{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-r01-b9049e57",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "6e176a05-df54-4726-ad34-e3fc9b3d1760",
"steps": [
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"role": "user",
"timestamp": "2026-09-29T01:22:44.614Z",
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"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"
}
]
},
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"timestamp": "2026-09-29T01:22:44.661Z",
"blocks": [
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"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"
}
]
},
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"timestamp": "2026-09-29T01:22:48.501Z",
"blocks": [
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"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:48.501Z",
"text": "Analyzing Music Discovery Requirements\n\nMy current focus is on dissecting the core problem of building this music-discovery ranker. I'm meticulously reviewing all existing documentation, including directory structures, available files, and the specific constraints we're working under, alongside the tools at our disposal. I'm also paying close attention to the prompt's explicit instructions, ensuring each requirement is met."
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"input": {
"strategic_intent": "Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker.",
"summary": "Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.",
"title": "Initial Exploration and Environment Setup"
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"summary": "Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.",
"title": "Initial Exploration and Environment Setup"
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"output": "Current topic: \"Initial Exploration and Environment Setup\"\nTopic summary: Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.\n\nStrategic Intent: Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker."
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"status": "success",
"timestamp": "2026-09-29T01:22:48.536Z",
"resultDisplay": "## 📂 Topic: **Initial Exploration and Environment Setup**\n\n**Summary:**\nStarting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.\n\n> [!STRATEGY]\n> **Intent:** Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker.",
"description": "Update topic to: \"Initial Exploration and Environment Setup\"",
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"timestamp": "2026-09-29T01:22:48.538Z",
"content": "Current topic: \"Initial Exploration and Environment Setup\"\nTopic summary: Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.\n\nStrategic Intent: Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker.",
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"callId": "list_directory__call_55760",
"name": "list_directory",
"input": {
"dir_path": "/task"
},
"recorded": {
"id": "list_directory__call_55760",
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"args": {
"dir_path": "/task"
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"error": "Path not in workspace: Attempted path \"/task\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app"
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"status": "error",
"timestamp": "2026-09-29T01:22:48.536Z",
"resultDisplay": "Path not in workspace: Attempted path \"/task\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"description": "Lists the names of files and subdirectories directly within a specified directory path. Can optionally ignore entries matching provided glob patterns.",
"displayName": "ReadFolder",
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},
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"timestamp": "2026-09-29T01:22:48.538Z",
"content": "Path not in workspace: Attempted path \"/task\" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app",
"isError": true
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"callId": "run_shell_command__call_55763",
"name": "run_shell_command",
"input": {
"description": "Check files in /task/data",
"command": "ls -lh /task/data"
},
"recorded": {
"id": "run_shell_command__call_55763",
"name": "run_shell_command",
"args": {
"description": "Check files in /task/data",
"command": "ls -lh /task/data"
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"result": [
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"output": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:48.536Z",
"resultDisplay": "total 340M\n-r--r--r-- 1 root root 342K Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 validation_preference_state.parquet",
"description": "ls -lh /task/data",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:48.538Z",
"content": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 01:16 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 01:16 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 01:16 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 01:16 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 01:16 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 01:16 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 01:16 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 01:16 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 01:16 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 01:16 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 01:16 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 01:16 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 01:16 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 01:16 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 01:17 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
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{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T01:22:50.471Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:50.471Z",
"callId": "run_shell_command__call_28049",
"name": "run_shell_command",
"input": {
"command": "ls -la /task",
"description": "List contents of /task"
},
"recorded": {
"id": "run_shell_command__call_28049",
"name": "run_shell_command",
"args": {
"command": "ls -la /task",
"description": "List contents of /task"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_28049",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt\nProcess Group PGID: 1615\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:50.485Z",
"resultDisplay": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt",
"description": "ls -la /task",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:50.485Z",
"content": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 01:20 .\ndrwxr-xr-x 1 root root 4096 Sep 29 01:22 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:17 data\n-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt\nProcess Group PGID: 1615\n",
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{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T01:22:52.732Z",
"blocks": [
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"timestamp": "2026-09-29T01:22:52.732Z",
"callId": "run_shell_command__call_30934",
"name": "run_shell_command",
"input": {
"description": "Read evaluate_public.py",
"command": "cat /task/evaluate_public.py"
},
"recorded": {
"id": "run_shell_command__call_30934",
"name": "run_shell_command",
"args": {
"description": "Read evaluate_public.py",
"command": "cat /task/evaluate_public.py"
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"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: 1618\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:52.766Z",
"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": "2026-09-29T01:22:52.767Z",
"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: 1618\n",
"isError": false
},
"responseMessageId": "82bfdec9-f8ce-47b4-8339-fc52d1727096"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:52.732Z",
"callId": "run_shell_command__call_30937",
"name": "run_shell_command",
"input": {
"command": "cat /task/requirements.txt",
"description": "Read requirements.txt"
},
"recorded": {
"id": "run_shell_command__call_30937",
"name": "run_shell_command",
"args": {
"command": "cat /task/requirements.txt",
"description": "Read requirements.txt"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30937",
"name": "run_shell_command",
"response": {
"output": "\nOutput: # 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: 1621\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:52.766Z",
"resultDisplay": "# 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": "cat /task/requirements.txt",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:52.767Z",
"content": "\nOutput: # 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: 1621\n",
"isError": false
},
"responseMessageId": "82bfdec9-f8ce-47b4-8339-fc52d1727096"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:52.732Z",
"callId": "run_shell_command__call_30943",
"name": "run_shell_command",
"input": {
"description": "Check compute resources (CPU, RAM, GPU)",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi"
},
"recorded": {
"id": "run_shell_command__call_30943",
"name": "run_shell_command",
"args": {
"description": "Check compute resources (CPU, RAM, GPU)",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30943",
"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\nExit Code: 127\nProcess Group PGID: 1624\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:52.766Z",
"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",
"description": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:52.767Z",
"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\nExit Code: 127\nProcess Group PGID: 1624\n",
"isError": false
},
"responseMessageId": "82bfdec9-f8ce-47b4-8339-fc52d1727096"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T01:22:55.160Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:55.160Z",
"callId": "run_shell_command__call_3069935",
"name": "run_shell_command",
"input": {
"description": "Check total memory",
"command": "cat /proc/meminfo | grep MemTotal"
},
"recorded": {
"id": "run_shell_command__call_3069935",
"name": "run_shell_command",
"args": {
"description": "Check total memory",
"command": "cat /proc/meminfo | grep MemTotal"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3069935",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988968 kB\nProcess Group PGID: 1630\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:55.179Z",
"resultDisplay": "MemTotal: 15988968 kB",
"description": "cat /proc/meminfo | grep MemTotal",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:55.180Z",
"content": "\nOutput: MemTotal: 15988968 kB\nProcess Group PGID: 1630\n",
"isError": false
},
"responseMessageId": "58943be9-6cc9-47d8-a1b7-eb3065cf95b3"
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:55.160Z",
"callId": "run_shell_command__call_3069937",
"name": "run_shell_command",
"input": {
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
},
"recorded": {
"id": "run_shell_command__call_3069937",
"name": "run_shell_command",
"args": {
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3069937",
"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: 1634\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:55.179Z",
"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": "2026-09-29T01:22:55.180Z",
"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: 1634\n",
"isError": false
},
"responseMessageId": "58943be9-6cc9-47d8-a1b7-eb3065cf95b3"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T01:22:58.242Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:22:58.242Z",
"text": "Assessing Current Resource Allocation\n\nMy current focus is on understanding our resource landscape. I'm examining the available RAM and vCPUs, and I'll soon be inspecting the `target_users` and `candidate_catalog` datasets, including their dimensions and data types, to gauge overall size and inform our next steps."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:22:58.242Z",
"callId": "run_shell_command__call_239418",
"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 count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog count:', len(catalog), catalog.columns.tolist())\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:', len(val_state), val_state.columns.tolist())\nprint(val_state.head())\ntest_state = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test preference state:', len(test_state), test_state.columns.tolist())\n\"",
"description": "Check target users, catalog, preference state shapes"
},
"recorded": {
"id": "run_shell_command__call_239418",
"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 count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog count:', len(catalog), catalog.columns.tolist())\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:', len(val_state), val_state.columns.tolist())\nprint(val_state.head())\ntest_state = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test preference state:', len(test_state), test_state.columns.tolist())\n\"",
"description": "Check target users, catalog, preference state shapes"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_239418",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\nCatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nValidation preference state: 469124 ['uid', 'item_id', 'state']\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nTest preference state: 484776 ['uid', 'item_id', 'state']\nProcess Group PGID: 1637\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:22:59.095Z",
"resultDisplay": "Targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\nCatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nValidation preference state: 469124 ['uid', 'item_id', 'state']\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nTest preference state: 484776 ['uid', 'item_id', 'state']",
"description": "Check target users, catalog, preference state shapes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:22:59.095Z",
"content": "\nOutput: Targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\nCatalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nValidation preference state: 469124 ['uid', 'item_id', 'state']\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nTest preference state: 484776 ['uid', 'item_id', 'state']\nProcess Group PGID: 1637\n",
"isError": false
},
"responseMessageId": "b00ec90c-ddf4-4978-8948-a849cf589bde"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T01:23:01.071Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:01.071Z",
"callId": "run_shell_command__call_130949",
"name": "run_shell_command",
"input": {
"description": "Check available libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'implicit', 'torch', 'scipy', 'sklearn', 'polars', 'pyarrow']:\n try:\n __import__(pkg)\n print(pkg, 'is AVAILABLE')\n except ImportError:\n print(pkg, 'is NOT available')\n\""
},
"recorded": {
"id": "run_shell_command__call_130949",
"name": "run_shell_command",
"args": {
"description": "Check available libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'implicit', 'torch', 'scipy', 'sklearn', 'polars', 'pyarrow']:\n try:\n __import__(pkg)\n print(pkg, 'is AVAILABLE')\n except ImportError:\n print(pkg, 'is NOT available')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_130949",
"name": "run_shell_command",
"response": {
"output": "\nOutput: lightgbm is NOT available\nxgboost is NOT available\ncatboost is NOT available\nimplicit is NOT available\ntorch is NOT available\nscipy is AVAILABLE\nsklearn is AVAILABLE\npolars is NOT available\npyarrow is AVAILABLE\nProcess Group PGID: 1654\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:02.583Z",
"resultDisplay": "lightgbm is NOT available\nxgboost is NOT available\ncatboost is NOT available\nimplicit is NOT available\ntorch is NOT available\nscipy is AVAILABLE\nsklearn is AVAILABLE\npolars is NOT available\npyarrow is AVAILABLE",
"description": "Check available libraries",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:02.584Z",
"content": "\nOutput: lightgbm is NOT available\nxgboost is NOT available\ncatboost is NOT available\nimplicit is NOT available\ntorch is NOT available\nscipy is AVAILABLE\nsklearn is AVAILABLE\npolars is NOT available\npyarrow is AVAILABLE\nProcess Group PGID: 1654\n",
"isError": false
},
"responseMessageId": "2377166c-0506-4155-9f52-918138e53309"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T01:23:04.995Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:04.995Z",
"callId": "run_shell_command__call_96145",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\nimport glob\n\nfor f in sorted(glob.glob('/task/data/*.parquet')):\n df = pd.read_parquet(f)\n ts_info = ''\n if 'timestamp' in df.columns:\n ts_info = f'min_ts: {df.timestamp.min()}, max_ts: {df.timestamp.max()}'\n print(f'{f.split(\\\"/\\\")[-1]}: {len(df):,} rows, cols: {df.columns.tolist()} {ts_info}')\n\"",
"description": "Summary of all parquet files"
},
"recorded": {
"id": "run_shell_command__call_96145",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\nimport glob\n\nfor f in sorted(glob.glob('/task/data/*.parquet')):\n df = pd.read_parquet(f)\n ts_info = ''\n if 'timestamp' in df.columns:\n ts_info = f'min_ts: {df.timestamp.min()}, max_ts: {df.timestamp.max()}'\n print(f'{f.split(\\\"/\\\")[-1]}: {len(df):,} rows, cols: {df.columns.tolist()} {ts_info}')\n\"",
"description": "Summary of all parquet files"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_96145",
"name": "run_shell_command",
"response": {
"output": "\nOutput: candidate_catalog.parquet: 30,000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] \ntarget_users.parquet: 8,456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens'] \ntest_preference_state.parquet: 484,776 rows, cols: ['uid', 'item_id', 'state'] \ntrain_dislikes.parquet: 101,075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 75, max_ts: 24786580\ntrain_likes.parquet: 814,831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 80, max_ts: 24786790\ntrain_listens.parquet: 43,484,159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 0, max_ts: 24786795\ntrain_undislikes.parquet: 19,960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 15950, max_ts: 24785395\ntrain_unlikes.parquet: 292,665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 250, max_ts: 24786570\nval_dislikes.parquet: 3,563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789920, max_ts: 25393280\nval_likes.parquet: 37,795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24788815, max_ts: 25393390\nval_listens.parquet: 1,501,697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 24788600, max_ts: 25393395\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24810715, max_ts: 25393035\nval_unlikes.parquet: 9,346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789005, max_ts: 25393385\nvalidation_preference_state.parquet: 469,124 rows, cols: ['uid', 'item_id', 'state']\nProcess Group PGID: 1672\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:06.745Z",
"resultDisplay": "candidate_catalog.parquet: 30,000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] \ntarget_users.parquet: 8,456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens'] \ntest_preference_state.parquet: 484,776 rows, cols: ['uid', 'item_id', 'state'] \ntrain_dislikes.parquet: 101,075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 75, max_ts: 24786580\ntrain_likes.parquet: 814,831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 80, max_ts: 24786790\ntrain_listens.parquet: 43,484,159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 0, max_ts: 24786795\ntrain_undislikes.parquet: 19,960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 15950, max_ts: 24785395\ntrain_unlikes.parquet: 292,665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 250, max_ts: 24786570\nval_dislikes.parquet: 3,563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789920, max_ts: 25393280\nval_likes.parquet: 37,795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24788815, max_ts: 25393390\nval_listens.parquet: 1,501,697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 24788600, max_ts: 25393395\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24810715, max_ts: 25393035\nval_unlikes.parquet: 9,346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789005, max_ts: 25393385\nvalidation_preference_state.parquet: 469,124 rows, cols: ['uid', 'item_id', 'state']",
"description": "Summary of all parquet files",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:06.747Z",
"content": "\nOutput: candidate_catalog.parquet: 30,000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] \ntarget_users.parquet: 8,456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens'] \ntest_preference_state.parquet: 484,776 rows, cols: ['uid', 'item_id', 'state'] \ntrain_dislikes.parquet: 101,075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 75, max_ts: 24786580\ntrain_likes.parquet: 814,831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 80, max_ts: 24786790\ntrain_listens.parquet: 43,484,159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 0, max_ts: 24786795\ntrain_undislikes.parquet: 19,960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 15950, max_ts: 24785395\ntrain_unlikes.parquet: 292,665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 250, max_ts: 24786570\nval_dislikes.parquet: 3,563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789920, max_ts: 25393280\nval_likes.parquet: 37,795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24788815, max_ts: 25393390\nval_listens.parquet: 1,501,697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 24788600, max_ts: 25393395\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24810715, max_ts: 25393035\nval_unlikes.parquet: 9,346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789005, max_ts: 25393385\nvalidation_preference_state.parquet: 469,124 rows, cols: ['uid', 'item_id', 'state']\nProcess Group PGID: 1672\n",
"isError": false
},
"responseMessageId": "cfe2f8ee-383c-411f-8963-2a4cacc0313a"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T01:23:13.499Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:13.499Z",
"text": "Analyzing Data Size\n\nMy focus has shifted to understanding the sheer volume of data. The training set alone has over 43 million entries, while the validation set contains a substantial 1.5 million. I'm currently examining the timestamp units within these datasets to get a clearer picture of the temporal scope."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:13.499Z",
"callId": "run_shell_command__call_25940",
"name": "run_shell_command",
"input": {
