"name": "run_shell_command",
"input": {
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Evaluate saved validation ranking with evaluate_public.py"
},
"recorded": {
"id": "run_shell_command__call_5769250",
"name": "run_shell_command",
"args": {
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Evaluate saved validation ranking with evaluate_public.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5769250",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009969,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2472\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:26:55.365Z",
"resultDisplay": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009969,\n \"quality_users\": 4263\n }\n}",
"description": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:26:55.368Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009969,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2472\n",
"isError": false
},
"responseMessageId": "1e6d8867-57ce-4690-a84b-74f9a51c093b"
}
]
},
{
"id": 63,
"role": "agent",
"timestamp": "2026-09-29T02:26:59.706Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:26:59.706Z",
"text": "Evaluating Model Performance\n\nI've successfully saved the validation ranking and initiated the public evaluation script. Initial results show a `quality_ndcg@10` of 0.009969 with 4263 users, which I'm now analyzing further."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:59.706Z",
"callId": "replace__call_125844",
"name": "replace",
"input": {
"new_string": " # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)",
"file_path": "/app/solution/train_val.py",
"instruction": "Fix subprocess call to use sys.executable and preserved environment",
"allow_multiple": false,
"old_string": " # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)"
},
"recorded": {
"id": "replace__call_125844",
"name": "replace",
"args": {
"new_string": " # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)",
"file_path": "/app/solution/train_val.py",
"instruction": "Fix subprocess call to use sys.executable and preserved environment",
"allow_multiple": false,
"old_string": " # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)"
},
"result": [
{
"functionResponse": {
"id": "replace__call_125844",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:26:59.716Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -110,8 +110,11 @@\n print(f\"Saved validation ranking to {output_path}\")\n \n # Run public evaluator\n- cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n- env = {\"PYTHONPATH\": \"/task\"}\n+ import os\n+ import sys\n+ cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n+ env = os.environ.copy()\n+ env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 5,
"model_removed_lines": 2,
"model_added_chars": 186,
"model_removed_chars": 100,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: # Run public evaluator... => # Run public evaluator...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:26:59.717Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n...",
"isError": false
},
"responseMessageId": "661379e1-17ca-456f-bdbe-986b2e839c7d"
}
]
},
{
"id": 64,
"role": "agent",
"timestamp": "2026-09-29T02:27:02.565Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:27:02.565Z",
"text": "Exploring Window Configurations\n\nI'm currently running `train_val.py` with an experimental setup: 5 epochs and 4 windows, each defined by specific cutoffs. I'm also evaluating the impact of `sample_listens = 40` within this configuration."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:27:02.565Z",
"callId": "run_shell_command__call_60378",
"name": "run_shell_command",
"input": {
"description": "Run train_val with 4 windows and 5 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30"
},
"recorded": {
"id": "run_shell_command__call_60378",
"name": "run_shell_command",
"args": {
"description": "Run train_val with 4 windows and 5 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_60378",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.84s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.35s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0946 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011541,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.011541\nEpoch 2/5: Loss=5.7236 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011515,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011515\nEpoch 3/5: Loss=5.4151 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.14s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011372,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.011372\nEpoch 4/5: Loss=5.1981 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010071,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.010071\nEpoch 5/5: Loss=5.0703 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009783,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009783\n=== Training Complete! Best Validation NDCG@10: 0.011541 ===\nProcess Group PGID: 2492\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:28:37.363Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.84s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.35s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0946 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011541,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.011541\nEpoch 2/5: Loss=5.7236 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011515,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011515\nEpoch 3/5: Loss=5.4151 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.14s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011372,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.011372\nEpoch 4/5: Loss=5.1981 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010071,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.010071\nEpoch 5/5: Loss=5.0703 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009783,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009783\n=== Training Complete! Best Validation NDCG@10: 0.011541 ===",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:28:37.365Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.84s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.35s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0946 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011541,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.011541\nEpoch 2/5: Loss=5.7236 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011515,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011515\nEpoch 3/5: Loss=5.4151 (7.8s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.14s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011372,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.011372\nEpoch 4/5: Loss=5.1981 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.06s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010071,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.010071\nEpoch 5/5: Loss=5.0703 (7.7s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009783,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009783\n=== Training Complete! Best Validation NDCG@10: 0.011541 ===\nProcess Group PGID: 2492\n",
"isError": false
},
"responseMessageId": "fffe4a9c-0097-4491-a4b8-c3d9ff0aafec"
}
]
},
{
"id": 65,
"role": "agent",
"timestamp": "2026-09-29T02:28:43.234Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:28:43.234Z",
"text": "Improving Recommendation Accuracy\n\nI've achieved a `quality_ndcg@10` of `0.011541` in Epoch 1, surpassing the popularity baseline. Subsequent epochs show a slight dip, indicating potential overfitting or need for further parameter tuning."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:28:43.234Z",
"callId": "run_shell_command__call_1772473",
"name": "run_shell_command",
"input": {
"description": "Check logq range",
"command": "PYTHONPATH=/app/solution python3 -c \"\nfrom pathlib import Path\nfrom data_prep import load_catalog_features\nDATA = Path('/task/data')\n_, _, _, logq = load_catalog_features(DATA)\nprint('logq min, mean, max:', logq.min(), logq.mean(), logq.max())\nprint('Range of logq:', logq.max() - logq.min())\n\""
},
"recorded": {
"id": "run_shell_command__call_1772473",
"name": "run_shell_command",
"args": {
"description": "Check logq range",
"command": "PYTHONPATH=/app/solution python3 -c \"\nfrom pathlib import Path\nfrom data_prep import load_catalog_features\nDATA = Path('/task/data')\n_, _, _, logq = load_catalog_features(DATA)\nprint('logq min, mean, max:', logq.min(), logq.mean(), logq.max())\nprint('Range of logq:', logq.max() - logq.min())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1772473",
"name": "run_shell_command",
"response": {
"output": "\nOutput: logq min, mean, max: -11.720305 -10.745149 -6.624825\nRange of logq: 5.0954804\nProcess Group PGID: 2618\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:28:47.278Z",
"resultDisplay": "logq min, mean, max: -11.720305 -10.745149 -6.624825\nRange of logq: 5.0954804",
"description": "Check logq range",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:28:47.280Z",
"content": "\nOutput: logq min, mean, max: -11.720305 -10.745149 -6.624825\nRange of logq: 5.0954804\nProcess Group PGID: 2618\n",
"isError": false
},
"responseMessageId": "30f7ab9b-38b7-49cf-a3e4-b3b12b85d964"
}
]
},
{
"id": 66,
"role": "agent",
"timestamp": "2026-09-29T02:28:53.165Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:28:53.165Z",
"text": "Analyzing LogQ Influence\n\nI'm seeing that the `logq` values, ranging from -11.7 to -6.6, significantly impact the logits. Subtracting a `target_item_logq` of around -10 effectively shifts all logits upwards by approximately 10. This is particularly noticeable for high `logq` items, like one at -6."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:28:53.165Z",
"text": "Testing LogQ Debiasing\n\nI'm evaluating the impact of scaling `logq` with a debiasing power $\\alpha$. Initial results suggest that $\\alpha=1.0$ over-penalizes popular items, leading to an extreme bias towards obscure content. I'll be testing $\\alpha$ values of $0.0$, $0.2$, and $0.5$ to find a more balanced approach."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:28:53.165Z",
"callId": "run_shell_command__call_1836848",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport sys\nsys.path.append('/app/solution')\nfrom train_val import main\n# Let's test with alpha = 0.0\n\"",
"description": "Check import"
},
"recorded": {
"id": "run_shell_command__call_1836848",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport sys\nsys.path.append('/app/solution')\nfrom train_val import main\n# Let's test with alpha = 0.0\n\"",
"description": "Check import"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1836848",
"name": "run_shell_command",
"response": {
"output": "\nOutput: (empty)\nProcess Group PGID: 2635\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:28:57.173Z",
"resultDisplay": "",
"description": "PYTHONPATH=/app/solution python3 -c \"\nimport sys\nsys.path.append('/app/solution')\nfrom train_val import main\n# Let's test with alpha = 0.0\n\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:28:57.174Z",
"content": "\nOutput: (empty)\nProcess Group PGID: 2635\n",
"isError": false
},
"responseMessageId": "341e5f94-56ab-4296-a9d3-6c8225932bbf"
}
]
},
{
"id": 67,
"role": "agent",
"timestamp": "2026-09-29T02:29:06.363Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:29:06.363Z",
"text": "Refining Scoring and Training\n\nI'm currently integrating `logq_weight` and temperature into the `TwoTowerRanker` and `train_val.py` scripts. Simultaneously, I'm analyzing the inference-time candidate item score computation, specifically examining the `torch.matmul(user_embeddings, cand_embeddings.t)` operation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:06.363Z",
"callId": "replace__call_5242297",
"name": "replace",
"input": {
"old_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(",
"file_path": "/app/solution/model.py",
"instruction": "Add logq_weight parameter to TwoTowerRanker forward_loss",
"new_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(",
"allow_multiple": false
},
"recorded": {
"id": "replace__call_5242297",
"name": "replace",
"args": {
"old_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(",
"file_path": "/app/solution/model.py",
"instruction": "Add logq_weight parameter to TwoTowerRanker forward_loss",
"new_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(",
"allow_multiple": false
},
"result": [
{
"functionResponse": {
"id": "replace__call_5242297",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n...\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:29:06.373Z",
"resultDisplay": {
"fileDiff": "Index: model.py\n===================================================================\n--- model.py\tCurrent\n+++ model.py\tProposed\n@@ -143,6 +143,7 @@\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n+ logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n@@ -167,6 +168,7 @@\n dropout=dropout,\n )\n self.temperature = temperature\n+ self.logq_weight = logq_weight\n \n def precompute_candidate_embeddings(\n self,\n",
"fileName": "model.py",
"filePath": "/app/solution/model.py",
"originalContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"newContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"diffStat": {
"model_added_lines": 2,
"model_removed_lines": 0,
"model_added_chars": 71,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/model.py: def __init__(... => def __init__(...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:29:06.374Z",
"content": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n...\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n...",
"isError": false
},
"responseMessageId": "50cdfbdf-7a2e-451b-944a-fe18e2e0338e"
}
]
},
{
"id": 68,
"role": "agent",
"timestamp": "2026-09-29T02:29:12.324Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:12.324Z",
"callId": "replace__call_930379",
"name": "replace",
"input": {
"old_string": " # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives",
"instruction": "Scale target_item_logq by self.logq_weight in forward_loss",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"new_string": " # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives"
},
"recorded": {
"id": "replace__call_930379",
"name": "replace",
"args": {
"old_string": " # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives",
"instruction": "Scale target_item_logq by self.logq_weight in forward_loss",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"new_string": " # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives"
},
"result": [
{
"functionResponse": {
"id": "replace__call_930379",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:29:12.334Z",
"resultDisplay": {
"fileDiff": "Index: model.py\n===================================================================\n--- model.py\tCurrent\n+++ model.py\tProposed\n@@ -270,8 +270,9 @@\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n \n # Popularity correction and temperature scaling\n- # sim / tau - logq\n- logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n+ logits = sim / self.temperature\n+ if self.logq_weight > 0.0:\n+ logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n \n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n",
"fileName": "model.py",
"filePath": "/app/solution/model.py",
"originalContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"newContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"diffStat": {
"model_added_lines": 3,
"model_removed_lines": 2,
"model_added_chars": 151,
"model_removed_chars": 97,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/model.py: # Popularity correctio... => # Popularity correctio...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:29:12.335Z",
"content": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n...",
"isError": false
},
"responseMessageId": "fc1c8f31-e605-40f5-80c5-d00b81bb959f"
}
]
},
{
"id": 69,
"role": "agent",
"timestamp": "2026-09-29T02:29:15.225Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:15.225Z",
"callId": "replace__call_5220445",
"name": "replace",
"input": {
"instruction": "Add logq_weight argument and pass to model in train_val.py",
"new_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"old_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
},
"recorded": {
"id": "replace__call_5220445",
"name": "replace",
"args": {
"instruction": "Add logq_weight argument and pass to model in train_val.py",
"new_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"old_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
},
"result": [
{
"functionResponse": {
"id": "replace__call_5220445",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:29:15.234Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -141,6 +141,7 @@\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n+ parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 1,
"model_removed_lines": 0,
"model_added_chars": 65,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: parser.add_argument(\"--tem... => parser.add_argument(\"--tem...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:29:15.235Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n...",
"isError": false
},
"responseMessageId": "0b467061-792e-4853-93c0-99d86cc688fe"
}
]
},
{
"id": 70,
"role": "agent",
"timestamp": "2026-09-29T02:29:18.443Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:18.443Z",
"callId": "replace__call_7668096",
"name": "replace",
"input": {
"old_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )",
"new_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py",
"instruction": "Pass logq_weight to TwoTowerRanker instance"
},
"recorded": {
"id": "replace__call_7668096",
"name": "replace",
"args": {
"old_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )",
"new_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py",
"instruction": "Pass logq_weight to TwoTowerRanker instance"
},
"result": [
{
"functionResponse": {
"id": "replace__call_7668096",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:29:18.451Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -201,6 +201,7 @@\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n+ logq_weight=args.logq_weight,\n )\n \n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 1,
"model_removed_lines": 0,
"model_added_chars": 37,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: model = TwoTowerRanker(... => model = TwoTowerRanker(...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:29:18.452Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n...",
"isError": false
},
"responseMessageId": "33b9fb5a-1f6b-418b-a3dc-132fe3b3911b"
}
]
},
{
"id": 71,
"role": "agent",
"timestamp": "2026-09-29T02:29:21.110Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:21.110Z",
"callId": "run_shell_command__call_2223229",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30 --logq_weight 0.0",
"description": "Run train_val with logq_weight 0.0"
},
"recorded": {
"id": "run_shell_command__call_2223229",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30 --logq_weight 0.0",
"description": "Run train_val with logq_weight 0.0"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2223229",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.74s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.33s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0840 (8.4s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00425,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.004250\nEpoch 2/5: Loss=5.7201 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005138,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.005138\nEpoch 3/5: Loss=5.4037 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004426,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.004426\nEpoch 4/5: Loss=5.1761 (8.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004201,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.004201\nEpoch 5/5: Loss=5.0458 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002985,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.002985\n=== Training Complete! Best Validation NDCG@10: 0.005138 ===\nProcess Group PGID: 2646\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:30:58.761Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.74s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.33s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0840 (8.4s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00425,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.004250\nEpoch 2/5: Loss=5.7201 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005138,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.005138\nEpoch 3/5: Loss=5.4037 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004426,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.004426\nEpoch 4/5: Loss=5.1761 (8.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004201,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.004201\nEpoch 5/5: Loss=5.0458 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002985,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.002985\n=== Training Complete! Best Validation NDCG@10: 0.005138 ===",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30 --logq_weight 0.0",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:30:58.762Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 192471 discovery training pairs across 4 windows.\nData preparation completed in 21.74s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.33s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.0840 (8.4s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00425,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.004250\nEpoch 2/5: Loss=5.7201 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005138,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.005138\nEpoch 3/5: Loss=5.4037 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004426,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.004426\nEpoch 4/5: Loss=5.1761 (8.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.13s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004201,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.004201\nEpoch 5/5: Loss=5.0458 (8.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.002985,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.002985\n=== Training Complete! Best Validation NDCG@10: 0.005138 ===\nProcess Group PGID: 2646\n",
"isError": false
},
"responseMessageId": "f8c3c53e-02dd-40ec-9e4b-fcd3eb29e56a"
}
]
},
{
"id": 72,
"role": "agent",
"timestamp": "2026-09-29T02:31:21.500Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Analyzing Logq Impact\n\nI'm observing a significant score improvement when `logq_weight` is adjusted to `0.0`, dropping from `0.011541` to `0.005138`. This contrasts with an earlier `1.0` setting that yielded the same initial score. I'm now delving into the reasons behind this substantial benefit."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Investigating Batch Bias\n\nMy focus has shifted to understanding how popular items skew recommendations. Without LogQ, popular items dominate user embeddings, causing collapses. I'm now exploring how different `logq_weight` and `temperature` values impact this bias, and inspecting the UserTower pooling mechanism."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Refining User Embeddings\n\nI'm investigating why the Item Tower incorporates richer item features into its MLP while the User Tower directly uses item embeddings. I'm considering how attention mechanisms, applied to recent listens within the User Tower, could potentially enhance its representation by capturing temporal dynamics, similar to how the Item Tower utilizes features."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Examining Data Ordering\n\nI'm realizing that the chronological order in my data, indicated by `groupby(\"uid\").cumcount()`, might be critical. Position 99 represents the most recent listen, and I'm questioning the proportion of users with active likes compared to listens and how this affects the discovery objective's relevance grading."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Evaluating Like-Listen Ratio\n\nI'm now scrutinizing the generated `pairs_list`, specifically observing the proportion of likes versus listens. This distinction seems critical, and I'm investigating its impact on the discovery objective's relevance grading, especially considering the differing weights assigned."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:31:21.500Z",
"text": "Adjusting Weight Priorities\n\nI'm realizing that likes are vastly undervalued in the current metric. A single organic like is seven times more valuable than a strong listen. I'm now exploring significantly increasing the weight for likes and rebalancing the ratio of likes to listens in sampling."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:31:21.500Z",
"callId": "run_shell_command__call_3854666",
"name": "run_shell_command",
"input": {
"description": "Check user full past listens vs tail(100)",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\n\nDATA = Path('/task/data')\ncutoff = 24180200\n# User's full listen history before cutoff\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', cutoff)]\n)\nall_hist = table.to_pandas().drop_duplicates()\nprint('Total past listen pairs before cutoff:', len(all_hist))\nall_hist_set = set(zip(all_hist.uid, all_hist.item_id))\n\n# Now let's see for 1000 pairs from our previous training pairs\nfrom data_prep import load_catalog_features, extract_user_histories\ncat_feats, item_to_idx, cat_items, _ = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nhist = extract_user_histories(DATA, cutoff, uids, item_to_idx)\nl_itms = set(hist['listen_items'][0].numpy())\nprint('User 0 tail listens count:', len(l_itms))\nuser0_full_hist = all_hist[all_hist.uid == uids[0]]\nprint('User 0 FULL past listens count:', len(user0_full_hist))\n\""
},
"recorded": {
"id": "run_shell_command__call_3854666",
"name": "run_shell_command",
"args": {
"description": "Check user full past listens vs tail(100)",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\n\nDATA = Path('/task/data')\ncutoff = 24180200\n# User's full listen history before cutoff\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', cutoff)]\n)\nall_hist = table.to_pandas().drop_duplicates()\nprint('Total past listen pairs before cutoff:', len(all_hist))\nall_hist_set = set(zip(all_hist.uid, all_hist.item_id))\n\n# Now let's see for 1000 pairs from our previous training pairs\nfrom data_prep import load_catalog_features, extract_user_histories\ncat_feats, item_to_idx, cat_items, _ = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nhist = extract_user_histories(DATA, cutoff, uids, item_to_idx)\nl_itms = set(hist['listen_items'][0].numpy())\nprint('User 0 tail listens count:', len(l_itms))\nuser0_full_hist = all_hist[all_hist.uid == uids[0]]\nprint('User 0 FULL past listens count:', len(user0_full_hist))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3854666",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total past listen pairs before cutoff: 12039859\nUser 0 tail listens count: 85\nUser 0 FULL past listens count: 781\nProcess Group PGID: 2772\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:31:46.989Z",
"resultDisplay": "Total past listen pairs before cutoff: 12039859\nUser 0 tail listens count: 85\nUser 0 FULL past listens count: 781",
"description": "Check user full past listens vs tail(100)",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:31:46.991Z",
"content": "\nOutput: Total past listen pairs before cutoff: 12039859\nUser 0 tail listens count: 85\nUser 0 FULL past listens count: 781\nProcess Group PGID: 2772\n",
"isError": false
},
"responseMessageId": "16adcc35-6634-4976-b7f5-bd064b0966f5"
}
]
},
{
"id": 73,
"role": "agent",
"timestamp": "2026-09-29T02:32:00.437Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:32:00.437Z",
"text": "Analyzing Listen Item Discrepancy\n\nI'm delving into why a significant portion of User 0's listening history wasn't reflected in the initial count. It appears a substantial number of items were filtered out, impacting the accuracy of `past_items_per_user` and consequently, my understanding of their target items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:32:00.437Z",
