{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-r05-b9049e57",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "744437f3-b94f-48e5-abb6-9e51f3e73986",
"steps": [
{
"id": 1,
"role": "user",
"timestamp": "2026-09-29T02:03:40.053Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T02:03:40.053Z",
"text": "\nThis is the Gemini CLI. We are setting up the context for our chat.\nToday's date is Tuesday, September 29, 2026 (formatted according to the user's locale).\nMy operating system is: linux\nThe project's temporary directory is: /home/agent/.gemini/tmp/app\n- **Workspace Directories:**\n - /app\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\n└───solution/\n\n\n"
}
]
},
{
"id": 2,
"role": "user",
"timestamp": "2026-09-29T02:03:40.096Z",
"blocks": [
{
"kind": "message",
"timestamp": "2026-09-29T02:03:40.096Z",
"text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n"
}
]
},
{
"id": 3,
"role": "agent",
"timestamp": "2026-09-29T02:03:43.669Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:03:43.669Z",
"text": "Focusing on Requirements\n\nI'm currently dissecting the core task and its associated demands, prioritizing immediate action and strict adherence to all stipulated guidelines to ensure security, system integrity, and efficient context delivery."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:03:43.669Z",
"callId": "update_topic__call_322750",
"name": "update_topic",
"input": {
"strategic_intent": "Explore environment, data files, evaluation script, and baseline requirements.",
"title": "Environment and Data Exploration",
"summary": "Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities."
},
"recorded": {
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"name": "update_topic",
"args": {
"strategic_intent": "Explore environment, data files, evaluation script, and baseline requirements.",
"title": "Environment and Data Exploration",
"summary": "Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities."
},
"result": [
{
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"id": "update_topic__call_322750",
"name": "update_topic",
"response": {
"output": "Current topic: \"Environment and Data Exploration\"\nTopic summary: Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities.\n\nStrategic Intent: Explore environment, data files, evaluation script, and baseline requirements."
}
}
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],
"status": "success",
"timestamp": "2026-09-29T02:03:43.677Z",
"resultDisplay": "## 📂 Topic: **Environment and Data Exploration**\n\n**Summary:**\nStarting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities.\n\n> [!STRATEGY]\n> **Intent:** Explore environment, data files, evaluation script, and baseline requirements.",
"description": "Update topic to: \"Environment and Data Exploration\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:03:43.678Z",
"content": "Current topic: \"Environment and Data Exploration\"\nTopic summary: Starting the discovery ranker project. Exploring available data in /task/data/, the evaluation scripts /task/evaluate_public.py and /task/ranking_evaluation.py, and checking environment capabilities.\n\nStrategic Intent: Explore environment, data files, evaluation script, and baseline requirements.",
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"responseMessageId": "d9ca1a16-346f-4e2d-8990-9d4df20f1d9e"
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{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T02:03:46.424Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:03:46.424Z",
"callId": "run_shell_command__call_1583700",
"name": "run_shell_command",
"input": {
"command": "ls -la /task && ls -la /task/data && nvidia-smi || true && lscpu | grep \"Model name\\|CPU(s):\" && free -h",
"description": "Check files in /task, /task/data, GPU availability, CPU info, and RAM."
},
"recorded": {
"id": "run_shell_command__call_1583700",
"name": "run_shell_command",
"args": {
"command": "ls -la /task && ls -la /task/data && nvidia-smi || true && lscpu | grep \"Model name\\|CPU(s):\" && free -h",
"description": "Check files in /task, /task/data, GPU availability, CPU info, and RAM."
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1583700",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:03 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 data\n-r--r--r-- 1 root root 5774 Sep 29 01:57 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:57 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:57 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:57 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:57 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:57 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:58 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:57 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:57 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:57 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:57 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:57 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:57 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:57 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:57 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:57 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:57 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:57 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:58 validation_preference_state.parquet\n/usr/bin/bash: line 4: nvidia-smi: command not found\nCPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1612\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:03:46.464Z",
"resultDisplay": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:03 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 data\n-r--r--r-- 1 root root 5774 Sep 29 01:57 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:57 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:57 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:57 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:57 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:57 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:58 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:57 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:57 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:57 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:57 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:57 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:57 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:57 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:57 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:57 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:57 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:57 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:58 validation_preference_state.parquet\n/usr/bin/bash: line 4: nvidia-smi: command not found\nCPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found",
"description": "ls -la /task && ls -la /task/data && nvidia-smi || true && lscpu | grep \"Model name\\|CPU(s):\" && free -h",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:03:46.465Z",
"content": "\nOutput: total 36\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 .\ndrwxr-xr-x 1 root root 4096 Sep 29 02:03 ..\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 data\n-r--r--r-- 1 root root 5774 Sep 29 01:57 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 29 01:57 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 29 01:57 requirements.txt\ntotal 347392\ndr-xr-xr-x 1 root root 4096 Sep 29 01:58 .\ndr-xr-xr-x 1 root root 4096 Sep 29 02:01 ..\n-r--r--r-- 1 root root 349352 Sep 29 01:57 candidate_catalog.parquet\n-r--r--r-- 1 root root 84127 Sep 29 01:57 target_users.parquet\n-r--r--r-- 1 root root 7736341 Sep 29 01:57 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1092329 Sep 29 01:58 test_preference_state.parquet\n-r--r--r-- 1 root root 857213 Sep 29 01:57 train_dislikes.parquet\n-r--r--r-- 1 root root 5374652 Sep 29 01:57 train_likes.parquet\n-r--r--r-- 1 root root 319183255 Sep 29 01:57 train_listens.parquet\n-r--r--r-- 1 root root 155988 Sep 29 01:57 train_undislikes.parquet\n-r--r--r-- 1 root root 1855885 Sep 29 01:57 train_unlikes.parquet\n-r--r--r-- 1 root root 36571 Sep 29 01:57 val_dislikes.parquet\n-r--r--r-- 1 root root 298484 Sep 29 01:57 val_likes.parquet\n-r--r--r-- 1 root root 9947609 Sep 29 01:57 val_listens.parquet\n-r--r--r-- 1 root root 7845 Sep 29 01:57 val_undislikes.parquet\n-r--r--r-- 1 root root 72536 Sep 29 01:57 val_unlikes.parquet\n-r--r--r-- 1 root root 7573821 Sep 29 01:57 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1061761 Sep 29 01:58 validation_preference_state.parquet\n/usr/bin/bash: line 4: nvidia-smi: command not found\nCPU(s): 8\nModel name: AMD EPYC 7R13 Processor\nNUMA node0 CPU(s): 0-7\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1612\n",
"isError": false
},
"responseMessageId": "7076eaf0-21ef-4843-89e6-54b5e26a2b9e"
}
]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T02:03:47.948Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:03:47.948Z",
"callId": "run_shell_command__call_42092",
"name": "run_shell_command",
"input": {
"description": "Check memory total and requirements.txt",
"command": "cat /proc/meminfo | grep MemTotal && cat /task/requirements.txt"
},
"recorded": {
"id": "run_shell_command__call_42092",
"name": "run_shell_command",
"args": {
"description": "Check memory total and requirements.txt",
"command": "cat /proc/meminfo | grep MemTotal && cat /task/requirements.txt"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_42092",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988968 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\nProcess Group PGID: 1620\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:03:47.963Z",
"resultDisplay": "MemTotal: 15988968 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1",
"description": "cat /proc/meminfo | grep MemTotal && cat /task/requirements.txt",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:03:47.964Z",
"content": "\nOutput: MemTotal: 15988968 kB\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\nProcess Group PGID: 1620\n",
"isError": false
},
"responseMessageId": "fb3acb3b-aef6-4d73-9167-8e0f5584f95e"
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]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T02:03:50.080Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:03:50.080Z",
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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: 1643\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:03:55.029Z",
"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",
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"renderOutputAsMarkdown": false
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"result": {
"timestamp": "2026-09-29T02:03:55.029Z",
"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: 1643\n",
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"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: 1646\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:03:56.318Z",
"resultDisplay": "\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }",
"description": "cat /task/ranking_evaluation.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:03:56.318Z",
"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: 1646\n",
"isError": false
},
"responseMessageId": "6a6c94eb-0252-4907-85ce-a5772c417c3c"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T02:04:00.035Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:00.035Z",
"text": "Examining Data Structures\n\nI'm now focusing on understanding the fundamental data shapes and characteristics. My next step involves executing a Python script to inspect key aspects of `target_users.parquet`, `candidate_catalog.parquet`, and the `train_*.parquet` and `val_*.parquet` files, specifically their column sets and item/user counts."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:00.035Z",
"callId": "run_shell_command__call_2430",
"name": "run_shell_command",
"input": {
"description": "Inspect dataset statistics and structure",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(f\"Target users: {len(targets)}, cols: {targets.columns.tolist()}, sample uids: {targets.uid.head(3).tolist()}\")\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(f\"Val elig uids: {len(val_elig[\\\"uids\\\"])}, items: {len(val_elig[\\\"item_ids\\\"])}, packed: {val_elig[\\\"packed_eligible\\\"].shape}\")\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(f\"Test elig uids: {len(test_elig[\\\"uids\\\"])}, items: {len(test_elig[\\\"item_ids\\\"])}, packed: {test_elig[\\\"packed_eligible\\\"].shape}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_2430",
"name": "run_shell_command",
"args": {
"description": "Inspect dataset statistics and structure",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(f\"Target users: {len(targets)}, cols: {targets.columns.tolist()}, sample uids: {targets.uid.head(3).tolist()}\")\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(f\"Val elig uids: {len(val_elig[\\\"uids\\\"])}, items: {len(val_elig[\\\"item_ids\\\"])}, packed: {val_elig[\\\"packed_eligible\\\"].shape}\")\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(f\"Test elig uids: {len(test_elig[\\\"uids\\\"])}, items: {len(test_elig[\\\"item_ids\\\"])}, packed: {test_elig[\\\"packed_eligible\\\"].shape}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_2430",
"name": "run_shell_command",
"response": {
"output": "\nOutput: File \"\", line 11\n print(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n ^\nSyntaxError: unexpected character after line continuation character\nExit Code: 1\nProcess Group PGID: 1649\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:00.061Z",
"resultDisplay": "File \"\", line 11\n print(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n ^\nSyntaxError: unexpected character after line continuation character",
"description": "Inspect dataset statistics and structure",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:00.062Z",
"content": "\nOutput: File \"\", line 11\n print(f\"Catalog items: {len(catalog)}, cols: {catalog.columns.tolist()}, head: {catalog.head(2).to_dict(orient=\\\"records\\\")}\")\n ^\nSyntaxError: unexpected character after line continuation character\nExit Code: 1\nProcess Group PGID: 1649\n",
"isError": false
},
"responseMessageId": "ed77406c-21c7-479e-90b4-17f032ecb14a"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T02:04:02.908Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:02.908Z",
"callId": "run_shell_command__call_424086",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(\"Target users:\", len(targets), targets.columns.tolist(), targets.uid.head(3).tolist())\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(\"Catalog items:\", len(catalog), catalog.columns.tolist(), catalog.head(2).to_dict(orient=\"records\"))\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Val elig uids:\", len(val_elig[\"uids\"]), \"items:\", len(val_elig[\"item_ids\"]), \"packed:\", val_elig[\"packed_eligible\"].shape)\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Test elig uids:\", len(test_elig[\"uids\"]), \"items:\", len(test_elig[\"item_ids\"]), \"packed:\", test_elig[\"packed_eligible\"].shape)\n'",
"description": "Inspect dataset shapes and column names"
},
"recorded": {
"id": "run_shell_command__call_424086",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(\"Target users:\", len(targets), targets.columns.tolist(), targets.uid.head(3).tolist())\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(\"Catalog items:\", len(catalog), catalog.columns.tolist(), catalog.head(2).to_dict(orient=\"records\"))\n\nfor f in sorted(DATA.glob(\"*.parquet\")):\n df = pd.read_parquet(f)\n print(f\"{f.name}: {len(df)} rows, cols: {df.columns.tolist()}\")\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Val elig uids:\", len(val_elig[\"uids\"]), \"items:\", len(val_elig[\"item_ids\"]), \"packed:\", val_elig[\"packed_eligible\"].shape)\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Test elig uids:\", len(test_elig[\"uids\"]), \"items:\", len(test_elig[\"item_ids\"]), \"packed:\", test_elig[\"packed_eligible\"].shape)\n'",
"description": "Inspect dataset shapes and column names"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_424086",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Target users: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] [100, 200, 300]\nCatalog items: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\ncandidate_catalog.parquet: 30000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: 8456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet: 484776 rows, cols: ['uid', 'item_id', 'state']\ntrain_dislikes.parquet: 101075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: 814831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: 43484159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: 19960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: 292665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: 3563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: 37795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: 1501697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: 9346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet: 469124 rows, cols: ['uid', 'item_id', 'state']\nVal elig uids: 8456 items: 30000 packed: (8456, 3750)\nTest elig uids: 8456 items: 30000 packed: (8456, 3750)\nProcess Group PGID: 1652\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:04.680Z",
"resultDisplay": "Target users: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] [100, 200, 300]\nCatalog items: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\ncandidate_catalog.parquet: 30000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: 8456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet: 484776 rows, cols: ['uid', 'item_id', 'state']\ntrain_dislikes.parquet: 101075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: 814831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: 43484159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: 19960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: 292665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: 3563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: 37795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: 1501697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: 9346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet: 469124 rows, cols: ['uid', 'item_id', 'state']\nVal elig uids: 8456 items: 30000 packed: (8456, 3750)\nTest elig uids: 8456 items: 30000 packed: (8456, 3750)",
"description": "Inspect dataset shapes and column names",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:04.681Z",
"content": "\nOutput: Target users: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] [100, 200, 300]\nCatalog items: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] [{'item_id': 5862961, 'positive_listens': 26615, 'organic_positive_listens': 20620, 'popularity_rank': 1}, {'item_id': 6901374, 'positive_listens': 25536, 'organic_positive_listens': 17347, 'popularity_rank': 2}]\ncandidate_catalog.parquet: 30000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet: 8456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet: 484776 rows, cols: ['uid', 'item_id', 'state']\ntrain_dislikes.parquet: 101075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet: 814831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet: 43484159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet: 19960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet: 292665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet: 3563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet: 37795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet: 1501697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet: 9346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet: 469124 rows, cols: ['uid', 'item_id', 'state']\nVal elig uids: 8456 items: 30000 packed: (8456, 3750)\nTest elig uids: 8456 items: 30000 packed: (8456, 3750)\nProcess Group PGID: 1652\n",
