{
"schemaVersion": 1,
"taskId": "yambda-discovery-ranking-v01",
"attemptId": "gemini-3-8-flash-high-basic-fixed-r03-d237f45e",
"taskChecksum": "06d023160277225da99fbf82a38920164f960785fd1fd9068f437996f389d90f",
"agentVersion": "0.61.0",
"model": "gemini-3.8-flash",
"sessionId": "6212091c-7fda-46d9-87f3-27fb24d0692b",
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"text": "\nThis is the Gemini CLI. We are setting up the context for our chat.\nToday's date is Tuesday, September 29, 2026 (formatted according to the user's locale).\nMy operating system is: linux\nThe project's temporary directory is: /home/agent/.gemini/tmp/app\n- **Workspace Directories:**\n - /app\n - /task\n- **Directory Structure:**\n\nShowing up to 200 items (files + folders).\n\n/app/\n└───solution/\nShowing up to 200 items (files + folders).\n\n/task/\n├───evaluate_public.py\n├───ranking_evaluation.py\n├───requirements.txt\n└───data/\n ├───candidate_catalog.parquet\n ├───target_users.parquet\n ├───test_eligible_candidates.npz\n ├───test_preference_state.parquet\n ├───train_dislikes.parquet\n ├───train_likes.parquet\n ├───train_listens.parquet\n ├───train_undislikes.parquet\n ├───train_unlikes.parquet\n ├───val_dislikes.parquet\n ├───val_likes.parquet\n ├───val_listens.parquet\n ├───val_undislikes.parquet\n ├───val_unlikes.parquet\n ├───validation_eligible_candidates.npz\n └───validation_preference_state.parquet\n\n\n"
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"text": "# Build a music-discovery ranker\n\nBuild the strongest ranking model you can for the \"Made for You\" shelf of a\nmusic streaming service. Per-user candidate eligibility, cutoff-specific\nexplicit preference state, and a temporal backtest are provided. Candidate\neligibility is platform-owned; all modeling within that candidate boundary is\nin scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n cutoff-correct candidate sets for every target user. Each contains aligned\n arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n the Boolean item mask with `np.unpackbits(packed_eligible[r],\n count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model, fitted representation, example\nsubmission, or modeling architecture is supplied. Build and tune the best model\nyou can, then refit the selected approach as described above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that builds the artifact and a concise\n`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission\nsaved throughout the attempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n connection managed by Harbor.\n- You have up to 30 minutes. Improve the model as much as possible within that\n limit. Structural validity alone is not sufficient; the final submission is\n also evaluated for ranking quality.\n"
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"text": "Analyzing Task Details\n\nI'm currently dissecting the prompt, paying close attention to the machine learning task, competitive aspects, and specific constraints. My focus is on understanding the requirements and the provided workspace setup before proceeding further."
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"text": "Investigating Workspace Constraints\n\nI'm now focusing on the strict environment limitations: read-only directories, no new packages, and a tight time budget. My priority is to understand the evaluation script and available tools to quickly establish a baseline and ensure valid submissions are always accessible."
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"resultDisplay": "## 📂 Topic: **Initial Exploration and Setup**\n\n**Summary:**\nStarting initial exploration of the task environment. Inspecting /task directory, dataset shapes, evaluation scripts, and installed packages.\n\n> [!STRATEGY]\n> **Intent:** Explore data files, requirements, and evaluation script to understand the problem and setup.",
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"output": "#!/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()\n"
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"content": "#!/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()\n",
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"output": "\"\"\"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 }\n"
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"content": "\"\"\"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 }\n",
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"output": "# 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\n"
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"content": "# 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\n",
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"output": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1612\n"
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"resultDisplay": "total 340M\n-r--r--r-- 1 root root 342K Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet",
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"result": {
"timestamp": "2026-09-29T04:19:55.801Z",
"content": "\nOutput: total 340M\n-r--r--r-- 1 root root 342K Sep 29 04:13 candidate_catalog.parquet\n-r--r--r-- 1 root root 83K Sep 29 04:13 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 29 04:13 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 29 04:13 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 29 04:13 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 29 04:13 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 29 04:13 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 29 04:13 train_unlikes.parquet\n-r--r--r-- 1 root root 36K Sep 29 04:13 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 29 04:13 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 29 04:13 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 29 04:13 val_undislikes.parquet\n-r--r--r-- 1 root root 71K Sep 29 04:13 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 29 04:13 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet\nProcess Group PGID: 1612\n",
"isError": false
},
"responseMessageId": "1718acf9-62a5-44de-afb1-1b9ab2ae7572"
}
]
},
{
"id": 4,
"role": "agent",
"timestamp": "2026-09-29T04:20:06.201Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:06.201Z",
"callId": "run_shell_command__call_262389",
"name": "run_shell_command",
"input": {
"description": "Check CPU cores and available memory",
"command": "nproc && free -h"
},
"recorded": {
"id": "run_shell_command__call_262389",
"name": "run_shell_command",
"args": {
"description": "Check CPU cores and available memory",
"command": "nproc && free -h"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_262389",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 8\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1615\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:06.217Z",
"resultDisplay": "8\n/usr/bin/bash: line 4: free: command not found",
"description": "nproc && free -h",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:06.218Z",
"content": "\nOutput: 8\n/usr/bin/bash: line 4: free: command not found\nExit Code: 127\nProcess Group PGID: 1615\n",
"isError": false
},
"responseMessageId": "7c74df70-dba5-4f07-9722-9591188c25fd"
}
]
},
{
"id": 5,
"role": "agent",
"timestamp": "2026-09-29T04:20:09.351Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:09.351Z",
"callId": "run_shell_command__call_417546",
"name": "run_shell_command",
"input": {
"command": "grep -i MemTotal /proc/meminfo && grep -i MemAvailable /proc/meminfo",
"description": "Check system memory"
},
"recorded": {
"id": "run_shell_command__call_417546",
"name": "run_shell_command",
"args": {
"command": "grep -i MemTotal /proc/meminfo && grep -i MemAvailable /proc/meminfo",
"description": "Check system memory"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_417546",
"name": "run_shell_command",
"response": {
"output": "\nOutput: MemTotal: 15988972 kB\nMemAvailable: 14687508 kB\nProcess Group PGID: 1619\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:09.366Z",
"resultDisplay": "MemTotal: 15988972 kB\nMemAvailable: 14687508 kB",
"description": "grep -i MemTotal /proc/meminfo && grep -i MemAvailable /proc/meminfo",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:09.366Z",
"content": "\nOutput: MemTotal: 15988972 kB\nMemAvailable: 14687508 kB\nProcess Group PGID: 1619\n",
"isError": false
},
"responseMessageId": "11294f8f-b645-492b-ae82-c1f417f75e49"
}
]
},
{
"id": 6,
"role": "agent",
"timestamp": "2026-09-29T04:20:14.462Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:14.462Z",
"text": "Exploring Data Structures\n\nI'm now focusing on understanding the data itself, checking the shapes and types of relevant structures like candidate sets and preference states. I plan to use a quick Python script or a one-liner to get a feel for the data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:14.462Z",
"callId": "run_shell_command__call_221159",
"name": "run_shell_command",
"input": {
"description": "Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"catalog:\", catalog.shape, catalog.columns)\nprint(catalog.head())\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(\"targets:\", targets.shape, targets.columns)\nprint(targets.head())\n\nval_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(\"val_pref:\", val_pref.shape, val_pref.columns)\nprint(val_pref.head())\n\ntest_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(\"test_pref:\", test_pref.shape, test_pref.columns)\nprint(test_pref.head())\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"val_npz:\", val_npz[\"uids\"].shape, val_npz[\"item_ids\"].shape, val_npz[\"packed_eligible\"].shape)\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nprint(\"test_npz:\", test_npz[\"uids\"].shape, test_npz[\"item_ids\"].shape, test_npz[\"packed_eligible\"].shape)\n'"
},
"recorded": {
"id": "run_shell_command__call_221159",
"name": "run_shell_command",
"args": {
"description": "Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"catalog:\", catalog.shape, catalog.columns)\nprint(catalog.head())\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(\"targets:\", targets.shape, targets.columns)\nprint(targets.head())\n\nval_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(\"val_pref:\", val_pref.shape, val_pref.columns)\nprint(val_pref.head())\n\ntest_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(\"test_pref:\", test_pref.shape, test_pref.columns)\nprint(test_pref.head())\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"val_npz:\", val_npz[\"uids\"].shape, val_npz[\"item_ids\"].shape, val_npz[\"packed_eligible\"].shape)\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nprint(\"test_npz:\", test_npz[\"uids\"].shape, test_npz[\"item_ids\"].shape, test_npz[\"packed_eligible\"].shape)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_221159",
"name": "run_shell_command",
"response": {
"output": "\nOutput: catalog: (30000, 4) Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntargets: (8456, 3) Index(['uid', 'recent_positive_listens', 'history_positive_listens'], dtype='object')\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nval_pref: (469124, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_pref: (484776, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nval_npz: (8456,) (30000,) (8456, 3750)\ntest_npz: (8456,) (30000,) (8456, 3750)\nProcess Group PGID: 1623\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:15.479Z",
"resultDisplay": "catalog: (30000, 4) Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntargets: (8456, 3) Index(['uid', 'recent_positive_listens', 'history_positive_listens'], dtype='object')\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nval_pref: (469124, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_pref: (484776, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nval_npz: (8456,) (30000,) (8456, 3750)\ntest_npz: (8456,) (30000,) (8456, 3750)",
"description": "Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:15.480Z",
"content": "\nOutput: catalog: (30000, 4) Index(['item_id', 'positive_listens', 'organic_positive_listens',\n 'popularity_rank'],\n dtype='object')\n item_id positive_listens organic_positive_listens popularity_rank\n0 5862961 26615 20620 1\n1 6901374 25536 17347 2\n2 5635052 24691 17194 3\n3 3542184 24075 17699 4\n4 9378983 24036 18195 5\ntargets: (8456, 3) Index(['uid', 'recent_positive_listens', 'history_positive_listens'], dtype='object')\n uid recent_positive_listens history_positive_listens\n0 100 844.0 2604\n1 200 50.0 286\n2 300 168.0 359\n3 600 1985.0 2423\n4 700 1776.0 6114\nval_pref: (469124, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\ntest_pref: (484776, 3) Index(['uid', 'item_id', 'state'], dtype='object')\n uid item_id state\n0 100 410868 liked\n1 100 732449 liked\n2 100 982903 liked\n3 100 1730273 liked\n4 100 2621343 liked\nval_npz: (8456,) (30000,) (8456, 3750)\ntest_npz: (8456,) (30000,) (8456, 3750)\nProcess Group PGID: 1623\n",
"isError": false
},
"responseMessageId": "d09436ed-fcde-43a5-bcf1-f3c64e6effa7"
}
]
},
{
"id": 7,
"role": "agent",
"timestamp": "2026-09-29T04:20:20.399Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:20.399Z",
"text": "Verifying Data Integrity\n\nI'm currently cross-referencing the user and item counts between the `val_npz` and `test_npz` datasets. My next step is to confirm the exact match of `uids` between the two and then analyze the average number of eligible items per user."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:20.399Z",
"callId": "run_shell_command__call_149412",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\ncounts = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"val eligible count per user: min=\", counts.min(), \"max=\", counts.max(), \"mean=\", counts.mean(), \"median=\", np.median(counts))\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\npacked_test = test_npz[\"packed_eligible\"]\ncounts_test = np.unpackbits(packed_test, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"test eligible count per user: min=\", counts_test.min(), \"max=\", counts_test.max(), \"mean=\", counts_test.mean(), \"median=\", np.median(counts_test))\n'",
"description": "Check distribution of eligible items per user"
},
"recorded": {
"id": "run_shell_command__call_149412",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\ncounts = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"val eligible count per user: min=\", counts.min(), \"max=\", counts.max(), \"mean=\", counts.mean(), \"median=\", np.median(counts))\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\npacked_test = test_npz[\"packed_eligible\"]\ncounts_test = np.unpackbits(packed_test, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"test eligible count per user: min=\", counts_test.min(), \"max=\", counts_test.max(), \"mean=\", counts_test.mean(), \"median=\", np.median(counts_test))\n'",
"description": "Check distribution of eligible items per user"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_149412",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val eligible count per user: min= 24657 max= 30000 mean= 29067.306291390727 median= 29242.0\ntest eligible count per user: min= 24552 max= 30000 mean= 29041.3178807947 median= 29220.0\nProcess Group PGID: 1640\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:21.472Z",
"resultDisplay": "val eligible count per user: min= 24657 max= 30000 mean= 29067.306291390727 median= 29242.0\ntest eligible count per user: min= 24552 max= 30000 mean= 29041.3178807947 median= 29220.0",
"description": "Check distribution of eligible items per user",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:21.473Z",
"content": "\nOutput: val eligible count per user: min= 24657 max= 30000 mean= 29067.306291390727 median= 29242.0\ntest eligible count per user: min= 24552 max= 30000 mean= 29041.3178807947 median= 29220.0\nProcess Group PGID: 1640\n",
"isError": false
},
"responseMessageId": "fbc84270-cc73-400f-a868-fa60220e5799"
}
]
},
{
"id": 8,
"role": "agent",
"timestamp": "2026-09-29T04:20:34.070Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:34.070Z",
"text": "Investigating Eligibility Logic\n\nI'm currently analyzing the eligibility of catalog items for users. It appears a significant majority, around 29,000 out of 30,000, are typically eligible. My next step is to delve into the specific criteria that determine this eligibility, likely involving user listening history and engagement signals."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:34.070Z",
"callId": "run_shell_command__call_362598",
"name": "run_shell_command",
"input": {
"description": "Check why items are ineligible",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\nu0 = uids[0]\nmask0 = np.unpackbits(packed[0], count=len(item_ids), bitorder=\"little\")\nineligible_items = set(item_ids[mask0 == 0])\nprint(f\"User {u0} has {len(ineligible_items)} ineligible items out of {len(item_ids)}\")\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nu0_pref = pref[pref.uid == u0]\nprint(f\"User {u0} pref items: {len(u0_pref)}, in ineligible: {len(set(u0_pref.item_id).intersection(ineligible_items))}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_362598",
"name": "run_shell_command",
"args": {