"description": "Inspect candidate eligibility structures",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val elig keys:', val_elig.files)\nprint('uids:', len(val_elig['uids']), 'item_ids:', len(val_elig['item_ids']), 'packed shape:', val_elig['packed_eligible'].shape)\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test elig keys:', test_elig.files)\nprint('uids:', len(test_elig['uids']), 'item_ids:', len(test_elig['item_ids']), 'packed shape:', test_elig['packed_eligible'].shape)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('uids match targets?:', np.array_equal(val_elig['uids'], targets.uid.values), np.array_equal(test_elig['uids'], targets.uid.values))\n\n# Check candidate count per user\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(val_elig['item_ids']), bitorder='little')\nprint('User 0 eligible count val:', np.sum(row0))\n\n# Sample candidate counts across users\ncounts = [np.sum(np.unpackbits(val_elig['packed_eligible'][i], count=len(val_elig['item_ids']), bitorder='little')) for i in range(100)]\nprint('Sample candidate counts (min, mean, max):', min(counts), np.mean(counts), max(counts))\n\""
},
"recorded": {
"id": "run_shell_command__call_25940",
"name": "run_shell_command",
"args": {
"description": "Inspect candidate eligibility structures",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val elig keys:', val_elig.files)\nprint('uids:', len(val_elig['uids']), 'item_ids:', len(val_elig['item_ids']), 'packed shape:', val_elig['packed_eligible'].shape)\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test elig keys:', test_elig.files)\nprint('uids:', len(test_elig['uids']), 'item_ids:', len(test_elig['item_ids']), 'packed shape:', test_elig['packed_eligible'].shape)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('uids match targets?:', np.array_equal(val_elig['uids'], targets.uid.values), np.array_equal(test_elig['uids'], targets.uid.values))\n\n# Check candidate count per user\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(val_elig['item_ids']), bitorder='little')\nprint('User 0 eligible count val:', np.sum(row0))\n\n# Sample candidate counts across users\ncounts = [np.sum(np.unpackbits(val_elig['packed_eligible'][i], count=len(val_elig['item_ids']), bitorder='little')) for i in range(100)]\nprint('Sample candidate counts (min, mean, max):', min(counts), np.mean(counts), max(counts))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_25940",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nTest elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nuids match targets?: True True\nUser 0 eligible count val: 29318\nSample candidate counts (min, mean, max): 25547 29098.94 29971\nProcess Group PGID: 1692\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:24.025Z",
"resultDisplay": "Val elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nTest elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nuids match targets?: True True\nUser 0 eligible count val: 29318\nSample candidate counts (min, mean, max): 25547 29098.94 29971",
"description": "Inspect candidate eligibility structures",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:24.026Z",
"content": "\nOutput: Val elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nTest elig keys: ['uids', 'item_ids', 'packed_eligible']\nuids: 8456 item_ids: 30000 packed shape: (8456, 3750)\nuids match targets?: True True\nUser 0 eligible count val: 29318\nSample candidate counts (min, mean, max): 25547 29098.94 29971\nProcess Group PGID: 1692\n",
"isError": false
},
"responseMessageId": "519a4f8c-cb17-4b9b-ad15-583dd7aa9f65"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T01:23:27.885Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:27.885Z",
"text": "Investigating Ineligibility\n\nI'm trying to pinpoint why nearly 900 items are deemed ineligible for the 8,456 target users, given the vast pool of 30,000 candidate items. My focus is on understanding the specific criteria causing this discrepancy."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:27.885Z",
"callId": "run_shell_command__call_75842",
"name": "run_shell_command",
"input": {
"description": "Check relationship between preference state and eligibility",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\n\n# Let's check for user 0\nuid0 = uids[0]\nuser0_state = val_state[val_state.uid == uid0]\nprint(f'User 0 (uid={uid0}) preference state:')\nprint(user0_state)\n\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(item_ids), bitorder='little')\nitem_map = {item: i for i, item in enumerate(item_ids)}\nstate_in_elig = [row0[item_map[it]] for it in user0_state.item_id if it in item_map]\nprint('Are user 0 liked/disliked items in eligible?', state_in_elig[:10])\n\""
},
"recorded": {
"id": "run_shell_command__call_75842",
"name": "run_shell_command",
"args": {
"description": "Check relationship between preference state and eligibility",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\n\n# Let's check for user 0\nuid0 = uids[0]\nuser0_state = val_state[val_state.uid == uid0]\nprint(f'User 0 (uid={uid0}) preference state:')\nprint(user0_state)\n\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(item_ids), bitorder='little')\nitem_map = {item: i for i, item in enumerate(item_ids)}\nstate_in_elig = [row0[item_map[it]] for it in user0_state.item_id if it in item_map]\nprint('Are user 0 liked/disliked items in eligible?', state_in_elig[:10])\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_75842",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 0 (uid=100) preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\n10 100 4943655 liked\n11 100 5590855 liked\n12 100 6568592 liked\n13 100 8200811 liked\n14 100 8661238 disliked\nAre user 0 liked/disliked items in eligible? [np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0)]\nProcess Group PGID: 1708\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:28.819Z",
"resultDisplay": "User 0 (uid=100) preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\n10 100 4943655 liked\n11 100 5590855 liked\n12 100 6568592 liked\n13 100 8200811 liked\n14 100 8661238 disliked\nAre user 0 liked/disliked items in eligible? [np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0)]",
"description": "Check relationship between preference state and eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:28.820Z",
"content": "\nOutput: User 0 (uid=100) preference state:\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\n5 100 3535902 liked\n6 100 3633039 liked\n7 100 4099299 liked\n8 100 4221787 liked\n9 100 4927727 disliked\n10 100 4943655 liked\n11 100 5590855 liked\n12 100 6568592 liked\n13 100 8200811 liked\n14 100 8661238 disliked\nAre user 0 liked/disliked items in eligible? [np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0)]\nProcess Group PGID: 1708\n",
"isError": false
},
"responseMessageId": "06fc7148-0546-458e-a7ac-8e33e48ccb15"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T01:23:32.071Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:32.071Z",
"text": "Analyzing User Preferences\n\nI'm currently examining how user preference state items influence eligibility, specifically noting exclusions. My focus has shifted to understanding the evaluation metric's behavior, particularly its calculation of relevant items within the validation window and the resulting scores."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:32.071Z",
"callId": "run_shell_command__call_1124229",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(5))\n\"",
"description": "Inspect candidate catalog"
},
"recorded": {
"id": "run_shell_command__call_1124229",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(5))\n\"",
"description": "Inspect candidate catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1124229",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nProcess Group PGID: 1726\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:32.871Z",
"resultDisplay": "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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000",
"description": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(5))\n\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:32.872Z",
"content": "\nOutput: 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\n5 5463340 22746 17398 6\n6 8213481 22505 17071 7\n7 906358 22238 17183 8\n8 2859641 18380 13704 9\n9 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n29995 1275692 163 81 29996\n29996 1280011 163 76 29997\n29997 1309246 163 138 29998\n29998 1634958 163 46 29999\n29999 1670524 163 73 30000\nProcess Group PGID: 1726\n",
"isError": false
},
"responseMessageId": "57e33106-59ed-4fe4-94c8-341c54381321"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T01:23:36.766Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:36.766Z",
"text": "Establishing Baseline Performance\n\nI'm currently focused on establishing a quick popularity baseline for the validation set. I'll save this to `/app/solution/val_pop.parquet` and then use `/task/evaluate_public.py` to gauge our initial performance. I'm also taking a moment to understand the mechanics of this evaluation script."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:36.766Z",
"callId": "run_shell_command__call_4848785",
"name": "run_shell_command",
"input": {
"description": "Check if elig.item_ids matches catalog.item_id",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = catalog.item_id.values\n\n# For each user, take the top 100 eligible items in catalog order\nrows = []\nfor uid in uids:\n row = elig.uid_row[uid]\n packed = elig.packed[row]\n # Fast bitmask check\n mask = np.unpackbits(packed, count=len(elig.item_ids), bitorder='little')\n # Filter items that are eligible\n # Wait: elig.item_ids might have a different order than catalog_items!\n # Let's check if elig.item_ids is the same order or different!\n\""
},
"recorded": {
"id": "run_shell_command__call_4848785",
"name": "run_shell_command",
"args": {
"description": "Check if elig.item_ids matches catalog.item_id",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = catalog.item_id.values\n\n# For each user, take the top 100 eligible items in catalog order\nrows = []\nfor uid in uids:\n row = elig.uid_row[uid]\n packed = elig.packed[row]\n # Fast bitmask check\n mask = np.unpackbits(packed, count=len(elig.item_ids), bitorder='little')\n # Filter items that are eligible\n # Wait: elig.item_ids might have a different order than catalog_items!\n # Let's check if elig.item_ids is the same order or different!\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4848785",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1743\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:37.551Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Check if elig.item_ids matches catalog.item_id",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:37.552Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 4, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1743\n",
"isError": false
},
"responseMessageId": "35205644-3d60-4218-b69b-dad9b0a4de1f"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T01:23:40.163Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:40.163Z",
"callId": "run_shell_command__call_18620",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Are item_ids identical to catalog?:', np.array_equal(elig.item_ids, catalog.item_id.values))\nif not np.array_equal(elig.item_ids, catalog.item_id.values):\n print('catalog[:5]:', catalog.item_id.values[:5])\n print('elig[:5]:', elig.item_ids[:5])\n\"",
"description": "Check item order between candidate_set and catalog"
},
"recorded": {
"id": "run_shell_command__call_18620",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Are item_ids identical to catalog?:', np.array_equal(elig.item_ids, catalog.item_id.values))\nif not np.array_equal(elig.item_ids, catalog.item_id.values):\n print('catalog[:5]:', catalog.item_id.values[:5])\n print('elig[:5]:', elig.item_ids[:5])\n\"",
"description": "Check item order between candidate_set and catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_18620",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Are item_ids identical to catalog?: True\nProcess Group PGID: 1754\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:41.096Z",
"resultDisplay": "Are item_ids identical to catalog?: True",
"description": "Check item order between candidate_set and catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:41.096Z",
"content": "\nOutput: Are item_ids identical to catalog?: True\nProcess Group PGID: 1754\n",
"isError": false
},
"responseMessageId": "936f343b-e5f1-483f-a613-1f8a91fa8984"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T01:23:44.004Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:44.004Z",
"text": "Confirming Item Order\n\nI've confirmed that both validation and test `item_ids` precisely match the order in `candidate_catalog.parquet`, which is by popularity rank. Now, I'm proceeding to examine the `item_ids` within `test_eligible_candidates.npz`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:44.004Z",
"callId": "run_shell_command__call_3790877",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\ntest_elig = CandidateSet('/task/data/test_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Test elig item_ids identical to catalog?:', np.array_equal(test_elig.item_ids, catalog.item_id.values))\nprint('Test elig uids identical to targets?:', np.array_equal(test_elig.uids, pd.read_parquet('/task/data/target_users.parquet').uid.values))\n\"",
"description": "Check test elig item_ids and uids"
},
"recorded": {
"id": "run_shell_command__call_3790877",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\ntest_elig = CandidateSet('/task/data/test_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Test elig item_ids identical to catalog?:', np.array_equal(test_elig.item_ids, catalog.item_id.values))\nprint('Test elig uids identical to targets?:', np.array_equal(test_elig.uids, pd.read_parquet('/task/data/target_users.parquet').uid.values))\n\"",
"description": "Check test elig item_ids and uids"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3790877",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Test elig item_ids identical to catalog?: True\nTest elig uids identical to targets?: True\nProcess Group PGID: 1771\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:44.928Z",
"resultDisplay": "Test elig item_ids identical to catalog?: True\nTest elig uids identical to targets?: True",
"description": "Check test elig item_ids and uids",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:44.929Z",
"content": "\nOutput: Test elig item_ids identical to catalog?: True\nTest elig uids identical to targets?: True\nProcess Group PGID: 1771\n",
"isError": false
},
"responseMessageId": "71be72e8-29b8-487a-8aea-4fd8c05a0b0a"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T01:23:49.444Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:23:49.444Z",
"text": "Confirming Data Integrity\n\nMy latest check confirms that both `val_elig` and `test_elig` datasets perfectly align with `candidate_catalog.parquet` and `target_users.parquet` in terms of item IDs and user IDs, respectively, maintaining the exact order. This rigorous verification reassures me of the data's foundational integrity."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:49.444Z",
"callId": "run_shell_command__call_8204",
"name": "run_shell_command",
"input": {
"description": "Generate popularity baseline for validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\n# For popularity baseline, for each user take top 100 eligible items in catalog order\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # eligible item indices:\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/val_pop.parquet', index=False)\nprint(f'Done in {time.time()-t0:.2f}s, rows={len(df)}')\n\""
},
"recorded": {
"id": "run_shell_command__call_8204",
"name": "run_shell_command",
"args": {
"description": "Generate popularity baseline for validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\n# For popularity baseline, for each user take top 100 eligible items in catalog order\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # eligible item indices:\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/val_pop.parquet', index=False)\nprint(f'Done in {time.time()-t0:.2f}s, rows={len(df)}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_8204",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Done in 1.13s, rows=845600\nProcess Group PGID: 1788\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:51.381Z",
"resultDisplay": "Done in 1.13s, rows=845600",
"description": "Generate popularity baseline for validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:51.381Z",
"content": "\nOutput: Done in 1.13s, rows=845600\nProcess Group PGID: 1788\n",
"isError": false
},
"responseMessageId": "988c40cd-10a8-404c-92b9-86bec73322f7"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T01:23:53.290Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:23:53.290Z",
"callId": "run_shell_command__call_2699",
"name": "run_shell_command",
"input": {
"description": "Evaluate popularity baseline on validation set",
"command": "python3 /task/evaluate_public.py /app/solution/val_pop.parquet"
},
"recorded": {
"id": "run_shell_command__call_2699",
"name": "run_shell_command",
"args": {
"description": "Evaluate popularity baseline on validation set",
"command": "python3 /task/evaluate_public.py /app/solution/val_pop.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2699",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/val_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1799\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:23:55.891Z",
"resultDisplay": "{\n \"model\": \"/app/solution/val_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "python3 /task/evaluate_public.py /app/solution/val_pop.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:23:55.892Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/val_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1799\n",
"isError": false
},
"responseMessageId": "05b06d8c-915a-4a5e-accc-fef5d16ed9ce"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T01:24:00.117Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:00.117Z",
"text": "Testing Submission Generation\n\nI've confirmed the evaluation metrics, with `quality_ndcg@10` at 0.01071 and over 4,000 users having relevant items. My next step is to see if I can swiftly produce a valid test submission using the current popularity baseline. This ensures we consistently have a functional deliverable."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:00.117Z",
"callId": "run_shell_command__call_7714",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nelig = np.load('/task/data/test_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint(f'Test submission created, rows={len(df)}')\n\"",
"description": "Create initial fallback test submission"
},
"recorded": {
"id": "run_shell_command__call_7714",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nelig = np.load('/task/data/test_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint(f'Test submission created, rows={len(df)}')\n\"",
"description": "Create initial fallback test submission"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_7714",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Test submission created, rows=845600\nProcess Group PGID: 1819\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:02.000Z",
"resultDisplay": "Test submission created, rows=845600",
"description": "Create initial fallback test submission",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:02.001Z",
"content": "\nOutput: Test submission created, rows=845600\nProcess Group PGID: 1819\n",
"isError": false
},
"responseMessageId": "f133c97d-7908-4ef3-b3e0-8ede429b17b7"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T01:24:04.238Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:04.238Z",
"callId": "run_shell_command__call_23450",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nframe = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nall_valid = all(\n elig.is_eligible(uid, item)\n for uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None)\n)\nprint('Submission valid?:', all_valid)\n\"",
"description": "Verify initial test submission integrity"
},
"recorded": {
"id": "run_shell_command__call_23450",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nframe = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nall_valid = all(\n elig.is_eligible(uid, item)\n for uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None)\n)\nprint('Submission valid?:', all_valid)\n\"",
"description": "Verify initial test submission integrity"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_23450",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Submission valid?: True\nProcess Group PGID: 1830\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:06.129Z",
"resultDisplay": "Submission valid?: True",
"description": "Verify initial test submission integrity",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:06.130Z",
"content": "\nOutput: Submission valid?: True\nProcess Group PGID: 1830\n",
"isError": false
},
"responseMessageId": "bd76dc0b-49fa-44a8-b389-e90432fe0f9d"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T01:24:11.961Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:11.961Z",
"text": "Exploring Data for Optimization\n\nI'm currently diving deep into the data, examining its structure and the nuances of the task. My focus is on identifying strategies to maximize the NDCG@10 score, considering various models and their potential impact."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:11.961Z",
"text": "Investigating Dataset Contents\n\nI'm now examining the `train_listens.parquet` file. My immediate focus is on understanding the column definitions and the temporal distribution of listen events. I'm also keen to verify the presence of all target users and the nuances of positive listen definitions as per the problem description."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:11.961Z",
"callId": "run_shell_command__call_2877414",
"name": "run_shell_command",
"input": {
"description": "Analyze user presence in train files",
"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)\n\nprint('Target users count:', len(target_uids))\n\n# Check target users in train_likes, train_dislikes, etc.\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes:', len(likes), 'for targets:', likes.uid.isin(target_uids).sum())\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Train dislikes:', len(dislikes), 'for targets:', dislikes.uid.isin(target_uids).sum())\n\n# How big is train_listens for target users vs other users?\n# Let's inspect a chunk or pyarrow dataset\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint('Total train listens:', len(table))\ndf_sample = table.slice(0, 1000000).to_pandas()\nprint('Sample listens in targets:', df_sample.uid.isin(target_uids).mean())\n\""