"isError": false
},
"responseMessageId": "30683acf-f375-4a86-905a-d64ebc608f01"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T02:04:10.133Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:10.132Z",
"text": "Assessing Data Scale\n\nMy current focus is on understanding the sheer volume of data we're dealing with. The `train_listens.parquet` file is immense, clocking in at over 43 million rows, with `val_listens.parquet` also substantial. I'm analyzing how our target users and candidate catalog items fit within this scale and the implications for storage."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:10.133Z",
"callId": "run_shell_command__call_1673371",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(targets.uid)\n\n# Check eligible items count\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Val eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\n\n# Check schema and metadata of train_listens\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nprint(\"train_listens num row groups:\", pf.num_row_groups)\nprint(\"train_listens schema:\", pf.schema)\n\n# Read 1 row group or sample\nsample = pf.read_row_group(0, columns=[\"uid\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]).to_pandas()\nprint(\"train_listens sample uids in targets:\", sample.uid.isin(target_uids).mean())\nprint(\"train_listens sample timestamp min/max:\", sample.timestamp.min(), sample.timestamp.max())\n\n# Preference state\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref state unique states:\", pref.state.value_counts().to_dict())\nprint(\"Validation pref uids in targets:\", pref.uid.isin(target_uids).mean())\n'",
"description": "Check eligibility counts, parquet row groups, and preference states"
},
"recorded": {
"id": "run_shell_command__call_1673371",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(targets.uid)\n\n# Check eligible items count\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Val eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\n\n# Check schema and metadata of train_listens\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nprint(\"train_listens num row groups:\", pf.num_row_groups)\nprint(\"train_listens schema:\", pf.schema)\n\n# Read 1 row group or sample\nsample = pf.read_row_group(0, columns=[\"uid\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]).to_pandas()\nprint(\"train_listens sample uids in targets:\", sample.uid.isin(target_uids).mean())\nprint(\"train_listens sample timestamp min/max:\", sample.timestamp.min(), sample.timestamp.max())\n\n# Preference state\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref state unique states:\", pref.state.value_counts().to_dict())\nprint(\"Validation pref uids in targets:\", pref.uid.isin(target_uids).mean())\n'",
"description": "Check eligibility counts, parquet row groups, and preference states"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1673371",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val eligible counts per user: min 24657 mean 29067.306291390727 max 30000\ntrain_listens num row groups: 42\ntrain_listens schema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_listens sample uids in targets: 0.9954624176025391\ntrain_listens sample timestamp min/max: 15 24786690\nValidation pref state unique states: {'liked': 418435, 'disliked': 50689}\nValidation pref uids in targets: 1.0\nProcess Group PGID: 1672\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:11.385Z",
"resultDisplay": "Val eligible counts per user: min 24657 mean 29067.306291390727 max 30000\ntrain_listens num row groups: 42\ntrain_listens schema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_listens sample uids in targets: 0.9954624176025391\ntrain_listens sample timestamp min/max: 15 24786690\nValidation pref state unique states: {'liked': 418435, 'disliked': 50689}\nValidation pref uids in targets: 1.0",
"description": "Check eligibility counts, parquet row groups, and preference states",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:11.387Z",
"content": "\nOutput: Val eligible counts per user: min 24657 mean 29067.306291390727 max 30000\ntrain_listens num row groups: 42\ntrain_listens schema: \nrequired group field_id=-1 schema {\n optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\ntrain_listens sample uids in targets: 0.9954624176025391\ntrain_listens sample timestamp min/max: 15 24786690\nValidation pref state unique states: {'liked': 418435, 'disliked': 50689}\nValidation pref uids in targets: 1.0\nProcess Group PGID: 1672\n",
"isError": false
},
"responseMessageId": "c2378f5a-fb1d-4270-a2d4-b2ecfb6a3a42"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T02:04:14.079Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:14.079Z",
"callId": "run_shell_command__call_120919",
"name": "run_shell_command",
"input": {
"description": "Check test eligibility and preference state",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nDATA = \"/task/data/\"\ntest_elig = np.load(DATA + \"test_eligible_candidates.npz\")\npacked = test_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Test eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\npref = pd.read_parquet(DATA + \"test_preference_state.parquet\")\nprint(\"Test pref state:\", pref.state.value_counts().to_dict(), len(pref))\n'"
},
"recorded": {
"id": "run_shell_command__call_120919",
"name": "run_shell_command",
"args": {
"description": "Check test eligibility and preference state",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nDATA = \"/task/data/\"\ntest_elig = np.load(DATA + \"test_eligible_candidates.npz\")\npacked = test_elig[\"packed_eligible\"]\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nprint(\"Test eligible counts per user: min\", unpacked.sum(axis=1).min(), \"mean\", unpacked.sum(axis=1).mean(), \"max\", unpacked.sum(axis=1).max())\npref = pd.read_parquet(DATA + \"test_preference_state.parquet\")\nprint(\"Test pref state:\", pref.state.value_counts().to_dict(), len(pref))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_120919",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Test eligible counts per user: min 24552 mean 29041.3178807947 max 30000\nTest pref state: {'liked': 432659, 'disliked': 52117} 484776\nProcess Group PGID: 1690\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:15.170Z",
"resultDisplay": "Test eligible counts per user: min 24552 mean 29041.3178807947 max 30000\nTest pref state: {'liked': 432659, 'disliked': 52117} 484776",
"description": "Check test eligibility and preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:15.171Z",
"content": "\nOutput: Test eligible counts per user: min 24552 mean 29041.3178807947 max 30000\nTest pref state: {'liked': 432659, 'disliked': 52117} 484776\nProcess Group PGID: 1690\n",
"isError": false
},
"responseMessageId": "04b30bd4-9eca-4b40-863c-dae6d8e99c95"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T02:04:18.135Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:18.135Z",
"text": "Exploring Baseline Performance\n\nI'm starting by establishing a simple baseline using candidate catalog popularity. The goal is to rank items for each user based on their existing `popularity_rank` from the `candidate_catalog.parquet` dataset to see how it performs."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:18.135Z",
"callId": "run_shell_command__call_120241",
"name": "run_shell_command",
"input": {
"description": "Ensure /app/solution exists and is writable",
"command": "mkdir -p /app/solution && ls -la /app/solution"
},
"recorded": {
"id": "run_shell_command__call_120241",
"name": "run_shell_command",
"args": {
"description": "Ensure /app/solution exists and is writable",
"command": "mkdir -p /app/solution && ls -la /app/solution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_120241",
"name": "run_shell_command",
"response": {
"output": "\nOutput: total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:01 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:01 ..\nProcess Group PGID: 1707\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:18.150Z",
"resultDisplay": "total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:01 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:01 ..",
"description": "mkdir -p /app/solution && ls -la /app/solution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:18.150Z",
"content": "\nOutput: total 8\ndrwxr-xr-x 2 agent agent 4096 Sep 29 02:01 .\ndrwxr-xr-x 3 agent agent 4096 Sep 29 02:01 ..\nProcess Group PGID: 1707\n",
"isError": false
},
"responseMessageId": "c55612e3-a863-4954-8fdf-6271760ac427"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T02:04:23.263Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:23.263Z",
"callId": "update_topic__call_31534",
"name": "update_topic",
"input": {
"title": "Baseline Evaluation and Benchmarking",
"strategic_intent": "Implement popularity baseline on validation set and evaluate with evaluate_public.py.",
"summary": "Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation."
},
"recorded": {
"id": "update_topic__call_31534",
"name": "update_topic",
"args": {
"title": "Baseline Evaluation and Benchmarking",
"strategic_intent": "Implement popularity baseline on validation set and evaluate with evaluate_public.py.",
"summary": "Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_31534",
"name": "update_topic",
"response": {
"output": "Current topic: \"Baseline Evaluation and Benchmarking\"\nTopic summary: Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation.\n\nStrategic Intent: Implement popularity baseline on validation set and evaluate with evaluate_public.py."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:23.266Z",
"resultDisplay": "## 📂 Topic: **Baseline Evaluation and Benchmarking**\n\n**Summary:**\nVerified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation.\n\n> [!STRATEGY]\n> **Intent:** Implement popularity baseline on validation set and evaluate with evaluate_public.py.",
"description": "Update topic to: \"Baseline Evaluation and Benchmarking\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:04:23.267Z",
"content": "Current topic: \"Baseline Evaluation and Benchmarking\"\nTopic summary: Verified data directory, schemas, candidate eligibility format, and evaluation protocol. Now implementing a popularity baseline on validation data and evaluating it using the official evaluate_public.py script. This will establish an initial benchmark and verify end-to-end evaluation.\n\nStrategic Intent: Implement popularity baseline on validation set and evaluate with evaluate_public.py.",
"isError": false
},
"responseMessageId": "4b12a1e5-557b-4ce6-9a39-c28550779791"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T02:04:25.717Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:25.717Z",
"callId": "run_shell_command__call_139907",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Catalog item_ids == val_elig item_ids?\", np.array_equal(catalog.item_id.values, val_elig[\"item_ids\"]))\nprint(\"Catalog item_ids head:\", catalog.item_id.head().tolist())\nprint(\"val_elig item_ids head:\", val_elig[\"item_ids\"][:5].tolist())\n'",
"description": "Check if candidate catalog and npz item_ids match"
},
"recorded": {
"id": "run_shell_command__call_139907",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nprint(\"Catalog item_ids == val_elig item_ids?\", np.array_equal(catalog.item_id.values, val_elig[\"item_ids\"]))\nprint(\"Catalog item_ids head:\", catalog.item_id.head().tolist())\nprint(\"val_elig item_ids head:\", val_elig[\"item_ids\"][:5].tolist())\n'",
"description": "Check if candidate catalog and npz item_ids match"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_139907",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog item_ids == val_elig item_ids? True\nCatalog item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]\nval_elig item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]\nProcess Group PGID: 1711\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:26.448Z",
"resultDisplay": "Catalog item_ids == val_elig item_ids? True\nCatalog item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]\nval_elig item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]",
"description": "Check if candidate catalog and npz item_ids match",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:26.449Z",
"content": "\nOutput: Catalog item_ids == val_elig item_ids? True\nCatalog item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]\nval_elig item_ids head: [5862961, 6901374, 5635052, 3542184, 9378983]\nProcess Group PGID: 1711\n",
"isError": false
},
"responseMessageId": "5b3a1ca2-c2f3-4bfa-ae69-94c084097942"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T02:04:31.542Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:31.542Z",
"callId": "run_shell_command__call_119768",
"name": "run_shell_command",
"input": {
"description": "Check test_elig item_ids match catalog",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Catalog item_ids == test_elig item_ids?\", np.array_equal(catalog.item_id.values, test_elig[\"item_ids\"]))\n'"
},
"recorded": {
"id": "run_shell_command__call_119768",
"name": "run_shell_command",
"args": {
"description": "Check test_elig item_ids match catalog",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Catalog item_ids == test_elig item_ids?\", np.array_equal(catalog.item_id.values, test_elig[\"item_ids\"]))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_119768",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog item_ids == test_elig item_ids? True\nProcess Group PGID: 1728\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:32.261Z",
"resultDisplay": "Catalog item_ids == test_elig item_ids? True",
"description": "Check test_elig item_ids match catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:32.262Z",
"content": "\nOutput: Catalog item_ids == test_elig item_ids? True\nProcess Group PGID: 1728\n",
"isError": false
},
"responseMessageId": "4aaa350c-6e9c-4df3-acec-4041f485cba7"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T02:04:34.662Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:34.662Z",
"callId": "run_shell_command__call_160587",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Targets == val_elig uids?\", np.array_equal(targets.uid.values, val_elig[\"uids\"]))\nprint(\"Targets == test_elig uids?\", np.array_equal(targets.uid.values, test_elig[\"uids\"]))\n'",
"description": "Check target users uids match npz uids"
},
"recorded": {
"id": "run_shell_command__call_160587",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nprint(\"Targets == val_elig uids?\", np.array_equal(targets.uid.values, val_elig[\"uids\"]))\nprint(\"Targets == test_elig uids?\", np.array_equal(targets.uid.values, test_elig[\"uids\"]))\n'",
"description": "Check target users uids match npz uids"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_160587",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Targets == val_elig uids? True\nTargets == test_elig uids? True\nProcess Group PGID: 1745\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:35.425Z",
"resultDisplay": "Targets == val_elig uids? True\nTargets == test_elig uids? True",
"description": "Check target users uids match npz uids",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:35.426Z",
"content": "\nOutput: Targets == val_elig uids? True\nTargets == test_elig uids? True\nProcess Group PGID: 1745\n",
"isError": false
},
"responseMessageId": "791d33bc-cd16-4248-87b4-0f8b3d90c316"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T02:04:41.315Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:41.315Z",
"text": "Verifying User Data\n\nMy focus has shifted to examining `validation_preference_state.parquet` and `test_preference_state.parquet`. I’m particularly interested in identifying the unique users present within the validation dataset and understanding the various states that are recorded for them."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:41.315Z",
"text": "Analyzing Preference States\n\nI’ve been looking at the `validation_preference_state.parquet` and `test_preference_state.parquet` files. My current thinking is that active dislikes for an item should significantly lower its ranking, potentially even filtering it out entirely. I’m also considering how active likes factor into the discovery objective."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:41.315Z",
"callId": "run_shell_command__call_318238",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state"
},
"recorded": {
"id": "run_shell_command__call_318238",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_318238",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1761\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:42.002Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'",
"description": "Check overlap of ground truth with preference state",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:42.003Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 5, in \nModuleNotFoundError: No module named 'ranking_evaluation'\nExit Code: 1\nProcess Group PGID: 1761\n",
"isError": false
},
"responseMessageId": "f2d63c51-487e-45ff-8e2c-914a6904a904"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T02:04:45.828Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:45.828Z",
"callId": "run_shell_command__call_155696",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state with pythonpath"
},
"recorded": {
"id": "run_shell_command__call_155696",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\nprint(\"Eligible organic strong listens count:\", sum(eligibility.is_eligible(u, i) for u, i in strong.itertuples(index=False)))\nprint(\"Total organic strong listens count:\", len(strong))\nprint(\"Eligible organic likes count:\", sum(eligibility.is_eligible(u, i) for u, i in organic_likes.itertuples(index=False)))\nprint(\"Total organic likes count:\", len(organic_likes))\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs = set(zip(strong.uid, strong.item_id))\nlike_pairs = set(zip(organic_likes.uid, organic_likes.item_id))\n\nprint(\"Strong in pref_likes:\", len(strong_pairs.intersection(pref_likes)))\nprint(\"Strong in pref_dislikes:\", len(strong_pairs.intersection(pref_dislikes)))\nprint(\"Likes in pref_likes:\", len(like_pairs.intersection(pref_likes)))\nprint(\"Likes in pref_dislikes:\", len(like_pairs.intersection(pref_dislikes)))\n'",
"description": "Check overlap of ground truth with preference state with pythonpath"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_155696",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Eligible organic strong listens count: 35597\nTotal organic strong listens count: 255608\nEligible organic likes count: 4417\nTotal organic likes count: 17195\nStrong in pref_likes: 55440\nStrong in pref_dislikes: 427\nLikes in pref_likes: 672\nLikes in pref_dislikes: 16\nProcess Group PGID: 1772\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:47.793Z",
