Agent trace

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-3f4b8ef8-9898-489a-aa53-f0591fcc299f",
    "process_id": "90378",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && cat /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,200p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "cat /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3240
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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    "process_id": "86707",
    "command": [
      "/bin/sh",
      "-lc",
      "python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, len(d), d.dtypes.to_dict())\n if 'timestamp' in d: print(' time', d.timestamp.min(), d.timestamp.max(),'uids',d.uid.nunique(),'items',d.item_id.nunique())\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); tar=pd.read_parquet(p/'target_users.parquet')\nprint('catalog head/tail',cat.head().to_dict('list'),cat.tail().to_dict('list'));print('targets',tar.head(),len(tar))\nfor st in ['validation','test']:\n z=np.load(p/f'{st}_eligible_candidates.npz');print(st,{k:z[k].shape for k in z.files},'eligible mean',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).mean(), 'minmax', np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).min(),np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).max())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, len(d), d.dtypes.to_dict())\n if 'timestamp' in d: print(' time', d.timestamp.min(), d.timestamp.max(),'uids',d.uid.nunique(),'items',d.item_id.nunique())\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); tar=pd.read_parquet(p/'target_users.parquet')\nprint('catalog head/tail',cat.head().to_dict('list'),cat.tail().to_dict('list'));print('targets',tar.head(),len(tar))\nfor st in ['validation','test']:\n z=np.load(p/f'{st}_eligible_candidates.npz');print(st,{k:z[k].shape for k in z.files},'eligible mean',np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).mean(), 'minmax', np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).min(),np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1).max())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3420
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pandas'\n"
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      "-lc",
      "which -a python python3; ls -l /opt/venv/bin/python* 2>/dev/null || true; find / -path '*/site-packages/pandas' -type d 2>/dev/null | head"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\n",
    "exit_code": 0,
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    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\n"
  }
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-41b1b474-f8d6-47b4-8ed3-cdc87d62b838",
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      "/bin/sh",
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      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, len(d), d.dtypes.to_dict())\n if 'timestamp' in d: print(' time', d.timestamp.min(), d.timestamp.max(),'uids',d.uid.nunique(),'items',d.item_id.nunique())\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); tar=pd.read_parquet(p/'target_users.parquet')\nprint('catalog',cat.head().to_dict('list'),cat.tail().to_dict('list'));print('targets',tar.head().to_dict('list'),len(tar))\nfor st in ['validation','test']:\n z=np.load(p/f'{st}_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1);print(st,{k:z[k].shape for k in z.files},'eligible',m.mean(),m.min(),m.max())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\np=Path('/task/data')\nfor f in sorted(p.glob('*.parquet')):\n d=pd.read_parquet(f)\n print(f.name, len(d), d.dtypes.to_dict())\n if 'timestamp' in d: print(' time', d.timestamp.min(), d.timestamp.max(),'uids',d.uid.nunique(),'items',d.item_id.nunique())\ncat=pd.read_parquet(p/'candidate_catalog.parquet'); tar=pd.read_parquet(p/'target_users.parquet')\nprint('catalog',cat.head().to_dict('list'),cat.tail().to_dict('list'));print('targets',tar.head().to_dict('list'),len(tar))\nfor st in ['validation','test']:\n z=np.load(p/f'{st}_eligible_candidates.npz'); m=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].sum(1);print(st,{k:z[k].shape for k in z.files},'eligible',m.mean(),m.min(),m.max())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet 30000 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet 8456 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_preference_state.parquet 484776 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet 101075 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 75 24786580 uids 5732 items 50876\ntrain_likes.parquet 814831 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 80 24786790 uids 8144 items 172303\ntrain_listens.parquet 43484159 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 0 24786795 uids 9238 items 845395\ntrain_undislikes.parquet 19960 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 15950 24785395 uids 2769 items 14725\ntrain_unlikes.parquet 292665 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 250 24786570 uids 6197 items 113072\nval_dislikes.parquet 3563 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789920 25393280 uids 955 items 3135\nval_likes.parquet 37795 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24788815 25393390 uids 4114 items 22902\nval_listens.parquet 1501697 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 24788600 25393395 uids 8196 items 191747\nval_undislikes.parquet 606 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24810715 25393035 uids 205 items 579\nval_unlikes.parquet 9346 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789005 25393385 uids 1313 items 8080\nvalidation_preference_state.parquet 469124 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ncatalog {'item_id': [5862961, 6901374, 5635052, 3542184, 9378983], 'positive_listens': [26615, 25536, 24691, 24075, 24036], 'organic_positive_listens': [20620, 17347, 17194, 17699, 18195], 'popularity_rank': [1, 2, 3, 4, 5]} {'item_id': [1275692, 1280011, 1309246, 1634958, 1670524], 'positive_listens': [163, 163, 163, 163, 163], 'organic_positive_listens': [81, 76, 138, 46, 73], 'popularity_rank': [29996, 29997, 29998, 29999, 30000]}\ntargets {'uid': [100, 200, 300, 600, 700], 'recent_positive_listens': [844.0, 50.0, 168.0, 1985.0, 1776.0], 'history_positive_listens': [2604, 286, 359, 2423, 6114]} 8456\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29067.306291390727 24657 30000\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29041.3178807947 24552 30000\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet 30000 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet 8456 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_preference_state.parquet 484776 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet 101075 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 75 24786580 uids 5732 items 50876\ntrain_likes.parquet 814831 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 80 24786790 uids 8144 items 172303\ntrain_listens.parquet 43484159 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 0 24786795 uids 9238 items 845395\ntrain_undislikes.parquet 19960 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 15950 24785395 uids 2769 items 14725\ntrain_unlikes.parquet 292665 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 250 24786570 uids 6197 items 113072\nval_dislikes.parquet 3563 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789920 25393280 uids 955 items 3135\nval_likes.parquet 37795 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24788815 25393390 uids 4114 items 22902\nval_listens.parquet 1501697 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 24788600 25393395 uids 8196 items 191747\nval_undislikes.parquet 606 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24810715 25393035 uids 205 items 579\nval_unlikes.parquet 9346 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789005 25393385 uids 1313 items 8080\nvalidation_preference_state.parquet 469124 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ncatalog {'item_id': [5862961, 6901374, 5635052, 3542184, 9378983], 'positive_listens': [26615, 25536, 24691, 24075, 24036], 'organic_positive_listens': [20620, 17347, 17194, 17699, 18195], 'popularity_rank': [1, 2, 3, 4, 5]} {'item_id': [1275692, 1280011, 1309246, 1634958, 1670524], 'positive_listens': [163, 163, 163, 163, 163], 'organic_positive_listens': [81, 76, 138, 46, 73], 'popularity_rank': [29996, 29997, 29998, 29999, 30000]}\ntargets {'uid': [100, 200, 300, 600, 700], 'recent_positive_listens': [844.0, 50.0, 168.0, 1985.0, 1776.0], 'history_positive_listens': [2604, 286, 359, 2423, 6114]} 8456\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29067.306291390727 24657 30000\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29041.3178807947 24552 30000\n",
    "exit_code": 0,
    "duration": {
      "secs": 3,
      "nanos": 242764080
    },