"callId": "run_shell_command__call_137084",
"name": "run_shell_command",
"input": {
"description": "Benchmark full past items extraction for all target users",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, rows={len(df)}')\n\ncutoff = 24180200\nt1 = time.time()\ndf_past = df[df.timestamp < cutoff]\n# Group by uid to get set of past items\npast_sets = df_past.groupby('uid')['item_id'].apply(set).to_dict()\nprint(f'Built full past sets for all {len(past_sets)} users in {time.time() - t1:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_137084",
"name": "run_shell_command",
"args": {
"description": "Benchmark full past items extraction for all target users",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, rows={len(df)}')\n\ncutoff = 24180200\nt1 = time.time()\ndf_past = df[df.timestamp < cutoff]\n# Group by uid to get set of past items\npast_sets = df_past.groupby('uid')['item_id'].apply(set).to_dict()\nprint(f'Built full past sets for all {len(past_sets)} users in {time.time() - t1:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_137084",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read and filtered in 3.53s, rows=43222297\nBuilt full past sets for all 8359 users in 6.11s\nProcess Group PGID: 2794\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:32:12.518Z",
"resultDisplay": "Read and filtered in 3.53s, rows=43222297\nBuilt full past sets for all 8359 users in 6.11s",
"description": "Benchmark full past items extraction for all target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:32:12.521Z",
"content": "\nOutput: Read and filtered in 3.53s, rows=43222297\nBuilt full past sets for all 8359 users in 6.11s\nProcess Group PGID: 2794\n",
"isError": false
},
"responseMessageId": "fca40bb4-5640-4a2a-8961-403e65af603e"
}
]
},
{
"id": 74,
"role": "agent",
"timestamp": "2026-09-29T02:32:28.220Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:32:28.220Z",
"text": "Processing All Past Items\n\nI've successfully compiled a complete set of all historical listening data across all users in under 7 seconds. This allows me to definitively identify discovery items by excluding anything present in a user's past listening history. Every remaining event is now confirmed as a true discovery."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:32:28.220Z",
"callId": "run_shell_command__call_5245441",
"name": "run_shell_command",
"input": {
"description": "Check true discovery counts in window 0",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(target_uids) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\nval_cutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nc0 = val_cutoff - (week + gap)\ndf_past0 = df[df.timestamp < c0]\npast0 = df_past0.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Target window 0: [c0 + gap, c0 + gap + week]\nw0_likes = likes[(likes.timestamp >= c0 + gap) & (likes.timestamp <= c0 + gap + week)]\nw0_listens = df[(df.timestamp >= c0 + gap) & (df.timestamp <= c0 + gap + week) & (df.is_organic == 1) & (df.played_ratio_pct >= 80) & (df.item_id.isin(cc_items))]\n\ndisc_likes = [itm for uid, itm in zip(w0_likes.uid, w0_likes.item_id) if itm not in past0.get(uid, set())]\ndisc_listens = [itm for uid, itm in zip(w0_listens.uid, w0_listens.item_id) if itm not in past0.get(uid, set())]\n\nprint(f'Window 0 True Discovery Likes: {len(disc_likes)} out of {len(w0_likes)}')\nprint(f'Window 0 True Discovery Strong Listens: {len(disc_listens)} out of {len(w0_listens)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_5245441",
"name": "run_shell_command",
"args": {
"description": "Check true discovery counts in window 0",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(target_uids) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\nval_cutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nc0 = val_cutoff - (week + gap)\ndf_past0 = df[df.timestamp < c0]\npast0 = df_past0.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Target window 0: [c0 + gap, c0 + gap + week]\nw0_likes = likes[(likes.timestamp >= c0 + gap) & (likes.timestamp <= c0 + gap + week)]\nw0_listens = df[(df.timestamp >= c0 + gap) & (df.timestamp <= c0 + gap + week) & (df.is_organic == 1) & (df.played_ratio_pct >= 80) & (df.item_id.isin(cc_items))]\n\ndisc_likes = [itm for uid, itm in zip(w0_likes.uid, w0_likes.item_id) if itm not in past0.get(uid, set())]\ndisc_listens = [itm for uid, itm in zip(w0_listens.uid, w0_listens.item_id) if itm not in past0.get(uid, set())]\n\nprint(f'Window 0 True Discovery Likes: {len(disc_likes)} out of {len(w0_likes)}')\nprint(f'Window 0 True Discovery Strong Listens: {len(disc_listens)} out of {len(w0_listens)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5245441",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Window 0 True Discovery Likes: 5420 out of 7774\nWindow 0 True Discovery Strong Listens: 67576 out of 304885\nProcess Group PGID: 2816\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:32:44.134Z",
"resultDisplay": "Window 0 True Discovery Likes: 5420 out of 7774\nWindow 0 True Discovery Strong Listens: 67576 out of 304885",
"description": "Check true discovery counts in window 0",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:32:44.136Z",
"content": "\nOutput: Window 0 True Discovery Likes: 5420 out of 7774\nWindow 0 True Discovery Strong Listens: 67576 out of 304885\nProcess Group PGID: 2816\n",
"isError": false
},
"responseMessageId": "09249ff3-444c-40cc-92d0-cae217d7270f"
}
]
},
{
"id": 75,
"role": "agent",
"timestamp": "2026-09-29T02:33:00.029Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:33:00.029Z",
"text": "Analyzing Listen Distribution\n\nMy current focus is on dissecting the listen data, particularly the significant proportion of repeat listens. It seems the model's training might have been overly influenced by existing user history, potentially hindering its exploration of new content."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:33:00.029Z",
"callId": "replace__call_7673046",
"name": "replace",
"input": {
"new_string": " # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"instruction": "Build full history set for each user to strictly filter out non-discovery targets",
"file_path": "/app/solution/data_prep.py",
"old_string": " histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"allow_multiple": false
},
"recorded": {
"id": "replace__call_7673046",
"name": "replace",
"args": {
"new_string": " # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"instruction": "Build full history set for each user to strictly filter out non-discovery targets",
"file_path": "/app/solution/data_prep.py",
"old_string": " histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"allow_multiple": false
},
"result": [
{
"functionResponse": {
"id": "replace__call_7673046",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n...\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:33:00.042Z",
"resultDisplay": {
"fileDiff": "Index: data_prep.py\n===================================================================\n--- data_prep.py\tCurrent\n+++ data_prep.py\tProposed\n@@ -286,6 +286,20 @@\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n \n+ # Load full target user listens to guarantee 100% true discovery filtering\n+ full_table = pq.read_table(\n+ data_dir / \"train_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\"],\n+ )\n+ all_listens_df = full_table.to_pandas()\n+ if include_val and (data_dir / \"val_listens.parquet\").exists():\n+ val_tbl_full = pq.read_table(\n+ data_dir / \"val_listens.parquet\",\n+ columns=[\"uid\", \"timestamp\", \"item_id\"],\n+ )\n+ all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n+ all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n+\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n \n@@ -304,42 +318,43 @@\n )\n histories_list.append(hist)\n \n- # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n- # Read past items from the history listen_items and likes\n- past_items_per_user: Dict[int, Set[int]] = {}\n- for r, uid in enumerate(target_uids):\n- # Items in listens and likes\n- l_itms = set(hist[\"listen_items\"][r].numpy())\n- lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n- past = l_itms.union(lk_itms)\n- past.discard(30000)\n- past_items_per_user[r] = past\n+ # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n+ df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n+ past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n \n+ # Add liked items before cutoff as well\n+ likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n+ for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n+ past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n+\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n \n- # Map and filter discovery likes (grade 3, weight 3.0)\n+ # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n \n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n- item_idx = item_to_idx.get(int(item))\n- if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n- pairs_list.append((w_idx, r, item_idx, 3.0))\n- user_seen_targets[r].add(item_idx)\n+ item_int = int(item)\n+ if item_int not in past_items_per_user.get(int(uid), set()):\n+ item_idx = item_to_idx.get(item_int)\n+ if item_idx is not None and item_idx not in user_seen_targets[r]:\n+ pairs_list.append((w_idx, r, item_idx, 5.0))\n+ user_seen_targets[r].add(item_idx)\n \n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n- items = group[\"item_id\"].to_numpy()\n+ user_past = past_items_per_user.get(int(uid), set())\n+ items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n- items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n+ items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n- item_idx = item_to_idx.get(int(itm))\n- if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n+ item_idx = item_to_idx.get(itm)\n+ if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n \n",
"fileName": "data_prep.py",
"filePath": "/app/solution/data_prep.py",
"originalContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"newContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"diffStat": {
"model_added_lines": 34,
"model_removed_lines": 19,
"model_added_chars": 1965,
"model_removed_chars": 1183,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/data_prep.py: histories_list: List[Dict[... => # Load full target user li...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:33:00.044Z",
"content": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n...\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n...",
"isError": false
},
"responseMessageId": "16bc504a-cb57-4a71-b943-d62c6a79dc3e"
}
]
},
{
"id": 76,
"role": "agent",
"timestamp": "2026-09-29T02:33:02.922Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:33:02.922Z",
"callId": "run_shell_command__call_131791",
"name": "run_shell_command",
"input": {
"description": "Run train_val with strict discovery filtering and logq_weight 0.5",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 0.5"
},
"recorded": {
"id": "run_shell_command__call_131791",
"name": "run_shell_command",
"args": {
"description": "Run train_val with strict discovery filtering and logq_weight 0.5",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 0.5"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_131791",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.79s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.39s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1895 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007081,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.007081\nEpoch 2/5: Loss=5.9163 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007211,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.007211\nEpoch 3/5: Loss=5.5643 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005033,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.005033\nEpoch 4/5: Loss=5.2741 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003885,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.003885\nEpoch 5/5: Loss=5.1058 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004044,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.004044\n=== Training Complete! Best Validation NDCG@10: 0.007211 ===\nProcess Group PGID: 2838\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:34:32.505Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.79s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.39s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1895 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007081,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.007081\nEpoch 2/5: Loss=5.9163 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007211,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.007211\nEpoch 3/5: Loss=5.5643 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005033,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.005033\nEpoch 4/5: Loss=5.2741 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003885,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.003885\nEpoch 5/5: Loss=5.1058 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004044,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.004044\n=== Training Complete! Best Validation NDCG@10: 0.007211 ===",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 0.5",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:34:34.666Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.79s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.39s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1895 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007081,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.007081\nEpoch 2/5: Loss=5.9163 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007211,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.007211\nEpoch 3/5: Loss=5.5643 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005033,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.005033\nEpoch 4/5: Loss=5.2741 (3.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.003885,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.003885\nEpoch 5/5: Loss=5.1058 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.004044,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.004044\n=== Training Complete! Best Validation NDCG@10: 0.007211 ===\nProcess Group PGID: 2838\n",
"isError": false
},
"responseMessageId": "5c68115e-b31d-4fe2-8661-1ba5eb82c996"
}
]
},
{
"id": 77,
"role": "agent",
"timestamp": "2026-09-29T02:34:48.165Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:34:48.165Z",
"text": "Analyzing LogQ Weights\n\nI'm examining the impact of different `logq_weight` values. It's clear LogQ plays a significant role, as seen with the distinct outcomes for `1.0`, `0.5`, and `0.0`. I'm continuing to investigate the `logq_weight = 1.0` scenario to fully understand its effect."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:34:48.165Z",
"callId": "run_shell_command__call_5045211",
"name": "run_shell_command",
"input": {
"description": "Run train_val with logq_weight 1.0",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 1.0"
},
"recorded": {
"id": "run_shell_command__call_5045211",
"name": "run_shell_command",
"args": {
"description": "Run train_val with logq_weight 1.0",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 1.0"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5045211",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.44s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.41s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1998 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009107,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.009107\nEpoch 2/5: Loss=5.9321 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00915,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.009150\nEpoch 3/5: Loss=5.5913 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.10s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007496,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007496\nEpoch 4/5: Loss=5.3021 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005305,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.005305\nEpoch 5/5: Loss=5.1320 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00526,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.005260\n=== Training Complete! Best Validation NDCG@10: 0.009150 ===\nProcess Group PGID: 2964\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:36:17.422Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.44s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.41s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1998 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009107,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.009107\nEpoch 2/5: Loss=5.9321 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00915,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.009150\nEpoch 3/5: Loss=5.5913 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.10s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007496,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007496\nEpoch 4/5: Loss=5.3021 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005305,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.005305\nEpoch 5/5: Loss=5.1320 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00526,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.005260\n=== Training Complete! Best Validation NDCG@10: 0.009150 ===",
"description": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 1.0",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:36:17.425Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nGenerated 76663 discovery training pairs across 3 windows.\nData preparation completed in 39.44s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.41s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.1998 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.009107,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.009107\nEpoch 2/5: Loss=5.9321 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00915,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.009150\nEpoch 3/5: Loss=5.5913 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.10s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007496,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007496\nEpoch 4/5: Loss=5.3021 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.005305,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.005305\nEpoch 5/5: Loss=5.1320 (3.2s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00526,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.005260\n=== Training Complete! Best Validation NDCG@10: 0.009150 ===\nProcess Group PGID: 2964\n",
"isError": false
},
"responseMessageId": "4c094c60-4274-4e9a-9a24-b0f1b519a2d2"
}
]
},
{
"id": 78,
"role": "agent",
"timestamp": "2026-09-29T02:36:32.287Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:36:32.287Z",
"text": "Improving Model Performance\n\nMy current focus is on refining the model's predictive accuracy. I've observed a notable decrease in the error score, from `0.011541` with a larger dataset (192,471 pairs) to `0.009150` with a reduced set of 76,663 pairs. This suggests that more comprehensive user-item interaction data significantly enhances recommender system performance by ensuring denser coverage."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:36:32.287Z",
"text": "Exploring Data Augmentation\n\nI'm considering ways to increase our training pairs for better model performance. The current interaction density per item is quite low. I plan to investigate strategies like increasing weekly cutoffs and sampling more listens per user, and I'll also re-evaluate how `item_logq` is computed and its impact on both training and retrieval, especially regarding the influence of item popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:36:32.287Z",
"text": "Validating Score Formulation\n\nI've realized my current approach might be ignoring global item popularity, which is a strong baseline. My new hypothesis is that the true ranking score should combine personalized affinity (the dot product $u^T v$) with a scaled global popularity term ($\\beta \\log P(v)$). I'm now investigating if the two-tower architecture can actually represent this combined score as a direct vector product."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:36:32.287Z",
"callId": "run_shell_command__call_5397026",
"name": "run_shell_command",
"input": {
"description": "Test combining personalized two-tower score with popularity",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\n# Catalog popularity feature: column 3 is norm_rank (0=most popular, 1=least), or log_pos (col 0)\n# Let's inspect cat_feats\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_5397026",
"name": "run_shell_command",
"args": {
"description": "Test combining personalized two-tower score with popularity",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\n# Catalog popularity feature: column 3 is norm_rank (0=most popular, 1=least), or log_pos (col 0)\n# Let's inspect cat_feats\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5397026",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 3090\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:36:36.246Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'evaluate_public'",
"description": "Test combining personalized two-tower score with popularity",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:36:36.248Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 8, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 3090\n",
"isError": false
},
"responseMessageId": "13e5d440-04b0-44d4-bbdd-df8e087d6691"
}
]
},
{
"id": 79,
"role": "agent",
"timestamp": "2026-09-29T02:36:55.195Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:36:55.195Z",
"callId": "run_shell_command__call_4059127",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate beta popularity weighting with two-tower dot product"
},
"recorded": {
"id": "run_shell_command__call_4059127",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate beta popularity weighting with two-tower dot product"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4059127",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 67: 3103 Killed PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 3101\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:37:16.794Z",
"resultDisplay": "/usr/bin/bash: line 67: 3103 Killed PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate beta popularity weighting with two-tower dot product",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:37:16.805Z",
"content": "\nOutput: /usr/bin/bash: line 67: 3103 Killed PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 3101\n",
"isError": false
},
"responseMessageId": "8b076dba-c09b-482e-a091-cb401c1e65e6"
}
]
},
{
"id": 80,
"role": "agent",
"timestamp": "2026-09-29T02:37:24.535Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:37:24.535Z",
"text": "Investigating Memory Allocation\n\nI'm currently probing the memory footprint of the `np.argsort` operation. It appears the internal buffer allocation within the loop is responsible for the OOM error. I'm exploring PyTorch's `torch.topk` as a more memory-efficient alternative."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:37:24.535Z",
"callId": "run_shell_command__call_32910",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\nbase_scores = torch.matmul(user_emb, cand_emb.t()) # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = torch.from_numpy(np.log1p(cc.positive_listens.values.astype(np.float32)))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_mask = torch.from_numpy(unpacked_eligible == 0)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n scores = base_scores + beta * log_pop_norm\n scores[ineligible_mask] = -1e9\n \n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Test popularity addition with torch.topk"
},
"recorded": {
"id": "run_shell_command__call_32910",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\nbase_scores = torch.matmul(user_emb, cand_emb.t()) # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = torch.from_numpy(np.log1p(cc.positive_listens.values.astype(np.float32)))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_mask = torch.from_numpy(unpacked_eligible == 0)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n scores = base_scores + beta * log_pop_norm\n scores[ineligible_mask] = -1e9\n \n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Test popularity addition with torch.topk"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_32910",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Beta = 0.00: NDCG@10 = 0.009150\nBeta = 0.05: NDCG@10 = 0.009835\nBeta = 0.10: NDCG@10 = 0.010169\nBeta = 0.15: NDCG@10 = 0.010700\nBeta = 0.20: NDCG@10 = 0.010400\nBeta = 0.30: NDCG@10 = 0.010395\nProcess Group PGID: 3126\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:37:44.171Z",
"resultDisplay": "Beta = 0.00: NDCG@10 = 0.009150\nBeta = 0.05: NDCG@10 = 0.009835\nBeta = 0.10: NDCG@10 = 0.010169\nBeta = 0.15: NDCG@10 = 0.010700\nBeta = 0.20: NDCG@10 = 0.010400\nBeta = 0.30: NDCG@10 = 0.010395",
"description": "Test popularity addition with torch.topk",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:37:44.174Z",
"content": "\nOutput: Beta = 0.00: NDCG@10 = 0.009150\nBeta = 0.05: NDCG@10 = 0.009835\nBeta = 0.10: NDCG@10 = 0.010169\nBeta = 0.15: NDCG@10 = 0.010700\nBeta = 0.20: NDCG@10 = 0.010400\nBeta = 0.30: NDCG@10 = 0.010395\nProcess Group PGID: 3126\n",
"isError": false
},
"responseMessageId": "93243d6a-979b-4598-89ea-9b1d53d23f86"
}
]
},
{
"id": 81,
"role": "agent",
"timestamp": "2026-09-29T02:37:50.966Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:37:50.966Z",
"text": "Investigating Model Performance\n\nI'm currently digging into the discrepancy in our model's performance. It seems a recent run with 76k samples achieved a better score (0.009150) than a previous run with 192k samples, which scored 0.011541. My focus is on understanding the root cause of this difference, especially the early epoch performance from the 192k sample run."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:37:50.966Z",
"text": "Refining User Representation\n\nI've identified a critical flaw in how liked items are being processed for validation. It appears the current method prioritizes `item_id` order over temporal recency, leading to a skewed representation of user preferences. I am investigating if timestamps exist and how to leverage them for more accurate historical data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:37:50.966Z",
"callId": "run_shell_command__call_234248",
"name": "run_shell_command",
"input": {
"description": "Check merging VPS liked with train_likes timestamps",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nmerged = pd.merge(vps[vps.state == 'liked'], likes[['uid', 'item_id', 'timestamp']], on=['uid', 'item_id'], how='inner')\nprint('VPS liked merged with train_likes:', len(merged), 'out of', len(vps[vps.state == 'liked']))\n\""
},
"recorded": {
"id": "run_shell_command__call_234248",
"name": "run_shell_command",
"args": {
"description": "Check merging VPS liked with train_likes timestamps",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nmerged = pd.merge(vps[vps.state == 'liked'], likes[['uid', 'item_id', 'timestamp']], on=['uid', 'item_id'], how='inner')\nprint('VPS liked merged with train_likes:', len(merged), 'out of', len(vps[vps.state == 'liked']))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_234248",
"name": "run_shell_command",
"response": {
"output": "\nOutput: VPS liked merged with train_likes: 428358 out of 418435\nProcess Group PGID: 3151\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:37:52.773Z",
"resultDisplay": "VPS liked merged with train_likes: 428358 out of 418435",
"description": "Check merging VPS liked with train_likes timestamps",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:37:52.774Z",
"content": "\nOutput: VPS liked merged with train_likes: 428358 out of 418435\nProcess Group PGID: 3151\n",
"isError": false
},
"responseMessageId": "5c15699e-da4a-440d-8316-4e991a58bbba"
}
]
},
{
"id": 82,
"role": "agent",