"resultDisplay": "Eligible organic strong listens count: 35597\nTotal organic strong listens count: 255608\nEligible organic likes count: 4417\nTotal organic likes count: 17195\nStrong in pref_likes: 55440\nStrong in pref_dislikes: 427\nLikes in pref_likes: 672\nLikes in pref_dislikes: 16",
"description": "Check overlap of ground truth with preference state with pythonpath",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:47.794Z",
"content": "\nOutput: Eligible organic strong listens count: 35597\nTotal organic strong listens count: 255608\nEligible organic likes count: 4417\nTotal organic likes count: 17195\nStrong in pref_likes: 55440\nStrong in pref_dislikes: 427\nLikes in pref_likes: 672\nLikes in pref_dislikes: 16\nProcess Group PGID: 1772\n",
"isError": false
},
"responseMessageId": "b7e21760-c32f-4fda-af2e-8191012a55e4"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T02:04:51.896Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:51.896Z",
"text": "Analyzing Eligible Counts\n\nI'm currently diving into the eligible organic strong listens and likes to pinpoint how many fall within `pref_likes`. It's crucial to isolate this subset for our analysis."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:51.896Z",
"callId": "run_shell_command__call_65977",
"name": "run_shell_command",
"input": {
"description": "Check how many eligible positive pairs are in pref_likes and pref_dislikes",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs_elig = set((u, i) for u, i in strong.itertuples(index=False) if eligibility.is_eligible(u, i))\nlike_pairs_elig = set((u, i) for u, i in organic_likes.itertuples(index=False) if eligibility.is_eligible(u, i))\n\nprint(\"Eligible strong in pref_likes:\", len(strong_pairs_elig.intersection(pref_likes)), \"out of\", len(strong_pairs_elig))\nprint(\"Eligible strong in pref_dislikes:\", len(strong_pairs_elig.intersection(pref_dislikes)))\nprint(\"Eligible likes in pref_likes:\", len(like_pairs_elig.intersection(pref_likes)), \"out of\", len(like_pairs_elig))\nprint(\"Eligible likes in pref_dislikes:\", len(like_pairs_elig.intersection(pref_dislikes)))\n'"
},
"recorded": {
"id": "run_shell_command__call_65977",
"name": "run_shell_command",
"args": {
"description": "Check how many eligible positive pairs are in pref_likes and pref_dislikes",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nlistens = pd.read_parquet(DATA / \"val_listens.parquet\")\nlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nstrong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n][[\"uid\", \"item_id\"]].drop_duplicates()\norganic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n][[\"uid\", \"item_id\"]].drop_duplicates()\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(pref[pref.state == \"liked\"].uid, pref[pref.state == \"liked\"].item_id))\npref_dislikes = set(zip(pref[pref.state == \"disliked\"].uid, pref[pref.state == \"disliked\"].item_id))\n\nstrong_pairs_elig = set((u, i) for u, i in strong.itertuples(index=False) if eligibility.is_eligible(u, i))\nlike_pairs_elig = set((u, i) for u, i in organic_likes.itertuples(index=False) if eligibility.is_eligible(u, i))\n\nprint(\"Eligible strong in pref_likes:\", len(strong_pairs_elig.intersection(pref_likes)), \"out of\", len(strong_pairs_elig))\nprint(\"Eligible strong in pref_dislikes:\", len(strong_pairs_elig.intersection(pref_dislikes)))\nprint(\"Eligible likes in pref_likes:\", len(like_pairs_elig.intersection(pref_likes)), \"out of\", len(like_pairs_elig))\nprint(\"Eligible likes in pref_dislikes:\", len(like_pairs_elig.intersection(pref_dislikes)))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_65977",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Eligible strong in pref_likes: 0 out of 35597\nEligible strong in pref_dislikes: 0\nEligible likes in pref_likes: 0 out of 4417\nEligible likes in pref_dislikes: 0\nProcess Group PGID: 1792\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:53.731Z",
"resultDisplay": "Eligible strong in pref_likes: 0 out of 35597\nEligible strong in pref_dislikes: 0\nEligible likes in pref_likes: 0 out of 4417\nEligible likes in pref_dislikes: 0",
"description": "Check how many eligible positive pairs are in pref_likes and pref_dislikes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:53.731Z",
"content": "\nOutput: Eligible strong in pref_likes: 0 out of 35597\nEligible strong in pref_dislikes: 0\nEligible likes in pref_likes: 0 out of 4417\nEligible likes in pref_dislikes: 0\nProcess Group PGID: 1792\n",
"isError": false
},
"responseMessageId": "fc6ed105-1514-494c-820c-39af399d99cb"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T02:04:58.098Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:04:58.098Z",
"text": "Analyzing Current Results\n\nMy analysis has revealed a complete lack of eligible entries across all preference categories. I'm now investigating the underlying data to understand why no matches are being found."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:04:58.098Z",
"callId": "run_shell_command__call_1722345",
"name": "run_shell_command",
"input": {
"description": "Check if any pref state item is eligible in validation",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i in pref.itertuples(index=False)))\n'"
},
"recorded": {
"id": "run_shell_command__call_1722345",
"name": "run_shell_command",
"args": {
"description": "Check if any pref state item is eligible in validation",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i in pref.itertuples(index=False)))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1722345",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 12, in \nValueError: too many values to unpack (expected 2)\nExit Code: 1\nProcess Group PGID: 1812\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:04:58.967Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 12, in \nValueError: too many values to unpack (expected 2)",
"description": "Check if any pref state item is eligible in validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:04:58.968Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 12, in \n File \"\", line 12, in \nValueError: too many values to unpack (expected 2)\nExit Code: 1\nProcess Group PGID: 1812\n",
"isError": false
},
"responseMessageId": "cc5dc5fa-0879-43d8-ad5b-c9b45a162ba9"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T02:05:01.548Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:01.548Z",
"callId": "run_shell_command__call_1325120",
"name": "run_shell_command",
"input": {
"description": "Check if any pref state item is eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i, s in pref.itertuples(index=False)))\n'"
},
"recorded": {
"id": "run_shell_command__call_1325120",
"name": "run_shell_command",
"args": {
"description": "Check if any pref state item is eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Are any pref items eligible?\", any(eligibility.is_eligible(u, i) for u, i, s in pref.itertuples(index=False)))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1325120",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Are any pref items eligible? False\nProcess Group PGID: 1830\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:03.054Z",
"resultDisplay": "Are any pref items eligible? False",
"description": "Check if any pref state item is eligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:03.055Z",
"content": "\nOutput: Are any pref items eligible? False\nProcess Group PGID: 1830\n",
"isError": false
},
"responseMessageId": "6dd9990b-83dd-4529-9f39-9db936ea3f6b"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T02:05:08.233Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:08.233Z",
"text": "Exploring Eligibility Factors\n\nI'm currently investigating why no items are appearing as eligible for validation. It seems the existing eligibility logic already filters out items the user has interacted with. I need to delve deeper into what other factors might be impacting eligibility."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:08.233Z",
"callId": "run_shell_command__call_151348",
"name": "run_shell_command",
"input": {
"description": "Check if train_listens items are eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n# check row group 0\nsample = pf.read_row_group(0, columns=[\"uid\", \"item_id\", \"played_ratio_pct\"]).to_pandas()\nprint(\"Total rows in sample:\", len(sample))\nsample_elig = [eligibility.is_eligible(u, i) for u, i in zip(sample.uid, sample.item_id)]\nprint(\"Eligible in train_listens sample:\", sum(sample_elig), \"out of\", len(sample_elig))\n'"
},
"recorded": {
"id": "run_shell_command__call_151348",
"name": "run_shell_command",
"args": {
"description": "Check if train_listens items are eligible",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n# check row group 0\nsample = pf.read_row_group(0, columns=[\"uid\", \"item_id\", \"played_ratio_pct\"]).to_pandas()\nprint(\"Total rows in sample:\", len(sample))\nsample_elig = [eligibility.is_eligible(u, i) for u, i in zip(sample.uid, sample.item_id)]\nprint(\"Eligible in train_listens sample:\", sum(sample_elig), \"out of\", len(sample_elig))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_151348",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total rows in sample: 1048576\nEligible in train_listens sample: 0 out of 1048576\nProcess Group PGID: 1848\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:09.793Z",
"resultDisplay": "Total rows in sample: 1048576\nEligible in train_listens sample: 0 out of 1048576",
"description": "Check if train_listens items are eligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:09.794Z",
"content": "\nOutput: Total rows in sample: 1048576\nEligible in train_listens sample: 0 out of 1048576\nProcess Group PGID: 1848\n",
"isError": false
},
"responseMessageId": "6569fc02-b23d-4b9b-800c-17a70b6641d6"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T02:05:12.503Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:12.503Z",
"callId": "run_shell_command__call_125956",
"name": "run_shell_command",
"input": {
"description": "Check first 5 row groups of train_listens for eligibility",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nfound_elig = 0\ntotal_checked = 0\nfor i in range(5):\n rg = pf.read_row_group(i, columns=[\"uid\", \"item_id\"]).to_pandas()\n for u, item in zip(rg.uid, rg.item_id):\n if eligibility.is_eligible(u, item):\n found_elig += 1\n total_checked += len(rg)\n print(f\"Group {i}: checked {total_checked}, found {found_elig}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_125956",
"name": "run_shell_command",
"args": {
"description": "Check first 5 row groups of train_listens for eligibility",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\npf = pq.ParquetFile(DATA / \"train_listens.parquet\")\nfound_elig = 0\ntotal_checked = 0\nfor i in range(5):\n rg = pf.read_row_group(i, columns=[\"uid\", \"item_id\"]).to_pandas()\n for u, item in zip(rg.uid, rg.item_id):\n if eligibility.is_eligible(u, item):\n found_elig += 1\n total_checked += len(rg)\n print(f\"Group {i}: checked {total_checked}, found {found_elig}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_125956",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Group 0: checked 1048576, found 0\nGroup 1: checked 2097152, found 0\nGroup 2: checked 3145728, found 0\nGroup 3: checked 4194304, found 0\nGroup 4: checked 5242880, found 0\nProcess Group PGID: 1862\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:17.170Z",
"resultDisplay": "Group 0: checked 1048576, found 0\nGroup 1: checked 2097152, found 0\nGroup 2: checked 3145728, found 0\nGroup 3: checked 4194304, found 0\nGroup 4: checked 5242880, found 0",
"description": "Check first 5 row groups of train_listens for eligibility",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:17.171Z",
"content": "\nOutput: Group 0: checked 1048576, found 0\nGroup 1: checked 2097152, found 0\nGroup 2: checked 3145728, found 0\nGroup 3: checked 4194304, found 0\nGroup 4: checked 5242880, found 0\nProcess Group PGID: 1862\n",
"isError": false
},
"responseMessageId": "e8700834-e76d-42fb-a980-6f1ff1ac8f83"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T02:05:22.245Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:22.245Z",
"text": "Understanding Discovery Models\n\nMy thoughts are currently focused on defining what \"discovery\" entails in this context. I'm working through the requirements for building a robust ranking model for a music streaming service's 'Made for You' shelf, specifically considering per-user candidate eligibility, cutoff-specific explicit preference states, and temporal backtesting."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:22.245Z",
"callId": "run_shell_command__call_279421",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n\nprint(\"Catalog cols:\", catalog.columns.tolist())\n# Catalog is already ordered by positive_listens popularity: catalog.popularity_rank == 1..30000\n# And catalog.item_id matches val_elig item_ids!\n# So column index 0 is rank 1, column index 1 is rank 2, etc.\n\n# For each user, we unpack bits and pick the first 100 eligible items!\n# Let us vectorize or do fast batch extraction.\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n # unpack\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # indices of eligible items\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_pop_baseline.parquet\")\nprint(f\"Saved baseline in {time.time()-t0:.2f}s, shape: {df.shape}\")\n' && python3 /task/evaluate_public.py /app/solution/val_pop_baseline.parquet",
"description": "Build and evaluate popularity baseline on validation set"
},
"recorded": {
"id": "run_shell_command__call_279421",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n\nprint(\"Catalog cols:\", catalog.columns.tolist())\n# Catalog is already ordered by positive_listens popularity: catalog.popularity_rank == 1..30000\n# And catalog.item_id matches val_elig item_ids!\n# So column index 0 is rank 1, column index 1 is rank 2, etc.\n\n# For each user, we unpack bits and pick the first 100 eligible items!\n# Let us vectorize or do fast batch extraction.\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n # unpack\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # indices of eligible items\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_pop_baseline.parquet\")\nprint(f\"Saved baseline in {time.time()-t0:.2f}s, shape: {df.shape}\")\n' && python3 /task/evaluate_public.py /app/solution/val_pop_baseline.parquet",
"description": "Build and evaluate popularity baseline on validation set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_279421",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nSaved baseline in 1.32s, shape: (845600, 3)\n{\n \"model\": \"/app/solution/val_pop_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1875\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:26.830Z",
"resultDisplay": "Catalog cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nSaved baseline in 1.32s, shape: (845600, 3)\n{\n \"model\": \"/app/solution/val_pop_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Build and evaluate popularity baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:26.830Z",
"content": "\nOutput: Catalog cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\nSaved baseline in 1.32s, shape: (845600, 3)\n{\n \"model\": \"/app/solution/val_pop_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1875\n",
"isError": false
},
"responseMessageId": "ef19e0f6-3b0d-49a4-b2e6-811dbcd0caad"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T02:05:31.549Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:31.549Z",
"text": "Generating Baseline Submission\n\nMy baseline popularity model is performing. It completes its run in 1.32 seconds, achieving a quality_ndcg@10 of 0.01071 and engaging 4263 out of 8456 users. I'm also generating a baseline submission for the test set to ensure a valid submission is always available."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:31.549Z",
"callId": "run_shell_command__call_335430",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nitem_ids = test_elig[\"item_ids\"]\npacked = test_elig[\"packed_eligible\"]\n\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nsub_path = \"/app/solution/submission.parquet\"\ndf.to_parquet(sub_path)\nprint(f\"Saved initial submission in {time.time()-t0:.2f}s, shape: {df.shape}\")\n\n# Validate using ranking_evaluation\ntarget_set = set(targets.uid.astype(int))\nvalidated = validate_ranking(df, target_set)\nelig = CandidateSet(DATA / \"test_eligible_candidates.npz\")\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Submission verified valid against test_eligible_candidates!\")\n'",
"description": "Generate and verify initial baseline submission for test set"
},
"recorded": {
"id": "run_shell_command__call_335430",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets.uid.values\n\ntest_elig = np.load(DATA / \"test_eligible_candidates.npz\")\nitem_ids = test_elig[\"item_ids\"]\npacked = test_elig[\"packed_eligible\"]\n\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_idx = np.where(mask == 1)[0][:100]\n top_items = item_ids[eligible_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nsub_path = \"/app/solution/submission.parquet\"\ndf.to_parquet(sub_path)\nprint(f\"Saved initial submission in {time.time()-t0:.2f}s, shape: {df.shape}\")\n\n# Validate using ranking_evaluation\ntarget_set = set(targets.uid.astype(int))\nvalidated = validate_ranking(df, target_set)\nelig = CandidateSet(DATA / \"test_eligible_candidates.npz\")\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Submission verified valid against test_eligible_candidates!\")\n'",
"description": "Generate and verify initial baseline submission for test set"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_335430",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Saved initial submission in 1.33s, shape: (845600, 3)\nSubmission verified valid against test_eligible_candidates!\nProcess Group PGID: 1910\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:34.704Z",
"resultDisplay": "Saved initial submission in 1.33s, shape: (845600, 3)\nSubmission verified valid against test_eligible_candidates!",
"description": "Generate and verify initial baseline submission for test set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:34.705Z",
"content": "\nOutput: Saved initial submission in 1.33s, shape: (845600, 3)\nSubmission verified valid against test_eligible_candidates!\nProcess Group PGID: 1910\n",
"isError": false
},
"responseMessageId": "fbe487a7-1491-403f-a898-d77614495a0d"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T02:05:37.059Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:37.059Z",
"text": "Confirming Initial Baseline\n\nMy initial baseline submission is now saved and has been successfully verified against both `validate_ranking` and `test_eligible_candidates.npz`. The next phase involves a deep dive into the data to inform the design of robust modeling strategies aimed at optimizing `quality_ndcg@10`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:37.059Z",
"callId": "update_topic__call_142881",
"name": "update_topic",
"input": {
"title": "Data Analysis and Recommender Architecture Research",
"strategic_intent": "Research data distributions, interaction types, and candidate recommendation algorithms.",
"summary": "Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality."