    "formatted_output": "candidate_catalog.parquet 30000 {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\ntarget_users.parquet 8456 {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\ntest_preference_state.parquet 484776 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ntrain_dislikes.parquet 101075 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 75 24786580 uids 5732 items 50876\ntrain_likes.parquet 814831 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 80 24786790 uids 8144 items 172303\ntrain_listens.parquet 43484159 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 0 24786795 uids 9238 items 845395\ntrain_undislikes.parquet 19960 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 15950 24785395 uids 2769 items 14725\ntrain_unlikes.parquet 292665 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 250 24786570 uids 6197 items 113072\nval_dislikes.parquet 3563 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789920 25393280 uids 955 items 3135\nval_likes.parquet 37795 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24788815 25393390 uids 4114 items 22902\nval_listens.parquet 1501697 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')}\n time 24788600 25393395 uids 8196 items 191747\nval_undislikes.parquet 606 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24810715 25393035 uids 205 items 579\nval_unlikes.parquet 9346 {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')}\n time 24789005 25393385 uids 1313 items 8080\nvalidation_preference_state.parquet 469124 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\ncatalog {'item_id': [5862961, 6901374, 5635052, 3542184, 9378983], 'positive_listens': [26615, 25536, 24691, 24075, 24036], 'organic_positive_listens': [20620, 17347, 17194, 17699, 18195], 'popularity_rank': [1, 2, 3, 4, 5]} {'item_id': [1275692, 1280011, 1309246, 1634958, 1670524], 'positive_listens': [163, 163, 163, 163, 163], 'organic_positive_listens': [81, 76, 138, 46, 73], 'popularity_rank': [29996, 29997, 29998, 29999, 30000]}\ntargets {'uid': [100, 200, 300, 600, 700], 'recent_positive_listens': [844.0, 50.0, 168.0, 1985.0, 1776.0], 'history_positive_listens': [2604, 286, 359, 2423, 6114]} 8456\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29067.306291390727 24657 30000\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} eligible 29041.3178807947 24552 30000\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-743212cf-75b5-4a67-9151-dce0ef100fcf",
    "changes": {
      "/app/solution/profile.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\ntargets = set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat = pd.read_parquet(D/'candidate_catalog.parquet')\nitems = set(cat.item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); its=z['item_ids'].astype(np.int64)\nur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(its)}\ndef eligible(u,i):\n r=ur.get(int(u)); c=ic.get(int(i)); return r is not None and c is not None and bool((z['packed_eligible'][r,c//8]>>(c%8))&1)\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nva=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nla=lk[(lk.uid.isin(targets))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ngold=pd.concat([va.assign(g=1),la.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\ngold=gold[[eligible(u,i) for u,i in gold[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('gold',len(gold),'users',gold.uid.nunique(),'likes',sum(gold.g==3))\nfor fam, cond in [('positive',None),('strongorg',None),('like',None)]:\n if fam=='like': h=pd.read_parquet(D/'train_likes.parquet',columns=['uid','item_id']); h=h[h.uid.isin(targets)]\n else:\n  h=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']);h=h[h.uid.isin(targets)]\n  h=h[h.played_ratio_pct >= (80 if fam=='strongorg' else 50)]\n  if fam=='strongorg':h=h[h.is_organic==1]\n h=h[['uid','item_id']].drop_duplicates(); m=gold.merge(h,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\n print(fam,'repeat',m.mean(), 'like repeat',m[gold.g.eq(3).to_numpy()].mean())\nprint('gold popularity quantiles',gold.merge(cat,on='item_id').popularity_rank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict())\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/profile.py\n",
    "stderr": ""
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-be3da3ca-4a1e-4421-badb-808e29283845",
    "changes": {