"description": "Check why items are ineligible",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\nu0 = uids[0]\nmask0 = np.unpackbits(packed[0], count=len(item_ids), bitorder=\"little\")\nineligible_items = set(item_ids[mask0 == 0])\nprint(f\"User {u0} has {len(ineligible_items)} ineligible items out of {len(item_ids)}\")\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nu0_pref = pref[pref.uid == u0]\nprint(f\"User {u0} pref items: {len(u0_pref)}, in ineligible: {len(set(u0_pref.item_id).intersection(ineligible_items))}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_362598",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User 100 has 682 ineligible items out of 30000\nUser 100 pref items: 15, in ineligible: 15\nProcess Group PGID: 1651\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:34.937Z",
"resultDisplay": "User 100 has 682 ineligible items out of 30000\nUser 100 pref items: 15, in ineligible: 15",
"description": "Check why items are ineligible",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:34.937Z",
"content": "\nOutput: User 100 has 682 ineligible items out of 30000\nUser 100 pref items: 15, in ineligible: 15\nProcess Group PGID: 1651\n",
"isError": false
},
"responseMessageId": "e48ee24a-3da6-476f-8014-6a6eaffac2b3"
}
]
},
{
"id": 9,
"role": "agent",
"timestamp": "2026-09-29T04:20:41.638Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:41.638Z",
"text": "Refining Model Logic\n\nI'm currently focusing on the core problem: building the most robust ranking model for a music streaming service's \"Made for You\" shelf. My current efforts are directed towards ensuring that previously interacted-with items are correctly excluded from recommendations, as organic behavior in the immediate subsequent window is paramount for relevance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:20:41.638Z",
"text": "Evaluating Ranking Precision\n\nMy focus has shifted to precisely implementing the grading system for our ranking model's validation. I'm ensuring that \"organic likes\" and substantial track listening (over 80%) are correctly mapped to the highest grade, with a keen eye on accurate DCG calculations and per-user NDCG averaging for robust metric assessment."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:41.638Z",
"callId": "run_shell_command__call_262944",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nimport numpy as np\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
"description": "Check validation labels statistics"
},
"recorded": {
"id": "run_shell_command__call_262944",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nimport numpy as np\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
"description": "Check validation labels statistics"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_262944",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 1669\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:41.665Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'evaluate_public'",
"description": "Check validation labels statistics",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:41.666Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 2, in \nModuleNotFoundError: No module named 'evaluate_public'\nExit Code: 1\nProcess Group PGID: 1669\n",
"isError": false
},
"responseMessageId": "c30fc451-9976-43a2-a529-664046b8bd08"
}
]
},
{
"id": 10,
"role": "agent",
"timestamp": "2026-09-29T04:20:45.193Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:20:45.193Z",
"callId": "run_shell_command__call_285006",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
"description": "Check validation labels statistics with PYTHONPATH"
},
"recorded": {
"id": "run_shell_command__call_285006",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
"description": "Check validation labels statistics with PYTHONPATH"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_285006",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total target users: 8456\nUsers with relevant items in val: 4263\nItems per relevant user: mean=8.86, median=4.0, max=442\nGrade 3 items: 4417, Grade 1 items: 33352\nProcess Group PGID: 1672\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:20:46.432Z",
"resultDisplay": "Total target users: 8456\nUsers with relevant items in val: 4263\nItems per relevant user: mean=8.86, median=4.0, max=442\nGrade 3 items: 4417, Grade 1 items: 33352",
"description": "Check validation labels statistics with PYTHONPATH",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:20:46.433Z",
"content": "\nOutput: Total target users: 8456\nUsers with relevant items in val: 4263\nItems per relevant user: mean=8.86, median=4.0, max=442\nGrade 3 items: 4417, Grade 1 items: 33352\nProcess Group PGID: 1672\n",
"isError": false
},
"responseMessageId": "01d98675-3bbf-4c22-85df-4ff61908842d"
}
]
},
{
"id": 11,
"role": "agent",
"timestamp": "2026-09-29T04:21:04.328Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:04.328Z",
"text": "Testing Popularity Baseline\n\nI'm assessing a simple popularity baseline against our validation set. My focus is on the NDCG@10 metric for users with relevant items, currently standing at 4263 out of 8456 total target users. I'm now calculating its performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:04.328Z",
"callId": "run_shell_command__call_142137",
"name": "run_shell_command",
"input": {
"description": "Compute popularity baseline NDCG@10",
"command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings, ndcg_at_k\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Catalog order\ncat_items = catalog[\"item_id\"].to_numpy()\n# Map item_id to index in val_npz item_ids\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\n# For each user, get top 100 eligible items by catalog popularity\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # which items in cat_cols are eligible?\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Popularity NDCG@10:\", res)\n'"
},
"recorded": {
"id": "run_shell_command__call_142137",
"name": "run_shell_command",
"args": {
"description": "Compute popularity baseline NDCG@10",
"command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings, ndcg_at_k\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Catalog order\ncat_items = catalog[\"item_id\"].to_numpy()\n# Map item_id to index in val_npz item_ids\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\n# For each user, get top 100 eligible items by catalog popularity\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # which items in cat_cols are eligible?\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Popularity NDCG@10:\", res)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_142137",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1692\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:06.328Z",
"resultDisplay": "Popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}",
"description": "Compute popularity baseline NDCG@10",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:06.329Z",
"content": "\nOutput: Popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}\nProcess Group PGID: 1692\n",
"isError": false
},
"responseMessageId": "f228550c-3a14-45f8-a24f-7cb661bf14a0"
}
]
},
{
"id": 12,
"role": "agent",
"timestamp": "2026-09-29T04:21:13.668Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:13.668Z",
"text": "Establishing Baseline Metric\n\nMy initial thought process has focused on establishing a baseline performance metric. I've identified NDCG@10 at 0.01071 as a promising starting point. My next immediate step is to confirm the exact file format and row count requirements for a valid submission."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:13.668Z",
"callId": "run_shell_command__call_190571",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nuids = test_npz[\"uids\"]\nitem_ids = test_npz[\"item_ids\"]\npacked = test_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\ncat_items = catalog[\"item_id\"].to_numpy()\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\nPath(\"/app/solution\").mkdir(parents=True, exist_ok=True)\ndf.to_parquet(\"/app/solution/submission.parquet\", index=False)\n\n# Validate\nvalidated = validate_ranking(df, targets)\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Baseline submission successfully created and validated!\")\n'",
"description": "Create initial baseline submission and validate against test_eligible_candidates"
},
"recorded": {
"id": "run_shell_command__call_190571",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nuids = test_npz[\"uids\"]\nitem_ids = test_npz[\"item_ids\"]\npacked = test_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\ncat_items = catalog[\"item_id\"].to_numpy()\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\nPath(\"/app/solution\").mkdir(parents=True, exist_ok=True)\ndf.to_parquet(\"/app/solution/submission.parquet\", index=False)\n\n# Validate\nvalidated = validate_ranking(df, targets)\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Baseline submission successfully created and validated!\")\n'",
"description": "Create initial baseline submission and validate against test_eligible_candidates"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_190571",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Baseline submission successfully created and validated!\nProcess Group PGID: 1712\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:16.896Z",
"resultDisplay": "Baseline submission successfully created and validated!",
"description": "Create initial baseline submission and validate against test_eligible_candidates",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:16.897Z",
"content": "\nOutput: Baseline submission successfully created and validated!\nProcess Group PGID: 1712\n",
"isError": false
},
"responseMessageId": "7687ff69-8399-4087-8337-0da246951da8"
}
]
},
{
"id": 13,
"role": "agent",
"timestamp": "2026-09-29T04:21:19.226Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:19.226Z",
"callId": "run_shell_command__call_149259",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(df.info())\nprint(df.head())\n'",
"description": "Verify submission.parquet format and row count"
},
"recorded": {
"id": "run_shell_command__call_149259",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(df.info())\nprint(df.head())\n'",
"description": "Verify submission.parquet format and row count"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_149259",
"name": "run_shell_command",
"response": {
"output": "\nOutput: \nRangeIndex: 845600 entries, 0 to 845599\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 uid 845600 non-null int64 \n 1 item_id 845600 non-null uint32\n 2 rank 845600 non-null int64 \ndtypes: int64(2), uint32(1)\nmemory usage: 16.1 MB\nNone\n uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\nProcess Group PGID: 1729\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:19.974Z",
"resultDisplay": "\nRangeIndex: 845600 entries, 0 to 845599\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 uid 845600 non-null int64 \n 1 item_id 845600 non-null uint32\n 2 rank 845600 non-null int64 \ndtypes: int64(2), uint32(1)\nmemory usage: 16.1 MB\nNone\n uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5",
"description": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(df.info())\nprint(df.head())\n'",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:19.976Z",
"content": "\nOutput: \nRangeIndex: 845600 entries, 0 to 845599\nData columns (total 3 columns):\n # Column Non-Null Count Dtype \n--- ------ -------------- ----- \n 0 uid 845600 non-null int64 \n 1 item_id 845600 non-null uint32\n 2 rank 845600 non-null int64 \ndtypes: int64(2), uint32(1)\nmemory usage: 16.1 MB\nNone\n uid item_id rank\n0 100 5862961 1\n1 100 6901374 2\n2 100 5635052 3\n3 100 3542184 4\n4 100 8213481 5\nProcess Group PGID: 1729\n",
"isError": false
},
"responseMessageId": "cf3f2a1a-1ce1-4601-9556-b17654c8d844"
}
]
},
{
"id": 14,
"role": "agent",
"timestamp": "2026-09-29T04:21:27.595Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:27.595Z",
"text": "Ensuring Data Type Compliance\n\nI'm focusing on ensuring type consistency. Specifically, I'm reviewing the `item_id`'s uint32 type against the `int64` cast in `validate_ranking` and plan to ensure it's int64 for `df.to_parquet` compliance. Next, I'll run `python3 /task/evaluate_public.py` with a popularity baseline validation parquet."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:27.595Z",
"callId": "run_shell_command__call_147947",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\n\ncat_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_baseline.parquet\", index=False)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_baseline.parquet",
"description": "Test evaluate_public.py on val_baseline.parquet"
},
"recorded": {
"id": "run_shell_command__call_147947",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\n\ncat_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_baseline.parquet\", index=False)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_baseline.parquet",
"description": "Test evaluate_public.py on val_baseline.parquet"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_147947",
"name": "run_shell_command",
"response": {
"output": "\nOutput: {\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1748\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:32.242Z",
"resultDisplay": "{\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}",
"description": "Test evaluate_public.py on val_baseline.parquet",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:32.242Z",
"content": "\nOutput: {\n \"model\": \"/app/solution/val_baseline.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.01071,\n \"quality_users\": 4263\n }\n}\nProcess Group PGID: 1748\n",
"isError": false
},
"responseMessageId": "899b8e51-9978-45a2-bd21-0f6b4f8a498a"
}
]
},
{
"id": 15,
"role": "agent",
"timestamp": "2026-09-29T04:21:39.546Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:39.546Z",
"text": "Exploring Dataset Potential\n\nMy current focus is on a deep dive into the dataset after confirming the successful execution of `evaluate_public.py`, which yielded a score of 0.01071. I'm now examining the available data files, specifically `train_listens.parquet` (305MB) and `train_likes.parquet`, to understand their structure and content more thoroughly."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:39.546Z",
"callId": "run_shell_command__call_174441",
"name": "run_shell_command",
"input": {
"description": "Inspect train_listens and train_likes",
"command": "python3 -c '\nimport pandas as pd\n\ndf_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\nprint(\"train_listens shape:\", df_listens.shape)\nprint(df_listens.head())\nprint(\"min timestamp:\", df_listens.timestamp.min(), \"max timestamp:\", df_listens.timestamp.max())\nprint(\"is_organic distribution:\\n\", df_listens.is_organic.value_counts())\nprint(\"played_ratio_pct distribution:\\n\", df_listens.played_ratio_pct.describe())\n\ndf_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\nprint(\"train_likes shape:\", df_likes.shape)\nprint(df_likes.head())\nprint(\"min timestamp:\", df_likes.timestamp.min(), \"max timestamp:\", df_likes.timestamp.max())\n'"
},
"recorded": {
"id": "run_shell_command__call_174441",
"name": "run_shell_command",
"args": {
"description": "Inspect train_listens and train_likes",
"command": "python3 -c '\nimport pandas as pd\n\ndf_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\nprint(\"train_listens shape:\", df_listens.shape)\nprint(df_listens.head())\nprint(\"min timestamp:\", df_listens.timestamp.min(), \"max timestamp:\", df_listens.timestamp.max())\nprint(\"is_organic distribution:\\n\", df_listens.is_organic.value_counts())\nprint(\"played_ratio_pct distribution:\\n\", df_listens.played_ratio_pct.describe())\n\ndf_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\nprint(\"train_likes shape:\", df_likes.shape)\nprint(df_likes.head())\nprint(\"min timestamp:\", df_likes.timestamp.min(), \"max timestamp:\", df_likes.timestamp.max())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_174441",
"name": "run_shell_command",
"response": {
"output": "\nOutput: train_listens shape: (43484159, 6)\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\nmin timestamp: 0 max timestamp: 24786795\nis_organic distribution:\n is_organic\n1 22477622\n0 21006537\nName: count, dtype: int64\nplayed_ratio_pct distribution:\n count 4.348416e+07\nmean 6.465021e+01\nstd 4.412183e+01\nmin 0.000000e+00\n25% 7.000000e+00\n50% 1.000000e+02\n75% 1.000000e+02\nmax 1.590000e+02\nName: played_ratio_pct, dtype: float64\ntrain_likes shape: (814831, 4)\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nmin timestamp: 80 max timestamp: 24786790\nProcess Group PGID: 1789\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:21:41.707Z",
"resultDisplay": "train_listens shape: (43484159, 6)\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\nmin timestamp: 0 max timestamp: 24786795\nis_organic distribution:\n is_organic\n1 22477622\n0 21006537\nName: count, dtype: int64\nplayed_ratio_pct distribution:\n count 4.348416e+07\nmean 6.465021e+01\nstd 4.412183e+01\nmin 0.000000e+00\n25% 7.000000e+00\n50% 1.000000e+02\n75% 1.000000e+02\nmax 1.590000e+02\nName: played_ratio_pct, dtype: float64\ntrain_likes shape: (814831, 4)\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nmin timestamp: 80 max timestamp: 24786790",
"description": "Inspect train_listens and train_likes",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:21:41.708Z",
