Agent trace

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{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-fc0163c6-5c90-46d9-8282-3c11caa1cd11",
    "process_id": "3418",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,200p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,160p' /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:58 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:58 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:58 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:58 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:58 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:58 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:58 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:58 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:58 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:58 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:58 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:58 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:58 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:58 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 validation_preference_state.parquet\n\"\"\"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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:58 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:58 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:58 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:58 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:58 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:58 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:58 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:58 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:58 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:58 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:58 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:58 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:58 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:58 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 validation_preference_state.parquet\n\"\"\"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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6440
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 23:58 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 23:58 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 23:58 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 23:58 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 23:58 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 23:58 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 23:58 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 23:58 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 23:58 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 23:58 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 23:58 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 23:58 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 23:58 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 23:58 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 23:58 validation_preference_state.parquet\n\"\"\"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#!/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 <validation-ranking.parquet|csv>\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 <validation-ranking.parquet|csv>\"\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# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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    "stdout": "total 36\ndr-xr-xr-x 1 root root 4096 Sep 25 00:11 .\ndrwxr-xr-x 1 root root 4096 Sep 25 00:15 ..\n-r--r--r-- 1 root root    0 Sep 24 23:58 .keep\ndr-xr-xr-x 1 root root 4096 Sep 24 23:58 data\n-r--r--r-- 1 root root 5774 Sep 24 23:58 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 24 23:58 ranking_evaluation.py\n-r--r--r-- 1 root root 1295 Sep 24 23:58 requirements.txt\n/task/data/val_likes.parquet\n/task/data/candidate_catalog.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/train_unlikes.parquet\n/task/data/train_likes.parquet\n/task/data/train_listens.parquet\n/task/data/validation_preference_state.parquet\n/task/data/test_preference_state.parquet\n/task/data/train_undislikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/val_unlikes.parquet\n/task/data/target_users.parquet\n/task/data/val_undislikes.parquet\n/task/data/val_dislikes.parquet\n/task/requirements.txt\n/task/evaluate_public.py\n/task/.keep\n/task/ranking_evaluation.py\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_MANAGED_BY_NPM=1\nCODEX_MANAGED_PACKAGE_ROOT=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex\nCODEX_SESSION_ID=01a0d5eb-3bb3-7850-905a-4a25a20c20c6\nCODEX_THREAD_ID=01a0d5eb-3bb3-7850-905a-4a25a20c20c6\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e3fea70142a8\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nNVM_BIN=/home/agent/.nvm/versions/node/v22.23.3/bin\nNVM_CD_FLAGS=\nNVM_DIR=/home/agent/.nvm\nNVM_INC=/home/agent/.nvm/versions/node/v22.23.3/include/node\nPAGER=cat\nPATH=/home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/codex-path:/home/agent/.nvm/versions/node/v22.23.3/bin:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/home/agent/.nvm/versions/node/v22.23.3/bin/codex\n",
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    "source": "unified_exec_startup",
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      "-lc",
      "ls -l /opt/task-venv/bin/python*; /opt/task-venv/bin/python - <<'PY'\nimport torch,pandas,pyarrow,numpy\nprint(torch.__version__,pandas.__version__)\nPY\n/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, os\nfor f in sorted(os.listdir('/task/data')):\n if f.endswith('.parquet'):\n  pf=pq.ParquetFile('/task/data/'+f)\n  print(f, pf.metadata.num_rows, pf.schema.names)\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load('/task/data/'+f)\n n=len(z['item_ids']); c=np.unpackbits(z['packed_eligible'],axis=1,count=n,bitorder='little').sum(1)\n print(f,{k:(z[k].shape,str(z[k].dtype)) for k in z.files},'eligible',c.mean(),c.min(),c.max())\nprint('targets',pd.read_parquet('/task/data/target_users.parquet').head())\nprint('catalog',pd.read_parquet('/task/data/candidate_catalog.parquet').head())\nPY"
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    "source": "unified_exec_startup",
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    "stdout": "2.8.0+cpu 2.3.3\ncandidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29067.306291390727 24657 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29041.3178807947 24552 30000\ntargets    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ncatalog    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\n",