"timestamp": "2026-09-29T02:38:30.473Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Prioritizing Timestamps\n\nI've confirmed nearly all active likes in `train_likes` have timestamps. Prioritizing these recent interactions over arbitrary `item_id` ordering seems to significantly improve performance. Next, I'm focusing on sequential and multi-interest attention within the user tower."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Developing User Interest Representations\n\nI'm exploring ways to capture diverse user interests beyond a single average vector. My current thinking involves splitting listens into short-term and long-term pools, incorporating explicit likes and dislikes, and concatenating these with user activity features into the User MLP."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Increasing Item Embeddings\n\nI'm considering increasing the item embedding dimension from 64 to 128. This offers significantly more expressive power for distinguishing item nuances, with a manageable parameter size increase of around 15MB, which remains computationally efficient.\n\n**Refining Training Cutoffs**\n\nI'm also evaluating several potential training data cutoffs, structured around weekly intervals and gaps, to optimize the learning process. My current thinking explores five distinct cutoff points for data segmentation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Adjusting Loss and Popularity\n\nI'm now focusing on the temperature parameter $\\tau$ in the sampled softmax, exploring values between 0.05 and 0.07. LogQ correction dramatically improved scores by mitigating sampling bias towards popular items, and I'm now investigating integrating popularity directly into the two-tower dot product, rather than as a separate inference term."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Incorporating Popularity\n\nI'm ensuring that all popularity-related signals are intrinsically part of the dot product. The item tower already has access to features like `norm_rank` and `log_pos` within `catalog_features`, allowing it to naturally incorporate popularity."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Adjusting Learning Rates\n\nI'm investigating the learning rate and regularization parameters to combat early overfitting. My current thinking suggests that a lower learning rate, increased weight decay, and higher dropout in the User Tower MLP, coupled with early stopping based on validation NDCG@10, will resolve the issue."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Experimenting with Architectures\n\nMy validation evaluation is now incredibly fast, taking under a second. This allows me to evaluate every epoch and save the best performing model. I'm refining the User Tower with specific pooling strategies for short-term and long-term listens, likes, and dislikes, alongside activity features, feeding into an MLP with dropout. The Item Tower will now include catalog feature projection before combining with the item embedding."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:30.473Z",
"text": "Structuring User Histories\n\nI've confirmed that the last 20 listens in `listen_items` already represent the most recent activity. This simplifies structuring short-term listens in the `UserTower` to directly slice these recent items and their corresponding weights. I am now proceeding to implement this."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:38:30.473Z",
"callId": "write_file__call_16944",
"name": "write_file",
"input": {
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
},
"recorded": {
"id": "write_file__call_16944",
"name": "write_file",
"args": {
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_16944",
"name": "write_file",
"response": {
"output": "Successfully overwrote file: /app/solution/model.py. Here is the updated code:\n...\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n...\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n...\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n...\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n...\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n...\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n...\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n...\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n...\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:38:30.488Z",
"resultDisplay": {
"fileDiff": "Index: model.py\n===================================================================\n--- model.py\tOriginal\n+++ model.py\tWritten\n@@ -16,7 +16,7 @@\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n- hidden_dim: int = 64,\n+ hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n@@ -60,16 +60,19 @@\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n- dropout: float = 0.1,\n+ dropout: float = 0.2,\n+ short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n+ self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n- in_dim = embed_dim * 3 + 32\n+ # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n+ in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n@@ -91,43 +94,38 @@\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n- \"\"\"Compute normalized user embeddings from interaction history.\n-\n- Args:\n- listen_items: LongTensor (B, max_listens)\n- listen_weights: FloatTensor (B, max_listens)\n- like_items: LongTensor (B, max_likes)\n- like_mask: FloatTensor (B, max_likes)\n- dislike_items: LongTensor (B, max_dislikes)\n- dislike_mask: FloatTensor (B, max_dislikes)\n- user_features: FloatTensor (B, user_feat_dim)\n-\n- Returns:\n- L2-normalized FloatTensor of shape (B, out_dim).\n- \"\"\"\n- # 1. Weighted listens pooling\n+ \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n+ # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n- pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n+ pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n \n- # 2. Likes pooling\n+ # 2. Short-term listens pooling (most recent short_window items)\n+ s_items = listen_items[:, -self.short_window :]\n+ s_weights = listen_weights[:, -self.short_window :]\n+ s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n+ s_norm_weights = s_weights / s_sum\n+ s_emb = self.item_embed(s_items)\n+ pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n+\n+ # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n \n- # 3. Dislikes pooling\n+ # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n \n- # 4. User features projection\n+ # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n \n- # 5. Combined MLP\n- combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n+ # 6. Combined MLP\n+ combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n \n@@ -141,9 +139,9 @@\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n- dropout: float = 0.1,\n+ dropout: float = 0.2,\n temperature: float = 0.07,\n- logq_weight: float = 0.0,\n+ logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n@@ -175,14 +173,7 @@\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n- \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n-\n- Args:\n- catalog_features: FloatTensor of shape (30000, 4).\n-\n- Returns:\n- FloatTensor of shape (30000, out_dim).\n- \"\"\"\n+ \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n@@ -200,14 +191,7 @@\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n- \"\"\"Compute user embeddings from interaction history.\n-\n- Args:\n- user_history: Dictionary containing history tensors.\n-\n- Returns:\n- FloatTensor of shape (N_users, out_dim).\n- \"\"\"\n+ \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n@@ -237,21 +221,7 @@\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n- \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n-\n- Args:\n- user_history_batch: Dict of user history tensors of batch size B.\n- target_item_indices: LongTensor of positive item indices (B,).\n- target_item_features: FloatTensor of positive item features (B, 4).\n- target_item_logq: FloatTensor of positive item logq (B,).\n- sample_weights: FloatTensor of positive sample weights (B,).\n- dislike_item_indices: Optional LongTensor of hard negative items (B,).\n- dislike_item_features: Optional FloatTensor (B, 4).\n- dislike_mask: Optional FloatTensor (B,).\n-\n- Returns:\n- Scalar loss tensor.\n- \"\"\"\n+ \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n@@ -267,7 +237,7 @@\n v_pos = self.item_tower(target_item_indices, target_item_features)\n \n # Dot product matrix (B, B)\n- sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n+ sim = torch.matmul(u, v_pos.t())\n \n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n@@ -279,16 +249,8 @@\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n \n- # Weight by grade importance (likes 3.0, listens 1.0)\n+ # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n \n- # Dislike penalty\n- if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n- v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n- dislike_sim = (u * v_neg).sum(dim=-1)\n- # Penalize similarity to disliked tracks\n- dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n- return infonce_loss + 0.2 * dislike_loss\n-\n return infonce_loss\n",
"fileName": "model.py",
"filePath": "/app/solution/model.py",
"originalContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"newContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"diffStat": {
"model_added_lines": 29,
"model_removed_lines": 67,
"model_added_chars": 1479,
"model_removed_chars": 2836,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "Writing to solution/model.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:38:30.490Z",
"content": "Successfully overwrote file: /app/solution/model.py. Here is the updated code:\n...\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n...\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n...\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n...\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n...\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n...\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n...\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n...\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n...\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"isError": false
},
"responseMessageId": "e299a018-0782-45bc-bcdc-5c5765a0e692"
}
]
},
{
"id": 83,
"role": "agent",
"timestamp": "2026-09-29T02:38:38.377Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:38:38.377Z",
"text": "Refining Preference Extraction\n\nI'm updating `data_prep.py` to sort user likes by timestamp within `extract_user_histories`. This ensures that only the most recent likes are consistently selected when building preference states."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:38:38.377Z",
"callId": "replace__call_3860058",
"name": "replace",
"input": {
"new_string": " if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:",
"old_string": " if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:",
"allow_multiple": false,
"instruction": "Order preference state likes by timestamp for recent preference prioritization",
"file_path": "/app/solution/data_prep.py"
},
"recorded": {
"id": "replace__call_3860058",
"name": "replace",
"args": {
"new_string": " if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:",
"old_string": " if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:",
"allow_multiple": false,
"instruction": "Order preference state likes by timestamp for recent preference prioritization",
"file_path": "/app/solution/data_prep.py"
},
"result": [
{
"functionResponse": {
"id": "replace__call_3860058",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:38:38.387Z",
"resultDisplay": {
"fileDiff": "Index: data_prep.py\n===================================================================\n--- data_prep.py\tCurrent\n+++ data_prep.py\tProposed\n@@ -151,6 +151,13 @@\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n+\n+ # Merge with likes to get timestamps for chronological ordering\n+ likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n+ if include_val and (data_dir / \"val_likes.parquet\").exists():\n+ likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n+ likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n+ liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n",
"fileName": "data_prep.py",
"filePath": "/app/solution/data_prep.py",
"originalContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"newContent": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\n pos_listens = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float32)\n org_listens = catalog_df[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n pop_rank = catalog_df[\"popularity_rank\"].to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n",
"diffStat": {
"model_added_lines": 7,
"model_removed_lines": 0,
"model_added_chars": 559,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/data_prep.py: if pref_state_path and pre... => if pref_state_path and pre...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:38:38.388Z",
"content": "Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:\n...\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n...",
"isError": false
},
"responseMessageId": "9e48b721-d6ae-4d20-9a50-9797d14f8cdf"
}
]
},
{
"id": 84,
"role": "agent",
"timestamp": "2026-09-29T02:38:41.063Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:38:41.063Z",
"callId": "run_shell_command__call_65123",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 5e-4",
"description": "Run train_val with updated architecture and 4 windows"
},
"recorded": {
"id": "run_shell_command__call_65123",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 5e-4",
"description": "Run train_val with updated architecture and 4 windows"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_65123",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.82s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.60s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.2144 (5.6s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010692,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.010692\nEpoch 2/5: Loss=6.0222 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008403,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.008403\nEpoch 3/5: Loss=5.7904 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007303,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007303\nEpoch 4/5: Loss=5.6115 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007151,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.007151\nEpoch 5/5: Loss=5.5162 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007181,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.007181\n=== Training Complete! Best Validation NDCG@10: 0.010692 ===\nProcess Group PGID: 3169\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:40:33.353Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.82s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.60s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.2144 (5.6s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010692,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.010692\nEpoch 2/5: Loss=6.0222 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008403,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.008403\nEpoch 3/5: Loss=5.7904 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007303,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007303\nEpoch 4/5: Loss=5.6115 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007151,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.007151\nEpoch 5/5: Loss=5.5162 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007181,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.007181\n=== Training Complete! Best Validation NDCG@10: 0.010692 ===",
"description": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 5e-4",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:40:33.356Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.82s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.60s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.2144 (5.6s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010692,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.010692\nEpoch 2/5: Loss=6.0222 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.008403,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.008403\nEpoch 3/5: Loss=5.7904 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007303,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.007303\nEpoch 4/5: Loss=5.6115 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007151,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.007151\nEpoch 5/5: Loss=5.5162 (5.5s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.007181,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.007181\n=== Training Complete! Best Validation NDCG@10: 0.010692 ===\nProcess Group PGID: 3169\n",
"isError": false
},
"responseMessageId": "207fe04d-dacb-4db7-b4c1-d51a216137ed"
}
]
},
{
"id": 85,
"role": "agent",
"timestamp": "2026-09-29T02:40:42.515Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:40:42.515Z",
"text": "Analyzing Training Dynamics\n\nI'm observing a peculiar trend during training. Specifically, at Epoch 1, my NDCG is around 0.011. Interestingly, as the loss on in-batch negatives decreases from 6.2 to 5.5, the validation NDCG actually drops. I'm trying to understand why minimizing this in-batch sampled softmax loss seems to be negatively impacting validation discovery."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:40:42.515Z",
"text": "Investigating Embedding Drift\n\nI'm realizing that the in-batch negatives, which are predominantly popular items, are driving my user embeddings away from the most common tracks. This seems to be inadvertently pushing the model towards retrieving obscure items during validation, as my embeddings are no longer strongly associated with the popular items that might otherwise be recommended."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:40:42.515Z",
"callId": "run_shell_command__call_5229939",
"name": "run_shell_command",
"input": {
"description": "Check popularity rank distribution of gold validation items",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\ngold_ranks = []\nfor uid, items in graded.items():\n for it in items:\n gold_ranks.append(item_pop_rank[it])\n\ngold_ranks = np.array(gold_ranks)\nprint(f'Gold items popularity ranks: min={gold_ranks.min()}, 25%={np.percentile(gold_ranks, 25):.0f}, median={np.median(gold_ranks):.0f}, 75%={np.percentile(gold_ranks, 75):.0f}, max={gold_ranks.max()}')\n\""
},
"recorded": {
"id": "run_shell_command__call_5229939",
"name": "run_shell_command",
"args": {
"description": "Check popularity rank distribution of gold validation items",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\ngold_ranks = []\nfor uid, items in graded.items():\n for it in items:\n gold_ranks.append(item_pop_rank[it])\n\ngold_ranks = np.array(gold_ranks)\nprint(f'Gold items popularity ranks: min={gold_ranks.min()}, 25%={np.percentile(gold_ranks, 25):.0f}, median={np.median(gold_ranks):.0f}, 75%={np.percentile(gold_ranks, 75):.0f}, max={gold_ranks.max()}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5229939",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Gold items popularity ranks: min=1, 25%=1733, median=5622, 75%=13374, max=30000\nProcess Group PGID: 3295\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:40:44.626Z",
"resultDisplay": "Gold items popularity ranks: min=1, 25%=1733, median=5622, 75%=13374, max=30000",
"description": "Check popularity rank distribution of gold validation items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:40:44.627Z",
"content": "\nOutput: Gold items popularity ranks: min=1, 25%=1733, median=5622, 75%=13374, max=30000\nProcess Group PGID: 3295\n",
"isError": false
},
"responseMessageId": "0f28d986-6f6e-407d-99d5-8ff81021ef9c"
}
]
},
{
"id": 86,
"role": "agent",
"timestamp": "2026-09-29T02:40:49.059Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:40:49.059Z",
"text": "Analyzing Gold Item Popularity\n\nI've been examining the popularity distribution of our gold validation items. It's striking that the median popularity rank sits at 5,622, with the 75th percentile extending to 13,374, indicating a good spread of high-value items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:40:49.059Z",
"callId": "run_shell_command__call_57134",
"name": "run_shell_command",
"input": {
"description": "Check popularity rank of predicted items at Epoch 5",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\nval = pd.read_parquet('/app/solution/validation.parquet')\npred_ranks = [item_pop_rank[it] for it in val.item_id.values]\npred_ranks = np.array(pred_ranks)\nprint(f'Predicted items popularity ranks: min={pred_ranks.min()}, 25%={np.percentile(pred_ranks, 25):.0f}, median={np.median(pred_ranks):.0f}, 75%={np.percentile(pred_ranks, 75):.0f}, max={pred_ranks.max()}')\n\""
},
"recorded": {
"id": "run_shell_command__call_57134",
"name": "run_shell_command",
"args": {
"description": "Check popularity rank of predicted items at Epoch 5",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\nval = pd.read_parquet('/app/solution/validation.parquet')\npred_ranks = [item_pop_rank[it] for it in val.item_id.values]\npred_ranks = np.array(pred_ranks)\nprint(f'Predicted items popularity ranks: min={pred_ranks.min()}, 25%={np.percentile(pred_ranks, 25):.0f}, median={np.median(pred_ranks):.0f}, 75%={np.percentile(pred_ranks, 75):.0f}, max={pred_ranks.max()}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_57134",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Predicted items popularity ranks: min=1, 25%=162, median=651, 75%=4160, max=29987\nProcess Group PGID: 3315\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:40:50.834Z",
"resultDisplay": "Predicted items popularity ranks: min=1, 25%=162, median=651, 75%=4160, max=29987",
"description": "Check popularity rank of predicted items at Epoch 5",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:40:50.836Z",
"content": "\nOutput: Predicted items popularity ranks: min=1, 25%=162, median=651, 75%=4160, max=29987\nProcess Group PGID: 3315\n",
"isError": false
},
"responseMessageId": "5b08558a-c218-49b3-8d65-851f29509ac3"
}
]
},
{
"id": 87,
"role": "agent",
"timestamp": "2026-09-29T02:41:02.799Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:02.799Z",
"text": "Analyzing Prediction Bias\n\nI'm investigating why my model is overpredicting highly popular items. The median popularity for predicted items is surprisingly low, but the top 25% are extremely concentrated at rank 162. This contrasts sharply with the gold items' much higher median popularity of 5,622. I'm examining `ItemTower` to pinpoint the source of this discrepancy."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:02.799Z",
"text": "Refining Embeddings & Bias\n\nI've identified that `feat_mlp` is feeding the same features to all items, potentially creating a dominant, shared component in item vectors aligned with `popularity_rank`. This, combined with normalization, could be pushing popular items higher. I'm now exploring how bypassing this shortcut or using explicit negative sampling might mitigate this."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:02.799Z",
"text": "Addressing Catalog Imbalance\n\nMy focus has shifted to the profound bias introduced by in-batch negatives. These only represent interacted items, heavily skewed towards the top 2,000. This means the vast majority of the catalog, 28,000 items, rarely gets updated, while popular items are disproportionately penalized. I'm now implementing mixed negative sampling to include uniform catalog negatives, ensuring all items receive appropriate penalty and the model learns a more accurate global ranking distribution."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:02.799Z",
"text": "Implementing Mixed Negatives\n\nI'm now actively incorporating mixed negative sampling into the model. This involves generating random catalog negatives alongside in-batch negatives for each user. I'm testing the implementation to ensure it's efficient and correctly addresses the catalog coverage issues and popularity distortions I've observed."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:41:02.799Z",
"callId": "run_shell_command__call_4460545",
"name": "run_shell_command",
"input": {
"description": "Test mixed negative sampling logits and loss",
"command": "python3 -c \"\nimport torch\nimport torch.nn.functional as F\n\nB = 8\nM = 16\nD = 64\n\nu = torch.randn(B, D)\nu = F.normalize(u, p=2, dim=-1)\n\nv_pos = torch.randn(B, D)\nv_pos = F.normalize(v_pos, p=2, dim=-1)\n\nv_neg = torch.randn(M, D)\nv_neg = F.normalize(v_neg, p=2, dim=-1)\n\nv_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\nsim = torch.matmul(u, v_all.t()) # (B, B + M)\nprint('sim shape:', sim.shape)\nlogits = sim / 0.07\ntargets = torch.arange(B)\nloss = F.cross_entropy(logits, targets)\nprint('loss:', loss.item())\n\""
},
"recorded": {
"id": "run_shell_command__call_4460545",
"name": "run_shell_command",
"args": {
"description": "Test mixed negative sampling logits and loss",
"command": "python3 -c \"\nimport torch\nimport torch.nn.functional as F\n\nB = 8\nM = 16\nD = 64\n\nu = torch.randn(B, D)\nu = F.normalize(u, p=2, dim=-1)\n\nv_pos = torch.randn(B, D)\nv_pos = F.normalize(v_pos, p=2, dim=-1)\n\nv_neg = torch.randn(M, D)\nv_neg = F.normalize(v_neg, p=2, dim=-1)\n\nv_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\nsim = torch.matmul(u, v_all.t()) # (B, B + M)\nprint('sim shape:', sim.shape)\nlogits = sim / 0.07\ntargets = torch.arange(B)\nloss = F.cross_entropy(logits, targets)\nprint('loss:', loss.item())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4460545",
"name": "run_shell_command",
"response": {
"output": "\nOutput: sim shape: torch.Size([8, 24])\nloss: 3.8542702198028564\nProcess Group PGID: 3332\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:41:05.765Z",
"resultDisplay": "sim shape: torch.Size([8, 24])\nloss: 3.8542702198028564",
"description": "Test mixed negative sampling logits and loss",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:41:05.766Z",
"content": "\nOutput: sim shape: torch.Size([8, 24])\nloss: 3.8542702198028564\nProcess Group PGID: 3332\n",
"isError": false
},
"responseMessageId": "5bcfc440-9342-42d5-ac03-b317c8b27dfe"
}
]
},
{
"id": 88,
"role": "agent",
"timestamp": "2026-09-29T02:41:19.931Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:19.931Z",
"text": "Analyzing Sampling Probabilities\n\nI'm currently dissecting the sampling probabilities within the Mixed Negative Sampling framework. My focus is on understanding the influence of popularity on in-batch items and the uniform sampling distribution for the negative items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:41:19.931Z",
"text": "Implementing Mixed Sampling\n\nI've successfully derived the exact Mixed Negative Sampling probability, aligning perfectly with established formulations. This means popular, mid-tier, and tail items are now handled with consistent mathematical rigor. I'm proceeding to implement this update in `model.py`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:41:19.931Z",
"callId": "replace__call_35957",
"name": "replace",
"input": {
"new_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"old_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss",
"instruction": "Implement mixed negative sampling in TwoTowerRanker forward_loss"
},
"recorded": {
"id": "replace__call_35957",
"name": "replace",
"args": {
"new_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"old_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss",
"instruction": "Implement mixed negative sampling in TwoTowerRanker forward_loss"
},
"result": [
{
"functionResponse": {
"id": "replace__call_35957",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:41:19.944Z",
"resultDisplay": {
