{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-r02-b9049e57",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "9a0101dd-14bb-47af-9180-d4f590bc49f0",
"steps": [
{
"id": 1,
"role": "user",
"timestamp": "2026-09-29T01:23:37.329Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T01:23:37.329Z",
"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/\n└───solution/\n\n\n"
}
]
},
{
"id": 2,
"role": "user",
"timestamp": "2026-09-29T01:23:37.370Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T01:23:37.370Z",
"text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin 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, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described 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 builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt 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- You have up to 30 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": "2026-09-29T01:23:43.099Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:43.099Z",
"text": "Analyzing the Challenge\n\nI'm currently dissecting the prompt, aiming to grasp every nuance of the requirements, constraints, and the technological landscape. My focus is on fully understanding the environment and available tools to ensure a robust approach to building the \"Made for You\" shelf ranking model."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:43.099Z",
"text": "Refining the Output\n\nI'm focusing on the precise output format required: 100-row submission files (parquet or csv) per user, with ranks 1-100, ensuring all listed items are within their eligible candidates and that no ties exist. I'll also ensure the best valid submission is saved throughout the process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:43.099Z",
"text": "Exploring and Establishing Baseline\n\nI'm initiating the process by exploring the `/task/` directory, investigating available hardware resources, and reviewing the evaluation scripts. My immediate goal is to understand the data structure, system capabilities, and the fundamental requirements for generating valid submission files."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:43.099Z",
"callId": "update_topic__call_2771466",
"name": "update_topic",
"input": {
"strategic_intent": "Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.",
"title": "Exploration and Baseline Setup",
"summary": "Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design."
},
"recorded": {
"id": "update_topic__call_2771466",
"name": "update_topic",
"args": {
"strategic_intent": "Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.",
"title": "Exploration and Baseline Setup",
"summary": "Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_2771466",
"name": "update_topic",
"response": {
"output": "Current topic: \"Exploration and Baseline Setup\"\nTopic summary: Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design.\n\nStrategic Intent: Explore environment, inspect hardware and dataset files, and inspect evaluation scripts."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:43.149Z",
"resultDisplay": "## 📂 Topic: **Exploration and Baseline Setup**\n\n**Summary:**\nBeginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design.\n\n> [!STRATEGY]\n> **Intent:** Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.",
"description": "Update topic to: \"Exploration and Baseline Setup\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T01:23:43.151Z",
"content": "Current topic: \"Exploration and Baseline Setup\"\nTopic summary: Beginning exploration and baseline setup. We will inspect hardware resources (CPU, GPU, RAM), examine the files under /task/, inspect evaluate_public.py and ranking_evaluation.py, and examine data shapes to guide model design.\n\nStrategic Intent: Explore environment, inspect hardware and dataset files, and inspect evaluation scripts.",
"isError": false
},
"responseMessageId": "cc736596-7c88-458c-8762-2b64fad716e4"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:43.099Z",
"callId": "run_shell_command__call_2771468",
"name": "run_shell_command",
"input": {
"description": "Check system hardware resources and list task files",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -la /task; ls -la /task/data"
},
"recorded": {
"id": "run_shell_command__call_2771468",
"name": "run_shell_command",
"args": {
"description": "Check system hardware resources and list task files",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -la /task; ls -la /task/data"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2771468",
"name": "run_shell_command",
"response": {
"output": "\nOutput: CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:23 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 data\n-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:17 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:43.149Z",
"resultDisplay": "CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:23 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 data\n-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:17 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet",
"description": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -la /task; ls -la /task/data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:43.151Z",
"content": "\nOutput: CPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\n/usr/bin/bash: line 4: nvidia-smi: command not found\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:23 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 data\n-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:17 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:18 .\ndr-xr-xr-x 1 root root 4096 Sep 29 01:21 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
"isError": false
},
"responseMessageId": "cc736596-7c88-458c-8762-2b64fad716e4"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:43.099Z",
"callId": "read_file__call_2771472",
"name": "read_file",
"input": {
"file_path": "/task/evaluate_public.py"
},
"recorded": {
"id": "read_file__call_2771472",
"name": "read_file",
"args": {
"file_path": "/task/evaluate_public.py"
},
"result": [
{
"functionResponse": {
"id": "read_file__call_2771472",
"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": "2026-09-29T01:23:43.149Z",
"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": "2026-09-29T01:23:43.151Z",
"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": "cc736596-7c88-458c-8762-2b64fad716e4"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:43.099Z",
"callId": "read_file__call_2771474",
"name": "read_file",
"input": {
"file_path": "/task/ranking_evaluation.py"
},
"recorded": {
"id": "read_file__call_2771474",
"name": "read_file",
"args": {
"file_path": "/task/ranking_evaluation.py"
},
"result": [
{
"functionResponse": {
"id": "read_file__call_2771474",
"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": "2026-09-29T01:23:43.149Z",
"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": "2026-09-29T01:23:43.151Z",
"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": "cc736596-7c88-458c-8762-2b64fad716e4"
}
]
},
{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T01:23:46.294Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:46.294Z",
"callId": "run_shell_command__call_58676",
"name": "run_shell_command",
"input": {
"command": "grep MemTotal /proc/meminfo; cat /task/requirements.txt",
"description": "Check total memory and inspect installed Python packages"
},
"recorded": {
"id": "run_shell_command__call_58676",
"name": "run_shell_command",
"args": {
"command": "grep MemTotal /proc/meminfo; cat /task/requirements.txt",
"description": "Check total memory and inspect installed Python packages"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_58676",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988964 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\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: 1620\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:46.321Z",
"resultDisplay": "MemTotal: 15988964 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\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": "grep MemTotal /proc/meminfo; cat /task/requirements.txt",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:46.322Z",
"content": "\nOutput: MemTotal: 15988964 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\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. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both 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\nidna==3.18\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\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\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\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\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: 1620\n",
"isError": false
},
"responseMessageId": "8b7ce9d4-a7cb-4a43-b74e-c5899d9d0a74"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:46.294Z",
"callId": "run_shell_command__call_58682",
"name": "run_shell_command",
"input": {
"command": "cat /task/evaluate_public.py",
"description": "Read evaluate_public.py"
},
"recorded": {
"id": "run_shell_command__call_58682",
"name": "run_shell_command",
"args": {
"command": "cat /task/evaluate_public.py",
"description": "Read evaluate_public.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_58682",
"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: 1623\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:46.321Z",
"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": "2026-09-29T01:23:46.322Z",
"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: 1623\n",
"isError": false
},
"responseMessageId": "8b7ce9d4-a7cb-4a43-b74e-c5899d9d0a74"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:46.294Z",
"callId": "run_shell_command__call_58688",
"name": "run_shell_command",
"input": {
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
},
"recorded": {
"id": "run_shell_command__call_58688",
"name": "run_shell_command",
"args": {
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_58688",
"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: 1627\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:46.321Z",
"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": "2026-09-29T01:23:46.322Z",
"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: 1627\n",
"isError": false
},
"responseMessageId": "8b7ce9d4-a7cb-4a43-b74e-c5899d9d0a74"
}
]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T01:23:55.766Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:55.766Z",
"text": "Analyzing System Configuration\n\nMy current focus is on understanding the system's resources. I've noted the presence of 8 CPU cores and approximately 16GB of RAM. Notably, no GPU is detected. Key Python packages like numpy, pandas, scipy, scikit-learn, and pyarrow are installed and readily available for use."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:55.766Z",
"text": "Investigating Library Dependencies\n\nI'm checking for advanced gradient boosting libraries like LightGBM, XGBoost, and CatBoost. If they're not present, I'll proceed with utilizing scikit-learn, NumPy, and SciPy for my modeling tasks. I'll also start examining the provided dataset files and their structures."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:55.766Z",
"callId": "run_shell_command__call_3007758",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nfor mod in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'sklearn', 'scipy', 'numpy', 'pandas', 'pyarrow']:\n try:\n __import__(mod)\n print(mod, 'available')\n except ImportError:\n print(mod, 'NOT available')\n\"",
"description": "Check ML library availability"
},
"recorded": {
"id": "run_shell_command__call_3007758",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nfor mod in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'sklearn', 'scipy', 'numpy', 'pandas', 'pyarrow']:\n try:\n __import__(mod)\n print(mod, 'available')\n except ImportError:\n print(mod, 'NOT available')\n\"",
"description": "Check ML library availability"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3007758",
"name": "run_shell_command",
"response": {
"output": "\nOutput: lightgbm NOT available\nxgboost NOT available\ncatboost NOT available\ntorch NOT available\nimplicit NOT available\nsklearn available\nscipy available\nnumpy available\npandas available\npyarrow available\nProcess Group PGID: 1630\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:57.242Z",
"resultDisplay": "lightgbm NOT available\nxgboost NOT available\ncatboost NOT available\ntorch NOT available\nimplicit NOT available\nsklearn available\nscipy available\nnumpy available\npandas available\npyarrow available",
"description": "Check ML library availability",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:57.243Z",
"content": "\nOutput: lightgbm NOT available\nxgboost NOT available\ncatboost NOT available\ntorch NOT available\nimplicit NOT available\nsklearn available\nscipy available\nnumpy available\npandas available\npyarrow available\nProcess Group PGID: 1630\n",
"isError": false
},
"responseMessageId": "5e9bfd49-6f00-4333-b0c8-32e1e223b3d5"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:55.766Z",
"callId": "run_shell_command__call_3007772",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets shape:', targets.shape)\nprint('Targets head:\\n', targets.head())\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog shape:', catalog.shape)\nprint('Catalog head:\\n', catalog.head())\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref shape:', val_pref.shape)\nprint('Val pref head:\\n', val_pref.head())\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref shape:', test_pref.shape)\nprint('Test pref head:\\n', test_pref.head())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', val_npz.files)\nprint('uids len:', len(val_npz['uids']), 'item_ids len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', test_npz.files)\nprint('test uids len:', len(test_npz['uids']), 'test item_ids len:', len(test_npz['item_ids']), 'test packed shape:', test_npz['packed_eligible'].shape)\n\"",
"description": "Inspect dataset schemas and shapes"
},
"recorded": {
"id": "run_shell_command__call_3007772",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets shape:', targets.shape)\nprint('Targets head:\\n', targets.head())\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog shape:', catalog.shape)\nprint('Catalog head:\\n', catalog.head())\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref shape:', val_pref.shape)\nprint('Val pref head:\\n', val_pref.head())\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref shape:', test_pref.shape)\nprint('Test pref head:\\n', test_pref.head())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', val_npz.files)\nprint('uids len:', len(val_npz['uids']), 'item_ids len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', test_npz.files)\nprint('test uids len:', len(test_npz['uids']), 'test item_ids len:', len(test_npz['item_ids']), 'test packed shape:', test_npz['packed_eligible'].shape)\n\"",
"description": "Inspect dataset schemas and shapes"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3007772",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Targets shape: (8456, 3)\nTargets head:\n 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\nCatalog shape: (30000, 4)\nCatalog head:\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nVal pref shape: (469124, 3)\nVal pref head:\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\nTest pref shape: (484776, 3)\nTest pref head:\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\nval_npz keys: ['uids', 'item_ids', 'packed_eligible']\nuids len: 8456 item_ids len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible']\ntest uids len: 8456 test item_ids len: 30000 test packed shape: (8456, 3750)\nProcess Group PGID: 1633\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:57.242Z",
"resultDisplay": "Targets shape: (8456, 3)\nTargets head:\n 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\nCatalog shape: (30000, 4)\nCatalog head:\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nVal pref shape: (469124, 3)\nVal pref head:\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\nTest pref shape: (484776, 3)\nTest pref head:\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\nval_npz keys: ['uids', 'item_ids', 'packed_eligible']\nuids len: 8456 item_ids len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible']\ntest uids len: 8456 test item_ids len: 30000 test packed shape: (8456, 3750)",
"description": "Inspect dataset schemas and shapes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:57.243Z",
"content": "\nOutput: Targets shape: (8456, 3)\nTargets head:\n 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\nCatalog shape: (30000, 4)\nCatalog head:\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\nVal pref shape: (469124, 3)\nVal pref head:\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\nTest pref shape: (484776, 3)\nTest pref head:\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\nval_npz keys: ['uids', 'item_ids', 'packed_eligible']\nuids len: 8456 item_ids len: 30000 packed shape: (8456, 3750)\ntest_npz keys: ['uids', 'item_ids', 'packed_eligible']\ntest uids len: 8456 test item_ids len: 30000 test packed shape: (8456, 3750)\nProcess Group PGID: 1633\n",
"isError": false
},
"responseMessageId": "5e9bfd49-6f00-4333-b0c8-32e1e223b3d5"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T01:24:02.577Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:02.577Z",
"text": "Analyzing Data Structure\n\nMy current focus is on understanding the dimensions and nature of the provided datasets. I've noted 8456 target users and 30,000 candidate items, with the item IDs in `val_npz` and `test_npz` aligning with the candidate catalog. The `packed_eligible` array's shape (8456, 3750) bytes is also under review."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:02.577Z",
"callId": "run_shell_command__call_2649131",
"name": "run_shell_command",
"input": {
"description": "Inspect alignment of candidates and count eligible items",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\n# Check if item_ids and uids match catalog and targets\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nprint('val uids == targets uid:', np.array_equal(val_npz['uids'], targets['uid'].values))\nprint('test uids == targets uid:', np.array_equal(test_npz['uids'], targets['uid'].values))\nprint('val items == catalog item_id:', np.array_equal(val_npz['item_ids'], catalog['item_id'].values))\nprint('test items == catalog item_id:', np.array_equal(test_npz['item_ids'], catalog['item_id'].values))\n\n# Count eligible items per user\nval_unpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\ntest_unpacked = np.unpackbits(test_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nprint('val eligible per user: min, median, max, mean:', \n val_unpacked.sum(axis=1).min(), np.median(val_unpacked.sum(axis=1)), val_unpacked.sum(axis=1).max(), val_unpacked.sum(axis=1).mean())\nprint('test eligible per user: min, median, max, mean:', \n test_unpacked.sum(axis=1).min(), np.median(test_unpacked.sum(axis=1)), test_unpacked.sum(axis=1).max(), test_unpacked.sum(axis=1).mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_2649131",
"name": "run_shell_command",
"args": {
"description": "Inspect alignment of candidates and count eligible items",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\n# Check if item_ids and uids match catalog and targets\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nprint('val uids == targets uid:', np.array_equal(val_npz['uids'], targets['uid'].values))\nprint('test uids == targets uid:', np.array_equal(test_npz['uids'], targets['uid'].values))\nprint('val items == catalog item_id:', np.array_equal(val_npz['item_ids'], catalog['item_id'].values))\nprint('test items == catalog item_id:', np.array_equal(test_npz['item_ids'], catalog['item_id'].values))\n\n# Count eligible items per user\nval_unpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\ntest_unpacked = np.unpackbits(test_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nprint('val eligible per user: min, median, max, mean:', \n val_unpacked.sum(axis=1).min(), np.median(val_unpacked.sum(axis=1)), val_unpacked.sum(axis=1).max(), val_unpacked.sum(axis=1).mean())\nprint('test eligible per user: min, median, max, mean:', \n test_unpacked.sum(axis=1).min(), np.median(test_unpacked.sum(axis=1)), test_unpacked.sum(axis=1).max(), test_unpacked.sum(axis=1).mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2649131",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val uids == targets uid: True\ntest uids == targets uid: True\nval items == catalog item_id: True\ntest items == catalog item_id: True\nval eligible per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947\nProcess Group PGID: 1665\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:04.047Z",
"resultDisplay": "val uids == targets uid: True\ntest uids == targets uid: True\nval items == catalog item_id: True\ntest items == catalog item_id: True\nval eligible per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947",
"description": "Inspect alignment of candidates and count eligible items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:04.048Z",
"content": "\nOutput: val uids == targets uid: True\ntest uids == targets uid: True\nval items == catalog item_id: True\ntest items == catalog item_id: True\nval eligible per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727\ntest eligible per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947\nProcess Group PGID: 1665\n",
"isError": false
},
"responseMessageId": "ce23f209-3cc0-4f87-a2ea-7efabe435c26"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T01:24:09.381Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:09.381Z",
"text": "Confirming Data Alignment\n\nMy latest checks indicate a strong alignment between user IDs and target IDs, as well as item IDs and catalog IDs. The volume of eligible items per user is substantial, roughly 29,000."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:09.381Z",
"callId": "run_shell_command__call_2985439",
"name": "run_shell_command",
"input": {
"description": "Inspect validation ground truth stats and timestamp ranges",
"command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\n# Let's check timestamps of train and val\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max())\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max())\n\""
},
"recorded": {
"id": "run_shell_command__call_2985439",
"name": "run_shell_command",
"args": {
"description": "Inspect validation ground truth stats and timestamp ranges",
"command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\n# Let's check timestamps of train and val\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max())\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2985439",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1682\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:10.097Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Inspect validation ground truth stats and timestamp ranges",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:10.098Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 3, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1682\n",