},
"recorded": {
"id": "run_shell_command__call_2877414",
"name": "run_shell_command",
"args": {
"description": "Analyze user presence in train files",
"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)\n\nprint('Target users count:', len(target_uids))\n\n# Check target users in train_likes, train_dislikes, etc.\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes:', len(likes), 'for targets:', likes.uid.isin(target_uids).sum())\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Train dislikes:', len(dislikes), 'for targets:', dislikes.uid.isin(target_uids).sum())\n\n# How big is train_listens for target users vs other users?\n# Let's inspect a chunk or pyarrow dataset\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint('Total train listens:', len(table))\ndf_sample = table.slice(0, 1000000).to_pandas()\nprint('Sample listens in targets:', df_sample.uid.isin(target_uids).mean())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2877414",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users count: 8456\nTrain likes: 814831 for targets: 690968\nTrain dislikes: 101075 for targets: 84266\nTotal train listens: 43484159\nSample listens in targets: 0.995437\nProcess Group PGID: 1848\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:13.374Z",
"resultDisplay": "Target users count: 8456\nTrain likes: 814831 for targets: 690968\nTrain dislikes: 101075 for targets: 84266\nTotal train listens: 43484159\nSample listens in targets: 0.995437",
"description": "Analyze user presence in train files",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:13.375Z",
"content": "\nOutput: Target users count: 8456\nTrain likes: 814831 for targets: 690968\nTrain dislikes: 101075 for targets: 84266\nTotal train listens: 43484159\nSample listens in targets: 0.995437\nProcess Group PGID: 1848\n",
"isError": false
},
"responseMessageId": "0749321d-811d-4653-b6ed-e6ea2acdafb1"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T01:24:35.643Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:35.643Z",
"text": "Focusing on Target Users\n\nI've confirmed that `train_listens` overwhelmingly comprises our target users, representing nearly all of its data. The sheer volume of listens per user is striking, and I'm now examining the temporal extent of this dataset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:35.643Z",
"callId": "run_shell_command__call_20177",
"name": "run_shell_command",
"input": {
"description": "Check listen timestamps distribution",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\nt_cutoff = 24786800\nt_90d = t_cutoff - 90 * 86400\nt_30d = t_cutoff - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'played_ratio_pct', 'is_organic'])\nts = table['timestamp']\n\nn_total = len(ts)\nn_90d = pc.sum(pc.greater_equal(ts, t_90d)).as_py()\nn_30d = pc.sum(pc.greater_equal(ts, t_30d)).as_py()\nprint(f'Total: {n_total:,}, in last 90d: {n_90d:,} ({n_90d/n_total:.1%}), in last 30d: {n_30d:,} ({n_30d/n_total:.1%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_20177",
"name": "run_shell_command",
"args": {
"description": "Check listen timestamps distribution",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\nt_cutoff = 24786800\nt_90d = t_cutoff - 90 * 86400\nt_30d = t_cutoff - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'played_ratio_pct', 'is_organic'])\nts = table['timestamp']\n\nn_total = len(ts)\nn_90d = pc.sum(pc.greater_equal(ts, t_90d)).as_py()\nn_30d = pc.sum(pc.greater_equal(ts, t_30d)).as_py()\nprint(f'Total: {n_total:,}, in last 90d: {n_90d:,} ({n_90d/n_total:.1%}), in last 30d: {n_30d:,} ({n_30d/n_total:.1%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_20177",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total: 43,484,159, in last 90d: 16,769,274 (38.6%), in last 30d: 6,126,103 (14.1%)\nProcess Group PGID: 1870\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:37.212Z",
"resultDisplay": "Total: 43,484,159, in last 90d: 16,769,274 (38.6%), in last 30d: 6,126,103 (14.1%)",
"description": "Check listen timestamps distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:37.213Z",
"content": "\nOutput: Total: 43,484,159, in last 90d: 16,769,274 (38.6%), in last 30d: 6,126,103 (14.1%)\nProcess Group PGID: 1870\n",
"isError": false
},
"responseMessageId": "094d0e86-6e40-4a57-a870-9144bb4ad8e2"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T01:24:41.648Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:24:41.648Z",
"text": "Analyzing Listen Patterns\n\nMy current focus is on dissecting listen patterns within specific timeframes. I'm particularly interested in understanding the \"target window behavior\" and identifying positive listens within the `val_listens.parquet` dataset, referencing the `validation_truth` definitions."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:24:41.648Z",
"callId": "run_shell_command__call_181955",
"name": "run_shell_command",
"input": {
"description": "Inspect validation ground truth statistics",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ntotal_users = len(truth)\ntotal_pairs = sum(len(items) for items in truth.values())\ngrade1_pairs = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ngrade3_pairs = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\n\nprint(f'Users with truth: {total_users}')\nprint(f'Total user-item pairs: {total_pairs}')\nprint(f'Grade 1 pairs: {grade1_pairs}, Grade 3 pairs: {grade3_pairs}')\n\""
},
"recorded": {
"id": "run_shell_command__call_181955",
"name": "run_shell_command",
"args": {
"description": "Inspect validation ground truth statistics",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ntotal_users = len(truth)\ntotal_pairs = sum(len(items) for items in truth.values())\ngrade1_pairs = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ngrade3_pairs = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\n\nprint(f'Users with truth: {total_users}')\nprint(f'Total user-item pairs: {total_pairs}')\nprint(f'Grade 1 pairs: {grade1_pairs}, Grade 3 pairs: {grade3_pairs}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_181955",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Users with truth: 4263\nTotal user-item pairs: 37769\nGrade 1 pairs: 33352, Grade 3 pairs: 4417\nProcess Group PGID: 1894\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:24:42.995Z",
"resultDisplay": "Users with truth: 4263\nTotal user-item pairs: 37769\nGrade 1 pairs: 33352, Grade 3 pairs: 4417",
"description": "Inspect validation ground truth statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:24:42.996Z",
"content": "\nOutput: Users with truth: 4263\nTotal user-item pairs: 37769\nGrade 1 pairs: 33352, Grade 3 pairs: 4417\nProcess Group PGID: 1894\n",
"isError": false
},
"responseMessageId": "de981072-a8e1-49df-aff1-4be69ffa3a48"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T01:25:12.613Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:12.613Z",
"text": "Exploring Item Engagement\n\nI'm currently delving into the characteristics of items users interact with, specifically examining whether they've encountered these items previously in the training data. This analysis aims to uncover patterns in user engagement with new vs. familiar content."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:12.613Z",
"callId": "run_shell_command__call_1044196",
"name": "run_shell_command",
"input": {
"description": "Check if truth pairs appeared in train listens",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Check a sample of train listens for these users\n# Read train_listens for target users\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\ntrain_pairs = set(zip(train_df['uid'], train_df['item_id']))\n\ntruth_pairs = []\nfor u, items in truth.items():\n for item in items:\n truth_pairs.append((u, item))\n\nin_train = sum((u, item) in train_pairs for u, item in truth_pairs)\nprint(f'Total truth pairs: {len(truth_pairs)}')\nprint(f'Truth pairs already listened in train: {in_train} ({in_train/len(truth_pairs):.2%})')\nprint(f'Truth pairs NEVER listened in train (discovery!): {len(truth_pairs) - in_train} ({(len(truth_pairs) - in_train)/len(truth_pairs):.2%})')\n\""
},
"recorded": {
"id": "run_shell_command__call_1044196",
"name": "run_shell_command",
"args": {
"description": "Check if truth pairs appeared in train listens",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Check a sample of train listens for these users\n# Read train_listens for target users\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\ntrain_pairs = set(zip(train_df['uid'], train_df['item_id']))\n\ntruth_pairs = []\nfor u, items in truth.items():\n for item in items:\n truth_pairs.append((u, item))\n\nin_train = sum((u, item) in train_pairs for u, item in truth_pairs)\nprint(f'Total truth pairs: {len(truth_pairs)}')\nprint(f'Truth pairs already listened in train: {in_train} ({in_train/len(truth_pairs):.2%})')\nprint(f'Truth pairs NEVER listened in train (discovery!): {len(truth_pairs) - in_train} ({(len(truth_pairs) - in_train)/len(truth_pairs):.2%})')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1044196",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total truth pairs: 37769\nTruth pairs already listened in train: 0 (0.00%)\nTruth pairs NEVER listened in train (discovery!): 37769 (100.00%)\nProcess Group PGID: 1914\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:34.854Z",
"resultDisplay": "Total truth pairs: 37769\nTruth pairs already listened in train: 0 (0.00%)\nTruth pairs NEVER listened in train (discovery!): 37769 (100.00%)",
"description": "Check if truth pairs appeared in train listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:34.855Z",
"content": "\nOutput: Total truth pairs: 37769\nTruth pairs already listened in train: 0 (0.00%)\nTruth pairs NEVER listened in train (discovery!): 37769 (100.00%)\nProcess Group PGID: 1914\n",
"isError": false
},
"responseMessageId": "c1d4dd76-e01e-4272-a334-85472c510f4c"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T01:25:38.798Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:38.798Z",
"text": "Discovering Exclusion Logic\n\nI've just realized something significant: my candidate selection process entirely prevents recommending tracks the user has already heard. This is a crucial design element, ensuring fresh discoveries are always presented."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:38.798Z",
"callId": "run_shell_command__call_8386",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\n# sample 1000 pairs from train_df\nsample = train_df.sample(1000, random_state=42)\neligible_count = sum(elig.is_eligible(uid, item) for uid, item in zip(sample.uid, sample.item_id))\nprint('Sample of train listens eligible in validation:', eligible_count)\n\"",
"description": "Verify candidate eligibility excludes previous listens"
},
"recorded": {
"id": "run_shell_command__call_8386",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\n# sample 1000 pairs from train_df\nsample = train_df.sample(1000, random_state=42)\neligible_count = sum(elig.is_eligible(uid, item) for uid, item in zip(sample.uid, sample.item_id))\nprint('Sample of train listens eligible in validation:', eligible_count)\n\"",
"description": "Verify candidate eligibility excludes previous listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_8386",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Sample of train listens eligible in validation: 0\nProcess Group PGID: 1938\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:41.866Z",
"resultDisplay": "Sample of train listens eligible in validation: 0",
"description": "Verify candidate eligibility excludes previous listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:41.867Z",
"content": "\nOutput: Sample of train listens eligible in validation: 0\nProcess Group PGID: 1938\n",
"isError": false
},
"responseMessageId": "4c21390d-b033-4ba2-b77c-dba5f6dcbe06"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T01:25:47.966Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:47.966Z",
"text": "Discovering New Content\n\nI'm focusing on how to ensure that all eligible candidates for a user are truly new discoveries. This means rigorously filtering out anything they've already engaged with."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:47.966Z",
"text": "Analyzing Candidate Discovery Methods\n\nMy current focus is on understanding how recommendation systems approach music discovery for users with absolutely no prior listening history. I'm exploring various collaborative filtering techniques and other methods, considering that I only have interaction data, not explicit metadata like artist or genre. I'm examining the available datasets to see what can be inferred."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:47.966Z",
"text": "Exploring Collaborative Filtering\n\nI'm delving into collaborative filtering techniques. Specifically, I'm evaluating Item-Item CF and various Matrix Factorization models like EASE, considering the scale of 30,000 candidate items in relation to the training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:47.966Z",
"callId": "run_shell_command__call_2769197",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_pandas().unique()\nprint('Unique items in train_listens:', len(items))\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\nprint('Catalog items count:', len(catalog_items))\nprint('Overlap:', len(set(items).intersection(catalog_items)))\n\"",
"description": "Check unique items in train_listens and overlap with catalog"
},
"recorded": {
"id": "run_shell_command__call_2769197",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_pandas().unique()\nprint('Unique items in train_listens:', len(items))\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\nprint('Catalog items count:', len(catalog_items))\nprint('Overlap:', len(set(items).intersection(catalog_items)))\n\"",
"description": "Check unique items in train_listens and overlap with catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2769197",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unique items in train_listens: 845395\nCatalog items count: 30000\nOverlap: 30000\nProcess Group PGID: 1962\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:49.503Z",
"resultDisplay": "Unique items in train_listens: 845395\nCatalog items count: 30000\nOverlap: 30000",
"description": "Check unique items in train_listens and overlap with catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:49.504Z",
"content": "\nOutput: Unique items in train_listens: 845395\nCatalog items count: 30000\nOverlap: 30000\nProcess Group PGID: 1962\n",
"isError": false
},
"responseMessageId": "80729ec4-93ca-488d-9c03-f746ded2608a"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T01:25:52.163Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:52.163Z",
"text": "Analyzing Catalog Discrepancy\n\nI'm currently examining the significant difference between the 845,395 unique items in `train_listens` and the much smaller `candidate_catalog` of 30,000, noting that all catalog items are present in the training data. My next step is to quantify how many `train_listens` specifically pertain to those 30,000 items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:52.163Z",
"callId": "run_shell_command__call_1645130",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nin_catalog = np.isin(items, list(catalog_items))\nprint(f'Total listens: {len(items):,}')\nprint(f'Listens to candidate catalog items: {np.sum(in_catalog):,} ({np.mean(in_catalog):.2%})')\n\"",
"description": "Check listen count to candidate catalog items"
},
"recorded": {
"id": "run_shell_command__call_1645130",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nin_catalog = np.isin(items, list(catalog_items))\nprint(f'Total listens: {len(items):,}')\nprint(f'Listens to candidate catalog items: {np.sum(in_catalog):,} ({np.mean(in_catalog):.2%})')\n\"",
"description": "Check listen count to candidate catalog items"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1645130",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total listens: 43,484,159\nListens to candidate catalog items: 31,249,613 (71.86%)\nProcess Group PGID: 1983\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:53.523Z",
"resultDisplay": "Total listens: 43,484,159\nListens to candidate catalog items: 31,249,613 (71.86%)",
"description": "Check listen count to candidate catalog items",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:53.524Z",
"content": "\nOutput: Total listens: 43,484,159\nListens to candidate catalog items: 31,249,613 (71.86%)\nProcess Group PGID: 1983\n",
"isError": false
},
"responseMessageId": "ae8d302d-505e-4a5a-ad1c-290b864de5e9"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T01:25:57.672Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:57.672Z",
"text": "Exploring Catalog Recommendations\n\nI'm currently investigating how our recommendation engine handles items outside the core 30,000 catalog. My recent focus has been on testing and validation to see if recommendations can extend beyond this heavily utilized subset, aiming to ensure comprehensive suggestions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:25:57.672Z",
"text": "Analyzing User Interaction Data\n\nMy recent focus has shifted to understanding the user-item interaction signals. I'm examining `listens`, `likes`, and `dislikes` data, and specifically checking if `validation_preference_state` accurately reflects all active explicit preferences at the training cutoff for validation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:25:57.672Z",
"callId": "run_shell_command__call_13628",
"name": "run_shell_command",
"input": {
"description": "Check state value counts",
"command": "python3 -c \"\nimport pandas as pd\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint(val_state.state.value_counts())\n\""
},
"recorded": {
"id": "run_shell_command__call_13628",
"name": "run_shell_command",
"args": {
"description": "Check state value counts",
"command": "python3 -c \"\nimport pandas as pd\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint(val_state.state.value_counts())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_13628",
"name": "run_shell_command",
"response": {
"output": "\nOutput: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nProcess Group PGID: 2003\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:25:58.517Z",
"resultDisplay": "state\nliked 418435\ndisliked 50689\nName: count, dtype: int64",
"description": "python3 -c \"\nimport pandas as pd\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint(val_state.state.value_counts())\n\"",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:25:58.518Z",
"content": "\nOutput: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nProcess Group PGID: 2003\n",
"isError": false
},
"responseMessageId": "728540fe-5116-4b24-b780-006fd994dc08"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T01:26:06.561Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:06.561Z",
"callId": "run_shell_command__call_2519066",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\nprint(f'Read table in {time.time()-t0:.2f}s')\n\ndf_cat = df[df.item_id.isin(catalog_items)]\nprint(f'Filtered to catalog in {time.time()-t0:.2f}s, rows={len(df_cat):,}')\n\nunique_pairs = df_cat[['uid', 'item_id']].drop_duplicates()\nprint(f'Unique (user, item) pairs in catalog: {len(unique_pairs):,}')\nprint(f'Avg unique items per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Analyze unique user-item pairs in catalog"
},
"recorded": {
"id": "run_shell_command__call_2519066",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\nprint(f'Read table in {time.time()-t0:.2f}s')\n\ndf_cat = df[df.item_id.isin(catalog_items)]\nprint(f'Filtered to catalog in {time.time()-t0:.2f}s, rows={len(df_cat):,}')\n\nunique_pairs = df_cat[['uid', 'item_id']].drop_duplicates()\nprint(f'Unique (user, item) pairs in catalog: {len(unique_pairs):,}')\nprint(f'Avg unique items per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Analyze unique user-item pairs in catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2519066",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read table in 0.85s\nFiltered to catalog in 4.50s, rows=31,249,613\nUnique (user, item) pairs in catalog: 7,965,936\nAvg unique items per user: 942.0\nProcess Group PGID: 2020\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:13.814Z",
"resultDisplay": "Read table in 0.85s\nFiltered to catalog in 4.50s, rows=31,249,613\nUnique (user, item) pairs in catalog: 7,965,936\nAvg unique items per user: 942.0",
"description": "Analyze unique user-item pairs in catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:13.815Z",
"content": "\nOutput: Read table in 0.85s\nFiltered to catalog in 4.50s, rows=31,249,613\nUnique (user, item) pairs in catalog: 7,965,936\nAvg unique items per user: 942.0\nProcess Group PGID: 2020\n",
"isError": false
},
"responseMessageId": "6f135011-f6a2-4cbd-a373-68f03cd5595e"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T01:26:24.449Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:24.449Z",
"text": "Exploring User Interaction\n\nMy recent thoughts reveal that, on average, each user has interacted with approximately 942 unique items out of a potential 30,000. This suggests a significant pool of roughly 29,058 remaining candidates for deeper exploration per user. Surprisingly, processing the entirety of the `train_listens.parquet` dataset proved to be remarkably efficient."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:24.449Z",
"text": "Refining Candidate Selection\n\nThe processing speed of the dataset is exceptional. My current focus is on defining \"relevance\" for the validation and test windows. I'm re-examining the objective, specifically the criteria for grade 3 (likes) and grade 1 (listens to 80%), and how they influence graded NDCG@10. I aim to significantly surpass the baseline popularity score by leveraging personalized recommendation techniques."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:24.449Z",
"text": "Evaluating Item-Item Similarity\n\nI'm shifting focus to evaluating item-item collaborative filtering approaches like cosine and BM25 similarity. I've confirmed the feasibility of computing the $30000 \\times 30000$ item co-occurrence matrix, fitting within memory constraints. My next step is to analyze the sparsity of this matrix and the impact of keeping top-k neighbors."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:24.449Z",