    "stderr": "",
    "aggregated_output": "2.8.0+cpu 2.3.3\ncandidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29067.306291390727 24657 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29041.3178807947 24552 30000\ntargets    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ncatalog    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\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 96442119
    },
    "formatted_output": "2.8.0+cpu 2.3.3\ncandidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29067.306291390727 24657 30000\ntest_eligible_candidates.npz {'uids': ((8456,), 'uint32'), 'item_ids': ((30000,), 'uint32'), 'packed_eligible': ((8456, 3750), 'uint8')} eligible 29041.3178807947 24552 30000\ntargets    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\ncatalog    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\n"
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}

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  "item": {
    "type": "CommandExecution",
    "id": "exec-12c542f6-f200-4337-957a-9d78e541772e",
    "process_id": "22237",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h; nproc; df -h /app"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h; nproc; df -h /app"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3311
    },
    "formatted_output": "/bin/sh: 1: free: not found\n8\nFilesystem      Size  Used Avail Use% Mounted on\noverlay          96G   15G   82G  16% /\n"
  }
}

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-a6b747f4-b407-4ab5-a244-eee27f2d126f",
    "process_id": "65357",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pyarrow.compute as pc, pyarrow as pa, pandas as pd, numpy as np, time\nT=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); C=set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.astype(int))\nfor fn in ['train_listens.parquet','train_likes.parquet','train_dislikes.parquet','val_listens.parquet','val_likes.parquet']:\n t=time.time(); n=nt=nc=0; us=set(); its=set()\n pf=pq.ParquetFile('/task/data/'+fn)\n cols=['uid','item_id']\n if 'listens' in fn: cols+=['played_ratio_pct','is_organic','timestamp']\n else: cols += ['is_organic','timestamp']\n for b in pf.iter_batches(batch_size=1000000,columns=cols):\n  d=b.to_pandas(); n+=len(d); d=d[d.uid.isin(T) & d.item_id.isin(C)]\n  nt+=len(d); us.update(d.uid.unique());its.update(d.item_id.unique())\n print(fn,n,nt,len(us),len(its),'sec',time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pyarrow.compute as pc, pyarrow as pa, pandas as pd, numpy as np, time\nT=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int)); C=set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.astype(int))\nfor fn in ['train_listens.parquet','train_likes.parquet','train_dislikes.parquet','val_listens.parquet','val_likes.parquet']:\n t=time.time(); n=nt=nc=0; us=set(); its=set()\n pf=pq.ParquetFile('/task/data/'+fn)\n cols=['uid','item_id']\n if 'listens' in fn: cols+=['played_ratio_pct','is_organic','timestamp']\n else: cols += ['is_organic','timestamp']\n for b in pf.iter_batches(batch_size=1000000,columns=cols):\n  d=b.to_pandas(); n+=len(d); d=d[d.uid.isin(T) & d.item_id.isin(C)]\n  nt+=len(d); us.update(d.uid.unique());its.update(d.item_id.unique())\n print(fn,n,nt,len(us),len(its),'sec',time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens.parquet 43484159 31062540 8452 30000 sec 8.034828901290894\ntrain_likes.parquet 814831 472889 6874 28606 sec 0.15238618850708008\ntrain_dislikes.parquet 101075 54901 4504 18602 sec 0.03159689903259277\nval_listens.parquet 1501697 999062 7574 29386 sec 0.2928323745727539\nval_likes.parquet 37795 18307 2882 9206 sec 0.019199848175048828\n",
    "stderr": "",
    "aggregated_output": "train_listens.parquet 43484159 31062540 8452 30000 sec 8.034828901290894\ntrain_likes.parquet 814831 472889 6874 28606 sec 0.15238618850708008\ntrain_dislikes.parquet 101075 54901 4504 18602 sec 0.03159689903259277\nval_listens.parquet 1501697 999062 7574 29386 sec 0.2928323745727539\nval_likes.parquet 37795 18307 2882 9206 sec 0.019199848175048828\n",
    "exit_code": 0,
    "duration": {
      "secs": 9,
      "nanos": 951256873
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}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-25acce7e-9b3e-4261-8931-e2fc3e1e66d9",
    "changes": {
      "/app/solution/explore_baselines.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Create leakage-safe diagnostic validation baselines.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nDATA = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef eligible(path):\n    z=np.load(path)\n    return z['uids'].astype(np.int64),z['item_ids'].astype(np.int64),np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').astype(bool)\n\ndef write_rank(path,uids,items,mask,base_order,boost=None):\n    rows=[]\n    pos={int(x):i for i,x in enumerate(items)}\n    for r,u in enumerate(uids):\n        chosen=[]\n        if boost is not None:\n            for x in boost.get(int(u),[]):\n                j=pos.get(int(x),-1)\n                if j>=0 and mask[r,j] and int(x) not in chosen: chosen.append(int(x))\n                if len(chosen)>=100: break\n        if len(chosen)<100:\n            for j in base_order:\n                x=int(items[j])\n                if mask[r,j] and x not in chosen: chosen.append(x)\n                if len(chosen)>=100: break\n        rows.extend((int(u),x,k+1) for k,x in enumerate(chosen))\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n\ndef main():\n    uids,items,mask=eligible(DATA/'validation_eligible_candidates.npz')\n    cat=pd.read_parquet(DATA/'candidate_catalog.parquet').set_index('item_id').loc[items]\n    pop=np.argsort(-cat.positive_listens.to_numpy(),kind='stable')\n    opop=np.argsort(-cat.organic_positive_listens.to_numpy(),kind='stable')\n    write_rank(OUT/'val_pop.parquet',uids,items,mask,pop)\n    write_rank(OUT/'val_opop.parquet',uids,items,mask,opop)\n    pref=pd.read_parquet(DATA/'validation_preference_state.parquet')\n    likes=pref[pref.state=='liked'].groupby('uid').item_id.apply(list).to_dict()\n    write_rank(OUT/'val_likes_pop.parquet',uids,items,mask,pop,likes)\n\n    targets=set(map(int,uids)); catalog=set(map(int,items)); parts=[]\n    pf=pq.ParquetFile(DATA/'train_listens.parquet')\n    for b in pf.iter_batches(batch_size=1_000_000,columns=['uid','item_id','timestamp','played_ratio_pct','is_organic']):\n        d=b.to_pandas()\n        d=d[d.uid.isin(targets)&d.item_id.isin(catalog)&(d.played_ratio_pct>=50)]\n        if len(d):\n            d['strong']=((d.played_ratio_pct>=80)&(d.is_organic==1)).astype(np.int16)\n            parts.append(d.