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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c6b54e21-e0e4-4c61-bf22-d04888840fa0",
    "process_id": "87177",
    "command": [
      "/bin/sh",
      "-lc",
      "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "pwd && find /task/data -maxdepth 1 -type f -printf '%f %s bytes\\n' | sort && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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": 4820
    },
    "formatted_output": "/app\ncandidate_catalog.parquet 349352 bytes\ntarget_users.parquet 84127 bytes\ntest_eligible_candidates.npz 7736341 bytes\ntest_preference_state.parquet 1092329 bytes\ntrain_dislikes.parquet 857213 bytes\ntrain_likes.parquet 5374652 bytes\ntrain_listens.parquet 319183255 bytes\ntrain_undislikes.parquet 155988 bytes\ntrain_unlikes.parquet 1855885 bytes\nval_dislikes.parquet 36571 bytes\nval_likes.parquet 298484 bytes\nval_listens.parquet 9947609 bytes\nval_undislikes.parquet 7845 bytes\nval_unlikes.parquet 72536 bytes\nvalidation_eligible_candidates.npz 7573821 bytes\nvalidation_preference_state.parquet 1061761 bytes\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": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root    0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root    3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root  171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root  920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root  302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root  304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root  297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root  305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root  316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root  344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root  315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root  301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root  325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root   21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root  307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root  321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root     2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root    18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root   327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root    21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root   102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root  1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1949 Sep 19 00:01 _compute.pxd\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root    0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root    3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root  171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root  920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root  302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root  304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root  297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root  305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root  316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root  344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root  315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root  301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root  325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root   21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root  307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root  321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root     2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root    18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root   327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root    21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root   102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root  1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1949 Sep 19 00:01 _compute.pxd\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3290
    },
    "formatted_output": "/opt/task-venv:\ntotal 32\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndrwxr-xr-x 1 root root 4096 Sep 19 00:00 ..\n-r--r--r-- 1 root root    0 Sep 19 00:01 .lock\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 bin\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 etc\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 include\ndr-xr-xr-x 1 root root 4096 Sep 19 00:00 lib\nlrwxrwxrwx 1 root root    3 Sep 19 00:00 lib64 -> lib\n-r--r--r-- 1 root root  171 Sep 19 00:00 pyvenv.cfg\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 share\n\n/opt/task-venv/bin:\ntotal 124\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 .\ndr-xr-xr-x 1 root root 4096 Sep 19 00:01 ..\n-r--r--r-- 1 root root 9033 Sep 19 00:01 Activate.ps1\n-r--r--r-- 1 root root 2158 Sep 19 00:01 activate\n-r--r--r-- 1 root root  920 Sep 19 00:01 activate.csh\n-r--r--r-- 1 root root 2195 Sep 19 00:01 activate.fish\n-r-xr-xr-x 1 root root  302 Sep 19 00:01 chardetect\n-r-xr-xr-x 1 root root  304 Sep 19 00:01 dotenv\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 f2py\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 idna\n-r-xr-xr-x 1 root root  297 Sep 19 00:01 isympy\n-r-xr-xr-x 1 root root  305 Sep 19 00:01 jsonschema\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupyter-migrate\n-r-xr-xr-x 1 root root  316 Sep 19 00:01 jupyter-troubleshoot\n-r-xr-xr-x 1 root root  344 Sep 19 00:01 jupyter-trust\n-r-xr-xr-x 1 root root  311 Sep 19 00:01 jupytext\n-r-xr-xr-x 1 root root  315 Sep 19 00:01 jupytext-config\n-r-xr-xr-x 1 root root  301 Sep 19 00:01 kaggle\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 markdown-it\n-r-xr-xr-x 1 root root  325 Sep 19 00:01 normalizer\n-r-xr-xr-x 1 root root  308 Sep 19 00:01 numpy-config\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3\n-r-xr-xr-x 1 root root  313 Sep 19 00:01 pip3.12\nlrwxrwxrwx 1 root root   21 Sep 19 00:00 python -> /usr/local/bin/python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3 -> python\nlrwxrwxrwx 1 root root    6 Sep 19 00:00 python3.12 -> python\n-r-xr-xr-x 1 root root  307 Sep 19 00:01 slugify\n-r-xr-xr-x 1 root root  321 Sep 19 00:01 torchfrtrace\n-r-xr-xr-x 1 root root  312 Sep 19 00:01 torchrun\n-r-xr-xr-x 1 root root  299 Sep 19 00:01 tqdm\ntotal 120204\n-r--r--r-- 1 root root     2195 Sep 19 00:01 __init__.pxd\n-r--r--r-- 1 root root    18119 Sep 19 00:01 __init__.py\n-r-xr-xr-x 1 root root   327160 Sep 19 00:01 _acero.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1440 Sep 19 00:01 _acero.pxd\n-r--r--r-- 1 root root    21301 Sep 19 00:01 _acero.pyx\n-r-xr-xr-x 1 root root   102240 Sep 19 00:01 _azurefs.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     5909 Sep 19 00:01 _azurefs.pyx\n-r-xr-xr-x 1 root root  1369056 Sep 19 00:01 _compute.cpython-312-x86_64-linux-gnu.so\n-r--r--r-- 1 root root     1949 Sep 19 00:01 _compute.pxd\n"