"text": "Testing SVD Efficiency\n\nI'm now investigating the computational feasibility and performance of Singular Value Decomposition (SVD) variants, specifically TruncatedSVD and RandomizedSVD, for reducing dimensionality. I'm keen to test their speed on the sparse interaction matrix and how quickly they can yield meaningful latent factors for user and item representations, aiming for dimensions like 64, 128, or 256."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:24.449Z",
"callId": "run_shell_command__call_72445",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Setup completed in {time.time()-t0:.2f}s')\n\"",
"description": "Test setup for evaluation script"
},
"recorded": {
"id": "run_shell_command__call_72445",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Setup completed in {time.time()-t0:.2f}s')\n\"",
"description": "Test setup for evaluation script"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_72445",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Setup completed in 0.61s\nProcess Group PGID: 2042\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:26.721Z",
"resultDisplay": "Setup completed in 0.61s",
"description": "Test setup for evaluation script",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:26.722Z",
"content": "\nOutput: Setup completed in 0.61s\nProcess Group PGID: 2042\n",
"isError": false
},
"responseMessageId": "697de520-c3f5-4b5e-ab70-0fe458a9163f"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T01:26:44.136Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Exploring Interaction Matrix Construction\n\nI'm currently focused on building an interaction matrix, leveraging `train_listens`, `train_likes`, and `validation_preference_state`. My next step is to evaluate Item-Item Cosine Similarity combined with BM25. I'll be paying close attention to the details of this approach."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Refining Similarity Calculations\n\nI'm refining the Item-Item Cosine Similarity calculation. I realized that by normalizing item columns first, the similarity matrix $S$ can be efficiently computed as $\\tilde{R}^T \\tilde{R}$. This also reveals a path to calculating user similarity vectors $v$ efficiently, which I'm exploring."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Evaluating Sparsity Strategies\n\nI'm now considering whether a dense similarity matrix or a top-k approach with BM25 is superior. I'm analyzing how popular items might influence a dense matrix and exploring the effectiveness of TF-IDF or BM25 weighting on the interaction matrix $R$. EASE is also on my radar."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Optimizing EASE Computation\n\nI've realized inverting the full $30000 \\times 30000$ matrix for EASE is computationally prohibitive. Fortunately, because the number of users ($U=8456$) is significantly less than items ($I=30000$), I can leverage the Woodbury matrix identity. This allows me to invert a much smaller $U \\times U$ matrix instead, drastically reducing the computational cost to seconds. This also provides an efficient way to compute the diagonal elements needed for predictions."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Deriving User-User Kernel\n\nI'm verifying a mathematical identity for EASE, specifically that $X(X^T X + \\lambda I)^{-1}$ is equivalent to $(X X^T + \\lambda I)^{-1} X$. This confirms I can use the smaller user-user Gram matrix $K = X X^T$ for computation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:44.136Z",
"text": "Calculating EASE Predictions Efficiently\n\nI've figured out how to efficiently calculate the diagonal elements $P_{ii}$ for EASE predictions. This involves a dot product between the $i$-th column of $X$ and the $i$-th column of $AX$. This then directly leads to the final prediction scores for unseen candidates, simplifying the scoring process considerably."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:44.136Z",
"callId": "run_shell_command__call_25580",
"name": "run_shell_command",
"input": {
"description": "Verify mathematical equivalence of Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\n\n# Small test: U=10, I=20\nnp.random.seed(42)\nX = (np.random.rand(10, 20) > 0.7).astype(float)\nlam = 10.0\n\n# Method 1: Standard EASE\nG = X.T @ X + lam * np.eye(20)\nP1 = la.inv(G)\nB1 = np.eye(20) - P1 / np.diag(P1)\npred1 = X @ B1\n\n# Method 2: Dual EASE via Kernel\nK = X @ X.T + lam * np.eye(10)\n# A = inv(K)\n# M = A @ X\nM = la.solve(K, X, assume_a='pos')\ncol_dot = np.sum(X * M, axis=0)\ndiag_P2 = (1.0 - col_dot) / lam\n\n# Check diag_P2 vs np.diag(P1)\nprint('Max diff in diag(P):', np.max(np.abs(np.diag(P1) - diag_P2)))\n\n# pred2 = X - M / diag_P2\npred2 = X - M / diag_P2[None, :]\nprint('Max diff in pred:', np.max(np.abs(pred1 - pred2)))\n\""
},
"recorded": {
"id": "run_shell_command__call_25580",
"name": "run_shell_command",
"args": {
"description": "Verify mathematical equivalence of Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\n\n# Small test: U=10, I=20\nnp.random.seed(42)\nX = (np.random.rand(10, 20) > 0.7).astype(float)\nlam = 10.0\n\n# Method 1: Standard EASE\nG = X.T @ X + lam * np.eye(20)\nP1 = la.inv(G)\nB1 = np.eye(20) - P1 / np.diag(P1)\npred1 = X @ B1\n\n# Method 2: Dual EASE via Kernel\nK = X @ X.T + lam * np.eye(10)\n# A = inv(K)\n# M = A @ X\nM = la.solve(K, X, assume_a='pos')\ncol_dot = np.sum(X * M, axis=0)\ndiag_P2 = (1.0 - col_dot) / lam\n\n# Check diag_P2 vs np.diag(P1)\nprint('Max diff in diag(P):', np.max(np.abs(np.diag(P1) - diag_P2)))\n\n# pred2 = X - M / diag_P2\npred2 = X - M / diag_P2[None, :]\nprint('Max diff in pred:', np.max(np.abs(pred1 - pred2)))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_25580",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Max diff in diag(P): 1.3877787807814457e-17\nMax diff in pred: 3.3306690738754696e-16\nProcess Group PGID: 2069\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:26:44.877Z",
"resultDisplay": "Max diff in diag(P): 1.3877787807814457e-17\nMax diff in pred: 3.3306690738754696e-16",
"description": "Verify mathematical equivalence of Dual EASE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:26:44.878Z",
"content": "\nOutput: Max diff in diag(P): 1.3877787807814457e-17\nMax diff in pred: 3.3306690738754696e-16\nProcess Group PGID: 2069\n",
"isError": false
},
"responseMessageId": "31b62caa-d7b5-4f6e-85a2-96dd84157ebb"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T01:26:48.522Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:26:48.522Z",
"text": "Confirming Exact Mathematical Equivalence\n\nI've confirmed an exact mathematical equivalence in the results. The maximum differences in both the diagonal of P and the predictions are astonishingly small, on the order of $10^{-17}$ and $10^{-16}$ respectively. This level of precision strongly suggests the theoretical equivalence holds computationally as well."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:26:48.522Z",
"callId": "run_shell_command__call_2440013",
"name": "run_shell_command",
"input": {
"description": "Benchmark Cholesky solve for Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\nimport time\n\nU = 8456\nI = 30000\nprint(f'Testing with U={U}, I={I}...')\nt0 = time.time()\nK = np.random.randn(U, U).astype(np.float32)\nK = K @ K.T + 500.0 * np.eye(U, dtype=np.float32)\nprint(f'K created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nc, lower = la.cho_factor(K, overwrite_a=True)\nprint(f'Cholesky factor in {time.time()-t1:.2f}s')\n\nt2 = time.time()\nX = np.random.randn(U, I).astype(np.float32)\nM = la.cho_solve((c, lower), X)\nprint(f'Cho solve in {time.time()-t2:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_2440013",
"name": "run_shell_command",
"args": {
"description": "Benchmark Cholesky solve for Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\nimport time\n\nU = 8456\nI = 30000\nprint(f'Testing with U={U}, I={I}...')\nt0 = time.time()\nK = np.random.randn(U, U).astype(np.float32)\nK = K @ K.T + 500.0 * np.eye(U, dtype=np.float32)\nprint(f'K created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nc, lower = la.cho_factor(K, overwrite_a=True)\nprint(f'Cholesky factor in {time.time()-t1:.2f}s')\n\nt2 = time.time()\nX = np.random.randn(U, I).astype(np.float32)\nM = la.cho_solve((c, lower), X)\nprint(f'Cho solve in {time.time()-t2:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2440013",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Testing with U=8456, I=30000...\nK created in 4.44s\nCholesky factor in 1.14s\nCho solve in 25.03s\nProcess Group PGID: 2086\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:19.902Z",
"resultDisplay": "Testing with U=8456, I=30000...\nK created in 4.44s\nCholesky factor in 1.14s\nCho solve in 25.03s",
"description": "Benchmark Cholesky solve for Dual EASE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:21.255Z",
"content": "\nOutput: Testing with U=8456, I=30000...\nK created in 4.44s\nCholesky factor in 1.14s\nCho solve in 25.03s\nProcess Group PGID: 2086\n",
"isError": false
},
"responseMessageId": "0dd61e9c-e486-4b71-a23c-69f0e4e51abf"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T01:27:24.712Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:24.712Z",
"text": "Optimizing Computation Time\n\nMy focus is on accelerating the `cho_solve` operation. It currently takes 25 seconds for 30,000 columns. I've broken down the process: K creation in 4.4s, Cholesky factor in 1.1s, and the cho_solve itself at 25s, for a total of around 30 seconds. I'm considering that X is actually a sparse matrix (CSR format) and exploring how that might be leveraged to improve efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:24.712Z",
"callId": "run_shell_command__call_9726",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\nprint(f'CSR created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nK = (X @ X.T).toarray()\nprint(f'K = X @ X.T in {time.time()-t1:.2f}s')\n\"",
"description": "Benchmark sparse X @ X.T"
},
"recorded": {
"id": "run_shell_command__call_9726",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\nprint(f'CSR created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nK = (X @ X.T).toarray()\nprint(f'K = X @ X.T in {time.time()-t1:.2f}s')\n\"",
"description": "Benchmark sparse X @ X.T"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_9726",
"name": "run_shell_command",
"response": {
"output": "\nOutput: CSR created in 0.36s\nK = X @ X.T in 7.17s\nProcess Group PGID: 2103\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:33.155Z",
"resultDisplay": "CSR created in 0.36s\nK = X @ X.T in 7.17s",
"description": "Benchmark sparse X @ X.T",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:33.156Z",
"content": "\nOutput: CSR created in 0.36s\nK = X @ X.T in 7.17s\nProcess Group PGID: 2103\n",
"isError": false
},
"responseMessageId": "e47c3750-7198-4b7e-9d22-49fe68ab0bec"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T01:27:37.857Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:37.857Z",
"text": "Optimizing Computational Bottlenecks\n\nI'm exploring ways to accelerate the `K = X @ X.T` operation, which currently takes 7.17s. I'm also investigating the performance of `cho_solve`, which currently stands at 25s. I'm considering if processing all 30,000 columns simultaneously is necessary and examining alternative models or faster methods like TruncatedSVD."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:37.857Z",
"callId": "run_shell_command__call_134127",
"name": "run_shell_command",
"input": {
"description": "Benchmark TruncatedSVD",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', n_iter=5, random_state=42)\nU_red = svd.fit_transform(X)\nV_red = svd.components_ # shape (128, I)\nprint(f'TruncatedSVD in {time.time()-t0:.2f}s, shapes: U={U_red.shape}, V={V_red.shape}')\n\n# Reconstructing scores: U_red @ V_red\nt1 = time.time()\nscores = U_red @ V_red # shape (U, I)\nprint(f'Matrix mult in {time.time()-t1:.2f}s, shape: {scores.shape}')\n\""
},
"recorded": {
"id": "run_shell_command__call_134127",
"name": "run_shell_command",
"args": {
"description": "Benchmark TruncatedSVD",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', n_iter=5, random_state=42)\nU_red = svd.fit_transform(X)\nV_red = svd.components_ # shape (128, I)\nprint(f'TruncatedSVD in {time.time()-t0:.2f}s, shapes: U={U_red.shape}, V={V_red.shape}')\n\n# Reconstructing scores: U_red @ V_red\nt1 = time.time()\nscores = U_red @ V_red # shape (U, I)\nprint(f'Matrix mult in {time.time()-t1:.2f}s, shape: {scores.shape}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_134127",
"name": "run_shell_command",
"response": {
"output": "\nOutput: TruncatedSVD in 4.70s, shapes: U=(8456, 128), V=(128, 30000)\nMatrix mult in 0.42s, shape: (8456, 30000)\nProcess Group PGID: 2120\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:45.187Z",
"resultDisplay": "TruncatedSVD in 4.70s, shapes: U=(8456, 128), V=(128, 30000)\nMatrix mult in 0.42s, shape: (8456, 30000)",
"description": "Benchmark TruncatedSVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:45.188Z",
"content": "\nOutput: TruncatedSVD in 4.70s, shapes: U=(8456, 128), V=(128, 30000)\nMatrix mult in 0.42s, shape: (8456, 30000)\nProcess Group PGID: 2120\n",
"isError": false
},
"responseMessageId": "3d472781-911c-489a-8ead-a226eabd3ba0"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T01:27:52.035Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:52.035Z",
"text": "Analyzing SVD and EASE\n\nI've finished running `TruncatedSVD`, which completed in 4.70 seconds, and the subsequent matrix multiplication at 0.42 seconds. My current focus is on evaluating both SVD and EASE performance on the validation set and exploring potential features for the user-item interaction matrix."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:27:52.035Z",
"text": "Investigating Interaction Signals\n\nI'm currently examining the signals within `train_listens.parquet`. My attention is particularly drawn to `played_ratio_pct` and `is_organic` to define positive interactions, and I'm exploring how recent timestamps can be incorporated, perhaps through a decay function."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:52.035Z",
"callId": "run_shell_command__call_219677",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Test evaluation directly on val_pop ranking\nfrom evaluate_public import load_submission\nranked = load_submission('/app/solution/val_pop.parquet', elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\"",
"description": "Check local evaluation harness"
},
"recorded": {
"id": "run_shell_command__call_219677",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Test evaluation directly on val_pop ranking\nfrom evaluate_public import load_submission\nranked = load_submission('/app/solution/val_pop.parquet', elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\"",
"description": "Check local evaluation harness"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_219677",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 15, in \n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\nExit Code: 1\nProcess Group PGID: 2138\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:53.380Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 15, in \n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'",
"description": "Check local evaluation harness",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:53.381Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 15, in \n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^\nAttributeError: 'str' object has no attribute 'suffix'\nExit Code: 1\nProcess Group PGID: 2138\n",
"isError": false
},
"responseMessageId": "a95d2e09-9cdd-4267-a104-396db9a21933"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T01:27:55.796Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:27:55.796Z",
"callId": "run_shell_command__call_61331",
"name": "run_shell_command",
"input": {
"description": "Check local evaluation harness with Path",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth, load_submission\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\nranked = load_submission(Path('/app/solution/val_pop.parquet'), elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\""
},
"recorded": {
"id": "run_shell_command__call_61331",
"name": "run_shell_command",
"args": {
"description": "Check local evaluation harness with Path",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth, load_submission\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\nranked = load_submission(Path('/app/solution/val_pop.parquet'), elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_61331",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Evaluation result: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 2158\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:27:58.427Z",
"resultDisplay": "Evaluation result: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}",
"description": "Check local evaluation harness with Path",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:27:58.428Z",
"content": "\nOutput: Evaluation result: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 2158\n",
"isError": false
},
"responseMessageId": "6fc12ef2-59db-4db7-9af2-415cdecc724b"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T01:28:02.020Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:02.020Z",
"text": "Refining Evaluation Metrics\n\nI'm currently focused on understanding the evaluation results, specifically the NDCG@10 score and the user count. I'm analyzing how the current evaluation methodology, which masks ineligible items per user from a score matrix, contributes to these figures."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:02.020Z",
"callId": "run_shell_command__call_3325928",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = 8456\nI = 30000\nscores = np.random.randn(U, I).astype(np.float32)\n\nt0 = time.time()\n# Test argpartition\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nrow_idx = np.arange(U)[:, None]\ntop100_scores = scores[row_idx, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nfinal_ranks = top100_idx[row_idx, sort_order]\nprint(f'Top 100 for all {U} users computed in {time.time()-t0:.2f}s, shape: {final_ranks.shape}')\n\"",
"description": "Benchmark vectorized top 100 extraction"
},
"recorded": {
"id": "run_shell_command__call_3325928",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = 8456\nI = 30000\nscores = np.random.randn(U, I).astype(np.float32)\n\nt0 = time.time()\n# Test argpartition\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nrow_idx = np.arange(U)[:, None]\ntop100_scores = scores[row_idx, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nfinal_ranks = top100_idx[row_idx, sort_order]\nprint(f'Top 100 for all {U} users computed in {time.time()-t0:.2f}s, shape: {final_ranks.shape}')\n\"",
"description": "Benchmark vectorized top 100 extraction"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_3325928",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Top 100 for all 8456 users computed in 0.98s, shape: (8456, 100)\nProcess Group PGID: 2178\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:08.096Z",
"resultDisplay": "Top 100 for all 8456 users computed in 0.98s, shape: (8456, 100)",
"description": "Benchmark vectorized top 100 extraction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:08.097Z",
"content": "\nOutput: Top 100 for all 8456 users computed in 0.98s, shape: (8456, 100)\nProcess Group PGID: 2178\n",
"isError": false
},
"responseMessageId": "7201a898-c779-4301-92b0-d22d94be21dc"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T01:28:10.929Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:10.929Z",
"text": "Refining Masking Logic\n\nI've successfully ranked all users against items rapidly. My current focus is on the masking verification step. I'm examining `val_elig.packed` to ensure the bit unpacking and subsequent score assignment for masked items correctly handles the exclusion as intended."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:10.929Z",
"callId": "run_shell_command__call_8273",
"name": "run_shell_command",
"input": {
"description": "Benchmark vectorized unpackbits",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nelig = np.load('/task/data/validation_eligible_candidates.npz')\npacked = elig['packed_eligible']\nU, I = packed.shape[0], len(elig['item_ids'])\n\nt0 = time.time()\n# Unpack all at once\nmask = np.unpackbits(packed, axis=1, count=I, bitorder='little')\nprint(f'Unpacked all in {time.time()-t0:.2f}s, mask shape: {mask.shape}')\n\""
},
"recorded": {
"id": "run_shell_command__call_8273",
"name": "run_shell_command",
"args": {
"description": "Benchmark vectorized unpackbits",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nelig = np.load('/task/data/validation_eligible_candidates.npz')\npacked = elig['packed_eligible']\nU, I = packed.shape[0], len(elig['item_ids'])\n\nt0 = time.time()\n# Unpack all at once\nmask = np.unpackbits(packed, axis=1, count=I, bitorder='little')\nprint(f'Unpacked all in {time.time()-t0:.2f}s, mask shape: {mask.shape}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_8273",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unpacked all in 0.03s, mask shape: (8456, 30000)\nProcess Group PGID: 2188\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:11.349Z",
"resultDisplay": "Unpacked all in 0.03s, mask shape: (8456, 30000)",
"description": "Benchmark vectorized unpackbits",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:11.350Z",
"content": "\nOutput: Unpacked all in 0.03s, mask shape: (8456, 30000)\nProcess Group PGID: 2188\n",
"isError": false
},
"responseMessageId": "4d7b88ad-ba00-40ea-9a30-0bbabc544271"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T01:28:14.513Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:14.513Z",
"text": "Optimizing Mask Processing\n\nI've just identified a critical bottleneck: unpacking candidate masks for all users currently takes 0.03 seconds per mask. This delay significantly impacts the overall model evaluation, pushing it to roughly 1 second per model. My current focus is on optimizing this unpacking process."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:14.513Z",
"callId": "update_topic__call_33165",
"name": "update_topic",
"input": {
"strategic_intent": "Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.",
"title": "Model Development and Validation Optimization",
"summary": "Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states."
},
"recorded": {
"id": "update_topic__call_33165",