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),last=('timestamp','max'),strong=('strong','sum')).reset_index())\n    h=pd.concat(parts,ignore_index=True).groupby(['uid','item_id'],sort=False).agg(n=('n','sum'),last=('last','max'),strong=('strong','sum')).reset_index()\n    # Several diagnostic orderings; these are not claimed as the final model.\n    cutoff=24_786_800\n    for name,score in {\n        'hist_count':np.log1p(h.n),\n        'hist_recent':np.log1p(h.n)*np.exp(-(cutoff-h['last'])/(86400*30)),\n        'hist_strong':np.log1p(h.n+3*h.strong)*np.exp(-(cutoff-h['last'])/(86400*60)),\n    }.items():\n        h['_s']=score\n        boost=h.sort_values(['uid','_s'],ascending=[True,False]).groupby('uid').item_id.apply(list).to_dict()\n        write_rank(OUT/f'val_{name}.parquet',uids,items,mask,pop,boost)\n\nif __name__=='__main__': main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/explore_baselines.py\n",
    "stderr": ""
  }
}

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    "command": [
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      "/opt/task-venv/bin/python solution/explore_baselines.py && for f in solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/explore_baselines.py && for f in solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 49,
      "nanos": 18239530
    },
    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n/opt/task-venv/lib/python3.12/site-packages/pandas/core/arraylike.py:399: RuntimeWarning: overflow encountered in exp\n  result = getattr(ufunc, method)(*inputs, **kwargs)\n{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-a4b4b933-8724-4dd7-8fe3-76021d445e24",
    "process_id": "96279",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh solution; for f in solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -lh solution; for f in solution/val_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 20,
      "nanos": 147941560
    },
    "formatted_output": "{\n  \"model\": \"solution/val_hist_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_recent.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_hist_strong.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_likes_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_opop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_users\": 4263\n  }\n}\n{\n  \"model\": \"solution/val_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

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  "thread_id": "01a0d5eb-3bb3-7850-905a-4a25a20c20c6",
  "turn_id": "01a0d5eb-3bc4-7200-b362-dc33e68e8d3b",
  "session_id": "01a0d5eb-3bb3-7850-905a-4a25a20c20c6",
  "root_turn_id": "01a0d5eb-3bc4-7200-b362-dc33e68e8d3b",
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  "usage": {
    "input_tokens": 26011,
    "cached_input_tokens": 25216,
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    "reasoning_output_tokens": 199,
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  },
  "turn_token_usage": {
    "input_tokens": 279902,
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    "reasoning_output_tokens": 1339,
    "total_tokens": 284399
  },
  "thread_token_usage": {
    "input_tokens": 279902,
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    "reasoning_output_tokens": 1339,
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-01a3342f-7b6d-44a5-bc99-f194394033dc",
    "process_id": "43921",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor f in ['train_listens.parquet','train_likes.parquet','val_listens.parquet']:\n p=pq.ParquetFile('/task/data/'+f); s=p.schema_arrow.get_field_index('timestamp')\n mins=[];maxs=[]\n for i in range(p.num_row_groups):\n  st=p.metadata.row_group(i).column(s).statistics\n  if st: mins.append(st.min);maxs.append(st.max)\n print(f,min(mins),max(maxs),p.num_row_groups)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfor f in ['train_listens.parquet','train_likes.parquet','val_listens.parquet']:\n p=pq.ParquetFile('/task/data/'+f); s=p.schema_arrow.get_field_index('timestamp')\n mins=[];maxs=[]\n for i in range(p.num_row_groups):\n  st=p.metadata.row_group(i).column(s).statistics\n  if st: mins.append(st.min);maxs.append(st.max)\n print(f,min(mins),max(maxs),p.num_row_groups)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens.parquet 0 24786795 42\ntrain_likes.parquet 80 24786790 1\nval_listens.parquet 24788600 25393395 2\n",
    "stderr": "",
    "aggregated_output": "train_listens.parquet 0 24786795 42\ntrain_likes.parquet 80 24786790 1\nval_listens.parquet 24788600 25393395 2\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 313644822
    },
    "formatted_output": "train_listens.parquet 0 24786795 42\ntrain_likes.parquet 80 24786790 1\nval_listens.parquet 24788600 25393395 2\n"
  }
}

{
  "type": "token_usage_record",
  "thread_id": "01a0d5eb-3bb3-7850-905a-4a25a20c20c6",
  "turn_id": "01a0d5eb-3bc4-7200-b362-dc33e68e8d3b",
  "session_id": "01a0d5eb-3bb3-7850-905a-4a25a20c20c6",
  "root_turn_id": "01a0d5eb-3bc4-7200-b362-dc33e68e8d3b",
  "response_id": "resp_0367c4963433099f016ab5bde767b887d29ac512bfd23413d2",
  "usage": {
    "input_tokens": 26460,
    "cached_input_tokens": 25856,
    "cache_write_input_tokens": 0,
    "output_tokens": 375,
    "reasoning_output_tokens": 148,
    "total_tokens": 26835
  },
  "turn_token_usage": {
    "input_tokens": 306362,
    "cached_input_tokens": 280192,
    "cache_write_input_tokens": 0,
    "output_tokens": 4872,
    "reasoning_output_tokens": 1487,
    "total_tokens": 311234
  },
  "thread_token_usage": {
    "input_tokens": 306362,
    "cached_input_tokens": 280192,
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    "reasoning_output_tokens": 1487,
    "total_tokens": 311234
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f377cfd5-7c17-4d9e-822e-58d0c5041be8",