},
"recorded": {
"id": "update_topic__call_142881",
"name": "update_topic",
"args": {
"title": "Data Analysis and Recommender Architecture Research",
"strategic_intent": "Research data distributions, interaction types, and candidate recommendation algorithms.",
"summary": "Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_142881",
"name": "update_topic",
"response": {
"output": "Current topic: \"Data Analysis and Recommender Architecture Research\"\nTopic summary: Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality.\n\nStrategic Intent: Research data distributions, interaction types, and candidate recommendation algorithms."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:37.061Z",
"resultDisplay": "## 📂 Topic: **Data Analysis and Recommender Architecture Research**\n\n**Summary:**\nBaseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality.\n\n> [!STRATEGY]\n> **Intent:** Research data distributions, interaction types, and candidate recommendation algorithms.",
"description": "Update topic to: \"Data Analysis and Recommender Architecture Research\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:05:37.061Z",
"content": "Current topic: \"Data Analysis and Recommender Architecture Research\"\nTopic summary: Baseline established: candidate catalog popularity achieves quality_ndcg@10 of 0.01071. Initial valid test submission created and verified against ranking_evaluation. Now researching user listening histories, item metadata/associations, implicit collaborative filtering, matrix factorization, and graph-based / co-occurrence methods to improve recommendation quality.\n\nStrategic Intent: Research data distributions, interaction types, and candidate recommendation algorithms.",
"isError": false
},
"responseMessageId": "de28352a-6716-4a86-be64-a92b1e4dbbac"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T02:05:42.139Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:42.139Z",
"text": "Analyzing Candidate Features\n\nI'm currently examining the available features within the candidate catalog. My focus is on understanding the distributions and relationships between `item_id`, `positive_listens`, `organic_positive_listens`, and `popularity_rank` to identify potent signals for recommendation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:42.139Z",
"callId": "run_shell_command__call_338102",
"name": "run_shell_command",
"input": {
"description": "Test organic popularity baseline on validation set",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\nuids = targets_frame.uid.values\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\n\n# Try ordering by organic_positive_listens\ncatalog_organic_order = catalog.sort_values(\"organic_positive_listens\", ascending=False).index.values\n\n# Test global popularity by organic_positive_listens\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # mask in organic order\n eligible_in_order = catalog_organic_order[mask[catalog_organic_order] == 1][:100]\n top_items = item_ids[eligible_in_order]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df)\nscores = score_rankings(ranked, graded)\nprint(\"Organic popularity baseline score:\", scores)\n'"
},
"recorded": {
"id": "run_shell_command__call_338102",
"name": "run_shell_command",
"args": {
"description": "Test organic popularity baseline on validation set",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\nuids = targets_frame.uid.values\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\nitem_ids = val_elig[\"item_ids\"]\npacked = val_elig[\"packed_eligible\"]\n\n# Try ordering by organic_positive_listens\ncatalog_organic_order = catalog.sort_values(\"organic_positive_listens\", ascending=False).index.values\n\n# Test global popularity by organic_positive_listens\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # mask in organic order\n eligible_in_order = catalog_organic_order[mask[catalog_organic_order] == 1][:100]\n top_items = item_ids[eligible_in_order]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df)\nscores = score_rankings(ranked, graded)\nprint(\"Organic popularity baseline score:\", scores)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_338102",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Organic popularity baseline score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1926\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:45.313Z",
"resultDisplay": "Organic popularity baseline score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}",
"description": "Test organic popularity baseline on validation set",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:45.314Z",
"content": "\nOutput: Organic popularity baseline score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 1926\n",
"isError": false
},
"responseMessageId": "e3e9daea-ac4d-4756-84fd-28974266d60c"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T02:05:49.733Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:49.733Z",
"text": "Exploring Personalization Metrics\n\nMy latest analysis shows `positive_listens` slightly outperformed `organic_positive_listens`. I'm now shifting focus to personalization. I'm examining available user history, specifically the `train_likes.parquet` dataset with its `uid`, `timestamp`, and `item_id` columns, to inform this next step."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:49.733Z",
"callId": "run_shell_command__call_20738",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\nprint(\"Train likes:\", len(train_likes), \"unique users:\", train_likes.uid.nunique(), \"unique items:\", train_likes.item_id.nunique())\nprint(\"Train likes candidate overlap:\", train_likes.item_id.isin(candidate_items).mean())\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref:\", len(pref), \"unique users:\", pref.uid.nunique(), \"unique items:\", pref.item_id.nunique())\nprint(\"Validation pref candidate overlap:\", pref.item_id.isin(candidate_items).mean())\n\n# Let us check timestamps across files\nprint(\"Train likes min/max timestamp:\", train_likes.timestamp.min(), train_likes.timestamp.max())\n'",
"description": "Check train likes and preference items vs candidate catalog"
},
"recorded": {
"id": "run_shell_command__call_20738",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntrain_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\nprint(\"Train likes:\", len(train_likes), \"unique users:\", train_likes.uid.nunique(), \"unique items:\", train_likes.item_id.nunique())\nprint(\"Train likes candidate overlap:\", train_likes.item_id.isin(candidate_items).mean())\n\npref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"Validation pref:\", len(pref), \"unique users:\", pref.uid.nunique(), \"unique items:\", pref.item_id.nunique())\nprint(\"Validation pref candidate overlap:\", pref.item_id.isin(candidate_items).mean())\n\n# Let us check timestamps across files\nprint(\"Train likes min/max timestamp:\", train_likes.timestamp.min(), train_likes.timestamp.max())\n'",
"description": "Check train likes and preference items vs candidate catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_20738",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Train likes: 814831 unique users: 8144 unique items: 172303\nTrain likes candidate overlap: 0.6809068383505291\nValidation pref: 469124 unique users: 7038 unique items: 28814\nValidation pref candidate overlap: 1.0\nTrain likes min/max timestamp: 80 24786790\nProcess Group PGID: 1946\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:50.657Z",
"resultDisplay": "Train likes: 814831 unique users: 8144 unique items: 172303\nTrain likes candidate overlap: 0.6809068383505291\nValidation pref: 469124 unique users: 7038 unique items: 28814\nValidation pref candidate overlap: 1.0\nTrain likes min/max timestamp: 80 24786790",
"description": "Check train likes and preference items vs candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:50.658Z",
"content": "\nOutput: Train likes: 814831 unique users: 8144 unique items: 172303\nTrain likes candidate overlap: 0.6809068383505291\nValidation pref: 469124 unique users: 7038 unique items: 28814\nValidation pref candidate overlap: 1.0\nTrain likes min/max timestamp: 80 24786790\nProcess Group PGID: 1946\n",
"isError": false
},
"responseMessageId": "ab8fb4de-796c-443f-b8e4-3435c8f7667a"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T02:05:53.420Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:05:53.420Z",
"text": "Analyzing Data Intersections\n\nI'm currently examining the overlap between training listens and the candidate catalog. Initial validation data shows a significant number of rows and unique users/items. The strong overlap in liked items is promising, but I need to understand how the training listen data fits into this."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:05:53.420Z",
"callId": "run_shell_command__call_311275",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport time\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"])\nt1 = time.time()\nprint(f\"Read train_listens in {t1-t0:.2f}s, num rows: {table.num_rows}\")\n'",
"description": "Benchmark train_listens read speed"
},
"recorded": {
"id": "run_shell_command__call_311275",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport time\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"])\nt1 = time.time()\nprint(f\"Read train_listens in {t1-t0:.2f}s, num rows: {table.num_rows}\")\n'",
"description": "Benchmark train_listens read speed"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_311275",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Read train_listens in 0.71s, num rows: 43484159\nProcess Group PGID: 1963\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:05:54.617Z",
"resultDisplay": "Read train_listens in 0.71s, num rows: 43484159",
"description": "Benchmark train_listens read speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:05:56.808Z",
"content": "\nOutput: Read train_listens in 0.71s, num rows: 43484159\nProcess Group PGID: 1963\n",
"isError": false
},
"responseMessageId": "0bfb5597-d3b0-4d78-898b-4e673f576f45"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T02:06:17.415Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:17.415Z",
"text": "Exploring PyArrow Speed\n\nI'm impressed by PyArrow's speed, processing over 43 million rows in under a second. My focus is shifting to understanding the core mechanics of recommender systems, specifically what drives successful discovery recommendations. I'm currently considering the necessity of user historical interaction data for effective training."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:17.415Z",
"text": "Considering Item Similarity\n\nMy current focus is on item-item similarity for recommendations. I'm evaluating whether a dense 30,000x30,000 matrix is feasible with 16GB RAM, or if a sparse representation is necessary given the memory constraints for this scale."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:17.415Z",
"text": "Evaluating Matrix Factorization\n\nMy current thoughts are exploring matrix factorization techniques. I'm realizing that with embeddings of dimensions 64 or 128, the resulting user and item matrices are quite manageable in size and can be used for rapid prediction generation, significantly outperforming previous estimations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:06:17.415Z",
"callId": "run_shell_command__call_119723",
"name": "run_shell_command",
"input": {
"description": "Check fraction of train_listens in candidate catalog",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"item_id\"])\nitems = table[\"item_id\"].to_numpy()\nin_cat = pd.Series(items).isin(candidate_items).mean()\nprint(f\"Fraction of train_listens in candidate_catalog: {in_cat:.4f}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_119723",
"name": "run_shell_command",
"args": {
"description": "Check fraction of train_listens in candidate catalog",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncandidate_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"item_id\"])\nitems = table[\"item_id\"].to_numpy()\nin_cat = pd.Series(items).isin(candidate_items).mean()\nprint(f\"Fraction of train_listens in candidate_catalog: {in_cat:.4f}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_119723",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Fraction of train_listens in candidate_catalog: 0.7186\nProcess Group PGID: 1985\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:06:21.554Z",
"resultDisplay": "Fraction of train_listens in candidate_catalog: 0.7186",
"description": "Check fraction of train_listens in candidate catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:06:21.554Z",
"content": "\nOutput: Fraction of train_listens in candidate_catalog: 0.7186\nProcess Group PGID: 1985\n",
"isError": false
},
"responseMessageId": "5cee914c-0c3d-43b6-af25-b189621bd900"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T02:06:26.069Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:26.069Z",
"text": "Analyzing User Preferences\n\nI'm focusing on understanding user preferences within our candidate catalog. Initial analysis shows a strong concentration of listens in this area. I'm currently investigating explicit likes and the validation preference state as key signals."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:06:26.069Z",
"callId": "run_shell_command__call_7700",
"name": "run_shell_command",
"input": {
"description": "Check timestamp ranges and duration",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nval_listens = pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"timestamp\"])\nval_likes = pd.read_parquet(DATA / \"val_likes.parquet\", columns=[\"timestamp\"])\n\nprint(\"Val listens min/max timestamp:\", val_listens.timestamp.min(), val_listens.timestamp.max())\nprint(\"Val duration (days):\", (val_listens.timestamp.max() - val_listens.timestamp.min()) / 86400)\nprint(\"Gap after train (s):\", val_listens.timestamp.min() - 24786800)\n'"
},
"recorded": {
"id": "run_shell_command__call_7700",
"name": "run_shell_command",
"args": {
"description": "Check timestamp ranges and duration",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nval_listens = pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"timestamp\"])\nval_likes = pd.read_parquet(DATA / \"val_likes.parquet\", columns=[\"timestamp\"])\n\nprint(\"Val listens min/max timestamp:\", val_listens.timestamp.min(), val_listens.timestamp.max())\nprint(\"Val duration (days):\", (val_listens.timestamp.max() - val_listens.timestamp.min()) / 86400)\nprint(\"Gap after train (s):\", val_listens.timestamp.min() - 24786800)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_7700",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Val listens min/max timestamp: 24788600 25393395\nVal duration (days): 6.99994212962963\nGap after train (s): 1800\nProcess Group PGID: 2006\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:06:26.840Z",
"resultDisplay": "Val listens min/max timestamp: 24788600 25393395\nVal duration (days): 6.99994212962963\nGap after train (s): 1800",
"description": "Check timestamp ranges and duration",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:06:26.841Z",
"content": "\nOutput: Val listens min/max timestamp: 24788600 25393395\nVal duration (days): 6.99994212962963\nGap after train (s): 1800\nProcess Group PGID: 2006\n",
"isError": false
},
"responseMessageId": "0b092838-3cc2-44d0-b2e9-34ff58684d26"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T02:06:29.891Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:06:29.891Z",
"callId": "run_shell_command__call_27687",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"timestamp\"])\nts = table[\"timestamp\"].to_numpy()\nprint(\"Min timestamp:\", ts.min(), \"Max timestamp:\", ts.max())\nprint(\"Total train range in days:\", (ts.max() - ts.min()) / 86400)\n# Percentiles:\nfor p in [10, 25, 50, 75, 90, 95]:\n print(f\"p{p}: {(ts.max() - np.percentile(ts, p)) / 86400:.1f} days before cutoff\")\n'",
"description": "Check train timestamp distribution"
},
"recorded": {
"id": "run_shell_command__call_27687",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"timestamp\"])\nts = table[\"timestamp\"].to_numpy()\nprint(\"Min timestamp:\", ts.min(), \"Max timestamp:\", ts.max())\nprint(\"Total train range in days:\", (ts.max() - ts.min()) / 86400)\n# Percentiles:\nfor p in [10, 25, 50, 75, 90, 95]:\n print(f\"p{p}: {(ts.max() - np.percentile(ts, p)) / 86400:.1f} days before cutoff\")\n'",
"description": "Check train timestamp distribution"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_27687",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Min timestamp: 0 Max timestamp: 24786795\nTotal train range in days: 286.88420138888887\np10: 248.4 days before cutoff\np25: 197.3 days before cutoff\np50: 122.0 days before cutoff\np75: 55.3 days before cutoff\np90: 21.1 days before cutoff\np95: 10.3 days before cutoff\nProcess Group PGID: 2027\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:06:31.899Z",
"resultDisplay": "Min timestamp: 0 Max timestamp: 24786795\nTotal train range in days: 286.88420138888887\np10: 248.4 days before cutoff\np25: 197.3 days before cutoff\np50: 122.0 days before cutoff\np75: 55.3 days before cutoff\np90: 21.1 days before cutoff\np95: 10.3 days before cutoff",
"description": "Check train timestamp distribution",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:06:31.901Z",
"content": "\nOutput: Min timestamp: 0 Max timestamp: 24786795\nTotal train range in days: 286.88420138888887\np10: 248.4 days before cutoff\np25: 197.3 days before cutoff\np50: 122.0 days before cutoff\np75: 55.3 days before cutoff\np90: 21.1 days before cutoff\np95: 10.3 days before cutoff\nProcess Group PGID: 2027\n",
"isError": false
},
"responseMessageId": "b78d3c5c-2ab8-4e10-a1f3-0d5d6120e25e"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T02:06:39.025Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:39.025Z",
"text": "Analyzing Validation Performance\n\nMy current focus is on dissecting the validation evaluation. I'm examining `evaluate_public.py` and its reliance on `validation_truth` to understand the model's performance on unseen data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:39.025Z",