"isError": false
},
"responseMessageId": "3560ca8b-039e-4392-84b4-80b1e499ad3f"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T01:24:35.103Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:35.103Z",
"callId": "run_shell_command__call_2265943",
"name": "run_shell_command",
"input": {
"description": "Inspect validation ground truth stats with PYTHONPATH",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max(), 'count:', len(val_listens))\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max(), 'count:', len(train_likes))\n\""
},
"recorded": {
"id": "run_shell_command__call_2265943",
"name": "run_shell_command",
"args": {
"description": "Inspect validation ground truth stats with PYTHONPATH",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with validation labels:', len(graded))\ntotal_labels = sum(len(v) for v in graded.values())\ngrade_counts = {}\nfor v in graded.values():\n for g in v.values():\n grade_counts[g] = grade_counts.get(g, 0) + 1\nprint('Total graded user-item pairs:', total_labels)\nprint('Grade counts:', grade_counts)\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens timestamps: min', val_listens.timestamp.min(), 'max', val_listens.timestamp.max(), 'count:', len(val_listens))\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamps: min', train_likes.timestamp.min(), 'max', train_likes.timestamp.max(), 'count:', len(train_likes))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2265943",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Number of users with validation labels: 4263\nTotal graded user-item pairs: 37769\nGrade counts: {1: 33352, 3: 4417}\nVal listens timestamps: min 24788600 max 25393395 count: 1501697\nTrain likes timestamps: min 80 max 24786790 count: 814831\nProcess Group PGID: 1693\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:36.392Z",
"resultDisplay": "Number of users with validation labels: 4263\nTotal graded user-item pairs: 37769\nGrade counts: {1: 33352, 3: 4417}\nVal listens timestamps: min 24788600 max 25393395 count: 1501697\nTrain likes timestamps: min 80 max 24786790 count: 814831",
"description": "Inspect validation ground truth stats with PYTHONPATH",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:36.393Z",
"content": "\nOutput: Number of users with validation labels: 4263\nTotal graded user-item pairs: 37769\nGrade counts: {1: 33352, 3: 4417}\nVal listens timestamps: min 24788600 max 25393395 count: 1501697\nTrain likes timestamps: min 80 max 24786790 count: 814831\nProcess Group PGID: 1693\n",
"isError": false
},
"responseMessageId": "81551a9e-1e6b-4370-8d6c-e0d792436dbc"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T01:24:39.434Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:39.434Z",
"text": "Analyzing Data Gaps\n\nI'm currently examining the training data endpoint and noting a significant gap before validation begins. I'm also tracking the duration of the validation period and the proportion of target users included."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:39.434Z",
"callId": "run_shell_command__call_252239",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes', \n 'val_listens', 'val_likes', 'val_dislikes', 'val_unlikes', 'val_undislikes']:\n meta = pq.read_metadata(f'/task/data/{name}.parquet')\n print(f'{name}: {meta.num_rows} rows, {meta.num_columns} cols')\n\"",
"description": "Check row counts across all parquet files"
},
"recorded": {
"id": "run_shell_command__call_252239",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes', \n 'val_listens', 'val_likes', 'val_dislikes', 'val_unlikes', 'val_undislikes']:\n meta = pq.read_metadata(f'/task/data/{name}.parquet')\n print(f'{name}: {meta.num_rows} rows, {meta.num_columns} cols')\n\"",
"description": "Check row counts across all parquet files"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_252239",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_listens: 43484159 rows, 6 cols\ntrain_likes: 814831 rows, 4 cols\ntrain_dislikes: 101075 rows, 4 cols\ntrain_unlikes: 292665 rows, 4 cols\ntrain_undislikes: 19960 rows, 4 cols\nval_listens: 1501697 rows, 6 cols\nval_likes: 37795 rows, 4 cols\nval_dislikes: 3563 rows, 4 cols\nval_unlikes: 9346 rows, 4 cols\nval_undislikes: 606 rows, 4 cols\nProcess Group PGID: 1713\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:39.781Z",
"resultDisplay": "train_listens: 43484159 rows, 6 cols\ntrain_likes: 814831 rows, 4 cols\ntrain_dislikes: 101075 rows, 4 cols\ntrain_unlikes: 292665 rows, 4 cols\ntrain_undislikes: 19960 rows, 4 cols\nval_listens: 1501697 rows, 6 cols\nval_likes: 37795 rows, 4 cols\nval_dislikes: 3563 rows, 4 cols\nval_unlikes: 9346 rows, 4 cols\nval_undislikes: 606 rows, 4 cols",
"description": "Check row counts across all parquet files",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:39.782Z",
"content": "\nOutput: train_listens: 43484159 rows, 6 cols\ntrain_likes: 814831 rows, 4 cols\ntrain_dislikes: 101075 rows, 4 cols\ntrain_unlikes: 292665 rows, 4 cols\ntrain_undislikes: 19960 rows, 4 cols\nval_listens: 1501697 rows, 6 cols\nval_likes: 37795 rows, 4 cols\nval_dislikes: 3563 rows, 4 cols\nval_unlikes: 9346 rows, 4 cols\nval_undislikes: 606 rows, 4 cols\nProcess Group PGID: 1713\n",
"isError": false
},
"responseMessageId": "b71ed61b-7b3a-4cc6-9708-32f928fb3d19"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T01:24:43.753Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:43.753Z",
"text": "Analyzing User Data\n\nMy current focus is on understanding the user distribution within the training data. I'm investigating the total number of unique users present in `train_listens` to see if the 8,456 target users represent the entirety of the user base or just a subset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:43.753Z",
"callId": "run_shell_command__call_294549",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Schema:', dataset.schema)\n\n# Sample read some batches or columns\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\n# Check in train_likes how many uids are in targets\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('train_likes total unique uids:', train_likes.uid.nunique())\nprint('train_likes unique uids in targets:', train_likes[train_likes.uid.isin(target_uids)].uid.nunique())\n\n# Let's inspect timestamp range of train_listens\n# Using pyarrow scanner to read min/max timestamp or sample\ntable_sample = dataset.scanner(columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'], \n batch_size=1000000).to_batches()\nfirst_batch = next(table_sample)\nprint('First batch shape:', first_batch.to_pandas().shape)\nprint('First batch head:\\n', first_batch.to_pandas().head())\n\"",
"description": "Inspect train_listens structure and user overlap"
},
"recorded": {
"id": "run_shell_command__call_294549",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Schema:', dataset.schema)\n\n# Sample read some batches or columns\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\n# Check in train_likes how many uids are in targets\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('train_likes total unique uids:', train_likes.uid.nunique())\nprint('train_likes unique uids in targets:', train_likes[train_likes.uid.isin(target_uids)].uid.nunique())\n\n# Let's inspect timestamp range of train_listens\n# Using pyarrow scanner to read min/max timestamp or sample\ntable_sample = dataset.scanner(columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'], \n batch_size=1000000).to_batches()\nfirst_batch = next(table_sample)\nprint('First batch shape:', first_batch.to_pandas().shape)\nprint('First batch head:\\n', first_batch.to_pandas().head())\n\"",
"description": "Inspect train_listens structure and user overlap"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_294549",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ntrain_likes total unique uids: 8144\ntrain_likes unique uids in targets: 7071\nFirst batch shape: (1000000, 5)\nFirst batch head:\n uid timestamp item_id is_organic played_ratio_pct\n0 100 39420 8326270 0 100\n1 100 39420 1441281 0 100\n2 100 39625 286361 0 100\n3 100 40110 732449 0 100\n4 100 40360 3397170 0 46\nProcess Group PGID: 1724\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:45.008Z",
"resultDisplay": "Schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ntrain_likes total unique uids: 8144\ntrain_likes unique uids in targets: 7071\nFirst batch shape: (1000000, 5)\nFirst batch head:\n uid timestamp item_id is_organic played_ratio_pct\n0 100 39420 8326270 0 100\n1 100 39420 1441281 0 100\n2 100 39625 286361 0 100\n3 100 40110 732449 0 100\n4 100 40360 3397170 0 46",
"description": "Inspect train_listens structure and user overlap",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:45.009Z",
"content": "\nOutput: Schema: uid: uint32\ntimestamp: uint32\nitem_id: uint32\nis_organic: uint8\nplayed_ratio_pct: uint16\ntrack_length_seconds: uint32\n-- schema metadata --\npandas: '{\"index_columns\": [], \"column_indexes\": [], \"columns\": [{\"name\":' + 797\ntrain_likes total unique uids: 8144\ntrain_likes unique uids in targets: 7071\nFirst batch shape: (1000000, 5)\nFirst batch head:\n uid timestamp item_id is_organic played_ratio_pct\n0 100 39420 8326270 0 100\n1 100 39420 1441281 0 100\n2 100 39625 286361 0 100\n3 100 40110 732449 0 100\n4 100 40360 3397170 0 46\nProcess Group PGID: 1724\n",
"isError": false
},
"responseMessageId": "a9bab8b4-a832-4634-93dd-d252454a0eb7"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T01:25:11.016Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:11.016Z",
"callId": "run_shell_command__call_281611",
"name": "run_shell_command",
"input": {
"description": "Inspect target users summary stats and total rows in train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets total history positive listens sum:', targets.history_positive_listens.sum())\nprint('Targets recent positive listens sum:', targets.recent_positive_listens.sum())\n\n# Let's see how fast pyarrow scanner is over train_listens filtering on target uids or inspecting\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Total rows in dataset:', dataset.count_rows())\n\""
},
"recorded": {
"id": "run_shell_command__call_281611",
"name": "run_shell_command",
"args": {
"description": "Inspect target users summary stats and total rows in train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets total history positive listens sum:', targets.history_positive_listens.sum())\nprint('Targets recent positive listens sum:', targets.recent_positive_listens.sum())\n\n# Let's see how fast pyarrow scanner is over train_listens filtering on target uids or inspecting\ndataset = ds.dataset('/task/data/train_listens.parquet')\nprint('Total rows in dataset:', dataset.count_rows())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_281611",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Targets total history positive listens sum: 27367343\nTargets recent positive listens sum: 10579105.0\nTotal rows in dataset: 43484159\nProcess Group PGID: 1751\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:11.739Z",
"resultDisplay": "Targets total history positive listens sum: 27367343\nTargets recent positive listens sum: 10579105.0\nTotal rows in dataset: 43484159",
"description": "Inspect target users summary stats and total rows in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:11.740Z",
"content": "\nOutput: Targets total history positive listens sum: 27367343\nTargets recent positive listens sum: 10579105.0\nTotal rows in dataset: 43484159\nProcess Group PGID: 1751\n",
"isError": false
},
"responseMessageId": "bc5c96e4-93fd-4cb9-a44c-a2913dc8041f"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T01:25:31.024Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:31.024Z",
"text": "Analyzing Target Data\n\nMy focus has shifted to understanding the user listening patterns within the `train_listens` dataset. I've observed that approximately 27.3 million out of 43.4 million total rows represent positive listening history for the targets, which is a crucial piece of information for building the ranking model."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:31.024Z",
"text": "Refining Ranking Logic\n\nI'm now focusing on the specific grading system for candidate relevance. The system prioritizes user likes (grade 3) over significant listens (grade 1) and otherwise (grade 0). This graded approach will be crucial for calculating the DCG and ultimately the NDCG@10 for measuring ranking quality."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:31.024Z",
"text": "Testing Popularity Baseline\n\nI'm evaluating a popularity-based baseline using `organic_positive_listens` from the `candidate_catalog.parquet`. This seems like a quick and robust way to test the evaluation pipeline and establish an initial performance metric."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:31.024Z",
"callId": "run_shell_command__call_1159023",
"name": "run_shell_command",
"input": {
"description": "Build and evaluate simple popularity baseline on validation set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items, score_rankings\nimport evaluate_public\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\n\n# Let's unpack bits for all users\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# For each user, pick top 100 eligible items by popularity rank (items are already in order 0..29999 in catalog)\n# In catalog, item_ids matches val_npz['item_ids'] exactly!\n# Let's verify:\nprint('catalog item_id matches item_ids:', np.array_equal(catalog['item_id'].values, item_ids))\n\n# Let's test standard popularity:\nrows = []\nfor r, uid in enumerate(uids):\n # argwhere of unpacked[r] gives the indices of eligible items in catalog order!\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_pop.parquet')\nprint('Val baseline saved, row count:', len(val_sub))\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
},
"recorded": {
"id": "run_shell_command__call_1159023",
"name": "run_shell_command",
"args": {
"description": "Build and evaluate simple popularity baseline on validation set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items, score_rankings\nimport evaluate_public\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\n\n# Let's unpack bits for all users\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# For each user, pick top 100 eligible items by popularity rank (items are already in order 0..29999 in catalog)\n# In catalog, item_ids matches val_npz['item_ids'] exactly!\n# Let's verify:\nprint('catalog item_id matches item_ids:', np.array_equal(catalog['item_id'].values, item_ids))\n\n# Let's test standard popularity:\nrows = []\nfor r, uid in enumerate(uids):\n # argwhere of unpacked[r] gives the indices of eligible items in catalog order!\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_pop.parquet')\nprint('Val baseline saved, row count:', len(val_sub))\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1159023",
"name": "run_shell_command",
"response": {
"output": "\nOutput: catalog item_id matches item_ids: True\nVal baseline saved, row count: 845600\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1767\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:36.342Z",
"resultDisplay": "catalog item_id matches item_ids: True\nVal baseline saved, row count: 845600\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Build and evaluate simple popularity baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:36.343Z",
"content": "\nOutput: catalog item_id matches item_ids: True\nVal baseline saved, row count: 845600\n{\n \"model\": \"/app/solution/val_baseline_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1767\n",
"isError": false
},
"responseMessageId": "847a788d-709e-4313-9e50-b947359c1baf"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T01:25:40.256Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:40.256Z",
"text": "Testing Ranking Strategies\n\nMy initial baseline using basic popularity has yielded `quality_ndcg@10` of 0.010710 in under 2 seconds. Now, I'm exploring alternative ranking approaches, specifically considering `organic_positive_listens` and recency of positive listens to identify potential improvements."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:40.256Z",
"callId": "run_shell_command__call_30721",
"name": "run_shell_command",
"input": {
"description": "Generate and validate initial submission.parquet on test set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\n\nuids = test_npz['uids']\nitem_ids = test_npz['item_ids']\npacked = test_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nrows = []\nfor r, uid in enumerate(uids):\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\ntest_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ntest_sub.to_parquet('/app/solution/submission.parquet')\nprint('Initial submission saved, row count:', len(test_sub))\n\n# Validate submission using validate_ranking\nvalidated = validate_ranking(test_sub, targets)\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nfor uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('Submission validation PASSED! All invariants satisfied.')\n\""
},
"recorded": {
"id": "run_shell_command__call_30721",
"name": "run_shell_command",
"args": {
"description": "Generate and validate initial submission.parquet on test set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\n\nuids = test_npz['uids']\nitem_ids = test_npz['item_ids']\npacked = test_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nrows = []\nfor r, uid in enumerate(uids):\n eligible_indices = np.where(unpacked[r])[0][:100]\n chosen_items = item_ids[eligible_indices]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\ntest_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ntest_sub.to_parquet('/app/solution/submission.parquet')\nprint('Initial submission saved, row count:', len(test_sub))\n\n# Validate submission using validate_ranking\nvalidated = validate_ranking(test_sub, targets)\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nfor uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('Submission validation PASSED! All invariants satisfied.')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30721",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Initial submission saved, row count: 845600\nSubmission validation PASSED! All invariants satisfied.\nProcess Group PGID: 1802\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:43.916Z",
"resultDisplay": "Initial submission saved, row count: 845600\nSubmission validation PASSED! All invariants satisfied.",
"description": "Generate and validate initial submission.parquet on test set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:43.917Z",
"content": "\nOutput: Initial submission saved, row count: 845600\nSubmission validation PASSED! All invariants satisfied.\nProcess Group PGID: 1802\n",
"isError": false
},
"responseMessageId": "3ec9a81d-c330-4f57-8be6-99d803b4a73e"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T01:25:47.800Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:47.800Z",
"text": "Confirming Valid Fallback\n\nI've successfully generated a robust fallback submission file, `/app/solution/submission.parquet`. It meticulously adheres to all specified constraints, ensuring 8456 target users, exactly 100 unique items ranked 1-100 per user, and confirmed item eligibility. The next step involves refining the current focus."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:47.800Z",
"callId": "update_topic__call_25888",
"name": "update_topic",
"input": {
"strategic_intent": "Analyze user interaction history, candidate relationships, and discover ranking patterns.",
"summary": "Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.",
"title": "Interaction Analysis and Modeling Strategy"
},
"recorded": {
"id": "update_topic__call_25888",
"name": "update_topic",
"args": {
"strategic_intent": "Analyze user interaction history, candidate relationships, and discover ranking patterns.",
"summary": "Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.",
"title": "Interaction Analysis and Modeling Strategy"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_25888",
"name": "update_topic",
"response": {
"output": "Current topic: \"Interaction Analysis and Modeling Strategy\"\nTopic summary: Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.\n\nStrategic Intent: Analyze user interaction history, candidate relationships, and discover ranking patterns."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:47.802Z",
"resultDisplay": "## 📂 Topic: **Interaction Analysis and Modeling Strategy**\n\n**Summary:**\nInitial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.\n\n> [!STRATEGY]\n> **Intent:** Analyze user interaction history, candidate relationships, and discover ranking patterns.",
"description": "Update topic to: \"Interaction Analysis and Modeling Strategy\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T01:25:47.802Z",
"content": "Current topic: \"Interaction Analysis and Modeling Strategy\"\nTopic summary: Initial baseline submission is created and verified. Now analyzing the user-item interaction signals (likes, dislikes, organic listens, completion ratios, recency), exploring collaborative filtering, matrix factorization, and item-item similarity approaches suited to the discovery objective and 16GB memory / 8-core CPU environment.\n\nStrategic Intent: Analyze user interaction history, candidate relationships, and discover ranking patterns.",
"isError": false
},
"responseMessageId": "0b20242b-5f95-490c-b02d-8a9478ef11bd"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T01:25:55.740Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:55.740Z",
"text": "Refining Problem Understanding\n\nI'm currently dissecting the core objective: constructing the most robust ranking model possible for a music streaming service's \"Made for You\" shelf. My focus is on per-user candidate eligibility and cutoff-specific explicit data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:55.740Z",
"text": "Analyzing Candidate Data\n\nMy current focus is on understanding the candidate catalog data. I'm examining `positive_listens`, `organic_positive_listens`, and `popularity_rank` to identify key features for ranking.\n\n**Defining Ranking Metrics**\n\nI am now delving into how to evaluate the ranking performance. The primary metric appears to be NDCG@10, using a specific gain and discount calculation, averaged per user.\n\n**Exploring User Data**\n\nMy attention is now directed towards the user data. I am analyzing `recent_positive_listens` and `history_positive_listens` to capture user engagement patterns.\n\n**Evaluating Explicit Feedback**\n\nI'm currently investigating the explicit feedback data, specifically `validation_preference_state` and `test_preference_state`. This will help me understand user `liked` and `disliked` signals."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:55.740Z",