"fileDiff": "Index: model.py\n===================================================================\n--- model.py\tCurrent\n+++ model.py\tProposed\n@@ -215,14 +215,28 @@\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n- target_item_logq: torch.Tensor,\n+ catalog_features: torch.Tensor,\n+ item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n- dislike_item_indices: Optional[torch.Tensor] = None,\n- dislike_item_features: Optional[torch.Tensor] = None,\n- dislike_mask: Optional[torch.Tensor] = None,\n+ num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n- \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n- # User vectors (B, D)\n+ \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n+\n+ Args:\n+ user_history_batch: Dict of user history tensors of batch size B.\n+ target_item_indices: LongTensor of positive item indices (B,).\n+ target_item_features: FloatTensor of positive item features (B, 4).\n+ catalog_features: All catalog features (30000, 4).\n+ item_logq: Precomputed log probabilities of catalog items (30000,).\n+ sample_weights: FloatTensor of sample weights (B,).\n+ num_uniform_negatives: Number of uniform random catalog negatives.\n+\n+ Returns:\n+ Scalar loss tensor.\n+ \"\"\"\n+ batch_size = len(target_item_indices)\n+\n+ # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n@@ -233,24 +247,44 @@\n user_features=user_history_batch[\"user_features\"],\n )\n \n- # Positive candidate vectors (B, D)\n+ # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n \n- # Dot product matrix (B, B)\n- sim = torch.matmul(u, v_pos.t())\n+ # 3. Uniform catalog negatives (M, D)\n+ if num_uniform_negatives > 0:\n+ neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n+ neg_features = catalog_features[neg_indices]\n+ v_neg = self.item_tower(neg_indices, neg_features)\n+ v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n+ all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n+ else:\n+ v_all = v_pos\n+ all_indices = target_item_indices\n \n- # Popularity correction and temperature scaling\n+ # 4. Dot product matrix (B, B + M)\n+ sim = torch.matmul(u, v_all.t())\n+\n+ # 5. Temperature scaling\n logits = sim / self.temperature\n+\n+ # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n- logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n+ # For in-batch items: logq from popularity\n+ # For uniform items: log(1 / N)\n+ total_items = float(len(catalog_features))\n+ uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n+ pos_logq = item_logq[target_item_indices]\n+ if num_uniform_negatives > 0:\n+ neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n+ cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n+ else:\n+ cand_logq = pos_logq\n+ logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n \n- # Cross entropy with in-batch negatives\n- batch_size = len(target_item_indices)\n+ # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n \n- # Weight by grade importance (likes 5.0, listens 1.0)\n+ # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n- infonce_loss = (per_sample_loss * norm_weights).mean()\n-\n- return infonce_loss\n+ return (per_sample_loss * norm_weights).mean()\n",
"fileName": "model.py",
"filePath": "/app/solution/model.py",
"originalContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"newContent": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()\n",
"diffStat": {
"model_added_lines": 51,
"model_removed_lines": 17,
"model_added_chars": 2397,
"model_removed_chars": 811,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/model.py: def forward_loss(... => def forward_loss(...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:41:19.946Z",
"content": "Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:\n...\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()\n",
"isError": false
},
"responseMessageId": "e603d6de-578e-41fa-9563-08e3cd3a4cf7"
}
]
},
{
"id": 89,
"role": "agent",
"timestamp": "2026-09-29T02:41:23.267Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:41:23.267Z",
"callId": "replace__call_129353",
"name": "replace",
"input": {
"instruction": "Update forward_loss call in train_val.py to support mixed negative sampling",
"new_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()",
"old_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
},
"recorded": {
"id": "replace__call_129353",
"name": "replace",
"args": {
"instruction": "Update forward_loss call in train_val.py to support mixed negative sampling",
"new_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()",
"old_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
},
"result": [
{
"functionResponse": {
"id": "replace__call_129353",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:41:23.276Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -50,8 +50,10 @@\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n- target_item_logq=b_logq,\n+ catalog_features=catalog_features,\n+ item_logq=item_logq,\n sample_weights=b_weights,\n+ num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 3,
"model_removed_lines": 1,
"model_added_chars": 116,
"model_removed_chars": 36,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: optimizer.zero_grad()... => optimizer.zero_grad()...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:41:23.277Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n...",
"isError": false
},
"responseMessageId": "2b2f0e5f-7b75-4e04-829e-8dc03a98ea64"
}
]
},
{
"id": 90,
"role": "agent",
"timestamp": "2026-09-29T02:41:28.947Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:41:28.947Z",
"callId": "run_shell_command__call_4063716",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with mixed negative sampling"
},
"recorded": {
"id": "run_shell_command__call_4063716",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with mixed negative sampling"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4063716",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.52s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.56s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.5575 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014781,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.014781\nEpoch 2/5: Loss=6.2701 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011245,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011245\nEpoch 3/5: Loss=5.9364 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010538,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.010538\nEpoch 4/5: Loss=5.6442 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00954,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.009540\nEpoch 5/5: Loss=5.4715 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00938,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009380\n=== Training Complete! Best Validation NDCG@10: 0.014781 ===\nProcess Group PGID: 3345\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:43:23.789Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.52s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.56s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.5575 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014781,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.014781\nEpoch 2/5: Loss=6.2701 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011245,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011245\nEpoch 3/5: Loss=5.9364 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010538,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.010538\nEpoch 4/5: Loss=5.6442 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00954,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.009540\nEpoch 5/5: Loss=5.4715 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00938,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009380\n=== Training Complete! Best Validation NDCG@10: 0.014781 ===",
"description": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:43:23.791Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.52s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.56s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training for 5 Epochs ===\nEpoch 1/5: Loss=6.5575 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014781,\n \"quality_users\": 4263\n }\n}\n\nEpoch 1 Public NDCG@10: 0.014781\nEpoch 2/5: Loss=6.2701 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011245,\n \"quality_users\": 4263\n }\n}\n\nEpoch 2 Public NDCG@10: 0.011245\nEpoch 3/5: Loss=5.9364 (6.0s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010538,\n \"quality_users\": 4263\n }\n}\n\nEpoch 3 Public NDCG@10: 0.010538\nEpoch 4/5: Loss=5.6442 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00954,\n \"quality_users\": 4263\n }\n}\n\nEpoch 4 Public NDCG@10: 0.009540\nEpoch 5/5: Loss=5.4715 (6.1s)\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.00938,\n \"quality_users\": 4263\n }\n}\n\nEpoch 5 Public NDCG@10: 0.009380\n=== Training Complete! Best Validation NDCG@10: 0.014781 ===\nProcess Group PGID: 3345\n",
"isError": false
},
"responseMessageId": "1e16e5ee-97b5-46fd-ad82-ddc1c2de9810"
}
]
},
{
"id": 91,
"role": "agent",
"timestamp": "2026-09-29T02:43:40.774Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:43:40.774Z",
"text": "Achieving Breakthrough NDCG\n\nMy validation NDCG@10 has seen a significant leap to **0.014781**. This represents a substantial enhancement compared to both the popularity baseline and our prior model, with the best performing state now saved for further analysis."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:43:40.774Z",
"text": "Investigating NDCG Peaks\n\nI'm focusing on the observed peak at Epoch 1 and subsequent decline in NDCG. I'm considering factors like learning rate, weight decay, and the number of uniform negatives. My hypothesis is that the model might be overfitting to specific negative samples or training data after the initial epoch, prompting exploration of more frequent evaluations and adjusted hyperparameters."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:43:40.774Z",
"callId": "replace__call_1267506",
"name": "replace",
"input": {
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"instruction": "Add intra-epoch evaluation every eval_steps steps",
"new_string": "def train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score",
"old_string": "def train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)"
},
"recorded": {
"id": "replace__call_1267506",
"name": "replace",
"args": {
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"instruction": "Add intra-epoch evaluation every eval_steps steps",
"new_string": "def train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score",
"old_string": "def train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)"
},
"result": [
{
"functionResponse": {
"id": "replace__call_1267506",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:43:40.787Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -16,7 +16,7 @@\n from model import TwoTowerRanker\n \n \n-def train_epoch(\n+def train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n@@ -26,46 +26,92 @@\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n+ val_user_history: dict[str, torch.Tensor],\n+ catalog_item_ids: np.ndarray,\n+ target_uids: np.ndarray,\n+ data_dir: Path,\n+ output_path: Path,\n+ epochs: int = 3,\n batch_size: int = 512,\n+ num_uniform_negatives: int = 512,\n+ eval_every_steps: int = 60,\n ) -> float:\n- model.train()\n- total_loss = 0.0\n- num_batches = 0\n-\n+ best_score = 0.0\n+ global_step = 0\n n_samples = len(target_items)\n- perm = torch.randperm(n_samples)\n+ item_logq_t = torch.from_numpy(item_logq)\n \n- for i in range(0, n_samples, batch_size):\n- idx = perm[i : min(i + batch_size, n_samples)]\n- b_uids = global_uids[idx]\n- b_items = target_items[idx]\n- b_weights = sample_weights[idx]\n+ for epoch in range(1, epochs + 1):\n+ model.train()\n+ perm = torch.randperm(n_samples)\n+ ep_loss = 0.0\n+ n_batches = 0\n \n- b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n- b_feats = catalog_features[b_items]\n- b_logq = item_logq[b_items]\n+ for i in range(0, n_samples, batch_size):\n+ idx = perm[i : min(i + batch_size, n_samples)]\n+ b_uids = global_uids[idx]\n+ b_items = target_items[idx]\n+ b_weights = sample_weights[idx]\n \n- optimizer.zero_grad()\n- loss = model.forward_loss(\n- user_history_batch=b_hist,\n- target_item_indices=b_items,\n- target_item_features=b_feats,\n+ b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n+ b_feats = catalog_features[b_items]\n+\n+ optimizer.zero_grad()\n+ loss = model.forward_loss(\n+ user_history_batch=b_hist,\n+ target_item_indices=b_items,\n+ target_item_features=b_feats,\n+ catalog_features=catalog_features,\n+ item_logq=item_logq_t,\n+ sample_weights=b_weights,\n+ num_uniform_negatives=num_uniform_negatives,\n+ )\n+ loss.backward()\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n+ optimizer.step()\n+ scheduler.step()\n+\n+ ep_loss += loss.item()\n+ n_batches += 1\n+ global_step += 1\n+\n+ if global_step % eval_every_steps == 0:\n+ avg_l = ep_loss / n_batches\n+ score = evaluate_on_validation(\n+ model=model,\n+ val_user_history=val_user_history,\n+ catalog_features=catalog_features,\n+ catalog_item_ids=catalog_item_ids,\n+ target_uids=target_uids,\n+ data_dir=data_dir,\n+ output_path=output_path,\n+ )\n+ print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n+ if score > best_score:\n+ best_score = score\n+ print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n+ torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n+ model.train()\n+\n+ # End of epoch evaluation\n+ score = evaluate_on_validation(\n+ model=model,\n+ val_user_history=val_user_history,\n catalog_features=catalog_features,\n- item_logq=item_logq,\n- sample_weights=b_weights,\n- num_uniform_negatives=256,\n+ catalog_item_ids=catalog_item_ids,\n+ target_uids=target_uids,\n+ data_dir=data_dir,\n+ output_path=output_path,\n )\n- loss.backward()\n- torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n- optimizer.step()\n- scheduler.step()\n+ print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n+ if score > best_score:\n+ best_score = score\n+ print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n+ torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n \n- total_loss += loss.item()\n- num_batches += 1\n+ return best_score\n \n- return total_loss / max(num_batches, 1)\n \n-\n def evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 76,
"model_removed_lines": 30,
"model_added_chars": 2943,
"model_removed_chars": 993,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: def train_epoch(... => def train_and_evaluate(...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:43:43.257Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n...",
"isError": false
},
"responseMessageId": "ce78526f-990b-4ad2-88ad-c94c4afa7d95"
}
]
},
{
"id": 92,
"role": "agent",
"timestamp": "2026-09-29T02:43:48.786Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:43:48.786Z",
"callId": "replace__call_1786130",
"name": "replace",
"input": {
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"new_string": " print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")",
"instruction": "Call train_and_evaluate from main",
"old_string": " print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")"
},
"recorded": {
"id": "replace__call_1786130",
"name": "replace",
"args": {
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"new_string": " print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")",
"instruction": "Call train_and_evaluate from main",
"old_string": " print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")"
},
"result": [
{
"functionResponse": {
"id": "replace__call_1786130",
"name": "replace",
"response": {
"output": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n..."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:43:48.798Z",
"resultDisplay": {
"fileDiff": "Index: train_val.py\n===================================================================\n--- train_val.py\tCurrent\n+++ train_val.py\tProposed\n@@ -256,40 +256,28 @@\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n \n- print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n- best_score = 0.0\n- for epoch in range(1, args.epochs + 1):\n- t_ep = time.time()\n- avg_loss = train_epoch(\n- model=model,\n- optimizer=optimizer,\n- scheduler=scheduler,\n- histories_stacked=histories_stacked,\n- global_uids=global_uids,\n- target_items=target_items,\n- sample_weights=sample_weights,\n- catalog_features=cat_feats,\n- item_logq=item_logq_t,\n- batch_size=args.batch_size,\n- )\n- ep_duration = time.time() - t_ep\n- print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n+ print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n+ best_score = train_and_evaluate(\n+ model=model,\n+ optimizer=optimizer,\n+ scheduler=scheduler,\n+ histories_stacked=histories_stacked,\n+ global_uids=global_uids,\n+ target_items=target_items,\n+ sample_weights=sample_weights,\n+ catalog_features=cat_feats,\n+ item_logq=item_logq,\n+ val_user_history=val_history,\n+ catalog_item_ids=cat_items,\n+ target_uids=target_uids,\n+ data_dir=data_dir,\n+ output_path=Path(args.output_path),\n+ epochs=args.epochs,\n+ batch_size=args.batch_size,\n+ num_uniform_negatives=512,\n+ eval_every_steps=40,\n+ )\n \n- # Validate\n- score = evaluate_on_validation(\n- model=model,\n- val_user_history=val_history,\n- catalog_features=cat_feats,\n- catalog_item_ids=cat_items,\n- target_uids=target_uids,\n- data_dir=data_dir,\n- output_path=Path(args.output_path),\n- )\n- print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n- if score > best_score:\n- best_score = score\n- torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n-\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n \n \n",
"fileName": "train_val.py",
"filePath": "/app/solution/train_val.py",
"originalContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"validation_preference_state.parquet\",\n )\n print(f\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 21,
"model_removed_lines": 33,
"model_added_chars": 692,
"model_removed_chars": 1202,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": false,
"isBuildFile": false
},
"description": "solution/train_val.py: print(f\"=== Starting Train... => print(f\"=== Starting Train...",
"displayName": "Edit",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:43:48.800Z",
"content": "Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:\n...\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n...",
"isError": false
},
"responseMessageId": "ad7c5e83-3506-4159-9aae-f4805e37a424"
}
]
},
{
"id": 93,
"role": "agent",
"timestamp": "2026-09-29T02:43:51.297Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:43:51.297Z",
"callId": "run_shell_command__call_5552733",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with intra-epoch checkpoints and 512 uniform negatives"
},
"recorded": {
"id": "run_shell_command__call_5552733",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with intra-epoch checkpoints and 512 uniform negatives"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_5552733",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.54s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.57s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010606,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9659 | Val NDCG@10=0.010606\n*** New best score: 0.010606! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011484,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9158 | Val NDCG@10=0.011484\n*** New best score: 0.011484! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012202,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8938 | Val NDCG@10=0.012202\n*** New best score: 0.012202! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014921,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8727 | Val NDCG@10=0.014921\n*** New best score: 0.014921! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01712,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8518 | Val NDCG@10=0.017120\n*** New best score: 0.017120! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017548,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017548\n*** New best score: 0.017548! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 2] Loss=6.7210 | Val NDCG@10=0.017774\n*** New best score: 0.017774! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016264,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 2] Loss=6.5972 | Val NDCG@10=0.016264\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015541,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5819 | Val NDCG@10=0.015541\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014918,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5692 | Val NDCG@10=0.014918\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014588,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5617 | Val NDCG@10=0.014588\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014989,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5526 | Val NDCG@10=0.014989\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014672,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.014672\n=== Training Complete! Best Validation NDCG@10: 0.017774 ===\nProcess Group PGID: 3471\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:46:05.359Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.54s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.57s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010606,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9659 | Val NDCG@10=0.010606\n*** New best score: 0.010606! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011484,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9158 | Val NDCG@10=0.011484\n*** New best score: 0.011484! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012202,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8938 | Val NDCG@10=0.012202\n*** New best score: 0.012202! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014921,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8727 | Val NDCG@10=0.014921\n*** New best score: 0.014921! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01712,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8518 | Val NDCG@10=0.017120\n*** New best score: 0.017120! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017548,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017548\n*** New best score: 0.017548! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 2] Loss=6.7210 | Val NDCG@10=0.017774\n*** New best score: 0.017774! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016264,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 2] Loss=6.5972 | Val NDCG@10=0.016264\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015541,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5819 | Val NDCG@10=0.015541\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014918,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5692 | Val NDCG@10=0.014918\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014588,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5617 | Val NDCG@10=0.014588\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014989,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5526 | Val NDCG@10=0.014989\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014672,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.014672\n=== Training Complete! Best Validation NDCG@10: 0.017774 ===",
"description": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:46:05.361Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nGenerated 122113 discovery training pairs across 4 windows.\nData preparation completed in 50.54s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.57s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010606,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9659 | Val NDCG@10=0.010606\n*** New best score: 0.010606! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011484,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9158 | Val NDCG@10=0.011484\n*** New best score: 0.011484! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012202,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8938 | Val NDCG@10=0.012202\n*** New best score: 0.012202! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014921,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8727 | Val NDCG@10=0.014921\n*** New best score: 0.014921! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01712,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8518 | Val NDCG@10=0.017120\n*** New best score: 0.017120! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017548,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017548\n*** New best score: 0.017548! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.47s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 2] Loss=6.7210 | Val NDCG@10=0.017774\n*** New best score: 0.017774! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.49s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016264,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 2] Loss=6.5972 | Val NDCG@10=0.016264\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.11s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015541,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5819 | Val NDCG@10=0.015541\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014918,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5692 | Val NDCG@10=0.014918\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.50s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014588,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5617 | Val NDCG@10=0.014588\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014989,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5526 | Val NDCG@10=0.014989\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.08s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.48s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014672,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.014672\n=== Training Complete! Best Validation NDCG@10: 0.017774 ===\nProcess Group PGID: 3471\n",
"isError": false
},
"responseMessageId": "568bf141-a0db-4c72-8723-9460d3b00801"
}
]
},
{
"id": 94,
"role": "agent",
"timestamp": "2026-09-29T02:46:33.762Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:46:33.762Z",
"text": "Achieving Improved NDCG\n\nI've achieved a new best score of 0.017774 on validation NDCG@10 at step 240. This represents a significant gain of over 66% compared to the baseline, and the intra-epoch evaluation clearly shows this peak."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:46:33.762Z",
"callId": "run_shell_command__call_60152",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Verify saved best_val_model.pt score"
},
"recorded": {
"id": "run_shell_command__call_60152",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Verify saved best_val_model.pt score"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_60152",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.017774\nProcess Group PGID: 3741\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:46:46.317Z",
"resultDisplay": "Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.017774",
"description": "Verify saved best_val_model.pt score",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:46:46.319Z",
"content": "\nOutput: Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.07s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017774,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.017774\nProcess Group PGID: 3741\n",
"isError": false
},
"responseMessageId": "a4c19773-00f5-4bdb-b851-2c902a794ac4"
}
]
},
{
"id": 95,
"role": "agent",
"timestamp": "2026-09-29T02:46:53.658Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:46:53.658Z",
"text": "Refining Performance Metrics\n\nMy verification of the 0.017774 score is complete. I'm now analyzing the `embed_dim` and `out_dim` parameters, specifically looking to see if increasing them from their current setting of 64 can further improve our results."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:46:53.658Z",
"callId": "run_shell_command__call_4321179",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96",
"description": "Experiment with embed_dim=96 and 5 windows"
},
"recorded": {
"id": "run_shell_command__call_4321179",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96",
"description": "Experiment with embed_dim=96 and 5 windows"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4321179",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.25s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.59s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===\nProcess Group PGID: 3784\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:49:43.773Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.25s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.59s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===",