"content": "\nOutput: train_listens shape: (43484159, 6)\n uid timestamp item_id is_organic played_ratio_pct track_length_seconds\n0 100 39420 8326270 0 100 170\n1 100 39420 1441281 0 100 105\n2 100 39625 286361 0 100 185\n3 100 40110 732449 0 100 240\n4 100 40360 3397170 0 46 130\nmin timestamp: 0 max timestamp: 24786795\nis_organic distribution:\n is_organic\n1 22477622\n0 21006537\nName: count, dtype: int64\nplayed_ratio_pct distribution:\n count 4.348416e+07\nmean 6.465021e+01\nstd 4.412183e+01\nmin 0.000000e+00\n25% 7.000000e+00\n50% 1.000000e+02\n75% 1.000000e+02\nmax 1.590000e+02\nName: played_ratio_pct, dtype: float64\ntrain_likes shape: (814831, 4)\n uid timestamp item_id is_organic\n0 100 44755 732449 1\n1 100 1155860 6568592 0\n2 100 1259125 5411243 1\n3 100 1260005 7371186 0\n4 100 1263935 4943655 0\nmin timestamp: 80 max timestamp: 24786790\nProcess Group PGID: 1789\n",
"isError": false
},
"responseMessageId": "5e22d9b0-2fa7-4c7c-822e-b4427f54c51c"
}
]
},
{
"id": 16,
"role": "agent",
"timestamp": "2026-09-29T04:21:46.323Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:21:46.323Z",
"text": "Processing Massive Datasets\n\nI'm currently grappling with the sheer scale of the `train_listens` dataset, which clocks in at an impressive 43.4 million rows. Despite its size, reading it into memory was surprisingly swift thanks to the efficient parquet format, occupying a manageable 305MB. My next step is to investigate the distribution of target users within this substantial data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:21:46.323Z",
"callId": "run_shell_command__call_148141",
"name": "run_shell_command",
"input": {
"description": "Check unique users in train_listens vs target users",
"command": "python3 -c '\nimport pandas as pd\ndf_targets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(df_targets.uid)\nprint(f\"Number of target users: {len(target_uids)}\")\n\n# Sample or check total unique users in train_listens\n# Using pyarrow to read only uid column\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nunique_uids = set(table[\"uid\"].to_pylist())\nprint(f\"Total unique uids in train_listens: {len(unique_uids)}\")\nprint(f\"Are all target uids in train_listens? {len(target_uids.intersection(unique_uids))}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_148141",
"name": "run_shell_command",
"args": {
"description": "Check unique users in train_listens vs target users",
"command": "python3 -c '\nimport pandas as pd\ndf_targets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(df_targets.uid)\nprint(f\"Number of target users: {len(target_uids)}\")\n\n# Sample or check total unique users in train_listens\n# Using pyarrow to read only uid column\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nunique_uids = set(table[\"uid\"].to_pylist())\nprint(f\"Total unique uids in train_listens: {len(unique_uids)}\")\nprint(f\"Are all target uids in train_listens? {len(target_uids.intersection(unique_uids))}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_148141",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Number of target users: 8456\nTotal unique uids in train_listens: 9238\nAre all target uids in train_listens? 8456\nProcess Group PGID: 1809\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:03.561Z",
"resultDisplay": "Number of target users: 8456\nTotal unique uids in train_listens: 9238\nAre all target uids in train_listens? 8456",
"description": "Check unique users in train_listens vs target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:03.562Z",
"content": "\nOutput: Number of target users: 8456\nTotal unique uids in train_listens: 9238\nAre all target uids in train_listens? 8456\nProcess Group PGID: 1809\n",
"isError": false
},
"responseMessageId": "3671cb40-94a3-4642-b67f-062fbf9ffbd6"
}
]
},
{
"id": 17,
"role": "agent",
"timestamp": "2026-09-29T04:22:06.856Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:06.856Z",
"text": "Analyzing User Data\n\nI'm currently examining the unique items within my training data, `train_listens`, and comparing them against the 30,000 items in the catalog. My focus is on identifying how many unique items are present in `train_listens` and which specific items are candidates for further analysis."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:06.856Z",
"callId": "run_shell_command__call_363778",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\nprint(\"Catalog items:\", len(cat_items))\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"item_id\"])\ntrain_items = set(table[\"item_id\"].to_pylist())\nprint(\"Train items:\", len(train_items))\nprint(\"Catalog items in train items:\", len(cat_items.intersection(train_items)))\n'",
"description": "Check items overlap between catalog and train_listens"
},
"recorded": {
"id": "run_shell_command__call_363778",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\nprint(\"Catalog items:\", len(cat_items))\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"item_id\"])\ntrain_items = set(table[\"item_id\"].to_pylist())\nprint(\"Train items:\", len(train_items))\nprint(\"Catalog items in train items:\", len(cat_items.intersection(train_items)))\n'",
"description": "Check items overlap between catalog and train_listens"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_363778",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Catalog items: 30000\nTrain items: 845395\nCatalog items in train items: 30000\nProcess Group PGID: 1830\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:25.681Z",
"resultDisplay": "Catalog items: 30000\nTrain items: 845395\nCatalog items in train items: 30000",
"description": "Check items overlap between catalog and train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:25.682Z",
"content": "\nOutput: Catalog items: 30000\nTrain items: 845395\nCatalog items in train items: 30000\nProcess Group PGID: 1830\n",
"isError": false
},
"responseMessageId": "26679a53-6f85-4943-a193-9647ff30b100"
}
]
},
{
"id": 18,
"role": "agent",
"timestamp": "2026-09-29T04:22:30.293Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:30.293Z",
"text": "Verifying Timestamp Boundaries\n\nAll 30,000 catalog items are now incorporated into the training set. My current focus is on scrutinizing the date range and timestamps, specifically noting that the training data concludes at timestamp 24786800. I'm investigating the units of this timestamp."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:30.293Z",
"callId": "run_shell_command__call_204106",
"name": "run_shell_command",
"input": {
"description": "Verify timestamps and units of val_listens",
"command": "python3 -c '\nimport pandas as pd\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(\"val_listens shape:\", val_listens.shape)\nprint(\"val_listens min ts:\", val_listens.timestamp.min(), \"max ts:\", val_listens.timestamp.max())\nprint(\"difference:\", val_listens.timestamp.max() - val_listens.timestamp.min())\nprint(\"gap from train end (24786800):\", val_listens.timestamp.min() - 24786800)\n'"
},
"recorded": {
"id": "run_shell_command__call_204106",
"name": "run_shell_command",
"args": {
"description": "Verify timestamps and units of val_listens",
"command": "python3 -c '\nimport pandas as pd\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(\"val_listens shape:\", val_listens.shape)\nprint(\"val_listens min ts:\", val_listens.timestamp.min(), \"max ts:\", val_listens.timestamp.max())\nprint(\"difference:\", val_listens.timestamp.max() - val_listens.timestamp.min())\nprint(\"gap from train end (24786800):\", val_listens.timestamp.min() - 24786800)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_204106",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val_listens shape: (1501697, 6)\nval_listens min ts: 24788600 max ts: 25393395\ndifference: 604795\ngap from train end (24786800): 1800\nProcess Group PGID: 1857\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:31.066Z",
"resultDisplay": "val_listens shape: (1501697, 6)\nval_listens min ts: 24788600 max ts: 25393395\ndifference: 604795\ngap from train end (24786800): 1800",
"description": "Verify timestamps and units of val_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:31.066Z",
"content": "\nOutput: val_listens shape: (1501697, 6)\nval_listens min ts: 24788600 max ts: 25393395\ndifference: 604795\ngap from train end (24786800): 1800\nProcess Group PGID: 1857\n",
"isError": false
},
"responseMessageId": "a2312407-8433-4c37-8c75-9af7e0bfe874"
}
]
},
{
"id": 19,
"role": "agent",
"timestamp": "2026-09-29T04:22:36.125Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:36.125Z",
"text": "Analyzing Data Gaps\n\nI'm currently examining the `val_listens` dataset, noting its substantial size over seven days and its specific start and end times. I'm also paying close attention to the defined gaps between training and testing windows. The instruction regarding distinct roles for public windows is also a key focus."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:36.125Z",
"callId": "run_shell_command__call_144982",
"name": "run_shell_command",
"input": {
"description": "Check values in validation preference state",
"command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(pref[\"state\"].value_counts())\n'"
},
"recorded": {
"id": "run_shell_command__call_144982",
"name": "run_shell_command",
"args": {
"description": "Check values in validation preference state",
"command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(pref[\"state\"].value_counts())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_144982",
"name": "run_shell_command",
"response": {
"output": "\nOutput: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nProcess Group PGID: 1877\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:36.886Z",
"resultDisplay": "state\nliked 418435\ndisliked 50689\nName: count, dtype: int64",
"description": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(pref[\"state\"].value_counts())\n'",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:36.886Z",
"content": "\nOutput: state\nliked 418435\ndisliked 50689\nName: count, dtype: int64\nProcess Group PGID: 1877\n",
"isError": false
},
"responseMessageId": "fc0946f4-c71a-4f8e-a738-d425a70c8068"
}
]
},
{
"id": 20,
"role": "agent",
"timestamp": "2026-09-29T04:22:45.020Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:45.020Z",
"text": "Analyzing User Interaction Data\n\nI'm currently investigating user interactions within the validation phase, specifically focusing on whether these interactions overlap with tracks previously engaged during training. My next step is to verify the extent of this overlap."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:22:45.020Z",
"text": "Investigating Validation Item Eligibility\n\nI'm digging into the implications of an item being \"eligible\" in validation. This means I need to clarify what triggers eligibility and how it relates to past interactions. My focus is on understanding the criteria for an item's inclusion in the validation set, especially if it was already encountered during training."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:45.020Z",
"callId": "run_shell_command__call_192162",
"name": "run_shell_command",
"input": {
"description": "Check if graded items appeared in user train_listens",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Let us check train_listens for these (uid, item) pairs\ntrain_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\n# sample 1000 graded pairs\npairs = []\nfor u, items in list(graded.items())[:500]:\n for it in items:\n pairs.append((u, it))\nsample_pairs = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total sample graded pairs: {len(sample_pairs)}\")\nmerged = sample_pairs.merge(train_listens[[\"uid\", \"item_id\", \"timestamp\"]], on=[\"uid\", \"item_id\"])\nprint(f\"Sample pairs present in train_listens: {len(merged)}\")\nif len(merged) > 0:\n print(merged.head())\n'"
},
"recorded": {
"id": "run_shell_command__call_192162",
"name": "run_shell_command",
"args": {
"description": "Check if graded items appeared in user train_listens",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Let us check train_listens for these (uid, item) pairs\ntrain_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\n# sample 1000 graded pairs\npairs = []\nfor u, items in list(graded.items())[:500]:\n for it in items:\n pairs.append((u, it))\nsample_pairs = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total sample graded pairs: {len(sample_pairs)}\")\nmerged = sample_pairs.merge(train_listens[[\"uid\", \"item_id\", \"timestamp\"]], on=[\"uid\", \"item_id\"])\nprint(f\"Sample pairs present in train_listens: {len(merged)}\")\nif len(merged) > 0:\n print(merged.head())\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_192162",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total sample graded pairs: 5013\nSample pairs present in train_listens: 0\nProcess Group PGID: 1894\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:22:51.071Z",
"resultDisplay": "Total sample graded pairs: 5013\nSample pairs present in train_listens: 0",
"description": "Check if graded items appeared in user train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:22:51.072Z",
"content": "\nOutput: Total sample graded pairs: 5013\nSample pairs present in train_listens: 0\nProcess Group PGID: 1894\n",
"isError": false
},
"responseMessageId": "dd319bd0-bf9a-4a4c-9db3-8d6af5a045d7"
}
]
},
{
"id": 21,
"role": "agent",
"timestamp": "2026-09-29T04:22:55.241Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:22:55.241Z",
"callId": "run_shell_command__call_347466",
"name": "run_shell_command",
"input": {
"description": "Check overlap across all graded pairs and train_listens",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\nimport pyarrow.parquet as pq\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Collect all (uid, item_id) in graded\npairs = []\nfor u, items in graded.items():\n for it in items:\n pairs.append((u, it))\ngraded_df = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total graded pairs: {len(graded_df)}\")\n\n# Check train_listens\nt_listens = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntrain_pairs = set(zip(t_listens[\"uid\"].to_pylist(), t_listens[\"item_id\"].to_pylist()))\noverlap = sum(1 for row in graded_df.itertuples(index=False) if (row.uid, row.item_id) in train_pairs)\nprint(f\"Overlap between graded pairs and train_listens: {overlap}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_347466",
"name": "run_shell_command",
"args": {
"description": "Check overlap across all graded pairs and train_listens",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\nimport pyarrow.parquet as pq\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Collect all (uid, item_id) in graded\npairs = []\nfor u, items in graded.items():\n for it in items:\n pairs.append((u, it))\ngraded_df = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total graded pairs: {len(graded_df)}\")\n\n# Check train_listens\nt_listens = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntrain_pairs = set(zip(t_listens[\"uid\"].to_pylist(), t_listens[\"item_id\"].to_pylist()))\noverlap = sum(1 for row in graded_df.itertuples(index=False) if (row.uid, row.item_id) in train_pairs)\nprint(f\"Overlap between graded pairs and train_listens: {overlap}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_347466",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total graded pairs: 37769\nOverlap between graded pairs and train_listens: 0\nProcess Group PGID: 1914\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:42.049Z",
"resultDisplay": "Total graded pairs: 37769\nOverlap between graded pairs and train_listens: 0",
"description": "Check overlap across all graded pairs and train_listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:23:42.049Z",
"content": "\nOutput: Total graded pairs: 37769\nOverlap between graded pairs and train_listens: 0\nProcess Group PGID: 1914\n",
"isError": false
},
"responseMessageId": "74e2b040-dcff-432a-8c8e-0bb78d46e8d9"
}
]
},
{
"id": 22,
"role": "agent",
"timestamp": "2026-09-29T04:23:52.724Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:52.724Z",
"text": "Refining Discovery Strategy\n\nI've confirmed a crucial detail: only items the user hasn't encountered during training are considered for discovery. This ensures a truly fresh and exploratory experience, prioritizing novel recommendations."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:52.724Z",
"text": "Filtering Candidate Items\n\nMy focus has shifted to refining how candidate items are filtered. I've confirmed that the system now excludes items already consumed or explicitly liked by the user. This ensures the discovery process prioritizes genuinely new content for each user."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:52.724Z",
"text": "Exploring Item-Item Similarity\n\nI'm investigating the potential of item-item collaborative filtering for discovery, specifically how to identify items with the highest similarity that the user hasn't encountered. I'm also considering efficient ways to handle a large item catalog, possibly through sparse representations or low-rank factorization to manage memory constraints."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:52.724Z",
"callId": "update_topic__call_265638",
"name": "update_topic",
"input": {
"title": "Researching Collaborative Filtering & Recency Models",
"summary": "Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.",
"strategic_intent": "Analyze user interaction patterns, recency, and test collaborative filtering methods."
},
"recorded": {
"id": "update_topic__call_265638",
"name": "update_topic",
"args": {
"title": "Researching Collaborative Filtering & Recency Models",
"summary": "Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.",
"strategic_intent": "Analyze user interaction patterns, recency, and test collaborative filtering methods."