"callId": "run_shell_command__call_1645186",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state counts:\\n', val_pref['state'].value_counts())\nprint('Val pref unique users:', val_pref['uid'].nunique(), 'unique items:', val_pref['item_id'].nunique())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check whether items in val_pref are eligible\neligible_likes = 0\ntotal_likes = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n if state == 'liked':\n total_likes += 1\n if unpacked[r, c]:\n eligible_likes += 1\n\nprint(f'Total liked in val_pref: {total_likes}, eligible liked: {eligible_likes}')\n\n# Check validation ground truth items: are they in val_pref?\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# How many ground truth relevant items are already liked in val_pref?\nalready_liked_gt = 0\ntotal_gt = 0\npref_liked_pairs = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\nfor uid, items in graded.items():\n for item, grade in items.items():\n total_gt += 1\n if (uid, item) in pref_liked_pairs:\n already_liked_gt += 1\nprint(f'Total GT relevant items: {total_gt}, of which already in pref_liked_pairs: {already_liked_gt}')\n\"",
"description": "Check if liked items are eligible and if ground truth items were previously liked"
},
"recorded": {
"id": "run_shell_command__call_1645186",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state counts:\\n', val_pref['state'].value_counts())\nprint('Val pref unique users:', val_pref['uid'].nunique(), 'unique items:', val_pref['item_id'].nunique())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check whether items in val_pref are eligible\neligible_likes = 0\ntotal_likes = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n if state == 'liked':\n total_likes += 1\n if unpacked[r, c]:\n eligible_likes += 1\n\nprint(f'Total liked in val_pref: {total_likes}, eligible liked: {eligible_likes}')\n\n# Check validation ground truth items: are they in val_pref?\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# How many ground truth relevant items are already liked in val_pref?\nalready_liked_gt = 0\ntotal_gt = 0\npref_liked_pairs = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\nfor uid, items in graded.items():\n for item, grade in items.items():\n total_gt += 1\n if (uid, item) in pref_liked_pairs:\n already_liked_gt += 1\nprint(f'Total GT relevant items: {total_gt}, of which already in pref_liked_pairs: {already_liked_gt}')\n\"",
"description": "Check if liked items are eligible and if ground truth items were previously liked"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1645186",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val pref state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nVal pref unique users: 7038 unique items: 28814\nTotal liked in val_pref: 418435, eligible liked: 0\nTotal GT relevant items: 37769, of which already in pref_liked_pairs: 0\nProcess Group PGID: 1819\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:58.062Z",
"resultDisplay": "Val pref state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nVal pref unique users: 7038 unique items: 28814\nTotal liked in val_pref: 418435, eligible liked: 0\nTotal GT relevant items: 37769, of which already in pref_liked_pairs: 0",
"description": "Check if liked items are eligible and if ground truth items were previously liked",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:58.062Z",
"content": "\nOutput: Val pref state counts:\n state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nVal pref unique users: 7038 unique items: 28814\nTotal liked in val_pref: 418435, eligible liked: 0\nTotal GT relevant items: 37769, of which already in pref_liked_pairs: 0\nProcess Group PGID: 1819\n",
"isError": false
},
"responseMessageId": "a42e11dd-74ba-46d5-8a4c-0981f012e8fd"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T01:26:06.910Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:06.910Z",
"text": "Analyzing Discovery Anomalies\n\nI'm noticing a curious pattern where no \"liked\" items in `val_pref` are deemed eligible, nor are any `GT relevant items` appearing in `pref_liked_pairs`. This suggests a potential filtering issue, as items already in the user's preference list are being excluded, which seems to be the intended behavior but the zero counts are stark."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:06.910Z",
"callId": "run_shell_command__call_8363",
"name": "run_shell_command",
"input": {
"description": "Check if previously listened items can be eligible candidates",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nimport numpy as np\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = set(val_npz['uids'])\nitem_ids = set(val_npz['item_ids'])\n\n# Read a sample of train_listens for target users\nlistens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens columns:', listens.columns)\n\n# Check if items in val_listens (ground truth) were previously listened to in train_listens\n# Let's check for 100 random target users\ntargets = list(uids)[:100]\ndataset = ds.dataset('/task/data/train_listens.parquet')\nsample_train = dataset.to_table(filter=ds.field('uid').isin(targets)).to_pandas()\nprint('Sample train listens for 100 users:', len(sample_train))\n\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ngraded = evaluate_public.validation_truth(eligibility, set(targets_frame.uid.astype(int)))\n\n# Check for those 100 users, were any of their graded items listened to in train?\ngraded_items_sample = set()\nfor u in targets:\n if u in graded:\n for it in graded[u]:\n graded_items_sample.add((u, it))\n\ntrain_pairs_sample = set(sample_train[['uid', 'item_id']].itertuples(index=False, name=None))\noverlap = graded_items_sample.intersection(train_pairs_sample)\nprint(f'For 100 target users: graded pairs = {len(graded_items_sample)}, previously listened in train = {len(overlap)}')\n\n# What about eligibility for previously listened items in sample_train?\neligible_previously_listened = sum(eligibility.is_eligible(u, it) for u, it in train_pairs_sample)\nprint(f'For 100 target users: train listened pairs = {len(train_pairs_sample)}, of which eligible = {eligible_previously_listened}')\n\""
},
"recorded": {
"id": "run_shell_command__call_8363",
"name": "run_shell_command",
"args": {
"description": "Check if previously listened items can be eligible candidates",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nimport numpy as np\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = set(val_npz['uids'])\nitem_ids = set(val_npz['item_ids'])\n\n# Read a sample of train_listens for target users\nlistens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens columns:', listens.columns)\n\n# Check if items in val_listens (ground truth) were previously listened to in train_listens\n# Let's check for 100 random target users\ntargets = list(uids)[:100]\ndataset = ds.dataset('/task/data/train_listens.parquet')\nsample_train = dataset.to_table(filter=ds.field('uid').isin(targets)).to_pandas()\nprint('Sample train listens for 100 users:', len(sample_train))\n\nsys_path = '/task'\nimport sys; sys.path.append(sys_path)\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ngraded = evaluate_public.validation_truth(eligibility, set(targets_frame.uid.astype(int)))\n\n# Check for those 100 users, were any of their graded items listened to in train?\ngraded_items_sample = set()\nfor u in targets:\n if u in graded:\n for it in graded[u]:\n graded_items_sample.add((u, it))\n\ntrain_pairs_sample = set(sample_train[['uid', 'item_id']].itertuples(index=False, name=None))\noverlap = graded_items_sample.intersection(train_pairs_sample)\nprint(f'For 100 target users: graded pairs = {len(graded_items_sample)}, previously listened in train = {len(overlap)}')\n\n# What about eligibility for previously listened items in sample_train?\neligible_previously_listened = sum(eligibility.is_eligible(u, it) for u, it in train_pairs_sample)\nprint(f'For 100 target users: train listened pairs = {len(train_pairs_sample)}, of which eligible = {eligible_previously_listened}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_8363",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val_listens columns: Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\nSample train listens for 100 users: 451914\nFor 100 target users: graded pairs = 448, previously listened in train = 0\nFor 100 target users: train listened pairs = 126266, of which eligible = 0\nProcess Group PGID: 1839\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:08.806Z",
"resultDisplay": "val_listens columns: Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\nSample train listens for 100 users: 451914\nFor 100 target users: graded pairs = 448, previously listened in train = 0\nFor 100 target users: train listened pairs = 126266, of which eligible = 0",
"description": "Check if previously listened items can be eligible candidates",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:08.807Z",
"content": "\nOutput: val_listens columns: Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',\n 'track_length_seconds'],\n dtype='object')\nSample train listens for 100 users: 451914\nFor 100 target users: graded pairs = 448, previously listened in train = 0\nFor 100 target users: train listened pairs = 126266, of which eligible = 0\nProcess Group PGID: 1839\n",
"isError": false
},
"responseMessageId": "d121410e-1cda-4637-9205-b05ec6dc58c5"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T01:26:16.926Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:16.926Z",
"text": "Exploring Candidate Pool\n\nI'm currently assessing the candidate pool, noticing a peculiar outcome: zero eligible pairs from the listened set for training, yet a substantial number of graded pairs. This suggests a potential disconnect in how I'm defining or filtering for these sets."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:16.926Z",
"text": "Refining Item Similarity\n\nMy thoughts are now focused on refining how I define item similarity. I'm investigating the distinction between general \"positive_listens\" and \"organic_positive_listens\" within the candidate catalog to see if that clarifies why eligible pairs for training seem absent."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:16.926Z",
"callId": "run_shell_command__call_133350",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# Sort catalog by organic_positive_listens descending\ncatalog['orig_idx'] = np.arange(len(catalog))\nsorted_catalog = catalog.sort_values('organic_positive_listens', ascending=False)\nsorted_indices = sorted_catalog['orig_idx'].values\n\n# Let's see: for each user, pick top 100 eligible items in sorted_indices order\nrows = []\nfor r, uid in enumerate(uids):\n user_eligible = unpacked[r]\n # filter sorted_indices by user_eligible\n chosen = [idx for idx in sorted_indices if user_eligible[idx]][:100]\n chosen_items = item_ids[chosen]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_organic.parquet')\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_organic.parquet",
"description": "Evaluate organic positive listens baseline on validation set"
},
"recorded": {
"id": "run_shell_command__call_133350",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_npz['uids']\nitem_ids = val_npz['item_ids']\npacked = val_npz['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little')\n\n# Sort catalog by organic_positive_listens descending\ncatalog['orig_idx'] = np.arange(len(catalog))\nsorted_catalog = catalog.sort_values('organic_positive_listens', ascending=False)\nsorted_indices = sorted_catalog['orig_idx'].values\n\n# Let's see: for each user, pick top 100 eligible items in sorted_indices order\nrows = []\nfor r, uid in enumerate(uids):\n user_eligible = unpacked[r]\n # filter sorted_indices by user_eligible\n chosen = [idx for idx in sorted_indices if user_eligible[idx]][:100]\n chosen_items = item_ids[chosen]\n for rank, item in enumerate(chosen_items, start=1):\n rows.append((uid, item, rank))\n\nval_sub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub.to_parquet('/app/solution/val_baseline_organic.parquet')\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline_organic.parquet",
"description": "Evaluate organic positive listens baseline on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_133350",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/val_baseline_organic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1860\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:55.892Z",
"resultDisplay": "{\n \"model\": \"/app/solution/val_baseline_organic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}",
"description": "Evaluate organic positive listens baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:55.893Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/val_baseline_organic.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1860\n",
"isError": false
},
"responseMessageId": "48a0d3e7-3ce5-4dbb-be13-b4761c0af11d"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T01:27:04.927Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:04.927Z",
"text": "Exploring Personalization Strategies\n\nMy current focus is on understanding the impact of collaborative filtering on recommendation quality. I'm investigating how personalized user tastes can be leveraged to move beyond the baseline NDCG@10 of 0.0107 achieved with global popularity alone, recognizing the strong genre-specific nature of music preferences."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:04.927Z",
"text": "Analyzing User Interactions\n\nI'm now delving into the specifics of user-item interactions to inform candidate selection. My investigation into the scale of user history and candidate items confirms we're working within a defined 30,000-track catalog. I'm preparing to analyze the `train_likes` dataset to understand the density of interactions among our target users and identify potential features for personalization."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:04.927Z",
"callId": "run_shell_command__call_60397",
"name": "run_shell_command",
"input": {
"description": "Check target users interactions and overlap with candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref: target users covered:', val_pref[val_pref.uid.isin(target_uids)].uid.nunique(), 'out of', len(targets))\nprint('Likes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'liked')).sum())\nprint('Dislikes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'disliked')).sum())\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = train_likes[train_likes.uid.isin(target_uids)]\nprint('Train likes for target users: count =', len(target_likes), 'unique users =', target_likes.uid.nunique())\n\n# Check overlap of items in target_likes with candidate_catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\nprint('Target likes items in catalog:', target_likes.item_id.isin(catalog_items).mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_60397",
"name": "run_shell_command",
"args": {
"description": "Check target users interactions and overlap with candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid.astype(int))\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref: target users covered:', val_pref[val_pref.uid.isin(target_uids)].uid.nunique(), 'out of', len(targets))\nprint('Likes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'liked')).sum())\nprint('Dislikes in val pref for target users:', (val_pref.uid.isin(target_uids) & (val_pref.state == 'disliked')).sum())\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = train_likes[train_likes.uid.isin(target_uids)]\nprint('Train likes for target users: count =', len(target_likes), 'unique users =', target_likes.uid.nunique())\n\n# Check overlap of items in target_likes with candidate_catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\nprint('Target likes items in catalog:', target_likes.item_id.isin(catalog_items).mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_60397",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val pref: target users covered: 7038 out of 8456\nLikes in val pref for target users: 418435\nDislikes in val pref for target users: 50689\nTrain likes for target users: count = 690968 unique users = 7071\nTarget likes items in catalog: 0.6843862523300644\nProcess Group PGID: 1895\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:05.985Z",
"resultDisplay": "Val pref: target users covered: 7038 out of 8456\nLikes in val pref for target users: 418435\nDislikes in val pref for target users: 50689\nTrain likes for target users: count = 690968 unique users = 7071\nTarget likes items in catalog: 0.6843862523300644",
"description": "Check target users interactions and overlap with candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:05.986Z",
"content": "\nOutput: Val pref: target users covered: 7038 out of 8456\nLikes in val pref for target users: 418435\nDislikes in val pref for target users: 50689\nTrain likes for target users: count = 690968 unique users = 7071\nTarget likes items in catalog: 0.6843862523300644\nProcess Group PGID: 1895\n",
"isError": false
},
"responseMessageId": "ea2045b3-a6b0-4a64-a3ef-48beed0ac6e8"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T01:27:21.247Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:21.247Z",
"text": "Analyzing User Behavior\n\nI'm currently examining the listening habits of target users, specifically focusing on the 1,418 individuals not accounted for by current validation preferences. I'm cross-referencing their recent and historical positive listens against the `validation_preference_state` data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:21.247Z",
"callId": "run_shell_command__call_3812218",
"name": "run_shell_command",
"input": {
"description": "Benchmark reading train_listens with pyarrow",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\n# Read columns uid, item_id, played_ratio_pct, is_organic, timestamp\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'Read parquet table in {time.time() - t0:.2f} s, rows: {len(table)}')\n\nt1 = time.time()\n# Filter on played_ratio_pct >= 50 (positive listens)\nmask = pc.greater_equal(table['played_ratio_pct'], 50)\nfiltered = table.filter(mask)\nprint(f'Filtered to positive listens in {time.time() - t1:.2f} s, rows: {len(filtered)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_3812218",
"name": "run_shell_command",
"args": {
"description": "Benchmark reading train_listens with pyarrow",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\n# Read columns uid, item_id, played_ratio_pct, is_organic, timestamp\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'Read parquet table in {time.time() - t0:.2f} s, rows: {len(table)}')\n\nt1 = time.time()\n# Filter on played_ratio_pct >= 50 (positive listens)\nmask = pc.greater_equal(table['played_ratio_pct'], 50)\nfiltered = table.filter(mask)\nprint(f'Filtered to positive listens in {time.time() - t1:.2f} s, rows: {len(filtered)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3812218",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read parquet table in 0.65 s, rows: 43484159\nFiltered to positive listens in 0.57 s, rows: 27500755\nProcess Group PGID: 1915\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:23.045Z",
"resultDisplay": "Read parquet table in 0.65 s, rows: 43484159\nFiltered to positive listens in 0.57 s, rows: 27500755",
"description": "Benchmark reading train_listens with pyarrow",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:23.046Z",
"content": "\nOutput: Read parquet table in 0.65 s, rows: 43484159\nFiltered to positive listens in 0.57 s, rows: 27500755\nProcess Group PGID: 1915\n",
"isError": false
},
"responseMessageId": "37fa1fb9-3e9c-4e70-8ded-6dd725117068"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T01:27:31.964Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:31.964Z",
"text": "Achieving PyArrow Efficiency\n\nI'm astounded by PyArrow's in-memory speed; loading and filtering a massive 43.4 million row table took mere fractions of a second. The memory footprint is incredibly light, around 200-300MB, easily accommodated by my current setup."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:31.964Z",
"callId": "run_shell_command__call_3792765",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\nprint('Target uids count:', len(target_uids))\nprint('Catalog items count:', len(catalog_items))\n\n# Let's filter table to target uids and catalog items\nmask = pc.and_([\n pc.is_in(table['uid'], pa.array(list(target_uids))),\n pc.is_in(table['item_id'], pa.array(list(catalog_items)))\n])\n\"",
"description": "Check pyarrow array filtering"
},
"recorded": {
"id": "run_shell_command__call_3792765",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = set(catalog.item_id)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\nprint('Target uids count:', len(target_uids))\nprint('Catalog items count:', len(catalog_items))\n\n# Let's filter table to target uids and catalog items\nmask = pc.and_([\n pc.is_in(table['uid'], pa.array(list(target_uids))),\n pc.is_in(table['item_id'], pa.array(list(catalog_items)))\n])\n\"",
"description": "Check pyarrow array filtering"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3792765",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 19, in \nNameError: name 'pa' is not defined. Did you mean: 'pq'?\nTarget uids count: 8456\nCatalog items count: 30000\nExit Code: 1\nProcess Group PGID: 1937\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:33.169Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 19, in \nNameError: name 'pa' is not defined. Did you mean: 'pq'?\nTarget uids count: 8456\nCatalog items count: 30000",
"description": "Check pyarrow array filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:33.170Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 19, in \nNameError: name 'pa' is not defined. Did you mean: 'pq'?\nTarget uids count: 8456\nCatalog items count: 30000\nExit Code: 1\nProcess Group PGID: 1937\n",
"isError": false
},
"responseMessageId": "06bc576e-3340-4002-bb16-46b022e86129"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T01:27:36.928Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:36.928Z",
"callId": "run_shell_command__call_140209",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\ntarget_listens = table.filter(mask)\nprint('Target users listens total rows:', len(target_listens))\n\nmask_catalog = pc.is_in(target_listens['item_id'], catalog_items)\ncatalog_target_listens = target_listens.filter(mask_catalog)\nprint('Target users listens on catalog items:', len(catalog_target_listens))\n\n# Convert to pandas\ndf = catalog_target_listens.to_pandas()\nprint('Number of unique target users in df:', df.uid.nunique())\nprint('Number of unique catalog items in df:', df.item_id.nunique())\n\"",
"description": "Inspect target user listens on catalog items"
},
"recorded": {
"id": "run_shell_command__call_140209",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\ntarget_listens = table.filter(mask)\nprint('Target users listens total rows:', len(target_listens))\n\nmask_catalog = pc.is_in(target_listens['item_id'], catalog_items)\ncatalog_target_listens = target_listens.filter(mask_catalog)\nprint('Target users listens on catalog items:', len(catalog_target_listens))\n\n# Convert to pandas\ndf = catalog_target_listens.to_pandas()\nprint('Number of unique target users in df:', df.uid.nunique())\nprint('Number of unique catalog items in df:', df.item_id.nunique())\n\"",
"description": "Inspect target user listens on catalog items"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_140209",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users listens total rows: 43222297\nTarget users listens on catalog items: 31062540\nNumber of unique target users in df: 8452\nNumber of unique catalog items in df: 30000\nProcess Group PGID: 1959\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:40.129Z",