    "process_id": "68189",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np,time\nitems=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy(); S=set(map(int,items)); n=0;us=set(); recent=0\np=pq.ParquetFile('/task/data/train_listens.parquet');t=time.time()\nfor b in p.iter_batches(batch_size=1000000,columns=['uid','item_id','timestamp','played_ratio_pct']):\n d=b.to_pandas(); d=d[(d.played_ratio_pct>=50)&d.item_id.isin(S)];n+=len(d);recent+=(d.timestamp>=24180200).sum();us.update(d.uid.unique())\nprint(n,len(us),recent,time.time()-t)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np,time\nitems=pd.read_parquet('/task/data/candidate_catalog.parquet').item_id.to_numpy(); S=set(map(int,items)); n=0;us=set(); recent=0\np=pq.ParquetFile('/task/data/train_listens.parquet');t=time.time()\nfor b in p.iter_batches(batch_size=1000000,columns=['uid','item_id','timestamp','played_ratio_pct']):\n d=b.to_pandas(); d=d[(d.played_ratio_pct>=50)&d.item_id.isin(S)];n+=len(d);recent+=(d.timestamp>=24180200).sum();us.update(d.uid.unique())\nprint(n,len(us),recent,time.time()-t)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "20056329 9135 675750 4.569034099578857\n",
    "stderr": "",
    "aggregated_output": "20056329 9135 675750 4.569034099578857\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
      "nanos": 976957629
    },
    "formatted_output": "20056329 9135 675750 4.569034099578857\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-e157270a-8ab1-4d59-a514-4439e73ae373",
    "changes": {
      "/app/solution/train_two_tower.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-to-item PyTorch two-tower ranker.\n\nThe script never forms a user-item matrix.  Rolling temporal examples contain a\nbounded history bag and one or more later, previously unseen organic positives.\nAt serving time candidate vectors are computed once and each request computes\nexactly one user vector; ranking is their inner product.\n\"\"\"\nfrom __future__ import annotations\nimport argparse, json, random, time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nDATA=Path('/task/data'); OUT=Path('/app/solution')\nTRAIN_END=24_786_800\nDAY=86_400\nHISTORY_DAYS=150\nMAX_HISTORY=240\nDIM=80\n\ndef seed_all(seed=20260925):\n    random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)\n\ndef load_events(include_val: bool):\n    \"\"\"Load only positive catalog interactions, bounded to useful recency.\"\"\"\n    catalog=pd.read_parquet(DATA/'candidate_catalog.parquet')\n    item_to_idx={int(x):i for i,x in enumerate(catalog.item_id)}\n    frames=[]\n    files=['train_listens.parquet']+(['val_listens.parquet'] if include_val else [])\n    min_ts=TRAIN_END-(HISTORY_DAYS+28)*DAY\n    for fn in files:\n        pf=pq.ParquetFile(DATA/fn)\n        for b in pf.iter_batches(batch_size=1_000_000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n            d=b.to_pandas()\n            d=d[(d.timestamp>=min_ts)&(d.played_ratio_pct>=50)]\n            ix=d.item_id.map(item_to_idx)\n            ok=ix.notna()\n            if ok.any():\n                q=d.loc[ok,['uid','timestamp','is_organic','played_ratio_pct']].copy()\n                q['item']=ix[ok].astype(np.int32).to_numpy()\n                q['strong']=((q.is_organic.to_numpy()==1)&(q.played_ratio_pct.to_numpy()>=80)).astype(np.uint8)\n                frames.append(q[['uid','timestamp','item','strong']])\n    ev=pd.concat(frames,ignore_index=True)\n    ev=ev.astype({'uid':'int32','timestamp':'int32','item':'int32','strong':'uint8'})\n    # Organic likes carry the higher-value discovery signal.\n    likes=[]\n    files=['train_likes.parquet']+(['val_likes.parquet'] if include_val else [])\n    for fn in files:\n        d=pd.read_parquet(DATA/fn,columns=['uid','timestamp','item_id','is_organic'])\n        d=d[(d.timestamp>=min_ts)&(d.is_organic==1)]\n        ix=d.item_id.map(item_to_idx); ok=ix.notna()\n        if ok.any():\n            q=d.loc[ok,['uid','timestamp']].copy();q['item']=ix[ok].astype(np.int32).to_numpy();q['strong']=np.uint8(3)\n            likes.append(q)\n    lk=pd.concat(likes,ignore_index=True).astype({'uid':'int32','timestamp':'int32','item':'int32','strong':'uint8'})\n    return catalog,ev,lk\n\ndef snapshot(ev,likes,cutoff):\n    \"\"\"Make bounded histories and next-week unseen relevant-label lists.\"\"\"\n    lo=cutoff-HISTORY_DAYS*DAY\n    h=ev[(ev.timestamp<cutoff)&(ev.timestamp>=lo)]\n    # One row per user/item. Recency, frequency, and strong plays weight the bag.\n    a=h.groupby(['uid','item'],sort=False).agg(last=('timestamp','max'),count=('timestamp','size'),strong=('strong','sum')).reset_index()\n    a=a.sort_values(['uid','last'],ascending=[True,False]).groupby('uid',sort=False).head(MAX_HISTORY)\n    a['weight']=(1+np.log1p(a['count'])+.35*np.log1p(a['strong']))*np.exp(-(cutoff-a['last'])/(75*DAY))\n    groups={int(u):(g.item.to_numpy(np.int32),g.weight.to_numpy(np.float32)) for u,g in a.groupby('uid',sort=False)}\n    end=cutoff+7*DAY\n    le=ev[(ev.timestamp>=cutoff)&(ev.timestamp<end)&(ev.strong>0)][['uid','item','strong']]\n    ll=likes[(likes.timestamp>=cutoff)&(likes.timestamp<end)][['uid','item','strong']]\n    lab=pd.concat([le,ll],ignore_index=True).groupby(['uid','item'],sort=False).strong.max().reset_index()\n    # Mimic eligibility novelty without ever constructing a global matrix.\n    hist_pairs=set(zip(a.uid.astype(int),a.item.astype(int)))\n    lab=lab[[((int(u),int(i)) not in hist_pairs) for u,i in lab[['uid','item']].itertuples(index=False,name=None)]]\n    bylab={int(u):(g.item.to_numpy(np.int32),g.strong.to_numpy(np.float32)) for u,g in lab.groupby('uid',sort=False)}\n    users=sorted(set(groups)&set(bylab))\n    return [(u,*groups[u],*bylab[u]) for u in users]\n\ndef inference_profiles(ev, cutoff, target_uids):\n    h=ev[(ev.timestamp<cutoff)&(ev.timestamp>=cutoff-HISTORY_DAYS*DAY)]\n    a=h.groupby(['uid','item'],sort=False).agg(last=('timestamp','max'),count=('timestamp','size'),strong=('strong','sum')).reset_index()\n    a=a.sort_values(['uid','last'],ascending=[True,False]).groupby('uid',sort=False).head(MAX_HISTORY)\n    a['weight']=(1+np.log1p(a['count'])+.35*np.log1p(a['strong']))*np.exp(-(cutoff-a['last'])/(75*DAY))\n    groups={int(u):(g.item.to_numpy(np.int32),g.weight.to_numpy(np.float32)) for u,g in a.groupby('uid',sort=False)}\n    prof=[]\n    for u in target_uids:\n        x,w=groups.get(int(u),(np.empty(0,np.int32),np.empty(0,np.float32)))\n        prof.append((int(u),x,w))\n    return prof\n\ndef pack_hist(examples):\n    n=len(examples); ids=np.full((n,MAX_HISTORY),-1,np.int64); weights=np.zeros((n,MAX_HISTORY),np.float32)\n    for r,e in enumerate(examples):\n        x,w=e[1],e[2]; k=min(len(x),MAX_HISTORY); ids[r,:k]=x[:k];weights[r,:k]=w[:k]\n    return ids,weights\n\nclass UserTower(nn.Module):\n    def __init__(self,n_items,dim):\n        super().