"text": "Assessing Data Eligibility\n\nI'm analyzing the validation set, specifically focusing on users with eligible organic listens or likes. Around 50% of target users meet this criterion within the validation window. I'm also starting to explore how to predict user engagement with candidate items."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:39.025Z",
"text": "Exploring Collaborative Filtering\n\nI'm now delving into collaborative filtering signals, considering user likes, organic listens, listen frequency, dislikes, and track recency. I'm also investigating how candidate items' popularity and user affinities influence recommendations. I'm starting to assess the feasibility of an item-item collaborative filtering model."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:06:39.025Z",
"callId": "run_shell_command__call_59221",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nuids = set(table[\"uid\"].to_numpy())\nprint(\"Total unique users in train_listens:\", len(uids))\ntargets = set(pq.read_table(\"/task/data/target_users.parquet\", columns=[\"uid\"])[\"uid\"].to_numpy())\nprint(\"Target users count:\", len(targets))\nprint(\"Target users in train_listens:\", len(targets.intersection(uids)))\n'",
"description": "Check unique users in train_listens vs targets"
},
"recorded": {
"id": "run_shell_command__call_59221",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nuids = set(table[\"uid\"].to_numpy())\nprint(\"Total unique users in train_listens:\", len(uids))\ntargets = set(pq.read_table(\"/task/data/target_users.parquet\", columns=[\"uid\"])[\"uid\"].to_numpy())\nprint(\"Target users count:\", len(targets))\nprint(\"Target users in train_listens:\", len(targets.intersection(uids)))\n'",
"description": "Check unique users in train_listens vs targets"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_59221",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total unique users in train_listens: 9238\nTarget users count: 8456\nTarget users in train_listens: 8456\nProcess Group PGID: 2049\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:06:41.961Z",
"resultDisplay": "Total unique users in train_listens: 9238\nTarget users count: 8456\nTarget users in train_listens: 8456",
"description": "Check unique users in train_listens vs targets",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:06:41.963Z",
"content": "\nOutput: Total unique users in train_listens: 9238\nTarget users count: 8456\nTarget users in train_listens: 8456\nProcess Group PGID: 2049\n",
"isError": false
},
"responseMessageId": "577108d2-d21e-4ae6-8924-7e5b2ed0a8da"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T02:06:52.544Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:06:52.544Z",
"text": "Confirming User Coverage\n\nIt's great to see that all 8,456 target users are accounted for within the `train_listens` dataset, which contains a total of 9,238 unique users. My next step is to determine the dimensions of our user-item matrix, $X$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:06:52.544Z",
"callId": "run_shell_command__call_16617",
"name": "run_shell_command",
"input": {
"description": "Count unique user-item pairs in train_listens",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"played_ratio_pct\"])\ndf = table.to_pandas()\ndf_cat = df[df.uid.isin(targets) & df.item_id.isin(cat_items)]\nprint(\"Total listens in catalog for targets:\", len(df_cat))\nunique_pairs = df_cat[[\"uid\", \"item_id\"]].drop_duplicates()\nprint(\"Unique (uid, item) pairs in catalog:\", len(unique_pairs))\n'"
},
"recorded": {
"id": "run_shell_command__call_16617",
"name": "run_shell_command",
"args": {
"description": "Count unique user-item pairs in train_listens",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ntable = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"played_ratio_pct\"])\ndf = table.to_pandas()\ndf_cat = df[df.uid.isin(targets) & df.item_id.isin(cat_items)]\nprint(\"Total listens in catalog for targets:\", len(df_cat))\nunique_pairs = df_cat[[\"uid\", \"item_id\"]].drop_duplicates()\nprint(\"Unique (uid, item) pairs in catalog:\", len(unique_pairs))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_16617",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total listens in catalog for targets: 31062540\nUnique (uid, item) pairs in catalog: 7868664\nProcess Group PGID: 2069\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:07:02.022Z",
"resultDisplay": "Total listens in catalog for targets: 31062540\nUnique (uid, item) pairs in catalog: 7868664",
"description": "Count unique user-item pairs in train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:07:02.023Z",
"content": "\nOutput: Total listens in catalog for targets: 31062540\nUnique (uid, item) pairs in catalog: 7868664\nProcess Group PGID: 2069\n",
"isError": false
},
"responseMessageId": "22f1e4c7-8798-45dc-9af4-54a42f027fcd"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T02:07:16.551Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:07:16.551Z",
"text": "Analyzing Data Density\n\nMy analysis reveals a 3.1% density from 7.86 million unique (uid, item) pairs out of a possible 253.68 million. I'm now considering incorporating `train_likes` and `validation_preference_state` to potentially increase this density."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:07:16.551Z",
"text": "Exploring Algorithmic Pathways\n\nI'm now exploring various algorithms to leverage the user-item matrix. My current focus is on how to best apply SVD with potential weighting schemes like TF-IDF, BM25, or even incorporating recency, and also on item-item collaborative filtering approaches."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:07:16.551Z",
"text": "Optimizing Matrix Computations\n\nI've realized we can avoid forming a large $30000 \\times 30000$ matrix. By using the associative property of matrix multiplication, I can compute $X X_{norm}^T$, resulting in a much smaller $8456 \\times 8456$ matrix. This approach significantly reduces memory and computation time compared to direct SVD on the full matrix."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:07:16.551Z",
"text": "Evaluating User-User Similarity\n\nI've determined that computing user-user cosine similarity is highly feasible given only 8,456 target users. The resulting similarity matrix, $W$, will be a manageable $8456 \\times 8456$, requiring approximately 286 MB, which is well within our computational limits. This contrasts sharply with the memory demands of dense EASE."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:07:16.551Z",
"callId": "run_shell_command__call_135330",
"name": "run_shell_command",
"input": {
"description": "Benchmark User-User CF sparse matrix operations",
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create a sparse matrix with 8456 rows, 30000 cols, ~8 million nonzeros (~3% dense)\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\nprint(\"Built CSR in\", time.time() - t0)\n\n# Normalize rows\nt0 = time.time()\nnorm = sp.linalg.norm(X, axis=1)\nnorm[norm == 0] = 1.0\nX_norm = sp.diags(1.0 / norm.ravel()) @ X\nprint(\"Normalized in\", time.time() - t0)\n\n# Multiply X_norm @ X_norm.T\nt0 = time.time()\nW = X_norm @ X_norm.T\nprint(\"W = X_norm @ X_norm.T took:\", time.time() - t0, \"seconds! Shape:\", W.shape)\n\n# Now predict: W @ X\nt0 = time.time()\n# For each user, predicted score is W_u @ X\n# We can do this in chunks or full!\nP = W @ X\nprint(\"P = W @ X took:\", time.time() - t0, \"seconds! Shape:\", P.shape)\n'"
},
"recorded": {
"id": "run_shell_command__call_135330",
"name": "run_shell_command",
"args": {
"description": "Benchmark User-User CF sparse matrix operations",
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\n# Create a sparse matrix with 8456 rows, 30000 cols, ~8 million nonzeros (~3% dense)\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\nprint(\"Built CSR in\", time.time() - t0)\n\n# Normalize rows\nt0 = time.time()\nnorm = sp.linalg.norm(X, axis=1)\nnorm[norm == 0] = 1.0\nX_norm = sp.diags(1.0 / norm.ravel()) @ X\nprint(\"Normalized in\", time.time() - t0)\n\n# Multiply X_norm @ X_norm.T\nt0 = time.time()\nW = X_norm @ X_norm.T\nprint(\"W = X_norm @ X_norm.T took:\", time.time() - t0, \"seconds! Shape:\", W.shape)\n\n# Now predict: W @ X\nt0 = time.time()\n# For each user, predicted score is W_u @ X\n# We can do this in chunks or full!\nP = W @ X\nprint(\"P = W @ X took:\", time.time() - t0, \"seconds! Shape:\", P.shape)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_135330",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Built CSR in 0.33393287658691406\nNormalized in 0.07563352584838867\nW = X_norm @ X_norm.T took: 6.302556276321411 seconds! Shape: (8456, 8456)\nP = W @ X took: 166.22440767288208 seconds! Shape: (8456, 30000)\nProcess Group PGID: 2095\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:10:10.426Z",
"resultDisplay": "Built CSR in 0.33393287658691406\nNormalized in 0.07563352584838867\nW = X_norm @ X_norm.T took: 6.302556276321411 seconds! Shape: (8456, 8456)\nP = W @ X took: 166.22440767288208 seconds! Shape: (8456, 30000)",
"description": "Benchmark User-User CF sparse matrix operations",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:10:10.427Z",
"content": "\nOutput: Built CSR in 0.33393287658691406\nNormalized in 0.07563352584838867\nW = X_norm @ X_norm.T took: 6.302556276321411 seconds! Shape: (8456, 8456)\nP = W @ X took: 166.22440767288208 seconds! Shape: (8456, 30000)\nProcess Group PGID: 2095\n",
"isError": false
},
"responseMessageId": "c288bcee-eac9-461b-a2d9-9d7e5c9f7ee7"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T02:10:23.118Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:10:23.118Z",
"callId": "run_shell_command__call_144207",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\n# Simulate top-100 neighbors per user\nk = 100\nw_rows = np.repeat(np.arange(n_users, dtype=np.int32), k)\nw_cols = np.random.randint(0, n_users, size=n_users * k, dtype=np.int32)\nw_data = np.random.rand(n_users * k).astype(np.float32)\n\nW_topk = sp.csr_matrix((w_data, (w_rows, w_cols)), shape=(n_users, n_users), dtype=np.float32)\n\nt0 = time.time()\nP = W_topk @ X\nprint(f\"W_topk @ X took: {time.time() - t0:.2f}s! Result shape: {P.shape}, nnz: {P.nnz}\")\n'",
"description": "Benchmark sparse top-k user CF"
},
"recorded": {
"id": "run_shell_command__call_144207",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\n# Simulate top-100 neighbors per user\nk = 100\nw_rows = np.repeat(np.arange(n_users, dtype=np.int32), k)\nw_cols = np.random.randint(0, n_users, size=n_users * k, dtype=np.int32)\nw_data = np.random.rand(n_users * k).astype(np.float32)\n\nW_topk = sp.csr_matrix((w_data, (w_rows, w_cols)), shape=(n_users, n_users), dtype=np.float32)\n\nt0 = time.time()\nP = W_topk @ X\nprint(f\"W_topk @ X took: {time.time() - t0:.2f}s! Result shape: {P.shape}, nnz: {P.nnz}\")\n'",
"description": "Benchmark sparse top-k user CF"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_144207",
"name": "run_shell_command",
"response": {
"output": "\nOutput: W_topk @ X took: 6.16s! Result shape: (8456, 30000), nnz: 242649503\nProcess Group PGID: 2112\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:10:30.546Z",
"resultDisplay": "W_topk @ X took: 6.16s! Result shape: (8456, 30000), nnz: 242649503",
"description": "Benchmark sparse top-k user CF",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:10:30.547Z",
"content": "\nOutput: W_topk @ X took: 6.16s! Result shape: (8456, 30000), nnz: 242649503\nProcess Group PGID: 2112\n",
"isError": false
},
"responseMessageId": "2d08142c-75ca-4fe7-a27b-23aaf5a793b7"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T02:10:33.929Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:10:33.929Z",
"callId": "run_shell_command__call_344525",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\nfor dim in [64, 128]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X) # (8456, dim)\n V = svd.components_ # (dim, 30000)\n print(f\"TruncatedSVD(dim={dim}) fit in {time.time()-t0:.2f}s\")\n t0 = time.time()\n # Compute dot product for top-100 items per user\n # In batches of users:\n preds = U[:1000] @ V\n print(f\"Batch 1000 users dot product took: {time.time()-t0:.4f}s\")\n'",
"description": "Benchmark TruncatedSVD and batch dot products"
},
"recorded": {
"id": "run_shell_command__call_344525",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nn_users = 8456\nn_items = 30000\nnnz = 8000000\nrows = np.random.randint(0, n_users, size=nnz, dtype=np.int32)\ncols = np.random.randint(0, n_items, size=nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(n_users, n_items), dtype=np.float32)\n\nfor dim in [64, 128]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X) # (8456, dim)\n V = svd.components_ # (dim, 30000)\n print(f\"TruncatedSVD(dim={dim}) fit in {time.time()-t0:.2f}s\")\n t0 = time.time()\n # Compute dot product for top-100 items per user\n # In batches of users:\n preds = U[:1000] @ V\n print(f\"Batch 1000 users dot product took: {time.time()-t0:.4f}s\")\n'",
"description": "Benchmark TruncatedSVD and batch dot products"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_344525",
"name": "run_shell_command",
"response": {
"output": "\nOutput: TruncatedSVD(dim=64) fit in 2.87s\nBatch 1000 users dot product took: 0.0178s\nTruncatedSVD(dim=128) fit in 4.35s\nBatch 1000 users dot product took: 0.0278s\nProcess Group PGID: 2129\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:10:43.246Z",
"resultDisplay": "TruncatedSVD(dim=64) fit in 2.87s\nBatch 1000 users dot product took: 0.0178s\nTruncatedSVD(dim=128) fit in 4.35s\nBatch 1000 users dot product took: 0.0278s",
"description": "Benchmark TruncatedSVD and batch dot products",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:10:43.247Z",
"content": "\nOutput: TruncatedSVD(dim=64) fit in 2.87s\nBatch 1000 users dot product took: 0.0178s\nTruncatedSVD(dim=128) fit in 4.35s\nBatch 1000 users dot product took: 0.0278s\nProcess Group PGID: 2129\n",
"isError": false
},
"responseMessageId": "2c1638b6-0431-45f1-9785-d58976f7fec8"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T02:10:53.356Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:10:53.356Z",
"text": "Analyzing Model Performance\n\nI've successfully fit the TruncatedSVD model in under 5 seconds. Batch dot product calculations are also proving efficient, coming in at a fraction of a second for all users. Now, I'm shifting focus to compare Item-Item CF with the current user-user and SVD approaches, with an eye towards discovery capabilities."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:10:53.356Z",
"callId": "run_shell_command__call_367682",
"name": "run_shell_command",
"input": {
"description": "Analyze validation ground truth items and their popularity ranks",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\n# graded is uid -> {item_id: grade}\nprint(f\"Num users with graded items: {len(graded)}\")\ntotal_grade3 = sum(sum(1 for g in items.values() if g == 3) for items in graded.values())\ntotal_grade1 = sum(sum(1 for g in items.values() if g == 1) for items in graded.values())\nprint(f\"Total grade 3: {total_grade3}, total grade 1: {total_grade1}\")\n\n# Check popularity rank of graded items\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\").set_index(\"item_id\")\nranks_grade3 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 3 and item in catalog.index]\nranks_grade1 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 1 and item in catalog.index]\n\nprint(f\"Grade 3 items popularity rank: mean {np.mean(ranks_grade3):.1f}, median {np.median(ranks_grade3):.1f}, p25 {np.percentile(ranks_grade3, 25):.1f}, p75 {np.percentile(ranks_grade3, 75):.1f}\")\nprint(f\"Grade 1 items popularity rank: mean {np.mean(ranks_grade1):.1f}, median {np.median(ranks_grade1):.1f}, p25 {np.percentile(ranks_grade1, 25):.1f}, p75 {np.percentile(ranks_grade1, 75):.1f}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_367682",
"name": "run_shell_command",
"args": {
"description": "Analyze validation ground truth items and their popularity ranks",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets)\n\n# graded is uid -> {item_id: grade}\nprint(f\"Num users with graded items: {len(graded)}\")\ntotal_grade3 = sum(sum(1 for g in items.values() if g == 3) for items in graded.values())\ntotal_grade1 = sum(sum(1 for g in items.values() if g == 1) for items in graded.values())\nprint(f\"Total grade 3: {total_grade3}, total grade 1: {total_grade1}\")\n\n# Check popularity rank of graded items\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\").set_index(\"item_id\")\nranks_grade3 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 3 and item in catalog.index]\nranks_grade1 = [catalog.loc[item, \"popularity_rank\"] for items in graded.values() for item, g in items.items() if g == 1 and item in catalog.index]\n\nprint(f\"Grade 3 items popularity rank: mean {np.mean(ranks_grade3):.1f}, median {np.median(ranks_grade3):.1f}, p25 {np.percentile(ranks_grade3, 25):.1f}, p75 {np.percentile(ranks_grade3, 75):.1f}\")\nprint(f\"Grade 1 items popularity rank: mean {np.mean(ranks_grade1):.1f}, median {np.median(ranks_grade1):.1f}, p25 {np.percentile(ranks_grade1, 25):.1f}, p75 {np.percentile(ranks_grade1, 75):.1f}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_367682",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Num users with graded items: 4263\nTotal grade 3: 4417, total grade 1: 33352\nGrade 3 items popularity rank: mean 8892.0, median 6371.0, p25 2107.0, p75 14181.0\nGrade 1 items popularity rank: mean 8406.3, median 5545.0, p25 1693.8, p75 13274.0\nProcess Group PGID: 2147\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:10:54.894Z",
"resultDisplay": "Num users with graded items: 4263\nTotal grade 3: 4417, total grade 1: 33352\nGrade 3 items popularity rank: mean 8892.0, median 6371.0, p25 2107.0, p75 14181.0\nGrade 1 items popularity rank: mean 8406.3, median 5545.0, p25 1693.8, p75 13274.0",