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{
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  "item": {
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    "id": "exec-74b075ae-151b-4601-a858-743b0abc17d9",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys, torch, pyarrow\nprint(sys.prefix,torch.__version__,pyarrow.__version__)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys, torch, pyarrow\nprint(sys.prefix,torch.__version__,pyarrow.__version__)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv 2.8.0+cpu 18.1.0\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv 2.8.0+cpu 18.1.0\n",
    "exit_code": 0,
    "duration": {
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    "formatted_output": "/opt/task-venv 2.8.0+cpu 18.1.0\n"
  }
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-be0fd6a5-1042-44d7-9619-c5295e8a0a94",
    "process_id": "37718",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p); m=pf.metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'cols',pf.schema_arrow.names)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nt=pd.read_parquet(D/'target_users.parquet'); c=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets',t.head(), t.shape, t.dtypes)\nprint('catalog head/tail\\n',c.head(),'\\n',c.tail(),c.dtypes)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n pf=pq.ParquetFile(p); m=pf.metadata\n print(p.name, 'rows',m.num_rows,'rowgroups',m.num_row_groups, 'cols',pf.schema_arrow.names)\nfor p in [D/'validation_eligible_candidates.npz',D/'test_eligible_candidates.npz']:\n z=np.load(p)\n print(p.name,{k:(z[k].shape,z[k].dtype) for k in z.files})\n packed=z['packed_eligible']; n=len(z['item_ids'])\n counts=np.unpackbits(packed,axis=1,count=n,bitorder='little').sum(1)\n print('eligible count',np.quantile(counts,[0,0.1,.5,.9,1]),counts.mean())\nt=pd.read_parquet(D/'target_users.parquet'); c=pd.read_parquet(D/'candidate_catalog.parquet')\nprint('targets',t.head(), t.shape, t.dtypes)\nprint('catalog head/tail\\n',c.head(),'\\n',c.tail(),c.dtypes)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657.  28038.5 29242.  29852.  30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\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 (8456, 3) uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\ncatalog head/tail\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657.  28038.5 29242.  29852.  30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\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 (8456, 3) uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\ncatalog head/tail\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 163212256
    },
    "formatted_output": "candidate_catalog.parquet rows 30000 rowgroups 1 cols ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet rows 8456 rowgroups 1 cols ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet rows 484776 rowgroups 1 cols ['uid', 'item_id', 'state']\ntrain_dislikes.parquet rows 101075 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet rows 814831 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet rows 43484159 rowgroups 42 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet rows 19960 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet rows 292665 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet rows 3563 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet rows 37795 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet rows 1501697 rowgroups 2 cols ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet rows 606 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet rows 9346 rowgroups 1 cols ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet rows 469124 rowgroups 1 cols ['uid', 'item_id', 'state']\nvalidation_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24657.  28038.5 29242.  29852.  30000. ] 29067.306291390727\ntest_eligible_candidates.npz {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}\neligible count [24552. 28001. 29220. 29835. 30000.] 29041.3178807947\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 (8456, 3) uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\ncatalog head/tail\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-6e18bf95-bbfa-484d-b175-ebbe65216bfd",
    "process_id": "49286",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nfor name in ['train_listens','val_listens']:\n t=time.time(); pf=pq.ParquetFile(D/f'{name}.parquet')\n n=nt=nc=npos=nstrong=0; users=set(); mint=10**20; maxt=0\n for b in pf.iter_batches(batch_size=1_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n  x=b.to_pandas(); n+=len(x); users.update(x.uid.unique().tolist()); mint=min(mint,int(x.timestamp.min())); maxt=max(maxt,int(x.timestamp.max()))\n  m=x.uid.isin(target); nt+=int(m.sum()); nc+=int((m & x.item_id.isin(cat)).sum()); npos+=int((m & (x.played_ratio_pct>=50)).sum()); nstrong+=int((m & (x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\n print(name,dict(rows=n,unique_users=len(users),target_rows=nt,target_catalog=nc,target_pos50=npos,target_organic80=nstrong,min_ts=mint,max_ts=maxt,secs=round(time.time()-t,2)))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np, time\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat=set(pd.read_parquet(D/'candidate_catalog.parquet').item_id.astype(int))\nfor name in ['train_listens','val_listens']:\n t=time.time(); pf=pq.ParquetFile(D/f'{name}.parquet')\n n=nt=nc=npos=nstrong=0; users=set(); mint=10**20; maxt=0\n for b in pf.iter_batches(batch_size=1_000_000, columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n  x=b.to_pandas(); n+=len(x); users.update(x.uid.unique().tolist()); mint=min(mint,int(x.timestamp.min())); maxt=max(maxt,int(x.timestamp.max()))\n  m=x.uid.isin(target); nt+=int(m.sum()); nc+=int((m & x.item_id.isin(cat)).sum()); npos+=int((m & (x.played_ratio_pct>=50)).sum()); nstrong+=int((m & (x.is_organic==1)&(x.played_ratio_pct>=80)).sum())\n print(name,dict(rows=n,unique_users=len(users),target_rows=nt,target_catalog=nc,target_pos50=npos,target_organic80=nstrong,min_ts=mint,max_ts=maxt,secs=round(time.time()-t,2)))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n",
    "stderr": "",