__init__(); self.context=nn.Embedding(n_items+1,dim,padding_idx=n_items)\n        self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim))\n    def forward(self,ids,weights):\n        pad=self.context.num_embeddings-1; x=self.context(ids.clamp_min(0).where(ids.ge(0),torch.full_like(ids,pad)))\n        z=(x*weights.unsqueeze(-1)).sum(1)/weights.sum(1,keepdim=True).clamp_min(1e-6)\n        return F.normalize(z+.35*self.net(z),dim=-1)\n\nclass ItemTower(nn.Module):\n    def __init__(self,n_items,dim,pop_feature):\n        super().__init__(); self.item=nn.Embedding(n_items,dim)\n        self.register_buffer('pop',torch.as_tensor(pop_feature,dtype=torch.float32).reshape(-1,1))\n        self.meta=nn.Sequential(nn.Linear(1,16),nn.GELU(),nn.Linear(16,dim))\n        self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim))\n    def forward(self,ids):\n        z=self.item(ids)+.15*self.meta(self.pop[ids])\n        return F.normalize(z+.35*self.net(z),dim=-1)\n\nclass TwoTower(nn.Module):\n    def __init__(self,n_items,pop):\n        super().__init__();self.user_tower=UserTower(n_items,DIM);self.item_tower=ItemTower(n_items,DIM,pop)\n        self.log_temperature=nn.Parameter(torch.tensor(2.7))\n    def user(self,ids,w): return self.user_tower(ids,w)\n    def item(self,ids): return self.item_tower(ids)\n\ndef train_model(model, examples, popularity, epochs=18):\n    ids,w=pack_hist(examples); n=len(examples); rng=np.random.default_rng(91027)\n    # Smoothed popularity produces useful hard negatives while retaining tail coverage.\n    negp=np.power(popularity.astype(np.float64)+5,.55);negp/=negp.sum()\n    opt=torch.optim.AdamW(model.parameters(),lr=2.5e-3,weight_decay=2e-5)\n    batch=256\n    for epoch in range(epochs):\n        order=rng.permutation(n); total=0.; seen=0\n        for st in range(0,n,batch):\n            ix=order[st:st+batch]\n            pos=np.empty(len(ix),np.int64); grade=np.empty(len(ix),np.float32)\n            for j,r in enumerate(ix):\n                li,lg=examples[r][3],examples[r][4]\n                p=lg.astype(np.float64);p/=p.sum(); k=rng.choice(len(li),p=p)\n                pos[j]=li[k];grade[j]=lg[k]\n            neg=rng.choice(len(popularity),size=(len(ix),48),p=negp).astype(np.int64)\n            ti=torch.from_numpy(ids[ix]);tw=torch.from_numpy(w[ix]);tp=torch.from_numpy(pos);tn=torch.from_numpy(neg)\n            u=model.user(ti,tw); pv=model.item(tp);nv=model.item(tn)\n            scale=model.log_temperature.exp().clamp(max=50)\n            ps=(u*pv).sum(-1)*scale; ns=torch.einsum('bd,bkd->bk',u,nv)*scale\n            wt=torch.from_numpy(1+.35*(grade-1))\n            loss=(F.softplus(-ps)*wt).mean()+F.softplus(ns).mean()\n            opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n            total+=float(loss)*len(ix);seen+=len(ix)\n        print(f'epoch {epoch+1:02d} loss={total/seen:.5f}',flush=True)\n    return model\n\[email protected]_grad()\ndef rank(model, profiles, eligibility_path, catalog, path):\n    z=np.load(eligibility_path);uids=z['uids'].astype(np.int64);items=z['item_ids'].astype(np.int64)\n    mask=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n    # Archives and catalog are aligned by ID, but map explicitly for robustness.\n    idx={int(x):i for i,x in enumerate(catalog.item_id)}; item_ix=np.asarray([idx[int(x)] for x in items],np.int64)\n    model.eval(); ivec=[]\n    for st in range(0,len(items),1024): ivec.append(model.item(torch.from_numpy(item_ix[st:st+1024])).cpu())\n    ivec=torch.cat(ivec)\n    pmap={u:(x,w) for u,x,w in profiles}; ordered=[(int(u),*pmap[int(u)]) for u in uids]\n    hi,hw=pack_hist(ordered); rows=[]\n    for st in range(0,len(uids),128):\n        uv=model.user(torch.from_numpy(hi[st:st+128]),torch.from_numpy(hw[st:st+128]))\n        score=([email protected]).numpy(); score[~mask[st:st+len(score)]]=-np.inf\n        top=np.argpartition(-score,99,axis=1)[:,:100]\n        top=top[np.arange(len(top))[:,None],np.argsort(-score[np.arange(len(top))[:,None],top],axis=1)]\n        for r,cols in enumerate(top): rows.extend((int(uids[st+r]),int(items[c]),k+1) for k,c in enumerate(cols))\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n\ndef main():\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);ap.add_argument('--epochs',type=int,default=18);args=ap.parse_args()\n    seed_all(); final=args.stage=='final';catalog,ev,likes=load_events(final)\n    cutoffs=[TRAIN_END-21*DAY,TRAIN_END-14*DAY,TRAIN_END-7*DAY]\n    if final: cutoffs.append(TRAIN_END)\n    examples=[]\n    for c in cutoffs:\n        s=snapshot(ev,likes,c);examples.extend(s);print('snapshot',c,'examples',len(s),flush=True)\n    pop=catalog.positive_listens.to_numpy(np.float32); popfeat=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\n    model=TwoTower(len(catalog),popfeat);train_model(model,examples,pop,args.epochs)\n    if final:\n        infer_cut=int(max(ev.timestamp.max(),likes.timestamp.max()))+1;elig=DATA/'test_eligible_candidates.npz';dest=OUT/'submission.parquet'\n    else:\n        infer_cut=TRAIN_END;elig=DATA/'validation_eligible_candidates.npz';dest=OUT/'validation.parquet'\n    target=np.load(elig)['uids'].astype(np.int64);profiles=inference_profiles(ev,infer_cut,target)\n    rank(model,profiles,elig,catalog,dest)\n    torch.save({'state_dict':model.state_dict(),'n_items':len(catalog),'dim':DIM,'stage':args.stage,'cutoff':infer_cut},OUT/f'model_{args.stage}.pt')\n    print(json.dumps({'stage':args.stage,'examples':len(examples),'ranking':str(dest),'cutoff':infer_cut}))\n\nif __name__=='__main__': main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/train_two_tower.py\n",
    "stderr": ""
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    "stdout": "snapshot 22972400 examples 4778\r\nsnapshot 23577200 examples 4862\r\nsnapshot 24182000 examples 5074\r\n/app/solution/train_two_tower.py:150: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(ix);seen+=len(ix)\r\nepoch 01 loss=1.80411\r\nepoch 02 loss=1.44031\r\nepoch 03 loss=1.39457\r\nepoch 04 loss=1.35050\r\nepoch 05 loss=1.29976\r\nepoch 06 loss=1.25690\r\nepoch 07 loss=1.22849\r\nepoch 08 loss=1.19416\r\nepoch 09 loss=1.17361\r\nepoch 10 loss=1.15088\r\nepoch 11 loss=1.13820\r\nepoch 12 loss=1.10626\r\nepoch 13 loss=1.09058\r\nepoch 14 loss=1.07269\r\nepoch 15 loss=1.05426\r\nepoch 16 loss=1.03660\r\nepoch 17 loss=1.02114\r\nepoch 18 loss=1.00319\r\n{\"stage\": \"validation\", \"examples\": 14714, \"ranking\": \"/app/solution/validation.parquet\", \"cutoff\": 24786800}\r\n",
    "stderr": "",