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_265638",
"name": "update_topic",
"response": {
"output": "Current topic: \"Researching Collaborative Filtering & Recency Models\"\nTopic summary: Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.\n\nStrategic Intent: Analyze user interaction patterns, recency, and test collaborative filtering methods."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:23:52.726Z",
"resultDisplay": "## 📂 Topic: **Researching Collaborative Filtering & Recency Models**\n\n**Summary:**\nCompleted initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.\n\n> [!STRATEGY]\n> **Intent:** Analyze user interaction patterns, recency, and test collaborative filtering methods.",
"description": "Update topic to: \"Researching Collaborative Filtering & Recency Models\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T04:23:52.726Z",
"content": "Current topic: \"Researching Collaborative Filtering & Recency Models\"\nTopic summary: Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.\n\nStrategic Intent: Analyze user interaction patterns, recency, and test collaborative filtering methods.",
"isError": false
},
"responseMessageId": "8ba233ea-db72-4451-b848-0842191fbd7a"
}
]
},
{
"id": 23,
"role": "agent",
"timestamp": "2026-09-29T04:23:58.664Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:23:58.664Z",
"text": "Analyzing Recent Interactions\n\nI'm currently exploring recent interactions, specifically aiming to script a Python test. My focus is on analyzing the temporal distribution of events within `train_listens` and `train_likes`. I'll also investigate processing speed by filtering `train_listens` by target users and timestamps."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:23:58.664Z",
"callId": "run_shell_command__call_319421",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets_frame.uid.astype(int))\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\ndays30 = cutoff - 30 * 86400\ndays14 = cutoff - 14 * 86400\ndays7 = cutoff - 7 * 86400\n\nprint(f\"Total rows: {len(listens)}\")\nt_in_target = listens.uid.isin(target_uids)\nprint(f\"Rows for target users: {t_in_target.sum()} ({t_in_target.mean():.1%})\")\nprint(f\"Rows in last 90 days: {(listens.timestamp >= days90).sum()}\")\nprint(f\"Rows in last 30 days: {(listens.timestamp >= days30).sum()}\")\nprint(f\"Rows in last 14 days: {(listens.timestamp >= days14).sum()}\")\nprint(f\"Rows in last 7 days: {(listens.timestamp >= days7).sum()}\")\n'",
"description": "Analyze rows by time window and target users"
},
"recorded": {
"id": "run_shell_command__call_319421",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets_frame.uid.astype(int))\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\ndays30 = cutoff - 30 * 86400\ndays14 = cutoff - 14 * 86400\ndays7 = cutoff - 7 * 86400\n\nprint(f\"Total rows: {len(listens)}\")\nt_in_target = listens.uid.isin(target_uids)\nprint(f\"Rows for target users: {t_in_target.sum()} ({t_in_target.mean():.1%})\")\nprint(f\"Rows in last 90 days: {(listens.timestamp >= days90).sum()}\")\nprint(f\"Rows in last 30 days: {(listens.timestamp >= days30).sum()}\")\nprint(f\"Rows in last 14 days: {(listens.timestamp >= days14).sum()}\")\nprint(f\"Rows in last 7 days: {(listens.timestamp >= days7).sum()}\")\n'",
"description": "Analyze rows by time window and target users"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_319421",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Total rows: 43484159\nRows for target users: 43222297 (99.4%)\nRows in last 90 days: 16769274\nRows in last 30 days: 6126103\nRows in last 14 days: 2920227\nRows in last 7 days: 1491699\nProcess Group PGID: 1944\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:24:02.841Z",
"resultDisplay": "Total rows: 43484159\nRows for target users: 43222297 (99.4%)\nRows in last 90 days: 16769274\nRows in last 30 days: 6126103\nRows in last 14 days: 2920227\nRows in last 7 days: 1491699",
"description": "Analyze rows by time window and target users",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:24:02.842Z",
"content": "\nOutput: Total rows: 43484159\nRows for target users: 43222297 (99.4%)\nRows in last 90 days: 16769274\nRows in last 30 days: 6126103\nRows in last 14 days: 2920227\nRows in last 7 days: 1491699\nProcess Group PGID: 1944\n",
"isError": false
},
"responseMessageId": "fcca8a6f-3fff-4708-ab56-b27a9fac9d42"
}
]
},
{
"id": 24,
"role": "agent",
"timestamp": "2026-09-29T04:24:45.876Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:45.875Z",
"text": "Analyzing Listen Data\n\nMy current focus is on understanding user listening habits. I'm reviewing recent data, particularly the last 90 and 30 days, noting the significant volume of activity. I'm investigating which specific items users are engaging with and if these align with our training data."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:24:45.876Z",
"callId": "run_shell_command__call_202292",
"name": "run_shell_command",
"input": {
"description": "Check fraction of recent listens in catalog",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"timestamp\", \"item_id\"])\nrecent = listens[listens.timestamp >= days90]\nin_cat = recent.item_id.isin(cat_items)\nprint(f\"Recent listens in catalog: {in_cat.sum()} / {len(recent)} ({in_cat.mean():.1%})\")\n'"
},
"recorded": {
"id": "run_shell_command__call_202292",
"name": "run_shell_command",
"args": {
"description": "Check fraction of recent listens in catalog",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"timestamp\", \"item_id\"])\nrecent = listens[listens.timestamp >= days90]\nin_cat = recent.item_id.isin(cat_items)\nprint(f\"Recent listens in catalog: {in_cat.sum()} / {len(recent)} ({in_cat.mean():.1%})\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_202292",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Recent listens in catalog: 12117965 / 16769274 (72.3%)\nProcess Group PGID: 1965\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:24:48.455Z",
"resultDisplay": "Recent listens in catalog: 12117965 / 16769274 (72.3%)",
"description": "Check fraction of recent listens in catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:24:48.456Z",
"content": "\nOutput: Recent listens in catalog: 12117965 / 16769274 (72.3%)\nProcess Group PGID: 1965\n",
"isError": false
},
"responseMessageId": "407da2d4-ea37-4257-ab03-2e956270d54b"
}
]
},
{
"id": 25,
"role": "agent",
"timestamp": "2026-09-29T04:24:52.863Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:24:52.863Z",
"text": "Analyzing Catalog Performance\n\nMy recent analysis reveals a strong focus on the candidate catalog, with over 72% of all listens originating from it. I'm now shifting to examine the `train_likes` and `validation_preference_state` datasets, with `train_likes` already showing a substantial 814,831 rows."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:24:52.863Z",
"callId": "run_shell_command__call_235070",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nin_cat = pref.item_id.isin(cat_items)\nprint(\"Pref items in catalog:\", in_cat.sum(), f\"({in_cat.mean():.1%})\")\nprint(\"Unique items in pref:\", pref.item_id.nunique())\n'",
"description": "Check pref items in catalog"
},
"recorded": {
"id": "run_shell_command__call_235070",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nin_cat = pref.item_id.isin(cat_items)\nprint(\"Pref items in catalog:\", in_cat.sum(), f\"({in_cat.mean():.1%})\")\nprint(\"Unique items in pref:\", pref.item_id.nunique())\n'",
"description": "Check pref items in catalog"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_235070",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Pref items in catalog: 469124 (100.0%)\nUnique items in pref: 28814\nProcess Group PGID: 1992\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:24:53.691Z",
"resultDisplay": "Pref items in catalog: 469124 (100.0%)\nUnique items in pref: 28814",
"description": "Check pref items in catalog",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:24:53.692Z",
"content": "\nOutput: Pref items in catalog: 469124 (100.0%)\nUnique items in pref: 28814\nProcess Group PGID: 1992\n",
"isError": false
},
"responseMessageId": "fa02d281-40f5-4691-b91b-b8b969aa27f6"
}
]
},
{
"id": 26,
"role": "agent",
"timestamp": "2026-09-29T04:25:00.908Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:00.908Z",
"text": "Completing Preference State\n\nAll active preference state items are now present in the candidate catalog. I'm currently verifying the extent to which catalog items are represented within the preference state and exploring the possibility of users unliking items after initial likes."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:00.908Z",
"text": "Refining Candidate Generation\n\nI've confirmed that `validation_preference_state` precisely captures users' current explicit likes and dislikes. Coupled with `train_listens` detailing listening habits, I'm now defining the discovery problem: recommending 100 new catalog tracks per user that they'll organically engage with or like within a week. My current focus is on how item popularity, both globally and recently, influences this prediction."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:00.908Z",
"callId": "run_shell_command__call_422538",
"name": "run_shell_command",
"input": {
"command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Sort catalog by organic_positive_listens descending\ncat_sorted = catalog.sort_values(\"organic_positive_listens\", ascending=False)\ncat_items = cat_sorted[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Organic popularity NDCG@10:\", res)\n'",
"description": "Check organic popularity baseline NDCG@10"
},
"recorded": {
"id": "run_shell_command__call_422538",
"name": "run_shell_command",
"args": {
"command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Sort catalog by organic_positive_listens descending\ncat_sorted = catalog.sort_values(\"organic_positive_listens\", ascending=False)\ncat_items = cat_sorted[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Organic popularity NDCG@10:\", res)\n'",
"description": "Check organic popularity baseline NDCG@10"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_422538",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 2009\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:02.963Z",
"resultDisplay": "Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}",
"description": "Check organic popularity baseline NDCG@10",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:02.964Z",
"content": "\nOutput: Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}\nProcess Group PGID: 2009\n",
"isError": false
},
"responseMessageId": "745d9a6c-3dd1-4581-8b81-59f10f0fe14f"
}
]
},
{
"id": 27,
"role": "agent",
"timestamp": "2026-09-29T04:25:09.581Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:09.581Z",
"text": "Exploring Personalization\n\nI'm now considering the significant impact of personalized collaborative filtering. Given the strong influence of individual user tastes in music, this approach is crucial and likely to yield substantial gains beyond just global popularity metrics."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:09.581Z",
"text": "Evaluating Collaborative Filtering\n\nI'm currently exploring the feasibility of implementing item-item collaborative filtering. My primary concern is the sparsity of the user-item interaction matrix given the scale of users and the catalog, which could impact the effectiveness of similarity calculations."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:09.581Z",
"callId": "run_shell_command__call_264537",
"name": "run_shell_command",
"input": {
"description": "Check unique pairs in recent listens",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id)}\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid)}\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(uid_to_idx))]\n\nprint(f\"Recent catalog listens for targets: {len(recent)}\")\nprint(f\"Unique (uid, item) pairs: {recent.groupby(['uid', 'item_id']).ngroups}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_264537",
"name": "run_shell_command",
"args": {
"description": "Check unique pairs in recent listens",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id)}\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid)}\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(uid_to_idx))]\n\nprint(f\"Recent catalog listens for targets: {len(recent)}\")\nprint(f\"Unique (uid, item) pairs: {recent.groupby(['uid', 'item_id']).ngroups}\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_264537",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 20, in \nNameError: name 'uid' is not defined. Did you mean: 'id'?\nRecent catalog listens for targets: 12099707\nExit Code: 1\nProcess Group PGID: 2029\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:17.302Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 20, in \nNameError: name 'uid' is not defined. Did you mean: 'id'?\nRecent catalog listens for targets: 12099707",
"description": "Check unique pairs in recent listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:17.303Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 20, in \nNameError: name 'uid' is not defined. Did you mean: 'id'?\nRecent catalog listens for targets: 12099707\nExit Code: 1\nProcess Group PGID: 2029\n",
"isError": false
},
"responseMessageId": "855c6783-8b2e-4b79-b10e-f008389af168"
}
]
},
{
"id": 28,
"role": "agent",
"timestamp": "2026-09-29T04:25:20.799Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:20.799Z",
"callId": "run_shell_command__call_367656",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport pandas as pd\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(target_uids))]\n\nn_unique = len(recent.drop_duplicates(subset=[\"uid\", \"item_id\"]))\nprint(f\"Unique (uid, item) pairs in last 90 days: {n_unique}\")\n'",
"description": "Check unique pairs in recent listens without error"
},
"recorded": {
"id": "run_shell_command__call_367656",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport pandas as pd\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(target_uids))]\n\nn_unique = len(recent.drop_duplicates(subset=[\"uid\", \"item_id\"]))\nprint(f\"Unique (uid, item) pairs in last 90 days: {n_unique}\")\n'",
"description": "Check unique pairs in recent listens without error"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_367656",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Unique (uid, item) pairs in last 90 days: 4306717\nProcess Group PGID: 2057\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:28.873Z",
"resultDisplay": "Unique (uid, item) pairs in last 90 days: 4306717",
"description": "Check unique pairs in recent listens without error",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:28.874Z",
"content": "\nOutput: Unique (uid, item) pairs in last 90 days: 4306717\nProcess Group PGID: 2057\n",
"isError": false
},
"responseMessageId": "40da5593-be6e-4a4c-8ad9-cada1b3ff05e"
}
]
},
{
"id": 29,
"role": "agent",
"timestamp": "2026-09-29T04:25:34.157Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:25:34.157Z",
"text": "Exploring Item-Item Similarity\n\nI'm currently investigating item-item similarity approaches, specifically cosine and BM25, to understand potential patterns. This analysis stems from the observed user engagement data showing an average of around 500 items per user, with 4.3 million unique pairs across 8,456 users and 30,000 items."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:25:34.157Z",
"callId": "run_shell_command__call_150559",
"name": "run_shell_command",
"input": {
"description": "Benchmark sparse matrix multiplication speed",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# test sparse dot\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nRtR = R.T @ R\nprint(f\"RtR shape: {RtR.shape}, nnz: {RtR.nnz}, time: {time.time() - t0:.2f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_150559",
"name": "run_shell_command",
"args": {
"description": "Benchmark sparse matrix multiplication speed",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# test sparse dot\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nRtR = R.T @ R\nprint(f\"RtR shape: {RtR.shape}, nnz: {RtR.nnz}, time: {time.time() - t0:.2f}s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_150559",
"name": "run_shell_command",
"response": {
"output": "\nOutput: RtR shape: (30000, 30000), nnz: 817434538, time: 21.02s\nProcess Group PGID: 2078\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:25:56.208Z",
"resultDisplay": "RtR shape: (30000, 30000), nnz: 817434538, time: 21.02s",
"description": "Benchmark sparse matrix multiplication speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:25:56.208Z",
"content": "\nOutput: RtR shape: (30000, 30000), nnz: 817434538, time: 21.02s\nProcess Group PGID: 2078\n",
"isError": false
},
"responseMessageId": "46a96767-404a-4cb2-a159-0608782a1d57"
}
]
},
{
"id": 30,
"role": "agent",
"timestamp": "2026-09-29T04:26:01.291Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:01.291Z",
"text": "Exploring Real-World Data\n\nMy recent experiments with uniformly random data yielded a high density of nonzeros, as expected due to the lack of inherent structure. I'm now shifting focus to real-world datasets, anticipating sparsity and community-driven clustering. The next step involves investigating the feasibility of applying SVD."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:01.291Z",
"callId": "run_shell_command__call_185540",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"TruncatedSVD k=64 time: {time.time() - t0:.2f}s, U shape: {U.shape}, V shape: {V.shape}\")\n'",
"description": "Benchmark TruncatedSVD speed"
},
"recorded": {
"id": "run_shell_command__call_185540",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"TruncatedSVD k=64 time: {time.time() - t0:.2f}s, U shape: {U.shape}, V shape: {V.shape}\")\n'",
"description": "Benchmark TruncatedSVD speed"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_185540",
"name": "run_shell_command",
"response": {
"output": "\nOutput: TruncatedSVD k=64 time: 2.12s, U shape: (8456, 64), V shape: (64, 30000)\nProcess Group PGID: 2101\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:26:05.177Z",
"resultDisplay": "TruncatedSVD k=64 time: 2.12s, U shape: (8456, 64), V shape: (64, 30000)",
"description": "Benchmark TruncatedSVD speed",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:26:05.178Z",
"content": "\nOutput: TruncatedSVD k=64 time: 2.12s, U shape: (8456, 64), V shape: (64, 30000)\nProcess Group PGID: 2101\n",
"isError": false
},
"responseMessageId": "a5d8e592-a862-480c-bb34-5dbddf797632"
}
]
},
{
"id": 31,
"role": "agent",
"timestamp": "2026-09-29T04:26:10.099Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:10.099Z",
"text": "Achieving Efficient SVD\n\nI'm pleased to report that a TruncatedSVD with k=64 completed in a swift 2.12 seconds. This yields $U$ (8456x64) and $V$ (64x30000), enabling rapid calculation of all predicted scores ($\\hat{Y}$) via $U @ V$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:10.099Z",
"callId": "run_shell_command__call_227417",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\n\nU = np.random.randn(8456, 64).astype(np.float32)\nV = np.random.randn(64, 30000).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V\nprint(f\"Matrix multiply time: {time.time() - t0:.3f}s, scores shape: {scores.shape}\")\n\nt0 = time.time()\n# test argpartition for top 100\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nprint(f\"Top 100 argpartition time: {time.time() - t0:.3f}s\")\n'",
"description": "Benchmark score calculation and top100 argpartition"
},
"recorded": {
"id": "run_shell_command__call_227417",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\n\nU = np.random.randn(8456, 64).astype(np.float32)\nV = np.random.randn(64, 30000).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V\nprint(f\"Matrix multiply time: {time.time() - t0:.3f}s, scores shape: {scores.shape}\")\n\nt0 = time.time()\n# test argpartition for top 100\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nprint(f\"Top 100 argpartition time: {time.time() - t0:.3f}s\")\n'",
"description": "Benchmark score calculation and top100 argpartition"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_227417",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Matrix multiply time: 0.204s, scores shape: (8456, 30000)\nTop 100 argpartition time: 0.968s\nProcess Group PGID: 2119\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:26:11.605Z",
"resultDisplay": "Matrix multiply time: 0.204s, scores shape: (8456, 30000)\nTop 100 argpartition time: 0.968s",
"description": "Benchmark score calculation and top100 argpartition",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:26:12.938Z",
"content": "\nOutput: Matrix multiply time: 0.204s, scores shape: (8456, 30000)\nTop 100 argpartition time: 0.968s\nProcess Group PGID: 2119\n",
"isError": false
},
"responseMessageId": "b374f59f-37bd-445d-adc4-349f93a2a3b3"
}
]
},
{
"id": 32,
"role": "agent",
"timestamp": "2026-09-29T04:26:20.948Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:20.948Z",
"text": "Achieving Rapid Iteration\n\nI'm thrilled by the performance gains; the entire evaluation cycle now takes mere seconds. This speed allows for incredibly swift iteration, enabling experimentation with various weighting schemes, recency half-lives, and interaction models."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:20.948Z",