    "aggregated_output": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n",
    "exit_code": 0,
    "duration": {
      "secs": 8,
      "nanos": 897843438
    },
    "formatted_output": "train_listens {'rows': 43484159, 'unique_users': 9238, 'target_rows': 43222297, 'target_catalog': 31062540, 'target_pos50': 27367343, 'target_organic80': 12123010, 'min_ts': 0, 'max_ts': 24786795, 'secs': 7.2}\nval_listens {'rows': 1501697, 'unique_users': 8196, 'target_rows': 1482553, 'target_catalog': 999062, 'target_pos50': 955678, 'target_organic80': 421615, 'min_ts': 24788600, 'max_ts': 25393395, 'secs': 0.26}\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-7a76aa95-80d0-44ef-a742-e1faf755cc6a",
    "changes": {
      "/app/solution/baseline.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Create a structurally valid popularity ranking for either cutoff.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\n\nDATA = Path(\"/task/data\")\n\n\ndef main() -> None:\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n    parser.add_argument(\"--output\", type=Path, required=True)\n    args = parser.parse_args()\n    archive = np.load(DATA / f\"{args.stage}_eligible_candidates.npz\")\n    uids = archive[\"uids\"].astype(np.int64)\n    items = archive[\"item_ids\"].astype(np.int64)\n    packed = archive[\"packed_eligible\"]\n    rows = []\n    for r, uid in enumerate(uids):\n        eligible = np.unpackbits(\n            packed[r], count=len(items), bitorder=\"little\"\n        ).astype(bool)\n        # The archive item order matches the popularity-ordered catalog.\n        chosen = items[np.flatnonzero(eligible)[:100]]\n        rows.append(\n            pd.DataFrame(\n                {\"uid\": uid, \"item_id\": chosen, \"rank\": np.arange(1, 101)}\n            )\n        )\n    args.output.parent.mkdir(parents=True, exist_ok=True)\n    pd.concat(rows, ignore_index=True).astype(\n        {\"uid\": \"int64\", \"item_id\": \"int64\", \"rank\": \"int64\"}\n    ).to_parquet(args.output, index=False)\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n",
    "stderr": ""
  }
}

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-36cfb31a-e144-4eeb-be04-ab7af5b5ec16",
    "process_id": "37824",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/baseline.py --stage validation --output solution/validation_pop.parquet && /opt/task-venv/bin/python solution/baseline.py --stage test --output solution/submission.parquet && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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      "secs": 9,
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      "/bin/sh",
      "-lc",
      "sed -n '1,8p' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\n# Compare exclusion with every historical catalog listen for a handful of users.\nuids=pd.read_parquet(D/'target_users.parquet').uid.astype(int).iloc[:20].tolist()\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'],filters=[('uid','in',uids)])\nz=np.load(D/'validation_eligible_candidates.npz'); items=z['item_ids']; col={int(v):i for i,v in enumerate(items)}; row={int(v):i for i,v in enumerate(z['uids'])}\nfor uid,g in x.groupby('uid'):\n uniq=set(map(int,g.item_id)) & set(col)\n bits=np.unpackbits(z['packed_eligible'][row[int(uid)]],count=len(items),bitorder='little')\n excluded={int(items[i]) for i in np.flatnonzero(bits==0)}\n print(uid,len(uniq),len(excluded),len(uniq&excluded),len(excluded-uniq))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1,8p' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\n# Compare exclusion with every historical catalog listen for a handful of users.\nuids=pd.read_parquet(D/'target_users.parquet').uid.astype(int).iloc[:20].tolist()\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id'],filters=[('uid','in',uids)])\nz=np.load(D/'validation_eligible_candidates.npz'); items=z['item_ids']; col={int(v):i for i,v in enumerate(items)}; row={int(v):i for i,v in enumerate(z['uids'])}\nfor uid,g in x.groupby('uid'):\n uniq=set(map(int,g.item_id)) & set(col)\n bits=np.unpackbits(z['packed_eligible'][row[int(uid)]],count=len(items),bitorder='little')\n excluded={int(items[i]) for i in np.flatnonzero(bits==0)}\n print(uid,len(uniq),len(excluded),len(uniq&excluded),len(excluded-uniq))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
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    "stderr": "",
    "aggregated_output": "100 682 682 682 0\n200 168 168 168 0\n300 69 69 69 0\n600 680 680 680 0\n700 1653 1653 1653 0\n800 2524 2524 2524 0\n900 334 334 334 0\n1000 499 499 499 0\n1100 1440 1440 1440 0\n1400 1262 1264 1262 2\n1500 524 524 524 0\n1600 1517 1524 1517 7\n1700 2164 2164 2164 0\n1900 2040 2041 2040 1\n2000 374 374 374 0\n2100 644 645 644 1\n2200 1096 1096 1096 0\n2300 1405 1406 1405 1\n2400 471 471 471 0\n2500 636 636 636 0\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 334035451
    },
    "formatted_output": "100 682 682 682 0\n200 168 168 168 0\n300 69 69 69 0\n600 680 680 680 0\n700 1653 1653 1653 0\n800 2524 2524 2524 0\n900 334 334 334 0\n1000 499 499 499 0\n1100 1440 1440 1440 0\n1400 1262 1264 1262 2\n1500 524 524 524 0\n1600 1517 1524 1517 7\n1700 2164 2164 2164 0\n1900 2040 2041 2040 1\n2000 374 374 374 0\n2100 644 645 644 1\n2200 1096 1096 1096 0\n2300 1405 1406 1405 1\n2400 471 471 471 0\n2500 636 636 636 0\n"
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}

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  "item": {
    "type": "CommandExecution",
    "id": "exec-4d0caf9c-35c8-484e-ac2f-189f4d1585c6",
    "process_id": "51257",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in [0,1,20,41]:\n x=p.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\n print(rg,len(x),x.head(2).to_dict('records'),x.tail(2).to_dict('records'),x.timestamp.min(),x.timestamp.max(),x.uid.nunique())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\np=pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in [0,1,20,41]:\n x=p.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\n print(rg,len(x),x.head(2).to_dict('records'),x.tail(2).to_dict('records'),x.timestamp.min(),x.timestamp.max(),x.uid.nunique())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242\n1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225\n20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251\n41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98\n",
    "stderr": "",