"description": "Experiment with embed_dim=96 and 5 windows",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:49:43.776Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.25s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.59s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===\nProcess Group PGID: 3784\n",
"isError": false
},
"responseMessageId": "b72e503a-753a-4024-ad1b-56015a42437e"
}
]
},
{
"id": 96,
"role": "agent",
"timestamp": "2026-09-29T02:49:55.950Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:49:55.949Z",
"text": "Achieving Significant Improvement\n\nI'm thrilled to report a substantial leap in performance! My validation NDCG@10 has reached an impressive 0.019376, representing an 81% relative improvement over the baseline of 0.010710. The progress has been remarkably stable, climbing incrementally through stages."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:49:55.950Z",
"callId": "run_shell_command__call_140006",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 6e-4 --embed_dim 128 --out_dim 128",
"description": "Experiment with embed_dim=128 and out_dim=128"
},
"recorded": {
"id": "run_shell_command__call_140006",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 6e-4 --embed_dim 128 --out_dim 128",
"description": "Experiment with embed_dim=128 and out_dim=128"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_140006",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 65.83s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011977,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9523 | Val NDCG@10=0.011977\n*** New best score: 0.011977! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.28s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012458,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9107 | Val NDCG@10=0.012458\n*** New best score: 0.012458! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012316,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8925 | Val NDCG@10=0.012316\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012337,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8767 | Val NDCG@10=0.012337\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.25s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015118,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8610 | Val NDCG@10=0.015118\n*** New best score: 0.015118! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016676,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8429 | Val NDCG@10=0.016676\n*** New best score: 0.016676! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.30s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018768,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8254 | Val NDCG@10=0.018768\n*** New best score: 0.018768! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018617,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.018617\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017452,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5896 | Val NDCG@10=0.017452\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017485,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5827 | Val NDCG@10=0.017485\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.29s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016044,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5715 | Val NDCG@10=0.016044\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015823,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5632 | Val NDCG@10=0.015823\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.31s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016519,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5536 | Val NDCG@10=0.016519\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.04s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0163,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5480 | Val NDCG@10=0.016300\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016186,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5458 | Val NDCG@10=0.016186\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016426,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016426\n=== Training Complete! Best Validation NDCG@10: 0.018768 ===\nProcess Group PGID: 4108\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:52:57.521Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 65.83s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011977,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9523 | Val NDCG@10=0.011977\n*** New best score: 0.011977! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.28s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012458,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9107 | Val NDCG@10=0.012458\n*** New best score: 0.012458! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012316,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8925 | Val NDCG@10=0.012316\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012337,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8767 | Val NDCG@10=0.012337\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.25s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015118,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8610 | Val NDCG@10=0.015118\n*** New best score: 0.015118! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016676,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8429 | Val NDCG@10=0.016676\n*** New best score: 0.016676! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.30s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018768,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8254 | Val NDCG@10=0.018768\n*** New best score: 0.018768! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018617,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.018617\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017452,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5896 | Val NDCG@10=0.017452\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017485,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5827 | Val NDCG@10=0.017485\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.29s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016044,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5715 | Val NDCG@10=0.016044\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015823,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5632 | Val NDCG@10=0.015823\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.31s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016519,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5536 | Val NDCG@10=0.016519\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.04s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0163,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5480 | Val NDCG@10=0.016300\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016186,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5458 | Val NDCG@10=0.016186\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016426,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016426\n=== Training Complete! Best Validation NDCG@10: 0.018768 ===",
"description": "Experiment with embed_dim=128 and out_dim=128",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:52:57.524Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 65.83s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011977,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9523 | Val NDCG@10=0.011977\n*** New best score: 0.011977! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.28s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012458,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9107 | Val NDCG@10=0.012458\n*** New best score: 0.012458! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012316,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.8925 | Val NDCG@10=0.012316\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012337,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8767 | Val NDCG@10=0.012337\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.25s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015118,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8610 | Val NDCG@10=0.015118\n*** New best score: 0.015118! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.54s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016676,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8429 | Val NDCG@10=0.016676\n*** New best score: 0.016676! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.30s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018768,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8254 | Val NDCG@10=0.018768\n*** New best score: 0.018768! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018617,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.018617\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017452,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.5896 | Val NDCG@10=0.017452\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017485,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.5827 | Val NDCG@10=0.017485\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.29s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016044,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5715 | Val NDCG@10=0.016044\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015823,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5632 | Val NDCG@10=0.015823\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.31s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.56s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016519,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5536 | Val NDCG@10=0.016519\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.04s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.0163,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5480 | Val NDCG@10=0.016300\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016186,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5458 | Val NDCG@10=0.016186\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.26s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.55s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016426,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016426\n=== Training Complete! Best Validation NDCG@10: 0.018768 ===\nProcess Group PGID: 4108\n",
"isError": false
},
"responseMessageId": "55cb9875-1630-48f2-abcd-82f09bc8aba0"
}
]
},
{
"id": 97,
"role": "agent",
"timestamp": "2026-09-29T02:53:06.373Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:53:06.373Z",
"text": "Testing Embedding Dimensions\n\nI'm exploring the impact of `embed_dim`, finding 96 performs slightly better than 128, offering a superior metric with improved speed. My next step is to assess the effect of increasing the training window size to six."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:53:06.373Z",
"callId": "run_shell_command__call_51505",
"name": "run_shell_command",
"input": {
"description": "Run train_val with 6 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 6 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
},
"recorded": {
"id": "run_shell_command__call_51505",
"name": "run_shell_command",
"args": {
"description": "Run train_val with 6 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 6 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_51505",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800, 21147200]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nExtracting user histories for training cutoff 5: timestamp=21147200...\nGenerated 178230 discovery training pairs across 6 windows.\nData preparation completed in 71.28s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011399,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9946 | Val NDCG@10=0.011399\n*** New best score: 0.011399! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012145,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9303 | Val NDCG@10=0.012145\n*** New best score: 0.012145! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013402,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9087 | Val NDCG@10=0.013402\n*** New best score: 0.013402! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013911,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8934 | Val NDCG@10=0.013911\n*** New best score: 0.013911! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014808,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8799 | Val NDCG@10=0.014808\n*** New best score: 0.014808! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016187,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8634 | Val NDCG@10=0.016187\n*** New best score: 0.016187! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017724,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8476 | Val NDCG@10=0.017724\n*** New best score: 0.017724! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018013,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 1] Loss=6.8328 | Val NDCG@10=0.018013\n*** New best score: 0.018013! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017491,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017491\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018002,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6126 | Val NDCG@10=0.018002\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016227,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.6077 | Val NDCG@10=0.016227\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016475,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5923 | Val NDCG@10=0.016475\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015871,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5801 | Val NDCG@10=0.015871\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015568,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5679 | Val NDCG@10=0.015568\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015686,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5607 | Val NDCG@10=0.015686\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015682,\n \"quality_users\": 4263\n }\n}\n\n[Step 600 | Ep 2] Loss=6.5577 | Val NDCG@10=0.015682\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015152,\n \"quality_users\": 4263\n }\n}\n\n[Step 640 | Ep 2] Loss=6.5543 | Val NDCG@10=0.015152\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015548,\n \"quality_users\": 4263\n }\n}\n\n[Step 680 | Ep 2] Loss=6.5490 | Val NDCG@10=0.015548\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016087,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016087\n=== Training Complete! Best Validation NDCG@10: 0.018013 ===\nProcess Group PGID: 4432\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:56:25.444Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800, 21147200]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nExtracting user histories for training cutoff 5: timestamp=21147200...\nGenerated 178230 discovery training pairs across 6 windows.\nData preparation completed in 71.28s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011399,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9946 | Val NDCG@10=0.011399\n*** New best score: 0.011399! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012145,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9303 | Val NDCG@10=0.012145\n*** New best score: 0.012145! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013402,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9087 | Val NDCG@10=0.013402\n*** New best score: 0.013402! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013911,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8934 | Val NDCG@10=0.013911\n*** New best score: 0.013911! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014808,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8799 | Val NDCG@10=0.014808\n*** New best score: 0.014808! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016187,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8634 | Val NDCG@10=0.016187\n*** New best score: 0.016187! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017724,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8476 | Val NDCG@10=0.017724\n*** New best score: 0.017724! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018013,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 1] Loss=6.8328 | Val NDCG@10=0.018013\n*** New best score: 0.018013! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017491,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017491\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018002,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6126 | Val NDCG@10=0.018002\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016227,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.6077 | Val NDCG@10=0.016227\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016475,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5923 | Val NDCG@10=0.016475\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015871,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5801 | Val NDCG@10=0.015871\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015568,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5679 | Val NDCG@10=0.015568\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015686,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5607 | Val NDCG@10=0.015686\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015682,\n \"quality_users\": 4263\n }\n}\n\n[Step 600 | Ep 2] Loss=6.5577 | Val NDCG@10=0.015682\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015152,\n \"quality_users\": 4263\n }\n}\n\n[Step 640 | Ep 2] Loss=6.5543 | Val NDCG@10=0.015152\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015548,\n \"quality_users\": 4263\n }\n}\n\n[Step 680 | Ep 2] Loss=6.5490 | Val NDCG@10=0.015548\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016087,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016087\n=== Training Complete! Best Validation NDCG@10: 0.018013 ===",
"description": "Run train_val with 6 windows and embed_dim 96",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:56:25.447Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800, 21147200]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nExtracting user histories for training cutoff 5: timestamp=21147200...\nGenerated 178230 discovery training pairs across 6 windows.\nData preparation completed in 71.28s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.62s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.011399,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9946 | Val NDCG@10=0.011399\n*** New best score: 0.011399! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012145,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9303 | Val NDCG@10=0.012145\n*** New best score: 0.012145! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013402,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9087 | Val NDCG@10=0.013402\n*** New best score: 0.013402! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013911,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8934 | Val NDCG@10=0.013911\n*** New best score: 0.013911! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014808,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8799 | Val NDCG@10=0.014808\n*** New best score: 0.014808! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016187,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8634 | Val NDCG@10=0.016187\n*** New best score: 0.016187! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017724,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8476 | Val NDCG@10=0.017724\n*** New best score: 0.017724! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018013,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 1] Loss=6.8328 | Val NDCG@10=0.018013\n*** New best score: 0.018013! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017491,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.017491\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018002,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6126 | Val NDCG@10=0.018002\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016227,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.6077 | Val NDCG@10=0.016227\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016475,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5923 | Val NDCG@10=0.016475\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015871,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5801 | Val NDCG@10=0.015871\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015568,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5679 | Val NDCG@10=0.015568\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015686,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5607 | Val NDCG@10=0.015686\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015682,\n \"quality_users\": 4263\n }\n}\n\n[Step 600 | Ep 2] Loss=6.5577 | Val NDCG@10=0.015682\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015152,\n \"quality_users\": 4263\n }\n}\n\n[Step 640 | Ep 2] Loss=6.5543 | Val NDCG@10=0.015152\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.015548,\n \"quality_users\": 4263\n }\n}\n\n[Step 680 | Ep 2] Loss=6.5490 | Val NDCG@10=0.015548\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016087,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.016087\n=== Training Complete! Best Validation NDCG@10: 0.018013 ===\nProcess Group PGID: 4432\n",
"isError": false
},
"responseMessageId": "d4148b37-39d6-46e0-a2da-b7ad41c6b6d2"
}
]
},
{
"id": 98,
"role": "agent",
"timestamp": "2026-09-29T02:56:52.482Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:56:52.482Z",
"text": "Achieving Higher Scores\n\nMy current focus is on pushing performance further. I've observed a promising NDCG@10 score of 0.019376 with a 5-window configuration. This suggests that exploring window sizes might be a fruitful avenue for improvement, and I'm keen to investigate this further to see if we can build upon this result."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:56:52.482Z",
"callId": "run_shell_command__call_2248375",
"name": "run_shell_command",
"input": {
"description": "Reproduce best model with 5 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
},
"recorded": {
"id": "run_shell_command__call_2248375",
"name": "run_shell_command",
"args": {
"description": "Reproduce best model with 5 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2248375",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.24s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.64s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.24s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===\nProcess Group PGID: 4810\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:59:42.415Z",
"resultDisplay": "=== Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.24s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.64s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.24s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===",
"description": "Reproduce best model with 5 windows and embed_dim 96",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:59:42.418Z",
"content": "\nOutput: === Loading Catalog and Users ===\nTraining cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Training Pairs ===\nExtracting user histories for training cutoff 0: timestamp=24180200...\nExtracting user histories for training cutoff 1: timestamp=23573600...\nExtracting user histories for training cutoff 2: timestamp=22967000...\nExtracting user histories for training cutoff 3: timestamp=22360400...\nExtracting user histories for training cutoff 4: timestamp=21753800...\nGenerated 151372 discovery training pairs across 5 windows.\nData preparation completed in 61.24s\n=== Extracting Validation Cutoff History ===\nValidation history ready in 3.64s\n=== Initializing Two-Tower Ranker ===\n=== Starting Training with Intra-Epoch Validation ===\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.21s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.53s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01299,\n \"quality_users\": 4263\n }\n}\n\n[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990\n*** New best score: 0.012990! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.012391,\n \"quality_users\": 4263\n }\n}\n\n[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01336,\n \"quality_users\": 4263\n }\n}\n\n[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360\n*** New best score: 0.013360! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014308,\n \"quality_users\": 4263\n }\n}\n\n[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308\n*** New best score: 0.014308! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.016339,\n \"quality_users\": 4263\n }\n}\n\n[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339\n*** New best score: 0.016339! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018645,\n \"quality_users\": 4263\n }\n}\n\n[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645\n*** New best score: 0.018645! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018652,\n \"quality_users\": 4263\n }\n}\n\n[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652\n*** New best score: 0.018652! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.52s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 1] Val NDCG@10=0.019376\n*** New best score: 0.019376! Saving model checkpoint...\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018836,\n \"quality_users\": 4263\n }\n}\n\n[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.018001,\n \"quality_users\": 4263\n }\n}\n\n[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017624,\n \"quality_users\": 4263\n }\n}\n\n[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.20s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017974,\n \"quality_users\": 4263\n }\n}\n\n[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.22s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017418,\n \"quality_users\": 4263\n }\n}\n\n[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.23s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017469,\n \"quality_users\": 4263\n }\n}\n\n[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.24s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017564,\n \"quality_users\": 4263\n }\n}\n\n[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564\nPrecomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.01s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.19s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.51s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\n\n[End of Epoch 2] Val NDCG@10=0.017218\n=== Training Complete! Best Validation NDCG@10: 0.019376 ===\nProcess Group PGID: 4810\n",
"isError": false
},
"responseMessageId": "92f15c88-d101-478f-8b70-811b0a6dfd86"
}
]
},
{
"id": 99,
"role": "agent",
"timestamp": "2026-09-29T02:59:47.495Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:59:47.495Z",
"callId": "run_shell_command__call_141300",
"name": "run_shell_command",
"input": {
"description": "Evaluate /app/solution/validation.parquet with public evaluator",
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet"
},
"recorded": {
"id": "run_shell_command__call_141300",
"name": "run_shell_command",
"args": {
"description": "Evaluate /app/solution/validation.parquet with public evaluator",
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_141300",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 5134\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:59:50.843Z",
"resultDisplay": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}",
"description": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:59:50.845Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017218,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 5134\n",
"isError": false
},
"responseMessageId": "8deebe8c-02b6-4017-9ff2-5317628edc54"
}
]
},
{
"id": 100,
"role": "agent",
"timestamp": "2026-09-29T02:59:55.944Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:59:55.944Z",
"text": "Refining Validation Output\n\nI'm focusing on ensuring the final `validation.parquet` precisely reflects the optimal performance point. My current effort involves regenerating this file using the best saved checkpoint to guarantee the accuracy of the reported metric, specifically targeting a value of 0.0."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:59:55.944Z",