"description": "Analyze validation ground truth items and their popularity ranks",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:10:54.894Z",
"content": "\nOutput: Num users with graded items: 4263\nTotal grade 3: 4417, total grade 1: 33352\nGrade 3 items popularity rank: mean 8892.0, median 6371.0, p25 2107.0, p75 14181.0\nGrade 1 items popularity rank: mean 8406.3, median 5545.0, p25 1693.8, p75 13274.0\nProcess Group PGID: 2147\n",
"isError": false
},
"responseMessageId": "2d1962bf-7c5c-4625-bfc1-ee0915c63180"
}
]
},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T02:11:00.991Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:11:00.991Z",
"callId": "update_topic__call_1724037",
"name": "update_topic",
"input": {
"strategic_intent": "Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.",
"summary": "Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.",
"title": "Matrix Factorization Prototype and Validation"
},
"recorded": {
"id": "update_topic__call_1724037",
"name": "update_topic",
"args": {
"strategic_intent": "Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.",
"summary": "Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.",
"title": "Matrix Factorization Prototype and Validation"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_1724037",
"name": "update_topic",
"response": {
"output": "Current topic: \"Matrix Factorization Prototype and Validation\"\nTopic summary: Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.\n\nStrategic Intent: Build matrix factorization (SVD) prototype on validation set and measure NDCG@10."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:11:00.993Z",
"resultDisplay": "## 📂 Topic: **Matrix Factorization Prototype and Validation**\n\n**Summary:**\nAnalyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.\n\n> [!STRATEGY]\n> **Intent:** Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.",
"description": "Update topic to: \"Matrix Factorization Prototype and Validation\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T02:11:00.994Z",
"content": "Current topic: \"Matrix Factorization Prototype and Validation\"\nTopic summary: Analyzed ground truth distribution: relevant items are widely distributed across catalog popularity (median rank ~6000), confirming that pure popularity ranking misses most user discoveries. Designing and implementing a TruncatedSVD matrix factorization model on user-item interaction matrix, evaluating with official validation metrics.\n\nStrategic Intent: Build matrix factorization (SVD) prototype on validation set and measure NDCG@10.",
"isError": false
},
"responseMessageId": "df67d3c5-c0b4-4e69-9a73-8dfd22fdf07d"
}
]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T02:11:24.241Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:11:24.241Z",
"text": "Designing Interaction Matrix\n\nI'm currently focusing on constructing the user-item interaction matrix. My immediate plan involves defining the features that will populate each (user, item) cell, considering users $u \\in \\{0, \\dots, 8455\\}$ and items $i \\in \\{0, \\dots, 2\\}$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:11:24.241Z",
"callId": "run_shell_command__call_34253",
"name": "run_shell_command",
"input": {
"description": "Prototype TruncatedSVD collaborative filtering and evaluate on validation",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\n\n# 1. Target users and catalog items\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 2. Load listens\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nprint(f\"Loaded train_listens in {time.time()-t0:.2f}s\")\n\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\n# Filter only target users and catalog items\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nprint(f\"Masked {mask.sum()} / {len(mask)} listens in {time.time()-t0:.2f}s\")\n\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\n# Weight calculation:\n# listens with played_ratio_pct >= 80 get 2.0, >= 50 get 1.0, < 20 get 0.1\n# organic gets 1.5x boost\n# time decay: exp(-days_ago / 60)\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\n\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\n# Build sparse matrix\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\nprint(f\"Built sparse user-item matrix in {time.time()-t0:.2f}s, nnz={X.nnz}\")\n\n# Add likes from train_likes and pref state\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + X_likes\n\n# Preference state likes\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\np_w = np.full(len(p_u), 5.0, dtype=np.float32)\nX = X + sp.csr_matrix((p_w, (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nprint(f\"Total matrix with likes: nnz={X.nnz}\")\n\n# Apply IDF weighting\n# idf = log(1 + n_users / (1 + df))\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# L2 normalize user rows\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Run TruncatedSVD\ndim = 128\nprint(f\"Fitting TruncatedSVD(dim={dim})...\")\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm) # (8456, dim)\nV = svd.components_ # (dim, 30000)\nprint(f\"TruncatedSVD fitted in {time.time()-t0:.2f}s\")\n\n# Evaluate ranking\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nrows = []\n# Batch score prediction\nbatch_size = 500\nfor start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V # shape (batch, 30000)\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n # Set ineligible scores to -inf\n user_scores = scores_batch[b_idx]\n user_scores[mask == 0] = -np.inf\n # Top 100 items: argpartition\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nprint(f\"SVD (dim={dim}) NDCG@10 score: {scores} in {time.time()-t0:.2f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_34253",
"name": "run_shell_command",
"args": {
"description": "Prototype TruncatedSVD collaborative filtering and evaluate on validation",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\n\n# 1. Target users and catalog items\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 2. Load listens\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nprint(f\"Loaded train_listens in {time.time()-t0:.2f}s\")\n\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\n# Filter only target users and catalog items\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nprint(f\"Masked {mask.sum()} / {len(mask)} listens in {time.time()-t0:.2f}s\")\n\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\n# Weight calculation:\n# listens with played_ratio_pct >= 80 get 2.0, >= 50 get 1.0, < 20 get 0.1\n# organic gets 1.5x boost\n# time decay: exp(-days_ago / 60)\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\n\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\n# Build sparse matrix\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\nprint(f\"Built sparse user-item matrix in {time.time()-t0:.2f}s, nnz={X.nnz}\")\n\n# Add likes from train_likes and pref state\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + X_likes\n\n# Preference state likes\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\np_w = np.full(len(p_u), 5.0, dtype=np.float32)\nX = X + sp.csr_matrix((p_w, (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nprint(f\"Total matrix with likes: nnz={X.nnz}\")\n\n# Apply IDF weighting\n# idf = log(1 + n_users / (1 + df))\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# L2 normalize user rows\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Run TruncatedSVD\ndim = 128\nprint(f\"Fitting TruncatedSVD(dim={dim})...\")\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm) # (8456, dim)\nV = svd.components_ # (dim, 30000)\nprint(f\"TruncatedSVD fitted in {time.time()-t0:.2f}s\")\n\n# Evaluate ranking\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nrows = []\n# Batch score prediction\nbatch_size = 500\nfor start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V # shape (batch, 30000)\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n # Set ineligible scores to -inf\n user_scores = scores_batch[b_idx]\n user_scores[mask == 0] = -np.inf\n # Top 100 items: argpartition\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nprint(f\"SVD (dim={dim}) NDCG@10 score: {scores} in {time.time()-t0:.2f}s\")\n'"
},
"result": [
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"output": "\nOutput: Loaded train_listens in 0.94s\nMasked 31062540 / 43484159 listens in 1.65s\nBuilt sparse user-item matrix in 11.66s, nnz=7868664\nTotal matrix with likes: nnz=7876748\nFitting TruncatedSVD(dim=128)...\nTruncatedSVD fitted in 16.63s\nSVD (dim=128) NDCG@10 score: {'quality_ndcg@10': 0.030842, 'quality_users': 4263} in 20.47s\nProcess Group PGID: 2167\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:11:46.403Z",
"resultDisplay": "Loaded train_listens in 0.94s\nMasked 31062540 / 43484159 listens in 1.65s\nBuilt sparse user-item matrix in 11.66s, nnz=7868664\nTotal matrix with likes: nnz=7876748\nFitting TruncatedSVD(dim=128)...\nTruncatedSVD fitted in 16.63s\nSVD (dim=128) NDCG@10 score: {'quality_ndcg@10': 0.030842, 'quality_users': 4263} in 20.47s",
"description": "Prototype TruncatedSVD collaborative filtering and evaluate on validation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:11:46.404Z",
"content": "\nOutput: Loaded train_listens in 0.94s\nMasked 31062540 / 43484159 listens in 1.65s\nBuilt sparse user-item matrix in 11.66s, nnz=7868664\nTotal matrix with likes: nnz=7876748\nFitting TruncatedSVD(dim=128)...\nTruncatedSVD fitted in 16.63s\nSVD (dim=128) NDCG@10 score: {'quality_ndcg@10': 0.030842, 'quality_users': 4263} in 20.47s\nProcess Group PGID: 2167\n",
"isError": false
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"responseMessageId": "ab6e7250-db6d-4e2d-9818-6787d865175a"
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]
},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T02:11:59.304Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:11:59.304Z",
"text": "Improving Recommendation Performance\n\nI've just achieved a nearly threefold improvement on the popularity baseline by implementing TruncatedSVD with 128 dimensions, which took only 20 seconds. My next step is to analyze the contributing factors to this success and explore additional methods to further elevate the recommendation score."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:11:59.304Z",
"callId": "run_shell_command__call_38984",
"name": "run_shell_command",
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"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n # Min-max normalize per row before adding popularity\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n'",
"description": "Experiment with SVD dimensions and popularity weighting"
},
"recorded": {
"id": "run_shell_command__call_38984",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n # Min-max normalize per row before adding popularity\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n'",
"description": "Experiment with SVD dimensions and popularity weighting"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_38984",
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"response": {
"output": "\nOutput: File \"\", line 114\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n ^\nSyntaxError: unexpected character after line continuation character\nExit Code: 1\nProcess Group PGID: 2196\n"
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}
}
],
"status": "success",
"timestamp": "2026-09-29T02:11:59.332Z",
"resultDisplay": "File \"\", line 114\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n ^\nSyntaxError: unexpected character after line continuation character",
"description": "Experiment with SVD dimensions and popularity weighting",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:11:59.333Z",
"content": "\nOutput: File \"\", line 114\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {scores[\\\"quality_ndcg@10\\\"]}\")\n ^\nSyntaxError: unexpected character after line continuation character\nExit Code: 1\nProcess Group PGID: 2196\n",
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]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T02:12:10.282Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:12:10.282Z",
"callId": "run_shell_command__call_95901",
"name": "run_shell_command",
"input": {
"description": "Run SVD dimension and popularity grid search",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {score_val}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_95901",
"name": "run_shell_command",
"args": {
"description": "Run SVD dimension and popularity grid search",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# Log popularity of catalog items\npop = np.log1p(catalog_df.positive_listens.values.astype(np.float32))\npop_norm = pop / pop.max()\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor dim in [128, 256]:\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n for alpha in [0.0, 0.05, 0.1, 0.2]:\n rows = []\n batch_size = 1000\n for start in range(0, n_users, batch_size):\n end = min(start + batch_size, n_users)\n scores_batch = U[start:end] @ V\n row_mins = scores_batch.min(axis=1, keepdims=True)\n row_maxs = scores_batch.max(axis=1, keepdims=True)\n row_ptp = np.where(row_maxs - row_mins > 1e-6, row_maxs - row_mins, 1.0)\n scores_norm = (scores_batch - row_mins) / row_ptp\n if alpha > 0:\n scores_norm = scores_norm + alpha * pop_norm\n\n for b_idx in range(end - start):\n u_idx_curr = start + b_idx\n uid = uids[u_idx_curr]\n mask = np.unpackbits(packed[u_idx_curr], count=n_items, bitorder=\"little\")\n user_scores = scores_norm[b_idx]\n user_scores[mask == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n top_items = item_ids[top100_idx]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"dim={dim}, alpha={alpha} -> NDCG@10: {score_val}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_95901",
"name": "run_shell_command",
"response": {
"output": "\nOutput: dim=128, alpha=0.0 -> NDCG@10: 0.030842\ndim=128, alpha=0.05 -> NDCG@10: 0.03095\ndim=128, alpha=0.1 -> NDCG@10: 0.030598\ndim=128, alpha=0.2 -> NDCG@10: 0.030346\ndim=256, alpha=0.0 -> NDCG@10: 0.029693\ndim=256, alpha=0.05 -> NDCG@10: 0.029324\ndim=256, alpha=0.1 -> NDCG@10: 0.029219\ndim=256, alpha=0.2 -> NDCG@10: 0.028426\nProcess Group PGID: 2199\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:13:03.982Z",
"resultDisplay": "dim=128, alpha=0.0 -> NDCG@10: 0.030842\ndim=128, alpha=0.05 -> NDCG@10: 0.03095\ndim=128, alpha=0.1 -> NDCG@10: 0.030598\ndim=128, alpha=0.2 -> NDCG@10: 0.030346\ndim=256, alpha=0.0 -> NDCG@10: 0.029693\ndim=256, alpha=0.05 -> NDCG@10: 0.029324\ndim=256, alpha=0.1 -> NDCG@10: 0.029219\ndim=256, alpha=0.2 -> NDCG@10: 0.028426",
"description": "Run SVD dimension and popularity grid search",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:13:03.983Z",
"content": "\nOutput: dim=128, alpha=0.0 -> NDCG@10: 0.030842\ndim=128, alpha=0.05 -> NDCG@10: 0.03095\ndim=128, alpha=0.1 -> NDCG@10: 0.030598\ndim=128, alpha=0.2 -> NDCG@10: 0.030346\ndim=256, alpha=0.0 -> NDCG@10: 0.029693\ndim=256, alpha=0.05 -> NDCG@10: 0.029324\ndim=256, alpha=0.1 -> NDCG@10: 0.029219\ndim=256, alpha=0.2 -> NDCG@10: 0.028426\nProcess Group PGID: 2199\n",
"isError": false
},
"responseMessageId": "5fd00f7e-1f49-4f6f-baed-771c072cce45"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T02:13:15.556Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:13:15.556Z",
"text": "Exploring Collaborative Filtering\n\nI'm currently delving into collaborative filtering approaches. My recent exploration with `dim=128` yielded a score of 0.03095, and now I'm transitioning to investigate User-User k-NN CF and Item-Item CF techniques. My immediate focus is on calculating user similarity for a target user to identify the most akin users."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:13:15.556Z",
"callId": "run_shell_command__call_121573",
"name": "run_shell_command",
"input": {
"description": "Test User-User k-NN Collaborative Filtering",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Compute User-User similarity in batches to find top-K neighbors\nt0 = time.time()\nprint(\"Computing User-User similarity...\")\n# We can do this in user batches:\nfor K in [50, 100, 200]:\n t_start = time.time()\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n # Cosine similarity between batch and all users: (batch, n_users)\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n # Set self-similarity to 0\n for i in range(b_end - b_start):\n sim_batch[i, b_start + i] = 0.0\n\n # Find top K neighbors\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n # Gather top K weights\n # Build batch sparse matrix of weights\n row_indices = np.repeat(np.arange(b_end - b_start), K)\n col_indices = topk_idx.ravel()\n weights_k = sim_batch[row_indices, col_indices]\n # Optional: zero out negative or very small weights\n weights_k[weights_k < 0] = 0\n\n # Sparse batch W_k: (batch, n_users)\n W_batch = sp.csr_matrix((weights_k, (row_indices, col_indices)), shape=(b_end - b_start, n_users), dtype=np.float32)\n\n # Predict scores: W_batch @ X_idf\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"User-User k-NN (K={K}) -> NDCG@10: {score_val} in {time.time()-t_start:.1f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_121573",
"name": "run_shell_command",
"args": {