"name": "update_topic",
"args": {
"strategic_intent": "Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.",
"title": "Model Development and Validation Optimization",
"summary": "Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_33165",
"name": "update_topic",
"response": {
"output": "Current topic: \"Model Development and Validation Optimization\"\nTopic summary: Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states.\n\nStrategic Intent: Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:14.516Z",
"resultDisplay": "## 📂 Topic: **Model Development and Validation Optimization**\n\n**Summary:**\nCompleted initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states.\n\n> [!STRATEGY]\n> **Intent:** Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.",
"description": "Update topic to: \"Model Development and Validation Optimization\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T01:28:14.517Z",
"content": "Current topic: \"Model Development and Validation Optimization\"\nTopic summary: Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states.\n\nStrategic Intent: Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.",
"isError": false
},
"responseMessageId": "1e817d61-b4de-4d6b-897b-d1fd0e59210d"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T01:28:25.135Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:25.135Z",
"text": "Testing Interaction Models\n\nI'm now evaluating various user-item interaction representations and models using the validation set. My current focus is on defining what data populates the interaction matrix $X$. I'm examining the columns in `train_listens` such as `uid`, `timestamp`, `item_id`, `is_organic`, and `played_`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:28:25.135Z",
"text": "Defining Interaction Weights\n\nI'm exploring different ways to weight user-item interactions. Specifically, I'm considering factors like play duration, organic plays, explicit likes, and dislikes from the preference state data. Time decay will also be incorporated into these interaction weights."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:28:25.135Z",
"callId": "run_shell_command__call_19058",
"name": "run_shell_command",
"input": {
"description": "Evaluate TruncatedSVD on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\""
},
"recorded": {
"id": "run_shell_command__call_19058",
"name": "run_shell_command",
"args": {
"description": "Evaluate TruncatedSVD on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_19058",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 107: 2200 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2198\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:28:58.080Z",
"resultDisplay": "/usr/bin/bash: line 107: 2200 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Evaluate TruncatedSVD on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:28:58.084Z",
"content": "\nOutput: /usr/bin/bash: line 107: 2200 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2198\n",
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]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T01:29:03.660Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:03.660Z",
"text": "Detecting Memory Leaks\n\nI've identified that an exit code of 137 signifies an Out Of Memory error. This seems to stem from the substantial size of `df_listens`, which, at 43.4 million rows, likely overloaded pandas during aggregation operations like `groupby`."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:03.660Z",
"text": "Filtering Data Effectively\n\nI've realized that using only recent listen data, specifically the last 90 days, drastically reduces the dataset size by 62% and should prevent memory issues during processing. This approach also aligns with the hypothesis that recent user taste is more predictive."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:03.660Z",
"callId": "run_shell_command__call_20539",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport time\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Test reading with filters\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\nprint('Total rows:', pf.metadata.num_rows)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], filters=[('timestamp', '>=', t_min)])\nprint(f'Read filtered table in {time.time()-t0:.2f}s, rows={len(table):,}')\n\"",
"description": "Test filtered pyarrow read"
},
"recorded": {
"id": "run_shell_command__call_20539",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport time\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Test reading with filters\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\nprint('Total rows:', pf.metadata.num_rows)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], filters=[('timestamp', '>=', t_min)])\nprint(f'Read filtered table in {time.time()-t0:.2f}s, rows={len(table):,}')\n\"",
"description": "Test filtered pyarrow read"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_20539",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Num row groups: 42\nTotal rows: 43484159\nRead filtered table in 0.79s, rows=16,769,274\nProcess Group PGID: 2227\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:29:04.962Z",
"resultDisplay": "Num row groups: 42\nTotal rows: 43484159\nRead filtered table in 0.79s, rows=16,769,274",
"description": "Test filtered pyarrow read",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:04.963Z",
"content": "\nOutput: Num row groups: 42\nTotal rows: 43484159\nRead filtered table in 0.79s, rows=16,769,274\nProcess Group PGID: 2227\n",
"isError": false
},
"responseMessageId": "cf38a011-17a8-411d-9343-17314ed27623"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T01:29:16.708Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:16.708Z",
"text": "Exploring Efficient Aggregation\n\nI'm currently focused on optimizing the aggregation of `(uid, item_id)` pairs. My primary concern is avoiding Out-of-Memory errors that arise from creating excessively large pandas DataFrames. I'm exploring alternative strategies to achieve this efficiency, leveraging existing mappings like `uid_to_idx` to streamline the process."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:16.708Z",
"text": "Testing Sparse Matrix Strategy\n\nI'm investigating a new approach to aggregate `(uid, item_id)` pairs. By mapping UIDs and item IDs to integer indices, I can generate a unique `pair_id`. This allows for direct aggregation into a numpy array using `np.bincount`, potentially avoiding OOM errors and offering significant speed advantages for dense matrices."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:16.708Z",
"callId": "run_shell_command__call_77585",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nN = 15000000\nU = 8456\nI = 30000\ntotal_cells = U * I\n\nt0 = time.time()\nu_idx = np.random.randint(0, U, N, dtype=np.int32)\nit_idx = np.random.randint(0, I, N, dtype=np.int32)\nweights = np.random.rand(N).astype(np.float32)\npair_id = u_idx.astype(np.int64) * I + it_idx\n\nt1 = time.time()\nprint(f'Setup done in {t1-t0:.2f}s')\n\n# np.bincount\nflat = np.bincount(pair_id, weights=weights, minlength=total_cells).astype(np.float32)\nX = flat.reshape(U, I)\nprint(f'bincount done in {time.time()-t1:.2f}s, X shape: {X.shape}, nonzeros: {np.count_nonzero(X):,}')\n\"",
"description": "Benchmark bincount matrix construction"
},
"recorded": {
"id": "run_shell_command__call_77585",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nN = 15000000\nU = 8456\nI = 30000\ntotal_cells = U * I\n\nt0 = time.time()\nu_idx = np.random.randint(0, U, N, dtype=np.int32)\nit_idx = np.random.randint(0, I, N, dtype=np.int32)\nweights = np.random.rand(N).astype(np.float32)\npair_id = u_idx.astype(np.int64) * I + it_idx\n\nt1 = time.time()\nprint(f'Setup done in {t1-t0:.2f}s')\n\n# np.bincount\nflat = np.bincount(pair_id, weights=weights, minlength=total_cells).astype(np.float32)\nX = flat.reshape(U, I)\nprint(f'bincount done in {time.time()-t1:.2f}s, X shape: {X.shape}, nonzeros: {np.count_nonzero(X):,}')\n\"",
"description": "Benchmark bincount matrix construction"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_77585",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Setup done in 0.44s\nbincount done in 0.40s, X shape: (8456, 30000), nonzeros: 14,565,541\nProcess Group PGID: 2249\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:29:18.180Z",
"resultDisplay": "Setup done in 0.44s\nbincount done in 0.40s, X shape: (8456, 30000), nonzeros: 14,565,541",
"description": "Benchmark bincount matrix construction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:18.181Z",
"content": "\nOutput: Setup done in 0.44s\nbincount done in 0.40s, X shape: (8456, 30000), nonzeros: 14,565,541\nProcess Group PGID: 2249\n",
"isError": false
},
"responseMessageId": "3fee1b27-dd30-4081-b772-82877e9f9e87"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T01:29:32.104Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:32.104Z",
"text": "Optimizing for speed\n\nThe bincount operation is complete, taking a mere 0.40 seconds with zero intermediate copies and no pandas groupby overhead. The outcome is a dense float32 matrix, ready for direct use or conversion to a sparse format. The next step is to test this with real data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:32.104Z",
"callId": "run_shell_command__call_359798",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_arr = table['uid'].to_numpy()\nit_arr = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Map to indices\n# Filter out non-catalog items\n# Fast lookup using array if possible or np.isin\nin_catalog = np.isin(it_arr, item_ids)\nu_arr = u_arr[in_catalog]\nit_arr = it_arr[in_catalog]\npr_arr = pr_arr[in_catalog]\norg_arr = org_arr[in_catalog]\nts_arr = ts_arr[in_catalog]\n\n# Map to integer indices\nu_idx = np.array([uid_to_idx[u] for u in u_arr], dtype=np.int32)\n# Since item_to_idx is 30,000 items, let's create a direct lookup array or dict\nitem_map_arr = np.full(max(item_ids) + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n item_map_arr[it] = idx\nit_idx = item_map_arr[it_arr]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run memory-efficient SVD evaluation"
},
"recorded": {
"id": "run_shell_command__call_359798",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_arr = table['uid'].to_numpy()\nit_arr = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Map to indices\n# Filter out non-catalog items\n# Fast lookup using array if possible or np.isin\nin_catalog = np.isin(it_arr, item_ids)\nu_arr = u_arr[in_catalog]\nit_arr = it_arr[in_catalog]\npr_arr = pr_arr[in_catalog]\norg_arr = org_arr[in_catalog]\nts_arr = ts_arr[in_catalog]\n\n# Map to integer indices\nu_idx = np.array([uid_to_idx[u] for u in u_arr], dtype=np.int32)\n# Since item_to_idx is 30,000 items, let's create a direct lookup array or dict\nitem_map_arr = np.full(max(item_ids) + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n item_map_arr[it] = idx\nit_idx = item_map_arr[it_arr]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run memory-efficient SVD evaluation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_359798",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 53, in \nKeyError: np.uint32(500)\nExit Code: 1\nProcess Group PGID: 2259\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:29:35.257Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 53, in \nKeyError: np.uint32(500)",
"description": "Run memory-efficient SVD evaluation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:35.258Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 53, in \nKeyError: np.uint32(500)\nExit Code: 1\nProcess Group PGID: 2259\n",
"isError": false
},
"responseMessageId": "012d828b-71b0-429a-9fc3-442780f6d9d2"
}
]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T01:29:44.049Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:29:44.049Z",
"text": "Investigating User Data\n\nI'm encountering a `KeyError` when trying to map user IDs to indices, specifically with `np.uint32(500)`. This seems to stem from `train_listens` containing users who aren't part of our target 8456 group, indicating a small but present 0.5% of non-target users within the dataset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:29:44.049Z",
"callId": "run_shell_command__call_12775",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run SVD evaluation with fixed filtering"
},
"recorded": {
"id": "run_shell_command__call_12775",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run SVD evaluation with fixed filtering"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_12775",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 119: 2290 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2288\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:29:58.138Z",
"resultDisplay": "/usr/bin/bash: line 119: 2290 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run SVD evaluation with fixed filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:29:58.158Z",
"content": "\nOutput: /usr/bin/bash: line 119: 2290 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2288\n",
"isError": false
},
"responseMessageId": "d8f5f2e3-82fe-4823-9748-5f5c263840fc"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T01:30:01.514Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:01.514Z",
"text": "Investigating Memory Consumption\n\nMy process was terminated due to excessive memory use. I'm now examining what might be consuming so much memory, starting by scrutinizing the `max_u` variable and its potential impact when used with `np.full`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:01.514Z",
"callId": "run_shell_command__call_282167",
"name": "run_shell_command",
"input": {
"description": "Check max uid and item_id",
"command": "python3 -c \"\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('max uid:', targets.uid.max(), 'min uid:', targets.uid.min())\nprint('max item_id:', catalog.item_id.max(), 'min item_id:', catalog.item_id.min())\n\""
},
"recorded": {
"id": "run_shell_command__call_282167",
"name": "run_shell_command",
"args": {
"description": "Check max uid and item_id",
"command": "python3 -c \"\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('max uid:', targets.uid.max(), 'min uid:', targets.uid.min())\nprint('max item_id:', catalog.item_id.max(), 'min item_id:', catalog.item_id.min())\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_282167",
"name": "run_shell_command",
"response": {
"output": "\nOutput: max uid: 1000000 min uid: 100\nmax item_id: 9390294 min item_id: 206\nProcess Group PGID: 2317\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:02.395Z",
"resultDisplay": "max uid: 1000000 min uid: 100\nmax item_id: 9390294 min item_id: 206",
"description": "Check max uid and item_id",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:02.397Z",
"content": "\nOutput: max uid: 1000000 min uid: 100\nmax item_id: 9390294 min item_id: 206\nProcess Group PGID: 2317\n",
"isError": false
},
"responseMessageId": "c5306c12-0f86-4246-951b-f42e1650d66e"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T01:30:06.803Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:06.803Z",
"text": "Exploring Memory Bottlenecks\n\nI've confirmed that the uid and item_id ranges aren't the source of our memory woes, as both are surprisingly manageable. My current focus is now on the `pq.read_table` operation itself and what might be consuming excessive memory during that process."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:06.803Z",
"callId": "run_shell_command__call_134449",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport os\nfor path in ['/sys/fs/cgroup/memory.max', '/sys/fs/cgroup/memory/memory.limit_in_bytes']:\n if os.path.exists(path):\n with open(path) as f:\n print(path, f.read().strip())\n\"",
"description": "Check container cgroup memory limit"
},
"recorded": {
"id": "run_shell_command__call_134449",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport os\nfor path in ['/sys/fs/cgroup/memory.max', '/sys/fs/cgroup/memory/memory.limit_in_bytes']:\n if os.path.exists(path):\n with open(path) as f:\n print(path, f.read().strip())\n\"",
"description": "Check container cgroup memory limit"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_134449",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /sys/fs/cgroup/memory.max 8589934592\nProcess Group PGID: 2334\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:06.834Z",
"resultDisplay": "/sys/fs/cgroup/memory.max 8589934592",
"description": "Check container cgroup memory limit",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:06.835Z",
"content": "\nOutput: /sys/fs/cgroup/memory.max 8589934592\nProcess Group PGID: 2334\n",
"isError": false
},
"responseMessageId": "5a8a498b-d5f5-4ae4-bd41-91641330be6d"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T01:30:16.131Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:16.131Z",
"text": "Discovering Memory Constraints\n\nI've realized the container's cgroup memory limit is set to 8 GB, significantly less than the host's 16 GB. This explains why processes are being OOM killed when memory usage spikes. I'll be implementing stricter memory management and ensuring temporary variables are promptly deleted."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:16.131Z",
"callId": "run_shell_command__call_130401",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor batch in pf.iter_batches(batch_size=2_000_000, \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'],\n filters=[('timestamp', '>=', t_min)]):\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)\n ts_arr = batch['timestamp'].to_numpy(zero_copy_only=False)\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Listens accumulated in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test streaming parquet read and accumulation"
},
"recorded": {
"id": "run_shell_command__call_130401",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor batch in pf.iter_batches(batch_size=2_000_000, \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'],\n filters=[('timestamp', '>=', t_min)]):\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)\n ts_arr = batch['timestamp'].to_numpy(zero_copy_only=False)\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Listens accumulated in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test streaming parquet read and accumulation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_130401",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 36, in \nTypeError: ParquetFile.iter_batches() got an unexpected keyword argument 'filters'\nExit Code: 1\nProcess Group PGID: 2337\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:16.999Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 36, in \nTypeError: ParquetFile.iter_batches() got an unexpected keyword argument 'filters'",
"description": "Test streaming parquet read and accumulation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:18.790Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 36, in \nTypeError: ParquetFile.iter_batches() got an unexpected keyword argument 'filters'\nExit Code: 1\nProcess Group PGID: 2337\n",
"isError": false
},
"responseMessageId": "613eabd2-398a-4d54-aeeb-8696223c030c"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T01:30:22.559Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:22.559Z",
"text": "Exploring Filter Options\n\nI'm currently investigating how to best apply filters when iterating through Parquet batches in pyarrow, specifically noting the absence of a `filters` argument in `ParquetFile.iter_batches()` for version 18.1.0. My focus is on exploring alternative approaches, such as utilizing `pq.read_table` with filters or examining the capabilities of `pq.ParquetDataset`, potentially by reading specific row groups."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:22.559Z",
"callId": "run_shell_command__call_290787",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\n# Read 1 row group\nt = pf.read_row_group(0, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'1 row group read in {time.time()-t0:.3f}s, rows={len(t):,}')\n\"",
"description": "Test reading single row group"
},
"recorded": {
"id": "run_shell_command__call_290787",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\n# Read 1 row group\nt = pf.read_row_group(0, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'1 row group read in {time.time()-t0:.3f}s, rows={len(t):,}')\n\"",
"description": "Test reading single row group"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_290787",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Num row groups: 42\n1 row group read in 0.021s, rows=1,048,576\nProcess Group PGID: 2354\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:22.989Z",
"resultDisplay": "Num row groups: 42\n1 row group read in 0.021s, rows=1,048,576",
"description": "Test reading single row group",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:22.990Z",
"content": "\nOutput: Num row groups: 42\n1 row group read in 0.021s, rows=1,048,576\nProcess Group PGID: 2354\n",
"isError": false
},
"responseMessageId": "94d2018c-91b9-4422-afed-d48315f29cca"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T01:30:29.467Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:29.467Z",
"text": "Benchmarking Row Group Performance\n\nI'm impressed by the speed; each row group processes in under 0.03 seconds, with minimal memory overhead. I'm now focusing on optimizing the complete accumulation across all row groups, incorporating row skipping based on `timestamp < t_min`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:29.467Z",
"callId": "run_shell_command__call_1644491",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Accumulation completed in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test complete streaming accumulation across row groups"
},
"recorded": {
"id": "run_shell_command__call_1644491",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Accumulation completed in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test complete streaming accumulation across row groups"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1644491",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Accumulation completed in 9.48s, nonzeros: 4,156,376\nProcess Group PGID: 2370\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:30:40.104Z",
"resultDisplay": "Accumulation completed in 9.48s, nonzeros: 4,156,376",
"description": "Test complete streaming accumulation across row groups",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:30:40.105Z",
"content": "\nOutput: Accumulation completed in 9.48s, nonzeros: 4,156,376\nProcess Group PGID: 2370\n",