"callId": "run_shell_command__call_79025",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=96, hidden_dim=128, out_dim=96)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Generate and verify validation.parquet from best_val_model.pt"
},
"recorded": {
"id": "run_shell_command__call_79025",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=96, hidden_dim=128, out_dim=96)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Generate and verify validation.parquet from best_val_model.pt"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_79025",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.60s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.019376\nProcess Group PGID: 5154\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:00:08.585Z",
"resultDisplay": "Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.60s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.019376",
"description": "Generate and verify validation.parquet from best_val_model.pt",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:00:08.588Z",
"content": "\nOutput: Precomputing candidate item embeddings offline...\nPrecomputed 30000 item embeddings in 0.02s\nComputing user embeddings for validation targets...\nComputed 8456 user embeddings in 0.12s\nRunning MIPS candidate retrieval and applying eligibility masks...\nMIPS retrieval completed in 0.60s\nSaved validation ranking to /app/solution/validation.parquet\nEvaluator Output:\n {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\n\nVerified saved best model score: 0.019376\nProcess Group PGID: 5154\n",
"isError": false
},
"responseMessageId": "e113719b-4762-400b-88f5-2941799ee63b"
}
]
},
{
"id": 101,
"role": "agent",
"timestamp": "2026-09-29T03:00:16.322Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:00:16.322Z",
"text": "Saving Best Score\n\nI've successfully saved `validation.parquet` with the optimal score of `0.019376`. My focus now shifts to the next stage: constructing and refining a validation ranker, utilizing `train_*` data for history and `val_*` for its performance evaluation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:00:16.322Z",
"callId": "update_topic__call_76277",
"name": "update_topic",
"input": {
"strategic_intent": "Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.",
"summary": "Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.",
"title": "Full Data Refit & Submission Generation"
},
"recorded": {
"id": "update_topic__call_76277",
"name": "update_topic",
"args": {
"strategic_intent": "Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.",
"summary": "Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.",
"title": "Full Data Refit & Submission Generation"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_76277",
"name": "update_topic",
"response": {
"output": "Current topic: \"Full Data Refit & Submission Generation\"\nTopic summary: Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.\n\nStrategic Intent: Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:00:16.326Z",
"resultDisplay": "## ๐ Topic: **Full Data Refit & Submission Generation**\n\n**Summary:**\nSuccessfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.\n\n> [!STRATEGY]\n> **Intent:** Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.",
"description": "Update topic to: \"Full Data Refit & Submission Generation\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T03:00:16.328Z",
"content": "Current topic: \"Full Data Refit & Submission Generation\"\nTopic summary: Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.\n\nStrategic Intent: Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.",
"isError": false
},
"responseMessageId": "2f4b0159-84ae-448a-826a-6e6377ae40d2"
}
]
},
{
"id": 102,
"role": "agent",
"timestamp": "2026-09-29T03:00:46.991Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:00:46.991Z",
"text": "Evaluating Hidden Test Logic\n\nI'm currently dissecting the intricacies of the final hidden-test phase. My focus is on meticulously understanding the distinct functionalities of the two public windows. I need to ensure a robust validation ranker is built and tuned solely on `train_*` history, with `val_*` serving as the subsequent outcomes. Following this, the selected approach must be refitted."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:00:46.991Z",
"text": "Refining Feature Usage\n\nI'm now focusing on clarifying how validation data is used. Specifically, validation events should be treated as observed history for feature generation during training, but not as direct outcomes for measuring validation ranking performance. The final ranker, however, will be refitted using this combined history."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:00:46.991Z",
"text": "Determining Refit Cutoffs\n\nI'm now establishing the precise training cutoff timestamps for refitting. With validation data now integrated into the training set, I'm shifting the original training cutoffs forward by seven days to encompass the validation window. This ensures the refitted model leverages the full historical context available up to the hidden test period."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:00:46.991Z",
"text": "Adjusting Training Boundaries\n\nI've shifted my training cutoffs forward by a week. This aligns the validation window perfectly as the target, allowing the model to learn organic discovery behaviors while still incorporating historical patterns from earlier weeks. Inference will now incorporate recent interaction histories and platform-provided preferences."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:00:46.991Z",
"callId": "write_file__call_252342",
"name": "write_file",
"input": {
"file_path": "/app/solution/refit_submission.py",
"content": "\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"test_preference_state.parquet\",\n include_val=True,\n )\n print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n"
},
"recorded": {
"id": "write_file__call_252342",
"name": "write_file",
"args": {
"file_path": "/app/solution/refit_submission.py",
"content": "\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"test_preference_state.parquet\",\n include_val=True,\n )\n print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_252342",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/refit_submission.py. Here is the updated code:\n\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"test_preference_state.parquet\",\n include_val=True,\n )\n print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:00:47.001Z",
"resultDisplay": {
"fileDiff": "Index: refit_submission.py\n===================================================================\n--- refit_submission.py\tOriginal\n+++ refit_submission.py\tWritten\n@@ -0,0 +1,231 @@\n+\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\n+from __future__ import annotations\n+\n+import argparse\n+import sys\n+import time\n+from pathlib import Path\n+\n+import numpy as np\n+import pandas as pd\n+import torch\n+\n+from data_prep import build_training_pairs, extract_user_histories, load_catalog_features\n+from model import TwoTowerRanker\n+\n+# Add /task to path for evaluation validators\n+sys.path.append(\"/task\")\n+from ranking_evaluation import CandidateSet, validate_ranking\n+\n+\n+def refit_and_submit(\n+ data_dir: Path,\n+ submission_path: Path,\n+ epochs: int = 1,\n+ steps_to_train: int = 280,\n+ batch_size: int = 512,\n+ lr: float = 7e-4,\n+ embed_dim: int = 96,\n+ hidden_dim: int = 128,\n+ out_dim: int = 96,\n+ temperature: float = 0.07,\n+ logq_weight: float = 1.0,\n+ num_windows: int = 6,\n+ sample_listens: int = 35,\n+ num_uniform_negatives: int = 512,\n+) -> None:\n+ torch.manual_seed(42)\n+ np.random.seed(42)\n+ torch.set_num_threads(8)\n+\n+ print(\"=== Loading Catalog and Users for Full Refit ===\")\n+ cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n+ target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n+ target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n+ targets_set = set(target_uids)\n+\n+ test_cutoff = 25393400\n+ val_cutoff = 24786800\n+ week_sec = 7 * 86400\n+ gap_sec = 1800\n+\n+ # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n+ cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n+ print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n+\n+ print(\"=== Building Full Training Pairs (including Val Window) ===\")\n+ t0 = time.time()\n+ histories_list, user_win, target_items, sample_weights = build_training_pairs(\n+ data_dir=data_dir,\n+ cutoffs=cutoffs,\n+ item_to_idx=item_to_idx,\n+ target_uids=target_uids,\n+ sample_listens_per_user=sample_listens,\n+ include_val=True,\n+ )\n+ print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n+\n+ # Stack user histories across windows\n+ keys = list(histories_list[0].keys())\n+ histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n+ n_users = len(target_uids)\n+ global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n+ item_logq_t = torch.from_numpy(item_logq)\n+\n+ print(\"=== Initializing Two-Tower Ranker ===\")\n+ model = TwoTowerRanker(\n+ num_catalog_items=30000,\n+ embed_dim=embed_dim,\n+ hidden_dim=hidden_dim,\n+ out_dim=out_dim,\n+ temperature=temperature,\n+ logq_weight=logq_weight,\n+ )\n+\n+ optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n+ total_steps = steps_to_train\n+ scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n+\n+ print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n+ model.train()\n+ n_samples = len(target_items)\n+ perm = torch.randperm(n_samples)\n+ global_step = 0\n+ t_train = time.time()\n+\n+ while global_step < steps_to_train:\n+ for i in range(0, n_samples, batch_size):\n+ if global_step >= steps_to_train:\n+ break\n+ idx = perm[i : min(i + batch_size, n_samples)]\n+ b_uids = global_uids[idx]\n+ b_items = target_items[idx]\n+ b_weights = sample_weights[idx]\n+\n+ b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n+ b_feats = cat_feats[b_items]\n+\n+ optimizer.zero_grad()\n+ loss = model.forward_loss(\n+ user_history_batch=b_hist,\n+ target_item_indices=b_items,\n+ target_item_features=b_feats,\n+ catalog_features=cat_feats,\n+ item_logq=item_logq_t,\n+ sample_weights=b_weights,\n+ num_uniform_negatives=num_uniform_negatives,\n+ )\n+ loss.backward()\n+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n+ optimizer.step()\n+ scheduler.step()\n+ global_step += 1\n+\n+ if global_step % 40 == 0:\n+ print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n+\n+ print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n+ torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n+\n+ # --- Generate Hidden Test Predictions ---\n+ print(\"=== Extracting Hidden-Test User Histories ===\")\n+ t0 = time.time()\n+ test_history = extract_user_histories(\n+ data_dir=data_dir,\n+ cutoff=test_cutoff,\n+ target_uids=target_uids,\n+ item_to_idx=item_to_idx,\n+ pref_state_path=data_dir / \"test_preference_state.parquet\",\n+ include_val=True,\n+ )\n+ print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n+\n+ print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n+ t0 = time.time()\n+ cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n+ print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n+\n+ print(\"=== Computing Target User Embeddings ===\")\n+ t0 = time.time()\n+ user_embeddings = model.compute_user_embeddings(test_history)\n+ print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n+\n+ print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n+ t0 = time.time()\n+ scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n+\n+ # Load test eligibility bitmask\n+ test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n+ unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n+ ineligible_mask = unpacked_test == 0\n+ scores[torch.from_numpy(ineligible_mask)] = -1e9\n+\n+ # Top 100 retrieval\n+ top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n+ print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n+\n+ print(\"=== Building Final Submission DataFrame ===\")\n+ rows = []\n+ for u_idx, uid in enumerate(target_uids):\n+ top_items = cat_items[top100[u_idx]]\n+ for r, item_id in enumerate(top_items, 1):\n+ rows.append((int(uid), int(item_id), r))\n+\n+ sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n+\n+ # Strict contract validation\n+ print(\"=== Validating Submission Contract ===\")\n+ validated = validate_ranking(sub_df, targets_set)\n+ print(\"Contract structure validation passed!\")\n+\n+ # Check candidate eligibility for all rows\n+ print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n+ is_valid_candidate = [\n+ test_eligibility.is_eligible(int(u), int(it))\n+ for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n+ ]\n+ if not all(is_valid_candidate):\n+ num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n+ raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n+ print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n+\n+ submission_path.parent.mkdir(parents=True, exist_ok=True)\n+ validated.to_parquet(submission_path, index=False)\n+ print(f\"Successfully saved final submission to {submission_path}\")\n+ print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n+\n+\n+def main():\n+ parser = argparse.ArgumentParser()\n+ parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n+ parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n+ parser.add_argument(\"--steps\", type=int, default=280)\n+ parser.add_argument(\"--batch_size\", type=int, default=512)\n+ parser.add_argument(\"--lr\", type=float, default=7e-4)\n+ parser.add_argument(\"--embed_dim\", type=int, default=96)\n+ parser.add_argument(\"--hidden_dim\", type=int, default=128)\n+ parser.add_argument(\"--out_dim\", type=int, default=96)\n+ parser.add_argument(\"--temperature\", type=float, default=0.07)\n+ parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n+ parser.add_argument(\"--num_windows\", type=int, default=6)\n+ parser.add_argument(\"--sample_listens\", type=int, default=35)\n+ args = parser.parse_args()\n+\n+ refit_and_submit(\n+ data_dir=Path(args.data_dir),\n+ submission_path=Path(args.submission_path),\n+ steps_to_train=args.steps,\n+ batch_size=args.batch_size,\n+ lr=args.lr,\n+ embed_dim=args.embed_dim,\n+ hidden_dim=args.hidden_dim,\n+ out_dim=args.out_dim,\n+ temperature=args.temperature,\n+ logq_weight=args.logq_weight,\n+ num_windows=args.num_windows,\n+ sample_listens=args.sample_listens,\n+ )\n+\n+\n+if __name__ == \"__main__\":\n+ main()\n",
"fileName": "refit_submission.py",
"filePath": "/app/solution/refit_submission.py",
"originalContent": "",
"newContent": "\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"test_preference_state.parquet\",\n include_val=True,\n )\n print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n",
"diffStat": {
"model_added_lines": 231,
"model_removed_lines": 0,
"model_added_chars": 8609,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/refit_submission.py",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T03:00:47.004Z",
"content": "Successfully created and wrote to new file: /app/solution/refit_submission.py. Here is the updated code:\n\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=data_dir / \"test_preference_state.parquet\",\n include_val=True,\n )\n print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n",
"isError": false
},
"responseMessageId": "317d82dc-641d-41d4-8ef4-e064c1914a30"
}
]
},
{
"id": 103,
"role": "agent",
"timestamp": "2026-09-29T03:00:56.000Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T03:00:56.000Z",
"callId": "run_shell_command__call_72562",
"name": "run_shell_command",
"input": {
"description": "Run refit_submission.py to generate final submission",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py --steps 280 --lr 7e-4 --embed_dim 96 --out_dim 96 --num_windows 6 --sample_listens 35"
},
"recorded": {
"id": "run_shell_command__call_72562",
"name": "run_shell_command",
"args": {
"description": "Run refit_submission.py to generate final submission",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py --steps 280 --lr 7e-4 --embed_dim 96 --out_dim 96 --num_windows 6 --sample_listens 35"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_72562",
"name": "run_shell_command",
"response": {
"output": "\nOutput: === Loading Catalog and Users for Full Refit ===\nRefit training cutoffs (6 windows): [24786800, 24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Full Training Pairs (including Val Window) ===\nExtracting user histories for training cutoff 0: timestamp=24786800...\nExtracting user histories for training cutoff 1: timestamp=24180200...\nExtracting user histories for training cutoff 2: timestamp=23573600...\nExtracting user histories for training cutoff 3: timestamp=22967000...\nExtracting user histories for training cutoff 4: timestamp=22360400...\nExtracting user histories for training cutoff 5: timestamp=21753800...\nGenerated 180980 discovery training pairs across 6 windows.\nData preparation completed in 74.25s, pairs=180980\n=== Initializing Two-Tower Ranker ===\n=== Training Refitted Two-Tower Model for 280 Steps ===\n[Step 40/280] Loss: 6.8969\n[Step 80/280] Loss: 6.8447\n[Step 120/280] Loss: 6.8488\n[Step 160/280] Loss: 6.7927\n[Step 200/280] Loss: 6.8334\n[Step 240/280] Loss: 6.8618\n[Step 280/280] Loss: 6.8466\nRefit training completed in 10.20s\n=== Extracting Hidden-Test User Histories ===\nTest user histories ready in 3.69s\n=== Precomputing Candidate Item Embeddings Offline ===\nCandidate embeddings shape=torch.Size([30000, 96]) computed in 0.02s\n=== Computing Target User Embeddings ===\nUser embeddings shape=torch.Size([8456, 96]) computed in 0.17s\n=== Computing MIPS Retrieval with Test Candidate Mask ===\nMIPS retrieval completed in 0.52s\n=== Building Final Submission DataFrame ===\n=== Validating Submission Contract ===\nContract structure validation passed!\nChecking candidate eligibility against test_eligible_candidates.npz...\n100% of submitted items are verified eligible in test_eligible_candidates.npz!\nSuccessfully saved final submission to /app/solution/submission.parquet\nRows: 845600, Users: 8456\nProcess Group PGID: 5197\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:02:33.541Z",
"resultDisplay": "=== Loading Catalog and Users for Full Refit ===\nRefit training cutoffs (6 windows): [24786800, 24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Full Training Pairs (including Val Window) ===\nExtracting user histories for training cutoff 0: timestamp=24786800...\nExtracting user histories for training cutoff 1: timestamp=24180200...\nExtracting user histories for training cutoff 2: timestamp=23573600...\nExtracting user histories for training cutoff 3: timestamp=22967000...\nExtracting user histories for training cutoff 4: timestamp=22360400...\nExtracting user histories for training cutoff 5: timestamp=21753800...\nGenerated 180980 discovery training pairs across 6 windows.\nData preparation completed in 74.25s, pairs=180980\n=== Initializing Two-Tower Ranker ===\n=== Training Refitted Two-Tower Model for 280 Steps ===\n[Step 40/280] Loss: 6.8969\n[Step 80/280] Loss: 6.8447\n[Step 120/280] Loss: 6.8488\n[Step 160/280] Loss: 6.7927\n[Step 200/280] Loss: 6.8334\n[Step 240/280] Loss: 6.8618\n[Step 280/280] Loss: 6.8466\nRefit training completed in 10.20s\n=== Extracting Hidden-Test User Histories ===\nTest user histories ready in 3.69s\n=== Precomputing Candidate Item Embeddings Offline ===\nCandidate embeddings shape=torch.Size([30000, 96]) computed in 0.02s\n=== Computing Target User Embeddings ===\nUser embeddings shape=torch.Size([8456, 96]) computed in 0.17s\n=== Computing MIPS Retrieval with Test Candidate Mask ===\nMIPS retrieval completed in 0.52s\n=== Building Final Submission DataFrame ===\n=== Validating Submission Contract ===\nContract structure validation passed!\nChecking candidate eligibility against test_eligible_candidates.npz...\n100% of submitted items are verified eligible in test_eligible_candidates.npz!\nSuccessfully saved final submission to /app/solution/submission.parquet\nRows: 845600, Users: 8456",
"description": "Run refit_submission.py to generate final submission",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:02:33.544Z",
"content": "\nOutput: === Loading Catalog and Users for Full Refit ===\nRefit training cutoffs (6 windows): [24786800, 24180200, 23573600, 22967000, 22360400, 21753800]\n=== Building Full Training Pairs (including Val Window) ===\nExtracting user histories for training cutoff 0: timestamp=24786800...\nExtracting user histories for training cutoff 1: timestamp=24180200...\nExtracting user histories for training cutoff 2: timestamp=23573600...\nExtracting user histories for training cutoff 3: timestamp=22967000...\nExtracting user histories for training cutoff 4: timestamp=22360400...\nExtracting user histories for training cutoff 5: timestamp=21753800...\nGenerated 180980 discovery training pairs across 6 windows.\nData preparation completed in 74.25s, pairs=180980\n=== Initializing Two-Tower Ranker ===\n=== Training Refitted Two-Tower Model for 280 Steps ===\n[Step 40/280] Loss: 6.8969\n[Step 80/280] Loss: 6.8447\n[Step 120/280] Loss: 6.8488\n[Step 160/280] Loss: 6.7927\n[Step 200/280] Loss: 6.8334\n[Step 240/280] Loss: 6.8618\n[Step 280/280] Loss: 6.8466\nRefit training completed in 10.20s\n=== Extracting Hidden-Test User Histories ===\nTest user histories ready in 3.69s\n=== Precomputing Candidate Item Embeddings Offline ===\nCandidate embeddings shape=torch.Size([30000, 96]) computed in 0.02s\n=== Computing Target User Embeddings ===\nUser embeddings shape=torch.Size([8456, 96]) computed in 0.17s\n=== Computing MIPS Retrieval with Test Candidate Mask ===\nMIPS retrieval completed in 0.52s\n=== Building Final Submission DataFrame ===\n=== Validating Submission Contract ===\nContract structure validation passed!\nChecking candidate eligibility against test_eligible_candidates.npz...\n100% of submitted items are verified eligible in test_eligible_candidates.npz!\nSuccessfully saved final submission to /app/solution/submission.parquet\nRows: 845600, Users: 8456\nProcess Group PGID: 5197\n",
"isError": false
},
"responseMessageId": "dbd47377-06bd-47c9-89d0-40febc37b648"
}
]
},
{
"id": 104,
"role": "agent",
"timestamp": "2026-09-29T03:03:18.864Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:03:18.864Z",
"text": "Confirming Submission Validity\n\nI've successfully validated the submission contract and confirmed that all submitted items meet the eligibility criteria defined in `test_eligible_candidates.npz`. The final submission has been saved."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:03:18.864Z",
"callId": "run_shell_command__call_1381377",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\n\nsub_path = Path('/app/solution/submission.parquet')\nprint('Checking submission file:', sub_path)\ndf = pd.read_parquet(sub_path)\nprint('Shape:', df.shape)\nprint('Columns:', df.columns.tolist())\nprint('Data types:\\n', df.dtypes)\n\nvalidated = validate_ranking(df, targets_set)\nprint('validate_ranking passed!')\n\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\nprint('Validating every single item against test_eligible_candidates.npz...')\ninvalids = []\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n invalids.append((uid, item))\n\nprint(f'Total invalid candidates: {len(invalids)}')\nassert len(invalids) == 0, 'Found invalid candidates!'\n\nprint('All 845,600 user-item pairs are 100% verified eligible!')\n\"",
"description": "Exhaustively verify final submission against all constraints and eligibility"
},
"recorded": {
"id": "run_shell_command__call_1381377",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\n\nsub_path = Path('/app/solution/submission.parquet')\nprint('Checking submission file:', sub_path)\ndf = pd.read_parquet(sub_path)\nprint('Shape:', df.shape)\nprint('Columns:', df.columns.tolist())\nprint('Data types:\\n', df.dtypes)\n\nvalidated = validate_ranking(df, targets_set)\nprint('validate_ranking passed!')\n\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\nprint('Validating every single item against test_eligible_candidates.npz...')\ninvalids = []\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n invalids.append((uid, item))\n\nprint(f'Total invalid candidates: {len(invalids)}')\nassert len(invalids) == 0, 'Found invalid candidates!'\n\nprint('All 845,600 user-item pairs are 100% verified eligible!')\n\"",
"description": "Exhaustively verify final submission against all constraints and eligibility"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1381377",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Checking submission file: /app/solution/submission.parquet\nShape: (845600, 3)\nColumns: ['uid', 'item_id', 'rank']\nData types:\n uid int64\nitem_id int64\nrank int64\ndtype: object\nvalidate_ranking passed!\nValidating every single item against test_eligible_candidates.npz...\nTotal invalid candidates: 0\nAll 845,600 user-item pairs are 100% verified eligible!\nProcess Group PGID: 5233\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:03:21.655Z",
"resultDisplay": "Checking submission file: /app/solution/submission.parquet\nShape: (845600, 3)\nColumns: ['uid', 'item_id', 'rank']\nData types:\n uid int64\nitem_id int64\nrank int64\ndtype: object\nvalidate_ranking passed!\nValidating every single item against test_eligible_candidates.npz...\nTotal invalid candidates: 0\nAll 845,600 user-item pairs are 100% verified eligible!",
"description": "Exhaustively verify final submission against all constraints and eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:03:21.657Z",
"content": "\nOutput: Checking submission file: /app/solution/submission.parquet\nShape: (845600, 3)\nColumns: ['uid', 'item_id', 'rank']\nData types:\n uid int64\nitem_id int64\nrank int64\ndtype: object\nvalidate_ranking passed!\nValidating every single item against test_eligible_candidates.npz...\nTotal invalid candidates: 0\nAll 845,600 user-item pairs are 100% verified eligible!\nProcess Group PGID: 5233\n",
"isError": false
},
"responseMessageId": "aa335d00-15d7-4f52-8cd9-dfd164162a2d"
}
]
},
{
"id": 105,
"role": "agent",
"timestamp": "2026-09-29T03:03:39.003Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:03:39.003Z",
"text": "Reviewing Deliverables\n\nI'm currently in Phase 6, focusing on verifying that all required deliverables, specifically the code for defining, training, and applying both towers, are present and correctly formatted as per the prompt's instructions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:03:39.003Z",
"text": "Documenting Model Details\n\nI'm now focused on creating a comprehensive `README.md` file for the `/app/solution/` directory. This document will clearly outline the two-tower neural network architecture, the training objective, the public validation score, and the final submission command. I'll ensure it reflects the PyTorch implementation and the specific feature projections used for item metadata."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:03:39.003Z",
"text": "Refining User Tower\n\nMy focus is now on the user tower. I'm implementing multi-scale pooling for listening taste, incorporating both short-term weighted pooling and long-term attention-based pooling with time decay. I'm also handling explicit likes and dislikes via mean pooling and incorporating user activity summaries before the final MLP projection."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:03:39.003Z",
"text": "Integrating Search and Objective\n\nI'm now integrating the Inner-Product Search mechanism, specifically the $U V^T$ calculation with platform eligibility bitmask filtering. My focus is also on refining the training objective, employing a mixed Negative Sampling InfoNCE approach with LogQ popularity debiasing, incorporating multi-window temporal backtesting, in-batch negatives, uniform catalog negatives, and relevance weighting for an optimized model."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:03:39.003Z",
"callId": "update_topic__call_66960",
"name": "update_topic",
"input": {
"strategic_intent": "Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.",
"title": "Documentation & Verification",
"summary": "Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands."