      "/app/solution/pop_baseline.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD=Path('/task/data'); O=Path('/app/solution')\ndef make(stage, out):\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n rows=[]\n for r,u in enumerate(z['uids']):\n  mask=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\n  top=z['item_ids'][mask][:100]\n  rows.extend((int(u),int(i),j+1) for j,i in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/out,index=False)\nmake('validation','validation_pop.parquet')\nmake('test','submission.parquet')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/pop_baseline.py\n",
    "stderr": ""
  }
}

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  "item": {
    "type": "CommandExecution",
    "id": "exec-811afd2b-bac7-4a38-91d0-d9e009dfec56",
    "process_id": "91415",
    "command": [
      "/bin/sh",
      "-lc",
      "ps aux | grep -E 'profile|pop_baseline|evaluate_public' | grep -v grep; ls -lh solution"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ps aux | grep -E 'profile|pop_baseline|evaluate_public' | grep -v grep; ls -lh solution"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/bin/sh: 1: ps: not found\ntotal 8.0K\n-rw-r--r-- 1 agent agent  581 Sep 24 23:01 pop_baseline.py\n-rw-r--r-- 1 agent agent 2.0K Sep 24 23:01 profile.py\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: ps: not found\ntotal 8.0K\n-rw-r--r-- 1 agent agent  581 Sep 24 23:01 pop_baseline.py\n-rw-r--r-- 1 agent agent 2.0K Sep 24 23:01 profile.py\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3780
    },
    "formatted_output": "/bin/sh: 1: ps: not found\ntotal 8.0K\n-rw-r--r-- 1 agent agent  581 Sep 24 23:01 pop_baseline.py\n-rw-r--r-- 1 agent agent 2.0K Sep 24 23:01 profile.py\n"
  }
}

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  "thread_token_usage": {
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{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-c66d398d-c5b1-43eb-bad9-629dda20eae4",
    "changes": {
      "/app/solution/profile.py": {
        "type": "update",
        "unified_diff": "@@ -20,9 +20,16 @@\n print('gold',len(gold),'users',gold.uid.nunique(),'likes',sum(gold.g==3))\n-for fam, cond in [('positive',None),('strongorg',None),('like',None)]:\n- if fam=='like': h=pd.read_parquet(D/'train_likes.parquet',columns=['uid','item_id']); h=h[h.uid.isin(targets)]\n- else:\n-  h=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']);h=h[h.uid.isin(targets)]\n-  h=h[h.played_ratio_pct >= (80 if fam=='strongorg' else 50)]\n-  if fam=='strongorg':h=h[h.is_organic==1]\n- h=h[['uid','item_id']].drop_duplicates(); m=gold.merge(h,on=['uid','item_id'],how='left',indicator=True)._merge.eq('both')\n+import pyarrow.parquet as pq\n+keys=(gold.uid.to_numpy(np.int64)<<32)|gold.item_id.to_numpy(np.int64)\n+for fam in ['positive','strongorg','like']:\n+ seen=set()\n+ path=D/('train_likes.parquet' if fam=='like' else 'train_listens.parquet')\n+ cols=['uid','item_id'] if fam=='like' else ['uid','item_id','is_organic','played_ratio_pct']\n+ for b in pq.ParquetFile(path).iter_batches(batch_size=500_000,columns=cols):\n+  x=b.to_pandas()\n+  if fam!='like':\n+   x=x[x.played_ratio_pct >= (80 if fam=='strongorg' else 50)]\n+   if fam=='strongorg': x=x[x.is_organic==1]\n+  kk=(x.uid.to_numpy(np.int64)<<32)|x.item_id.to_numpy(np.int64)\n+  seen.update(set(kk[np.isin(kk,keys)]))\n+ m=np.isin(keys,np.fromiter(seen,dtype=np.int64))\n  print(fam,'repeat',m.mean(), 'like repeat',m[gold.g.eq(3).to_numpy()].mean())\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/profile.py\n",
    "stderr": ""
  }
}

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      "/app/solution/train_ranker.py": {
        "type": "add",
        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply an ANN-compatible neural two-tower discovery ranker.\n\nNo user ID parameter is used.  User representations are functions of timestamped\ninteraction histories; item representations can be materialized independently.\n\"\"\"\nfrom __future__ import annotations\nimport argparse, json, math, random\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, TensorDataset\n\nDATA=Path('/task/data'); OUT=Path('/app/solution')\nTRAIN_END=24_786_800; WINDOW=604_800; GAP=1_800\nHASH=131_071; MAX_HIST=160; DIM=64\n\ndef read_events(name, columns, lo=None, hi=None, include_val=False):\n    filters=[]\n    if lo is not None: filters.append(('timestamp','>=',int(lo)))\n    if hi is not None: filters.append(('timestamp','<=',int(hi)))\n    a=pd.read_parquet(DATA/f'train_{name}.parquet',columns=columns,filters=filters or None)\n    if include_val:\n        b=pd.read_parquet(DATA/f'val_{name}.parquet',columns=columns,\n                          filters=filters or None)\n        a=pd.concat([a,b],ignore_index=True)\n    return a\n\ndef make_history(cutoff, uid_to_row, include_val=False, preference_file=None):\n    \"\"\"Return fixed-size hashed histories and weights, built linearly from events.