    "aggregated_output": "0 1048576 [{'uid': 100, 'timestamp': 39420, 'item_id': 8326270, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 100, 'timestamp': 39420, 'item_id': 1441281, 'is_organic': 0, 'played_ratio_pct': 100}] [{'uid': 26500, 'timestamp': 10880440, 'item_id': 5240843, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 4473119, 'is_organic': 1, 'played_ratio_pct': 1}] 15 24786690 242\n1 1048576 [{'uid': 26500, 'timestamp': 10880440, 'item_id': 6935357, 'is_organic': 1, 'played_ratio_pct': 2}, {'uid': 26500, 'timestamp': 10880440, 'item_id': 5777014, 'is_organic': 1, 'played_ratio_pct': 28}] [{'uid': 50300, 'timestamp': 13407860, 'item_id': 8552709, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 50300, 'timestamp': 13407860, 'item_id': 956298, 'is_organic': 1, 'played_ratio_pct': 100}] 185 24786725 225\n20 1048576 [{'uid': 475900, 'timestamp': 15677130, 'item_id': 5365235, 'is_organic': 1, 'played_ratio_pct': 0}, {'uid': 475900, 'timestamp': 15677280, 'item_id': 673655, 'is_organic': 1, 'played_ratio_pct': 100}] [{'uid': 502800, 'timestamp': 1588120, 'item_id': 798719, 'is_organic': 1, 'played_ratio_pct': 100}, {'uid': 502800, 'timestamp': 1588335, 'item_id': 8030729, 'is_organic': 1, 'played_ratio_pct': 100}] 40 24786795 251\n41 492543 [{'uid': 989500, 'timestamp': 2340320, 'item_id': 2429958, 'is_organic': 0, 'played_ratio_pct': 5}, {'uid': 989500, 'timestamp': 2340340, 'item_id': 1838448, 'is_organic': 1, 'played_ratio_pct': 8}] [{'uid': 1000000, 'timestamp': 24599500, 'item_id': 6682095, 'is_organic': 0, 'played_ratio_pct': 100}, {'uid': 1000000, 'timestamp': 24611315, 'item_id': 4352630, 'is_organic': 0, 'played_ratio_pct': 16}] 20 24786690 98\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 461318269
    },
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  "item": {
    "type": "FileChange",
    "id": "exec-7246e620-1af5-46b2-9437-d88a5db48cb2",
    "changes": {
      "/app/solution/experiment_popularity.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Validation-only popularity recency sweep used during model selection.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nO = Path('/app/solution')\nEND = 24786800\ncat = pd.read_parquet(D/'candidate_catalog.parquet')\nitems = cat.item_id.to_numpy(np.int64)\nidx = pd.Index(items)\nscores = {f'listen_{days}d': np.zeros(len(items), np.float64) for days in [3,7,14,30,60,90,180]}\nscores['organic_all'] = cat.organic_positive_listens.to_numpy(np.float64)\nscores['positive_all'] = cat.positive_listens.to_numpy(np.float64)\nfor batch in pq.ParquetFile(D/'train_listens.parquet').iter_batches(batch_size=1_000_000,\n        columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n    x=batch.to_pandas()\n    pos=idx.get_indexer(x.item_id)\n    good=(pos>=0)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80)\n    age=END-x.timestamp.to_numpy()\n    for days in [3,7,14,30,60,90,180]:\n        m=good&(age < days*86400)\n        scores[f'listen_{days}d'] += np.bincount(pos[m], minlength=len(items))\nlikes=pd.read_parquet(D/'train_likes.parquet',columns=['timestamp','item_id','is_organic'])\nlp=idx.get_indexer(likes.item_id); age=END-likes.timestamp.to_numpy()\nfor days in [7,30,90]:\n    m=(lp>=0)&(likes.is_organic.to_numpy()==1)&(age<days*86400)\n    scores[f'like_{days}d']=np.bincount(lp[m],minlength=len(items)).astype(float)\nz=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; uids=z['uids'].astype(np.int64)\ndef write(name, score):\n    order=np.argsort(-score,kind='stable'); out=[]\n    for r,uid in enumerate(uids):\n        elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n        chosen=order[elig[order]][:100]\n        out.append(pd.DataFrame({'uid':uid,'item_id':items[chosen],'rank':np.arange(1,101)}))\n    pd.concat(out,ignore_index=True).to_parquet(O/f'validation_{name}.parquet',index=False)\nfor k,v in scores.items():\n    write(k,v)\n# A few sensible blends, transformed to limit head domination.\nfor days in [7,14,30,60,90]:\n    a=np.log1p(scores[f'listen_{days}d'])\n    b=np.log1p(scores.get(f'like_{days}d', scores['like_30d']))\n    for w in [.1,.3,1.0]: write(f'blend_{days}d_{w}', a+w*b)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/experiment_popularity.py\n",
    "stderr": ""
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f645acfb-7f2b-477d-9698-fe3cd1c55058",
    "process_id": "20808",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/experiment_popularity.py && for f in solution/validation_listen_*.parquet solution/validation_*all.parquet solution/validation_like_*.parquet solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/  */ /g'; echo; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/experiment_popularity.py && for f in solution/validation_listen_*.parquet solution/validation_*all.parquet solution/validation_like_*.parquet solution/validation_blend_*.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\" | tr '\\n' ' ' | sed 's/  */ /g'; echo; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{ \"model\": \"solution/validation_listen_14d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_180d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.011908, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_3d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.025721, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_60d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015802, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_organic_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_positive_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.027007, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028128, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.024913, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016949, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019394, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019823, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016831, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.017736, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018951, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029046, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030068, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031211, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016406, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016467, \"quality_users\": 4263 } } \n",
    "stderr": "",