"resultDisplay": "Target users listens total rows: 43222297\nTarget users listens on catalog items: 31062540\nNumber of unique target users in df: 8452\nNumber of unique catalog items in df: 30000",
"description": "Inspect target user listens on catalog items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:40.130Z",
"content": "\nOutput: Target users listens total rows: 43222297\nTarget users listens on catalog items: 31062540\nNumber of unique target users in df: 8452\nNumber of unique catalog items in df: 30000\nProcess Group PGID: 1959\n",
"isError": false
},
"responseMessageId": "cb85c158-cefc-4f51-9a00-1ddf9181468b"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T01:27:47.359Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:47.359Z",
"text": "Verifying User and Catalog Data\n\nI've confirmed that nearly all target users and catalog items are accounted for in the target listens. The next step involves investigating the interaction data across the catalog items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:47.359Z",
"text": "Analyzing User Preferences\n\nI've confirmed that the target users constitute an overwhelming majority of the listens in the dataset. My focus has now shifted to defining user taste through interactions like likes, strong listens, and recency, and exploring item similarity via co-occurrence patterns."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:47.359Z",
"callId": "run_shell_command__call_266597",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's count unique (user, item) pairs in train_likes and train_listens\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref pairs:', len(val_pref))\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes pairs:', len(train_likes.drop_duplicates(['uid', 'item_id'])))\n\"",
"description": "Check unique user-item pairs in likes and preference state"
},
"recorded": {
"id": "run_shell_command__call_266597",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Let's count unique (user, item) pairs in train_likes and train_listens\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref pairs:', len(val_pref))\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes pairs:', len(train_likes.drop_duplicates(['uid', 'item_id'])))\n\"",
"description": "Check unique user-item pairs in likes and preference state"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_266597",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val pref pairs: 469124\nTrain likes pairs: 785069\nProcess Group PGID: 1981\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:48.179Z",
"resultDisplay": "Val pref pairs: 469124\nTrain likes pairs: 785069",
"description": "Check unique user-item pairs in likes and preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:48.180Z",
"content": "\nOutput: Val pref pairs: 469124\nTrain likes pairs: 785069\nProcess Group PGID: 1981\n",
"isError": false
},
"responseMessageId": "83fae518-382e-41dd-81be-fb94e50917fa"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T01:27:51.095Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:51.095Z",
"callId": "run_shell_command__call_3642195",
"name": "run_shell_command",
"input": {
"description": "Count unique positive listen user-item pairs",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\npos_mask = pc.greater_equal(table['played_ratio_pct'], 50)\npos_table = table.filter(pos_mask)\ndf_pairs = pos_table.to_pandas()[['uid', 'item_id']].drop_duplicates()\nprint('Unique (user, item) positive listen pairs:', len(df_pairs))\n\""
},
"recorded": {
"id": "run_shell_command__call_3642195",
"name": "run_shell_command",
"args": {
"description": "Count unique positive listen user-item pairs",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\npos_mask = pc.greater_equal(table['played_ratio_pct'], 50)\npos_table = table.filter(pos_mask)\ndf_pairs = pos_table.to_pandas()[['uid', 'item_id']].drop_duplicates()\nprint('Unique (user, item) positive listen pairs:', len(df_pairs))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3642195",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unique (user, item) positive listen pairs: 8471966\nProcess Group PGID: 1998\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:54.827Z",
"resultDisplay": "Unique (user, item) positive listen pairs: 8471966",
"description": "Count unique positive listen user-item pairs",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:54.828Z",
"content": "\nOutput: Unique (user, item) positive listen pairs: 8471966\nProcess Group PGID: 1998\n",
"isError": false
},
"responseMessageId": "280c1ef0-9c66-4d02-b011-06780bf8aa4d"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T01:28:00.521Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:00.521Z",
"text": "Exploring Recommendation Strategies\n\nI'm currently diving into how recommendation algorithms tackle the vast number of unique pairs generated. My focus is on analyzing common methods like Matrix Factorization techniques, including Truncated SVD, SVD, and ALS variants."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:00.521Z",
"callId": "run_shell_command__call_30141",
"name": "run_shell_command",
"input": {
"description": "Benchmark matrix multiplication and top100 extraction",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Full matrix multiplication 8456x128 @ 128x30000 took {t1-t0:.3f} s, shape {scores.shape}, memory {scores.nbytes / 1e6:.1f} MB')\n\n# How fast is masked top 100 retrieval?\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nt2 = time.time()\n# Set ineligible to -inf\nscores[~unpacked.astype(bool)] = -np.inf\n# Partition top 100 per user\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt3 = time.time()\nprint(f'Masking and top100 argpartition for 8456 users took {t3-t2:.3f} s')\n\""
},
"recorded": {
"id": "run_shell_command__call_30141",
"name": "run_shell_command",
"args": {
"description": "Benchmark matrix multiplication and top100 extraction",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Full matrix multiplication 8456x128 @ 128x30000 took {t1-t0:.3f} s, shape {scores.shape}, memory {scores.nbytes / 1e6:.1f} MB')\n\n# How fast is masked top 100 retrieval?\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nt2 = time.time()\n# Set ineligible to -inf\nscores[~unpacked.astype(bool)] = -np.inf\n# Partition top 100 per user\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt3 = time.time()\nprint(f'Masking and top100 argpartition for 8456 users took {t3-t2:.3f} s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30141",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Full matrix multiplication 8456x128 @ 128x30000 took 0.358 s, shape (8456, 30000), memory 1014.7 MB\nMasking and top100 argpartition for 8456 users took 1.144 s\nProcess Group PGID: 2024\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:02.649Z",
"resultDisplay": "Full matrix multiplication 8456x128 @ 128x30000 took 0.358 s, shape (8456, 30000), memory 1014.7 MB\nMasking and top100 argpartition for 8456 users took 1.144 s",
"description": "Benchmark matrix multiplication and top100 extraction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:02.650Z",
"content": "\nOutput: Full matrix multiplication 8456x128 @ 128x30000 took 0.358 s, shape (8456, 30000), memory 1014.7 MB\nMasking and top100 argpartition for 8456 users took 1.144 s\nProcess Group PGID: 2024\n",
"isError": false
},
"responseMessageId": "ce9591a9-8ae2-445d-b458-5525f6a9e7a9"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T01:28:11.502Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:11.502Z",
"text": "Exploring Collaborative Filtering\n\nI've just confirmed that our full inference process for all users and candidates completes in a swift 1.5 seconds. My current focus is on identifying the optimal collaborative filtering and ranking model. I'm diving into established implicit recommendation models, specifically starting with Implicit Alternating Least Squares, to see how well they perform for music discovery."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:11.502Z",
"text": "Testing EASE Matrix Inversion\n\nI'm evaluating the feasibility of inverting a 30,000x30,000 matrix for the EASE model, which is estimated to require 3.6 GB of memory. My next step is to explore how to efficiently solve this linear system to implement EASE."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:11.502Z",
"text": "Evaluating EASE Model Scaling\n\nI've shifted focus to User-User EASE after realizing the Item-Item EASE matrix inversion is computationally prohibitive on current hardware. An 8456x8456 matrix is manageable, requiring ~286 MB and completing inversion in ~3 seconds. I'm now exploring combining this with other approaches like SVD, popularity, and explicit preferences."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:11.502Z",
"callId": "run_shell_command__call_4671876",
"name": "run_shell_command",
"input": {
"description": "Inspect listen timestamp distribution over time windows",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'is_organic', 'played_ratio_pct'])\ntimestamps = table['timestamp'].to_numpy()\nprint('Train listens timestamps: min', timestamps.min(), 'max', timestamps.max())\ncutoff = 24786800\nday = 86400\nfor days in [7, 14, 30, 90, 180, 365]:\n cnt = np.sum(timestamps >= (cutoff - days * day))\n print(f'Past {days:3d} days: {cnt:10d} listens ({cnt/len(timestamps)*100:.1f}%)')\n\""
},
"recorded": {
"id": "run_shell_command__call_4671876",
"name": "run_shell_command",
"args": {
"description": "Inspect listen timestamp distribution over time windows",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'is_organic', 'played_ratio_pct'])\ntimestamps = table['timestamp'].to_numpy()\nprint('Train listens timestamps: min', timestamps.min(), 'max', timestamps.max())\ncutoff = 24786800\nday = 86400\nfor days in [7, 14, 30, 90, 180, 365]:\n cnt = np.sum(timestamps >= (cutoff - days * day))\n print(f'Past {days:3d} days: {cnt:10d} listens ({cnt/len(timestamps)*100:.1f}%)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4671876",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Train listens timestamps: min 0 max 24786795\nPast 7 days: 1491699 listens (3.4%)\nPast 14 days: 2920227 listens (6.7%)\nPast 30 days: 6126103 listens (14.1%)\nPast 90 days: 16769274 listens (38.6%)\nPast 180 days: 30187038 listens (69.4%)\nPast 365 days: 43484159 listens (100.0%)\nProcess Group PGID: 2034\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:12.781Z",
"resultDisplay": "Train listens timestamps: min 0 max 24786795\nPast 7 days: 1491699 listens (3.4%)\nPast 14 days: 2920227 listens (6.7%)\nPast 30 days: 6126103 listens (14.1%)\nPast 90 days: 16769274 listens (38.6%)\nPast 180 days: 30187038 listens (69.4%)\nPast 365 days: 43484159 listens (100.0%)",
"description": "Inspect listen timestamp distribution over time windows",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:12.781Z",
"content": "\nOutput: Train listens timestamps: min 0 max 24786795\nPast 7 days: 1491699 listens (3.4%)\nPast 14 days: 2920227 listens (6.7%)\nPast 30 days: 6126103 listens (14.1%)\nPast 90 days: 16769274 listens (38.6%)\nPast 180 days: 30187038 listens (69.4%)\nPast 365 days: 43484159 listens (100.0%)\nProcess Group PGID: 2034\n",
"isError": false
},
"responseMessageId": "52170cdb-90ef-40cd-b996-aad96feae6a6"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T01:28:17.554Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:17.554Z",
"text": "Analyzing Listen Counts\n\nMy current focus is on understanding listen volume trends across different timeframes. I've noted that the past 7 days and the validation window show remarkably similar listen counts, suggesting a stable recent period. The 30-day and 90-day figures provide broader context for this recent stability."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:17.554Z",
"callId": "run_shell_command__call_14474",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n\nall_gt_items = []\nfor u, items in graded.items():\n for it, g in items.items():\n all_gt_items.append((u, it, g, catalog.loc[it, 'popularity_rank'] if it in catalog.index else -1))\n\ndf_gt = pd.DataFrame(all_gt_items, columns=['uid', 'item_id', 'grade', 'pop_rank'])\nprint('GT popularity rank summary:')\nprint(df_gt['pop_rank'].describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]))\n\nprint('\\nBreakdown by grade:')\nprint(df_gt.groupby('grade')['pop_rank'].describe(percentiles=[0.25, 0.5, 0.75]))\n\"",
"description": "Analyze popularity rank of validation ground truth items"
},
"recorded": {
"id": "run_shell_command__call_14474",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet').set_index('item_id')\n\nall_gt_items = []\nfor u, items in graded.items():\n for it, g in items.items():\n all_gt_items.append((u, it, g, catalog.loc[it, 'popularity_rank'] if it in catalog.index else -1))\n\ndf_gt = pd.DataFrame(all_gt_items, columns=['uid', 'item_id', 'grade', 'pop_rank'])\nprint('GT popularity rank summary:')\nprint(df_gt['pop_rank'].describe(percentiles=[0.1, 0.25, 0.5, 0.75, 0.9]))\n\nprint('\\nBreakdown by grade:')\nprint(df_gt.groupby('grade')['pop_rank'].describe(percentiles=[0.25, 0.5, 0.75]))\n\"",
"description": "Analyze popularity rank of validation ground truth items"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_14474",
"name": "run_shell_command",
"response": {
"output": "\nOutput: GT popularity rank summary:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n10% 428.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\n90% 21680.200000\nmax 30000.000000\nName: pop_rank, dtype: float64\n\nBreakdown by grade:\n count mean std ... 50% 75% max\ngrade ... \n1 33352.0 8406.287659 8124.885100 ... 5545.0 13274.0 30000.0\n3 4417.0 8891.975323 8053.409263 ... 6371.0 14181.0 29989.0\n\n[2 rows x 8 columns]\nProcess Group PGID: 2058\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:19.093Z",
"resultDisplay": "GT popularity rank summary:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n10% 428.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\n90% 21680.200000\nmax 30000.000000\nName: pop_rank, dtype: float64\n\nBreakdown by grade:\n count mean std ... 50% 75% max\ngrade ... \n1 33352.0 8406.287659 8124.885100 ... 5545.0 13274.0 30000.0\n3 4417.0 8891.975323 8053.409263 ... 6371.0 14181.0 29989.0\n\n[2 rows x 8 columns]",
"description": "Analyze popularity rank of validation ground truth items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:19.094Z",
"content": "\nOutput: GT popularity rank summary:\ncount 37769.000000\nmean 8463.087744\nstd 8117.953262\nmin 1.000000\n10% 428.000000\n25% 1733.000000\n50% 5622.000000\n75% 13374.000000\n90% 21680.200000\nmax 30000.000000\nName: pop_rank, dtype: float64\n\nBreakdown by grade:\n count mean std ... 50% 75% max\ngrade ... \n1 33352.0 8406.287659 8124.885100 ... 5545.0 13274.0 30000.0\n3 4417.0 8891.975323 8053.409263 ... 6371.0 14181.0 29989.0\n\n[2 rows x 8 columns]\nProcess Group PGID: 2058\n",
"isError": false
},
"responseMessageId": "9c1e0b7b-0d2a-4d3d-b4b0-41a01b6a5399"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T01:28:27.658Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:27.658Z",
"text": "Analyzing Ranking Performance\n\nI've been examining the distribution of ground truth items within the ranked list. The median is at 5,622, with 75% below 13,374 and 10% above 21,680. This clearly indicates that a simple popularity model, restricted to top 100 items, will significantly underperform due to the broad discovery patterns observed."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:27.658Z",
"text": "Exploring Interaction Data\n\nI'm now considering how to construct a user-item interaction matrix and evaluate collaborative filtering. My focus is on leveraging `validation_preference_state`, `train_likes`, and `train_listens`, particularly distinguishing organic listens and applying time-weighting to account for recency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:27.658Z",
"callId": "run_shell_command__call_24364",
"name": "run_shell_command",
"input": {
"description": "Check indexing and build preference matrix prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nprint('Starting SVD prototype...')\nt0 = time.time()\n\n# 1. Load target users and catalog items\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nn_users = len(targets)\nn_items = len(catalog)\nprint(f'n_users: {n_users}, n_items: {n_items}')\n\n# 2. Load validation preference state (explicit likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_likes = val_pref[val_pref.state == 'liked']\nval_dislikes = val_pref[val_pref.state == 'disliked']\n\npref_u = [uid_to_idx[u] for u in val_likes.uid if u in uid_to_idx and val_likes.item_id.iloc[0] in item_to_idx] # wait, fix indexing\npref_rows = []\npref_cols = []\npref_vals = []\nfor u, it in zip(val_likes.uid, val_likes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(5.0)\n\nfor u, it in zip(val_dislikes.uid, val_dislikes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(-3.0)\n\nprint(f'Preference entries: {len(pref_rows)} in {time.time() - t0:.2f} s')\n\""
},
"recorded": {
"id": "run_shell_command__call_24364",
"name": "run_shell_command",
"args": {
"description": "Check indexing and build preference matrix prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nprint('Starting SVD prototype...')\nt0 = time.time()\n\n# 1. Load target users and catalog items\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nn_users = len(targets)\nn_items = len(catalog)\nprint(f'n_users: {n_users}, n_items: {n_items}')\n\n# 2. Load validation preference state (explicit likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_likes = val_pref[val_pref.state == 'liked']\nval_dislikes = val_pref[val_pref.state == 'disliked']\n\npref_u = [uid_to_idx[u] for u in val_likes.uid if u in uid_to_idx and val_likes.item_id.iloc[0] in item_to_idx] # wait, fix indexing\npref_rows = []\npref_cols = []\npref_vals = []\nfor u, it in zip(val_likes.uid, val_likes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(5.0)\n\nfor u, it in zip(val_dislikes.uid, val_dislikes.item_id):\n if u in uid_to_idx and it in item_to_idx:\n pref_rows.append(uid_to_idx[u])\n pref_cols.append(item_to_idx[it])\n pref_vals.append(-3.0)\n\nprint(f'Preference entries: {len(pref_rows)} in {time.time() - t0:.2f} s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_24364",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Starting SVD prototype...\nn_users: 8456, n_items: 30000\nPreference entries: 469124 in 3.30 s\nProcess Group PGID: 2078\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:32.442Z",
"resultDisplay": "Starting SVD prototype...\nn_users: 8456, n_items: 30000\nPreference entries: 469124 in 3.30 s",
"description": "Check indexing and build preference matrix prototype",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:32.442Z",
"content": "\nOutput: Starting SVD prototype...\nn_users: 8456, n_items: 30000\nPreference entries: 469124 in 3.30 s\nProcess Group PGID: 2078\n",
"isError": false
},
"responseMessageId": "213b3360-8cd4-4589-b14a-0031a4a6fb3f"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T01:28:37.196Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:37.196Z",
"text": "Analyzing Train Listens\n\nI'm examining the `train_listens` dataset, noting its substantial 43.4 million rows and columns like `uid`, `item_id`, `played_ratio_pct`, `is_organic`, and `timestamp`. My next step is to determine the optimal processing approach for this data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:37.196Z",
"callId": "run_shell_command__call_307893",
"name": "run_shell_command",
"input": {
"description": "Test filtering and loading train_listens into pandas",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\n# Read only columns needed\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\n# Filter to target users and catalog items and positive listens (>= 50%)\n# Or what about listens in the past 120 days?\ncutoff = 24786800\nmask = pc.and_([\n pc.is_in(table['uid'], target_uids),\n pc.is_in(table['item_id'], catalog_items),\n pc.greater_equal(table['played_ratio_pct'], 50)\n])\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s')\nprint(df.head())\n\""
},
"recorded": {
"id": "run_shell_command__call_307893",
"name": "run_shell_command",
"args": {
"description": "Test filtering and loading train_listens into pandas",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\n# Read only columns needed\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\n# Filter to target users and catalog items and positive listens (>= 50%)\n# Or what about listens in the past 120 days?\ncutoff = 24786800\nmask = pc.and_([\n pc.is_in(table['uid'], target_uids),\n pc.is_in(table['item_id'], catalog_items),\n pc.greater_equal(table['played_ratio_pct'], 50)\n])\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s')\nprint(df.head())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_307893",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 23, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py\", line 241, in wrapper\n raise TypeError(\nTypeError: and_ takes 2 positional argument(s), but 1 were given\nRead table in 0.41 s\nExit Code: 1\nProcess Group PGID: 2102\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:39.399Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 23, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py\", line 241, in wrapper\n raise TypeError(\nTypeError: and_ takes 2 positional argument(s), but 1 were given\nRead table in 0.41 s",
"description": "Test filtering and loading train_listens into pandas",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:39.399Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 23, in \n File \"/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py\", line 241, in wrapper\n raise TypeError(\nTypeError: and_ takes 2 positional argument(s), but 1 were given\nRead table in 0.41 s\nExit Code: 1\nProcess Group PGID: 2102\n",
"isError": false
},
"responseMessageId": "b2dea9ac-88c0-4b49-a3d7-750a92f6b70f"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T01:28:46.194Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:46.193Z",
"text": "Refining Boolean Expressions\n\nI'm currently optimizing how I construct complex boolean masks in PyArrow compute. Instead of chaining multiple `pc.and_` calls, I'm exploring using a more concise syntax, like the `&` operator directly with `pc.is_in` and `pc.greater_equal` for efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:46.194Z",
"callId": "run_shell_command__call_2710415",
"name": "run_shell_command",
"input": {
"description": "Filter positive target listens with pyarrow compute",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s, memory: {df.memory_usage().sum() / 1e6:.1f} MB')\nprint(df.head())\n\""
},
"recorded": {
"id": "run_shell_command__call_2710415",
"name": "run_shell_command",
"args": {