\"\"\"\n    lo=cutoff-180*86400\n    cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\n    x=read_events('listens',cols,lo,cutoff,include_val)\n    x=x[(x.uid.isin(uid_to_row)) & (x.played_ratio_pct>=50)].copy()\n    # Retain only recent events before converting to dense history tensors.\n    x=x.sort_values(['uid','timestamp']).groupby('uid',sort=False).tail(MAX_HIST)\n    n=len(uid_to_row); ids=np.zeros((n,MAX_HIST),np.int64); w=np.zeros((n,MAX_HIST),np.float32)\n    for uid,g in x.groupby('uid',sort=False):\n        r=uid_to_row[int(uid)]; k=min(len(g),MAX_HIST); g=g.iloc[-k:]\n        ids[r,-k:]=(g.item_id.to_numpy(np.int64)%HASH)+1\n        age=(cutoff-g.timestamp.to_numpy(np.float64))/86400\n        strength=np.minimum(g.played_ratio_pct.to_numpy(np.float32),100)/100\n        w[r,-k:]=(0.35+strength)*(1+0.35*g.is_organic.to_numpy())*np.exp(-age/75)\n    # Explicit likes are a high-signal second channel. Add them by replacing oldest slots.\n    if preference_file:\n        p=pd.read_parquet(DATA/preference_file)\n        p=p[(p.state=='liked') & p.uid.isin(uid_to_row)]\n        for uid,g in p.groupby('uid',sort=False):\n            r=uid_to_row[int(uid)]; vals=((g.item_id.to_numpy(np.int64)[-32:]%HASH)+1)\n            k=len(vals); ids[r,:k]=vals; w[r,:k]=2.5\n    else:\n        p=read_events('likes',['uid','timestamp','item_id','is_organic'],lo,cutoff,include_val)\n        p=p[p.uid.isin(uid_to_row)].sort_values(['uid','timestamp']).groupby('uid').tail(24)\n        for uid,g in p.groupby('uid',sort=False):\n            r=uid_to_row[int(uid)]; vals=((g.item_id.to_numpy(np.int64)%HASH)+1); k=len(vals)\n            ids[r,:k]=vals; w[r,:k]=2.2*(1+.25*g.is_organic.to_numpy())\n    # Four stable activity descriptors; no identity information.\n    nz=(w>0); feat=np.stack([np.log1p(nz.sum(1)),np.log1p(w.sum(1)),\n        (w[:,-32:]>0).sum(1)/32, np.maximum(w,axis=1)],1).astype(np.float32)\n    feat[:,0:2]/=6\n    return ids,w,feat\n\ndef outcome_pairs(start,end,uid_to_row,item_to_col,include_val=False, history_raw=None):\n    l=read_events('listens',['uid','timestamp','item_id','is_organic','played_ratio_pct'],start,end,include_val)\n    l=l[(l.uid.isin(uid_to_row))&(l.item_id.isin(item_to_col))&(l.is_organic==1)&(l.played_ratio_pct>=80)]\n    a=read_events('likes',['uid','timestamp','item_id','is_organic'],start,end,include_val)\n    a=a[(a.uid.isin(uid_to_row))&(a.item_id.isin(item_to_col))&(a.is_organic==1)]\n    q=pd.concat([l[['uid','item_id']].assign(weight=1.0),a[['uid','item_id']].assign(weight=3.0)])\n    q=q.groupby(['uid','item_id'],as_index=False).weight.max()\n    # Sampling likes more often approximates graded gain without changing the serving score.\n    rows=np.array([uid_to_row[int(v)] for v in q.uid],np.int64)\n    items=np.array([item_to_col[int(v)] for v in q.item_id],np.int64)\n    rep=np.where(q.weight.to_numpy()>1,3,1)\n    return np.repeat(rows,rep),np.repeat(items,rep)\n\nclass UserTower(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.history=nn.Embedding(HASH+1,48,padding_idx=0)\n        self.net=nn.Sequential(nn.Linear(52,96),nn.ReLU(),nn.Linear(96,DIM),nn.LayerNorm(DIM))\n    def forward(self,ids,weights,features):\n        e=self.history(ids)\n        pooled=(e*weights.unsqueeze(-1)).sum(1)/weights.sum(1,keepdim=True).clamp_min(.1)\n        return self.net(torch.cat([pooled,features],1))\n\nclass ItemTower(nn.Module):\n    def __init__(self,n_items):\n        super().__init__()\n        self.item=nn.Embedding(n_items,48)\n        self.net=nn.Sequential(nn.Linear(51,96),nn.ReLU(),nn.Linear(96,DIM),nn.LayerNorm(DIM))\n        self.bias=nn.Embedding(n_items,1)\n        nn.init.zeros_(self.bias.weight)\n    def forward(self,item_idx,item_features):\n        z=self.net(torch.cat([self.item(item_idx),item_features],1))\n        # A constant user coordinate turns the item bias into an exact dot-product term.\n        return torch.cat([z[:,:-1],self.bias(item_idx).squeeze(1).unsqueeze(1)],1)\n\nclass TwoTower(nn.Module):\n    def __init__(self,n_items):\n        super().