    "aggregated_output": "snapshot 22972400 examples 4778\r\nsnapshot 23577200 examples 4862\r\nsnapshot 24182000 examples 5074\r\n/app/solution/train_two_tower.py:150: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(ix);seen+=len(ix)\r\nepoch 01 loss=1.80411\r\nepoch 02 loss=1.44031\r\nepoch 03 loss=1.39457\r\nepoch 04 loss=1.35050\r\nepoch 05 loss=1.29976\r\nepoch 06 loss=1.25690\r\nepoch 07 loss=1.22849\r\nepoch 08 loss=1.19416\r\nepoch 09 loss=1.17361\r\nepoch 10 loss=1.15088\r\nepoch 11 loss=1.13820\r\nepoch 12 loss=1.10626\r\nepoch 13 loss=1.09058\r\nepoch 14 loss=1.07269\r\nepoch 15 loss=1.05426\r\nepoch 16 loss=1.03660\r\nepoch 17 loss=1.02114\r\nepoch 18 loss=1.00319\r\n{\"stage\": \"validation\", \"examples\": 14714, \"ranking\": \"/app/solution/validation.parquet\", \"cutoff\": 24786800}\r\n",
    "exit_code": 0,
    "duration": {
      "secs": 77,
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      },
      "/app/solution/train_two_tower.py": {
        "type": "update",
        "unified_diff": "@@ -154,3 +154,3 @@\n @torch.no_grad()\n-def rank(model, profiles, eligibility_path, catalog, path):\n+def rank(model, profiles, eligibility_path, catalog, path, pop_blend=.0):\n     z=np.load(eligibility_path);uids=z['uids'].astype(np.int64);items=z['item_ids'].astype(np.int64)\n@@ -162,2 +162,5 @@\n     ivec=torch.cat(ivec)\n+    # A final popularity coordinate is still a strict two-tower dot product:\n+    # item=[learned_vector, alpha*popularity], user=[history_vector, 1].\n+    popcoord=torch.as_tensor(1/np.log2(catalog.popularity_rank.to_numpy(np.float32)+1))[torch.from_numpy(item_ix)]\n     pmap={u:(x,w) for u,x,w in profiles}; ordered=[(int(u),*pmap[int(u)]) for u in uids]\n@@ -166,3 +169,3 @@\n         uv=model.user(torch.from_numpy(hi[st:st+128]),torch.from_numpy(hw[st:st+128]))\n-        score=([email protected]).numpy(); score[~mask[st:st+len(score)]]=-np.inf\n+        score=([email protected]+pop_blend*popcoord.unsqueeze(0)).numpy(); score[~mask[st:st+len(score)]]=-np.inf\n         top=np.argpartition(-score,99,axis=1)[:,:100]\n@@ -173,3 +176,3 @@\n def main():\n-    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);ap.add_argument('--epochs',type=int,default=18);args=ap.parse_args()\n+    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);ap.add_argument('--epochs',type=int,default=18);ap.add_argument('--pop-blend',type=float,default=.04);args=ap.parse_args()\n     seed_all(); final=args.stage=='final';catalog,ev,likes=load_events(final)\n@@ -187,4 +190,4 @@\n     target=np.load(elig)['uids'].astype(np.int64);profiles=inference_profiles(ev,infer_cut,target)\n-    rank(model,profiles,elig,catalog,dest)\n-    torch.save({'state_dict':model.state_dict(),'n_items':len(catalog),'dim':DIM,'stage':args.stage,'cutoff':infer_cut},OUT/f'model_{args.stage}.pt')\n+    rank(model,profiles,elig,catalog,dest,args.pop_blend)\n+    torch.save({'state_dict':model.state_dict(),'n_items':len(catalog),'dim':DIM+1,'latent_dim':DIM,'pop_blend':args.pop_blend,'stage':args.stage,'cutoff':infer_cut},OUT/f'model_{args.stage}.pt')\n     print(json.dumps({'stage':args.stage,'examples':len(examples),'ranking':str(dest),'cutoff':infer_cut}))\n",
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    "stdout": "snapshot 21158000 examples 4504\r\nsnapshot 21762800 examples 4636\r\nsnapshot 22367600 examples 4753\r\nsnapshot 22972400 examples 4778\r\nsnapshot 23577200 examples 4862\r\nsnapshot 24182000 examples 5074\r\nsnapshot 24786800 examples 5323\r\n/app/solution/train_two_tower.py:150: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(ix);seen+=len(ix)\r\nepoch 01 loss=1.60970\r\nepoch 02 loss=1.39591\r\nepoch 03 loss=1.33841\r\nepoch 04 loss=1.29467\r\nepoch 05 loss=1.26178\r\nepoch 06 loss=1.23293\r\nepoch 07 loss=1.21133\r\nepoch 08 loss=1.18849\r\nepoch 09 loss=1.17438\r\nepoch 10 loss=1.15019\r\nepoch 11 loss=1.13003\r\nepoch 12 loss=1.10894\r\nepoch 13 loss=1.08751\r\nepoch 14 loss=1.07053\r\nepoch 15 loss=1.04453\r\nepoch 16 loss=1.02523\r\nepoch 17 loss=1.00264\r\nepoch 18 loss=0.98204\r\n{\"stage\": \"final\", \"examples\": 33930, \"ranking\": \"/app/solution/submission.parquet\", \"cutoff\": 25393396}\r\n",
    "stderr": "",
    "aggregated_output": "snapshot 21158000 examples 4504\r\nsnapshot 21762800 examples 4636\r\nsnapshot 22367600 examples 4753\r\nsnapshot 22972400 examples 4778\r\nsnapshot 23577200 examples 4862\r\nsnapshot 24182000 examples 5074\r\nsnapshot 24786800 examples 5323\r\n/app/solution/train_two_tower.py:150: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(ix);seen+=len(ix)\r\nepoch 01 loss=1.60970\r\nepoch 02 loss=1.39591\r\nepoch 03 loss=1.33841\r\nepoch 04 loss=1.29467\r\nepoch 05 loss=1.26178\r\nepoch 06 loss=1.23293\r\nepoch 07 loss=1.21133\r\nepoch 08 loss=1.18849\r\nepoch 09 loss=1.17438\r\nepoch 10 loss=1.15019\r\nepoch 11 loss=1.13003\r\nepoch 12 loss=1.10894\r\nepoch 13 loss=1.08751\r\nepoch 14 loss=1.07053\r\nepoch 15 loss=1.04453\r\nepoch 16 loss=1.02523\r\nepoch 17 loss=1.00264\r\nepoch 18 loss=0.98204\r\n{\"stage\": \"final\", \"examples\": 33930, \"ranking\": \"/app/solution/submission.parquet\", \"cutoff\": 25393396}\r\n",
    "exit_code": 0,
    "duration": {
      "secs": 157,
      "nanos": 347609486
    },
    "formatted_output": "snapshot 21158000 examples 4504\r\nsnapshot 21762800 examples 4636\r\nsnapshot 22367600 examples 4753\r\nsnapshot 22972400 examples 4778\r\nsnapshot 23577200 examples 4862\r\nsnapshot 24182000 examples 5074\r\nsnapshot 24786800 examples 5323\r\n/app/solution/train_two_tower.py:150: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\r\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\r\n  total+=float(loss)*len(ix);seen+=len(ix)\r\nepoch 01 loss=1.60970\r\nepoch 02 loss=1.39591\r\nepoch 03 loss=1.33841\r\nepoch 04 loss=1.29467\r\nepoch 05 loss=1.26178\r\nepoch 06 loss=1.23293\r\nepoch 07 loss=1.21133\r\nepoch 08 loss=1.18849\r\nepoch 09 loss=1.17438\r\nepoch 10 loss=1.15019\r\nepoch 11 loss=1.13003\r\nepoch 12 loss=1.10894\r\nepoch 13 loss=1.08751\r\nepoch 14 loss=1.07053\r\nepoch 15 loss=1.04453\r\nepoch 16 loss=1.02523\r\nepoch 17 loss=1.00264\r\nepoch 18 loss=0.98204\r\n{\"stage\": \"final\", \"examples\": 33930, \"ranking\": \"/app/solution/submission.parquet\", \"cutoff\": 25393396}\r\n"
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}

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  "item": {
    "type": "FileChange",
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    "changes": {