"description": "Test User-User k-NN Collaborative Filtering",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n# Likes\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n# IDF\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Compute User-User similarity in batches to find top-K neighbors\nt0 = time.time()\nprint(\"Computing User-User similarity...\")\n# We can do this in user batches:\nfor K in [50, 100, 200]:\n t_start = time.time()\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n # Cosine similarity between batch and all users: (batch, n_users)\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n # Set self-similarity to 0\n for i in range(b_end - b_start):\n sim_batch[i, b_start + i] = 0.0\n\n # Find top K neighbors\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n # Gather top K weights\n # Build batch sparse matrix of weights\n row_indices = np.repeat(np.arange(b_end - b_start), K)\n col_indices = topk_idx.ravel()\n weights_k = sim_batch[row_indices, col_indices]\n # Optional: zero out negative or very small weights\n weights_k[weights_k < 0] = 0\n\n # Sparse batch W_k: (batch, n_users)\n W_batch = sp.csr_matrix((weights_k, (row_indices, col_indices)), shape=(b_end - b_start, n_users), dtype=np.float32)\n\n # Predict scores: W_batch @ X_idf\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"User-User k-NN (K={K}) -> NDCG@10: {score_val} in {time.time()-t_start:.1f}s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_121573",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computing User-User similarity...\nUser-User k-NN (K=50) -> NDCG@10: 0.02183 in 18.4s\nUser-User k-NN (K=100) -> NDCG@10: 0.02683 in 19.9s\nUser-User k-NN (K=200) -> NDCG@10: 0.029547 in 22.6s\nProcess Group PGID: 2228\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:14:30.450Z",
"resultDisplay": "Computing User-User similarity...\nUser-User k-NN (K=50) -> NDCG@10: 0.02183 in 18.4s\nUser-User k-NN (K=100) -> NDCG@10: 0.02683 in 19.9s\nUser-User k-NN (K=200) -> NDCG@10: 0.029547 in 22.6s",
"description": "Test User-User k-NN Collaborative Filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:14:30.451Z",
"content": "\nOutput: Computing User-User similarity...\nUser-User k-NN (K=50) -> NDCG@10: 0.02183 in 18.4s\nUser-User k-NN (K=100) -> NDCG@10: 0.02683 in 19.9s\nUser-User k-NN (K=200) -> NDCG@10: 0.029547 in 22.6s\nProcess Group PGID: 2228\n",
"isError": false
},
"responseMessageId": "c61d1df3-4c17-4c48-89ba-c7f11f277f92"
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]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T02:14:41.475Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:14:41.475Z",
"callId": "run_shell_command__call_79089",
"name": "run_shell_command",
"input": {
"description": "Evaluate ensemble of SVD + User-User kNN",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# SVD\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nK = 300\nfor w_knn in [0.0, 0.2, 0.4, 0.6, 0.8]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n # SVD score\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_79089",
"name": "run_shell_command",
"args": {
"description": "Evaluate ensemble of SVD + User-User kNN",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n# SVD\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nK = 300\nfor w_knn in [0.0, 0.2, 0.4, 0.6, 0.8]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n # SVD score\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_79089",
"name": "run_shell_command",
"response": {
"output": "\nOutput: w_knn=0.0 -> NDCG@10: 0.030842\nw_knn=0.2 -> NDCG@10: 0.03214\nw_knn=0.4 -> NDCG@10: 0.033015\nw_knn=0.6 -> NDCG@10: 0.032672\nw_knn=0.8 -> NDCG@10: 0.032124\nProcess Group PGID: 2257\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:16:52.246Z",
"resultDisplay": "w_knn=0.0 -> NDCG@10: 0.030842\nw_knn=0.2 -> NDCG@10: 0.03214\nw_knn=0.4 -> NDCG@10: 0.033015\nw_knn=0.6 -> NDCG@10: 0.032672\nw_knn=0.8 -> NDCG@10: 0.032124",
"description": "Evaluate ensemble of SVD + User-User kNN",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:16:54.592Z",
"content": "\nOutput: w_knn=0.0 -> NDCG@10: 0.030842\nw_knn=0.2 -> NDCG@10: 0.03214\nw_knn=0.4 -> NDCG@10: 0.033015\nw_knn=0.6 -> NDCG@10: 0.032672\nw_knn=0.8 -> NDCG@10: 0.032124\nProcess Group PGID: 2257\n",
"isError": false
},
"responseMessageId": "ee8d8308-45aa-428c-8a53-c02e1bf981f1"
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]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T02:17:02.866Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:02.866Z",
"text": "Improving NDCG Score\n\nMy latest exploration into signal weighting, specifically at `w_knn=0.4`, yielded a significant improvement in NDCG@10, boosting it to 0.033015. I'm now shifting focus to investigate the efficacy of Item-Item Collaborative Filtering and its potential synergy with User-User CF."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:02.866Z",
"text": "Exploring Item-Item Similarity\n\nI'm now diving into Item-Item Collaborative Filtering. My current thought process is centered on efficiently computing item similarity, potentially leveraging matrix operations like $Y^T Y$ where $Y$ is column-normalized IDF data. I'm also considering the calculation $(X Y^T) Y$ for scoring."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:02.866Z",
"callId": "run_shell_command__call_30725",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# test on a block of 30000 cols, say 5000 at a time\nB = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B\nprint(f\"Dense (8456, 8456) @ (8456, 5000) took {time.time()-t0:.2f}s\")\n'",
"description": "Benchmark BLAS dense matrix multiplication"
},
"recorded": {
"id": "run_shell_command__call_30725",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\n# test on a block of 30000 cols, say 5000 at a time\nB = np.random.randn(8456, 5000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B\nprint(f\"Dense (8456, 8456) @ (8456, 5000) took {time.time()-t0:.2f}s\")\n'",
"description": "Benchmark BLAS dense matrix multiplication"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_30725",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Dense (8456, 8456) @ (8456, 5000) took 3.31s\nProcess Group PGID: 2286\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:17:08.609Z",
"resultDisplay": "Dense (8456, 8456) @ (8456, 5000) took 3.31s",
"description": "Benchmark BLAS dense matrix multiplication",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:17:08.609Z",
"content": "\nOutput: Dense (8456, 8456) @ (8456, 5000) took 3.31s\nProcess Group PGID: 2286\n",
"isError": false
},
"responseMessageId": "169dc618-310e-421a-8b6d-717049d3cc50"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T02:17:21.061Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:21.061Z",
"text": "Calculating User-User Similarity\n\nI've just confirmed the computational feasibility of generating the User-User similarity matrix. At this rate, processing all 30,000 columns should only take around 20 seconds, which is excellent news. Now I need to clarify the definition of $A$."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:17:21.061Z",
"text": "Refining Similarity Calculations\n\nI've confirmed that block-wise dense matrix multiplication significantly accelerates computation by leveraging multithreaded OpenBLAS. I am now investigating whether limiting neighbor count (k-NN) or applying power transforms to similarity weights yields superior recommendation results compared to using the entire similarity matrix."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:17:21.061Z",
"callId": "run_shell_command__call_40920",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [200, 300, 500]:\n for power in [1.0, 2.0]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n if power != 1.0:\n w_k = w_k ** power\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K}, power={power} -> NDCG@10: {score_val}\")\n'",
"description": "Test KNN K and similarity power"
},
"recorded": {
"id": "run_shell_command__call_40920",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [200, 300, 500]:\n for power in [1.0, 2.0]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n if power != 1.0:\n w_k = w_k ** power\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K}, power={power} -> NDCG@10: {score_val}\")\n'",
"description": "Test KNN K and similarity power"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_40920",
"name": "run_shell_command",
"response": {
"output": "\nOutput: K=200, power=1.0 -> NDCG@10: 0.029547\nK=200, power=2.0 -> NDCG@10: 0.026768\nK=300, power=1.0 -> NDCG@10: 0.030823\nK=300, power=2.0 -> NDCG@10: 0.027552\nK=500, power=1.0 -> NDCG@10: 0.032421\nK=500, power=2.0 -> NDCG@10: 0.0295\nProcess Group PGID: 2296\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:20:07.222Z",
"resultDisplay": "K=200, power=1.0 -> NDCG@10: 0.029547\nK=200, power=2.0 -> NDCG@10: 0.026768\nK=300, power=1.0 -> NDCG@10: 0.030823\nK=300, power=2.0 -> NDCG@10: 0.027552\nK=500, power=1.0 -> NDCG@10: 0.032421\nK=500, power=2.0 -> NDCG@10: 0.0295",
"description": "Test KNN K and similarity power",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:20:07.223Z",
"content": "\nOutput: K=200, power=1.0 -> NDCG@10: 0.029547\nK=200, power=2.0 -> NDCG@10: 0.026768\nK=300, power=1.0 -> NDCG@10: 0.030823\nK=300, power=2.0 -> NDCG@10: 0.027552\nK=500, power=1.0 -> NDCG@10: 0.032421\nK=500, power=2.0 -> NDCG@10: 0.0295\nProcess Group PGID: 2296\n",
"isError": false
},
"responseMessageId": "7141c5fe-c0df-4312-a0d2-966d98ac04d0"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T02:20:17.792Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:20:17.792Z",
"text": "Exploring K-Value Effects\n\nI'm currently investigating the impact of varying the K value, specifically testing K=1000 and K=1500 to see how they influence the output compared to K=500. Additionally, I'm exploring the implications of not truncating K at all, or setting the similarity threshold to a minimum of 0, by analyzing results after setting `sim_batch[sim_batch < 0] = 0`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:20:17.792Z",
"callId": "run_shell_command__call_99846",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [800, 1200]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K} -> NDCG@10: {score_val}\")\n'",
"description": "Test K=800 and K=1200 User-User CF"
},
"recorded": {
"id": "run_shell_command__call_99846",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nfor K in [800, 1200]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n scores_batch = (W_batch @ X_idf).toarray()\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"K={K} -> NDCG@10: {score_val}\")\n'",
"description": "Test K=800 and K=1200 User-User CF"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_99846",
"name": "run_shell_command",
"response": {
"output": "\nOutput: K=800 -> NDCG@10: 0.032383\nK=1200 -> NDCG@10: 0.032886\nProcess Group PGID: 2325\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:21:48.501Z",
"resultDisplay": "K=800 -> NDCG@10: 0.032383\nK=1200 -> NDCG@10: 0.032886",
"description": "Test K=800 and K=1200 User-User CF",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:21:48.501Z",
"content": "\nOutput: K=800 -> NDCG@10: 0.032383\nK=1200 -> NDCG@10: 0.032886\nProcess Group PGID: 2325\n",
"isError": false
},
"responseMessageId": "fbf0a40a-81bf-4ed9-a8dd-b53b2a848758"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T02:22:01.346Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:22:01.346Z",
"text": "Evaluating Combinations\n\nMy recent focus has been on exploring hybrid recommendation approaches. I'm observing promising results when combining User-User similarities with Singular Value Decomposition, particularly with higher K values for User-User and varying dimensionality for SVD. I'm keen to push these combinations further."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:22:01.346Z",
"text": "Investigating Item-Item\n\nI'm now examining Item-Item Collaborative Filtering. I've mapped out the process, realizing item embeddings can be derived from the transposed normalized user-item matrix, enabling direct calculation of item-item similarities and, subsequently, recommendation scores using a concise matrix operation."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:22:01.346Z",
"callId": "run_shell_command__call_25433",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# Column normalize X_idf (items)\n# norm of each column\nitem_norms = sp.linalg.norm(X_idf, axis=0)\nitem_norms[item_norms == 0] = 1.0\nY = X_idf.multiply(1.0 / item_norms).tocsr() # (8456, 30000)\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Let us normalize user rows of X_idf\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_unorm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nt0 = time.time()\nprint(\"Computing E = X_unorm @ Y.T...\")\nE = (X_unorm @ Y.T).toarray() # (8456, 8456)\nprint(f\"E computed in {time.time()-t0:.2f}s\")\n\n# Now score in batches of users:\nrows = []\nbatch_size = 1000\nfor b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n # scores: E[b_start:b_end] @ Y -> shape (b_len, 30000)\n scores_batch = E[b_start:b_end] @ Y # dense @ sparse is supported or convert Y block\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nscore_val = scores[\"quality_ndcg@10\"]\nprint(f\"Item-Item Cosine CF -> NDCG@10: {score_val} in {time.time()-t0:.2f}s\")\n'",
"description": "Test Item-Item Cosine Collaborative Filtering"
},
"recorded": {
"id": "run_shell_command__call_25433",
"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 pathlib import Path\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\n# 1. Build matrix\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\n\nt_max = 24786800.0\ndays_ago = (t_max - l_ts[mask]) / 86400.0\ntime_weight = np.exp(-days_ago / 60.0)\nlisten_weight = np.where(l_ratio[mask] >= 80, 2.0, np.where(l_ratio[mask] >= 50, 1.0, 0.2))\norg_weight = np.where(l_org[mask] == 1, 1.5, 1.0)\nweights = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = [uid_to_idx[u] for u in likes_df.uid]\nl_i = [item_to_idx[i] for i in likes_df.item_id]\nlikes_days = (t_max - likes_df.timestamp.values) / 86400.0\nl_w = 5.0 * np.exp(-likes_days / 60.0)\nX = X + sp.csr_matrix((l_w, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = [uid_to_idx[u] for u in pref_likes.uid]\np_i = [item_to_idx[i] for i in pref_likes.item_id]\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\n# Column normalize X_idf (items)\n# norm of each column\nitem_norms = sp.linalg.norm(X_idf, axis=0)\nitem_norms[item_norms == 0] = 1.0\nY = X_idf.multiply(1.0 / item_norms).tocsr() # (8456, 30000)\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Let us normalize user rows of X_idf\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_unorm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\nt0 = time.time()\nprint(\"Computing E = X_unorm @ Y.T...\")\nE = (X_unorm @ Y.T).toarray() # (8456, 8456)\nprint(f\"E computed in {time.time()-t0:.2f}s\")\n\n# Now score in batches of users:\nrows = []\nbatch_size = 1000\nfor b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n # scores: E[b_start:b_end] @ Y -> shape (b_len, 30000)\n scores_batch = E[b_start:b_end] @ Y # dense @ sparse is supported or convert Y block\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores_batch[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\ndf_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\nranked = ranked_items(df_sub)\nscores = score_rankings(ranked, graded)\nscore_val = scores[\"quality_ndcg@10\"]\nprint(f\"Item-Item Cosine CF -> NDCG@10: {score_val} in {time.time()-t0:.2f}s\")\n'",
"description": "Test Item-Item Cosine Collaborative Filtering"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_25433",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computing E = X_unorm @ Y.T...\nE computed in 11.40s\nItem-Item Cosine CF -> NDCG@10: 0.017004 in 31.74s\nProcess Group PGID: 2354\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:22:46.977Z",
"resultDisplay": "Computing E = X_unorm @ Y.T...\nE computed in 11.40s\nItem-Item Cosine CF -> NDCG@10: 0.017004 in 31.74s",
"description": "Test Item-Item Cosine Collaborative Filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:22:46.978Z",
"content": "\nOutput: Computing E = X_unorm @ Y.T...\nE computed in 11.40s\nItem-Item Cosine CF -> NDCG@10: 0.017004 in 31.74s\nProcess Group PGID: 2354\n",
"isError": false
},
"responseMessageId": "08723cf0-450d-4255-822c-e480c8626294"
}
]
},
{
"id": 51,
"role": "agent",
"timestamp": "2026-09-29T02:23:02.530Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:23:02.530Z",
"text": "Exploring Performance Differences\n\nMy current thoughts are focused on understanding the significant performance disparity observed. I'm analyzing why SVD and User-User k-NN are outperforming the full dense Item-Item approach, suspecting SVD's orthogonal dense components are key to capturing nuanced latent factors like genres and moods."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:23:02.530Z",
"text": "Optimizing SVD Parameters\n\nI'm now delving into the finer points of SVD optimization. Specifically, I'm investigating the impact of various weighting schemes for interactions, including time decay half-lives, listen completion, organic vs. recommendation boosts, and differential weighting for likes and dislikes. I'm also considering how matrix preprocessing choices might influence these outcomes."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:23:02.530Z",
"callId": "run_shell_command__call_1140679",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=60.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n if decay_days is not None:\n time_weight = np.exp(-days_ago / decay_days)\n else:\n time_weight = 1.0\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n # Likes\n if decay_days is not None:\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n else:\n likes_time = 1.0\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n # TF transform on X.data\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n elif tf_mode == \"sqrt\":\n X.data = np.sqrt(X.data)\n\n # IDF\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n # Normalize rows\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nprint(\"decay=60, log, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=128))\nprint(\"decay=60, sqrt, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"sqrt\", dim=128))\nprint(\"decay=60, raw, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"raw\", dim=128))\nprint(\"decay=30, log, dim=128:\", evaluate_config(decay_days=30, tf_mode=\"log\", dim=128))\nprint(\"decay=90, log, dim=128:\", evaluate_config(decay_days=90, tf_mode=\"log\", dim=128))\nprint(\"decay=60, log, dim=160:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=160))\n'",