"description": "Filter positive target listens with pyarrow compute",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f} s')\n\nt1 = time.time()\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask)\nprint(f'Filtered to positive target listens in {time.time() - t1:.2f} s, count: {len(filtered)}')\n\ndf = filtered.to_pandas()\nprint(f'Converted to pandas in {time.time() - t1:.2f} s, memory: {df.memory_usage().sum() / 1e6:.1f} MB')\nprint(df.head())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2710415",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read table in 0.40 s\nFiltered to positive target listens in 1.30 s, count: 19959853\nConverted to pandas in 1.34 s, memory: 299.4 MB\n uid item_id timestamp played_ratio_pct is_organic\n0 100 8326270 39420 100 0\n1 100 286361 39625 100 0\n2 100 732449 40110 100 0\n3 100 7849270 40380 100 0\n4 100 1449307 40640 100 0\nProcess Group PGID: 2124\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:48.770Z",
"resultDisplay": "Read table in 0.40 s\nFiltered to positive target listens in 1.30 s, count: 19959853\nConverted to pandas in 1.34 s, memory: 299.4 MB\n uid item_id timestamp played_ratio_pct is_organic\n0 100 8326270 39420 100 0\n1 100 286361 39625 100 0\n2 100 732449 40110 100 0\n3 100 7849270 40380 100 0\n4 100 1449307 40640 100 0",
"description": "Filter positive target listens with pyarrow compute",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:48.771Z",
"content": "\nOutput: Read table in 0.40 s\nFiltered to positive target listens in 1.30 s, count: 19959853\nConverted to pandas in 1.34 s, memory: 299.4 MB\n uid item_id timestamp played_ratio_pct is_organic\n0 100 8326270 39420 100 0\n1 100 286361 39625 100 0\n2 100 732449 40110 100 0\n3 100 7849270 40380 100 0\n4 100 1449307 40640 100 0\nProcess Group PGID: 2124\n",
"isError": false
},
"responseMessageId": "c57a6ce1-0db8-4811-b160-909501462ddb"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T01:29:08.264Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:08.264Z",
"text": "Analyzing Listener Behaviors\n\nI'm currently delving into listener interaction data, specifically investigating which signals best predict future organic discovery. My immediate focus is on analyzing the validation window metrics like `val_listens` and `val_likes` to uncover these predictive patterns."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:08.264Z",
"text": "Testing Collaborative Filtering\n\nMy current focus is on evaluating collaborative filtering for predicting organic discovery. I'm constructing a user-item interaction matrix using a weighted approach that incorporates likes, high-percentage organic listens, and other listen data, while also considering dislikes as negative signals. This will allow me to test direct collaborative filtering principles."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:08.264Z",
"text": "Exploring Collaborative Filtering Duality\n\nI've confirmed the mathematical equivalence between user-user and item-item linear collaborative filtering. This realization streamlines our approach, allowing us to leverage the associative property for efficient computation and explore non-linear transformations on user embeddings."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:08.264Z",
"text": "Implementing Collaborative Filtering\n\nI'm now implementing and testing various collaborative filtering techniques, including cosine similarity, EASE, and KNN. My current focus is on measuring their performance using a dedicated evaluation script, as the EASE model inversion appears to be computationally feasible."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:08.264Z",
"callId": "run_shell_command__call_91046",
"name": "run_shell_command",
"input": {
"description": "Build and evaluate User-User Collaborative Filtering prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nt0 = time.time()\nprint('Loading targets, catalog, preferences...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load validation preferences (likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\n# Load train listens (positive listens, played_ratio >= 50)\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\nprint(f'Data loaded in {time.time() - t0:.2f} s. Positive listens: {len(filtered)}')\n\n# Build user-item weights\n# Group listens by (uid, item_id)\n# Recency weighting: cutoff = 24786800, half-life = 90 days = 7776000 sec\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\n# Boost organic listens\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\n# Boost completion >= 80%\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\n# Aggregate listens per (uid, item_id)\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n# Cap weight per (uid, item_id) to avoid heavy repeat skew (e.g. log1p)\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\n# Combine with explicit likes: likes get an additional boost\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0 # strong positive\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nprint(f'Combined interaction entries: {len(combined)}')\n\n# Build sparse matrix R\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\nprint(f'Sparse matrix R built: shape {R.shape}, nnz {R.nnz}')\n\n# Normalize R (L2 row normalization for cosine similarity)\nrow_norms = sparse.linalg.norm(R, axis=1)\nrow_norms[row_norms == 0] = 1.0\nR_norm = R.multiply(1.0 / row_norms[:, np.newaxis]).tocsr()\n\n# Compute User-User Cosine Similarity: S = R_norm @ R_norm.T (8456 x 8456)\nt1 = time.time()\nS = (R_norm @ R_norm.T).toarray()\nprint(f'User-user similarity computed in {time.time() - t1:.2f} s, shape {S.shape}')\n\n# Zero out diagonal\nnp.fill_diagonal(S, 0)\n# Sharpen similarities (e.g. S**2 or S**3 to emphasize top taste neighbors)\nS = np.maximum(S, 0)\nS = np.power(S, 2.0)\n\n# Predict scores: Pred = S @ R_norm or S @ R\nt2 = time.time()\nscores = S @ R.toarray()\nprint(f'Prediction scores computed in {time.time() - t2:.2f} s, shape {scores.shape}')\n\n# Mask ineligible candidates using validation_eligible_candidates.npz\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\n# Get top 100 per user\nt3 = time.time()\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_cf.parquet')\nprint(f'Sub built in {time.time() - t3:.2f} s. Ready for evaluation.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_cf.parquet"
},
"recorded": {
"id": "run_shell_command__call_91046",
"name": "run_shell_command",
"args": {
"description": "Build and evaluate User-User Collaborative Filtering prototype",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nt0 = time.time()\nprint('Loading targets, catalog, preferences...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load validation preferences (likes)\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\n# Load train listens (positive listens, played_ratio >= 50)\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\nprint(f'Data loaded in {time.time() - t0:.2f} s. Positive listens: {len(filtered)}')\n\n# Build user-item weights\n# Group listens by (uid, item_id)\n# Recency weighting: cutoff = 24786800, half-life = 90 days = 7776000 sec\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\n# Boost organic listens\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\n# Boost completion >= 80%\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\n# Aggregate listens per (uid, item_id)\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n# Cap weight per (uid, item_id) to avoid heavy repeat skew (e.g. log1p)\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\n# Combine with explicit likes: likes get an additional boost\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0 # strong positive\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nprint(f'Combined interaction entries: {len(combined)}')\n\n# Build sparse matrix R\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\nprint(f'Sparse matrix R built: shape {R.shape}, nnz {R.nnz}')\n\n# Normalize R (L2 row normalization for cosine similarity)\nrow_norms = sparse.linalg.norm(R, axis=1)\nrow_norms[row_norms == 0] = 1.0\nR_norm = R.multiply(1.0 / row_norms[:, np.newaxis]).tocsr()\n\n# Compute User-User Cosine Similarity: S = R_norm @ R_norm.T (8456 x 8456)\nt1 = time.time()\nS = (R_norm @ R_norm.T).toarray()\nprint(f'User-user similarity computed in {time.time() - t1:.2f} s, shape {S.shape}')\n\n# Zero out diagonal\nnp.fill_diagonal(S, 0)\n# Sharpen similarities (e.g. S**2 or S**3 to emphasize top taste neighbors)\nS = np.maximum(S, 0)\nS = np.power(S, 2.0)\n\n# Predict scores: Pred = S @ R_norm or S @ R\nt2 = time.time()\nscores = S @ R.toarray()\nprint(f'Prediction scores computed in {time.time() - t2:.2f} s, shape {scores.shape}')\n\n# Mask ineligible candidates using validation_eligible_candidates.npz\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\n# Get top 100 per user\nt3 = time.time()\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_cf.parquet')\nprint(f'Sub built in {time.time() - t3:.2f} s. Ready for evaluation.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_cf.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_91046",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading targets, catalog, preferences...\nData loaded in 1.84 s. Positive listens: 19959853\nCombined interaction entries: 5558048\nSparse matrix R built: shape (8456, 30000), nnz 5558048\nUser-user similarity computed in 5.89 s, shape (8456, 8456)\nPrediction scores computed in 19.82 s, shape (8456, 30000)\nSub built in 1.89 s. Ready for evaluation.\n{\n \"model\": \"/app/solution/val_test_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014732,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2146\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:29:45.597Z",
"resultDisplay": "Loading targets, catalog, preferences...\nData loaded in 1.84 s. Positive listens: 19959853\nCombined interaction entries: 5558048\nSparse matrix R built: shape (8456, 30000), nnz 5558048\nUser-user similarity computed in 5.89 s, shape (8456, 8456)\nPrediction scores computed in 19.82 s, shape (8456, 30000)\nSub built in 1.89 s. Ready for evaluation.\n{\n \"model\": \"/app/solution/val_test_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014732,\n \"quality_users\": 4263\n }\n}",
"description": "Build and evaluate User-User Collaborative Filtering prototype",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:45.598Z",
"content": "\nOutput: Loading targets, catalog, preferences...\nData loaded in 1.84 s. Positive listens: 19959853\nCombined interaction entries: 5558048\nSparse matrix R built: shape (8456, 30000), nnz 5558048\nUser-user similarity computed in 5.89 s, shape (8456, 8456)\nPrediction scores computed in 19.82 s, shape (8456, 30000)\nSub built in 1.89 s. Ready for evaluation.\n{\n \"model\": \"/app/solution/val_test_cf.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.014732,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2146\n",
"isError": false
},
"responseMessageId": "842492af-0479-4a0c-887e-b1dd630304fe"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T01:29:52.813Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:52.813Z",
"text": "Achieving Early Wins\n\nMy initial exploration into User-User CF has yielded promising results. The prototype immediately boosted popularity baseline by a remarkable 37.5%, demonstrating significant potential with just a 30-second pipeline execution."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:52.813Z",
"text": "Refining Predictive Power\n\nI'm now focusing on the underlying mechanics of the User-User CF. It seems the current prediction $\\hat{R} = S R$ overemphasizes globally popular items. I'm exploring normalization strategies, like IDF or BM25 weighting, and considering a shift to Item-Item CF, to enhance predictive accuracy and avoid generic recommendations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:52.813Z",
"callId": "run_shell_command__call_4232",
"name": "run_shell_command",
"input": {
"description": "Benchmark sparse matrix multiplication for item-item similarity",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Suppose R is 8456 x 30000 with 5.5M non-zeros\n# Let's test R.T @ R or chunked item-item\nR = sp.random(8456, 30000, density=5.5e6/(8456*30000), format='csr', dtype=np.float32)\nR_csc = R.tocsc()\n\nt0 = time.time()\n# If we chunk items in blocks of 2000\nscores_sample = R_csc[:, :2000].T @ R_csc\nprint(f'2000 items x 30000 items multiplication took {time.time()-t0:.2f} s')\n\""
},
"recorded": {
"id": "run_shell_command__call_4232",
"name": "run_shell_command",
"args": {
"description": "Benchmark sparse matrix multiplication for item-item similarity",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Suppose R is 8456 x 30000 with 5.5M non-zeros\n# Let's test R.T @ R or chunked item-item\nR = sp.random(8456, 30000, density=5.5e6/(8456*30000), format='csr', dtype=np.float32)\nR_csc = R.tocsc()\n\nt0 = time.time()\n# If we chunk items in blocks of 2000\nscores_sample = R_csc[:, :2000].T @ R_csc\nprint(f'2000 items x 30000 items multiplication took {time.time()-t0:.2f} s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4232",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 2000 items x 30000 items multiplication took 1.68 s\nProcess Group PGID: 2193\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:05.795Z",
"resultDisplay": "2000 items x 30000 items multiplication took 1.68 s",
"description": "Benchmark sparse matrix multiplication for item-item similarity",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:07.755Z",
"content": "\nOutput: 2000 items x 30000 items multiplication took 1.68 s\nProcess Group PGID: 2193\n",
"isError": false
},
"responseMessageId": "0d93e3c6-9713-4019-98c6-7e75bed35b91"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T01:30:17.450Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:17.450Z",
"text": "Optimizing Matrix Multiplication\n\nMy focus is now on accelerating matrix multiplication. I've observed impressive speedups, reducing operations from what initially seemed prohibitive to manageable computations. I'm exploring SVD decomposition as a more efficient alternative to direct $R^T R$ calculation, specifically investigating Truncated SVD for its potential in recommender systems."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:17.450Z",
"callId": "run_shell_command__call_110438",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n# Try BM25 or TF-IDF weighting on R before SVD!\n# In recommender systems, BM25 / TF-IDF transformation of user-item matrix yields massive gains!\n# Let's test BM25 on R:\n# Item frequency: number of users who interacted with item\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\n# Apply TF-IDF or BM25:\nR_tfidf = R.multiply(idf)\n\nprint('Running TruncatedSVD(n_components=128)...')\nt1 = time.time()\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU_sigma = svd.fit_transform(R_tfidf) # shape: (8456, 128)\nV = svd.components_ # shape: (128, 30000)\nprint(f'SVD fitted in {time.time() - t1:.2f} s, explained variance: {svd.explained_variance_ratio_.sum():.4f}')\n\n# Predict scores\nscores = U_sigma @ V\nprint('Scores shape:', scores.shape)\n\n# Mask ineligible\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_svd.parquet')\nprint('SVD sub ready for eval.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_svd.parquet",
"description": "Test BM25 + TruncatedSVD on validation set"
},
"recorded": {
"id": "run_shell_command__call_110438",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n# Try BM25 or TF-IDF weighting on R before SVD!\n# In recommender systems, BM25 / TF-IDF transformation of user-item matrix yields massive gains!\n# Let's test BM25 on R:\n# Item frequency: number of users who interacted with item\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\n# Apply TF-IDF or BM25:\nR_tfidf = R.multiply(idf)\n\nprint('Running TruncatedSVD(n_components=128)...')\nt1 = time.time()\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU_sigma = svd.fit_transform(R_tfidf) # shape: (8456, 128)\nV = svd.components_ # shape: (128, 30000)\nprint(f'SVD fitted in {time.time() - t1:.2f} s, explained variance: {svd.explained_variance_ratio_.sum():.4f}')\n\n# Predict scores\nscores = U_sigma @ V\nprint('Scores shape:', scores.shape)\n\n# Mask ineligible\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\nscores[~unpacked.astype(bool)] = -np.inf\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nrows_sub = []\nfor r in range(n_users):\n user_scores = scores[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n sorted_top100 = top100_idx[np.argsort(-user_scores[top100_idx])]\n uid = uids[r]\n for rank, idx in enumerate(sorted_top100, start=1):\n rows_sub.append((uid, item_ids[idx], rank))\n\nsub = pd.DataFrame(rows_sub, columns=['uid', 'item_id', 'rank'])\nsub.to_parquet('/app/solution/val_test_svd.parquet')\nprint('SVD sub ready for eval.')\n\" && python3 /task/evaluate_public.py /app/solution/val_test_svd.parquet",
"description": "Test BM25 + TruncatedSVD on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_110438",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Running TruncatedSVD(n_components=128)...\nSVD fitted in 5.81 s, explained variance: 0.2978\nScores shape: (8456, 30000)\nSVD sub ready for eval.\n{\n \"model\": \"/app/solution/val_test_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017015,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2210\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:36.288Z",
"resultDisplay": "Running TruncatedSVD(n_components=128)...\nSVD fitted in 5.81 s, explained variance: 0.2978\nScores shape: (8456, 30000)\nSVD sub ready for eval.\n{\n \"model\": \"/app/solution/val_test_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017015,\n \"quality_users\": 4263\n }\n}",
"description": "Test BM25 + TruncatedSVD on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:36.289Z",
"content": "\nOutput: Running TruncatedSVD(n_components=128)...\nSVD fitted in 5.81 s, explained variance: 0.2978\nScores shape: (8456, 30000)\nSVD sub ready for eval.\n{\n \"model\": \"/app/solution/val_test_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.017015,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 2210\n",
"isError": false
},
"responseMessageId": "ff091a56-04d2-43fe-b0f6-db3a863e8ac6"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T01:30:48.559Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:48.559Z",
"text": "Improving Recommendation Scores\n\nI've just achieved a score of 0.017015 with BM25 combined with TruncatedSVD(128), a noticeable improvement over previous methods like popularity and User-User CF, and it processed remarkably fast at just 5.8 seconds. I'm now considering further avenues for enhancement."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:48.559Z",
"text": "Experimenting with SVD Components\n\nI'm actively investigating the impact of varying SVD components, testing values like 64, 128, 192, 256, and 320 on the validation set to optimize performance before refitting on the full training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:48.559Z",
"callId": "run_shell_command__call_1239779",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256, 320]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_tfidf)\n V = svd.components_\n pred = U_s @ V\n ndcg = eval_scores(pred)\n print(f'k={k:3d}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t:.1f}s)')\n\"",
"description": "Evaluate TruncatedSVD with varying n_components"
},
"recorded": {
"id": "run_shell_command__call_1239779",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256, 320]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_tfidf)\n V = svd.components_\n pred = U_s @ V\n ndcg = eval_scores(pred)\n print(f'k={k:3d}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t:.1f}s)')\n\"",
"description": "Evaluate TruncatedSVD with varying n_components"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1239779",
"name": "run_shell_command",
"response": {
"output": "\nOutput: k= 64: NDCG@10 = 0.014744 (time: 6.1s)\nk=128: NDCG@10 = 0.017015 (time: 8.1s)\nk=192: NDCG@10 = 0.017975 (time: 12.5s)\nk=256: NDCG@10 = 0.017798 (time: 15.6s)\nk=320: NDCG@10 = 0.018002 (time: 20.2s)\nProcess Group PGID: 2257\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:31:58.969Z",
"resultDisplay": "k= 64: NDCG@10 = 0.014744 (time: 6.1s)\nk=128: NDCG@10 = 0.017015 (time: 8.1s)\nk=192: NDCG@10 = 0.017975 (time: 12.5s)\nk=256: NDCG@10 = 0.017798 (time: 15.6s)\nk=320: NDCG@10 = 0.018002 (time: 20.2s)",
"description": "Evaluate TruncatedSVD with varying n_components",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:31:58.970Z",
"content": "\nOutput: k= 64: NDCG@10 = 0.014744 (time: 6.1s)\nk=128: NDCG@10 = 0.017015 (time: 8.1s)\nk=192: NDCG@10 = 0.017975 (time: 12.5s)\nk=256: NDCG@10 = 0.017798 (time: 15.6s)\nk=320: NDCG@10 = 0.018002 (time: 20.2s)\nProcess Group PGID: 2257\n",
"isError": false
},
"responseMessageId": "682d5991-2263-4923-9524-2004bc118d7f"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T01:32:10.158Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:10.158Z",
"text": "Exploring Dislike Influence\n\nI'm now investigating how incorporating `train_dislikes` might refine our results. Specifically, I'm exploring strategies like assigning negative weights to disliked items or modifying their neighborhood influence to potentially improve performance beyond the current k=192 and k=320 outcomes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:10.158Z",
"text": "Testing EASE Variants\n\nI'm now exploring the impact of different `half_life` durations on our models, alongside investigating the effectiveness of user row normalization with TF-IDF/LSI. Additionally, I plan to evaluate User-User EASE with various lambda values and consider incorporating non-target users into item embeddings."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:32:10.158Z",
"callId": "run_shell_command__call_1649838",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"",
"description": "Test User-User EASE with various regularization parameters"
},
"recorded": {
"id": "run_shell_command__call_1649838",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"",
"description": "Test User-User EASE with various regularization parameters"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1649838",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 101: 2288 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2286\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:32:41.435Z",
"resultDisplay": "/usr/bin/bash: line 101: 2288 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"",
"description": "Test User-User EASE with various regularization parameters",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:32:41.439Z",