    "aggregated_output": "{ \"model\": \"solution/validation_listen_14d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_180d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.011908, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_3d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.025721, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_60d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015802, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_organic_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_positive_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.027007, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028128, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.024913, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016949, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019394, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019823, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016831, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.017736, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018951, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029046, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030068, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031211, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016406, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016467, \"quality_users\": 4263 } } \n",
    "exit_code": 0,
    "duration": {
      "secs": 134,
      "nanos": 103602986
    },
    "formatted_output": "{ \"model\": \"solution/validation_listen_14d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.022878, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_180d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.011908, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.013314, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_3d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.025721, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_60d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015802, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028131, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_listen_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.015822, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_organic_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.010577, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_positive_all.parquet\", \"scores\": { \"quality_ndcg@10\": 0.01071, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_30d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018429, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_7d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029297, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_like_90d.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016217, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.027007, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.028128, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_14d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.024913, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016949, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019394, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_30d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.019823, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016831, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.017736, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_60d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.018951, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.029046, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.030068, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_7d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.031211, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.1.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016405, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_0.3.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016406, \"quality_users\": 4263 } } \n{ \"model\": \"solution/validation_blend_90d_1.0.parquet\", \"scores\": { \"quality_ndcg@10\": 0.016467, \"quality_users\": 4263 } } \n"
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}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-771b161b-b636-4bf6-afec-a51e00b2be6a",
    "changes": {
      "/app/solution/train_two_tower.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-based PyTorch two-tower ranker.\n\nThe script deliberately works with event streams and bounded history bags.  It\nnever creates a user-by-item matrix.  Run with ``--stage validation`` for model\nselection and ``--stage test`` for the final refit on train + validation data.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport math\nimport random\nimport time\nfrom pathlib import Path\n\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\")\nOUT = Path(\"/app/solution\")\nTRAIN_END = 24_786_800\nWINDOW = 604_800\nGAP = 1_800\nFINAL_END = TRAIN_END + GAP + WINDOW\nPAD_HISTORY = 256\nPAD_RECENT = 96\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef load_events(stage: str, item_index: pd.Index, uid_index: pd.Index):\n    \"\"\"Read compact catalog event arrays, retaining no dense interaction grid.\"\"\"\n    families = [\"train_listens\"] + ([\"val_listens\"] if stage == \"test\" else [])\n    chunks = {k: [] for k in (\"uid\", \"time\", \"item\", \"ratio\", \"organic\")}\n    for family in families:\n        pf = pq.ParquetFile(DATA / f\"{family}.parquet\")\n        for batch in pf.iter_batches(\n            batch_size=1_000_000,\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n        ):\n            x = batch.to_pandas()\n            ii = item_index.get_indexer(x.item_id)\n            ui = uid_index.get_indexer(x.uid)\n            keep = (ii >= 0) & (ui >= 0)\n            chunks[\"uid\"].append(ui[keep].astype(np.int16))\n            chunks[\"time\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\n            chunks[\"item\"].append(ii[keep].astype(np.int16))\n            chunks[\"ratio\"].append(x.played_ratio_pct.to_numpy()[keep].astype(np.int16))\n            chunks[\"organic\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\n    ev = {k: np.concatenate(v) for k, v in chunks.items()}\n    # Files are user-major individually.  Sorting also merges train and val for refit.