},
"recorded": {
"id": "update_topic__call_66960",
"name": "update_topic",
"args": {
"strategic_intent": "Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.",
"title": "Documentation & Verification",
"summary": "Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_66960",
"name": "update_topic",
"response": {
"output": "Current topic: \"Documentation & Verification\"\nTopic summary: Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands.\n\nStrategic Intent: Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:03:39.005Z",
"resultDisplay": "## ๐ Topic: **Documentation & Verification**\n\n**Summary:**\nCompleted Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands.\n\n> [!STRATEGY]\n> **Intent:** Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.",
"description": "Update topic to: \"Documentation & Verification\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T03:03:39.007Z",
"content": "Current topic: \"Documentation & Verification\"\nTopic summary: Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands.\n\nStrategic Intent: Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.",
"isError": false
},
"responseMessageId": "47db4bf5-c498-48be-a912-2a3504cd47b9"
}
]
},
{
"id": 106,
"role": "agent",
"timestamp": "2026-09-29T03:03:56.562Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T03:03:56.562Z",
"callId": "write_file__call_4398383",
"name": "write_file",
"input": {
"file_path": "/app/solution/README.md",
"content": "# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n"
},
"recorded": {
"id": "write_file__call_4398383",
"name": "write_file",
"args": {
"file_path": "/app/solution/README.md",
"content": "# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_4398383",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:\n# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:03:56.570Z",
"resultDisplay": {
"fileDiff": "Index: README.md\n===================================================================\n--- README.md\tOriginal\n+++ README.md\tWritten\n@@ -0,0 +1,112 @@\n+# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n+\n+## Overview\n+This solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n+\n+---\n+\n+## 1. Model Architecture\n+\n+The ranker strictly decomposes into two independent PyTorch neural towers:\n+\n+### Item Tower (`ItemTower` in `model.py`)\n+- **Inputs**:\n+ - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n+ - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n+ - Catalog Metadata Features (4 dimensions):\n+ - Standardized $\\log(1 + \\text{positive\\_listens})$\n+ - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n+ - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n+ - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n+- **Architecture**:\n+ - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n+ - Concatenation of item embedding (96d) and feature projection (32d).\n+ - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n+ - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n+- **Offline Precomputation**:\n+ - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n+\n+### User Tower (`UserTower` in `model.py`)\n+- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n+ 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n+ 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n+ 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n+ 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n+ 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n+- **Architecture**:\n+ - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n+ - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n+ - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n+ - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n+\n+### Relevance Scoring & Latency-Efficient Retrieval\n+- **Exact Decomposition**:\n+ $$\\text{RelevanceScore}(u, v) = u^T v$$\n+ The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n+- **MIPS Latency**:\n+ At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n+\n+---\n+\n+## 2. Training Objective\n+\n+The model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n+\n+1. **Temporal Slicing & True Discovery Filtering**:\n+ - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n+ - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n+ - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n+2. **Grade Importance Weighting**:\n+ - Relevant items are weighted by their contribution to graded NDCG@10:\n+ - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n+ - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n+3. **Mixed Negative Sampling**:\n+ - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n+ - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n+4. **LogQ Debiasing**:\n+ - Logits are corrected by item sampling frequency:\n+ $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n+ where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n+\n+---\n+\n+## 3. Public Validation Score\n+\n+The validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n+\n+- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n+- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n+- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n+- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n+\n+Evaluation command:\n+```sh\n+python /task/evaluate_public.py /app/solution/validation.parquet\n+```\n+\n+---\n+\n+## 4. Final Submission Command\n+\n+To reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n+\n+```sh\n+PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n+ --steps 280 \\\n+ --lr 7e-4 \\\n+ --embed_dim 96 \\\n+ --out_dim 96 \\\n+ --num_windows 6 \\\n+ --sample_listens 35 \\\n+ --submission_path /app/solution/submission.parquet\n+```\n+\n+### Artifacts in `/app/solution/`\n+- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n+- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n+- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n+- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n+- `train_val.py`: Validation training loop and public evaluator integration.\n+- `refit_submission.py`: Full data refitting and final test submission generation.\n+- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n+- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n",
"fileName": "README.md",
"filePath": "/app/solution/README.md",
"originalContent": "",
"newContent": "# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n",
"diffStat": {
"model_added_lines": 112,
"model_removed_lines": 0,
"model_added_chars": 7352,
"model_removed_chars": 0,
"user_added_lines": 0,
"user_removed_lines": 0,
"user_added_chars": 0,
"user_removed_chars": 0
},
"isNewFile": true,
"isBuildFile": false
},
"description": "Writing to solution/README.md",
"displayName": "WriteFile",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T03:03:58.793Z",
"content": "Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:\n# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n",
"isError": false
},
"responseMessageId": "77ce3d29-dce3-42e2-8589-c7370a924c3c"
}
]
},
{
"id": 107,
"role": "agent",
"timestamp": "2026-09-29T03:04:01.165Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T03:04:01.165Z",
"callId": "run_shell_command__call_149182",
"name": "run_shell_command",
"input": {
"command": "ls -lh /app/solution",
"description": "List files in /app/solution"
},
"recorded": {
"id": "run_shell_command__call_149182",
"name": "run_shell_command",
"args": {
"command": "ls -lh /app/solution",
"description": "List files in /app/solution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_149182",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 26M\n-rw-r--r-- 1 agent agent 7.3K Sep 29 03:03 README.md\n-rw-r--r-- 1 agent agent 12M Sep 29 02:58 best_val_model.pt\n-rw-r--r-- 1 agent agent 18K Sep 29 02:38 data_prep.py\n-rw-r--r-- 1 agent agent 12M Sep 29 03:02 final_refitted_model.pt\n-rw-r--r-- 1 agent agent 11K Sep 29 02:41 model.py\n-rw-r--r-- 1 agent agent 8.7K Sep 29 03:00 refit_submission.py\n-rw-r--r-- 1 agent agent 1.2M Sep 29 03:02 submission.parquet\n-rw-r--r-- 1 agent agent 11K Sep 29 02:43 train_val.py\n-rw-r--r-- 1 agent agent 1.3M Sep 29 03:00 validation.parquet\nProcess Group PGID: 5253\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:04:01.187Z",
"resultDisplay": "total 26M\n-rw-r--r-- 1 agent agent 7.3K Sep 29 03:03 README.md\n-rw-r--r-- 1 agent agent 12M Sep 29 02:58 best_val_model.pt\n-rw-r--r-- 1 agent agent 18K Sep 29 02:38 data_prep.py\n-rw-r--r-- 1 agent agent 12M Sep 29 03:02 final_refitted_model.pt\n-rw-r--r-- 1 agent agent 11K Sep 29 02:41 model.py\n-rw-r--r-- 1 agent agent 8.7K Sep 29 03:00 refit_submission.py\n-rw-r--r-- 1 agent agent 1.2M Sep 29 03:02 submission.parquet\n-rw-r--r-- 1 agent agent 11K Sep 29 02:43 train_val.py\n-rw-r--r-- 1 agent agent 1.3M Sep 29 03:00 validation.parquet",
"description": "ls -lh /app/solution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:04:01.189Z",
"content": "\nOutput: total 26M\n-rw-r--r-- 1 agent agent 7.3K Sep 29 03:03 README.md\n-rw-r--r-- 1 agent agent 12M Sep 29 02:58 best_val_model.pt\n-rw-r--r-- 1 agent agent 18K Sep 29 02:38 data_prep.py\n-rw-r--r-- 1 agent agent 12M Sep 29 03:02 final_refitted_model.pt\n-rw-r--r-- 1 agent agent 11K Sep 29 02:41 model.py\n-rw-r--r-- 1 agent agent 8.7K Sep 29 03:00 refit_submission.py\n-rw-r--r-- 1 agent agent 1.2M Sep 29 03:02 submission.parquet\n-rw-r--r-- 1 agent agent 11K Sep 29 02:43 train_val.py\n-rw-r--r-- 1 agent agent 1.3M Sep 29 03:00 validation.parquet\nProcess Group PGID: 5253\n",
"isError": false
},
"responseMessageId": "a0fd9970-8074-441a-832a-e48298c1a5d3"
}
]
},
{
"id": 108,
"role": "agent",
"timestamp": "2026-09-29T03:04:04.206Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:04:04.206Z",
"text": "Validating Final Datasets\n\nI'm currently performing a thorough check of both `validation.parquet` and `submission.parquet`. This involves running `evaluate_public.py` against `validation.parquet` to ensure it outputs valid JSON with the `quality_ndcg@10` metric, and also confirming the integrity of candidate eligibility through `ranking_evaluation.validate_ranking`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:04:04.206Z",
"callId": "run_shell_command__call_77073",
"name": "run_shell_command",
"input": {
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Final check on validation.parquet using evaluate_public.py"
},
"recorded": {
"id": "run_shell_command__call_77073",
"name": "run_shell_command",
"args": {
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Final check on validation.parquet using evaluate_public.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_77073",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 5256\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:04:07.534Z",
"resultDisplay": "{\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}",
"description": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:04:07.537Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/validation.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019376,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 5256\n",
"isError": false
},
"responseMessageId": "c67371a8-dd12-4927-9682-705c3f147b62"
}
]
},
{
"id": 109,
"role": "agent",
"timestamp": "2026-09-29T03:04:15.068Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T03:04:15.068Z",
"callId": "run_shell_command__call_166229",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet')['uid'].values)\nsub = pd.read_parquet('/app/solution/submission.parquet')\nvalidated = validate_ranking(sub, targets)\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n raise ValueError(f'Ineligible pair ({uid}, {item})')\n\nprint('ALL SUBMISSION CHECKS PASSED PERFECTLY!')\nprint(f'Rows: {len(validated)}, Unique UIDs: {validated.uid.nunique()}, Items per UID: exactly {len(validated) // validated.uid.nunique()}')\n\"",
"description": "Final sanity check of submission.parquet"
},
"recorded": {
"id": "run_shell_command__call_166229",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet')['uid'].values)\nsub = pd.read_parquet('/app/solution/submission.parquet')\nvalidated = validate_ranking(sub, targets)\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n raise ValueError(f'Ineligible pair ({uid}, {item})')\n\nprint('ALL SUBMISSION CHECKS PASSED PERFECTLY!')\nprint(f'Rows: {len(validated)}, Unique UIDs: {validated.uid.nunique()}, Items per UID: exactly {len(validated) // validated.uid.nunique()}')\n\"",
"description": "Final sanity check of submission.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_166229",
"name": "run_shell_command",
"response": {
"output": "\nOutput: ALL SUBMISSION CHECKS PASSED PERFECTLY!\nRows: 845600, Unique UIDs: 8456, Items per UID: exactly 100\nProcess Group PGID: 5276\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:04:17.879Z",
"resultDisplay": "ALL SUBMISSION CHECKS PASSED PERFECTLY!\nRows: 845600, Unique UIDs: 8456, Items per UID: exactly 100",
"description": "Final sanity check of submission.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T03:04:17.882Z",
"content": "\nOutput: ALL SUBMISSION CHECKS PASSED PERFECTLY!\nRows: 845600, Unique UIDs: 8456, Items per UID: exactly 100\nProcess Group PGID: 5276\n",
"isError": false
},
"responseMessageId": "3e81fb26-33b9-49f9-a1dd-2e5c2160ea48"
}
]
},
{
"id": 110,
"role": "agent",
"timestamp": "2026-09-29T03:04:22.105Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T03:04:22.105Z",
"text": "Confirming All Preparations\n\nI've double-checked all components; everything is verified and primed for the final operational sequence. I'll initiate the `update_topic` function to provide a comprehensive project recap as per the model's requirements for multi-turn tasks."
},
{
"kind": "tool",
"timestamp": "2026-09-29T03:04:22.105Z",
"callId": "update_topic__call_109825",
"name": "update_topic",
"input": {
"title": "Project Completion & Verification",
"summary": "Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.",
"strategic_intent": "Final synthesis and deliverable verification of the two-tower neural ranker."
},
"recorded": {
"id": "update_topic__call_109825",
"name": "update_topic",
"args": {
"title": "Project Completion & Verification",
"summary": "Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.",
"strategic_intent": "Final synthesis and deliverable verification of the two-tower neural ranker."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_109825",
"name": "update_topic",
"response": {
"output": "Current topic: \"Project Completion & Verification\"\nTopic summary: Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.\n\nStrategic Intent: Final synthesis and deliverable verification of the two-tower neural ranker."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T03:04:22.108Z",
"resultDisplay": "## ๐ Topic: **Project Completion & Verification**\n\n**Summary:**\nSuccessfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.\n\n> [!STRATEGY]\n> **Intent:** Final synthesis and deliverable verification of the two-tower neural ranker.",
"description": "Update topic to: \"Project Completion & Verification\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T03:04:22.111Z",
"content": "Current topic: \"Project Completion & Verification\"\nTopic summary: Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.\n\nStrategic Intent: Final synthesis and deliverable verification of the two-tower neural ranker.",
"isError": false
},
"responseMessageId": "c3431537-c7a5-450c-9025-9f7694304b1f"
}
]
},
{
"id": 111,
"role": "agent",
"timestamp": "2026-09-29T03:04:33.721Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T03:04:33.721Z",
"text": "# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\nA latency-efficient CPU PyTorch two-tower neural network ranker has been designed, implemented, tuned, and deployed for the \"Made for You\" discovery shelf under `/app/solution/`.\n\n---\n\n## 1. Architecture & Design\n\nThe model strictly decomposes into two independent PyTorch neural towers (`/app/solution/model.py`), satisfying all latency, decomposition, and production-scale constraints:\n\n### Item Tower (`ItemTower`)\n- **Inputs**:\n - `item_id` index in $[0, 30000]$ (index 30,000 reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4d): standardized $\\log(1 + \\text{positive\\_listens})$, standardized $\\log(1 + \\text{organic\\_listens})$, organic listen ratio, and normalized popularity rank.\n- **Layers**:\n - Feature projection MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Combination and output MLP (`Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, 96)`).\n - $L_2$ Normalization to unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Indexing**: Precomputes all 30,000 candidate item embeddings into an offline matrix $V \\in \\mathbb{R}^{30000 \\times 96}$ in <0.02s.\n\n### User Tower (`UserTower`)\n- **Strict History-Derived Representation** (**No user-ID lookup table**, eliminating global user-item interaction matrices and supporting cold/streaming users):\n 1. **Short-Term Listens Pooling**: Weighted pooling over the most recent 20 positive listens ($\\ge 50\\%$ completion) to capture immediate session context.\n 2. **Long-Term Listens Pooling**: Exponential time-decayed attention pooling ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$) over the past 100 positive listens with completion ratio and organic discovery weighting.\n 3. **Explicit Likes Pooling**: Mean-pooled embeddings over active liked tracks from preference states and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled embeddings over active disliked tracks from preference states and explicit dislikes.\n 5. **User Volume & Engagement Features (6d)**: Standardized logs of historical and recent listens, organic ratio, played ratio average, and like/dislike counts.\n- **Layers**:\n - Activity projection MLP (`Linear(6, 32) -> LayerNorm -> GELU`).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, 96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency Budget\n- **Exact Decomposition**:\n $$\\text{Score}(u, v) = u^T v$$\n- **Request-Time Latency**: Generating user embeddings for all 8,456 target users takes ~0.17s (~20 $\\mu$s per user). Scoring all 30,000 catalog items via MIPS matrix multiplication $U V^T$ and retrieving top-100 items with platform eligibility masks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Scheme & Objective\n\n- **Temporal Sliding Backtests & Strict Discovery Filtering**:\n To replicate the platform's candidate eligibility boundary (where items already heard or liked are filtered out), training pairs were constructed using 6 historical weekly cutoffs. Only items the user had never interacted with prior to the cutoff were retained as positive targets.\n- **Relevance Weighting**:\n Grade 3 organic discovery likes were weighted 5.0 (reflecting the $2^3 - 1 = 7$ metric gain), while grade 1 strong organic listens were weighted 1.0.\n- **Mixed Negative Sampling & LogQ Debiasing**:\n In-batch negative sampling was augmented with 512 uniform random negatives per batch sampled across all 30,000 catalog candidates. Logits were debiased by empirical sampling probabilities ($\\log q(v)$), preventing head collapse while preserving personal taste alignment.\n\n---\n\n## 3. Public Validation Benchmark\n\nValidation was evaluated with `/task/evaluate_public.py` on the public validation window:\n\n| Model / Approach | Public NDCG@10 | Evaluated Users | Relative Improvement |\n| :--- | :---: | :---: | :---: |\n| Catalog Popularity Baseline | `0.010710` | 4,263 | Baseline |\n| Two-Tower (Pure In-Batch Softmax) | `0.011541` | 4,263 | +7.8% |\n| **Two-Tower (Mixed Negatives + Multi-Scale History)** | **`0.019376`** | **4,263** | **+80.9%** |\n\n---\n\n## 4. Deliverables & Verification\n\nThe final deliverable files under `/app/solution/` include:\n- `submission.parquet`: The final deliverable containing 845,600 rows (exactly 100 rows per user for all 8,456 target users, ranks 1โ100, no duplicates, with 100% of items verified against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` module definitions.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop with intra-epoch checkpoints and evaluator integration.\n- `refit_submission.py`: Pipeline for refitting on `train_* + val_*` and generating `submission.parquet`.\n- `README.md`: System documentation detailing architecture, loss function, validation score, and reproduction commands."
}
]
}
],
"source": {
"format": "gemini-cli-session",
"sha256": "b6fabf9242730cd5ca94b8185d2d97eaa4349f6ce5bf306b6e50a3382797555c",
"records": 546,
"sessionMetadata": {
"sessionId": "a666d72d-5c39-4e8f-8a1d-19dfce207292",
"projectHash": "f53b52ad6d21cceb72dfa78fb67614fe14f110c58e68412b01508d6a485501c3",
"startTime": "2026-09-29T02:12:14.462Z",
"lastUpdated": "2026-09-29T02:12:14.462Z",
"kind": "main"
},
"sessionUpdates": 219,
"usageRecords": [
{
"messageId": "56cbabdc-b1d2-4d6b-8de9-8e3f99dc4a9e",
"timestamp": "2026-09-29T02:12:24.740Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 9963,
"output": 204,
"cached": 0,
"thoughts": 1150,
"tool": 0,
"total": 11317
}
},
{
"messageId": "90bb0b8c-9a48-4973-9b7d-4c0f9352d30b",
"timestamp": "2026-09-29T02:12:27.570Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 12323,
"output": 120,
"cached": 7675,
"thoughts": 87,
"tool": 0,
"total": 12530
}
},
{
"messageId": "98bd8ada-e69e-4e5f-876e-adb2d76594a4",
"timestamp": "2026-09-29T02:12:30.019Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 17021,
"output": 41,
"cached": 11671,
"thoughts": 17,
"tool": 0,
"total": 17079
}
},
{
"messageId": "e2acd1ee-b21d-40b8-ac26-cccf58eca37a",
"timestamp": "2026-09-29T02:12:34.240Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 17289,
"output": 42,
"cached": 11663,
"thoughts": 14,
"tool": 0,
"total": 17345
}
},
{
"messageId": "6af6647f-86a2-4e10-901a-2a7f2619bde8",
"timestamp": "2026-09-29T02:12:38.807Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 17435,
"output": 360,
"cached": 11652,
"thoughts": 172,
"tool": 0,
"total": 17967
}
},
{
"messageId": "75ae4ecc-dd79-457c-9526-6f2b6b16cfac",
"timestamp": "2026-09-29T02:12:43.858Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 18341,
"output": 350,
"cached": 15555,
"thoughts": 46,
"tool": 0,
"total": 18737
}
},
{
"messageId": "e42b2c27-b914-4305-a5e4-f1e64305755f",
"timestamp": "2026-09-29T02:12:57.533Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 19303,
"output": 411,
"cached": 15574,
"thoughts": 185,
"tool": 0,
"total": 19899
}
},
{
"messageId": "16393594-9374-400e-847d-8e465ff91677",
"timestamp": "2026-09-29T02:13:01.921Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 20044,
"output": 144,
"cached": 15583,
"thoughts": 67,
"tool": 0,
"total": 20255
}
},
{
"messageId": "fc2a5fb0-4f66-40f7-8b10-ba8eedbb643c",
"timestamp": "2026-09-29T02:13:07.105Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 20519,
"output": 248,
"cached": 15582,
"thoughts": 106,
"tool": 0,
"total": 20873
}
},
{
"messageId": "e1a76902-d0f6-4e63-b2fb-2312b99cda3a",
"timestamp": "2026-09-29T02:13:19.924Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 20994,
"output": 337,
"cached": 15581,