"content": "\nOutput: /usr/bin/bash: line 101: 2288 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\ncutoff = 24786800\nhalf_life = 90 * 86400\nfiltered['weight'] = np.exp(-(cutoff - filtered['timestamp']) / half_life)\nfiltered['weight'] *= np.where(filtered['is_organic'] == 1, 2.0, 1.0)\nfiltered['weight'] *= np.where(filtered['played_ratio_pct'] >= 80, 1.5, 1.0)\n\nagg_listens = filtered.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\nagg_listens['weight'] = np.log1p(agg_listens['weight'])\n\nagg_likes = likes[['uid', 'item_id']].copy()\nagg_likes['weight'] = 3.0\n\ncombined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['weight'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\nR_tfidf = R.multiply(idf).tocsr()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef eval_scores(raw_scores):\n s = raw_scores.copy()\n s[~unpacked.astype(bool)] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = s[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('Computing G = R_tfidf @ R_tfidf.T...')\nt0 = time.time()\n# L2-normalize rows of R_tfidf\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R_tfidf, norm='l2', axis=1)\nG = (R_norm @ R_norm.T).toarray()\nprint(f'Gram matrix G computed in {time.time() - t0:.2f} s')\n\nfor reg in [10.0, 50.0, 100.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n pred = B @ R_tfidf.toarray()\n ndcg = eval_scores(pred)\n print(f'EASE reg={reg:6.1f}: NDCG@10 = {ndcg:.6f} (time: {time.time()-t1:.1f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2286\n",
"isError": false
},
"responseMessageId": "af97e141-61d0-4c33-93cc-bcda228c9157"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T01:32:45.243Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:45.243Z",
"text": "Investigating Resource Issues\n\nI'm currently trying to understand why a process was terminated. It seems the memory demands of converting TF-IDF matrices to dense arrays and subsequent matrix multiplications are leading to out-of-memory errors, specifically exit code 137. I'm exploring optimizations for these steps."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:32:45.243Z",
"callId": "run_shell_command__call_34304",
"name": "run_shell_command",
"input": {
"description": "Check psutil availability",
"command": "python3 -c \"\nimport gc, os, psutil\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\np = psutil.Process()\nprint('Initial RSS:', p.memory_info().rss / 1e6, 'MB')\n\""
},
"recorded": {
"id": "run_shell_command__call_34304",
"name": "run_shell_command",
"args": {
"description": "Check psutil availability",
"command": "python3 -c \"\nimport gc, os, psutil\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\np = psutil.Process()\nprint('Initial RSS:', p.memory_info().rss / 1e6, 'MB')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_34304",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'\nExit Code: 1\nProcess Group PGID: 2315\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:32:45.275Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'",
"description": "Check psutil availability",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:32:45.277Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'psutil'\nExit Code: 1\nProcess Group PGID: 2315\n",
"isError": false
},
"responseMessageId": "47ddc5c6-fdca-46d2-b246-00c8fd29e549"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T01:33:01.096Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:01.096Z",
"text": "Refining Memory Metrics\n\nI'm currently exploring methods for memory measurement, considering alternatives like `/proc/self/status` and `/proc/meminfo` since `psutil` isn't available. My immediate focus is on extracting `VmRSS` for more precise data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:01.096Z",
"text": "Evaluating Event Weights\n\nI am now investigating how to best weigh various user interaction events to predict future organic discovery. My current focus is on analyzing the correlation of explicit likes, dislikes, different levels of organic listens, non-organic listens, and repeat listens against discovery. I'm also exploring the impact of time decay on these interactions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:01.096Z",
"text": "Tuning Ranking Functions\n\nI'm now deeply engaged in optimizing the IDF component of our ranking function, specifically experimenting with different exponent values ($\\gamma$). The aim is to fine-tune the penalty for extremely popular items. I'm also evaluating user vector normalization strategies before and after SVD to mitigate popularity bias in retrieval."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:33:01.096Z",
"callId": "run_shell_command__call_251813",
"name": "run_shell_command",
"input": {
"description": "Test recency half-life on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800\n if half_life_days is not None:\n hl = half_life_days * 86400\n w = np.exp(-(cutoff - filtered['timestamp'].values) / hl)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32) # (k, 30000)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_251813",
"name": "run_shell_command",
"args": {
"description": "Test recency half-life on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800\n if half_life_days is not None:\n hl = half_life_days * 86400\n w = np.exp(-(cutoff - filtered['timestamp'].values) / hl)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32) # (k, 30000)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_251813",
"name": "run_shell_command",
"response": {
"output": "\nOutput: :52: RuntimeWarning: overflow encountered in exp\nTraceback (most recent call last):\n File \"\", line 104, in \n File \"\", line 82, in run_experiment\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 226, in fit_transform\n X = validate_data(self, X, accept_sparse=[\"csr\", \"csc\"], ensure_min_features=2)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 2954, in validate_data\n out = check_array(X, input_name=\"X\", **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input X contains infinity or a value too large for dtype('float64').\nLoading base data...\nTesting half_life_days...\nExit Code: 1\nProcess Group PGID: 2318\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:33:08.910Z",
"resultDisplay": ":52: RuntimeWarning: overflow encountered in exp\nTraceback (most recent call last):\n File \"\", line 104, in \n File \"\", line 82, in run_experiment\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 226, in fit_transform\n X = validate_data(self, X, accept_sparse=[\"csr\", \"csc\"], ensure_min_features=2)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 2954, in validate_data\n out = check_array(X, input_name=\"X\", **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input X contains infinity or a value too large for dtype('float64').\nLoading base data...\nTesting half_life_days...",
"description": "Test recency half-life on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:33:08.911Z",
"content": "\nOutput: :52: RuntimeWarning: overflow encountered in exp\nTraceback (most recent call last):\n File \"\", line 104, in \n File \"\", line 82, in run_experiment\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n return fit_method(estimator, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 226, in fit_transform\n X = validate_data(self, X, accept_sparse=[\"csr\", \"csc\"], ensure_min_features=2)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 2954, in validate_data\n out = check_array(X, input_name=\"X\", **check_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input X contains infinity or a value too large for dtype('float64').\nLoading base data...\nTesting half_life_days...\nExit Code: 1\nProcess Group PGID: 2318\n",
"isError": false
},
"responseMessageId": "9703eb0c-0188-484d-a935-6c59d224aea9"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T01:33:18.449Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:18.449Z",
"text": "Investigating Overflow Error\n\nI'm tracking down an `overflow encountered in exp` error. It seems to stem from the calculation `np.exp(-(cutoff - timestamp) / hl)`. The `(cutoff - timestamp)` difference is proving quite large, potentially leading to an excessively large negative exponent when divided by `hl`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:33:18.449Z",
"callId": "run_shell_command__call_3989675",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n if half_life_days is not None:\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Test half life with correct float casting"
},
"recorded": {
"id": "run_shell_command__call_3989675",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\nprint('Loading base data...')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=90, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n if half_life_days is not None:\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n else:\n w = np.ones(len(filtered), dtype=np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing half_life_days...')\nfor hl in [30, 60, 90, 180, None]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Test half life with correct float casting"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3989675",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading base data...\nTesting half_life_days...\nhalf_life=30: NDCG = 0.030037 (14.7s)\nhalf_life=60: NDCG = 0.026516 (14.6s)\nhalf_life=90: NDCG = 0.025394 (14.6s)\nhalf_life=180: NDCG = 0.022800 (14.6s)\nhalf_life=None: NDCG = 0.020161 (14.8s)\nProcess Group PGID: 2347\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:34:35.805Z",
"resultDisplay": "Loading base data...\nTesting half_life_days...\nhalf_life=30: NDCG = 0.030037 (14.7s)\nhalf_life=60: NDCG = 0.026516 (14.6s)\nhalf_life=90: NDCG = 0.025394 (14.6s)\nhalf_life=180: NDCG = 0.022800 (14.6s)\nhalf_life=None: NDCG = 0.020161 (14.8s)",
"description": "Test half life with correct float casting",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:34:35.806Z",
"content": "\nOutput: Loading base data...\nTesting half_life_days...\nhalf_life=30: NDCG = 0.030037 (14.7s)\nhalf_life=60: NDCG = 0.026516 (14.6s)\nhalf_life=90: NDCG = 0.025394 (14.6s)\nhalf_life=180: NDCG = 0.022800 (14.6s)\nhalf_life=None: NDCG = 0.020161 (14.8s)\nProcess Group PGID: 2347\n",
"isError": false
},
"responseMessageId": "1b3fecbe-6adc-4900-bedc-66b862710f18"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T01:34:45.579Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:34:45.579Z",
"text": "Discovering Temporal Relevance\n\nI'm seeing a significant improvement in NDCG with a half-life of 30 days, reaching 0.030037. This indicates music taste exhibits strong temporal locality, with recent listening habits proving far more predictive than older ones or no temporal weighting at all."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:34:45.579Z",
"callId": "run_shell_command__call_34482",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=30, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [10, 14, 20, 25, 30, 40]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl:2d}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Fine-tune recency half-life around 10-40 days"
},
"recorded": {
"id": "run_shell_command__call_34482",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=30, organic_boost=2.0, strong_boost=1.5, idf_gamma=1.0, k=192, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = 3.0\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [10, 14, 20, 25, 30, 40]:\n t = time.time()\n sc = run_experiment(half_life_days=hl, idf_gamma=1.0, k=192)\n print(f'half_life={hl:2d}: NDCG = {sc:.6f} ({time.time()-t:.1f}s)')\n\"",
"description": "Fine-tune recency half-life around 10-40 days"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_34482",
"name": "run_shell_command",
"response": {
"output": "\nOutput: half_life=10: NDCG = 0.032219 (15.2s)\nhalf_life=14: NDCG = 0.032966 (15.2s)\nhalf_life=20: NDCG = 0.031735 (15.3s)\nhalf_life=25: NDCG = 0.030114 (15.2s)\nhalf_life=30: NDCG = 0.030037 (15.3s)\nhalf_life=40: NDCG = 0.029155 (15.2s)\nProcess Group PGID: 2376\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:36:21.119Z",
"resultDisplay": "half_life=10: NDCG = 0.032219 (15.2s)\nhalf_life=14: NDCG = 0.032966 (15.2s)\nhalf_life=20: NDCG = 0.031735 (15.3s)\nhalf_life=25: NDCG = 0.030114 (15.2s)\nhalf_life=30: NDCG = 0.030037 (15.3s)\nhalf_life=40: NDCG = 0.029155 (15.2s)",
"description": "Fine-tune recency half-life around 10-40 days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:36:21.120Z",
"content": "\nOutput: half_life=10: NDCG = 0.032219 (15.2s)\nhalf_life=14: NDCG = 0.032966 (15.2s)\nhalf_life=20: NDCG = 0.031735 (15.3s)\nhalf_life=25: NDCG = 0.030114 (15.2s)\nhalf_life=30: NDCG = 0.030037 (15.3s)\nhalf_life=40: NDCG = 0.029155 (15.2s)\nProcess Group PGID: 2376\n",
"isError": false
},
"responseMessageId": "ea82c246-23ff-424c-b0f2-fb31d07e5ff4"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T01:36:35.098Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:35.098Z",
"text": "Improving NDCG Scores\n\nI'm thrilled to report a significant leap in NDCG, now sitting at 0.032966, a threefold increase from our baseline! My next steps involve systematically exploring different half-life values (7, 10, 12, 14, 16 days) and fine-tuning the `organic_boost` parameter, considering values like 3.0 and 4.0."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:36:35.098Z",
"callId": "run_shell_command__call_139188",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_boost=2.0, strong_boost=1.5, like_weight=3.0, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = like_weight\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing like_weight with hl=14...')\nfor lw in [1.0, 3.0, 5.0, 8.0, 12.0]:\n sc = run_experiment(half_life_days=14, like_weight=lw, k=192)\n print(f'like_weight={lw:4.1f}: NDCG = {sc:.6f}')\n\nprint('\\nTesting organic_boost with hl=14...')\nfor ob in [1.0, 2.0, 3.0, 4.0]:\n sc = run_experiment(half_life_days=14, organic_boost=ob, k=192)\n print(f'organic_boost={ob:4.1f}: NDCG = {sc:.6f}')\n\"",
"description": "Test like_weight and organic_boost effects"
},
"recorded": {
"id": "run_shell_command__call_139188",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = val_pref[val_pref.state == 'liked']\ndislikes = val_pref[val_pref.state == 'disliked']\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\nts_float = filtered['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_boost=2.0, strong_boost=1.5, like_weight=3.0, idf_gamma=1.0, k=256, alpha_norm=0.0):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n w = np.exp(-(cutoff - ts_float) / hl).astype(np.float32)\n \n w *= np.where(filtered['is_organic'].values == 1, organic_boost, 1.0).astype(np.float32)\n w *= np.where(filtered['played_ratio_pct'].values >= 80, strong_boost, 1.0).astype(np.float32)\n \n df_w = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w})\n agg_listens = df_w.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n agg_listens['w'] = np.log1p(agg_listens['w'])\n \n agg_likes = likes[['uid', 'item_id']].copy()\n agg_likes['w'] = like_weight\n \n combined = pd.concat([agg_listens, agg_likes]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n if idf_gamma > 0:\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n else:\n R_mat = R\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n if alpha_norm > 0:\n item_norms = np.linalg.norm(V, axis=0) + 1e-8\n V = V / (item_norms ** alpha_norm)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nprint('Testing like_weight with hl=14...')\nfor lw in [1.0, 3.0, 5.0, 8.0, 12.0]:\n sc = run_experiment(half_life_days=14, like_weight=lw, k=192)\n print(f'like_weight={lw:4.1f}: NDCG = {sc:.6f}')\n\nprint('\\nTesting organic_boost with hl=14...')\nfor ob in [1.0, 2.0, 3.0, 4.0]:\n sc = run_experiment(half_life_days=14, organic_boost=ob, k=192)\n print(f'organic_boost={ob:4.1f}: NDCG = {sc:.6f}')\n\"",
"description": "Test like_weight and organic_boost effects"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_139188",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Testing like_weight with hl=14...\nlike_weight= 1.0: NDCG = 0.034554\nlike_weight= 3.0: NDCG = 0.032966\nlike_weight= 5.0: NDCG = 0.028768\nlike_weight= 8.0: NDCG = 0.024070\nlike_weight=12.0: NDCG = 0.020298\n\nTesting organic_boost with hl=14...\norganic_boost= 1.0: NDCG = 0.030516\norganic_boost= 2.0: NDCG = 0.032966\norganic_boost= 3.0: NDCG = 0.033981\norganic_boost= 4.0: NDCG = 0.034452\nProcess Group PGID: 2405\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:38:56.617Z",
"resultDisplay": "Testing like_weight with hl=14...\nlike_weight= 1.0: NDCG = 0.034554\nlike_weight= 3.0: NDCG = 0.032966\nlike_weight= 5.0: NDCG = 0.028768\nlike_weight= 8.0: NDCG = 0.024070\nlike_weight=12.0: NDCG = 0.020298\n\nTesting organic_boost with hl=14...\norganic_boost= 1.0: NDCG = 0.030516\norganic_boost= 2.0: NDCG = 0.032966\norganic_boost= 3.0: NDCG = 0.033981\norganic_boost= 4.0: NDCG = 0.034452",
"description": "Test like_weight and organic_boost effects",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:38:56.618Z",
"content": "\nOutput: Testing like_weight with hl=14...\nlike_weight= 1.0: NDCG = 0.034554\nlike_weight= 3.0: NDCG = 0.032966\nlike_weight= 5.0: NDCG = 0.028768\nlike_weight= 8.0: NDCG = 0.024070\nlike_weight=12.0: NDCG = 0.020298\n\nTesting organic_boost with hl=14...\norganic_boost= 1.0: NDCG = 0.030516\norganic_boost= 2.0: NDCG = 0.032966\norganic_boost= 3.0: NDCG = 0.033981\norganic_boost= 4.0: NDCG = 0.034452\nProcess Group PGID: 2405\n",
"isError": false
},
"responseMessageId": "0f299f12-d008-4607-95ae-79570305fb8e"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T01:39:08.832Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:39:08.832Z",
"text": "Exploring Like Weight\n\nMy current focus is on understanding the impact of `like_weight`. A value of 1.0 yielded a significant jump in NDCG to 0.034554. I'm investigating this because `validation_preference_state` likes, lacking timestamps, are treated as all-time, contrasting with the recency of fresh listens. Overly high weights appear to overshadow recent interactions."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:39:08.832Z",
"callId": "run_shell_command__call_8868",
"name": "run_shell_command",
"input": {
"description": "Inspect timestamps and organic flag in train_likes",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamp summary:')\nprint(train_likes['timestamp'].describe())\nprint('Train likes is_organic value counts:')\nprint(train_likes['is_organic'].value_counts())\n\""
},
"recorded": {
"id": "run_shell_command__call_8868",
"name": "run_shell_command",
"args": {
"description": "Inspect timestamps and organic flag in train_likes",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes timestamp summary:')\nprint(train_likes['timestamp'].describe())\nprint('Train likes is_organic value counts:')\nprint(train_likes['is_organic'].value_counts())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_8868",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Train likes timestamp summary:\ncount 8.148310e+05\nmean 1.360703e+07\nstd 7.373538e+06\nmin 8.000000e+01\n25% 7.173508e+06\n50% 1.433050e+07\n75% 2.034649e+07\nmax 2.478679e+07\nName: timestamp, dtype: float64\nTrain likes is_organic value counts:\nis_organic\n1 461839\n0 352992\nName: count, dtype: int64\nProcess Group PGID: 2434\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:39:09.616Z",
"resultDisplay": "Train likes timestamp summary:\ncount 8.148310e+05\nmean 1.360703e+07\nstd 7.373538e+06\nmin 8.000000e+01\n25% 7.173508e+06\n50% 1.433050e+07\n75% 2.034649e+07\nmax 2.478679e+07\nName: timestamp, dtype: float64\nTrain likes is_organic value counts:\nis_organic\n1 461839\n0 352992\nName: count, dtype: int64",
"description": "Inspect timestamps and organic flag in train_likes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:39:09.617Z",
"content": "\nOutput: Train likes timestamp summary:\ncount 8.148310e+05\nmean 1.360703e+07\nstd 7.373538e+06\nmin 8.000000e+01\n25% 7.173508e+06\n50% 1.433050e+07\n75% 2.034649e+07\nmax 2.478679e+07\nName: timestamp, dtype: float64\nTrain likes is_organic value counts:\nis_organic\n1 461839\n0 352992\nName: count, dtype: int64\nProcess Group PGID: 2434\n",
"isError": false
},
"responseMessageId": "32bb84be-51ec-4dc4-a4cb-a6760ad0c9e3"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T01:39:22.438Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:39:22.438Z",
"text": "Analyzing Interaction Data\n\nMy recent focus has been on the temporal aspects of user interactions. I've confirmed that both 'likes' and 'unlikes' are timestamped, and I'm now investigating the implications of 'unlikes' effectively reversing a 'like'. The next step involves a similar temporal analysis for 'dislikes' and 'undislikes'."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:39:22.438Z",
"callId": "run_shell_command__call_148347",
"name": "run_shell_command",
"input": {
"description": "Test timestamped decayed likes and organic multipliers",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load train likes with timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_listen_mult=3.0, strong_listen_mult=1.5, like_base_mult=4.0, organic_like_mult=2.0, idf_gamma=1.0, k=192):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n \n # Listens weight\n w_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens *= np.where(filtered['is_organic'].values == 1, organic_listen_mult, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, strong_listen_mult, 1.0).astype(np.float32)\n \n # Likes weight with timestamp decay\n w_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * like_base_mult\n w_likes *= np.where(train_likes['is_organic'].values == 1, organic_like_mult, 1.0).astype(np.float32)\n \n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n \n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [7, 10, 14, 21]:\n for org_l in [2.0, 4.0, 6.0]:\n sc = run_experiment(half_life_days=hl, organic_listen_mult=org_l, k=192)\n print(f'hl={hl:2d}, org_l={org_l:3.1f}: NDCG = {sc:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_148347",
"name": "run_shell_command",
"args": {
"description": "Test timestamped decayed likes and organic multipliers",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\n# Load train likes with timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ndef run_experiment(half_life_days=14, organic_listen_mult=3.0, strong_listen_mult=1.5, like_base_mult=4.0, organic_like_mult=2.0, idf_gamma=1.0, k=192):\n cutoff = 24786800.0\n hl = half_life_days * 86400.0\n \n # Listens weight\n w_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens *= np.where(filtered['is_organic'].values == 1, organic_listen_mult, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, strong_listen_mult, 1.0).astype(np.float32)\n \n # Likes weight with timestamp decay\n w_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * like_base_mult\n w_likes *= np.where(train_likes['is_organic'].values == 1, organic_like_mult, 1.0).astype(np.float32)\n \n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n \n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** idf_gamma\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n pred = U_s @ V\n pred[~unpacked] = -np.inf\n \n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n \n score = evaluate_public.score(ranked, graded)['quality_ndcg@10']\n return score\n\nfor hl in [7, 10, 14, 21]:\n for org_l in [2.0, 4.0, 6.0]:\n sc = run_experiment(half_life_days=hl, organic_listen_mult=org_l, k=192)\n print(f'hl={hl:2d}, org_l={org_l:3.1f}: NDCG = {sc:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_148347",
"name": "run_shell_command",
"response": {
"output": "\nOutput: hl= 7, org_l=2.0: NDCG = 0.035128\nhl= 7, org_l=4.0: NDCG = 0.035530\nhl= 7, org_l=6.0: NDCG = 0.035835\nhl=10, org_l=2.0: NDCG = 0.036816\nhl=10, org_l=4.0: NDCG = 0.037473\nhl=10, org_l=6.0: NDCG = 0.037265\nhl=14, org_l=2.0: NDCG = 0.037327\nhl=14, org_l=4.0: NDCG = 0.037709\nhl=14, org_l=6.0: NDCG = 0.037453\nhl=21, org_l=2.0: NDCG = 0.036951\nhl=21, org_l=4.0: NDCG = 0.037333\nhl=21, org_l=6.0: NDCG = 0.036983\nProcess Group PGID: 2451\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:42:16.056Z",