\n    order = np.lexsort((ev[\"time\"], ev[\"uid\"]))\n    ev = {k: v[order] for k, v in ev.items()}\n    return ev\n\n\ndef load_action(stage: str, family: str, item_index: pd.Index, uid_index: pd.Index):\n    names = [f\"train_{family}\"] + ([f\"val_{family}\"] if stage == \"test\" else [])\n    out = {k: [] for k in (\"uid\", \"time\", \"item\", \"organic\")}\n    for name in names:\n        x = pd.read_parquet(DATA / f\"{name}.parquet\")\n        ii = item_index.get_indexer(x.item_id)\n        ui = uid_index.get_indexer(x.uid)\n        keep = (ii >= 0) & (ui >= 0)\n        out[\"uid\"].append(ui[keep].astype(np.int16))\n        out[\"time\"].append(x.timestamp.to_numpy()[keep].astype(np.int32))\n        out[\"item\"].append(ii[keep].astype(np.int16))\n        out[\"organic\"].append(x.is_organic.to_numpy()[keep].astype(np.int8))\n    out = {k: np.concatenate(v) if v else np.empty(0, np.int32) for k, v in out.items()}\n    order = np.lexsort((out[\"time\"], out[\"uid\"]))\n    return {k: v[order] for k, v in out.items()}\n\n\ndef group_bounds(uid_array: np.ndarray, n_users: int):\n    counts = np.bincount(uid_array.astype(np.int64), minlength=n_users)\n    ends = np.cumsum(counts)\n    return np.r_[0, ends[:-1]], ends\n\n\ndef recent_unique(values: np.ndarray, limit: int) -> np.ndarray:\n    if len(values) == 0:\n        return values.astype(np.int64)\n    rev = values[::-1]\n    _, first = np.unique(rev, return_index=True)\n    return rev[np.sort(first)[:limit]].astype(np.int64)\n\n\ndef frequent_unique(values: np.ndarray, limit: int) -> np.ndarray:\n    if len(values) == 0:\n        return values.astype(np.int64)\n    ids, counts = np.unique(values, return_counts=True)\n    # Stable item-id tie breaking makes the cache and training reproducible.\n    order = np.lexsort((ids, -counts))[:limit]\n    return ids[order].astype(np.int64)\n\n\ndef population_features(ev, likes, cutoffs: np.ndarray, n_items: int) -> np.ndarray:\n    \"\"\"Cutoff-correct item features used by the trainable item MLP.\"\"\"\n    feats = []\n    t, item = ev[\"time\"], ev[\"item\"].astype(np.int64)\n    strong = (ev[\"organic\"] == 1) & (ev[\"ratio\"] >= 80)\n    lt, li = likes[\"time\"], likes[\"item\"].astype(np.int64)\n    lo = likes[\"organic\"] == 1\n    for cutoff in cutoffs:\n        cols = []\n        for days in (3, 7, 14, 30):\n            m = strong & (t < cutoff) & (t >= cutoff - days * 86400)\n            cols.append(np.bincount(item[m], minlength=n_items))\n        m = lo & (lt < cutoff) & (lt >= cutoff - 7 * 86400)\n        cols.append(np.bincount(li[m], minlength=n_items))\n        m = strong & (t < cutoff)\n        cols.append(np.bincount(item[m], minlength=n_items))\n        x = np.stack(cols, axis=1).astype(np.float32)\n        x = np.log1p(x)\n        # Cross-sectional standardization keeps different historical cutoffs aligned.\n        x = (x - x.mean(0, keepdims=True)) / (x.std(0, keepdims=True) + 1e-5)\n        feats.append(x)\n    return np.stack(feats)\n\n\ndef make_profiles(stage: str, ev, likes, n_users: int, n_items: int):\n    end = TRAIN_END if stage == \"validation\" else FINAL_END\n    # Validation uses only earlier pseudo-futures.  Refit adds the actual public\n    # window as a training future via cutoff=end-(WINDOW+GAP).\n    cutoffs = np.asarray([end - (j + 1) * (WINDOW + GAP) for j in range(12)], np.int64)\n    pop = population_features(ev, likes, np.r_[cutoffs, end], n_items)\n    es, ee = group_bounds(ev[\"uid\"], n_users)\n    ls, le = group_bounds(likes[\"uid\"], n_users)\n    histories, recents, user_feats, label_lists, cutoff_ids = [], [], [], [], []\n    for ci, cutoff in enumerate(cutoffs):\n        y0, y1 = cutoff + GAP, cutoff + GAP + WINDOW\n        for u in range(n_users):\n            sl = slice(es[u], ee[u])\n            tt, ii = ev[\"time\"][sl], ev[\"item\"][sl].astype(np.int64)\n            before = tt < cutoff\n            if not before.any():\n                continue\n            seen = np.unique(ii[before])\n            pos = before & (ev[\"ratio\"][sl] >= 50)\n            pos_items = ii[pos]\n            if len(pos_items) < 5:\n                continue\n            target_m = (\n                (tt >= y0) & (tt < y1) & (ev[\"organic\"][sl] == 1)\n                & (ev[\"ratio\"][sl] >= 80)\n            )\n            targets = np.setdiff1d(np.unique(ii[target_m]), seen, assume_unique=False)\n            # Organic likes are oversampled to respect their seven-fold DCG gain.\n            lsl = slice(ls[u], le[u])\n            ltt, li = likes[\"time\"][lsl], likes[\"item\"][lsl].astype(np.int64)\n            lm = (ltt >= y0) & (ltt < y1) & (likes[\"organic\"][lsl] == 1)\n            liked_targets = np.setdiff1d(np.unique(li[lm]), seen, assume_unique=False)\n            if len(targets) == 0 and len(liked_targets) == 0:\n                continue\n            labels = np.r_[targets, np.repeat(liked_targets, 4)].astype(np.int64)\n            histories.append(frequent_unique(pos_items, PAD_HISTORY))\n            # Recent channel only considers the last 60 days before this cutoff.\n            rpos = pos & (tt >= cutoff - 60 * 86400)\n            recents.append(recent_unique(ii[rpos], PAD_RECENT))\n            nstrong = int((before & (ev[\"organic\"][sl] == 1) & (ev[\"ratio\"][sl] >= 80)).sum())\n            user_feats.append([\n                math.log1p(len(np.unique(pos_items))),\n                math.log1p(int(pos.sum())),\n                math.log1p(nstrong),\n                len(recents[-1]) / PAD_RECENT,\n            ])\n            label_lists.append(labels)\n            cutoff_ids.append(ci)\n    return cutoffs, pop, histories, recents, np.asarray(user_feats, np.float32), label_lists, np.asarray(cutoff_ids)\n\n\ndef make_inference_profiles(stage: str, ev, n_users: int):\n    cutoff = TRAIN_END if stage == \"validation\" else FINAL_END\n    es, ee = group_bounds(ev[\"uid\"], n_users)\n    histories, recents, feats = [], [], []\n    for u in range(n_users):\n        sl = slice(es[u], ee[u]); tt = ev[\"time\"][sl]\n        ii = ev[\"item\"][sl].astype(np.int64)\n        before = tt <= cutoff\n        pos = before & (ev[\"ratio\"][sl] >= 50)\n        pi = ii[pos]\n        histories.append(frequent_unique(pi, PAD_HISTORY))\n        recents.append(recent_unique(ii[pos & (tt >= cutoff - 60 * 86400)], PAD_RECENT))\n        nstrong = int((before & (ev[\"organic\"][sl] == 1) & (ev[\"ratio\"][sl] >= 80)).sum())\n        feats.append([math.log1p(len(np.unique(pi))), math.log1p(int(pos.sum())),\n                      math.log1p(nstrong), len(recents[-1]) / PAD_RECENT])\n    return histories, recents, np.asarray(feats, np.float32)\n\n\ndef pad_bags(bags: list[np.ndarray], width: int, pad: int) -> np.ndarray:\n    out = np.full((len(bags), width), pad, dtype=np.int64)\n    for r, x in enumerate(bags):\n        out[r, : min(width, len(x))] = x[:width]\n    return out\n\n\nclass UserTower(nn.Module):\n    \"\"\"History-only user encoder; there is intentionally no user-ID table.\"\"\"\n    def __init__(self, n_items: int, dim: int = 64):\n        super().