__init__(); self.user_tower=UserTower(); self.item_tower=ItemTower(n_items)\n\ndef build(stage):\n    torch.set_num_threads(max(1,min(8,torch.get_num_threads())))\n    np.random.seed(2026); random.seed(2026); torch.manual_seed(2026)\n    targets=pd.read_parquet(DATA/'target_users.parquet').uid.astype(np.int64).to_numpy()\n    uid_to_row={int(v):i for i,v in enumerate(targets)}\n    cat=pd.read_parquet(DATA/'candidate_catalog.parquet')\n    item_ids=cat.item_id.astype(np.int64).to_numpy(); item_to_col={int(v):i for i,v in enumerate(item_ids)}\n    pop=np.log1p(cat.positive_listens.to_numpy(np.float32)); org=np.log1p(cat.organic_positive_listens.to_numpy(np.float32))\n    rank=np.log1p(cat.popularity_rank.to_numpy(np.float32))\n    item_feat=np.stack([(pop-pop.mean())/pop.std(),(org-org.mean())/org.std(),(rank-rank.mean())/rank.std()],1).astype(np.float32)\n    if stage=='validation': end=TRAIN_END; include_val=False; pref='validation_preference_state.parquet'; elig_stage='validation'\n    else: end=25_393_400; include_val=True; pref='test_preference_state.parquet'; elig_stage='test'\n    # Two chronological, cutoff-correct pseudo-backtests.\n    scenarios=[]; train_rows=[]; train_items=[]\n    for j in (2,1):\n        cutoff=end-j*(WINDOW+GAP)\n        ids,w,f=make_history(cutoff,uid_to_row,include_val)\n        rr,ii=outcome_pairs(cutoff+GAP,cutoff+GAP+WINDOW,uid_to_row,item_to_col,include_val)\n        scenarios.append((ids,w,f)); train_rows.append(np.c_[np.full(len(rr),len(scenarios)-1),rr]); train_items.append(ii)\n        print('scenario',j,'cutoff',cutoff,'samples',len(rr),flush=True)\n    pairs=np.concatenate(train_rows); labels=np.concatenate(train_items)\n    # A third scenario uses the most recent completed window in final refitting.\n    model=TwoTower(len(item_ids)); opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n    ds=TensorDataset(torch.from_numpy(pairs),torch.from_numpy(labels))\n    loader=DataLoader(ds,batch_size=384,shuffle=True,drop_last=True)\n    ft=torch.from_numpy(item_feat)\n    model.train()\n    for epoch in range(8):\n        total=0.; n=0\n        for pair,y in loader:\n            hs=[]\n            for s in range(len(scenarios)):\n                mask=pair[:,0]==s\n                if mask.any():\n                    ix=pair[mask,1].numpy(); ids,w,f=scenarios[s]\n                    hs.append((mask,model.user_tower(torch.from_numpy(ids[ix]),torch.from_numpy(w[ix]),torch.from_numpy(f[ix]))))\n            u=torch.empty((len(y),DIM));\n            for mask,val in hs:u[mask]=val\n            v=model.item_tower(y,ft[y]); logits=([email protected])/7.0\n            loss=nn.functional.cross_entropy(logits,torch.arange(len(y)))\n            opt.zero_grad();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n            total+=float(loss)*len(y);n+=len(y)\n        print('epoch',epoch+1,'loss',round(total/n,4),flush=True)\n    torch.save({'model':model.state_dict(),'item_ids':item_ids,'item_feat':item_feat},OUT/f'model_{stage}.pt')\n    # Materialize independent tower outputs, then score eligible candidates in chunks.\n    infer_ids,infer_w,infer_f=make_history(end,uid_to_row,include_val,pref)\n    model.eval();\n    with torch.no_grad():\n        allv=[]\n        for a in range(0,len(item_ids),2048):\n            ix=torch.arange(a,min(a+2048,len(item_ids))); allv.append(model.item_tower(ix,ft[ix]))\n        V=torch.cat(allv)\n    z=np.load(DATA/f'{elig_stage}_eligible_candidates.npz'); packed=z['packed_eligible'].copy()\n    # Eligibility files share target and catalog order, but map explicitly for robustness.\n    eu=z['uids'].astype(np.int64); ei=z['item_ids'].astype(np.int64); er={int(x):i for i,x in enumerate(eu)}\n    ecol=np.array([item_to_col[int(x)] for x in ei],np.int64)\n    rows=[]\n    with torch.no_grad():\n        for a in range(0,len(targets),128):\n            b=min(a+128,len(targets)); U=model.user_tower(torch.from_numpy(infer_ids[a:b]),torch.from_numpy(infer_w[a:b]),torch.from_numpy(infer_f[a:b]))\n            # Last coordinate is fixed to one, making item bias a dot-product component.\n            U[:,-1]=1.; scores=([email protected]).numpy()\n            for j,u in enumerate(targets[a:b]):\n                mask=np.unpackbits(packed[er[int(u)]],count=len(ei),bitorder='little').astype(bool)\n                allowed=ecol[mask]; s=scores[j,allowed]\n                top=allowed[np.argpartition(s,-100)[-100:]]; top=top[np.argsort(scores[j,top])[::-1]]\n                rows.extend((int(u),int(item_ids[k]),r+1) for r,k in enumerate(top))\n    name='validation.parquet' if stage=='validation' else 'submission.parquet'\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/name,index=False)\n    print('wrote',name,len(rows),flush=True)\n\nif __name__=='__main__':\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);build(ap.parse_args().stage)\n"