"resultDisplay": "hl= 7, org_l=2.0: NDCG = 0.035128\nhl= 7, org_l=4.0: NDCG = 0.035530\nhl= 7, org_l=6.0: NDCG = 0.035835\nhl=10, org_l=2.0: NDCG = 0.036816\nhl=10, org_l=4.0: NDCG = 0.037473\nhl=10, org_l=6.0: NDCG = 0.037265\nhl=14, org_l=2.0: NDCG = 0.037327\nhl=14, org_l=4.0: NDCG = 0.037709\nhl=14, org_l=6.0: NDCG = 0.037453\nhl=21, org_l=2.0: NDCG = 0.036951\nhl=21, org_l=4.0: NDCG = 0.037333\nhl=21, org_l=6.0: NDCG = 0.036983",
"description": "Test timestamped decayed likes and organic multipliers",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:42:16.057Z",
"content": "\nOutput: hl= 7, org_l=2.0: NDCG = 0.035128\nhl= 7, org_l=4.0: NDCG = 0.035530\nhl= 7, org_l=6.0: NDCG = 0.035835\nhl=10, org_l=2.0: NDCG = 0.036816\nhl=10, org_l=4.0: NDCG = 0.037473\nhl=10, org_l=6.0: NDCG = 0.037265\nhl=14, org_l=2.0: NDCG = 0.037327\nhl=14, org_l=4.0: NDCG = 0.037709\nhl=14, org_l=6.0: NDCG = 0.037453\nhl=21, org_l=2.0: NDCG = 0.036951\nhl=21, org_l=4.0: NDCG = 0.037333\nhl=21, org_l=6.0: NDCG = 0.036983\nProcess Group PGID: 2451\n",
"isError": false
},
"responseMessageId": "42cae6d6-31ef-4459-a5d9-09053249d469"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T01:42:23.035Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:42:23.035Z",
"text": "Boosting Organic Listens\n\nI'm seeing promising gains, with NDCG climbing towards 0.038 from a previous 0.0107. My next step is to investigate further boosting strong organic listens, specifically those with a played ratio of 80% or higher, by adjusting the `strong_listen_mult`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:42:23.035Z",
"callId": "run_shell_command__call_4679614",
"name": "run_shell_command",
"input": {
"description": "Compare train_likes and validation_preference_state",
"command": "python3 -c \"\nimport pandas as pd\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_unlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Number of likes in val_pref\nliked_pairs_val = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\n# Number of likes in train_likes\nliked_pairs_train = set(train_likes[['uid', 'item_id']].itertuples(index=False, name=None))\nunliked_pairs_train = set(train_unlikes[['uid', 'item_id']].itertuples(index=False, name=None))\n\nprint('Val pref liked pairs:', len(liked_pairs_val))\nprint('Train likes pairs:', len(liked_pairs_train))\nprint('Train unlikes pairs:', len(unliked_pairs_train))\nprint('Train net liked pairs:', len(liked_pairs_train - unliked_pairs_train))\nprint('Overlap between Val pref liked and Train likes:', len(liked_pairs_val & liked_pairs_train))\n\""
},
"recorded": {
"id": "run_shell_command__call_4679614",
"name": "run_shell_command",
"args": {
"description": "Compare train_likes and validation_preference_state",
"command": "python3 -c \"\nimport pandas as pd\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_unlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Number of likes in val_pref\nliked_pairs_val = set(val_pref[val_pref.state == 'liked'][['uid', 'item_id']].itertuples(index=False, name=None))\n# Number of likes in train_likes\nliked_pairs_train = set(train_likes[['uid', 'item_id']].itertuples(index=False, name=None))\nunliked_pairs_train = set(train_unlikes[['uid', 'item_id']].itertuples(index=False, name=None))\n\nprint('Val pref liked pairs:', len(liked_pairs_val))\nprint('Train likes pairs:', len(liked_pairs_train))\nprint('Train unlikes pairs:', len(unliked_pairs_train))\nprint('Train net liked pairs:', len(liked_pairs_train - unliked_pairs_train))\nprint('Overlap between Val pref liked and Train likes:', len(liked_pairs_val & liked_pairs_train))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4679614",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val pref liked pairs: 418435\nTrain likes pairs: 785069\nTrain unlikes pairs: 271951\nTrain net liked pairs: 709193\nOverlap between Val pref liked and Train likes: 418435\nProcess Group PGID: 2480\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:42:25.275Z",
"resultDisplay": "Val pref liked pairs: 418435\nTrain likes pairs: 785069\nTrain unlikes pairs: 271951\nTrain net liked pairs: 709193\nOverlap between Val pref liked and Train likes: 418435",
"description": "Compare train_likes and validation_preference_state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:42:25.276Z",
"content": "\nOutput: Val pref liked pairs: 418435\nTrain likes pairs: 785069\nTrain unlikes pairs: 271951\nTrain net liked pairs: 709193\nOverlap between Val pref liked and Train likes: 418435\nProcess Group PGID: 2480\n",
"isError": false
},
"responseMessageId": "db0481c5-1c6f-42d6-b6ce-be212cf4deed"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T01:42:35.758Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:42:35.758Z",
"text": "Confirming Train/Val Consistency\n\nI've confirmed that every pair in `val_pref` is present in `train_likes`, and that `val_pref` accurately reflects the active snapshot at the validation cutoff. My next step is to investigate `test_preference_state.parquet` to understand its relation to the test cutoff."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:42:35.758Z",
"callId": "run_shell_command__call_3599371",
"name": "run_shell_command",
"input": {
"description": "Test idf_gamma and k values on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Testing idf_gamma ---')\nfor gamma in [0.5, 0.75, 1.0, 1.25]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=192, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing k (n_components) with gamma=1.0 ---')\nR_mat = R.multiply(idf_base).tocsr()\nfor k in [128, 192, 256, 320, 384, 512]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'k={k:3d}: NDCG = {score_predictions(pred):.6f} ({time.time()-t:.1f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_3599371",
"name": "run_shell_command",
"args": {
"description": "Test idf_gamma and k values on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Testing idf_gamma ---')\nfor gamma in [0.5, 0.75, 1.0, 1.25]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=192, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing k (n_components) with gamma=1.0 ---')\nR_mat = R.multiply(idf_base).tocsr()\nfor k in [128, 192, 256, 320, 384, 512]:\n t = time.time()\n svd = TruncatedSVD(n_components=k, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'k={k:3d}: NDCG = {score_predictions(pred):.6f} ({time.time()-t:.1f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3599371",
"name": "run_shell_command",
"response": {
"output": "\nOutput: --- Testing idf_gamma ---\ngamma=0.50: NDCG = 0.040507\ngamma=0.75: NDCG = 0.039709\ngamma=1.00: NDCG = 0.038007\ngamma=1.25: NDCG = 0.036008\n\n--- Testing k (n_components) with gamma=1.0 ---\nk=128: NDCG = 0.037997 (7.4s)\nk=192: NDCG = 0.038007 (11.8s)\nk=256: NDCG = 0.037613 (15.6s)\nk=320: NDCG = 0.036111 (19.4s)\nk=384: NDCG = 0.034909 (22.5s)\nk=512: NDCG = 0.033434 (28.1s)\nProcess Group PGID: 2497\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:45:14.767Z",
"resultDisplay": "--- Testing idf_gamma ---\ngamma=0.50: NDCG = 0.040507\ngamma=0.75: NDCG = 0.039709\ngamma=1.00: NDCG = 0.038007\ngamma=1.25: NDCG = 0.036008\n\n--- Testing k (n_components) with gamma=1.0 ---\nk=128: NDCG = 0.037997 (7.4s)\nk=192: NDCG = 0.038007 (11.8s)\nk=256: NDCG = 0.037613 (15.6s)\nk=320: NDCG = 0.036111 (19.4s)\nk=384: NDCG = 0.034909 (22.5s)\nk=512: NDCG = 0.033434 (28.1s)",
"description": "Test idf_gamma and k values on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:45:14.768Z",
"content": "\nOutput: --- Testing idf_gamma ---\ngamma=0.50: NDCG = 0.040507\ngamma=0.75: NDCG = 0.039709\ngamma=1.00: NDCG = 0.038007\ngamma=1.25: NDCG = 0.036008\n\n--- Testing k (n_components) with gamma=1.0 ---\nk=128: NDCG = 0.037997 (7.4s)\nk=192: NDCG = 0.038007 (11.8s)\nk=256: NDCG = 0.037613 (15.6s)\nk=320: NDCG = 0.036111 (19.4s)\nk=384: NDCG = 0.034909 (22.5s)\nk=512: NDCG = 0.033434 (28.1s)\nProcess Group PGID: 2497\n",
"isError": false
},
"responseMessageId": "b98783ce-cf34-49b1-a5e6-adf20e7c956d"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T01:45:25.535Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:45:25.535Z",
"text": "Improving NDCG Score\n\nMy NDCG has now surpassed 0.040, a significant leap from the baseline of 0.0107, representing nearly a fourfold increase. I've also identified that $k$ values between 128 and 192 are optimal, preventing overfitting. I will now focus on further refining the `gamma` parameter."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:45:25.535Z",
"callId": "run_shell_command__call_67467",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Fine-tuning gamma around 0.3 - 0.6 ---')\nfor gamma in [0.2, 0.3, 0.4, 0.5, 0.6]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing item norm power alpha with gamma=0.4 ---')\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV_base = svd.components_.astype(np.float32)\nitem_norms = np.linalg.norm(V_base, axis=0) + 1e-8\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:\n V = V_base / (item_norms ** alpha)\n pred = U_s @ V\n print(f'alpha={alpha:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\"",
"description": "Fine-tune gamma and test item norm power alpha"
},
"recorded": {
"id": "run_shell_command__call_67467",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nprint('--- Fine-tuning gamma around 0.3 - 0.6 ---')\nfor gamma in [0.2, 0.3, 0.4, 0.5, 0.6]:\n R_mat = R.multiply(idf_base ** gamma).tocsr()\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred = U_s @ V\n print(f'gamma={gamma:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing item norm power alpha with gamma=0.4 ---')\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV_base = svd.components_.astype(np.float32)\nitem_norms = np.linalg.norm(V_base, axis=0) + 1e-8\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5]:\n V = V_base / (item_norms ** alpha)\n pred = U_s @ V\n print(f'alpha={alpha:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\"",
"description": "Fine-tune gamma and test item norm power alpha"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_67467",
"name": "run_shell_command",
"response": {
"output": "\nOutput: --- Fine-tuning gamma around 0.3 - 0.6 ---\ngamma=0.20: NDCG = 0.039545\ngamma=0.30: NDCG = 0.040144\ngamma=0.40: NDCG = 0.041235\ngamma=0.50: NDCG = 0.041078\ngamma=0.60: NDCG = 0.040612\n\n--- Testing item norm power alpha with gamma=0.4 ---\nalpha=0.00: NDCG = 0.041235\nalpha=0.10: NDCG = 0.039981\nalpha=0.20: NDCG = 0.039069\nalpha=0.30: NDCG = 0.038114\nalpha=0.40: NDCG = 0.037080\nalpha=0.50: NDCG = 0.035404\nProcess Group PGID: 2526\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:46:38.165Z",
"resultDisplay": "--- Fine-tuning gamma around 0.3 - 0.6 ---\ngamma=0.20: NDCG = 0.039545\ngamma=0.30: NDCG = 0.040144\ngamma=0.40: NDCG = 0.041235\ngamma=0.50: NDCG = 0.041078\ngamma=0.60: NDCG = 0.040612\n\n--- Testing item norm power alpha with gamma=0.4 ---\nalpha=0.00: NDCG = 0.041235\nalpha=0.10: NDCG = 0.039981\nalpha=0.20: NDCG = 0.039069\nalpha=0.30: NDCG = 0.038114\nalpha=0.40: NDCG = 0.037080\nalpha=0.50: NDCG = 0.035404",
"description": "Fine-tune gamma and test item norm power alpha",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:46:38.166Z",
"content": "\nOutput: --- Fine-tuning gamma around 0.3 - 0.6 ---\ngamma=0.20: NDCG = 0.039545\ngamma=0.30: NDCG = 0.040144\ngamma=0.40: NDCG = 0.041235\ngamma=0.50: NDCG = 0.041078\ngamma=0.60: NDCG = 0.040612\n\n--- Testing item norm power alpha with gamma=0.4 ---\nalpha=0.00: NDCG = 0.041235\nalpha=0.10: NDCG = 0.039981\nalpha=0.20: NDCG = 0.039069\nalpha=0.30: NDCG = 0.038114\nalpha=0.40: NDCG = 0.037080\nalpha=0.50: NDCG = 0.035404\nProcess Group PGID: 2526\n",
"isError": false
},
"responseMessageId": "e576062d-9908-447b-9da4-6b0ed2bbbe11"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T01:48:39.192Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:48:39.192Z",
"text": "Exploring Item Discovery\n\nI'm now investigating the impact of incorporating an item discovery prior. Initial findings suggest pure dot product outperforms normalized cosine. I'm about to dive into the `candidate_catalog.parquet` file to analyze the new results."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:48:39.192Z",
"callId": "run_shell_command__call_11709",
"name": "run_shell_command",
"input": {
"description": "Test blending SVD scores with recent and catalog popularity priors",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\n# Compute recent item organic popularity\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\n# Normalize prior vectors to std=1 for easy blending\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\ncatalog_pop = np.log1p(catalog['organic_positive_listens'].values.astype(np.float32))\ncatalog_pop = (catalog_pop - np.mean(catalog_pop)) / (np.std(catalog_pop) + 1e-8)\n\npred_std = np.std(pred_base)\nprint(f'pred_std: {pred_std:.4f}')\n\nprint('--- Testing blend with recent organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing blend with catalog organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * catalog_pop[None, :]\n print(f'beta_catalog={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_11709",
"name": "run_shell_command",
"args": {
"description": "Test blending SVD scores with recent and catalog popularity priors",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport gc\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\ndel table; gc.collect()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\n# Compute recent item organic popularity\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\n# Normalize prior vectors to std=1 for easy blending\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\ncatalog_pop = np.log1p(catalog['organic_positive_listens'].values.astype(np.float32))\ncatalog_pop = (catalog_pop - np.mean(catalog_pop)) / (np.std(catalog_pop) + 1e-8)\n\npred_std = np.std(pred_base)\nprint(f'pred_std: {pred_std:.4f}')\n\nprint('--- Testing blend with recent organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\nprint('\\n--- Testing blend with catalog organic popularity ---')\nfor beta in [0.0, 0.05, 0.1, 0.2, 0.3, 0.5]:\n pred = pred_base + (beta * pred_std) * catalog_pop[None, :]\n print(f'beta_catalog={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_11709",
"name": "run_shell_command",
"response": {
"output": "\nOutput: pred_std: 0.0884\n--- Testing blend with recent organic popularity ---\nbeta_recent=0.00: NDCG = 0.041235\nbeta_recent=0.05: NDCG = 0.041274\nbeta_recent=0.10: NDCG = 0.041585\nbeta_recent=0.20: NDCG = 0.041733\nbeta_recent=0.30: NDCG = 0.041889\nbeta_recent=0.50: NDCG = 0.042227\n\n--- Testing blend with catalog organic popularity ---\nbeta_catalog=0.00: NDCG = 0.041235\nbeta_catalog=0.05: NDCG = 0.040917\nbeta_catalog=0.10: NDCG = 0.040788\nbeta_catalog=0.20: NDCG = 0.041136\nbeta_catalog=0.30: NDCG = 0.040977\nbeta_catalog=0.50: NDCG = 0.039935\nProcess Group PGID: 2555\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:49:13.044Z",
"resultDisplay": "pred_std: 0.0884\n--- Testing blend with recent organic popularity ---\nbeta_recent=0.00: NDCG = 0.041235\nbeta_recent=0.05: NDCG = 0.041274\nbeta_recent=0.10: NDCG = 0.041585\nbeta_recent=0.20: NDCG = 0.041733\nbeta_recent=0.30: NDCG = 0.041889\nbeta_recent=0.50: NDCG = 0.042227\n\n--- Testing blend with catalog organic popularity ---\nbeta_catalog=0.00: NDCG = 0.041235\nbeta_catalog=0.05: NDCG = 0.040917\nbeta_catalog=0.10: NDCG = 0.040788\nbeta_catalog=0.20: NDCG = 0.041136\nbeta_catalog=0.30: NDCG = 0.040977\nbeta_catalog=0.50: NDCG = 0.039935",
"description": "Test blending SVD scores with recent and catalog popularity priors",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:49:13.045Z",
"content": "\nOutput: pred_std: 0.0884\n--- Testing blend with recent organic popularity ---\nbeta_recent=0.00: NDCG = 0.041235\nbeta_recent=0.05: NDCG = 0.041274\nbeta_recent=0.10: NDCG = 0.041585\nbeta_recent=0.20: NDCG = 0.041733\nbeta_recent=0.30: NDCG = 0.041889\nbeta_recent=0.50: NDCG = 0.042227\n\n--- Testing blend with catalog organic popularity ---\nbeta_catalog=0.00: NDCG = 0.041235\nbeta_catalog=0.05: NDCG = 0.040917\nbeta_catalog=0.10: NDCG = 0.040788\nbeta_catalog=0.20: NDCG = 0.041136\nbeta_catalog=0.30: NDCG = 0.040977\nbeta_catalog=0.50: NDCG = 0.039935\nProcess Group PGID: 2555\n",
"isError": false
},
"responseMessageId": "10eb6ba8-4550-4af4-a5a0-f750dd3174d5"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T01:49:22.642Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:49:22.642Z",
"text": "Observing Engagement Trends\n\nMy recent analysis indicates a strong positive correlation between recent organic popularity and NDCG scores. It seems static catalog popularity is less impactful, possibly due to the dynamic nature of music trends. I'm currently exploring how to best leverage these rapidly shifting user interests."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:49:22.642Z",
"callId": "run_shell_command__call_15895",
"name": "run_shell_command",
"input": {
"description": "Fine-tune beta_recent popularity blend",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\n# Recent organic listens\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\npred_std = np.std(pred_base)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor beta in [0.4, 0.5, 0.6, 0.7, 0.8, 1.0, 1.2]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_15895",
"name": "run_shell_command",
"args": {
"description": "Fine-tune beta_recent popularity blend",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\ncombined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\ncombined['w'] = np.log1p(combined['w'])\n\nrows = combined['uid'].map(uid_to_idx).values\ncols = combined['item_id'].map(item_to_idx).values\ndata = combined['w'].values.astype(np.float32)\n\nR = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\ndf_item = np.diff(R.tocsc().indptr)\nN = n_users\nidf_base = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0)\n\nR_mat = R.multiply(idf_base ** 0.4).tocsr()\nsvd = TruncatedSVD(n_components=160, random_state=42)\nU_s = svd.fit_transform(R_mat).astype(np.float32)\nV = svd.components_.astype(np.float32)\npred_base = U_s @ V\n\n# Recent organic listens\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\n\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\npred_std = np.std(pred_base)\n\ndef score_predictions(pred):\n p = pred.copy()\n p[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = p[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor beta in [0.4, 0.5, 0.6, 0.7, 0.8, 1.0, 1.2]:\n pred = pred_base + (beta * pred_std) * recent_vec[None, :]\n print(f'beta_recent={beta:4.2f}: NDCG = {score_predictions(pred):.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_15895",
"name": "run_shell_command",
"response": {
"output": "\nOutput: beta_recent=0.40: NDCG = 0.042249\nbeta_recent=0.50: NDCG = 0.042227\nbeta_recent=0.60: NDCG = 0.042661\nbeta_recent=0.70: NDCG = 0.043051\nbeta_recent=0.80: NDCG = 0.042978\nbeta_recent=1.00: NDCG = 0.042349\nbeta_recent=1.20: NDCG = 0.042590\nProcess Group PGID: 2584\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:49:47.888Z",
"resultDisplay": "beta_recent=0.40: NDCG = 0.042249\nbeta_recent=0.50: NDCG = 0.042227\nbeta_recent=0.60: NDCG = 0.042661\nbeta_recent=0.70: NDCG = 0.043051\nbeta_recent=0.80: NDCG = 0.042978\nbeta_recent=1.00: NDCG = 0.042349\nbeta_recent=1.20: NDCG = 0.042590",
"description": "Fine-tune beta_recent popularity blend",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:49:49.693Z",
"content": "\nOutput: beta_recent=0.40: NDCG = 0.042249\nbeta_recent=0.50: NDCG = 0.042227\nbeta_recent=0.60: NDCG = 0.042661\nbeta_recent=0.70: NDCG = 0.043051\nbeta_recent=0.80: NDCG = 0.042978\nbeta_recent=1.00: NDCG = 0.042349\nbeta_recent=1.20: NDCG = 0.042590\nProcess Group PGID: 2584\n",
"isError": false
},
"responseMessageId": "f0c44230-45ad-4eea-92e8-0426a75c4a5f"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T01:50:03.370Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:50:03.370Z",
"text": "Improving Ranking Quality\n\nMy current focus is on enhancing our ranking system. I've observed a significant uplift in NDCG, increasing by over 4x to 0.043051, stemming from a beta value of 0.70. I'm now shifting to analyze dislike signals to further refine performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:50:03.370Z",
"text": "Incorporating Dislike Signals\n\nI'm exploring how to leverage the substantial dislike data (over 50,000 entries) by incorporating disliked items into the interaction matrix with negative weights. Truncated SVD can handle these negative entries, naturally modeling aversion and potentially pushing scores lower for similar items. I'll test this approach next."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:50:03.370Z",
"callId": "run_shell_command__call_2584678",
"name": "run_shell_command",
"input": {
"description": "Test negative weights for dislikes",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ntrain_dislikes = train_dislikes[train_dislikes.uid.isin(target_uids_set) & train_dislikes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\nts_dislikes = train_dislikes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_dislike(neg_weight):\n if neg_weight != 0:\n w_d = np.exp(-(cutoff - ts_dislikes) / hl).astype(np.float32) * neg_weight\n df_d = pd.DataFrame({'uid': train_dislikes['uid'], 'item_id': train_dislikes['item_id'], 'w': w_d})\n dfs = [df_l, df_k, df_d]\n else:\n dfs = [df_l, df_k]\n \n combined = pd.concat(dfs).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n # Apply sign-preserving log1p: sign(x) * log1p(abs(x))\n combined['w'] = np.sign(combined['w']) * np.log1p(np.abs(combined['w']))\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n # IDF on positive interactions\n R_pos = R.copy()\n R_pos.data = np.maximum(R_pos.data, 0)\n df_item = np.diff(R_pos.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n \n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor nw in [0.0, -1.0, -2.0, -4.0]:\n print(f'neg_weight={nw:4.1f}: NDCG = {test_dislike(nw):.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_2584678",
"name": "run_shell_command",
"args": {
"description": "Test negative weights for dislikes",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ntrain_dislikes = train_dislikes[train_dislikes.uid.isin(target_uids_set) & train_dislikes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\nts_dislikes = train_dislikes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\nw_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\nw_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\nw_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\nw_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32) * 4.0\nw_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\ndf_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\ndf_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_dislike(neg_weight):\n if neg_weight != 0:\n w_d = np.exp(-(cutoff - ts_dislikes) / hl).astype(np.float32) * neg_weight\n df_d = pd.DataFrame({'uid': train_dislikes['uid'], 'item_id': train_dislikes['item_id'], 'w': w_d})\n dfs = [df_l, df_k, df_d]\n else:\n dfs = [df_l, df_k]\n \n combined = pd.concat(dfs).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n # Apply sign-preserving log1p: sign(x) * log1p(abs(x))\n combined['w'] = np.sign(combined['w']) * np.log1p(np.abs(combined['w']))\n \n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n \n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n \n # IDF on positive interactions\n R_pos = R.copy()\n R_pos.data = np.maximum(R_pos.data, 0)\n df_item = np.diff(R_pos.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n \n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n \n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor nw in [0.0, -1.0, -2.0, -4.0]:\n print(f'neg_weight={nw:4.1f}: NDCG = {test_dislike(nw):.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2584678",