__init__()\n        self.pad = n_items\n        self.long_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n        self.recent_embedding = nn.Embedding(n_items + 1, dim, padding_idx=n_items)\n        self.numeric = nn.Sequential(nn.Linear(4, 16), nn.ReLU())\n        self.mlp = nn.Sequential(nn.Linear(dim * 2 + 16, 128), nn.ReLU(),\n                                 nn.Linear(128, dim))\n\n    @staticmethod\n    def masked_mean(table: nn.Embedding, ids: torch.Tensor, pad: int):\n        mask = (ids != pad).unsqueeze(-1)\n        return (table(ids) * mask).sum(1) / mask.sum(1).clamp_min(1)\n\n    def forward(self, history, recent, numeric):\n        h = self.masked_mean(self.long_embedding, history, self.pad)\n        r = self.masked_mean(self.recent_embedding, recent, self.pad)\n        return self.mlp(torch.cat([h, r, self.numeric(numeric)], dim=1))\n\n\nclass ItemTower(nn.Module):\n    \"\"\"Offline-precomputable candidate encoder.\"\"\"\n    def __init__(self, n_items: int, n_features: int, dim: int = 64):\n        super().__init__()\n        self.id_embedding = nn.Embedding(n_items, dim)\n        self.feature_mlp = nn.Sequential(nn.Linear(n_features, 32), nn.ReLU(),\n                                         nn.Linear(32, dim))\n        self.combine = nn.Sequential(nn.Linear(dim * 2, 96), nn.ReLU(),\n                                     nn.Linear(96, dim))\n\n    def forward(self, item_ids, features):\n        return self.combine(torch.cat([self.id_embedding(item_ids),\n                                       self.feature_mlp(features)], dim=-1))\n\n\nclass TwoTower(nn.Module):\n    def __init__(self, n_items: int, n_features: int, dim: int = 64):\n        super().__init__()\n        self.user_tower = UserTower(n_items, dim)\n        self.item_tower = ItemTower(n_items, n_features, dim)\n        self.log_scale = nn.Parameter(torch.tensor(math.log(1.0 / math.sqrt(dim))))\n\n    def scores(self, u, v):\n        return (u.unsqueeze(1) * v).sum(-1) * self.log_scale.exp().clamp(max=2.0)\n\n\ndef train_model(model, histories, recents, user_feats, labels, cutoff_ids, pop,\n                epochs: int, seed: int):\n    h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, pop.shape[1]))\n    r = torch.from_numpy(pad_bags(recents, PAD_RECENT, pop.shape[1]))\n    uf = torch.from_numpy(user_feats)\n    pfeat = torch.from_numpy(pop.astype(np.float32))\n    opt = torch.optim.AdamW(model.parameters(), lr=2e-3, weight_decay=2e-5)\n    rng = np.random.default_rng(seed)\n    n, batch_size, candidates = len(labels), 256, 40\n    model.train()\n    for epoch in range(epochs):\n        order = rng.permutation(n); losses = []\n        for start in range(0, n, batch_size):\n            rows = order[start:start + batch_size]\n            b = len(rows)\n            positives = np.asarray([x[rng.integers(len(x))] for x in (labels[j] for j in rows)])\n            neg = rng.integers(0, pop.shape[1], size=(b, candidates - 1), dtype=np.int64)\n            cand = np.concatenate([positives[:, None], neg], axis=1)\n            # Put the positive at a random column to prevent positional shortcuts.\n            pos_col = rng.integers(candidates, size=b)\n            cand[np.arange(b), 0], cand[np.arange(b), pos_col] = (\n                cand[np.arange(b), pos_col].copy(), cand[np.arange(b), 0].copy())\n            bt = torch.from_numpy(rows)\n            ct = torch.from_numpy(cand)\n            cids = torch.from_numpy(cutoff_ids[rows]).long()\n            u = model.user_tower(h[bt], r[bt], uf[bt])\n            cf = pfeat[cids[:, None], ct]\n            v = model.item_tower(ct, cf)\n            logits = model.scores(u, v)\n            loss = F.cross_entropy(logits, torch.from_numpy(pos_col).long())\n            opt.zero_grad(set_to_none=True); loss.backward()\n            torch.nn.utils.clip_grad_norm_(model.parameters(), 5.0)\n            opt.step(); losses.append(float(loss))\n        print(f\"epoch={epoch + 1} profiles={n} loss={np.mean(losses):.5f}\", flush=True)\n    return model\n\n\[email protected]_mode()\ndef rank(model, stage: str, histories, recents, user_feats, final_pop,\n         uids: np.ndarray, items: np.ndarray, packed: np.ndarray, output: Path):\n    model.eval(); n_items = len(items)\n    h = torch.from_numpy(pad_bags(histories, PAD_HISTORY, n_items))\n    r = torch.from_numpy(pad_bags(recents, PAD_RECENT, n_items))\n    uf = torch.from_numpy(user_feats)\n    item_ids = torch.arange(n_items)\n    item_vec = model.item_tower(item_ids, torch.from_numpy(final_pop.astype(np.float32)))\n    frames = []\n    for start in range(0, len(uids), 128):\n        stop = min(len(uids), start + 128)\n        user_vec = model.user_tower(h[start:stop], r[start:stop], uf[start:stop])\n        scores = user_vec @ item_vec.T\n        eligibility = np.unpackbits(packed[start:stop], axis=1, count=n_items,\n                                    bitorder=\"little\").astype(bool)\n        scores[~torch.from_numpy(eligibility)] = -torch.inf\n        top = torch.topk(scores, 100, dim=1, sorted=True).indices.numpy()\n        for j, row in enumerate(top):\n            frames.append(pd.DataFrame({\"uid\": int(uids[start + j]),\n                                        \"item_id\": items[row],\n                                        \"rank\": np.arange(1, 101)}))\n    pd.concat(frames, ignore_index=True).astype(\n        {\"uid\": \"int64\", \"item_id\": \"int64\", \"rank\": \"int64\"}\n    ).to_parquet(output, index=False)\n\n\ndef main():\n    ap = argparse.ArgumentParser()\n    ap.add_argument(\"--stage\", choices=[\"validation\", \"test\"], required=True)\n    ap.add_argument(\"--output\", type=Path, required=True)\n    ap.add_argument(\"--epochs\", type=int, default=8)\n    ap.add_argument(\"--seed\", type=int, default=20260919)\n    args = ap.parse_args(); seed_everything(args.seed)\n    torch.set_num_threads(min(16, torch.get_num_threads()))\n    catalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    targets = pd.read_parquet(DATA / \"target_users.parquet\")\n    items = catalog.item_id.to_numpy(np.int64); uids = targets.uid.to_numpy(np.int64)\n    print(\"loading compact event streams\", flush=True); started = time.time()\n    ev = load_events(args.stage, pd.Index(items), pd.Index(uids))\n    likes = load_action(args.stage, \"likes\", pd.Index(items), pd.Index(uids))\n    print(f\"catalog listens={len(ev['uid']):,} likes={len(likes['uid']):,} seconds={time.time()-started:.1f}\", flush=True)\n    cutoffs, pop, histories, recents, uf, labels, cids = make_profiles(\n        args.stage, ev, likes, len(uids), len(items))\n    print(f\"training profiles={len(labels):,} mean_labels={np.mean([len(x) for x in labels]):.2f}\", flush=True)\n    model = TwoTower(len(items), pop.shape[-1])\n    train_model(model, histories, recents, uf, labels, cids, pop, args.epochs, args.seed)\n    ih, ir, iuf = make_inference_profiles(args.stage, ev, len(uids))\n    z = np.load(DATA / f\"{args.stage}_eligible_candidates.npz\")\n    rank(model, args.stage, ih, ir, iuf, pop[-1], z[\"uids\"].astype(np.int64),\n         z[\"item_ids\"].astype(np.int64), z[\"packed_eligible\"], args.output)\n    torch.save({\"user_tower\": model.user_tower.state_dict(),\n                \"item_tower\": model.item_tower.state_dict(),\n                \"config\": {\"dimension\": 64, \"item_features\": int(pop.shape[-1]),\n                           \"stage\": args.stage, \"seed\": args.seed}},\n               OUT / f\"two_tower_{args.stage}.pt\")\n    print(json.dumps({\"output\": str(args.output), \"seconds\": round(time.time()-started, 1)}))\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/train_two_tower.py\n",