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made for You two-tower ranker\n\n`train_two_tower.py` implements a strict CPU PyTorch two-tower retrieval model. The user tower encodes up to 240 distinct positive-history tracks from the prior 150 days using a trainable weighted embedding bag and residual MLP. The item tower uses a separate trainable item embedding, popularity-feature MLP, and residual MLP. Both serving outputs have 81 dimensions: 80 learned normalized coordinates plus one calibrated popularity coordinate. The only final score is the dot product of these vectors. Thus all 30,000 item vectors can be computed offline, while a request computes one history-derived user vector; there is no user-ID embedding.\n\nTraining uses six rolling, leakage-safe temporal snapshots. Each snapshot predicts organic 80%-complete listens and organic likes in the next week that were absent from the preceding history. It optimizes sampled logistic contrastive loss with 48 smoothed-popularity negatives per positive. No global user-item matrix is constructed or factorized. For the final fit, the public validation window is added as a seventh supervised snapshot and as observed user history.\n\nPublic validation NDCG@10 is **0.018017** over 4,263 scored users. The selected popularity-coordinate weight is 1.1. For comparison, catalog popularity scored 0.010710. Validation events were not used as validation features or training labels during selection.\n\nRun the final refit and submission generation with:\n\n```sh\nOMP_NUM_THREADS=8 MKL_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/train_two_tower.py --stage final --epochs 18 --pop-blend 1.1\n```\n\nThis writes `submission.parquet` and `model_final.pt` under `/app/solution/`.\n"
      },
      "/app/solution/train_two_tower.py": {
        "type": "update",
        "unified_diff": "@@ -108,2 +108,5 @@\n         return F.normalize(z+.35*self.net(z),dim=-1)\n+    def serving_vector(self,ids,weights):\n+        z=self.forward(ids,weights)\n+        return torch.cat((z,torch.ones((len(z),1),device=z.device)),dim=-1)\n \n@@ -115,2 +118,5 @@\n         self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim))\n+        order=torch.argsort(torch.as_tensor(pop_feature),descending=True)\n+        rank=torch.empty(n_items,dtype=torch.float32);rank[order]=torch.arange(1,n_items+1,dtype=torch.float32)\n+        self.register_buffer('retrieval_pop',1/torch.log2(rank+1),persistent=False)\n     def forward(self,ids):\n@@ -118,2 +124,5 @@\n         return F.normalize(z+.35*self.net(z),dim=-1)\n+    def serving_vector(self,ids,pop_blend):\n+        z=self.forward(ids)\n+        return torch.cat((z,pop_blend*self.retrieval_pop[ids,None]),dim=-1)\n \n@@ -125,2 +134,4 @@\n     def item(self,ids): return self.item_tower(ids)\n+    def user_vector(self,ids,w): return self.user_tower.serving_vector(ids,w)\n+    def item_vector(self,ids,pop_blend): return self.item_tower.serving_vector(ids,pop_blend)\n \n@@ -149,3 +160,3 @@\n             opt.zero_grad();loss.backward();torch.nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n-            total+=float(loss)*len(ix);seen+=len(ix)\n+            total+=float(loss.detach())*len(ix);seen+=len(ix)\n         print(f'epoch {epoch+1:02d} loss={total/seen:.5f}',flush=True)\n@@ -160,7 +171,4 @@\n     model.eval(); ivec=[]\n-    for st in range(0,len(items),1024): ivec.append(model.item(torch.from_numpy(item_ix[st:st+1024])).cpu())\n+    for st in range(0,len(items),1024): ivec.append(model.item_vector(torch.from_numpy(item_ix[st:st+1024]),pop_blend).cpu())\n     ivec=torch.cat(ivec)\n-    # A final popularity coordinate is still a strict two-tower dot product:\n-    # item=[learned_vector, alpha*popularity], user=[history_vector, 1].\n-    popcoord=torch.as_tensor(1/np.log2(catalog.popularity_rank.to_numpy(np.float32)+1))[torch.from_numpy(item_ix)]\n     pmap={u:(x,w) for u,x,w in profiles}; ordered=[(int(u),*pmap[int(u)]) for u in uids]\n@@ -168,4 +176,4 @@\n     for st in range(0,len(uids),128):\n-        uv=model.user(torch.from_numpy(hi[st:st+128]),torch.from_numpy(hw[st:st+128]))\n-        score=([email protected]+pop_blend*popcoord.unsqueeze(0)).numpy(); score[~mask[st:st+len(score)]]=-np.inf\n+        uv=model.user_vector(torch.from_numpy(hi[st:st+128]),torch.from_numpy(hw[st:st+128]))\n+        score=([email protected]).numpy(); score[~mask[st:st+len(score)]]=-np.inf\n         top=np.argpartition(-score,99,axis=1)[:,:100]\n@@ -176,3 +184,3 @@\n def main():\n-    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);ap.add_argument('--epochs',type=int,default=18);ap.add_argument('--pop-blend',type=float,default=.04);args=ap.parse_args()\n+    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);ap.add_argument('--epochs',type=int,default=18);ap.add_argument('--pop-blend',type=float,default=1.1);args=ap.parse_args()\n     seed_all(); final=args.stage=='final';catalog,ev,likes=load_events(final)\n",
        "move_path": null
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_two_tower.py\n",
    "stderr": ""
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  "item": {
    "type": "FileChange",
    "id": "exec-46524828-2301-45f3-b744-61a109af1b2d",
    "changes": {
      "/app/solution/tune_serving.py": {
        "type": "update",
        "unified_diff": "@@ -9,3 +9,3 @@\n pf=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\n-m=t.TwoTower(len(cat),pf); ck=torch.load('/app/solution/model_validation.pt',weights_only=False);m.load_state_dict(ck['state_dict'])\n+m=t.TwoTower(len(cat),pf,1/np.log2(cat.popularity_rank.to_numpy(np.float32)+1)); ck=torch.load('/app/solution/model_validation.pt',weights_only=False);m.load_state_dict(ck['state_dict'])\n elig=t.DATA/'validation_eligible_candidates.npz';uids=np.load(elig)['uids'].astype(np.int64)\n",
        "move_path": null
      },
      "/app/solution/train_two_tower.py": {
        "type": "update",
        "unified_diff": "@@ -113,3 +113,3 @@\n class ItemTower(nn.Module):\n-    def __init__(self,n_items,dim,pop_feature):\n+    def __init__(self,n_items,dim,pop_feature,retrieval_pop=None):\n         super().__init__(); self.item=nn.Embedding(n_items,dim)\n@@ -118,5 +118,6 @@\n         self.net=nn.Sequential(nn.LayerNorm(dim),nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim))\n-        order=torch.argsort(torch.as_tensor(pop_feature),descending=True)\n-        rank=torch.empty(n_items,dtype=torch.float32);rank[order]=torch.arange(1,n_items+1,dtype=torch.float32)\n-        self.register_buffer('retrieval_pop',1/torch.log2(rank+1),persistent=False)\n+        if retrieval_pop is None:\n+            order=np.argsort(-np.asarray(pop_feature),kind='stable');rank=np.empty(n_items,np.float32);rank[order]=np.arange(1,n_items+1)\n+            retrieval_pop=1/np.log2(rank+1)\n+        self.register_buffer('retrieval_pop',torch.as_tensor(retrieval_pop,dtype=torch.float32),persistent=False)\n     def forward(self,ids):\n@@ -129,4 +130,4 @@\n class TwoTower(nn.Module):\n-    def __init__(self,n_items,pop):\n-        super().__init__();self.user_tower=UserTower(n_items,DIM);self.item_tower=ItemTower(n_items,DIM,pop)\n+    def __init__(self,n_items,pop,retrieval_pop=None):\n+        super().__init__();self.user_tower=UserTower(n_items,DIM);self.item_tower=ItemTower(n_items,DIM,pop,retrieval_pop)\n         self.log_temperature=nn.Parameter(torch.tensor(2.7))\n@@ -192,3 +193,4 @@\n     pop=catalog.positive_listens.to_numpy(np.float32); popfeat=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\n-    model=TwoTower(len(catalog),popfeat);train_model(model,examples,pop,args.epochs)\n+    retrieval_pop=1/np.log2(catalog.popularity_rank.to_numpy(np.float32)+1)\n+    model=TwoTower(len(catalog),popfeat,retrieval_pop);train_model(model,examples,pop,args.epochs)\n     if final:\n",