"text": "Exploring Feature Dimensions\n\nI'm now considering a broader spectrum of signal types, including explicit likes and implicit completion rates. I'm also evaluating different dimensionality reduction techniques and normalization strategies, aiming to integrate these with popularity biases for a more robust hybrid approach. Direct evaluation via `evaluate_public.py` is key."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:20.948Z",
"callId": "run_shell_command__call_264899",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# test 43M nonzeros\nN, M = 8456, 845000\nnnz = 20000000 # test 20M\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nprint(f\"TruncatedSVD 20M nnz time: {time.time() - t0:.2f}s\")\n'",
"description": "Benchmark TruncatedSVD on large sparse matrix"
},
"recorded": {
"id": "run_shell_command__call_264899",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# test 43M nonzeros\nN, M = 8456, 845000\nnnz = 20000000 # test 20M\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nprint(f\"TruncatedSVD 20M nnz time: {time.time() - t0:.2f}s\")\n'",
"description": "Benchmark TruncatedSVD on large sparse matrix"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_264899",
"name": "run_shell_command",
"response": {
"output": "\nOutput: TruncatedSVD 20M nnz time: 25.17s\nProcess Group PGID: 2129\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:26:48.966Z",
"resultDisplay": "TruncatedSVD 20M nnz time: 25.17s",
"description": "Benchmark TruncatedSVD on large sparse matrix",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:26:48.967Z",
"content": "\nOutput: TruncatedSVD 20M nnz time: 25.17s\nProcess Group PGID: 2129\n",
"isError": false
},
"responseMessageId": "4427eed6-72ff-4a5a-8a19-fda8be0fd24b"
}
]
},
{
"id": 33,
"role": "agent",
"timestamp": "2026-09-29T04:26:53.304Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:53.304Z",
"text": "Evaluating Candidate Performance\n\nMy recent focus has been on understanding the performance implications of candidate item selection. I've noted that processing 20 million non-zero entries took 25 seconds. I'm now analyzing the candidate items, recognizing that our ranking operations are effectively confined to a 30,000-item catalog, as the test and validation sets exclusively draw from this subset."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:53.304Z",
"callId": "run_shell_command__call_214485",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"cat len:\", len(cat))\nprint(\"val len:\", len(val_npz[\"item_ids\"]))\nprint(\"test len:\", len(test_npz[\"item_ids\"]))\nprint(\"val == test item_ids:\", np.array_equal(val_npz[\"item_ids\"], test_npz[\"item_ids\"]))\nprint(\"cat items == set(val item_ids):\", set(cat.item_id) == set(val_npz[\"item_ids\"]))\n'",
"description": "Verify candidate items alignment"
},
"recorded": {
"id": "run_shell_command__call_214485",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"cat len:\", len(cat))\nprint(\"val len:\", len(val_npz[\"item_ids\"]))\nprint(\"test len:\", len(test_npz[\"item_ids\"]))\nprint(\"val == test item_ids:\", np.array_equal(val_npz[\"item_ids\"], test_npz[\"item_ids\"]))\nprint(\"cat items == set(val item_ids):\", set(cat.item_id) == set(val_npz[\"item_ids\"]))\n'",
"description": "Verify candidate items alignment"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_214485",
"name": "run_shell_command",
"response": {
"output": "\nOutput: cat len: 30000\nval len: 30000\ntest len: 30000\nval == test item_ids: True\ncat items == set(val item_ids): True\nProcess Group PGID: 2147\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:26:54.065Z",
"resultDisplay": "cat len: 30000\nval len: 30000\ntest len: 30000\nval == test item_ids: True\ncat items == set(val item_ids): True",
"description": "Verify candidate items alignment",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:26:54.066Z",
"content": "\nOutput: cat len: 30000\nval len: 30000\ntest len: 30000\nval == test item_ids: True\ncat items == set(val item_ids): True\nProcess Group PGID: 2147\n",
"isError": false
},
"responseMessageId": "41756678-809b-4790-9bb8-202729d5a0cd"
}
]
},
{
"id": 34,
"role": "agent",
"timestamp": "2026-09-29T04:26:57.552Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:26:57.552Z",
"text": "Confirming Data Integrity\n\nI've confirmed that the `item_ids` in both `val_npz` and `test_npz` perfectly match the 30,000 items listed in `candidate_catalog.parquet`. Similarly, the `uids` across `val_npz`, `test_npz`, and `target_users` are identical. My next step is to verify the order of these `uids`."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:26:57.552Z",
"callId": "run_shell_command__call_204376",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"val == test uids:\", np.array_equal(val_npz[\"uids\"], test_npz[\"uids\"]))\nprint(\"val == targets uids:\", np.array_equal(val_npz[\"uids\"], targets[\"uid\"].to_numpy()))\n'",
"description": "Verify target uids alignment"
},
"recorded": {
"id": "run_shell_command__call_204376",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"val == test uids:\", np.array_equal(val_npz[\"uids\"], test_npz[\"uids\"]))\nprint(\"val == targets uids:\", np.array_equal(val_npz[\"uids\"], targets[\"uid\"].to_numpy()))\n'",
"description": "Verify target uids alignment"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_204376",
"name": "run_shell_command",
"response": {
"output": "\nOutput: val == test uids: True\nval == targets uids: True\nProcess Group PGID: 2164\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:26:58.297Z",
"resultDisplay": "val == test uids: True\nval == targets uids: True",
"description": "Verify target uids alignment",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:26:58.298Z",
"content": "\nOutput: val == test uids: True\nval == targets uids: True\nProcess Group PGID: 2164\n",
"isError": false
},
"responseMessageId": "9b7f003d-d098-4b23-8421-64ca9599cceb"
}
]
},
{
"id": 35,
"role": "agent",
"timestamp": "2026-09-29T04:27:02.087Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:02.087Z",
"text": "Confirming Data Alignment\n\nI've confirmed that all rows and columns across `val_npz`, `test_npz`, `target_users`, and `candidate_catalog` are perfectly aligned. The user IDs (`uids`) count 8456, and item IDs (`item_ids`) are 30000. Each row index $r$ corresponds to user $r$, and column index $c$ to item $c$."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:27:02.087Z",
"callId": "run_shell_command__call_153768",
"name": "run_shell_command",
"input": {
"description": "Check item_ids order between catalog and npz",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"Are item_ids in exact same order as catalog?\", np.array_equal(cat[\"item_id\"].to_numpy(), val_npz[\"item_ids\"]))\n'"
},
"recorded": {
"id": "run_shell_command__call_153768",
"name": "run_shell_command",
"args": {
"description": "Check item_ids order between catalog and npz",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"Are item_ids in exact same order as catalog?\", np.array_equal(cat[\"item_id\"].to_numpy(), val_npz[\"item_ids\"]))\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_153768",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Are item_ids in exact same order as catalog? True\nProcess Group PGID: 2180\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:27:02.841Z",
"resultDisplay": "Are item_ids in exact same order as catalog? True",
"description": "Check item_ids order between catalog and npz",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:27:02.842Z",
"content": "\nOutput: Are item_ids in exact same order as catalog? True\nProcess Group PGID: 2180\n",
"isError": false
},
"responseMessageId": "8c53b2b1-35f9-4edc-9eb9-3a88cce90cce"
}
]
},
{
"id": 36,
"role": "agent",
"timestamp": "2026-09-29T04:27:17.064Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:17.064Z",
"text": "Confirming Index Alignment\n\nI've confirmed that the `item_ids` in both validation and test eligible candidate files perfectly align with the `candidate_catalog.parquet`. This direct correspondence simplifies the mapping process significantly for subsequent steps."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:17.064Z",
"text": "Leveraging User-Item Matrix\n\nMy current focus is on constructing a comprehensive user-item matrix using a combination of `train_listens`, `train_likes`, and `validation_preference_state`. This unified data source will allow me to evaluate explicit positive and negative signals from `validation_preference_state`, alongside the quality signals from organic `train_likes` for my experiments."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:17.064Z",
"text": "Exploring Factorization Methods\n\nI'm now exploring low-rank matrix factorization techniques like TruncatedSVD, SVD, and iALS. The goal is to leverage the constructed user-item matrix, incorporating factors like listen completion, organic engagement, and recency, to generate predictions for eligible candidates."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:27:17.064Z",
"callId": "run_shell_command__call_174239",
"name": "run_shell_command",
"input": {
"description": "Initial SVD model experiment on recent listens",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\n# Group by uid, item_id and compute count of listens with played_ratio_pct >= 50\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = [uid_to_row[u] for u in grouped.uid]\ncols = [item_to_col[it] for it in grouped.item_id]\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (len(target_uids), len(cat_items))), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\n# Run TruncatedSVD\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\n# Predict scores\nscores = U @ V\n\n# Generate top 100 for each user\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'"
},
"recorded": {
"id": "run_shell_command__call_174239",
"name": "run_shell_command",
"args": {
"description": "Initial SVD model experiment on recent listens",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\n# Group by uid, item_id and compute count of listens with played_ratio_pct >= 50\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = [uid_to_row[u] for u in grouped.uid]\ncols = [item_to_col[it] for it in grouped.item_id]\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (len(target_uids), len(cat_items))), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\n# Run TruncatedSVD\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\n# Predict scores\nscores = U @ V\n\n# Generate top 100 for each user\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_174239",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Traceback (most recent call last):\n File \"\", line 49, in \n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 56, in __init__\n coo = self._coo_container(arg1, shape=shape, dtype=dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 219, in _check\n if self.nnz > 0:\n ^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 349, in nnz\n return self._getnnz()\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in _getnnz\n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in \n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^\nTypeError: len() of unsized object\nLoading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.1s]\nExit Code: 1\nProcess Group PGID: 2197\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:27:25.618Z",
"resultDisplay": "Traceback (most recent call last):\n File \"\", line 49, in \n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 56, in __init__\n coo = self._coo_container(arg1, shape=shape, dtype=dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 219, in _check\n if self.nnz > 0:\n ^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 349, in nnz\n return self._getnnz()\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in _getnnz\n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in \n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^\nTypeError: len() of unsized object\nLoading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.1s]",
"description": "Initial SVD model experiment on recent listens",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:27:25.619Z",
"content": "\nOutput: Traceback (most recent call last):\n File \"\", line 49, in \n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py\", line 56, in __init__\n coo = self._coo_container(arg1, shape=shape, dtype=dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 101, in __init__\n self._check()\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 219, in _check\n if self.nnz > 0:\n ^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py\", line 349, in nnz\n return self._getnnz()\n ^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in _getnnz\n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py\", line 168, in \n if any(len(idx) != nnz for idx in self.coords):\n ^^^^^^^^\nTypeError: len() of unsized object\nLoading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.1s]\nExit Code: 1\nProcess Group PGID: 2197\n",
"isError": false
},
"responseMessageId": "3a383a9a-e735-4aa8-b4c6-292edbba1d85"
}
]
},
{
"id": 37,
"role": "agent",
"timestamp": "2026-09-29T04:27:34.077Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:34.077Z",
"text": "Fixing SciPy Array Types\n\nI'm addressing a recent discovery in SciPy version 1.15.3 regarding array types. It seems `rows` and `cols` are now expected to be NumPy arrays, not lists. I'm updating the code to reflect this, ensuring `rows` and `cols` are correctly converted to `np.int32` arrays."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:27:34.077Z",
"callId": "run_shell_command__call_146661",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = np.array([uid_to_row[u] for u in grouped.uid], dtype=np.int32)\ncols = np.array([item_to_col[it] for it in grouped.item_id], dtype=np.int32)\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (rows, cols)), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD fitted. Explained var: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\nscores = U @ V\nprint(f\"Scores computed. [{time.time()-t0:.1f}s]\")\n\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'",
"description": "Run initial SVD experiment with fixed sparse matrix creation"
},
"recorded": {
"id": "run_shell_command__call_146661",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = np.array([uid_to_row[u] for u in grouped.uid], dtype=np.int32)\ncols = np.array([item_to_col[it] for it in grouped.item_id], dtype=np.int32)\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (rows, cols)), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD fitted. Explained var: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\nscores = U @ V\nprint(f\"Scores computed. [{time.time()-t0:.1f}s]\")\n\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'",
"description": "Run initial SVD experiment with fixed sparse matrix creation"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_146661",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Loading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.0s]\nR shape: (8456, 30000), nnz: 3000810 [7.2s]\nSVD fitted. Explained var: 0.2716 [9.0s]\nScores computed. [9.2s]\nTruncatedSVD k=64 NDCG@10: {'quality_ndcg@10': 0.02495, 'quality_users': 4263} [10.5s]\nProcess Group PGID: 2233\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:27:46.218Z",
"resultDisplay": "Loading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.0s]\nR shape: (8456, 30000), nnz: 3000810 [7.2s]\nSVD fitted. Explained var: 0.2716 [9.0s]\nScores computed. [9.2s]\nTruncatedSVD k=64 NDCG@10: {'quality_ndcg@10': 0.02495, 'quality_users': 4263} [10.5s]",
"description": "Run initial SVD experiment with fixed sparse matrix creation",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:27:46.219Z",
"content": "\nOutput: Loading train listens (last 90 days)... [0.7s]\nRecent listens: 16769274 [1.3s]\nFiltered listens: 12099707 [5.0s]\nR shape: (8456, 30000), nnz: 3000810 [7.2s]\nSVD fitted. Explained var: 0.2716 [9.0s]\nScores computed. [9.2s]\nTruncatedSVD k=64 NDCG@10: {'quality_ndcg@10': 0.02495, 'quality_users': 4263} [10.5s]\nProcess Group PGID: 2233\n",
"isError": false
},
"responseMessageId": "9b039186-bb55-432f-b559-e62e6c496459"
}
]
},
{
"id": 38,
"role": "agent",
"timestamp": "2026-09-29T04:27:57.159Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:57.159Z",
"text": "Improving NDCG Performance\n\nMy recent experiments show a significant jump in NDCG@10 from a mere 0.01071 (using popularity) to an impressive 0.02495 with a basic TruncatedSVD. This is over a 2.3x improvement! Next, I'm focusing on incorporating user preference data like likes to further enhance these results."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:27:57.159Z",
"text": "Exploring Item-Item Interactions\n\nI'm now considering the impact of explicit signals like likes, and weighting organic listens higher. I'm also exploring recency weighting and tuning the number of components for SVD. A key next step is comparing Item-Item Collaborative Filtering with cosine similarity against my current SVD approach."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:27:57.159Z",
"callId": "run_shell_command__call_148421",
"name": "run_shell_command",
"input": {
"description": "Benchmark Item-Item CF via associative multiply",
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nN, M = 8456, 30000\nnnz = 3000000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\n# Normalize items\ncol_norms = np.asarray(np.sqrt(R.power(2).sum(axis=0))).flatten()\ncol_norms[col_norms == 0] = 1.0\nP = R.multiply(1.0 / col_norms).tocsr()\n\nt0 = time.time()\n# Associative multiply: (R @ P.T) @ P\nRP = R @ P.T # 8456 x 8456 sparse\nprint(f\"R @ P.T nnz: {RP.nnz}, time: {time.time()-t0:.2f}s\")\nt1 = time.time()\nscores = RP @ P # 8456 x 30000\nprint(f\"RP @ P shape: {scores.shape}, nnz: {scores.nnz}, time: {time.time()-t1:.2f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_148421",
"name": "run_shell_command",
"args": {
"description": "Benchmark Item-Item CF via associative multiply",
"command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nN, M = 8456, 30000\nnnz = 3000000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\n# Normalize items\ncol_norms = np.asarray(np.sqrt(R.power(2).sum(axis=0))).flatten()\ncol_norms[col_norms == 0] = 1.0\nP = R.multiply(1.0 / col_norms).tocsr()\n\nt0 = time.time()\n# Associative multiply: (R @ P.T) @ P\nRP = R @ P.T # 8456 x 8456 sparse\nprint(f\"R @ P.T nnz: {RP.nnz}, time: {time.time()-t0:.2f}s\")\nt1 = time.time()\nscores = RP @ P # 8456 x 30000\nprint(f\"RP @ P shape: {scores.shape}, nnz: {scores.nnz}, time: {time.time()-t1:.2f}s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_148421",
"name": "run_shell_command",
"response": {
"output": "\nOutput: R @ P.T nnz: 70371236, time: 2.04s\nRP @ P shape: (8456, 30000), nnz: 253680000, time: 58.20s\nProcess Group PGID: 2263\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:28:58.349Z",
"resultDisplay": "R @ P.T nnz: 70371236, time: 2.04s\nRP @ P shape: (8456, 30000), nnz: 253680000, time: 58.20s",
"description": "Benchmark Item-Item CF via associative multiply",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:28:58.349Z",
"content": "\nOutput: R @ P.T nnz: 70371236, time: 2.04s\nRP @ P shape: (8456, 30000), nnz: 253680000, time: 58.20s\nProcess Group PGID: 2263\n",
"isError": false
},
"responseMessageId": "e4d94d09-3b93-42d1-a8cd-d913e14090a6"
}
]
},
{
"id": 39,
"role": "agent",
"timestamp": "2026-09-29T04:29:04.214Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:29:04.214Z",
"text": "Exploring SVD Efficiency\n\nI'm observing a significant performance disparity. RP@P is proving quite slow due to dense matrix expansion, whereas TruncatedSVD is remarkably fast for fitting and multiplication. I'm now investigating TruncatedSVD's behavior with normalized data."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:29:04.214Z",
"text": "Testing Normalization Techniques\n\nI'm now exploring how TF-IDF and BM25 transformations, alongside various normalization and feature engineering strategies like played ratio and recency, impact SVD performance and NDCG@ metrics."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:29:04.214Z",
"callId": "update_topic__call_157364",
"name": "update_topic",
"input": {
"summary": "Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.",
"strategic_intent": "Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.",
"title": "Feature Engineering and Matrix Weighting Optimization"
},
"recorded": {
"id": "update_topic__call_157364",
"name": "update_topic",
"args": {
"summary": "Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.",
"strategic_intent": "Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.",
"title": "Feature Engineering and Matrix Weighting Optimization"
},
"result": [
{
"functionResponse": {
"id": "update_topic__call_157364",
"name": "update_topic",
"response": {
"output": "Current topic: \"Feature Engineering and Matrix Weighting Optimization\"\nTopic summary: Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.\n\nStrategic Intent: Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states."