    "stderr": ""
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{
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  "item": {
    "type": "CommandExecution",
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      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/train_two_tower.py --stage validation --output solution/validation_two_tower.parquet --epochs 6"
    ],
    "cwd": "file:///app",
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      }
    ],
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    "status": "completed",
    "stdout": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.9\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.09883\nepoch=2 profiles=44451 loss=2.94168\nepoch=3 profiles=44451 loss=2.83037\nepoch=4 profiles=44451 loss=2.76422\nepoch=5 profiles=44451 loss=2.71072\nepoch=6 profiles=44451 loss=2.65645\n{\"output\": \"solution/validation_two_tower.parquet\", \"seconds\": 78.1}\n",
    "stderr": "",
    "aggregated_output": "loading compact event streams\ncatalog listens=31,062,540 likes=472,889 seconds=3.9\ntraining profiles=44,451 mean_labels=12.70\n/app/solution/train_two_tower.py:286: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  opt.step(); losses.append(float(loss))\nepoch=1 profiles=44451 loss=3.09883\nepoch=2 profiles=44451 loss=2.94168\nepoch=3 profiles=44451 loss=2.83037\nepoch=4 profiles=44451 loss=2.76422\nepoch=5 profiles=44451 loss=2.71072\nepoch=6 profiles=44451 loss=2.65645\n{\"output\": \"solution/validation_two_tower.parquet\", \"seconds\": 78.1}\n",
    "exit_code": 0,
    "duration": {
      "secs": 82,
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        "type": "update",
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      "/app/solution/run_final.sh": {
        "type": "add",
        "content": "#!/bin/sh\nset -eu\ncd /app\n/opt/task-venv/bin/python solution/train_two_tower.py \\\n  --stage test \\\n  --output solution/submission_neural_only.parquet \\\n  --epochs 10 \\\n  --negative-power 0.25 \\\n  --seed 20260919\n/opt/task-venv/bin/python solution/calibrate_ranker.py \\\n  --stage test \\\n  --checkpoint solution/two_tower_test.pt \\\n  --prefix solution/submission_exact \\\n  --mode raw \\\n  --alphas 10\ncp solution/submission_exact_a10.parquet solution/submission.parquet\n\n"
      },
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made for You two-tower ranker\n\n## Model\n\nThe ranker is a CPU PyTorch two-tower model with no user-ID embedding and no\nglobal user-item matrix. The user tower separately embeds (a) the 256 most\nfrequent positive-history catalog tracks and (b) 96 unique tracks from the\nlast 60 days, mean-pools each bag, adds four activity features, and maps the\nresult through an MLP to 64 dimensions. The item tower combines a trainable\nitem embedding with an MLP over six cutoff-correct item statistics (organic\n80%-listen counts at 3/7/14/30 days, seven-day organic likes, and lifetime\norganic 80%-listens) and emits a 64-dimensional vector.\n\nA validation-selected trend coordinate is appended to both outputs: `1` on\nthe user side and 10 times the standardized sum of log seven-day organic\n80%-listen and organic-like counts on the item side. The deployed vectors\ntherefore have 65 dimensions and the complete relevance score—including the\ntrend term—is exactly one dot product. Candidate vectors can be computed\noffline, and serving computes only one history-based user vector before MIPS.\n\n## Training\n\nTraining uses twelve rolling, cutoff-correct seven-day pseudo-future windows.\nPositives are previously unseen catalog tracks with an organic 80% listen;\norganic likes are oversampled fourfold to reflect their larger graded gain.\nThe loss is a 40-way sampled-softmax cross entropy with negatives sampled from\nthe empirical future-target distribution raised to power 0.25. Histories are\nstrictly earlier than each pseudo cutoff. The final fit uses `train_* + val_*`,\nso validation events become observed history and the public validation window\nbecomes the newest training pseudo-future.\n\n## Validation\n\nThe selected single-tower-seed model plus trend coordinate achieved public\ngraded **NDCG@10 = 0.032947** over 4,263 scored users. For reference, the\nseven-day trend alone scored 0.031211, the original neural model alone scored\n0.023610, and lifetime popularity scored 0.010710.\n\n## Reproduce the final submission\n\nFrom `/app`, run:\n\n```sh\nsh /app/solution/run_final.sh\n```\n\nThis refits on train plus validation and writes\n`/app/solution/submission.parquet`.\n"
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    "stdout": "Success. Updated the following files:\nA /app/solution/run_final.sh\nA /app/solution/README.md\n",
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nx=validate_ranking(pd.read_parquet(p),targets).sort_values(['uid','rank'])\nz=np.load(D/'test_eligible_candidates.npz')\nassert np.array_equal(x.uid.unique(),z['uids'].astype(np.int64))\nitems=z['item_ids'].astype(np.int64); order=np.argsort(items)\nvals=x.item_id.to_numpy(); loc=np.searchsorted(items[order],vals); cols=order[loc]\nrows=np.repeat(np.arange(len(z['uids'])),100)\nelig=((z['packed_eligible'][rows,cols//8] >> (cols%8)) & 1).astype(bool)\nassert elig.all()\nprint({'path':str(p),'rows':len(x),'users':x.uid.nunique(),'all_test_eligible':bool(elig.all()),'bytes':p.stat().st_size,'rank_min':int(x['rank'].min()),'rank_max':int(x['rank'].max())})\nPY\nls -lh solution/submission.parquet solution/two_tower_test.pt solution/README.md solution/run_final.sh"
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    "stdout": "validation catalog_order_equal True\ntest catalog_order_equal True\n",
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