"isError": false
},
"responseMessageId": "6dabcaf9-b4b7-4a0d-b954-317eaab81171"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T01:30:52.426Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:52.426Z",
"text": "Analyzing Data Interactions\n\nMy recent focus has been on integrating explicit user preferences, specifically `liked` and `disliked` items, with the accumulated listening interaction matrix (`flat`). This step is crucial for refining the model's understanding of user tastes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:30:52.426Z",
"text": "Integrating Explicit Preferences\n\nI've been verifying the inclusion of `train_likes` and `train_dislikes` by examining `validation_preference_state` to ensure it captures active explicit preferences at the training cutoff. This is a key step for improving recommendation accuracy by directly incorporating user feedback."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:30:52.426Z",
"callId": "run_shell_command__call_129631",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"",
"description": "Test Dual EASE model on validation set"
},
"recorded": {
"id": "run_shell_command__call_129631",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"",
"description": "Test Dual EASE model on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_129631",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 130: 2389 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2387\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:32:00.877Z",
"resultDisplay": "/usr/bin/bash: line 130: 2389 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"",
"description": "Test Dual EASE model on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:32:00.897Z",
"content": "\nOutput: /usr/bin/bash: line 130: 2389 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"\nExit Code: 137\nProcess Group PGID: 2387\n",
"isError": false
},
"responseMessageId": "470ae904-550f-4d8f-971f-fc65207c1b5b"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T01:32:07.196Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:07.196Z",
"text": "Investigating Memory Allocation\n\nI'm currently probing memory usage, as it appears the `K = X @ X.T` operation was unexpectedly terminated. My suspicion is memory constraints, given that `X` and its `flat` representation each consume roughly 1GB. I'm examining how `X`'s reshape operation might be impacting available memory."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:32:07.196Z",
"callId": "run_shell_command__call_107640",
"name": "run_shell_command",
"input": {
"description": "Test sparse X and K memory usage",
"command": "python3 -c \"\nimport resource\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nprint_mem('Start')\n# Create sparse matrix with 4.2M nonzeros\nU, I = 8456, 30000\nnnz = 4200000\nrows = np.random.randint(0, U, nnz, dtype=np.int32)\ncols = np.random.randint(0, I, nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX_sp = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\ndel rows, cols, data\ngc.collect()\nprint_mem('X_sp created')\n\nt0 = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint_mem(f'K = X_sp @ X_sp.T in {time.time()-t0:.2f}s')\n\""
},
"recorded": {
"id": "run_shell_command__call_107640",
"name": "run_shell_command",
"args": {
"description": "Test sparse X and K memory usage",
"command": "python3 -c \"\nimport resource\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nprint_mem('Start')\n# Create sparse matrix with 4.2M nonzeros\nU, I = 8456, 30000\nnnz = 4200000\nrows = np.random.randint(0, U, nnz, dtype=np.int32)\ncols = np.random.randint(0, I, nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX_sp = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\ndel rows, cols, data\ngc.collect()\nprint_mem('X_sp created')\n\nt0 = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint_mem(f'K = X_sp @ X_sp.T in {time.time()-t0:.2f}s')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_107640",
"name": "run_shell_command",
"response": {
"output": "\nOutput: [Start] Peak RSS: 65.1 MB\n[X_sp created] Peak RSS: 146.3 MB\n[K = X_sp @ X_sp.T in 3.22s] Peak RSS: 916.4 MB\nProcess Group PGID: 2414\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:32:11.485Z",
"resultDisplay": "[Start] Peak RSS: 65.1 MB\n[X_sp created] Peak RSS: 146.3 MB\n[K = X_sp @ X_sp.T in 3.22s] Peak RSS: 916.4 MB",
"description": "Test sparse X and K memory usage",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:32:11.486Z",
"content": "\nOutput: [Start] Peak RSS: 65.1 MB\n[X_sp created] Peak RSS: 146.3 MB\n[K = X_sp @ X_sp.T in 3.22s] Peak RSS: 916.4 MB\nProcess Group PGID: 2414\n",
"isError": false
},
"responseMessageId": "4da591cf-7ece-4b47-a644-2b8c83f441ef"
}
]
},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T01:32:20.577Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:20.577Z",
"text": "Optimizing Memory Allocation\n\nI'm focusing on reducing memory usage by exploring ways to construct `X_sp` directly, bypassing the large 1 GB `flat` array. My recent calculations show peak RSS at just 916 MB, which is encouraging. I'm currently analyzing the `train_listens` row groups, specifically `u_idx`, `it_idx`, and `w`, to achieve this efficiency."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:32:20.577Z",
"callId": "run_shell_command__call_94969",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nt0 = time.time()\nprint_mem('Init')\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\ngc.collect()\n\nprint_mem('Concatenated')\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\nprint_mem('X_sp built')\nprint(f'X_sp ready in {time.time()-t0:.2f}s, shape={X_sp.shape}, nnz={X_sp.nnz:,}')\n\"",
"description": "Test ultra-lean sparse matrix construction"
},
"recorded": {
"id": "run_shell_command__call_94969",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nt0 = time.time()\nprint_mem('Init')\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\ngc.collect()\n\nprint_mem('Concatenated')\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\nprint_mem('X_sp built')\nprint(f'X_sp ready in {time.time()-t0:.2f}s, shape={X_sp.shape}, nnz={X_sp.nnz:,}')\n\"",
"description": "Test ultra-lean sparse matrix construction"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_94969",
"name": "run_shell_command",
"response": {
"output": "\nOutput: [Init] Peak RSS: 131.1 MB\n[Concatenated] Peak RSS: 628.9 MB\n[X_sp built] Peak RSS: 694.2 MB\nX_sp ready in 2.23s, shape=(8456, 30000), nnz=4,412,668\nProcess Group PGID: 2431\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:32:23.925Z",
"resultDisplay": "[Init] Peak RSS: 131.1 MB\n[Concatenated] Peak RSS: 628.9 MB\n[X_sp built] Peak RSS: 694.2 MB\nX_sp ready in 2.23s, shape=(8456, 30000), nnz=4,412,668",
"description": "Test ultra-lean sparse matrix construction",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:32:23.926Z",
"content": "\nOutput: [Init] Peak RSS: 131.1 MB\n[Concatenated] Peak RSS: 628.9 MB\n[X_sp built] Peak RSS: 694.2 MB\nX_sp ready in 2.23s, shape=(8456, 30000), nnz=4,412,668\nProcess Group PGID: 2431\n",
"isError": false
},
"responseMessageId": "2451ee7a-31e0-4b1f-9b28-71aab3b688e5"
}
]
},
{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T01:32:36.148Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:32:36.148Z",
"text": "Achieving Optimization Goals\n\nMy recent focus has been on optimizing performance, and I'm thrilled to report significant gains. I've successfully reduced `X_sp` execution time to an impressive 2.23 seconds while keeping peak RSS below 700 MB. Now, I'm delving into the specifics of SVD and EASE, specifically testing TruncatedSVD to further refine our scoring mechanisms."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:32:36.148Z",
"callId": "run_shell_command__call_1767988",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nfor n_comp in [32, 64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n V_factor = svd.components_\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = U_factor[b_start:b_end] @ V_factor\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run batched SVD evaluation on validation"
},
"recorded": {
"id": "run_shell_command__call_1767988",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nfor n_comp in [32, 64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n V_factor = svd.components_\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = U_factor[b_start:b_end] @ V_factor\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run batched SVD evaluation on validation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1767988",
"name": "run_shell_command",
"response": {
"output": "\nOutput: SVD n_comp= 32: NDCG@10 = 0.018734 (took 3.94s)\nSVD n_comp= 64: NDCG@10 = 0.018742 (took 4.49s)\nSVD n_comp=128: NDCG@10 = 0.018088 (took 6.06s)\nSVD n_comp=256: NDCG@10 = 0.016818 (took 8.90s)\nProcess Group PGID: 2456\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:33:04.005Z",
"resultDisplay": "SVD n_comp= 32: NDCG@10 = 0.018734 (took 3.94s)\nSVD n_comp= 64: NDCG@10 = 0.018742 (took 4.49s)\nSVD n_comp=128: NDCG@10 = 0.018088 (took 6.06s)\nSVD n_comp=256: NDCG@10 = 0.016818 (took 8.90s)",
"description": "Run batched SVD evaluation on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:33:04.006Z",
"content": "\nOutput: SVD n_comp= 32: NDCG@10 = 0.018734 (took 3.94s)\nSVD n_comp= 64: NDCG@10 = 0.018742 (took 4.49s)\nSVD n_comp=128: NDCG@10 = 0.018088 (took 6.06s)\nSVD n_comp=256: NDCG@10 = 0.016818 (took 8.90s)\nProcess Group PGID: 2456\n",
"isError": false
},
"responseMessageId": "ee6bcb08-4e4b-41bc-8741-41a05f449e43"
}
]
},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T01:33:23.540Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:23.540Z",
"text": "Testing New Algorithms\n\nI've successfully boosted NDCG@10 significantly with SVD and am now exploring Dual EASE and other methods. My current focus is on identifying algorithms that can offer even greater improvements beyond SVD's performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:33:23.540Z",
"text": "Implementing Dual EASE\n\nI'm analyzing Dual EASE's implementation with our batched, low-memory constraints. The Cholesky factorization of $K_{reg}$ is manageable at 286 MB. I'm now exploring solving for $M$ in column blocks to further optimize memory usage, which appears feasible."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:33:23.540Z",
"callId": "run_shell_command__call_4096819",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nprint(f'Building K...')\nt_k = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint(f'K ready in {time.time()-t_k:.2f}s')\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0]:\n t_ease = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n \n # Solve in item chunks to keep memory low\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n \n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X_sp[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n \n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n \n # Predicted scores for unseen items: -M * inv_diag_P\n # Scale M by -inv_diag_P\n M *= -inv_diag_P[None, :]\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = M[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam:6.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_ease:.2f}s)')\n\"",
"description": "Evaluate Dual EASE on validation set"
},
"recorded": {
"id": "run_shell_command__call_4096819",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nprint(f'Building K...')\nt_k = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint(f'K ready in {time.time()-t_k:.2f}s')\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0]:\n t_ease = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n \n # Solve in item chunks to keep memory low\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n \n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X_sp[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n \n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n \n # Predicted scores for unseen items: -M * inv_diag_P\n # Scale M by -inv_diag_P\n M *= -inv_diag_P[None, :]\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = M[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam:6.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_ease:.2f}s)')\n\"",
"description": "Evaluate Dual EASE on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4096819",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Building K...\nK ready in 5.45s\nDual EASE lam= 100.0: NDCG@10 = 0.008167 (took 24.86s)\nDual EASE lam= 500.0: NDCG@10 = 0.009555 (took 24.92s)\nDual EASE lam=1000.0: NDCG@10 = 0.010752 (took 24.87s)\nDual EASE lam=2000.0: NDCG@10 = 0.012705 (took 24.89s)\nProcess Group PGID: 2483\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:35:12.348Z",
"resultDisplay": "Building K...\nK ready in 5.45s\nDual EASE lam= 100.0: NDCG@10 = 0.008167 (took 24.86s)\nDual EASE lam= 500.0: NDCG@10 = 0.009555 (took 24.92s)\nDual EASE lam=1000.0: NDCG@10 = 0.010752 (took 24.87s)\nDual EASE lam=2000.0: NDCG@10 = 0.012705 (took 24.89s)",
"description": "Evaluate Dual EASE on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:35:12.349Z",
"content": "\nOutput: Building K...\nK ready in 5.45s\nDual EASE lam= 100.0: NDCG@10 = 0.008167 (took 24.86s)\nDual EASE lam= 500.0: NDCG@10 = 0.009555 (took 24.92s)\nDual EASE lam=1000.0: NDCG@10 = 0.010752 (took 24.87s)\nDual EASE lam=2000.0: NDCG@10 = 0.012705 (took 24.89s)\nProcess Group PGID: 2483\n",
"isError": false
},
"responseMessageId": "972fddc7-2352-4673-8769-b11c096b284e"
}
]
},
{
"id": 54,
"role": "agent",
"timestamp": "2026-09-29T01:35:30.687Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:35:30.687Z",
"text": "Analyzing SVD Performance\n\nI'm focusing on why SVD significantly outperformed Dual EASE in the recent test. It appears SVD's lower rank latent space effectively captures broader user preferences, enabling generalization beyond direct co-occurrences, which is crucial for music discovery where user taste is nuanced."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:35:30.687Z",
"text": "Optimizing SVD Parameters\n\nI'm now investigating hyperparameter tuning for SVD. Specifically, I'm testing the impact of different normalization techniques like TF-IDF and BM25 on the user-item matrix, alongside exploring various numbers of latent components and their influence on performance metrics."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:35:30.687Z",
"callId": "run_shell_command__call_157497",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\n\n# Function to build X given half_life, t_min_days, organic_mult\ndef build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n if min_ratio > 0:\n ratio_mask = (pr_arr >= min_ratio)\n if not np.any(ratio_mask):\n continue\n pr_arr = pr_arr[ratio_mask]\n time_mask_idx = np.where(time_mask)[0][ratio_mask]\n else:\n time_mask_idx = np.where(time_mask)[0]\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Evaluate function\ndef eval_factors(U_factor, V_factor, pop_weight=0.0):\n log_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_factor[b_start:b_end] @ V_factor).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Test base X\nX_sp = build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0)\nfor n_comp in [48, 64, 80]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n score_pure = eval_factors(U_f, V_f, pop_weight=0.0)\n print(f'SVD n_comp={n_comp}, pure NDCG@10 = {score_pure:.6f}')\n for pw in [0.01, 0.03, 0.05, 0.1]:\n score_pop = eval_factors(U_f, V_f, pop_weight=pw)\n print(f' + pop_weight={pw}: NDCG@10 = {score_pop:.6f}')\n\"",
"description": "Evaluate SVD hyperparameters and popularity blending"
},
"recorded": {
"id": "run_shell_command__call_157497",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\n\n# Function to build X given half_life, t_min_days, organic_mult\ndef build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n if min_ratio > 0:\n ratio_mask = (pr_arr >= min_ratio)\n if not np.any(ratio_mask):\n continue\n pr_arr = pr_arr[ratio_mask]\n time_mask_idx = np.where(time_mask)[0][ratio_mask]\n else:\n time_mask_idx = np.where(time_mask)[0]\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Evaluate function\ndef eval_factors(U_factor, V_factor, pop_weight=0.0):\n log_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_factor[b_start:b_end] @ V_factor).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Test base X\nX_sp = build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0)\nfor n_comp in [48, 64, 80]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n score_pure = eval_factors(U_f, V_f, pop_weight=0.0)\n print(f'SVD n_comp={n_comp}, pure NDCG@10 = {score_pure:.6f}')\n for pw in [0.01, 0.03, 0.05, 0.1]:\n score_pop = eval_factors(U_f, V_f, pop_weight=pw)\n print(f' + pop_weight={pw}: NDCG@10 = {score_pop:.6f}')\n\"",
"description": "Evaluate SVD hyperparameters and popularity blending"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_157497",
"name": "run_shell_command",
"response": {
"output": "\nOutput: SVD n_comp=48, pure NDCG@10 = 0.019382\n + pop_weight=0.01: NDCG@10 = 0.019535\n + pop_weight=0.03: NDCG@10 = 0.019844\n + pop_weight=0.05: NDCG@10 = 0.019932\n + pop_weight=0.1: NDCG@10 = 0.019699\nSVD n_comp=64, pure NDCG@10 = 0.019016\n + pop_weight=0.01: NDCG@10 = 0.019301\n + pop_weight=0.03: NDCG@10 = 0.019635\n + pop_weight=0.05: NDCG@10 = 0.019690\n + pop_weight=0.1: NDCG@10 = 0.019813\nSVD n_comp=80, pure NDCG@10 = 0.019169\n + pop_weight=0.01: NDCG@10 = 0.019459\n + pop_weight=0.03: NDCG@10 = 0.019739\n + pop_weight=0.05: NDCG@10 = 0.020100\n + pop_weight=0.1: NDCG@10 = 0.020543\nProcess Group PGID: 2510\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:36:18.772Z",
"resultDisplay": "SVD n_comp=48, pure NDCG@10 = 0.019382\n + pop_weight=0.01: NDCG@10 = 0.019535\n + pop_weight=0.03: NDCG@10 = 0.019844\n + pop_weight=0.05: NDCG@10 = 0.019932\n + pop_weight=0.1: NDCG@10 = 0.019699\nSVD n_comp=64, pure NDCG@10 = 0.019016\n + pop_weight=0.01: NDCG@10 = 0.019301\n + pop_weight=0.03: NDCG@10 = 0.019635\n + pop_weight=0.05: NDCG@10 = 0.019690\n + pop_weight=0.1: NDCG@10 = 0.019813\nSVD n_comp=80, pure NDCG@10 = 0.019169\n + pop_weight=0.01: NDCG@10 = 0.019459\n + pop_weight=0.03: NDCG@10 = 0.019739\n + pop_weight=0.05: NDCG@10 = 0.020100\n + pop_weight=0.1: NDCG@10 = 0.020543",
"description": "Evaluate SVD hyperparameters and popularity blending",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:36:18.773Z",
"content": "\nOutput: SVD n_comp=48, pure NDCG@10 = 0.019382\n + pop_weight=0.01: NDCG@10 = 0.019535\n + pop_weight=0.03: NDCG@10 = 0.019844\n + pop_weight=0.05: NDCG@10 = 0.019932\n + pop_weight=0.1: NDCG@10 = 0.019699\nSVD n_comp=64, pure NDCG@10 = 0.019016\n + pop_weight=0.01: NDCG@10 = 0.019301\n + pop_weight=0.03: NDCG@10 = 0.019635\n + pop_weight=0.05: NDCG@10 = 0.019690\n + pop_weight=0.1: NDCG@10 = 0.019813\nSVD n_comp=80, pure NDCG@10 = 0.019169\n + pop_weight=0.01: NDCG@10 = 0.019459\n + pop_weight=0.03: NDCG@10 = 0.019739\n + pop_weight=0.05: NDCG@10 = 0.020100\n + pop_weight=0.1: NDCG@10 = 0.020543\nProcess Group PGID: 2510\n",
"isError": false
},
"responseMessageId": "70097a7a-8bfc-4180-97d2-1435934fb573"
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]
},
{
"id": 55,
"role": "agent",
"timestamp": "2026-09-29T01:36:28.014Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:28.014Z",
"text": "Improving NDCG@10 Score\n\nMy NDCG@10 has now surpassed 0.02! I'm seeing positive trends as I increase `n_comp` to 80 and `pop_weight`. I'll now explore further increases to `pop_weight` and experiment with organic popularity."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:36:28.014Z",
"callId": "run_shell_command__call_25264",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\n# Let's run a test comparing organic_pop vs positive_listens pop and different weights\n\"",
"description": "Test script setup"
},
"recorded": {
"id": "run_shell_command__call_25264",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\n# Let's run a test comparing organic_pop vs positive_listens pop and different weights\n\"",
"description": "Test script setup"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_25264",
"name": "run_shell_command",
"response": {
"output": "\nOutput: (empty)\nProcess Group PGID: 2537\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:36:30.313Z",
"resultDisplay": "",
"description": "Test script setup",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:36:30.314Z",
"content": "\nOutput: (empty)\nProcess Group PGID: 2537\n",
"isError": false
},
"responseMessageId": "59f34f0d-9423-4874-87e9-61b975ac404c"
}
]
},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-29T01:36:46.441Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:46.441Z",
"text": "Exploring Normalization Strategies\n\nI'm currently investigating various normalization techniques for the raw listen counts. Specifically, I'm considering options like TF-IDF and BM25, and their potential application to address the wide dynamic range observed in user-item interaction weights, aiming for a more balanced data representation."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:36:46.441Z",
"text": "Testing Log-Scaling and Normalization\n\nI'm now exploring a log-scaling transformation `np.log1p(X_sp.data)`. Following this, I'm assessing the impact of L2 normalizing user rows before SVD to mitigate high-activity user influence. I'm also considering item-item collaborative filtering with cosine similarity and top-K neighbors."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:36:46.441Z",