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:29:04.216Z",
"resultDisplay": "## 📂 Topic: **Feature Engineering and Matrix Weighting Optimization**\n\n**Summary:**\nEstablished baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.\n\n> [!STRATEGY]\n> **Intent:** Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.",
"description": "Update topic to: \"Feature Engineering and Matrix Weighting Optimization\"",
"displayName": "Update Topic Context",
"renderOutputAsMarkdown": true
},
"result": {
"timestamp": "2026-09-29T04:29:04.217Z",
"content": "Current topic: \"Feature Engineering and Matrix Weighting Optimization\"\nTopic summary: Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.\n\nStrategic Intent: Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.",
"isError": false
},
"responseMessageId": "877974d7-2cac-4666-9d59-5168c1220883"
}
]
},
{
"id": 40,
"role": "agent",
"timestamp": "2026-09-29T04:29:22.240Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:29:22.240Z",
"text": "Designing Evaluation Harness\n\nI'm currently focused on developing the Python evaluation harness, specifically the `/app/solution/tune_weights.py` script. My primary goal is to efficiently load the data once and then construct the sparse user-item interaction matrix, taking into account the recency half-life parameter."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:29:22.240Z",
"callId": "write_file__call_268196",
"name": "write_file",
"input": {
"file_path": "/app/solution/tune_weights.py",
"instruction": "Write hyperparameter tuning script for weighting and SVD ranker",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n"
},
"recorded": {
"id": "write_file__call_268196",
"name": "write_file",
"args": {
"file_path": "/app/solution/tune_weights.py",
"instruction": "Write hyperparameter tuning script for weighting and SVD ranker",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_268196",
"name": "write_file",
"response": {
"output": "Successfully created and wrote to new file: /app/solution/tune_weights.py. Here is the updated code:\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:29:22.254Z",
"resultDisplay": {
"fileDiff": "Index: tune_weights.py\n===================================================================\n--- tune_weights.py\tOriginal\n+++ tune_weights.py\tWritten\n@@ -0,0 +1,150 @@\n+import time\n+import numpy as np\n+import pandas as pd\n+import scipy.sparse as sp\n+from sklearn.decomposition import TruncatedSVD\n+from sklearn.feature_extraction.text import TfidfTransformer\n+import sys\n+sys.path.append(\"/task\")\n+from ranking_evaluation import score_rankings\n+from evaluate_public import validation_truth, CandidateSet\n+from pathlib import Path\n+\n+def main():\n+ t0 = time.time()\n+ targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n+ target_uids = targets_frame.uid.to_numpy()\n+ uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n+\n+ catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n+ cat_items = catalog.item_id.to_numpy()\n+ item_to_col = {item: i for i, item in enumerate(cat_items)}\n+\n+ val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n+ packed = val_npz[\"packed_eligible\"]\n+ eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n+ graded = validation_truth(eligibility, set(target_uids))\n+\n+ cutoff = 24786800\n+ days90 = cutoff - 90 * 86400\n+\n+ print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n+ listens = pd.read_parquet(\n+ \"/task/data/train_listens.parquet\",\n+ columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n+ )\n+ # Filter to recent 90 days, target users, catalog items\n+ mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n+ listens = listens[mask].copy()\n+ print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n+\n+ # Load preference state\n+ pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n+ pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n+ print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n+\n+ # Convert uids and item_ids to row and col indices\n+ listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n+ listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n+\n+ pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n+ pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n+ likes_only = pref_targets[pref_targets.state == \"liked\"]\n+\n+ # Helper function to evaluate given weights\n+ def evaluate_model(\n+ half_life_days=30.0,\n+ organic_mult=2.0,\n+ strong_mult=2.0,\n+ like_weight=10.0,\n+ n_components=128,\n+ use_tfidf=False,\n+ norm_rows=False,\n+ pop_alpha=0.0\n+ ):\n+ t_start = time.time()\n+ # Compute event weights\n+ delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n+ recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n+ \n+ # Base completion weight\n+ completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n+ completion = np.clip(completion, 0.0, 1.5)\n+ # Strong listen boost\n+ strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n+ # Organic boost\n+ organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n+ \n+ event_weight = recency_weight * completion * strong_boost * organic_boost\n+ \n+ # Aggregate weights per (row, col)\n+ # Using coo matrix summing duplicates\n+ rows = listens[\"row\"].to_numpy()\n+ cols = listens[\"col\"].to_numpy()\n+ \n+ # Add likes\n+ like_rows = likes_only[\"row\"].to_numpy()\n+ like_cols = likes_only[\"col\"].to_numpy()\n+ like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n+ \n+ all_rows = np.concatenate([rows, like_rows])\n+ all_cols = np.concatenate([cols, like_cols])\n+ all_weights = np.concatenate([event_weight, like_weights])\n+ \n+ R = sp.csr_matrix(\n+ (all_weights, (all_rows, all_cols)),\n+ shape=(len(target_uids), len(cat_items)),\n+ dtype=np.float32\n+ )\n+ # Log1p transform\n+ R.data = np.log1p(R.data)\n+ \n+ if use_tfidf:\n+ tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n+ R_transformed = tfidf.fit_transform(R)\n+ elif norm_rows:\n+ from sklearn.preprocessing import normalize\n+ R_transformed = normalize(R, norm=\"l2\", axis=1)\n+ else:\n+ R_transformed = R\n+\n+ svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n+ U = svd.fit_transform(R_transformed)\n+ V = svd.components_\n+ \n+ scores = U @ V\n+ \n+ if pop_alpha > 0.0:\n+ # Add popularity prior\n+ pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n+ pop_std = pop.std()\n+ pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n+ scores = scores + pop_alpha * pop_norm\n+ \n+ ranked = {}\n+ for r in range(len(target_uids)):\n+ uid = int(target_uids[r])\n+ mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n+ user_scores = scores[r].copy()\n+ user_scores[~mask_eligible] = -1e9\n+ top100_idx = np.argpartition(-user_scores, 100)[:100]\n+ top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n+ ranked[uid] = list(cat_items[top100_idx])\n+ \n+ res = score_rankings(ranked, graded)\n+ score = res[\"quality_ndcg@10\"]\n+ print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n+ return score\n+\n+ print(\"\\n--- Running parameter experiments ---\")\n+ evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n+ evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n+ evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n+ evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n+ evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n+ evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n+ evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n+ evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n+\n+if __name__ == \"__main__\":\n+ main()\n",
"fileName": "tune_weights.py",
"filePath": "/app/solution/tune_weights.py",
"originalContent": "",
"newContent": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n",
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"result": {
"timestamp": "2026-09-29T04:29:22.255Z",
"content": "Successfully created and wrote to new file: /app/solution/tune_weights.py. Here is the updated code:\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n",
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},
{
"id": 41,
"role": "agent",
"timestamp": "2026-09-29T04:29:30.273Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:29:30.273Z",
"callId": "run_shell_command__call_384790",
"name": "run_shell_command",
"input": {
"description": "Run tuning experiments on validation set",
"command": "python3 /app/solution/tune_weights.py"
},
"recorded": {
"id": "run_shell_command__call_384790",
"name": "run_shell_command",
"args": {
"description": "Run tuning experiments on validation set",
"command": "python3 /app/solution/tune_weights.py"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_384790",
"name": "run_shell_command",
"response": {
"output": "\nOutput: /opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py:1713: RuntimeWarning: divide by zero encountered in log\n np.log(X.data, X.data)\nLoading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\nPref state rows: 469124 [10.8s]\n\n--- Running parameter experiments ---\nHL=30.0d org=1.0 str=1.0 like=0.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03080 (4.9s)\nHL=30.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03359 (4.5s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03491 (4.5s)\nHL=7.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03527 (4.4s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=128 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03459 (5.2s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=256 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03370 (7.5s)\nTraceback (most recent call last):\n File \"/app/solution/tune_weights.py\", line 150, in \n main()\n File \"/app/solution/tune_weights.py\", line 146, in main\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n File \"/app/solution/tune_weights.py\", line 104, in evaluate_model\n R_transformed = tfidf.fit_transform(R)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 894, in fit_transform\n return self.fit(X, **fit_params).transform(X)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1722, in transform\n X = normalize(X, norm=self.norm, copy=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py\", line 218, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 1979, in normalize\n X = check_array(\n ^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input contains infinity or a value too large for dtype('float32').\nExit Code: 1\nProcess Group PGID: 2286\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:30:14.884Z",
"resultDisplay": "/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py:1713: RuntimeWarning: divide by zero encountered in log\n np.log(X.data, X.data)\nLoading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\nPref state rows: 469124 [10.8s]\n\n--- Running parameter experiments ---\nHL=30.0d org=1.0 str=1.0 like=0.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03080 (4.9s)\nHL=30.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03359 (4.5s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03491 (4.5s)\nHL=7.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03527 (4.4s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=128 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03459 (5.2s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=256 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03370 (7.5s)\nTraceback (most recent call last):\n File \"/app/solution/tune_weights.py\", line 150, in \n main()\n File \"/app/solution/tune_weights.py\", line 146, in main\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n File \"/app/solution/tune_weights.py\", line 104, in evaluate_model\n R_transformed = tfidf.fit_transform(R)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 894, in fit_transform\n return self.fit(X, **fit_params).transform(X)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1722, in transform\n X = normalize(X, norm=self.norm, copy=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py\", line 218, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 1979, in normalize\n X = check_array(\n ^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input contains infinity or a value too large for dtype('float32').",
"description": "python3 /app/solution/tune_weights.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:30:14.885Z",
"content": "\nOutput: /opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py:1713: RuntimeWarning: divide by zero encountered in log\n np.log(X.data, X.data)\nLoading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\nPref state rows: 469124 [10.8s]\n\n--- Running parameter experiments ---\nHL=30.0d org=1.0 str=1.0 like=0.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03080 (4.9s)\nHL=30.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03359 (4.5s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03491 (4.5s)\nHL=7.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03527 (4.4s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=128 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03459 (5.2s)\nHL=14.0d org=2.0 str=2.0 like=10.0 k=256 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03370 (7.5s)\nTraceback (most recent call last):\n File \"/app/solution/tune_weights.py\", line 150, in \n main()\n File \"/app/solution/tune_weights.py\", line 146, in main\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n File \"/app/solution/tune_weights.py\", line 104, in evaluate_model\n R_transformed = tfidf.fit_transform(R)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n data_to_wrap = f(self, X, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 894, in fit_transform\n return self.fit(X, **fit_params).transform(X)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py\", line 1722, in transform\n X = normalize(X, norm=self.norm, copy=False)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py\", line 218, in wrapper\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py\", line 1979, in normalize\n X = check_array(\n ^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 1012, in check_array\n array = _ensure_sparse_format(\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 647, in _ensure_sparse_format\n _assert_all_finite(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 120, in _assert_all_finite\n _assert_all_finite_element_wise(\n File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py\", line 169, in _assert_all_finite_element_wise\n raise ValueError(msg_err)\nValueError: Input contains infinity or a value too large for dtype('float32').\nExit Code: 1\nProcess Group PGID: 2286\n",
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]
},
{
"id": 42,
"role": "agent",
"timestamp": "2026-09-29T04:30:36.093Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:30:36.093Z",
"text": "Analyzing Pre-TFIDF Data\n\nI'm examining performance metrics prior to addressing the TF-IDF issue. Initial runs with varying historical lengths and organization/stringency parameters show promising, though slightly lower, accuracy scores. I'm particularly noting the slight improvements seen with the 14-day historical window."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:30:36.093Z",
"text": "Improving Scoring Model\n\nThe model's score has significantly improved, exceeding a 3.3x baseline increase through cumulative adjustments like adding likes and weighting organic completion. I'm now investigating TF-IDF transformer errors, specifically the `log(0)` issue, and exploring alternative IDF calculation methods and normalization techniques. Further tuning of half-life, multipliers, like weights, and the inclusion of dislikes is planned."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:30:36.093Z",
"callId": "write_file__call_157033",
"name": "write_file",
"input": {
"instruction": "Update tuning script to sweep half-life, organic/strong multipliers, like weight, components, idf, and normalization",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/tune_weights.py"
},
"recorded": {
"id": "write_file__call_157033",
"name": "write_file",
"args": {
"instruction": "Update tuning script to sweep half-life, organic/strong multipliers, like weight, components, idf, and normalization",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/tune_weights.py"
},
"result": [
{
"functionResponse": {
"id": "write_file__call_157033",
"name": "write_file",
"response": {
"output": "Successfully overwrote file: /app/solution/tune_weights.py. Here is the updated code:\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n...\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n...\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:30:36.116Z",
"resultDisplay": {