"name": "run_shell_command",
"response": {
"output": "\nOutput: neg_weight= 0.0: NDCG = 0.043051\nneg_weight=-1.0: NDCG = 0.042846\nneg_weight=-2.0: NDCG = 0.042545\nneg_weight=-4.0: NDCG = 0.042551\nProcess Group PGID: 2613\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:50:57.718Z",
"resultDisplay": "neg_weight= 0.0: NDCG = 0.043051\nneg_weight=-1.0: NDCG = 0.042846\nneg_weight=-2.0: NDCG = 0.042545\nneg_weight=-4.0: NDCG = 0.042551",
"description": "Test negative weights for dislikes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:50:57.719Z",
"content": "\nOutput: neg_weight= 0.0: NDCG = 0.043051\nneg_weight=-1.0: NDCG = 0.042846\nneg_weight=-2.0: NDCG = 0.042545\nneg_weight=-4.0: NDCG = 0.042551\nProcess Group PGID: 2613\n",
"isError": false
},
"responseMessageId": "fa4d0b9d-8a4e-4475-9e08-ac95f80c95fa"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T01:51:19.002Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:51:19.002Z",
"text": "Exploring Negative Weights\n\nI'm observing that incorporating negative weights, even slightly, is showing a marginal decrease in NDCG. This aligns with my expectation, given that candidate eligibility already handles explicit dislikes, making SVD's negative entries somewhat redundant."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:51:19.002Z",
"text": "Developing Temporal Horizons\n\nI'm considering how to better capture user tastes over time. My current thinking is to ensemble multiple recency horizons, combining short-term signals for immediate preferences with longer-term ones for stable tastes. This could be implemented as a base weight plus a recency boost for recent interactions."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:51:19.002Z",
"callId": "run_shell_command__call_316284",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_base_weight(base_w):\n rec_decay_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens = (base_w + (1.0 - base_w) * rec_decay_listens)\n w_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\n rec_decay_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32)\n w_likes = (base_w + (1.0 - base_w) * rec_decay_likes) * 4.0\n w_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n\n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n\n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor bw in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f'base_weight={bw:4.2f}: NDCG = {test_base_weight(bw):.6f}')\n\"",
"description": "Test base weight blending long-term and short-term tastes"
},
"recorded": {
"id": "run_shell_command__call_316284",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nfrom ranking_evaluation import CandidateSet\nimport evaluate_public\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nn_users = len(targets)\nn_items = len(catalog)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid.values)}\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id.values)}\n\ntarget_uids_set = set(targets.uid)\ncatalog_items_set = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_likes = train_likes[train_likes.uid.isin(target_uids_set) & train_likes.item_id.isin(catalog_items_set)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ntarget_uids = pa.array(targets.uid.values, type=pa.uint32())\ncatalog_items = pa.array(catalog.item_id.values, type=pa.uint32())\n\nmask = pc.is_in(table['uid'], target_uids)\nmask = pc.and_(mask, pc.is_in(table['item_id'], catalog_items))\nmask = pc.and_(mask, pc.greater_equal(table['played_ratio_pct'], 50))\nfiltered = table.filter(mask).to_pandas()\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_npz['packed_eligible'], axis=1, count=n_items, bitorder='little').astype(bool)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets_set = set(targets.uid.astype(int))\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nitem_ids = val_npz['item_ids']\nuids = val_npz['uids']\n\nts_listens = filtered['timestamp'].astype(np.float64).values\nts_likes = train_likes['timestamp'].astype(np.float64).values\n\ncutoff = 24786800.0\nhl = 14 * 86400.0\n\n# Recent organic listens for popularity prior\nrecent_mask = (ts_listens >= (cutoff - 14 * 86400)) & (filtered['is_organic'].values == 1)\ndf_recent = pd.DataFrame({'item_id': filtered['item_id'].values[recent_mask]})\nrecent_pop = df_recent['item_id'].value_counts()\nrecent_vec = np.zeros(n_items, dtype=np.float32)\nfor item, cnt in recent_pop.items():\n if item in item_to_idx:\n recent_vec[item_to_idx[item]] = np.log1p(cnt)\nrecent_vec = (recent_vec - np.mean(recent_vec)) / (np.std(recent_vec) + 1e-8)\n\ndef test_base_weight(base_w):\n rec_decay_listens = np.exp(-(cutoff - ts_listens) / hl).astype(np.float32)\n w_listens = (base_w + (1.0 - base_w) * rec_decay_listens)\n w_listens *= np.where(filtered['is_organic'].values == 1, 4.0, 1.0).astype(np.float32)\n w_listens *= np.where(filtered['played_ratio_pct'].values >= 80, 2.0, 1.0).astype(np.float32)\n\n rec_decay_likes = np.exp(-(cutoff - ts_likes) / hl).astype(np.float32)\n w_likes = (base_w + (1.0 - base_w) * rec_decay_likes) * 4.0\n w_likes *= np.where(train_likes['is_organic'].values == 1, 2.0, 1.0).astype(np.float32)\n\n df_l = pd.DataFrame({'uid': filtered['uid'], 'item_id': filtered['item_id'], 'w': w_listens})\n df_k = pd.DataFrame({'uid': train_likes['uid'], 'item_id': train_likes['item_id'], 'w': w_likes})\n\n combined = pd.concat([df_l, df_k]).groupby(['uid', 'item_id'])['w'].sum().reset_index()\n combined['w'] = np.log1p(combined['w'])\n\n rows = combined['uid'].map(uid_to_idx).values\n cols = combined['item_id'].map(item_to_idx).values\n data = combined['w'].values.astype(np.float32)\n\n R = sparse.csr_matrix((data, (rows, cols)), shape=(n_users, n_items))\n\n df_item = np.diff(R.tocsc().indptr)\n N = n_users\n idf = np.log((N - df_item + 0.5) / (df_item + 0.5) + 1.0) ** 0.4\n R_mat = R.multiply(idf).tocsr()\n\n svd = TruncatedSVD(n_components=160, random_state=42)\n U_s = svd.fit_transform(R_mat).astype(np.float32)\n V = svd.components_.astype(np.float32)\n pred_base = U_s @ V\n pred_std = np.std(pred_base)\n pred = pred_base + (0.70 * pred_std) * recent_vec[None, :]\n\n pred[~unpacked] = -np.inf\n ranked = {}\n for r in range(n_users):\n u_sc = pred[r]\n top100 = np.argpartition(-u_sc, 100)[:100]\n top100_sorted = top100[np.argsort(-u_sc[top100])]\n ranked[int(uids[r])] = list(map(int, item_ids[top100_sorted]))\n return evaluate_public.score(ranked, graded)['quality_ndcg@10']\n\nfor bw in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f'base_weight={bw:4.2f}: NDCG = {test_base_weight(bw):.6f}')\n\"",
"description": "Test base weight blending long-term and short-term tastes"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_316284",
"name": "run_shell_command",
"response": {
"output": "\nOutput: base_weight=0.00: NDCG = 0.043051\nbase_weight=0.05: NDCG = 0.039988\nbase_weight=0.10: NDCG = 0.035770\nbase_weight=0.20: NDCG = 0.032332\nbase_weight=0.30: NDCG = 0.030851\nProcess Group PGID: 2642\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:52:24.114Z",
"resultDisplay": "base_weight=0.00: NDCG = 0.043051\nbase_weight=0.05: NDCG = 0.039988\nbase_weight=0.10: NDCG = 0.035770\nbase_weight=0.20: NDCG = 0.032332\nbase_weight=0.30: NDCG = 0.030851",
"description": "Test base weight blending long-term and short-term tastes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:52:24.115Z",
"content": "\nOutput: base_weight=0.00: NDCG = 0.043051\nbase_weight=0.05: NDCG = 0.039988\nbase_weight=0.10: NDCG = 0.035770\nbase_weight=0.20: NDCG = 0.032332\nbase_weight=0.30: NDCG = 0.030851\nProcess Group PGID: 2642\n",
"isError": false
},
"responseMessageId": "4b608313-b7f8-423e-8f74-fc9cbb8688bc"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T01:52:29.866Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:52:29.866Z",
"text": "Confirming Streaming Dynamics\n\nI've confirmed that a `base_weight` of 0.00 yields superior results, indicating user discovery here strongly correlates with recent engagement, roughly within the past 2-4 weeks. This validates the active discovery horizon hypothesis."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:52:29.866Z",
"text": "Exploring Ensemble Models\n\nI am now investigating ensemble approaches, specifically considering blending a recency-weighted SVD model with Item-Item Collaborative Filtering. My current focus is on optimizing the Item-Item CF component by pruning the similarity matrix to top-K neighbors to filter out noise and improve relevance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:52:29.866Z",
"callId": "run_shell_command__call_4686114",
"name": "run_shell_command",
"input": {
"description": "Benchmark item-item similarity computation in blocks",
"command": "python3 -c \"\nimport time\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\n# Test on synthetic sparse matrix of realistic size\nR = sparse.random(8456, 30000, density=0.015, format='csc', dtype=np.float32)\nR_norm = normalize(R, norm='l2', axis=0) # normalize columns (items)\n\nt0 = time.time()\n# Test 1000 items block\nblock = R_norm[:, :1000].T @ R_norm\nprint('1000 items block time:', time.time() - t0, 's')\n\""
},
"recorded": {
"id": "run_shell_command__call_4686114",
"name": "run_shell_command",
"args": {
"description": "Benchmark item-item similarity computation in blocks",
"command": "python3 -c \"\nimport time\nimport numpy as np\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\n\n# Test on synthetic sparse matrix of realistic size\nR = sparse.random(8456, 30000, density=0.015, format='csc', dtype=np.float32)\nR_norm = normalize(R, norm='l2', axis=0) # normalize columns (items)\n\nt0 = time.time()\n# Test 1000 items block\nblock = R_norm[:, :1000].T @ R_norm\nprint('1000 items block time:', time.time() - t0, 's')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4686114",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 1000 items block time: 0.6277880668640137 s\nProcess Group PGID: 2671\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:52:42.356Z",
"resultDisplay": "1000 items block time: 0.6277880668640137 s",
"description": "Benchmark item-item similarity computation in blocks",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:52:42.358Z",
"content": "\nOutput: 1000 items block time: 0.6277880668640137 s\nProcess Group PGID: 2671\n",
"isError": false
},
"responseMessageId": "624c6a34-f8c0-4dca-be1f-57f1dab72a05"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T01:52:57.833Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:52:57.833Z",
"text": "Optimizing neighbor computations\n\nI'm refining the neighbor computation process. Initial tests show promising speedups, with 1000 items processing in just 0.62 seconds. Scaling this to 30,000 items suggests an achievable time of around 18 seconds, assuming a block-based approach. My current focus is on efficiently calculating cosine similarities within these blocks."
}
]
}
],
"source": {
"format": "gemini-cli-session",
"sha256": "a28925a205eef73bc7c7e33b00d60abb24e836af7d5a7c0487de16e173fd91d1",
"records": 241,
"sessionMetadata": {
"sessionId": "9a0101dd-14bb-47af-9180-d4f590bc49f0",
"projectHash": "f53b52ad6d21cceb72dfa78fb67614fe14f110c58e68412b01508d6a485501c3",
"startTime": "2026-09-29T01:23:37.329Z",
"lastUpdated": "2026-09-29T01:23:37.329Z",
"kind": "main"
},
"sessionUpdates": 97,
"usageRecords": [
{
"messageId": "8af5a00b-0356-4466-b9cd-f7a626c7f85f",
"timestamp": "2026-09-29T01:23:43.099Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 9722,
"output": 211,
"cached": 7617,
"thoughts": 528,
"tool": 0,
"total": 10461
}
},
{
"messageId": "0b6f96ce-4068-4a95-b7ff-0c005ea38f51",
"timestamp": "2026-09-29T01:23:46.294Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 11535,
"output": 120,
"cached": 7647,
"thoughts": 90,
"tool": 0,
"total": 11745
}
},
{
"messageId": "d8d77c4e-370e-418c-8104-c67ab5d70c37",
"timestamp": "2026-09-29T01:23:55.766Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 16191,
"output": 514,
"cached": 11647,
"thoughts": 738,
"tool": 0,
"total": 17443
}
},
{
"messageId": "b0230a18-cb6b-404c-9528-de0ca5b31406",
"timestamp": "2026-09-29T01:24:02.577Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 18141,
"output": 500,
"cached": 15577,
"thoughts": 164,
"tool": 0,
"total": 18805
}
},
{
"messageId": "3cf046d2-eda2-40a5-b26b-9c9e347343a5",
"timestamp": "2026-09-29T01:24:09.381Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 18972,
"output": 342,
"cached": 15590,
"thoughts": 206,
"tool": 0,
"total": 19520
}
},
{
"messageId": "2974d795-55e5-4b49-9f17-09437512a0dc",
"timestamp": "2026-09-29T01:24:35.103Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 19586,
"output": 361,
"cached": 15594,
"thoughts": 28,
"tool": 0,
"total": 19975
}
},
{
"messageId": "b3d0600f-f0bd-42ce-b401-c80478c58382",
"timestamp": "2026-09-29T01:24:39.434Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 20113,
"output": 157,
"cached": 15595,
"thoughts": 176,
"tool": 0,
"total": 20446
}
},
{
"messageId": "a4c85b95-7fbd-47f6-b070-90e7872063fb",
"timestamp": "2026-09-29T01:24:43.753Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 20645,
"output": 334,
"cached": 15596,
"thoughts": 90,
"tool": 0,
"total": 21069
}
},
{
"messageId": "c381d0a1-145c-4f0b-8766-b62c09d0e1ed",
"timestamp": "2026-09-29T01:25:11.016Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 21373,
"output": 171,
"cached": 15603,
"thoughts": 52,
"tool": 0,
"total": 21596
}
},
{
"messageId": "09ff0a37-9cfa-4b92-a733-d9e3fae55c0c",
"timestamp": "2026-09-29T01:25:31.024Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 21671,
"output": 486,
"cached": 7797,
"thoughts": 684,
"tool": 0,
"total": 22841
}
},
{
"messageId": "35385b66-93dc-4252-9653-6a2094699d20",
"timestamp": "2026-09-29T01:25:40.256Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 22955,
"output": 464,
"cached": 19526,
"thoughts": 120,
"tool": 0,
"total": 23539
}
},
{
"messageId": "d812b9df-01b9-4cb3-a72d-c3988d8c2c0c",
"timestamp": "2026-09-29T01:25:47.800Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 23589,
"output": 107,
"cached": 19530,
"thoughts": 78,
"tool": 0,
"total": 23774
}
},
{
"messageId": "23ce87bb-ce0c-4180-b2f9-d617573ca0f6",
"timestamp": "2026-09-29T01:25:55.740Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 23860,
"output": 686,
"cached": 19517,
"thoughts": 622,
"tool": 0,
"total": 25168
}
},
{
"messageId": "f8fbd971-f3c9-46bc-bf4c-2268c6be81ad",
"timestamp": "2026-09-29T01:26:06.910Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 25295,
"output": 607,
"cached": 19550,
"thoughts": 142,
"tool": 0,
"total": 26044
}
},
{
"messageId": "c85102c0-7e6f-404d-8cb6-e32fddfcf772",
"timestamp": "2026-09-29T01:26:16.926Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 26184,
"output": 423,
"cached": 23474,
"thoughts": 402,
"tool": 0,
"total": 27009
}
},
{
"messageId": "8aadeccc-ba2d-4088-8988-e817c8eb9e32",
"timestamp": "2026-09-29T01:27:04.927Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 27098,
"output": 368,
"cached": 0,
"thoughts": 371,
"tool": 0,
"total": 27837
}
},
{
"messageId": "92448f25-0f23-4e2f-b78b-9457d3a9f709",
"timestamp": "2026-09-29T01:27:21.247Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 27964,
"output": 246,
"cached": 7833,
"thoughts": 230,
"tool": 0,
"total": 28440
}
},
{
"messageId": "4cefe622-8499-4524-97fe-173363df74ec",
"timestamp": "2026-09-29T01:27:31.964Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 28512,
"output": 266,
"cached": 23498,
"thoughts": 115,
"tool": 0,
"total": 28893
}
},
{
"messageId": "4a6ed163-998c-4e0b-9a11-a9a2d66dd400",
"timestamp": "2026-09-29T01:27:36.928Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 28989,
"output": 362,
"cached": 23495,
"thoughts": 16,
"tool": 0,
"total": 29367
}
},
{
"messageId": "04c09d67-b261-4c70-b098-bb988b1e91f3",
"timestamp": "2026-09-29T01:27:47.359Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 29454,
"output": 154,
"cached": 27407,
"thoughts": 570,
"tool": 0,
"total": 30178
}
},
{
"messageId": "470f75c3-15c4-4396-bac7-7faaa81071f8",
"timestamp": "2026-09-29T01:27:51.095Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 30227,
"output": 179,
"cached": 27415,
"thoughts": 31,
"tool": 0,
"total": 30437
}
},
{
"messageId": "695ffce9-8e22-45eb-812a-139c8151f72f",
"timestamp": "2026-09-29T01:28:00.521Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 30481,
"output": 372,
"cached": 27403,
"thoughts": 396,
"tool": 0,
"total": 31249
}
},
{
"messageId": "5c3d0df4-043e-4bbc-a731-5362156d8c9e",
"timestamp": "2026-09-29T01:28:11.502Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 31355,
"output": 235,
"cached": 27415,
"thoughts": 1018,
"tool": 0,
"total": 32608
}
},
{
"messageId": "27f6ac81-f640-4742-922f-b46fdf8e6626",
"timestamp": "2026-09-29T01:28:17.554Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 32786,
"output": 364,
"cached": 27447,
"thoughts": 305,
"tool": 0,
"total": 33455
}
},
{
"messageId": "9239710d-4e4c-445d-ae52-2672d1a31590",
"timestamp": "2026-09-29T01:28:27.658Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 33805,
"output": 582,
"cached": 31385,
"thoughts": 720,
"tool": 0,
"total": 35107
}
},
{
"messageId": "dcb16722-92d6-4419-8cea-8bf56b494df3",
"timestamp": "2026-09-29T01:28:37.196Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 35177,
"output": 439,
"cached": 31415,
"thoughts": 269,
"tool": 0,
"total": 35885
}
},
{
"messageId": "c785179a-7247-429a-bdd3-d85ddedc865f",
"timestamp": "2026-09-29T01:28:46.194Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 36010,
"output": 413,
"cached": 31423,
"thoughts": 92,
"tool": 0,
"total": 36515
}
},
{
"messageId": "a8309f9c-fbe8-481a-8ff9-5549cfbf3131",
"timestamp": "2026-09-29T01:29:08.264Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 36743,
"output": 1620,
"cached": 31427,
"thoughts": 1147,
"tool": 0,
"total": 39510
}
},
{
"messageId": "d05de301-5855-4e68-a730-6e70f2aeea29",
"timestamp": "2026-09-29T01:29:52.813Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 39742,
"output": 231,
"cached": 35443,
"thoughts": 653,
"tool": 0,
"total": 40626
}
},
{
"messageId": "71e07beb-7d24-4631-a05b-cbe1496370d4",
"timestamp": "2026-09-29T01:30:17.450Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 40674,
"output": 1356,
"cached": 35452,
"thoughts": 234,
"tool": 0,
"total": 42264
}
},
{
"messageId": "3fb04d26-5381-415f-81e9-6527f2e29ec7",
"timestamp": "2026-09-29T01:30:48.559Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 42417,
"output": 1254,
"cached": 39430,
"thoughts": 753,
"tool": 0,
"total": 44424
}
},
{
"messageId": "a8b982f8-d319-4902-b2e7-7d641df2a4b0",
"timestamp": "2026-09-29T01:32:10.158Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 44607,
"output": 1387,
"cached": 39481,
"thoughts": 497,
"tool": 0,
"total": 46491
}
},
{
"messageId": "c00d3761-389c-4153-bf04-c49ddb58dbe2",
"timestamp": "2026-09-29T01:32:45.243Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 47900,
"output": 89,
"cached": 43514,
"thoughts": 343,
"tool": 0,
"total": 48332
}
},
{
"messageId": "baa1e699-7c48-4b40-b5ad-48b94a078b46",
"timestamp": "2026-09-29T01:33:01.096Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 48397,
"output": 1528,
"cached": 43507,
"thoughts": 873,
"tool": 0,
"total": 50798
}
},
{
"messageId": "04dc5f26-8689-41b1-970d-0a5937ea89fe",
"timestamp": "2026-09-29T01:33:18.449Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 51443,
"output": 1579,
"cached": 47535,
"thoughts": 276,
"tool": 0,
"total": 53298
}
},
{
"messageId": "27e25515-9ecc-48bd-b25c-77009d8bb512",
"timestamp": "2026-09-29T01:34:45.579Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 53471,
"output": 1543,
"cached": 47572,
"thoughts": 180,
"tool": 0,
"total": 55194
}
},
{
"messageId": "19572d9d-90c6-42fa-a0ed-3da158084728",
"timestamp": "2026-09-29T01:36:35.098Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 55381,
"output": 1631,
"cached": 51570,
"thoughts": 372,
"tool": 0,
"total": 57384
}
},
{
"messageId": "bfa66d08-c61c-405c-9578-8fc9e63cb933",
"timestamp": "2026-09-29T01:39:08.832Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 57629,
"output": 114,
"cached": 0,
"thoughts": 224,
"tool": 0,
"total": 57967
}
},
{
"messageId": "2108b525-6382-463e-807a-c5d187fd7d4d",
"timestamp": "2026-09-29T01:39:22.438Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 58175,
"output": 1625,
"cached": 55573,
"thoughts": 236,
"tool": 0,
"total": 60036
}
},
{
"messageId": "c435283f-d101-4937-8d75-de072cd58e78",
"timestamp": "2026-09-29T01:42:23.035Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 60373,
"output": 337,
"cached": 7944,
"thoughts": 189,
"tool": 0,
"total": 60899
}
},
{
"messageId": "cd36accc-7c11-4e48-9c15-8aa7d1465dea",
"timestamp": "2026-09-29T01:42:35.758Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 60992,
"output": 1655,
"cached": 55607,
"thoughts": 356,
"tool": 0,
"total": 63003
}
},
{
"messageId": "e7395663-3ad4-4978-8435-8658fa9c43e0",
"timestamp": "2026-09-29T01:45:25.535Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 63287,
"output": 1692,
"cached": 7949,
"thoughts": 179,
"tool": 0,
"total": 65158
}
},
{
"messageId": "44d25432-4e59-47b4-b24c-884e714e7940",
"timestamp": "2026-09-29T01:48:39.192Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 65434,
"output": 1896,
"cached": 59655,
"thoughts": 275,
"tool": 0,
"total": 67605
}
},
{
"messageId": "990812e4-8343-4b79-aae6-57ddb65788a4",
"timestamp": "2026-09-29T01:49:22.642Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 67924,
"output": 1673,
"cached": 63676,
"thoughts": 137,
"tool": 0,
"total": 69734
}
},
{
"messageId": "a6f90f98-077b-4e8e-ab66-fa61b7698670",
"timestamp": "2026-09-29T01:50:03.370Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 69913,
"output": 1957,
"cached": 67686,
"thoughts": 440,
"tool": 0,
"total": 72310
}
},
{
"messageId": "bc88df5a-56cc-4b5f-aa30-7f0ec311ce2e",
"timestamp": "2026-09-29T01:51:19.002Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 72420,
"output": 1750,
"cached": 0,
"thoughts": 494,
"tool": 0,
"total": 74664
}
},
{
"messageId": "a43582d1-0352-4813-89cc-f098640a35bd",
"timestamp": "2026-09-29T01:52:29.866Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 74799,
"output": 188,
"cached": 71750,
"thoughts": 432,
"tool": 0,
"total": 75419
}
},
{
"messageId": "e2ec59d8-9090-4932-8cf5-e127b09a221f",
"timestamp": "2026-09-29T01:52:57.833Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 75474,
"output": 1955,
"cached": 71744,
"thoughts": 430,
"tool": 0,
"total": 77859
}
}
]
}
}