"description": "Systematic parameter search for SVD collaborative filtering"
},
"recorded": {
"id": "run_shell_command__call_1140679",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=60.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n if decay_days is not None:\n time_weight = np.exp(-days_ago / decay_days)\n else:\n time_weight = 1.0\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n # Likes\n if decay_days is not None:\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n else:\n likes_time = 1.0\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n # TF transform on X.data\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n elif tf_mode == \"sqrt\":\n X.data = np.sqrt(X.data)\n\n # IDF\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n # Normalize rows\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nprint(\"decay=60, log, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=128))\nprint(\"decay=60, sqrt, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"sqrt\", dim=128))\nprint(\"decay=60, raw, dim=128:\", evaluate_config(decay_days=60, tf_mode=\"raw\", dim=128))\nprint(\"decay=30, log, dim=128:\", evaluate_config(decay_days=30, tf_mode=\"log\", dim=128))\nprint(\"decay=90, log, dim=128:\", evaluate_config(decay_days=90, tf_mode=\"log\", dim=128))\nprint(\"decay=60, log, dim=160:\", evaluate_config(decay_days=60, tf_mode=\"log\", dim=160))\n'",
"description": "Systematic parameter search for SVD collaborative filtering"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_1140679",
"name": "run_shell_command",
"response": {
"output": "\nOutput: decay=60, log, dim=128: 0.030127\ndecay=60, sqrt, dim=128: 0.030251\ndecay=60, raw, dim=128: 0.030643\ndecay=30, log, dim=128: 0.033705\ndecay=90, log, dim=128: 0.026958\ndecay=60, log, dim=160: 0.030901\nProcess Group PGID: 2383\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:24:10.308Z",
"resultDisplay": "decay=60, log, dim=128: 0.030127\ndecay=60, sqrt, dim=128: 0.030251\ndecay=60, raw, dim=128: 0.030643\ndecay=30, log, dim=128: 0.033705\ndecay=90, log, dim=128: 0.026958\ndecay=60, log, dim=160: 0.030901",
"description": "Systematic parameter search for SVD collaborative filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:24:10.309Z",
"content": "\nOutput: decay=60, log, dim=128: 0.030127\ndecay=60, sqrt, dim=128: 0.030251\ndecay=60, raw, dim=128: 0.030643\ndecay=30, log, dim=128: 0.033705\ndecay=90, log, dim=128: 0.026958\ndecay=60, log, dim=160: 0.030901\nProcess Group PGID: 2383\n",
"isError": false
},
"responseMessageId": "c2f13a23-1737-4b08-9c56-a9a7301ca257"
}
]
},
{
"id": 52,
"role": "agent",
"timestamp": "2026-09-29T02:24:40.958Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:24:40.958Z",
"text": "Exploring Decay Parameter\n\nI'm investigating the impact of the `decay` parameter on performance. Initial tests with `decay=30` yield a value of 0.033705. I observed a trend where higher decay values seem to correlate with better results, with `decay=90` achieving 0.026958. My next step is to explore decay values between 15 and 30 to refine this understanding, as recent listening trends strongly influence music discovery."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:24:40.958Z",
"callId": "run_shell_command__call_43630",
"name": "run_shell_command",
"input": {
"description": "Fine-grained search on decay days",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=30.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [10, 15, 20, 25, 30, 40]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_43630",
"name": "run_shell_command",
"args": {
"description": "Fine-grained search on decay days",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=30.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [10, 15, 20, 25, 30, 40]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_43630",
"name": "run_shell_command",
"response": {
"output": "\nOutput: decay=10: 0.038638\ndecay=15: 0.038085\ndecay=20: 0.036539\ndecay=25: 0.035227\ndecay=30: 0.033705\ndecay=40: 0.032595\nProcess Group PGID: 2412\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:25:48.276Z",
"resultDisplay": "decay=10: 0.038638\ndecay=15: 0.038085\ndecay=20: 0.036539\ndecay=25: 0.035227\ndecay=30: 0.033705\ndecay=40: 0.032595",
"description": "Fine-grained search on decay days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:25:48.277Z",
"content": "\nOutput: decay=10: 0.038638\ndecay=15: 0.038085\ndecay=20: 0.036539\ndecay=25: 0.035227\ndecay=30: 0.033705\ndecay=40: 0.032595\nProcess Group PGID: 2412\n",
"isError": false
},
"responseMessageId": "86f89b53-66f0-4ba7-a965-1d99a2c3e0f3"
}
]
},
{
"id": 53,
"role": "agent",
"timestamp": "2026-09-29T02:26:03.118Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T02:26:03.118Z",
"callId": "run_shell_command__call_167236",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=10.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [3, 5, 7, 10, 12]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'",
"description": "Test very short decay days"
},
"recorded": {
"id": "run_shell_command__call_167236",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\n# Load tables once\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef evaluate_config(decay_days=10.0, tf_mode=\"log\", dim=128, organic_boost=2.0, like_weight=5.0):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, organic_boost, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = like_weight * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), like_weight, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n if tf_mode == \"log\":\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor d in [3, 5, 7, 10, 12]:\n print(f\"decay={d}: {evaluate_config(decay_days=d, dim=128)}\")\n'",
"description": "Test very short decay days"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_167236",
"name": "run_shell_command",
"response": {
"output": "\nOutput: decay=3: 0.035435\ndecay=5: 0.038131\ndecay=7: 0.038788\ndecay=10: 0.038638\ndecay=12: 0.038616\nProcess Group PGID: 2441\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:27:01.787Z",
"resultDisplay": "decay=3: 0.035435\ndecay=5: 0.038131\ndecay=7: 0.038788\ndecay=10: 0.038638\ndecay=12: 0.038616",
"description": "Test very short decay days",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:27:01.788Z",
"content": "\nOutput: decay=3: 0.035435\ndecay=5: 0.038131\ndecay=7: 0.038788\ndecay=10: 0.038638\ndecay=12: 0.038616\nProcess Group PGID: 2441\n",
"isError": false
},
"responseMessageId": "bf87e345-997e-42bf-8fc0-5e736f5ca2c3"
}
]
},
{
"id": 54,
"role": "agent",
"timestamp": "2026-09-29T02:27:50.995Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:27:50.995Z",
"text": "Refining Decay Parameters\n\nI've pinpointed a decay rate of 7, corresponding to a 1-week half-life. This precisely aligns with the validation window duration. My current focus is on leveraging recent listening activity, specifically within the 7-14 day window, as it seems to offer the most potent indicator of a user's imminent discovery. I'm now proceeding to test this refined approach."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:27:50.995Z",
"callId": "run_shell_command__call_48371",
"name": "run_shell_command",
"input": {
"description": "Test long-term base weight mixed with exponential decay",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef test_time_mix(base_weight=0.1, decay_days=7.0, dim=128):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = base_weight + (1.0 - base_weight) * np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, 2.0, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = base_weight + (1.0 - base_weight) * np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = 5.0 * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor base in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f\"base={base}: {test_time_mix(base_weight=base, decay_days=7.0)}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_48371",
"name": "run_shell_command",
"args": {
"description": "Test long-term base weight mixed with exponential decay",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\n\ndef test_time_mix(base_weight=0.1, decay_days=7.0, dim=128):\n days_ago = (t_max - l_ts_sub) / 86400.0\n time_weight = base_weight + (1.0 - base_weight) * np.exp(-days_ago / decay_days)\n\n listen_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\n org_weight = np.where(l_org_sub == 1, 2.0, 1.0)\n w_listens = listen_weight * org_weight * time_weight\n\n X = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\n likes_time = base_weight + (1.0 - base_weight) * np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\n w_likes = 5.0 * likes_time\n X = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\n X = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\n X.data = np.log1p(X.data)\n\n df = np.bincount(X.nonzero()[1], minlength=n_items)\n idf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\n X_idf = X.multiply(idf).tocsr()\n\n user_norms = sp.linalg.norm(X_idf, axis=1)\n user_norms[user_norms == 0] = 1.0\n X_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\n svd = TruncatedSVD(n_components=dim, random_state=42)\n U = svd.fit_transform(X_norm)\n V = svd.components_\n\n rows = []\n batch_size = 1000\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n scores = U[b_start:b_end] @ V\n for i in range(b_end - b_start):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = scores[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n return scores[\"quality_ndcg@10\"]\n\nfor base in [0.0, 0.05, 0.1, 0.2, 0.3]:\n print(f\"base={base}: {test_time_mix(base_weight=base, decay_days=7.0)}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_48371",
"name": "run_shell_command",
"response": {
"output": "\nOutput: base=0.0: 0.038788\nbase=0.05: 0.032644\nbase=0.1: 0.028814\nbase=0.2: 0.025656\nbase=0.3: 0.024137\nProcess Group PGID: 2470\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:28:49.061Z",
"resultDisplay": "base=0.0: 0.038788\nbase=0.05: 0.032644\nbase=0.1: 0.028814\nbase=0.2: 0.025656\nbase=0.3: 0.024137",
"description": "Test long-term base weight mixed with exponential decay",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:28:49.062Z",
"content": "\nOutput: base=0.0: 0.038788\nbase=0.05: 0.032644\nbase=0.1: 0.028814\nbase=0.2: 0.025656\nbase=0.3: 0.024137\nProcess Group PGID: 2470\n",
"isError": false
},
"responseMessageId": "eafbbf08-1445-4869-9198-6db41803ecac"
}
]
},
{
"id": 55,
"role": "agent",
"timestamp": "2026-09-29T02:29:07.803Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:29:07.803Z",
"text": "Analyzing Decay Factor\n\nMy current focus is on understanding the impact of `base=0.0`, which signifies pure exponential decay with a 7-day window. It appears significantly more effective than retaining older interaction data. This is because stale, long-past interactions from over 100 days ago seem to dilute the listener's current musical engagement, especially when they're actively exploring new content."
},
{
"kind": "tool",
"timestamp": "2026-09-29T02:29:07.803Z",
"callId": "run_shell_command__call_168804",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\ndecay_days = 7.0\n\ndays_ago = (t_max - l_ts_sub) / 86400.0\ntime_weight = np.exp(-days_ago / decay_days)\nlisten_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\norg_weight = np.where(l_org_sub == 1, 2.0, 1.0)\nw_listens = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\nw_likes = 5.0 * likes_time\nX = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nX.data = np.log1p(X.data)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\ndim = 128\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nK = 500\nfor w_knn in [0.0, 0.1, 0.2, 0.3, 0.4]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'",
"description": "Test SVD + User-User kNN with 7-day decay"
},
"recorded": {
"id": "run_shell_command__call_168804",
"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 pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: idx for idx, uid in enumerate(uids)}\nn_users = len(uids)\n\ncatalog_df = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_ids = catalog_df.item_id.values\nitem_to_idx = {item: idx for idx, item in enumerate(item_ids)}\nn_items = len(item_ids)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, set(uids))\n\nval_elig = np.load(DATA / \"validation_eligible_candidates.npz\")\npacked = val_elig[\"packed_eligible\"]\n\nlistens_table = pq.read_table(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nl_uid = listens_table[\"uid\"].to_numpy()\nl_item = listens_table[\"item_id\"].to_numpy()\nl_ts = listens_table[\"timestamp\"].to_numpy()\nl_org = listens_table[\"is_organic\"].to_numpy()\nl_ratio = listens_table[\"played_ratio_pct\"].to_numpy()\n\nmask = np.isin(l_uid, uids) & np.isin(l_item, item_ids)\nu_idx = np.array([uid_to_idx[u] for u in l_uid[mask]], dtype=np.int32)\ni_idx = np.array([item_to_idx[i] for i in l_item[mask]], dtype=np.int32)\nl_ts_sub = l_ts[mask]\nl_org_sub = l_org[mask]\nl_ratio_sub = l_ratio[mask]\n\nlikes_df = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.uid.isin(uids) & likes_df.item_id.isin(item_ids)]\nl_u = np.array([uid_to_idx[u] for u in likes_df.uid], dtype=np.int32)\nl_i = np.array([item_to_idx[i] for i in likes_df.item_id], dtype=np.int32)\nlikes_ts = likes_df.timestamp.values\n\npref_df = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = pref_df[(pref_df.state == \"liked\") & pref_df.uid.isin(uids) & pref_df.item_id.isin(item_ids)]\np_u = np.array([uid_to_idx[u] for u in pref_likes.uid], dtype=np.int32)\np_i = np.array([item_to_idx[i] for i in pref_likes.item_id], dtype=np.int32)\n\nt_max = 24786800.0\ndecay_days = 7.0\n\ndays_ago = (t_max - l_ts_sub) / 86400.0\ntime_weight = np.exp(-days_ago / decay_days)\nlisten_weight = np.where(l_ratio_sub >= 80, 2.5, np.where(l_ratio_sub >= 50, 1.0, 0.2))\norg_weight = np.where(l_org_sub == 1, 2.0, 1.0)\nw_listens = listen_weight * org_weight * time_weight\n\nX = sp.csr_matrix((w_listens, (u_idx, i_idx)), shape=(n_users, n_items), dtype=np.float32)\n\nlikes_time = np.exp(-((t_max - likes_ts) / 86400.0) / decay_days)\nw_likes = 5.0 * likes_time\nX = X + sp.csr_matrix((w_likes, (l_u, l_i)), shape=(n_users, n_items), dtype=np.float32)\nX = X + sp.csr_matrix((np.full(len(p_u), 5.0, dtype=np.float32), (p_u, p_i)), shape=(n_users, n_items), dtype=np.float32)\n\nX.data = np.log1p(X.data)\n\ndf = np.bincount(X.nonzero()[1], minlength=n_items)\nidf = np.log1p(n_users / (1.0 + df)).astype(np.float32)\nX_idf = X.multiply(idf).tocsr()\n\nuser_norms = sp.linalg.norm(X_idf, axis=1)\nuser_norms[user_norms == 0] = 1.0\nX_norm = sp.diags(1.0 / user_norms.ravel()) @ X_idf\n\ndim = 128\nsvd = TruncatedSVD(n_components=dim, random_state=42)\nU = svd.fit_transform(X_norm)\nV = svd.components_\n\nK = 500\nfor w_knn in [0.0, 0.1, 0.2, 0.3, 0.4]:\n rows = []\n batch_size = 500\n for b_start in range(0, n_users, batch_size):\n b_end = min(b_start + batch_size, n_users)\n b_len = b_end - b_start\n\n s_svd = U[b_start:b_end] @ V\n s_svd_min = s_svd.min(axis=1, keepdims=True)\n s_svd_max = s_svd.max(axis=1, keepdims=True)\n s_svd = (s_svd - s_svd_min) / (s_svd_max - s_svd_min + 1e-6)\n\n if w_knn > 0:\n sim_batch = (X_norm[b_start:b_end] @ X_norm.T).toarray()\n for i in range(b_len):\n sim_batch[i, b_start + i] = 0.0\n topk_idx = np.argpartition(sim_batch, -K, axis=1)[:, -K:]\n row_idx = np.repeat(np.arange(b_len), K)\n col_idx = topk_idx.ravel()\n w_k = sim_batch[row_idx, col_idx]\n w_k[w_k < 0] = 0\n W_batch = sp.csr_matrix((w_k, (row_idx, col_idx)), shape=(b_len, n_users), dtype=np.float32)\n s_knn = (W_batch @ X_idf).toarray()\n s_knn_min = s_knn.min(axis=1, keepdims=True)\n s_knn_max = s_knn.max(axis=1, keepdims=True)\n s_knn = (s_knn - s_knn_min) / (s_knn_max - s_knn_min + 1e-6)\n s_final = (1.0 - w_knn) * s_svd + w_knn * s_knn\n else:\n s_final = s_svd\n\n for i in range(b_len):\n u_curr = b_start + i\n uid = uids[u_curr]\n mask = np.unpackbits(packed[u_curr], count=n_items, bitorder=\"little\")\n u_scores = s_final[i]\n u_scores[mask == 0] = -np.inf\n top100 = np.argpartition(u_scores, -100)[-100:]\n top100 = top100[np.argsort(-u_scores[top100])]\n top_items = item_ids[top100]\n for rank_idx, item in enumerate(top_items, 1):\n rows.append((uid, item, rank_idx))\n\n df_sub = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n ranked = ranked_items(df_sub)\n scores = score_rankings(ranked, graded)\n score_val = scores[\"quality_ndcg@10\"]\n print(f\"w_knn={w_knn} -> NDCG@10: {score_val}\")\n'",
"description": "Test SVD + User-User kNN with 7-day decay"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_168804",
"name": "run_shell_command",
"response": {
"output": "\nOutput: w_knn=0.0 -> NDCG@10: 0.038788\nw_knn=0.1 -> NDCG@10: 0.041966\nw_knn=0.2 -> NDCG@10: 0.04431\nw_knn=0.3 -> NDCG@10: 0.047341\nw_knn=0.4 -> NDCG@10: 0.04895\nProcess Group PGID: 2499\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T02:31:34.504Z",
"resultDisplay": "w_knn=0.0 -> NDCG@10: 0.038788\nw_knn=0.1 -> NDCG@10: 0.041966\nw_knn=0.2 -> NDCG@10: 0.04431\nw_knn=0.3 -> NDCG@10: 0.047341\nw_knn=0.4 -> NDCG@10: 0.04895",
"description": "Test SVD + User-User kNN with 7-day decay",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T02:31:34.505Z",
"content": "\nOutput: w_knn=0.0 -> NDCG@10: 0.038788\nw_knn=0.1 -> NDCG@10: 0.041966\nw_knn=0.2 -> NDCG@10: 0.04431\nw_knn=0.3 -> NDCG@10: 0.047341\nw_knn=0.4 -> NDCG@10: 0.04895\nProcess Group PGID: 2499\n",
"isError": false
},
"responseMessageId": "b55e2401-36a2-40a5-a6d8-682eabe3b48f"
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]
},
{
"id": 56,
"role": "agent",
"timestamp": "2026-09-29T02:32:17.877Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T02:32:17.877Z",
"text": "Exploring w_knn weights\n\nI've been investigating the impact of varying `w_knn` weights. With a value of 0.4, I've achieved an NDCG@10 of 0.048950, which is a significant improvement, nearly five times better than the baseline. I'm now curious to explore weights of 0.5, 0.6, and beyond."
}
]
}
],
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