"fileDiff": "Index: tune_weights.py\n===================================================================\n--- tune_weights.py\tOriginal\n+++ tune_weights.py\tWritten\n@@ -3,7 +3,7 @@\n import pandas as pd\n import scipy.sparse as sp\n from sklearn.decomposition import TruncatedSVD\n-from sklearn.feature_extraction.text import TfidfTransformer\n+from sklearn.preprocessing import normalize\n import sys\n sys.path.append(\"/task\")\n from ranking_evaluation import score_rankings\n@@ -33,7 +33,6 @@\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n- # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n@@ -41,86 +40,74 @@\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n- print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n \n- # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n \n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n+ dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n \n- # Helper function to evaluate given weights\n- def evaluate_model(\n- half_life_days=30.0,\n+ def build_matrix(\n+ half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n- n_components=128,\n- use_tfidf=False,\n- norm_rows=False,\n- pop_alpha=0.0\n+ dislike_weight=0.0,\n+ use_idf=False,\n+ norm_type=\"none\"\n ):\n- t_start = time.time()\n- # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n- \n- # Base completion weight\n- completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n- completion = np.clip(completion, 0.0, 1.5)\n- # Strong listen boost\n+ completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n- # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n- \n event_weight = recency_weight * completion * strong_boost * organic_boost\n- \n- # Aggregate weights per (row, col)\n- # Using coo matrix summing duplicates\n- rows = listens[\"row\"].to_numpy()\n- cols = listens[\"col\"].to_numpy()\n- \n- # Add likes\n- like_rows = likes_only[\"row\"].to_numpy()\n- like_cols = likes_only[\"col\"].to_numpy()\n- like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n- \n- all_rows = np.concatenate([rows, like_rows])\n- all_cols = np.concatenate([cols, like_cols])\n- all_weights = np.concatenate([event_weight, like_weights])\n- \n+\n+ rows = [listens[\"row\"].to_numpy()]\n+ cols = [listens[\"col\"].to_numpy()]\n+ weights = [event_weight]\n+\n+ if like_weight > 0 and len(likes_only) > 0:\n+ rows.append(likes_only[\"row\"].to_numpy())\n+ cols.append(likes_only[\"col\"].to_numpy())\n+ weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n+\n+ if dislike_weight > 0 and len(dislikes_only) > 0:\n+ rows.append(dislikes_only[\"row\"].to_numpy())\n+ cols.append(dislikes_only[\"col\"].to_numpy())\n+ weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n+\n+ all_rows = np.concatenate(rows)\n+ all_cols = np.concatenate(cols)\n+ all_weights = np.concatenate(weights)\n+\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n- # Log1p transform\n- R.data = np.log1p(R.data)\n- \n- if use_tfidf:\n- tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n- R_transformed = tfidf.fit_transform(R)\n- elif norm_rows:\n- from sklearn.preprocessing import normalize\n- R_transformed = normalize(R, norm=\"l2\", axis=1)\n- else:\n- R_transformed = R\n+ # Apply signed log1p: sign(x) * log1p(|x|)\n+ pos_mask = R.data > 0\n+ neg_mask = R.data < 0\n+ R.data[pos_mask] = np.log1p(R.data[pos_mask])\n+ R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n \n- svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n- U = svd.fit_transform(R_transformed)\n- V = svd.components_\n- \n- scores = U @ V\n- \n- if pop_alpha > 0.0:\n- # Add popularity prior\n- pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n- pop_std = pop.std()\n- pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n- scores = scores + pop_alpha * pop_norm\n- \n+ if use_idf:\n+ df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n+ idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n+ # Multiply columns by idf\n+ R = R.dot(sp.diags(idf.astype(np.float32)))\n+\n+ if norm_type == \"l2_row\":\n+ R = normalize(R, norm=\"l2\", axis=1)\n+ elif norm_type == \"l2_col\":\n+ R = normalize(R, norm=\"l2\", axis=0)\n+\n+ return R\n+\n+ def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n@@ -133,18 +120,45 @@\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n- print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n+ print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n \n- print(\"\\n--- Running parameter experiments ---\")\n- evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n- evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n- evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n- evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n- evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n- evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n- evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n- evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n+ print(\"\\n--- Tuning Half-Life ---\")\n+ for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n+ R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n+ svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n+ scores = svd.fit_transform(R) @ svd.components_\n+ evaluate_scores(scores, f\"HL={hl}d\")\n \n+ print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n+ for org in [1.5, 2.0, 3.0, 4.0]:\n+ for str_m in [1.5, 2.0, 3.0]:\n+ R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n+ svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n+ scores = svd.fit_transform(R) @ svd.components_\n+ evaluate_scores(scores, f\"org={org} str={str_m}\")\n+\n+ print(\"\\n--- Tuning Like Weight ---\")\n+ for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n+ R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n+ svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n+ scores = svd.fit_transform(R) @ svd.components_\n+ evaluate_scores(scores, f\"like_weight={lw}\")\n+\n+ print(\"\\n--- Tuning SVD Components ---\")\n+ R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n+ for k in [32, 48, 64, 80, 96, 128]:\n+ svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n+ scores = svd.fit_transform(R) @ svd.components_\n+ evaluate_scores(scores, f\"k={k}\")\n+\n+ print(\"\\n--- Testing IDF and Normalization ---\")\n+ for idf_val in [False, True]:\n+ for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n+ R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n+ svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n+ scores = svd.fit_transform(R) @ svd.components_\n+ evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n+\n if __name__ == \"__main__\":\n main()\n",
"fileName": "tune_weights.py",
"filePath": "/app/solution/tune_weights.py",
"originalContent": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n",
"newContent": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n",
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"result": {
"timestamp": "2026-09-29T04:30:36.118Z",
"content": "Successfully overwrote file: /app/solution/tune_weights.py. Here is the updated code:\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n...\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n...\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n",
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},
{
"id": 43,
"role": "agent",
"timestamp": "2026-09-29T04:30:38.702Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:30:38.702Z",
"callId": "run_shell_command__call_206713",
"name": "run_shell_command",
"input": {
"description": "Run parameter tuning experiments",
"command": "python3 /app/solution/tune_weights.py"
},
"recorded": {
"id": "run_shell_command__call_206713",
"name": "run_shell_command",
"args": {
"description": "Run parameter tuning experiments",
"command": "python3 /app/solution/tune_weights.py"
},
"result": [
{
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"response": {
"output": "\nOutput: Loading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\n\n--- Tuning Half-Life ---\n[HL=3.0d] NDCG@10: 0.03181\n[HL=5.0d] NDCG@10: 0.03448\n[HL=7.0d] NDCG@10: 0.03527\n[HL=10.0d] NDCG@10: 0.03608\n[HL=14.0d] NDCG@10: 0.03491\n[HL=21.0d] NDCG@10: 0.03424\n\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\n[org=1.5 str=1.5] NDCG@10: 0.03347\n[org=1.5 str=2.0] NDCG@10: 0.03446\n[org=1.5 str=3.0] NDCG@10: 0.03580\n[org=2.0 str=1.5] NDCG@10: 0.03454\n[org=2.0 str=2.0] NDCG@10: 0.03527\n[org=2.0 str=3.0] NDCG@10: 0.03642\n[org=3.0 str=1.5] NDCG@10: 0.03570\n[org=3.0 str=2.0] NDCG@10: 0.03726\n[org=3.0 str=3.0] NDCG@10: 0.03742\n[org=4.0 str=1.5] NDCG@10: 0.03680\n[org=4.0 str=2.0] NDCG@10: 0.03756\n[org=4.0 str=3.0] NDCG@10: 0.03817\n\n--- Tuning Like Weight ---\n[like_weight=5.0] NDCG@10: 0.03714\n[like_weight=10.0] NDCG@10: 0.03527\n[like_weight=15.0] NDCG@10: 0.03438\n[like_weight=25.0] NDCG@10: 0.03322\n[like_weight=40.0] NDCG@10: 0.03201\n\n--- Tuning SVD Components ---\n[k=32] NDCG@10: 0.03309\n[k=48] NDCG@10: 0.03291\n[k=64] NDCG@10: 0.03438\n[k=80] NDCG@10: 0.03337\n[k=96] NDCG@10: 0.03426\n[k=128] NDCG@10: 0.03362\n\n--- Testing IDF and Normalization ---\n[idf=False norm=none] NDCG@10: 0.03438\n[idf=False norm=l2_row] NDCG@10: 0.03260\n[idf=False norm=l2_col] NDCG@10: 0.01218\n[idf=True norm=none] NDCG@10: 0.03309\n[idf=True norm=l2_row] NDCG@10: 0.03729\n[idf=True norm=l2_col] NDCG@10: 0.01218\nProcess Group PGID: 2320\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:33:24.641Z",
"resultDisplay": "Loading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\n\n--- Tuning Half-Life ---\n[HL=3.0d] NDCG@10: 0.03181\n[HL=5.0d] NDCG@10: 0.03448\n[HL=7.0d] NDCG@10: 0.03527\n[HL=10.0d] NDCG@10: 0.03608\n[HL=14.0d] NDCG@10: 0.03491\n[HL=21.0d] NDCG@10: 0.03424\n\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\n[org=1.5 str=1.5] NDCG@10: 0.03347\n[org=1.5 str=2.0] NDCG@10: 0.03446\n[org=1.5 str=3.0] NDCG@10: 0.03580\n[org=2.0 str=1.5] NDCG@10: 0.03454\n[org=2.0 str=2.0] NDCG@10: 0.03527\n[org=2.0 str=3.0] NDCG@10: 0.03642\n[org=3.0 str=1.5] NDCG@10: 0.03570\n[org=3.0 str=2.0] NDCG@10: 0.03726\n[org=3.0 str=3.0] NDCG@10: 0.03742\n[org=4.0 str=1.5] NDCG@10: 0.03680\n[org=4.0 str=2.0] NDCG@10: 0.03756\n[org=4.0 str=3.0] NDCG@10: 0.03817\n\n--- Tuning Like Weight ---\n[like_weight=5.0] NDCG@10: 0.03714\n[like_weight=10.0] NDCG@10: 0.03527\n[like_weight=15.0] NDCG@10: 0.03438\n[like_weight=25.0] NDCG@10: 0.03322\n[like_weight=40.0] NDCG@10: 0.03201\n\n--- Tuning SVD Components ---\n[k=32] NDCG@10: 0.03309\n[k=48] NDCG@10: 0.03291\n[k=64] NDCG@10: 0.03438\n[k=80] NDCG@10: 0.03337\n[k=96] NDCG@10: 0.03426\n[k=128] NDCG@10: 0.03362\n\n--- Testing IDF and Normalization ---\n[idf=False norm=none] NDCG@10: 0.03438\n[idf=False norm=l2_row] NDCG@10: 0.03260\n[idf=False norm=l2_col] NDCG@10: 0.01218\n[idf=True norm=none] NDCG@10: 0.03309\n[idf=True norm=l2_row] NDCG@10: 0.03729\n[idf=True norm=l2_col] NDCG@10: 0.01218",
"description": "python3 /app/solution/tune_weights.py",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:33:24.643Z",
"content": "\nOutput: Loading data... [0.7s]\nFiltered listens: 12099707 rows [10.7s]\n\n--- Tuning Half-Life ---\n[HL=3.0d] NDCG@10: 0.03181\n[HL=5.0d] NDCG@10: 0.03448\n[HL=7.0d] NDCG@10: 0.03527\n[HL=10.0d] NDCG@10: 0.03608\n[HL=14.0d] NDCG@10: 0.03491\n[HL=21.0d] NDCG@10: 0.03424\n\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\n[org=1.5 str=1.5] NDCG@10: 0.03347\n[org=1.5 str=2.0] NDCG@10: 0.03446\n[org=1.5 str=3.0] NDCG@10: 0.03580\n[org=2.0 str=1.5] NDCG@10: 0.03454\n[org=2.0 str=2.0] NDCG@10: 0.03527\n[org=2.0 str=3.0] NDCG@10: 0.03642\n[org=3.0 str=1.5] NDCG@10: 0.03570\n[org=3.0 str=2.0] NDCG@10: 0.03726\n[org=3.0 str=3.0] NDCG@10: 0.03742\n[org=4.0 str=1.5] NDCG@10: 0.03680\n[org=4.0 str=2.0] NDCG@10: 0.03756\n[org=4.0 str=3.0] NDCG@10: 0.03817\n\n--- Tuning Like Weight ---\n[like_weight=5.0] NDCG@10: 0.03714\n[like_weight=10.0] NDCG@10: 0.03527\n[like_weight=15.0] NDCG@10: 0.03438\n[like_weight=25.0] NDCG@10: 0.03322\n[like_weight=40.0] NDCG@10: 0.03201\n\n--- Tuning SVD Components ---\n[k=32] NDCG@10: 0.03309\n[k=48] NDCG@10: 0.03291\n[k=64] NDCG@10: 0.03438\n[k=80] NDCG@10: 0.03337\n[k=96] NDCG@10: 0.03426\n[k=128] NDCG@10: 0.03362\n\n--- Testing IDF and Normalization ---\n[idf=False norm=none] NDCG@10: 0.03438\n[idf=False norm=l2_row] NDCG@10: 0.03260\n[idf=False norm=l2_col] NDCG@10: 0.01218\n[idf=True norm=none] NDCG@10: 0.03309\n[idf=True norm=l2_row] NDCG@10: 0.03729\n[idf=True norm=l2_col] NDCG@10: 0.01218\nProcess Group PGID: 2320\n",
"isError": false
},
"responseMessageId": "95e51b5d-f80e-4375-8e81-0ee8005accc2"
}
]
},
{
"id": 44,
"role": "agent",
"timestamp": "2026-09-29T04:33:44.650Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:33:44.650Z",
"text": "Analyzing Engagement Patterns\n\nI'm noticing a clear trend where a recency half-life of 10 days and higher organic/strong multipliers are driving better engagement metrics. It appears lower \"like weight\" values also contribute positively, suggesting overemphasis can dilute impact."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:33:44.650Z",
"text": "Refining Interaction Weighting\n\nI'm now exploring the nuanced interaction of listen weights, specifically hypothesizing that over-weighting likes might be diluting their impact. I'm considering a low 'like_weight' and a significant disparity between organic and non-organic listen weights, potentially setting non-organic to 0.2 and organic to 1.0 for a 5.0x ratio."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:33:44.650Z",
"callId": "run_shell_command__call_328629",
"name": "run_shell_command",
"input": {
"description": "Test combinations of weighting and normalization",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\ndislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\ndef test_config(hl, org_mult, str_mult, non_org_mult, lw, dw, use_idf, norm_l2, k=64):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n rec = np.exp(-np.log(2) * delta_days / hl)\n comp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n s_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, str_mult, 1.0)\n o_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, org_mult, non_org_mult)\n weights = rec * comp * s_boost * o_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n vals = [weights]\n\n if lw > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=lw, dtype=np.float32))\n\n if dw > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n vals.append(np.full(len(dislikes_only), fill_value=-dw, dtype=np.float32))\n\n R = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_l2:\n R = normalize(R, norm=\"l2\", axis=1)\n\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"hl={hl} org={org_mult} non_org={non_org_mult} str={str_mult} lw={lw} dw={dw} idf={use_idf} norm={norm_l2} k={k} -> {score:.5f}\")\n return scores, score\n\nprint(\"--- Testing configurations ---\")\ntest_config(hl=10.0, org_mult=4.0, str_mult=3.0, non_org_mult=1.0, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.5, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=2.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True, k=96)\n'"
},
"recorded": {
"id": "run_shell_command__call_328629",
"name": "run_shell_command",
"args": {
"description": "Test combinations of weighting and normalization",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\ndislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\ndef test_config(hl, org_mult, str_mult, non_org_mult, lw, dw, use_idf, norm_l2, k=64):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n rec = np.exp(-np.log(2) * delta_days / hl)\n comp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n s_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, str_mult, 1.0)\n o_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, org_mult, non_org_mult)\n weights = rec * comp * s_boost * o_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n vals = [weights]\n\n if lw > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=lw, dtype=np.float32))\n\n if dw > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n vals.append(np.full(len(dislikes_only), fill_value=-dw, dtype=np.float32))\n\n R = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_l2:\n R = normalize(R, norm=\"l2\", axis=1)\n\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"hl={hl} org={org_mult} non_org={non_org_mult} str={str_mult} lw={lw} dw={dw} idf={use_idf} norm={norm_l2} k={k} -> {score:.5f}\")\n return scores, score\n\nprint(\"--- Testing configurations ---\")\ntest_config(hl=10.0, org_mult=4.0, str_mult=3.0, non_org_mult=1.0, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.5, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=2.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True, k=96)\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_328629",
"name": "run_shell_command",
"response": {
"output": "\nOutput: --- Testing configurations ---\nhl=10.0 org=4.0 non_org=1.0 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03817\nhl=10.0 org=5.0 non_org=0.5 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03862\nhl=10.0 org=5.0 non_org=0.2 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03698\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03689\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=False norm=False k=64 -> 0.03758\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=2.0 idf=False norm=False k=64 -> 0.03701\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=64 -> 0.03714\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=96 -> 0.03660\nProcess Group PGID: 2366\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:34:33.715Z",
"resultDisplay": "--- Testing configurations ---\nhl=10.0 org=4.0 non_org=1.0 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03817\nhl=10.0 org=5.0 non_org=0.5 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03862\nhl=10.0 org=5.0 non_org=0.2 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03698\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03689\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=False norm=False k=64 -> 0.03758\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=2.0 idf=False norm=False k=64 -> 0.03701\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=64 -> 0.03714\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=96 -> 0.03660",
"description": "Test combinations of weighting and normalization",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:34:33.716Z",
"content": "\nOutput: --- Testing configurations ---\nhl=10.0 org=4.0 non_org=1.0 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03817\nhl=10.0 org=5.0 non_org=0.5 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03862\nhl=10.0 org=5.0 non_org=0.2 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03698\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03689\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=False norm=False k=64 -> 0.03758\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=2.0 idf=False norm=False k=64 -> 0.03701\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=64 -> 0.03714\nhl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=96 -> 0.03660\nProcess Group PGID: 2366\n",
"isError": false
},
"responseMessageId": "bef7a395-a65f-4210-8ccf-b3e1b4b2c5e8"
}
]
},
{
"id": 45,
"role": "agent",
"timestamp": "2026-09-29T04:34:45.049Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:34:45.049Z",
"text": "Testing New Parameters\n\nI've achieved a score of 0.03862 with the current configuration. My next steps involve exploring variations around `non_org` at 0.5 and 0.7, and `org` at 6.0 and 8.0. I'll also be adjusting the half-life to 8, 10, and 12 days to see how these impact performance."