"callId": "run_shell_command__call_4274",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u, all_it, all_w = [], [], []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\n\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Build Item-Item Cosine Similarity Matrix\n# We can normalize columns of X_sp (items)\n# X_csc for fast column ops\nX_csc = X_sp.tocsc()\n# For cosine, clip negative weights to 0 for similarity\nX_pos = X_csc.copy()\nX_pos.data = np.maximum(0, X_pos.data)\nX_norm = normalize(X_pos, norm='l2', axis=0) # unit norm columns\n\nprint(f'Computing Item-Item similarity in chunks...')\nt_sim = time.time()\n# S_sparse: for each chunk of items, compute dot with all items, keep top K\nK_nn = 100\nrows_s, cols_s, data_s = [], [], []\n\nchunk_size = 2000\nfor c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n # items in chunk: (c_end - c_start)\n chunk_items = X_norm[:, c_start:c_end]\n # sim matrix: (c_end - c_start) x I\n sim_chunk = (chunk_items.T @ X_norm).toarray() # dense chunk\n # zero out diagonal\n for idx_i in range(c_end - c_start):\n sim_chunk[idx_i, c_start + idx_i] = 0.0\n \n # top K for each item in chunk\n top_k_idx = np.argpartition(-sim_chunk, K_nn, axis=1)[:, :K_nn]\n for idx_i in range(c_end - c_start):\n top_cols = top_k_idx[idx_i]\n top_vals = sim_chunk[idx_i, top_cols]\n # filter positive\n pos_mask = top_vals > 0\n top_cols = top_cols[pos_mask]\n top_vals = top_vals[pos_mask]\n \n rows_s.extend([c_start + idx_i] * len(top_cols))\n cols_s.extend(top_cols)\n data_s.extend(top_vals)\n\nS_sparse = sp.csr_matrix((data_s, (rows_s, cols_s)), shape=(I, I), dtype=np.float32)\nprint(f'Item similarity S built in {time.time()-t_sim:.2f}s, nnz={S_sparse.nnz:,}')\n\n# User scores: X_sp @ S_sparse\nt_score = time.time()\n# Multiply in batches\nranked_dict = {}\nbatch_size = 1000\nlog_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\nfor b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (X_sp[b_start:b_end] @ S_sparse).toarray()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n\nres = score_rankings(ranked_dict, truth)\nprint(f'Item-Item CF pure NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate Item-Item CF on validation set"
},
"recorded": {
"id": "run_shell_command__call_4274",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u, all_it, all_w = [], [], []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\n\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Build Item-Item Cosine Similarity Matrix\n# We can normalize columns of X_sp (items)\n# X_csc for fast column ops\nX_csc = X_sp.tocsc()\n# For cosine, clip negative weights to 0 for similarity\nX_pos = X_csc.copy()\nX_pos.data = np.maximum(0, X_pos.data)\nX_norm = normalize(X_pos, norm='l2', axis=0) # unit norm columns\n\nprint(f'Computing Item-Item similarity in chunks...')\nt_sim = time.time()\n# S_sparse: for each chunk of items, compute dot with all items, keep top K\nK_nn = 100\nrows_s, cols_s, data_s = [], [], []\n\nchunk_size = 2000\nfor c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n # items in chunk: (c_end - c_start)\n chunk_items = X_norm[:, c_start:c_end]\n # sim matrix: (c_end - c_start) x I\n sim_chunk = (chunk_items.T @ X_norm).toarray() # dense chunk\n # zero out diagonal\n for idx_i in range(c_end - c_start):\n sim_chunk[idx_i, c_start + idx_i] = 0.0\n \n # top K for each item in chunk\n top_k_idx = np.argpartition(-sim_chunk, K_nn, axis=1)[:, :K_nn]\n for idx_i in range(c_end - c_start):\n top_cols = top_k_idx[idx_i]\n top_vals = sim_chunk[idx_i, top_cols]\n # filter positive\n pos_mask = top_vals > 0\n top_cols = top_cols[pos_mask]\n top_vals = top_vals[pos_mask]\n \n rows_s.extend([c_start + idx_i] * len(top_cols))\n cols_s.extend(top_cols)\n data_s.extend(top_vals)\n\nS_sparse = sp.csr_matrix((data_s, (rows_s, cols_s)), shape=(I, I), dtype=np.float32)\nprint(f'Item similarity S built in {time.time()-t_sim:.2f}s, nnz={S_sparse.nnz:,}')\n\n# User scores: X_sp @ S_sparse\nt_score = time.time()\n# Multiply in batches\nranked_dict = {}\nbatch_size = 1000\nlog_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\nfor b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (X_sp[b_start:b_end] @ S_sparse).toarray()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n\nres = score_rankings(ranked_dict, truth)\nprint(f'Item-Item CF pure NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate Item-Item CF on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_4274",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computing Item-Item similarity in chunks...\nItem similarity S built in 24.33s, nnz=2,997,411\nItem-Item CF pure NDCG@10 = 0.011673\nProcess Group PGID: 2564\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:37:20.491Z",
"resultDisplay": "Computing Item-Item similarity in chunks...\nItem similarity S built in 24.33s, nnz=2,997,411\nItem-Item CF pure NDCG@10 = 0.011673",
"description": "Evaluate Item-Item CF on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:37:20.492Z",
"content": "\nOutput: Computing Item-Item similarity in chunks...\nItem similarity S built in 24.33s, nnz=2,997,411\nItem-Item CF pure NDCG@10 = 0.011673\nProcess Group PGID: 2564\n",
"isError": false
},
"responseMessageId": "4e473e7c-6952-44d8-9ca4-de62b9f93914"
}
]
},
{
"id": 57,
"role": "agent",
"timestamp": "2026-09-29T01:37:34.056Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:34.056Z",
"text": "Analyzing SVD Dominance\n\nI'm focusing on understanding why SVD is outperforming item-item collaborative filtering so significantly. The sparsity of the interaction matrix appears to be a key factor contributing to SVD's superior performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:34.056Z",
"text": "Refining SVD Input\n\nI'm now exploring the impact of different time windows and decay factors on SVD's performance, specifically testing 180, 90, and 45-day windows with various half-lives. I'm also investigating the inclusion of `train_likes.parquet` and clarifying the scope of `validation_preference_state.parquet`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:37:34.056Z",
"callId": "run_shell_command__call_411819",
"name": "run_shell_command",
"input": {
"description": "Check organic and played ratio distributions",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\norg = table['is_organic'].to_numpy()\npr = table['played_ratio_pct'].to_numpy()\nprint(f'Organic listen fraction: {org.mean():.2%}')\nprint(f'Played >= 80% fraction: {(pr >= 80).mean():.2%}')\nprint(f'Organic and >= 80%: {((org == 1) & (pr >= 80)).mean():.2%}')\n\""
},
"recorded": {
"id": "run_shell_command__call_411819",
"name": "run_shell_command",
"args": {
"description": "Check organic and played ratio distributions",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\norg = table['is_organic'].to_numpy()\npr = table['played_ratio_pct'].to_numpy()\nprint(f'Organic listen fraction: {org.mean():.2%}')\nprint(f'Played >= 80% fraction: {(pr >= 80).mean():.2%}')\nprint(f'Organic and >= 80%: {((org == 1) & (pr >= 80)).mean():.2%}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_411819",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Organic listen fraction: 51.69%\nPlayed >= 80% fraction: 59.23%\nOrganic and >= 80%: 28.00%\nProcess Group PGID: 2591\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:37:35.142Z",
"resultDisplay": "Organic listen fraction: 51.69%\nPlayed >= 80% fraction: 59.23%\nOrganic and >= 80%: 28.00%",
"description": "Check organic and played ratio distributions",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:37:35.143Z",
"content": "\nOutput: Organic listen fraction: 51.69%\nPlayed >= 80% fraction: 59.23%\nOrganic and >= 80%: 28.00%\nProcess Group PGID: 2591\n",
"isError": false
},
"responseMessageId": "927d42fa-9199-4a35-8b92-7043078a9fc4"
}
]
},
{
"id": 58,
"role": "agent",
"timestamp": "2026-09-29T01:37:49.289Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:37:49.289Z",
"text": "Analyzing Listen Data\n\nI'm focusing on the low organic listen percentage and its implications. Specifically, I'm questioning the weighting of skips. If a listen has a played ratio below 30%, it's a clear skip. I'm considering if skips should have negative or zero weight, as positively weighting a track a user skipped after only 5 seconds seems counterproductive."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:37:49.289Z",
"callId": "run_shell_command__call_41393",
"name": "run_shell_command",
"input": {
"description": "Test discrete ratio weights and organic pop weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Preload candidate catalog popularity signals\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\nlog_tot_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=0.0):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * pop_vec[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Function to build X given parameters\ndef build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0, like_w=4.0, dislike_w=-2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n \n # Played ratio scoring\n pr = pr_arr[valid]\n rat = np.where(pr >= 80, 1.0, np.where(pr >= 50, 0.4, -skip_penalty)).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Test half_life_days=45, organic_mult=2.0\nX_sp = build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0)\n\nfor n_comp in [64, 80, 96]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n for pw in [0.05, 0.1, 0.15, 0.2]:\n score_org = evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=pw)\n print(f'n_comp={n_comp}, pw_org={pw:.2f}: NDCG@10 = {score_org:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_41393",
"name": "run_shell_command",
"args": {
"description": "Test discrete ratio weights and organic pop weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Preload candidate catalog popularity signals\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\nlog_tot_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=0.0):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * pop_vec[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Function to build X given parameters\ndef build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0, like_w=4.0, dislike_w=-2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n \n # Played ratio scoring\n pr = pr_arr[valid]\n rat = np.where(pr >= 80, 1.0, np.where(pr >= 50, 0.4, -skip_penalty)).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Test half_life_days=45, organic_mult=2.0\nX_sp = build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0)\n\nfor n_comp in [64, 80, 96]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n for pw in [0.05, 0.1, 0.15, 0.2]:\n score_org = evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=pw)\n print(f'n_comp={n_comp}, pw_org={pw:.2f}: NDCG@10 = {score_org:.6f}')\n\""
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"output": "\nOutput: n_comp=64, pw_org=0.05: NDCG@10 = 0.019745\nn_comp=64, pw_org=0.10: NDCG@10 = 0.020004\nn_comp=64, pw_org=0.15: NDCG@10 = 0.019890\nn_comp=64, pw_org=0.20: NDCG@10 = 0.019773\nn_comp=80, pw_org=0.05: NDCG@10 = 0.020798\nn_comp=80, pw_org=0.10: NDCG@10 = 0.021160\nn_comp=80, pw_org=0.15: NDCG@10 = 0.021262\nn_comp=80, pw_org=0.20: NDCG@10 = 0.021121\nn_comp=96, pw_org=0.05: NDCG@10 = 0.020271\nn_comp=96, pw_org=0.10: NDCG@10 = 0.021136\nn_comp=96, pw_org=0.15: NDCG@10 = 0.020850\nn_comp=96, pw_org=0.20: NDCG@10 = 0.020710\nProcess Group PGID: 2613\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:38:34.608Z",
"resultDisplay": "n_comp=64, pw_org=0.05: NDCG@10 = 0.019745\nn_comp=64, pw_org=0.10: NDCG@10 = 0.020004\nn_comp=64, pw_org=0.15: NDCG@10 = 0.019890\nn_comp=64, pw_org=0.20: NDCG@10 = 0.019773\nn_comp=80, pw_org=0.05: NDCG@10 = 0.020798\nn_comp=80, pw_org=0.10: NDCG@10 = 0.021160\nn_comp=80, pw_org=0.15: NDCG@10 = 0.021262\nn_comp=80, pw_org=0.20: NDCG@10 = 0.021121\nn_comp=96, pw_org=0.05: NDCG@10 = 0.020271\nn_comp=96, pw_org=0.10: NDCG@10 = 0.021136\nn_comp=96, pw_org=0.15: NDCG@10 = 0.020850\nn_comp=96, pw_org=0.20: NDCG@10 = 0.020710",
"description": "Test discrete ratio weights and organic pop weights",
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},
"result": {
"timestamp": "2026-09-29T01:38:34.609Z",
"content": "\nOutput: n_comp=64, pw_org=0.05: NDCG@10 = 0.019745\nn_comp=64, pw_org=0.10: NDCG@10 = 0.020004\nn_comp=64, pw_org=0.15: NDCG@10 = 0.019890\nn_comp=64, pw_org=0.20: NDCG@10 = 0.019773\nn_comp=80, pw_org=0.05: NDCG@10 = 0.020798\nn_comp=80, pw_org=0.10: NDCG@10 = 0.021160\nn_comp=80, pw_org=0.15: NDCG@10 = 0.021262\nn_comp=80, pw_org=0.20: NDCG@10 = 0.021121\nn_comp=96, pw_org=0.05: NDCG@10 = 0.020271\nn_comp=96, pw_org=0.10: NDCG@10 = 0.021136\nn_comp=96, pw_org=0.15: NDCG@10 = 0.020850\nn_comp=96, pw_org=0.20: NDCG@10 = 0.020710\nProcess Group PGID: 2613\n",
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]
},
{
"id": 59,
"role": "agent",
"timestamp": "2026-09-29T01:38:38.325Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:38:38.325Z",
"text": "Improving NDCG Performance\n\nI've achieved a near doubling of NDCG@10 to 0.021262 with n_comp=80 and pw_org=0.15. Now, I'm investigating further gains by exploring normalization techniques for user vectors in $X_{\\text{sp}}$, specifically L2 and L1 norms prior to SVD."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:38:38.325Z",
"callId": "run_shell_command__call_21467",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\n\n# Test IDF weighting on X_sp\n# Let's import the previous setup logic and test IDF scaling\n\"",
"description": "Prepare IDF test"
},
"recorded": {
"id": "run_shell_command__call_21467",
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"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\n\n# Test IDF weighting on X_sp\n# Let's import the previous setup logic and test IDF scaling\n\"",
"description": "Prepare IDF test"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_21467",
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"output": "\nOutput: (empty)\nProcess Group PGID: 2640\n"
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],
"status": "success",
"timestamp": "2026-09-29T01:38:39.920Z",
"resultDisplay": "",
"description": "Prepare IDF test",
"displayName": "Shell",
"renderOutputAsMarkdown": false
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"result": {
"timestamp": "2026-09-29T01:38:39.921Z",
"content": "\nOutput: (empty)\nProcess Group PGID: 2640\n",
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},
{
"id": 60,
"role": "agent",
"timestamp": "2026-09-29T01:38:53.241Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:38:53.241Z",
"text": "Exploring Ranking Factors\n\nI'm currently evaluating the impact of L2 user normalization, varying item IDF weighting (0.25, 0.5, and 1.0), and applying log1p transformations to weights. My aim is to refine our ranking algorithm by testing these parameters."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:38:53.241Z",
"callId": "run_shell_command__call_142273",
"name": "run_shell_command",
"input": {
"description": "Test normalizations and transforms",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 4.0, -2.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n return X\n\nX_base = build_matrix(half_life_days=45, t_min_days=90)\n\n# Test 1: User L2 Normalization\nX_user_norm = normalize(X_base, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_user_norm)\nV_f = svd.components_\nprint('User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 2: Log1p transform on positive values\nX_log = X_base.copy()\npos_idx = X_log.data > 0\nX_log.data[pos_idx] = np.log1p(X_log.data[pos_idx])\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log)\nV_f = svd.components_\nprint('Log1p NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 3: Log1p + User L2 Normalization\nX_log_norm = normalize(X_log, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log_norm)\nV_f = svd.components_\nprint('Log1p + User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 4: Item IDF weighting\n# df per item\ndf_item = np.diff(X_base.tocsc().indptr)\nidf = np.log((U + 1.0) / (df_item + 1.0)) + 1.0\nfor p in [0.25, 0.5]:\n idf_diag = sp.diags(idf ** p)\n X_idf = X_base @ idf_diag\n svd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_idf)\n V_f = svd.components_\n print(f'IDF^{p} NDCG@10:', evaluate(U_f, V_f, 0.15))\n\""
},
"recorded": {
"id": "run_shell_command__call_142273",
"name": "run_shell_command",
"args": {
"description": "Test normalizations and transforms",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 4.0, -2.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n return X\n\nX_base = build_matrix(half_life_days=45, t_min_days=90)\n\n# Test 1: User L2 Normalization\nX_user_norm = normalize(X_base, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_user_norm)\nV_f = svd.components_\nprint('User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 2: Log1p transform on positive values\nX_log = X_base.copy()\npos_idx = X_log.data > 0\nX_log.data[pos_idx] = np.log1p(X_log.data[pos_idx])\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log)\nV_f = svd.components_\nprint('Log1p NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 3: Log1p + User L2 Normalization\nX_log_norm = normalize(X_log, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log_norm)\nV_f = svd.components_\nprint('Log1p + User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 4: Item IDF weighting\n# df per item\ndf_item = np.diff(X_base.tocsc().indptr)\nidf = np.log((U + 1.0) / (df_item + 1.0)) + 1.0\nfor p in [0.25, 0.5]:\n idf_diag = sp.diags(idf ** p)\n X_idf = X_base @ idf_diag\n svd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_idf)\n V_f = svd.components_\n print(f'IDF^{p} NDCG@10:', evaluate(U_f, V_f, 0.15))\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_142273",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User L2 normalized NDCG@10: 0.010972\nLog1p NDCG@10: 0.026368\nLog1p + User L2 normalized NDCG@10: 0.011793\nIDF^0.25 NDCG@10: 0.02251\nIDF^0.5 NDCG@10: 0.021536\nProcess Group PGID: 2658\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:39:31.515Z",
"resultDisplay": "User L2 normalized NDCG@10: 0.010972\nLog1p NDCG@10: 0.026368\nLog1p + User L2 normalized NDCG@10: 0.011793\nIDF^0.25 NDCG@10: 0.02251\nIDF^0.5 NDCG@10: 0.021536",
"description": "Test normalizations and transforms",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:39:31.517Z",
"content": "\nOutput: User L2 normalized NDCG@10: 0.010972\nLog1p NDCG@10: 0.026368\nLog1p + User L2 normalized NDCG@10: 0.011793\nIDF^0.25 NDCG@10: 0.02251\nIDF^0.5 NDCG@10: 0.021536\nProcess Group PGID: 2658\n",
"isError": false
},
"responseMessageId": "e4f84ee6-fd45-4b8d-88a0-f04a9efa3135"
}
]
},
{
"id": 61,
"role": "agent",
"timestamp": "2026-09-29T01:40:07.956Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:40:07.956Z",
"text": "Analyzing Log1p Impact\n\nI'm seeing a significant jump in NDCG@10 to 0.026368 after applying Log1p compression. This improvement, up from 0.021262, suggests that accounting for the power-law distribution of listen frequencies, where some tracks are vastly more popular, is a beneficial strategy."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:40:07.956Z",
"callId": "run_shell_command__call_43657",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [64, 80, 100, 128]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n for pw in [0.0, 0.05, 0.1, 0.15, 0.2]:\n score = evaluate(U_f, V_f, pop_weight=pw)\n print(f'n_comp={n_comp}, pw={pw:.2f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Sweep components and popularity weights for log1p SVD"
},
"recorded": {
"id": "run_shell_command__call_43657",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [64, 80, 100, 128]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n for pw in [0.0, 0.05, 0.1, 0.15, 0.2]:\n score = evaluate(U_f, V_f, pop_weight=pw)\n print(f'n_comp={n_comp}, pw={pw:.2f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Sweep components and popularity weights for log1p SVD"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_43657",
"name": "run_shell_command",
"response": {
"output": "\nOutput: n_comp=64, pw=0.00: NDCG@10 = 0.031635\nn_comp=64, pw=0.05: NDCG@10 = 0.029762\nn_comp=64, pw=0.10: NDCG@10 = 0.028191\nn_comp=64, pw=0.15: NDCG@10 = 0.026146\nn_comp=64, pw=0.20: NDCG@10 = 0.023952\nn_comp=80, pw=0.00: NDCG@10 = 0.031213\nn_comp=80, pw=0.05: NDCG@10 = 0.029205\nn_comp=80, pw=0.10: NDCG@10 = 0.027956\nn_comp=80, pw=0.15: NDCG@10 = 0.026271\nn_comp=80, pw=0.20: NDCG@10 = 0.024379\nn_comp=100, pw=0.00: NDCG@10 = 0.031578\nn_comp=100, pw=0.05: NDCG@10 = 0.029687\nn_comp=100, pw=0.10: NDCG@10 = 0.028371\nn_comp=100, pw=0.15: NDCG@10 = 0.026645\nn_comp=100, pw=0.20: NDCG@10 = 0.024692\nn_comp=128, pw=0.00: NDCG@10 = 0.031919\nn_comp=128, pw=0.05: NDCG@10 = 0.029963\nn_comp=128, pw=0.10: NDCG@10 = 0.028236\nn_comp=128, pw=0.15: NDCG@10 = 0.026469\nn_comp=128, pw=0.20: NDCG@10 = 0.024727\nProcess Group PGID: 2685\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:41:18.466Z",
"resultDisplay": "n_comp=64, pw=0.00: NDCG@10 = 0.031635\nn_comp=64, pw=0.05: NDCG@10 = 0.029762\nn_comp=64, pw=0.10: NDCG@10 = 0.028191\nn_comp=64, pw=0.15: NDCG@10 = 0.026146\nn_comp=64, pw=0.20: NDCG@10 = 0.023952\nn_comp=80, pw=0.00: NDCG@10 = 0.031213\nn_comp=80, pw=0.05: NDCG@10 = 0.029205\nn_comp=80, pw=0.10: NDCG@10 = 0.027956\nn_comp=80, pw=0.15: NDCG@10 = 0.026271\nn_comp=80, pw=0.20: NDCG@10 = 0.024379\nn_comp=100, pw=0.00: NDCG@10 = 0.031578\nn_comp=100, pw=0.05: NDCG@10 = 0.029687\nn_comp=100, pw=0.10: NDCG@10 = 0.028371\nn_comp=100, pw=0.15: NDCG@10 = 0.026645\nn_comp=100, pw=0.20: NDCG@10 = 0.024692\nn_comp=128, pw=0.00: NDCG@10 = 0.031919\nn_comp=128, pw=0.05: NDCG@10 = 0.029963\nn_comp=128, pw=0.10: NDCG@10 = 0.028236\nn_comp=128, pw=0.15: NDCG@10 = 0.026469\nn_comp=128, pw=0.20: NDCG@10 = 0.024727",
"description": "Sweep components and popularity weights for log1p SVD",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:41:19.971Z",
"content": "\nOutput: n_comp=64, pw=0.00: NDCG@10 = 0.031635\nn_comp=64, pw=0.05: NDCG@10 = 0.029762\nn_comp=64, pw=0.10: NDCG@10 = 0.028191\nn_comp=64, pw=0.15: NDCG@10 = 0.026146\nn_comp=64, pw=0.20: NDCG@10 = 0.023952\nn_comp=80, pw=0.00: NDCG@10 = 0.031213\nn_comp=80, pw=0.05: NDCG@10 = 0.029205\nn_comp=80, pw=0.10: NDCG@10 = 0.027956\nn_comp=80, pw=0.15: NDCG@10 = 0.026271\nn_comp=80, pw=0.20: NDCG@10 = 0.024379\nn_comp=100, pw=0.00: NDCG@10 = 0.031578\nn_comp=100, pw=0.05: NDCG@10 = 0.029687\nn_comp=100, pw=0.10: NDCG@10 = 0.028371\nn_comp=100, pw=0.15: NDCG@10 = 0.026645\nn_comp=100, pw=0.20: NDCG@10 = 0.024692\nn_comp=128, pw=0.00: NDCG@10 = 0.031919\nn_comp=128, pw=0.05: NDCG@10 = 0.029963\nn_comp=128, pw=0.10: NDCG@10 = 0.028236\nn_comp=128, pw=0.15: NDCG@10 = 0.026469\nn_comp=128, pw=0.20: NDCG@10 = 0.024727\nProcess Group PGID: 2685\n",
"isError": false
},
"responseMessageId": "91a04a35-02db-43c0-be94-211827667683"
}
]
},
{
"id": 62,
"role": "agent",
"timestamp": "2026-09-29T01:41:38.786Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:41:38.786Z",
"text": "Discovering Pure SVD Power\n\nI've hit on something significant! It turns out that after log-scaling, pure SVD, even with just 128 components, surprisingly outperforms models that incorporate popularity. The log-scaling seems to amplify the true personalized signal, pushing popularity into a detrimental role."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:41:38.786Z",
"callId": "run_shell_command__call_1543349",
"name": "run_shell_command",
"input": {
"description": "Evaluate SVD 160/200, Dual EASE on log1p, and Ensembling",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_1543349",