        "move_path": null
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      "/app/solution/tune_profiles.py": {
        "type": "update",
        "unified_diff": "@@ -6,3 +6,3 @@\n pf=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\n-m=t.TwoTower(len(cat),pf);m.load_state_dict(torch.load(t.OUT/'model_validation.pt',weights_only=False)['state_dict'])\n+m=t.TwoTower(len(cat),pf,1/np.log2(cat.popularity_rank.to_numpy(np.float32)+1));m.load_state_dict(torch.load(t.OUT/'model_validation.pt',weights_only=False)['state_dict'])\n elig=t.DATA/'validation_eligible_candidates.npz';uids=np.load(elig)['uids'].astype(np.int64)\n",
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{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-eeaad4d5-59d2-4bba-b868-59c04e05956a",
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    "command": [
      "/bin/sh",
      "-lc",
      "cp solution/validation_pop1.10.parquet solution/validation.parquet\n/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/explore_baselines.py solution/tune_serving.py solution/tune_profiles.py\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd,torch\nsys.path.insert(0,'/app/solution');import train_two_tower as t\np=Path('/app/solution/submission.parquet'); d=pd.read_parquet(p)\nz=np.load('/task/data/test_eligible_candidates.npz');uids=z['uids'].astype(np.int64);items=z['item_ids'].astype(np.int64);packed=z['packed_eligible']\nassert list(d.columns)==['uid','item_id','rank'];assert len(d)==len(uids)*100\nassert set(d.uid)==set(uids.tolist());assert (d.groupby('uid').size()==100).all();assert not d.duplicated(['uid','item_id']).any()\nassert d.groupby('uid')['rank'].apply(lambda x:set(x)==set(range(1,101))).all()\nur={int(u):r for r,u in enumerate(uids)};ic={int(x):c for c,x in enumerate(items)}\nassert all((int(packed[ur[int(u)],ic[int(i)]//8])>>(ic[int(i)]%8))&1 for u,i in d[['uid','item_id']].itertuples(index=False,name=None))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');pop=cat.positive_listens.to_numpy(np.float32);pf=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\nm=t.TwoTower(len(cat),pf,1/np.log2(cat.popularity_rank.to_numpy(np.float32)+1));ck=torch.load('/app/solution/model_final.pt',weights_only=False);m.load_state_dict(ck['state_dict']);m.eval()\nwith torch.no_grad():\n uv=m.user_vector(torch.zeros((1,t.MAX_HISTORY),dtype=torch.long),torch.ones((1,t.MAX_HISTORY)));iv=m.item_vector(torch.arange(3),ck['pop_blend']);[email protected]\nassert uv.shape==(1,81) and iv.shape==(3,81) and s.shape==(1,3)\nprint({'rows':len(d),'users':d.uid.nunique(),'eligible':'all','uid_dtype':str(d.uid.dtype),'item_dtype':str(d.item_id.dtype),'rank_dtype':str(d['rank'].dtype),'tower_dims':(tuple(uv.shape),tuple(iv.shape)),'model_load':'ok','bytes':p.stat().st_size})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp solution/validation_pop1.10.parquet solution/validation.parquet\n/opt/task-venv/bin/python -m py_compile solution/train_two_tower.py solution/explore_baselines.py solution/tune_serving.py solution/tune_profiles.py\n/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport sys,numpy as np,pandas as pd,torch\nsys.path.insert(0,'/app/solution');import train_two_tower as t\np=Path('/app/solution/submission.parquet'); d=pd.read_parquet(p)\nz=np.load('/task/data/test_eligible_candidates.npz');uids=z['uids'].astype(np.int64);items=z['item_ids'].astype(np.int64);packed=z['packed_eligible']\nassert list(d.columns)==['uid','item_id','rank'];assert len(d)==len(uids)*100\nassert set(d.uid)==set(uids.tolist());assert (d.groupby('uid').size()==100).all();assert not d.duplicated(['uid','item_id']).any()\nassert d.groupby('uid')['rank'].apply(lambda x:set(x)==set(range(1,101))).all()\nur={int(u):r for r,u in enumerate(uids)};ic={int(x):c for c,x in enumerate(items)}\nassert all((int(packed[ur[int(u)],ic[int(i)]//8])>>(ic[int(i)]%8))&1 for u,i in d[['uid','item_id']].itertuples(index=False,name=None))\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet');pop=cat.positive_listens.to_numpy(np.float32);pf=(np.log1p(pop)-np.log1p(pop).mean())/(np.log1p(pop).std()+1e-6)\nm=t.TwoTower(len(cat),pf,1/np.log2(cat.popularity_rank.to_numpy(np.float32)+1));ck=torch.load('/app/solution/model_final.pt',weights_only=False);m.load_state_dict(ck['state_dict']);m.eval()\nwith torch.no_grad():\n uv=m.user_vector(torch.zeros((1,t.MAX_HISTORY),dtype=torch.long),torch.ones((1,t.MAX_HISTORY)));iv=m.item_vector(torch.arange(3),ck['pop_blend']);[email protected]\nassert uv.shape==(1,81) and iv.shape==(3,81) and s.shape==(1,3)\nprint({'rows':len(d),'users':d.uid.nunique(),'eligible':'all','uid_dtype':str(d.uid.dtype),'item_dtype':str(d.item_id.dtype),'rank_dtype':str(d['rank'].dtype),'tower_dims':(tuple(uv.shape),tuple(iv.shape)),'model_load':'ok','bytes':p.stat().st_size})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'eligible': 'all', 'uid_dtype': 'int64', 'item_dtype': 'int64', 'rank_dtype': 'int64', 'tower_dims': ((1, 81), (3, 81)), 'model_load': 'ok', 'bytes': 1655100}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018017,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'eligible': 'all', 'uid_dtype': 'int64', 'item_dtype': 'int64', 'rank_dtype': 'int64', 'tower_dims': ((1, 81), (3, 81)), 'model_load': 'ok', 'bytes': 1655100}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018017,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 8,
      "nanos": 508677878
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'eligible': 'all', 'uid_dtype': 'int64', 'item_dtype': 'int64', 'rank_dtype': 'int64', 'tower_dims': ((1, 81), (3, 81)), 'model_load': 'ok', 'bytes': 1655100}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.018017,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}