},
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:34:45.049Z",
"text": "Incorporating Recency Decay\n\nI'm now considering incorporating recency decay for `train_likes`, as their timestamps offer valuable temporal information. I'm also exploring item frequency calibration to address potential biases in SVD scores and investigating ensembling various SVD models with different half-lives, alongside item-item similarity."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:34:45.049Z",
"callId": "run_shell_command__call_378313",
"name": "run_shell_command",
"input": {
"description": "Benchmark Cholesky factorization for 5000x5000",
"command": "python3 -c '\nimport time\nimport numpy as np\n\n# test 5000 x 5000\nA = np.random.randn(5000, 5000).astype(np.float32)\nG = A.T @ A + 10.0 * np.eye(5000, dtype=np.float32)\nt0 = time.time()\nnp.linalg.cholesky(G)\nprint(f\"5000x5000 Cholesky time: {time.time()-t0:.2f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_378313",
"name": "run_shell_command",
"args": {
"description": "Benchmark Cholesky factorization for 5000x5000",
"command": "python3 -c '\nimport time\nimport numpy as np\n\n# test 5000 x 5000\nA = np.random.randn(5000, 5000).astype(np.float32)\nG = A.T @ A + 10.0 * np.eye(5000, dtype=np.float32)\nt0 = time.time()\nnp.linalg.cholesky(G)\nprint(f\"5000x5000 Cholesky time: {time.time()-t0:.2f}s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_378313",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 5000x5000 Cholesky time: 0.72s\nProcess Group PGID: 2400\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:34:47.163Z",
"resultDisplay": "5000x5000 Cholesky time: 0.72s",
"description": "Benchmark Cholesky factorization for 5000x5000",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:34:47.164Z",
"content": "\nOutput: 5000x5000 Cholesky time: 0.72s\nProcess Group PGID: 2400\n",
"isError": false
},
"responseMessageId": "f8c2848b-a97a-4bd3-bcec-56ac39f12e8f"
}
]
},
{
"id": 46,
"role": "agent",
"timestamp": "2026-09-29T04:34:53.153Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:34:53.153Z",
"text": "Accelerating Cholesky Factorization\n\nI'm thrilled by the speed of the 5000x5000 Cholesky, clocking in at just 0.72s. I'm now contemplating scaling this up to 15,000x15,000 and even 30,000x30,000. Based on the $O(N^3)$ complexity, I estimate the larger matrices will take around 2.5 minutes. However, a question arises: is full EASE necessary?"
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:34:53.153Z",
"callId": "run_shell_command__call_329816",
"name": "run_shell_command",
"input": {
"description": "Benchmark 8456x8456 Cholesky time",
"command": "python3 -c '\nimport time\nimport numpy as np\n\nN = 8456\nA = np.random.randn(N, 64).astype(np.float32)\nG = A @ A.T + 10.0 * np.eye(N, dtype=np.float32)\n\nt0 = time.time()\nL = np.linalg.cholesky(G)\nprint(f\"8456x8456 Cholesky time: {time.time()-t0:.2f}s\")\n'"
},
"recorded": {
"id": "run_shell_command__call_329816",
"name": "run_shell_command",
"args": {
"description": "Benchmark 8456x8456 Cholesky time",
"command": "python3 -c '\nimport time\nimport numpy as np\n\nN = 8456\nA = np.random.randn(N, 64).astype(np.float32)\nG = A @ A.T + 10.0 * np.eye(N, dtype=np.float32)\n\nt0 = time.time()\nL = np.linalg.cholesky(G)\nprint(f\"8456x8456 Cholesky time: {time.time()-t0:.2f}s\")\n'"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_329816",
"name": "run_shell_command",
"response": {
"output": "\nOutput: 8456x8456 Cholesky time: 2.97s\nProcess Group PGID: 2410\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:34:56.635Z",
"resultDisplay": "8456x8456 Cholesky time: 2.97s",
"description": "Benchmark 8456x8456 Cholesky time",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:34:58.276Z",
"content": "\nOutput: 8456x8456 Cholesky time: 2.97s\nProcess Group PGID: 2410\n",
"isError": false
},
"responseMessageId": "234b3cfc-258f-4705-82ff-25d8d75083e2"
}
]
},
{
"id": 47,
"role": "agent",
"timestamp": "2026-09-29T04:35:11.299Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:35:11.299Z",
"text": "Calculating Cholesky Efficiency\n\nMy recent computations show an $8456 \\times 8456$ Cholesky decomposition completing in just 2.97 seconds, which is quite promising. My next step is to delve into the specifics of user-user EASE, particularly how to predict a user's interactions by modeling them based on other users' behaviors."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:35:11.299Z",
"callId": "run_shell_command__call_255101",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nt0 = time.time()\nprint(\"Computing G = R @ R.T...\")\nG = (R @ R.T).toarray()\nprint(f\"G computed in {time.time()-t0:.2f}s\")\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # dense x sparse\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test User-EASE collaborative filtering"
},
"recorded": {
"id": "run_shell_command__call_255101",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nt0 = time.time()\nprint(\"Computing G = R @ R.T...\")\nG = (R @ R.T).toarray()\nprint(f\"G computed in {time.time()-t0:.2f}s\")\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # dense x sparse\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test User-EASE collaborative filtering"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_255101",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Computing G = R @ R.T...\nG computed in 4.57s\nUser-EASE lam=100.0 -> NDCG@10: 0.02989 (27.08s)\nUser-EASE lam=500.0 -> NDCG@10: 0.03617 (26.94s)\nUser-EASE lam=1000.0 -> NDCG@10: 0.03871 (27.14s)\nUser-EASE lam=2000.0 -> NDCG@10: 0.04170 (27.07s)\nUser-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.89s)\nProcess Group PGID: 2420\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:37:43.951Z",
"resultDisplay": "Computing G = R @ R.T...\nG computed in 4.57s\nUser-EASE lam=100.0 -> NDCG@10: 0.02989 (27.08s)\nUser-EASE lam=500.0 -> NDCG@10: 0.03617 (26.94s)\nUser-EASE lam=1000.0 -> NDCG@10: 0.03871 (27.14s)\nUser-EASE lam=2000.0 -> NDCG@10: 0.04170 (27.07s)\nUser-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.89s)",
"description": "Test User-EASE collaborative filtering",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:37:43.952Z",
"content": "\nOutput: Computing G = R @ R.T...\nG computed in 4.57s\nUser-EASE lam=100.0 -> NDCG@10: 0.02989 (27.08s)\nUser-EASE lam=500.0 -> NDCG@10: 0.03617 (26.94s)\nUser-EASE lam=1000.0 -> NDCG@10: 0.03871 (27.14s)\nUser-EASE lam=2000.0 -> NDCG@10: 0.04170 (27.07s)\nUser-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.89s)\nProcess Group PGID: 2420\n",
"isError": false
},
"responseMessageId": "4b7a4df7-1f49-4789-82ab-efff260de6ec"
}
]
},
{
"id": 48,
"role": "agent",
"timestamp": "2026-09-29T04:37:57.982Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:37:57.982Z",
"text": "Refining User-EASE\n\nI've successfully boosted the User-EASE metric to 0.04417 with a lambda of 5000, a significant leap from previous methods like Naive and Tuned SVD. The convergence was remarkably swift. Now, I'm exploring the impact of even higher lambda values, such as 10,000 and 20,000, to further optimize performance."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:37:57.982Z",
"callId": "run_shell_command__call_153659",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nG = (R @ R.T).toarray()\n\nfor lam in [5000.0, 10000.0, 20000.0, 40000.0, 100000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test higher lambda values for User-EASE"
},
"recorded": {
"id": "run_shell_command__call_153659",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nG = (R @ R.T).toarray()\n\nfor lam in [5000.0, 10000.0, 20000.0, 40000.0, 100000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test higher lambda values for User-EASE"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_153659",
"name": "run_shell_command",
"response": {
"output": "\nOutput: User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.98s)\nUser-EASE lam=10000.0 -> NDCG@10: 0.04491 (26.89s)\nUser-EASE lam=20000.0 -> NDCG@10: 0.04296 (27.27s)\nUser-EASE lam=40000.0 -> NDCG@10: 0.04076 (27.10s)\nUser-EASE lam=100000.0 -> NDCG@10: 0.03593 (27.20s)\nProcess Group PGID: 2460\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:40:31.587Z",
"resultDisplay": "User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.98s)\nUser-EASE lam=10000.0 -> NDCG@10: 0.04491 (26.89s)\nUser-EASE lam=20000.0 -> NDCG@10: 0.04296 (27.27s)\nUser-EASE lam=40000.0 -> NDCG@10: 0.04076 (27.10s)\nUser-EASE lam=100000.0 -> NDCG@10: 0.03593 (27.20s)",
"description": "Test higher lambda values for User-EASE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:40:31.588Z",
"content": "\nOutput: User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.98s)\nUser-EASE lam=10000.0 -> NDCG@10: 0.04491 (26.89s)\nUser-EASE lam=20000.0 -> NDCG@10: 0.04296 (27.27s)\nUser-EASE lam=40000.0 -> NDCG@10: 0.04076 (27.10s)\nUser-EASE lam=100000.0 -> NDCG@10: 0.03593 (27.20s)\nProcess Group PGID: 2460\n",
"isError": false
},
"responseMessageId": "f0f98f64-a15e-4fbb-b969-5347a06346af"
}
]
},
{
"id": 49,
"role": "agent",
"timestamp": "2026-09-29T04:42:21.395Z",
"blocks": [
{
"kind": "reasoning",
"representation": "summary",
"timestamp": "2026-09-29T04:42:21.395Z",
"text": "Testing Lambda Values\n\nI'm exploring lambda values around 10000, specifically testing 7500, 10000, 12500, and 15000. I've also started investigating the impact of row-wise normalization of users prior to calculating G."
},
{
"kind": "tool",
"timestamp": "2026-09-29T04:42:21.395Z",
"callId": "run_shell_command__call_162060",
"name": "run_shell_command",
"input": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [10.0, 50.0, 100.0, 200.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # user weights applied to original R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"Cosine User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test Cosine User-EASE"
},
"recorded": {
"id": "run_shell_command__call_162060",
"name": "run_shell_command",
"args": {
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [10.0, 50.0, 100.0, 200.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # user weights applied to original R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"Cosine User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
"description": "Test Cosine User-EASE"
},
"result": [
{
"functionResponse": {
"id": "run_shell_command__call_162060",
"name": "run_shell_command",
"response": {
"output": "\nOutput: Cosine User-EASE lam=10.0 -> NDCG@10: 0.04518 (27.08s)\nCosine User-EASE lam=50.0 -> NDCG@10: 0.03998 (27.08s)\nCosine User-EASE lam=100.0 -> NDCG@10: 0.03626 (27.21s)\nCosine User-EASE lam=200.0 -> NDCG@10: 0.03315 (27.14s)\nCosine User-EASE lam=500.0 -> NDCG@10: 0.02964 (27.01s)\nCosine User-EASE lam=1000.0 -> NDCG@10: 0.02779 (27.10s)\nProcess Group PGID: 2506\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:45:22.059Z",
"resultDisplay": "Cosine User-EASE lam=10.0 -> NDCG@10: 0.04518 (27.08s)\nCosine User-EASE lam=50.0 -> NDCG@10: 0.03998 (27.08s)\nCosine User-EASE lam=100.0 -> NDCG@10: 0.03626 (27.21s)\nCosine User-EASE lam=200.0 -> NDCG@10: 0.03315 (27.14s)\nCosine User-EASE lam=500.0 -> NDCG@10: 0.02964 (27.01s)\nCosine User-EASE lam=1000.0 -> NDCG@10: 0.02779 (27.10s)",
"description": "Test Cosine User-EASE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:45:22.060Z",
"content": "\nOutput: Cosine User-EASE lam=10.0 -> NDCG@10: 0.04518 (27.08s)\nCosine User-EASE lam=50.0 -> NDCG@10: 0.03998 (27.08s)\nCosine User-EASE lam=100.0 -> NDCG@10: 0.03626 (27.21s)\nCosine User-EASE lam=200.0 -> NDCG@10: 0.03315 (27.14s)\nCosine User-EASE lam=500.0 -> NDCG@10: 0.02964 (27.01s)\nCosine User-EASE lam=1000.0 -> NDCG@10: 0.02779 (27.10s)\nProcess Group PGID: 2506\n",
"isError": false
},
"responseMessageId": "b8e9a2b4-f3aa-4a15-8697-1641a6b6951c"
}
]
},
{
"id": 50,
"role": "agent",
"timestamp": "2026-09-29T04:45:32.921Z",
"blocks": [
{
"kind": "tool",
"timestamp": "2026-09-29T04:45:32.921Z",
"callId": "run_shell_command__call_377070",
"name": "run_shell_command",
"input": {
"description": "Test fine lambda grid and R vs R_norm for Cosine User-EASE",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [1.0, 2.0, 5.0, 10.0, 15.0]:\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n \n # Try B @ R vs B @ R_norm\n for use_r_norm in [False, True]:\n scores = B @ (R_norm if use_r_norm else R)\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"lam={lam} use_r_norm={use_r_norm} -> NDCG@10: {score:.5f}\")\n'"
},
"recorded": {
"id": "run_shell_command__call_377070",
"name": "run_shell_command",
"args": {
"description": "Test fine lambda grid and R vs R_norm for Cosine User-EASE",
"command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [1.0, 2.0, 5.0, 10.0, 15.0]:\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n \n # Try B @ R vs B @ R_norm\n for use_r_norm in [False, True]:\n scores = B @ (R_norm if use_r_norm else R)\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"lam={lam} use_r_norm={use_r_norm} -> NDCG@10: {score:.5f}\")\n'"
},
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"output": "\nOutput: lam=1.0 use_r_norm=False -> NDCG@10: 0.03891\nlam=1.0 use_r_norm=True -> NDCG@10: 0.03356\nlam=2.0 use_r_norm=False -> NDCG@10: 0.04149\nlam=2.0 use_r_norm=True -> NDCG@10: 0.03741\nlam=5.0 use_r_norm=False -> NDCG@10: 0.04439\nlam=5.0 use_r_norm=True -> NDCG@10: 0.04081\nlam=10.0 use_r_norm=False -> NDCG@10: 0.04518\nlam=10.0 use_r_norm=True -> NDCG@10: 0.04312\nlam=15.0 use_r_norm=False -> NDCG@10: 0.04467\nlam=15.0 use_r_norm=True -> NDCG@10: 0.04337\nProcess Group PGID: 2552\n"
}
}
}
],
"status": "success",
"timestamp": "2026-09-29T04:48:58.713Z",
"resultDisplay": "lam=1.0 use_r_norm=False -> NDCG@10: 0.03891\nlam=1.0 use_r_norm=True -> NDCG@10: 0.03356\nlam=2.0 use_r_norm=False -> NDCG@10: 0.04149\nlam=2.0 use_r_norm=True -> NDCG@10: 0.03741\nlam=5.0 use_r_norm=False -> NDCG@10: 0.04439\nlam=5.0 use_r_norm=True -> NDCG@10: 0.04081\nlam=10.0 use_r_norm=False -> NDCG@10: 0.04518\nlam=10.0 use_r_norm=True -> NDCG@10: 0.04312\nlam=15.0 use_r_norm=False -> NDCG@10: 0.04467\nlam=15.0 use_r_norm=True -> NDCG@10: 0.04337",
"description": "Test fine lambda grid and R vs R_norm for Cosine User-EASE",
"displayName": "Shell",
"renderOutputAsMarkdown": false
},
"result": {
"timestamp": "2026-09-29T04:48:58.715Z",
"content": "\nOutput: lam=1.0 use_r_norm=False -> NDCG@10: 0.03891\nlam=1.0 use_r_norm=True -> NDCG@10: 0.03356\nlam=2.0 use_r_norm=False -> NDCG@10: 0.04149\nlam=2.0 use_r_norm=True -> NDCG@10: 0.03741\nlam=5.0 use_r_norm=False -> NDCG@10: 0.04439\nlam=5.0 use_r_norm=True -> NDCG@10: 0.04081\nlam=10.0 use_r_norm=False -> NDCG@10: 0.04518\nlam=10.0 use_r_norm=True -> NDCG@10: 0.04312\nlam=15.0 use_r_norm=False -> NDCG@10: 0.04467\nlam=15.0 use_r_norm=True -> NDCG@10: 0.04337\nProcess Group PGID: 2552\n",
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},
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"role": "agent",
"timestamp": "2026-09-29T04:49:31.517Z",
"blocks": [
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"representation": "summary",
"timestamp": "2026-09-29T04:49:31.517Z",
"text": "Exploring Ensemble Methods\n\nI'm exploring how ensembling Cosine User-EASE with TruncatedSVD might improve performance. My current focus is understanding how these distinct structural representations, User-EASE and SVD, might complement each other when combined."
}
]
}
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