"thoughts": 548,
"tool": 0,
"total": 21879
}
},
{
"messageId": "6b629400-bf3e-4314-8f56-3e4de7ccb8a5",
"timestamp": "2026-09-29T02:13:29.069Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 21945,
"output": 345,
"cached": 19496,
"thoughts": 38,
"tool": 0,
"total": 22328
}
},
{
"messageId": "1382745c-3bb9-4b96-b8bf-29ea7f24fa4c",
"timestamp": "2026-09-29T02:13:37.345Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 22450,
"output": 443,
"cached": 19496,
"thoughts": 157,
"tool": 0,
"total": 23050
}
},
{
"messageId": "00ec363f-35cb-4797-8009-2da5731d798a",
"timestamp": "2026-09-29T02:13:51.721Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 23105,
"output": 342,
"cached": 19501,
"thoughts": 1000,
"tool": 0,
"total": 24447
}
},
{
"messageId": "193c089e-cd6e-4a1d-baee-ed547b268054",
"timestamp": "2026-09-29T02:14:11.470Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 24711,
"output": 473,
"cached": 19542,
"thoughts": 10,
"tool": 0,
"total": 25194
}
},
{
"messageId": "ccdd9bf7-11fa-4c2e-8879-27c72df6b887",
"timestamp": "2026-09-29T02:14:16.584Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 25283,
"output": 143,
"cached": 19543,
"thoughts": 136,
"tool": 0,
"total": 25562
}
},
{
"messageId": "ce049118-2aea-4aea-aa3c-936aca3175a8",
"timestamp": "2026-09-29T02:14:22.257Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 25610,
"output": 277,
"cached": 23443,
"thoughts": 127,
"tool": 0,
"total": 26014
}
},
{
"messageId": "c828a530-e07c-4003-a1b5-e3cf4c849f7d",
"timestamp": "2026-09-29T02:14:36.470Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 26113,
"output": 273,
"cached": 23442,
"thoughts": 1261,
"tool": 0,
"total": 27647
}
},
{
"messageId": "58894fd5-bd02-4cfc-a027-e7baf7698745",
"timestamp": "2026-09-29T02:14:50.737Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 27731,
"output": 423,
"cached": 23484,
"thoughts": 111,
"tool": 0,
"total": 28265
}
},
{
"messageId": "746ded12-29c5-4ebf-8530-15c316b55bd2",
"timestamp": "2026-09-29T02:14:56.553Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 28436,
"output": 308,
"cached": 23489,
"thoughts": 131,
"tool": 0,
"total": 28875
}
},
{
"messageId": "b8fe15d5-151d-485c-8f91-f67333142ad9",
"timestamp": "2026-09-29T02:15:06.693Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 28958,
"output": 222,
"cached": 23488,
"thoughts": 725,
"tool": 0,
"total": 29905
}
},
{
"messageId": "fbce7152-d3ca-4fb3-991f-293afaa38125",
"timestamp": "2026-09-29T02:15:14.230Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 30005,
"output": 250,
"cached": 27422,
"thoughts": 483,
"tool": 0,
"total": 30738
}
},
{
"messageId": "04e4fd2d-d8a9-4297-b02a-4515c021d872",
"timestamp": "2026-09-29T02:15:32.521Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 30787,
"output": 135,
"cached": 27430,
"thoughts": 1851,
"tool": 0,
"total": 32773
}
},
{
"messageId": "86d81abf-10f4-46f7-9b4b-1772993b9c39",
"timestamp": "2026-09-29T02:15:40.604Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 32812,
"output": 154,
"cached": 27483,
"thoughts": 539,
"tool": 0,
"total": 33505
}
},
{
"messageId": "7fd5ca29-aac0-4407-a642-53f6682f80a8",
"timestamp": "2026-09-29T02:15:44.531Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 33576,
"output": 93,
"cached": 31415,
"thoughts": 108,
"tool": 0,
"total": 33777
}
},
{
"messageId": "a4ad6dd2-dc81-4869-b347-20c69eafaf29",
"timestamp": "2026-09-29T02:15:51.573Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 34324,
"output": 439,
"cached": 31421,
"thoughts": 266,
"tool": 0,
"total": 35029
}
},
{
"messageId": "ec94ee8d-4cb6-46a1-b00d-85a0bf2a4292",
"timestamp": "2026-09-29T02:16:07.611Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 35101,
"output": 230,
"cached": 31427,
"thoughts": 109,
"tool": 0,
"total": 35440
}
},
{
"messageId": "bab7d67c-e431-4ac4-85d4-8b9a4390e9f9",
"timestamp": "2026-09-29T02:16:16.591Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 35480,
"output": 40,
"cached": 31419,
"thoughts": 263,
"tool": 0,
"total": 35783
}
},
{
"messageId": "cd5a0e61-3512-4cf4-b2c0-ddc9782204b5",
"timestamp": "2026-09-29T02:16:57.900Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 35458,
"output": 2193,
"cached": 0,
"thoughts": 3416,
"tool": 0,
"total": 41067
}
},
{
"messageId": "9bb03ef4-c62e-45b0-b92c-0856af8dc91d",
"timestamp": "2026-09-29T02:17:05.982Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 43251,
"output": 22,
"cached": 31746,
"thoughts": 53,
"tool": 0,
"total": 43326
}
},
{
"messageId": "bf803200-c19a-4a51-892b-9d495b225ab8",
"timestamp": "2026-09-29T02:17:12.428Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 43760,
"output": 109,
"cached": 0,
"thoughts": 40,
"tool": 0,
"total": 43909
}
},
{
"messageId": "9d79279c-890c-4e3a-9787-9fd521437447",
"timestamp": "2026-09-29T02:17:19.815Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 43997,
"output": 365,
"cached": 39502,
"thoughts": 527,
"tool": 0,
"total": 44889
}
},
{
"messageId": "6a5c4c15-dfcb-4629-8a14-169a24556ff6",
"timestamp": "2026-09-29T02:17:38.933Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 44991,
"output": 306,
"cached": 39511,
"thoughts": 366,
"tool": 0,
"total": 45663
}
},
{
"messageId": "ab9e183d-2dbd-40b6-8b0c-0f071089c64f",
"timestamp": "2026-09-29T02:17:53.594Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 45735,
"output": 483,
"cached": 43464,
"thoughts": 628,
"tool": 0,
"total": 46846
}
},
{
"messageId": "0369814f-65e5-4f1b-a26c-c8fec948ea7b",
"timestamp": "2026-09-29T02:18:02.557Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 47110,
"output": 611,
"cached": 43485,
"thoughts": 250,
"tool": 0,
"total": 47971
}
},
{
"messageId": "b97c1f89-3f36-4430-bb2e-6da458ed43b2",
"timestamp": "2026-09-29T02:18:35.715Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 48090,
"output": 216,
"cached": 43494,
"thoughts": 1197,
"tool": 0,
"total": 49503
}
},
{
"messageId": "dc274038-b110-4a88-982e-2293b30cd525",
"timestamp": "2026-09-29T02:18:47.235Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 49559,
"output": 36,
"cached": 47472,
"thoughts": 949,
"tool": 0,
"total": 50544
}
},
{
"messageId": "9152c37c-c0df-4c15-8352-642021a4fe1c",
"timestamp": "2026-09-29T02:18:52.434Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 50627,
"output": 423,
"cached": 47483,
"thoughts": 232,
"tool": 0,
"total": 51282
}
},
{
"messageId": "ca351465-0171-44bc-914b-039c7e366ca9",
"timestamp": "2026-09-29T02:18:58.595Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 51347,
"output": 38,
"cached": 47482,
"thoughts": 31,
"tool": 0,
"total": 51416
}
},
{
"messageId": "f35ec6ed-6816-4417-b26b-a2bda48091f2",
"timestamp": "2026-09-29T02:19:10.877Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 51476,
"output": 579,
"cached": 47463,
"thoughts": 1095,
"tool": 0,
"total": 53150
}
},
{
"messageId": "50323b19-ee4b-448e-b9c5-f46566936fe5",
"timestamp": "2026-09-29T02:19:31.743Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 53345,
"output": 249,
"cached": 47498,
"thoughts": 2096,
"tool": 0,
"total": 55690
}
},
{
"messageId": "43947b09-9cf4-4a01-bf59-447605b91a86",
"timestamp": "2026-09-29T02:19:45.569Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 55744,
"output": 398,
"cached": 51508,
"thoughts": 167,
"tool": 0,
"total": 56309
}
},
{
"messageId": "4e537466-8c7c-40eb-afab-16abb5a75244",
"timestamp": "2026-09-29T02:19:51.457Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 56441,
"output": 249,
"cached": 51505,
"thoughts": 103,
"tool": 0,
"total": 56793
}
},
{
"messageId": "87b5a685-9756-4ce3-aa6a-376362365551",
"timestamp": "2026-09-29T02:19:58.675Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 56840,
"output": 341,
"cached": 51494,
"thoughts": 141,
"tool": 0,
"total": 57322
}
},
{
"messageId": "d8cd7112-fe72-412f-80d1-ccbbded760ec",
"timestamp": "2026-09-29T02:20:11.163Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 57405,
"output": 723,
"cached": 51489,
"thoughts": 301,
"tool": 0,
"total": 58429
}
},
{
"messageId": "c48ac843-d853-4d87-9df8-0cf8a6c7df83",
"timestamp": "2026-09-29T02:20:52.895Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 58598,
"output": 194,
"cached": 55463,
"thoughts": 152,
"tool": 0,
"total": 58944
}
},
{
"messageId": "84f44f94-d527-485f-b724-be6fca235532",
"timestamp": "2026-09-29T02:21:07.459Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 59037,
"output": 363,
"cached": 55453,
"thoughts": 1350,
"tool": 0,
"total": 60750
}
},
{
"messageId": "89b09c09-f769-433f-8fdb-2a3d99fbcb76",
"timestamp": "2026-09-29T02:22:33.834Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 60850,
"output": 5788,
"cached": 55484,
"thoughts": 544,
"tool": 0,
"total": 67182
}
},
{
"messageId": "1c3ee70b-18fc-4816-962d-5f93eeabd563",
"timestamp": "2026-09-29T02:23:21.439Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 72961,
"output": 338,
"cached": 59748,
"thoughts": 26,
"tool": 0,
"total": 73325
}
},
{
"messageId": "c9f76956-f616-4160-b719-13cee3218dff",
"timestamp": "2026-09-29T02:23:45.552Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 73600,
"output": 342,
"cached": 67708,
"thoughts": 126,
"tool": 0,
"total": 74068
}
},
{
"messageId": "1b710c39-fa3d-43cb-a286-7c8faa454d5f",
"timestamp": "2026-09-29T02:23:52.137Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 74189,
"output": 110,
"cached": 71682,
"thoughts": 60,
"tool": 0,
"total": 74359
}
},
{
"messageId": "15f6c303-a9e4-4a96-bf62-4f24ec1d9c66",
"timestamp": "2026-09-29T02:23:55.581Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 74589,
"output": 345,
"cached": 71659,
"thoughts": 23,
"tool": 0,
"total": 74957
}
},
{
"messageId": "a1df90ac-af3f-48f8-872e-b76136d12958",
"timestamp": "2026-09-29T02:24:19.727Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 75101,
"output": 120,
"cached": 71649,
"thoughts": 718,
"tool": 0,
"total": 75939
}
},
{
"messageId": "436fefb9-d891-4d0d-8081-d79caf64084d",
"timestamp": "2026-09-29T02:24:51.379Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 76038,
"output": 3270,
"cached": 71646,
"thoughts": 2282,
"tool": 0,
"total": 81590
}
},
{
"messageId": "215dfabc-626f-4819-be45-60a7b554a597",
"timestamp": "2026-09-29T02:25:08.070Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 84851,
"output": 528,
"cached": 75826,
"thoughts": 32,
"tool": 0,
"total": 85411
}
},
{
"messageId": "1af1e0e0-4402-463f-b65f-305a1a8dce17",
"timestamp": "2026-09-29T02:25:29.805Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 85647,
"output": 107,
"cached": 79813,
"thoughts": 53,
"tool": 0,
"total": 85807
}
},
{
"messageId": "8127c7dc-2b66-41ca-809c-5fbeac291c3b",
"timestamp": "2026-09-29T02:25:53.212Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 85893,
"output": 2857,
"cached": 83781,
"thoughts": 942,
"tool": 0,
"total": 89692
}
},
{
"messageId": "2cd44e56-7a21-4641-bd7a-8fceec9b763a",
"timestamp": "2026-09-29T02:26:07.771Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 92540,
"output": 72,
"cached": 83914,
"thoughts": 39,
"tool": 0,
"total": 92651
}
},
{
"messageId": "90c84270-b0fa-4985-b9e5-21a4b1214598",
"timestamp": "2026-09-29T02:26:17.307Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 92831,
"output": 96,
"cached": 87893,
"thoughts": 60,
"tool": 0,
"total": 92987
}
},
{
"messageId": "e2d35947-d9c1-4e52-9b60-8f49d0a97ff6",
"timestamp": "2026-09-29T02:26:19.480Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 93137,
"output": 69,
"cached": 87867,
"thoughts": 9,
"tool": 0,
"total": 93215
}
},
{
"messageId": "2501b114-ab67-48cc-9f73-a3ade78d497f",
"timestamp": "2026-09-29T02:26:52.042Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 93826,
"output": 50,
"cached": 87859,
"thoughts": 290,
"tool": 0,
"total": 94166
}
},
{
"messageId": "1f239a39-2062-460b-be95-e88364ba33f9",
"timestamp": "2026-09-29T02:26:59.706Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 94251,
"output": 216,
"cached": 91839,
"thoughts": 275,
"tool": 0,
"total": 94742
}
},
{
"messageId": "bc4852e4-4d8f-42d2-85a3-dcbbca66b59d",
"timestamp": "2026-09-29T02:27:02.565Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 94947,
"output": 70,
"cached": 91822,
"thoughts": 135,
"tool": 0,
"total": 95152
}
},
{
"messageId": "e9b9f1f3-90d9-42bd-8336-fa52938a5210",
"timestamp": "2026-09-29T02:28:43.234Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 96375,
"output": 124,
"cached": 91832,
"thoughts": 402,
"tool": 0,
"total": 96901
}
},
{
"messageId": "3b45571a-2e02-4d21-b220-d2385079db44",
"timestamp": "2026-09-29T02:28:53.165Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 96980,
"output": 71,
"cached": 91822,
"thoughts": 413,
"tool": 0,
"total": 97464
}
},
{
"messageId": "c6f9599b-7679-44a5-9cde-8175ef42d8ed",
"timestamp": "2026-09-29T02:29:06.363Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 97493,
"output": 827,
"cached": 91811,
"thoughts": 293,
"tool": 0,
"total": 98613
}
},
{
"messageId": "9a7d1dbf-492f-41a3-b4e0-625960de1f35",
"timestamp": "2026-09-29T02:29:12.324Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 98886,
"output": 180,
"cached": 95803,
"thoughts": 26,
"tool": 0,
"total": 99092
}
},
{
"messageId": "7cdfc549-a3ed-497b-afdc-575779b15d8a",
"timestamp": "2026-09-29T02:29:15.225Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 99265,
"output": 169,
"cached": 95778,
"thoughts": 34,
"tool": 0,
"total": 99468
}
},
{
"messageId": "f6e10a25-8dae-47e2-b9a1-7a55c8951541",
"timestamp": "2026-09-29T02:29:18.443Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 99676,
"output": 213,
"cached": 95755,
"thoughts": 15,
"tool": 0,
"total": 99904
}
},
{
"messageId": "ace0972d-98e4-4ef0-8b18-0e2f0352d2a3",
"timestamp": "2026-09-29T02:29:21.110Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 100090,
"output": 80,
"cached": 95732,
"thoughts": 39,
"tool": 0,
"total": 100209
}
},
{
"messageId": "1d6d530c-48be-42ea-ad58-604c7a678f76",
"timestamp": "2026-09-29T02:31:21.500Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 101431,
"output": 392,
"cached": 95741,
"thoughts": 2032,
"tool": 0,
"total": 103855
}
},
{
"messageId": "746c896d-29ab-4fb6-8cf7-8dc9385340fd",
"timestamp": "2026-09-29T02:32:00.437Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 103921,
"output": 300,
"cached": 99768,
"thoughts": 298,
"tool": 0,
"total": 104519
}
},
{
"messageId": "08700af4-5974-4dc2-9378-d279887a05ff",
"timestamp": "2026-09-29T02:32:28.220Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 104586,
"output": 627,
"cached": 99760,
"thoughts": 297,
"tool": 0,
"total": 105510
}
},
{
"messageId": "44f535c2-4f2c-4d6e-8097-27d8b3d3a39d",
"timestamp": "2026-09-29T02:33:00.029Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 105580,
"output": 2084,
"cached": 99760,
"thoughts": 351,
"tool": 0,
"total": 108015
}
},
{
"messageId": "34c76e9f-f1ff-45a1-93e7-da3caeaedede",
"timestamp": "2026-09-29T02:33:02.922Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 109219,
"output": 84,
"cached": 103807,
"thoughts": 39,
"tool": 0,
"total": 109342
}
},
{
"messageId": "618c287a-8b3c-446f-9528-3e313ac552ed",
"timestamp": "2026-09-29T02:34:48.165Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 110532,
"output": 80,
"cached": 0,
"thoughts": 109,
"tool": 0,
"total": 110721
}
},
{
"messageId": "de36aa0a-f9d8-46fe-8555-51851bf51edc",
"timestamp": "2026-09-29T02:36:32.287Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 111909,
"output": 920,
"cached": 107816,
"thoughts": 1045,
"tool": 0,
"total": 113874
}
},
{
"messageId": "1cbc74c4-8825-40b7-a406-9e691098e878",
"timestamp": "2026-09-29T02:36:55.195Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 113940,
"output": 910,
"cached": 111834,
"thoughts": 26,
"tool": 0,
"total": 114876
}
},
{
"messageId": "fabbf019-30de-441d-8614-4a3de640e84e",
"timestamp": "2026-09-29T02:37:24.535Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 115807,
"output": 900,
"cached": 111855,
"thoughts": 199,
"tool": 0,
"total": 116906
}
},
{
"messageId": "67814f55-6471-4638-8d4d-2a4790cee4df",
"timestamp": "2026-09-29T02:37:50.966Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 117075,
"output": 175,
"cached": 111860,
"thoughts": 620,
"tool": 0,
"total": 117870
}
},
{
"messageId": "668eef9a-cbb8-487d-9c0c-d8f0d39c1d6e",
"timestamp": "2026-09-29T02:38:30.473Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 117920,
"output": 2979,
"cached": 115851,
"thoughts": 2641,
"tool": 0,
"total": 123540
}
},
{
"messageId": "968bf4c1-0cf2-489b-91a0-d3dd16bdcca3",
"timestamp": "2026-09-29T02:38:38.377Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 125439,
"output": 444,
"cached": 115999,
"thoughts": 315,
"tool": 0,
"total": 126198
}
},
{
"messageId": "82691771-4173-4d41-9616-522ff42e5a72",
"timestamp": "2026-09-29T02:38:41.063Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 126575,
"output": 87,
"cached": 123991,
"thoughts": 92,
"tool": 0,
"total": 126754
}
},
{
"messageId": "7e808a93-f59a-4f0b-85d6-73c5393a6aed",
"timestamp": "2026-09-29T02:40:42.515Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 127977,
"output": 337,
"cached": 0,
"thoughts": 460,
"tool": 0,
"total": 128774
}
},
{
"messageId": "0ff23eb4-bf48-4332-9f59-8d2e7feb1d11",
"timestamp": "2026-09-29T02:40:49.059Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 128842,
"output": 259,
"cached": 123993,
"thoughts": 201,
"tool": 0,
"total": 129302
}
},
{
"messageId": "26ca2185-2cdf-4894-8e47-51457fae4bd7",
"timestamp": "2026-09-29T02:41:02.799Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 129367,
"output": 264,
"cached": 123981,
"thoughts": 1172,
"tool": 0,
"total": 130803
}
},
{
"messageId": "5f6cf1e9-d962-4e13-9515-764d5cee9337",
"timestamp": "2026-09-29T02:41:19.931Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 130864,
"output": 1633,
"cached": 127990,
"thoughts": 416,
"tool": 0,
"total": 132913
}
},
{
"messageId": "1194fd25-747c-4f11-9499-6ea9dced926a",
"timestamp": "2026-09-29T02:41:23.267Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 133948,
"output": 259,
"cached": 128025,
"thoughts": 34,
"tool": 0,
"total": 134241
}
},
{
"messageId": "0eed81d4-0e2b-407a-af37-9f8ead080c18",
"timestamp": "2026-09-29T02:41:28.947Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 134400,
"output": 84,
"cached": 132002,
"thoughts": 64,
"tool": 0,
"total": 134548
}
},
{
"messageId": "479fa0ba-42b0-4d10-bc50-9bed7e64ae0d",
"timestamp": "2026-09-29T02:43:40.774Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 135769,
"output": 1578,
"cached": 0,
"thoughts": 624,
"tool": 0,
"total": 137971
}
},
{
"messageId": "c8e78221-6c24-464e-98a5-82771a3b7976",
"timestamp": "2026-09-29T02:43:48.786Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 139104,
"output": 701,
"cached": 132049,
"thoughts": 22,
"tool": 0,
"total": 139827
}
},
{
"messageId": "b62b1ac8-53ea-4767-992c-7cb5ead84584",
"timestamp": "2026-09-29T02:43:51.297Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 140198,
"output": 92,
"cached": 136041,
"thoughts": 31,
"tool": 0,
"total": 140321
}
},
{
"messageId": "da5f9234-3d3a-48e8-aa48-a506057cc3ea",
"timestamp": "2026-09-29T02:46:33.762Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 143126,
"output": 431,
"cached": 0,
"thoughts": 299,
"tool": 0,
"total": 143856
}
},
{
"messageId": "4055b591-bf2c-48c4-9490-e1b9c4ec9637",
"timestamp": "2026-09-29T02:46:53.658Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 144048,
"output": 102,
"cached": 140079,
"thoughts": 384,
"tool": 0,
"total": 144534
}
},
{
"messageId": "d4636f29-bbbe-453e-b51b-d5b05ef391ad",
"timestamp": "2026-09-29T02:49:55.950Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 147931,
"output": 109,
"cached": 0,
"thoughts": 241,
"tool": 0,
"total": 148281
}
},
{
"messageId": "276393ee-8dc2-4263-86b2-d7ccd6a968be",
"timestamp": "2026-09-29T02:53:06.373Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 151638,
"output": 105,
"cached": 0,
"thoughts": 277,
"tool": 0,
"total": 152020
}
},
{
"messageId": "5914fe8c-1eb6-432b-b7a7-a71f0aeaf54d",
"timestamp": "2026-09-29T02:56:52.482Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 156032,
"output": 105,
"cached": 0,
"thoughts": 430,
"tool": 0,
"total": 156567
}
},
{
"messageId": "f32fec91-5e23-4c93-aa90-9ab0970eca81",
"timestamp": "2026-09-29T02:59:47.495Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 159964,
"output": 52,
"cached": 0,
"thoughts": 85,
"tool": 0,
"total": 160101
}
},
{
"messageId": "8a4190c6-f9da-40b3-951e-def6f2af2bb1",
"timestamp": "2026-09-29T02:59:55.944Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 160186,
"output": 440,
"cached": 156325,
"thoughts": 79,
"tool": 0,
"total": 160705
}
},
{
"messageId": "03ce807a-91fa-46f6-bf64-19f128a4de26",
"timestamp": "2026-09-29T03:00:16.322Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 160897,
"output": 181,
"cached": 156315,
"thoughts": 180,
"tool": 0,
"total": 161258
}
},
{
"messageId": "2dbb4dc2-8f04-4ecc-b248-e6b7bf88d48c",
"timestamp": "2026-09-29T03:00:46.991Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 161418,
"output": 2865,
"cached": 156297,
"thoughts": 1333,
"tool": 0,
"total": 165616
}
},
{
"messageId": "2f2d26db-a3a0-415f-b18b-2ce7502ec0dd",
"timestamp": "2026-09-29T03:00:56.000Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 168472,
"output": 95,
"cached": 160426,
"thoughts": 51,
"tool": 0,
"total": 168618
}
},
{
"messageId": "c9f56d73-93f1-4b8e-aa21-27b5a2489d37",
"timestamp": "2026-09-29T03:03:18.864Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 169293,
"output": 351,
"cached": 164428,
"thoughts": 253,
"tool": 0,
"total": 169897
}
},
{
"messageId": "6e3ffdb2-9bc3-40eb-9a6a-b658e63cb61d",
"timestamp": "2026-09-29T03:03:39.003Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 170043,
"output": 122,
"cached": 164419,
"thoughts": 994,
"tool": 0,
"total": 171159
}
},
{
"messageId": "bf5183f5-b309-4626-b471-45a3fe148273",
"timestamp": "2026-09-29T03:03:56.562Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 171260,
"output": 2143,
"cached": 168425,
"thoughts": 13,
"tool": 0,
"total": 173416
}
},
{
"messageId": "76bbe2b8-9402-4dbf-b0b5-991b69eab236",
"timestamp": "2026-09-29T03:04:01.165Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 175550,
"output": 36,
"cached": 168487,
"thoughts": 23,
"tool": 0,
"total": 175609
}
},
{
"messageId": "f5b456c8-8768-46e8-bf8d-a706977a0581",
"timestamp": "2026-09-29T03:04:04.206Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 175921,
"output": 52,
"cached": 172481,
"thoughts": 84,
"tool": 0,
"total": 176057
}
},
{
"messageId": "e1b7cced-2a91-4915-a554-015fed35ca5a",
"timestamp": "2026-09-29T03:04:15.068Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 176142,
"output": 249,
"cached": 172461,
"thoughts": 50,
"tool": 0,
"total": 176441
}
},
{
"messageId": "5b08f288-4d94-4272-a990-6ca5f8d29ee3",
"timestamp": "2026-09-29T03:04:22.105Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 176508,
"output": 250,
"cached": 172443,
"thoughts": 66,
"tool": 0,
"total": 176824
}
},
{
"messageId": "176991d3-0d9e-448d-a15d-d265c885abdb",
"timestamp": "2026-09-29T03:04:33.721Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 177053,
"output": 1454,
"cached": 172426,
"thoughts": 11,
"tool": 0,
"total": 178518
}
}
]
}
}