"name": "run_shell_command",
"args": {
"description": "Evaluate SVD 160/200, Dual EASE on log1p, and Ensembling",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1543349",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /usr/bin/bash: line 149: 2714 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 2712\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:42:40.036Z",
"resultDisplay": "/usr/bin/bash: line 149: 2714 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\"",
"description": "Evaluate SVD 160/200, Dual EASE on log1p, and Ensembling",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:42:40.045Z",
"content": "\nOutput: /usr/bin/bash: line 149: 2714 Killed python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\"\nExit Code: 137\nProcess Group PGID: 2712\n",
"isError": false
},
"responseMessageId": "1bb91b9b-70a8-4ae3-9d52-f0480204125b"
}
]
},
{
"id": 63,
"role": "agent",
"timestamp": "2026-09-29T01:42:51.462Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:42:51.462Z",
"text": "Analyzing Memory Footprint\n\nI'm currently scrutinizing the memory demands of my calculations. Specifically, I'm noting that `scores_svd`, a substantial $8456 \\times 30000$ matrix, consumes about 1 GB. This is a key factor as I move into the second loop, where it's joined by several other large arrays like `K`, `M`, `scores_ease`, and their normalized counterparts."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:42:51.462Z",
"callId": "run_shell_command__call_12369",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [96, 128, 160, 200, 256]:\n t1 = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n score = evaluate_factors(U_f, V_f)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {score:.6f} (took {time.time()-t1:.2f}s)')\n del U_f, V_f, svd\n gc.collect()\n\"",
"description": "Evaluate SVD components with lean batched memory"
},
"recorded": {
"id": "run_shell_command__call_12369",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [96, 128, 160, 200, 256]:\n t1 = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n score = evaluate_factors(U_f, V_f)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {score:.6f} (took {time.time()-t1:.2f}s)')\n del U_f, V_f, svd\n gc.collect()\n\"",
"description": "Evaluate SVD components with lean batched memory"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_12369",
"name": "run_shell_command",
"response": {
"output": "\nOutput: SVD n_comp= 96: NDCG@10 = 0.032038 (took 5.82s)\nSVD n_comp=128: NDCG@10 = 0.031919 (took 7.34s)\nSVD n_comp=160: NDCG@10 = 0.031208 (took 8.52s)\nSVD n_comp=200: NDCG@10 = 0.030494 (took 10.06s)\nSVD n_comp=256: NDCG@10 = 0.030458 (took 12.72s)\nProcess Group PGID: 2739\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:43:40.608Z",
"resultDisplay": "SVD n_comp= 96: NDCG@10 = 0.032038 (took 5.82s)\nSVD n_comp=128: NDCG@10 = 0.031919 (took 7.34s)\nSVD n_comp=160: NDCG@10 = 0.031208 (took 8.52s)\nSVD n_comp=200: NDCG@10 = 0.030494 (took 10.06s)\nSVD n_comp=256: NDCG@10 = 0.030458 (took 12.72s)",
"description": "Evaluate SVD components with lean batched memory",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:43:40.612Z",
"content": "\nOutput: SVD n_comp= 96: NDCG@10 = 0.032038 (took 5.82s)\nSVD n_comp=128: NDCG@10 = 0.031919 (took 7.34s)\nSVD n_comp=160: NDCG@10 = 0.031208 (took 8.52s)\nSVD n_comp=200: NDCG@10 = 0.030494 (took 10.06s)\nSVD n_comp=256: NDCG@10 = 0.030458 (took 12.72s)\nProcess Group PGID: 2739\n",
"isError": false
},
"responseMessageId": "760f3d05-0e30-4ae9-8e0c-2b0f4ffcc75d"
}
]
},
{
"id": 64,
"role": "agent",
"timestamp": "2026-09-29T01:43:50.674Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:43:50.674Z",
"text": "Tuning Data Parameters\n\nI'm currently exploring how different `half_life_days` and `t_min_days` values impact performance. Initial runs with `n_comp=96` yielded an NDCG@10 of 0.032038. I'm systematically testing a range of values for both parameters to identify optimal settings."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:43:50.674Z",
"callId": "run_shell_command__call_326658",
"name": "run_shell_command",
"input": {
"description": "Evaluate half-life variations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [20, 35, 50, 70]:\n X = build_matrix(half_life_days=hl, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_326658",
"name": "run_shell_command",
"args": {
"description": "Evaluate half-life variations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [20, 35, 50, 70]:\n X = build_matrix(half_life_days=hl, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_326658",
"name": "run_shell_command",
"response": {
"output": "\nOutput: half_life=20d: NDCG@10 = 0.033544\nhalf_life=35d: NDCG@10 = 0.032493\nhalf_life=50d: NDCG@10 = 0.031782\nhalf_life=70d: NDCG@10 = 0.031005\nProcess Group PGID: 2766\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:44:24.297Z",
"resultDisplay": "half_life=20d: NDCG@10 = 0.033544\nhalf_life=35d: NDCG@10 = 0.032493\nhalf_life=50d: NDCG@10 = 0.031782\nhalf_life=70d: NDCG@10 = 0.031005",
"description": "Evaluate half-life variations",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:44:24.298Z",
"content": "\nOutput: half_life=20d: NDCG@10 = 0.033544\nhalf_life=35d: NDCG@10 = 0.032493\nhalf_life=50d: NDCG@10 = 0.031782\nhalf_life=70d: NDCG@10 = 0.031005\nProcess Group PGID: 2766\n",
"isError": false
},
"responseMessageId": "0188f6ef-3a26-4041-b49c-c73bad9401fc"
}
]
},
{
"id": 65,
"role": "agent",
"timestamp": "2026-09-29T01:44:34.199Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T01:44:34.199Z",
"callId": "run_shell_command__call_28962",
"name": "run_shell_command",
"input": {
"description": "Fine-grain search for optimal half-life",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [7, 10, 14, 20]:\n X = build_matrix(half_life_days=hl, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_28962",
"name": "run_shell_command",
"args": {
"description": "Fine-grain search for optimal half-life",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [7, 10, 14, 20]:\n X = build_matrix(half_life_days=hl, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_28962",
"name": "run_shell_command",
"response": {
"output": "\nOutput: half_life= 7d: NDCG@10 = 0.032981\nhalf_life=10d: NDCG@10 = 0.033948\nhalf_life=14d: NDCG@10 = 0.034023\nhalf_life=20d: NDCG@10 = 0.033817\nProcess Group PGID: 2793\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:45:05.625Z",
"resultDisplay": "half_life= 7d: NDCG@10 = 0.032981\nhalf_life=10d: NDCG@10 = 0.033948\nhalf_life=14d: NDCG@10 = 0.034023\nhalf_life=20d: NDCG@10 = 0.033817",
"description": "Fine-grain search for optimal half-life",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:45:05.626Z",
"content": "\nOutput: half_life= 7d: NDCG@10 = 0.032981\nhalf_life=10d: NDCG@10 = 0.033948\nhalf_life=14d: NDCG@10 = 0.034023\nhalf_life=20d: NDCG@10 = 0.033817\nProcess Group PGID: 2793\n",
"isError": false
},
"responseMessageId": "df076b1a-8395-41a2-8b75-8cea7fd1870f"
}
]
},
{
"id": 66,
"role": "agent",
"timestamp": "2026-09-29T01:45:16.418Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:45:16.418Z",
"text": "Testing Parameters\n\nI've successfully evaluated the NDCG@10 for a 14-day half-life, achieving 0.034023. My next step is to explore the interplay of `organic_mult` and `like_w` under this established half-life. I'm currently testing variations of `like_w` (3, 5, 8, 12) and `organic_mult` (1.5, 2.0)."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:45:16.418Z",
"callId": "run_shell_command__call_26289",
"name": "run_shell_command",
"input": {
"description": "Search over organic multiplier and like weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [1.5, 2.0, 3.0]:\n for lw in [3.0, 6.0, 10.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=lw, dislike_w=-3.0, organic_mult=org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org_mult={org_m:.1f}, like_w={lw:.1f}: NDCG@10 = {score:.6f}')\n\""
},
"recorded": {
"id": "run_shell_command__call_26289",
"name": "run_shell_command",
"args": {
"description": "Search over organic multiplier and like weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [1.5, 2.0, 3.0]:\n for lw in [3.0, 6.0, 10.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=lw, dislike_w=-3.0, organic_mult=org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org_mult={org_m:.1f}, like_w={lw:.1f}: NDCG@10 = {score:.6f}')\n\""
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_26289",
"name": "run_shell_command",
"response": {
"output": "\nOutput: org_mult=1.5, like_w=3.0: NDCG@10 = 0.033124\norg_mult=1.5, like_w=6.0: NDCG@10 = 0.032162\norg_mult=1.5, like_w=10.0: NDCG@10 = 0.032840\norg_mult=2.0, like_w=3.0: NDCG@10 = 0.033992\norg_mult=2.0, like_w=6.0: NDCG@10 = 0.034090\norg_mult=2.0, like_w=10.0: NDCG@10 = 0.034108\norg_mult=3.0, like_w=3.0: NDCG@10 = 0.035300\norg_mult=3.0, like_w=6.0: NDCG@10 = 0.035780\norg_mult=3.0, like_w=10.0: NDCG@10 = 0.034943\nProcess Group PGID: 2820\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:46:22.299Z",
"resultDisplay": "org_mult=1.5, like_w=3.0: NDCG@10 = 0.033124\norg_mult=1.5, like_w=6.0: NDCG@10 = 0.032162\norg_mult=1.5, like_w=10.0: NDCG@10 = 0.032840\norg_mult=2.0, like_w=3.0: NDCG@10 = 0.033992\norg_mult=2.0, like_w=6.0: NDCG@10 = 0.034090\norg_mult=2.0, like_w=10.0: NDCG@10 = 0.034108\norg_mult=3.0, like_w=3.0: NDCG@10 = 0.035300\norg_mult=3.0, like_w=6.0: NDCG@10 = 0.035780\norg_mult=3.0, like_w=10.0: NDCG@10 = 0.034943",
"description": "Search over organic multiplier and like weights",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:46:22.300Z",
"content": "\nOutput: org_mult=1.5, like_w=3.0: NDCG@10 = 0.033124\norg_mult=1.5, like_w=6.0: NDCG@10 = 0.032162\norg_mult=1.5, like_w=10.0: NDCG@10 = 0.032840\norg_mult=2.0, like_w=3.0: NDCG@10 = 0.033992\norg_mult=2.0, like_w=6.0: NDCG@10 = 0.034090\norg_mult=2.0, like_w=10.0: NDCG@10 = 0.034108\norg_mult=3.0, like_w=3.0: NDCG@10 = 0.035300\norg_mult=3.0, like_w=6.0: NDCG@10 = 0.035780\norg_mult=3.0, like_w=10.0: NDCG@10 = 0.034943\nProcess Group PGID: 2820\n",
"isError": false
},
"responseMessageId": "9a7b4fe8-bd78-4f27-8d01-9e65dff5f714"
}
]
},
{
"id": 67,
"role": "agent",
"timestamp": "2026-09-29T01:48:52.112Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:48:52.112Z",
"text": "Testing Multiplier Impact\n\nI'm exploring how altering the organic multiplier affects NDCG@10. Initial findings with a multiplier of 3.0 yielded 0.035780. Now, I'm investigating higher multipliers (4.0, 5.0) and the impact of downweighting non-organic listens."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:48:52.112Z",
"callId": "run_shell_command__call_37673",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=3.0, non_org_mult=1.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, non_org_mult).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [3.0, 4.0, 5.0]:\n for non_org_m in [0.2, 0.5, 1.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=org_m, non_org_mult=non_org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org={org_m:.1f}, non_org={non_org_m:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test organic vs non-organic relative weighting"
},
"recorded": {
"id": "run_shell_command__call_37673",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=3.0, non_org_mult=1.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, non_org_mult).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [3.0, 4.0, 5.0]:\n for non_org_m in [0.2, 0.5, 1.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=org_m, non_org_mult=non_org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org={org_m:.1f}, non_org={non_org_m:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test organic vs non-organic relative weighting"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_37673",
"name": "run_shell_command",
"response": {
"output": "\nOutput: org=3.0, non_org=0.2: NDCG@10 = 0.034776\norg=3.0, non_org=0.5: NDCG@10 = 0.036133\norg=3.0, non_org=1.0: NDCG@10 = 0.035780\norg=4.0, non_org=0.2: NDCG@10 = 0.035782\norg=4.0, non_org=0.5: NDCG@10 = 0.036248\norg=4.0, non_org=1.0: NDCG@10 = 0.036237\norg=5.0, non_org=0.2: NDCG@10 = 0.035872\norg=5.0, non_org=0.5: NDCG@10 = 0.036697\norg=5.0, non_org=1.0: NDCG@10 = 0.036811\nProcess Group PGID: 2847\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:49:58.804Z",
"resultDisplay": "org=3.0, non_org=0.2: NDCG@10 = 0.034776\norg=3.0, non_org=0.5: NDCG@10 = 0.036133\norg=3.0, non_org=1.0: NDCG@10 = 0.035780\norg=4.0, non_org=0.2: NDCG@10 = 0.035782\norg=4.0, non_org=0.5: NDCG@10 = 0.036248\norg=4.0, non_org=1.0: NDCG@10 = 0.036237\norg=5.0, non_org=0.2: NDCG@10 = 0.035872\norg=5.0, non_org=0.5: NDCG@10 = 0.036697\norg=5.0, non_org=1.0: NDCG@10 = 0.036811",
"description": "Test organic vs non-organic relative weighting",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:49:58.805Z",
"content": "\nOutput: org=3.0, non_org=0.2: NDCG@10 = 0.034776\norg=3.0, non_org=0.5: NDCG@10 = 0.036133\norg=3.0, non_org=1.0: NDCG@10 = 0.035780\norg=4.0, non_org=0.2: NDCG@10 = 0.035782\norg=4.0, non_org=0.5: NDCG@10 = 0.036248\norg=4.0, non_org=1.0: NDCG@10 = 0.036237\norg=5.0, non_org=0.2: NDCG@10 = 0.035872\norg=5.0, non_org=0.5: NDCG@10 = 0.036697\norg=5.0, non_org=1.0: NDCG@10 = 0.036811\nProcess Group PGID: 2847\n",
"isError": false
},
"responseMessageId": "bb3c3133-2328-417e-a7ae-897a21e869d8"
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]
},
{
"id": 68,
"role": "agent",
"timestamp": "2026-09-29T01:50:19.021Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:50:19.021Z",
"text": "Observing Performance Gains\n\nMy recent evaluations show a continued upward trend in NDCG@10, currently at 0.036811. This represents a significant 3.44x improvement over the baseline. I'm now exploring the impact of increasing the 'org' parameter to 6.0."
},
{
"kind": "tool",
"timestamp": "2026-09-29T01:50:19.021Z",
"callId": "run_shell_command__call_2643",
"name": "run_shell_command",
"input": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(ratio_low=0.0, ratio_mid=0.3, ratio_high=1.0):\n half_life = 14 * 86400\n t_min = cutoff - 60 * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 5.0, 1.0).astype(np.float32)\n \n pr = pr_arr[valid]\n rat = np.where(pr >= 80, ratio_high, np.where(pr >= 50, ratio_mid, ratio_low)).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 6.0, -3.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor r_low in [0.0, -0.5, 0.1]:\n for r_mid in [0.2, 0.4, 0.6]:\n X = build_matrix(ratio_low=r_low, ratio_mid=r_mid, ratio_high=1.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'r_low={r_low:.1f}, r_mid={r_mid:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test ratio scoring thresholds"
},
"recorded": {
"id": "run_shell_command__call_2643",
"name": "run_shell_command",
"args": {
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(ratio_low=0.0, ratio_mid=0.3, ratio_high=1.0):\n half_life = 14 * 86400\n t_min = cutoff - 60 * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 5.0, 1.0).astype(np.float32)\n \n pr = pr_arr[valid]\n rat = np.where(pr >= 80, ratio_high, np.where(pr >= 50, ratio_mid, ratio_low)).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 6.0, -3.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor r_low in [0.0, -0.5, 0.1]:\n for r_mid in [0.2, 0.4, 0.6]:\n X = build_matrix(ratio_low=r_low, ratio_mid=r_mid, ratio_high=1.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'r_low={r_low:.1f}, r_mid={r_mid:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test ratio scoring thresholds"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2643",
"name": "run_shell_command",
"response": {
"output": "\nOutput: r_low=0.0, r_mid=0.2: NDCG@10 = 0.035598\nr_low=0.0, r_mid=0.4: NDCG@10 = 0.035635\nr_low=0.0, r_mid=0.6: NDCG@10 = 0.035516\nr_low=-0.5, r_mid=0.2: NDCG@10 = 0.020860\nr_low=-0.5, r_mid=0.4: NDCG@10 = 0.021453\nr_low=-0.5, r_mid=0.6: NDCG@10 = 0.022195\nr_low=0.1, r_mid=0.2: NDCG@10 = 0.036099\nr_low=0.1, r_mid=0.4: NDCG@10 = 0.035879\nr_low=0.1, r_mid=0.6: NDCG@10 = 0.036049\nProcess Group PGID: 2874\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T01:51:25.681Z",
"resultDisplay": "r_low=0.0, r_mid=0.2: NDCG@10 = 0.035598\nr_low=0.0, r_mid=0.4: NDCG@10 = 0.035635\nr_low=0.0, r_mid=0.6: NDCG@10 = 0.035516\nr_low=-0.5, r_mid=0.2: NDCG@10 = 0.020860\nr_low=-0.5, r_mid=0.4: NDCG@10 = 0.021453\nr_low=-0.5, r_mid=0.6: NDCG@10 = 0.022195\nr_low=0.1, r_mid=0.2: NDCG@10 = 0.036099\nr_low=0.1, r_mid=0.4: NDCG@10 = 0.035879\nr_low=0.1, r_mid=0.6: NDCG@10 = 0.036049",
"description": "Test ratio scoring thresholds",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:51:25.682Z",
"content": "\nOutput: r_low=0.0, r_mid=0.2: NDCG@10 = 0.035598\nr_low=0.0, r_mid=0.4: NDCG@10 = 0.035635\nr_low=0.0, r_mid=0.6: NDCG@10 = 0.035516\nr_low=-0.5, r_mid=0.2: NDCG@10 = 0.020860\nr_low=-0.5, r_mid=0.4: NDCG@10 = 0.021453\nr_low=-0.5, r_mid=0.6: NDCG@10 = 0.022195\nr_low=0.1, r_mid=0.2: NDCG@10 = 0.036099\nr_low=0.1, r_mid=0.4: NDCG@10 = 0.035879\nr_low=0.1, r_mid=0.6: NDCG@10 = 0.036049\nProcess Group PGID: 2874\n",
"isError": false
},
"responseMessageId": "948314ad-410a-4b1f-8685-d76d8b974da4"
}
]
},
{
"id": 69,
"role": "agent",
"timestamp": "2026-09-29T01:51:42.691Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:51:42.691Z",
"text": "Testing Ratio Calculations\n\nMy recent explorations involved tweaking the `r_low` parameter, finding a value of 0.1 to be marginally more effective than 0.0 or negative alternatives. This builds upon prior experimentation where a threshold of 0.4 for ratios below 80 resulted in a score of 0.036811. I am now investigating clipping the ratio."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T01:51:42.691Z",
"text": "Exploring Blending Strategies\n\nI'm now considering multi-model ensembling. Specifically, I want to investigate blending different SVD models, perhaps trained with varying random seeds or half-lives, to see if averaging their predictions yields improvements. I'm also tentatively looking into iALS but am concerned about implementation speed."
},
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"description": "Evaluate multi-temporal SVD ensemble",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=5.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\n# Model 1: half_life = 10 days\nX1 = build_matrix(half_life_days=10, t_min_days=45)\nsvd1 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\nU1 = svd1.fit_transform(X1)\nV1 = svd1.components_\n\n# Model 2: half_life = 25 days\nX2 = build_matrix(half_life_days=25, t_min_days=90)\nsvd2 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=123)\nU2 = svd2.fit_transform(X2)\nV2 = svd2.components_\n\n# Model 3: half_life = 60 days\nX3 = build_matrix(half_life_days=60, t_min_days=120)\nsvd3 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=456)\nU3 = svd3.fit_transform(X3)\nV3 = svd3.components_\n\ndef eval_ensemble(weights):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n s1 = (U1[b_start:b_end] @ V1).astype(np.float32)\n s2 = (U2[b_start:b_end] @ V2).astype(np.float32)\n s3 = (U3[b_start:b_end] @ V3).astype(np.float32)\n \n # Standardize per user within batch\n s1 = (s1 - s1.mean(axis=1, keepdims=True)) / (s1.std(axis=1, keepdims=True) + 1e-6)\n s2 = (s2 - s2.mean(axis=1, keepdims=True)) / (s2.std(axis=1, keepdims=True) + 1e-6)\n s3 = (s3 - s3.mean(axis=1, keepdims=True)) / (s3.std(axis=1, keepdims=True) + 1e-6)\n \n b_scores = weights[0] * s1 + weights[1] * s2 + weights[2] * s3\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\nprint('Model 1 alone:', eval_ensemble([1.0, 0.0, 0.0]))\nprint('Model 2 alone:', eval_ensemble([0.0, 1.0, 0.0]))\nprint('Model 3 alone:', eval_ensemble([0.0, 0.0, 1.0]))\nprint('Ensemble 1+2 (0.6, 0.4):', eval_ensemble([0.6, 0.4, 0.0]))\nprint('Ensemble 1+2+3 (0.5, 0.35, 0.15):', eval_ensemble([0.5, 0.35, 0.15]))\nprint('Ensemble 1+2+3 (0.6, 0.3, 0.1):', eval_ensemble([0.6, 0.3, 0.1]))\n\""
},
"recorded": {
"id": "run_shell_command__call_220495",
"name": "run_shell_command",
"args": {
"description": "Evaluate multi-temporal SVD ensemble",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=5.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\n# Model 1: half_life = 10 days\nX1 = build_matrix(half_life_days=10, t_min_days=45)\nsvd1 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\nU1 = svd1.fit_transform(X1)\nV1 = svd1.components_\n\n# Model 2: half_life = 25 days\nX2 = build_matrix(half_life_days=25, t_min_days=90)\nsvd2 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=123)\nU2 = svd2.fit_transform(X2)\nV2 = svd2.components_\n\n# Model 3: half_life = 60 days\nX3 = build_matrix(half_life_days=60, t_min_days=120)\nsvd3 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=456)\nU3 = svd3.fit_transform(X3)\nV3 = svd3.components_\n\ndef eval_ensemble(weights):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n s1 = (U1[b_start:b_end] @ V1).astype(np.float32)\n s2 = (U2[b_start:b_end] @ V2).astype(np.float32)\n s3 = (U3[b_start:b_end] @ V3).astype(np.float32)\n \n # Standardize per user within batch\n s1 = (s1 - s1.mean(axis=1, keepdims=True)) / (s1.std(axis=1, keepdims=True) + 1e-6)\n s2 = (s2 - s2.mean(axis=1, keepdims=True)) / (s2.std(axis=1, keepdims=True) + 1e-6)\n s3 = (s3 - s3.mean(axis=1, keepdims=True)) / (s3.std(axis=1, keepdims=True) + 1e-6)\n \n b_scores = weights[0] * s1 + weights[1] * s2 + weights[2] * s3\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\nprint('Model 1 alone:', eval_ensemble([1.0, 0.0, 0.0]))\nprint('Model 2 alone:', eval_ensemble([0.0, 1.0, 0.0]))\nprint('Model 3 alone:', eval_ensemble([0.0, 0.0, 1.0]))\nprint('Ensemble 1+2 (0.6, 0.4):', eval_ensemble([0.6, 0.4, 0.0]))\nprint('Ensemble 1+2+3 (0.5, 0.35, 0.15):', eval_ensemble([0.5, 0.35, 0.15]))\nprint('Ensemble 1+2+3 (0.6, 0.3, 0.1):', eval_ensemble([0.6, 0.3, 0.1]))\n\""
},
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"output": "\nOutput: Model 1 alone: 0.037002\nModel 2 alone: 0.035264\nModel 3 alone: 0.031669\nEnsemble 1+2 (0.6, 0.4): 0.036813\nEnsemble 1+2+3 (0.5, 0.35, 0.15): 0.03658\nEnsemble 1+2+3 (0.6, 0.3, 0.1): 0.036885\nProcess Group PGID: 2901\n"
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"timestamp": "2026-09-29T01:52:37.340Z",
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"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T01:52:37.342Z",
"content": "\nOutput: Model 1 alone: 0.037002\nModel 2 alone: 0.035264\nModel 3 alone: 0.031669\nEnsemble 1+2 (0.6, 0.4): 0.036813\nEnsemble 1+2+3 (0.5, 0.35, 0.15): 0.03658\nEnsemble 1+2+3 (0.6, 0.3, 0.1): 0.036885\nProcess Group PGID: 2901\n",
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