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    "stdout": "Success. Updated the following files:\nA /app/solution/train_ranker.py\n",
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    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
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      "-lc",
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    ],
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      {
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        "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python -u solution/train_ranker.py --stage validation"
      }
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    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "scenario 2 cutoff 23573600 samples 185191\nscenario 1 cutoff 24180200 samples 191483\ngold 37769 users 4263 likes 4417\npositive repeat 0.0 like repeat 0.0\nstrongorg repeat 0.0 like repeat 0.0\nlike repeat 0.0 like repeat 0.0\ngold popularity quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0}\nTraceback (most recent call last):\n  File \"/app/solution/train_ranker.py\", line 176, in <module>\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);build(ap.parse_args().stage)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train_ranker.py\", line 126, in build\n    model=TwoTower(len(item_ids)); opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 37, in __init__\n    super().__init__(\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 101, in __init__\n    super().__init__(params, defaults)\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 400, in __init__\n    self.add_param_group(cast(dict, param_group))\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_compile.py\", line 46, in inner\n    import torch._dynamo\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/__init__.py\", line 13, in <module>\n    from . import config, convert_frame, eval_frame, resume_execution\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py\", line 26, in <module>\n    import cProfile\n  File \"/usr/local/lib/python3.12/cProfile.py\", line 24, in <module>\n    run.__doc__ = _pyprofile.run.__doc__\n                  ^^^^^^^^^^^^^^\nAttributeError: module 'profile' has no attribute 'run'\n",
    "stderr": "",
    "aggregated_output": "scenario 2 cutoff 23573600 samples 185191\nscenario 1 cutoff 24180200 samples 191483\ngold 37769 users 4263 likes 4417\npositive repeat 0.0 like repeat 0.0\nstrongorg repeat 0.0 like repeat 0.0\nlike repeat 0.0 like repeat 0.0\ngold popularity quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0}\nTraceback (most recent call last):\n  File \"/app/solution/train_ranker.py\", line 176, in <module>\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);build(ap.parse_args().stage)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train_ranker.py\", line 126, in build\n    model=TwoTower(len(item_ids)); opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 37, in __init__\n    super().__init__(\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 101, in __init__\n    super().__init__(params, defaults)\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 400, in __init__\n    self.add_param_group(cast(dict, param_group))\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_compile.py\", line 46, in inner\n    import torch._dynamo\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/__init__.py\", line 13, in <module>\n    from . import config, convert_frame, eval_frame, resume_execution\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py\", line 26, in <module>\n    import cProfile\n  File \"/usr/local/lib/python3.12/cProfile.py\", line 24, in <module>\n    run.__doc__ = _pyprofile.run.__doc__\n                  ^^^^^^^^^^^^^^\nAttributeError: module 'profile' has no attribute 'run'\n",
    "exit_code": 1,
    "duration": {
      "secs": 25,
      "nanos": 390104936
    },
    "formatted_output": "scenario 2 cutoff 23573600 samples 185191\nscenario 1 cutoff 24180200 samples 191483\ngold 37769 users 4263 likes 4417\npositive repeat 0.0 like repeat 0.0\nstrongorg repeat 0.0 like repeat 0.0\nlike repeat 0.0 like repeat 0.0\ngold popularity quantiles {0.0: 1.0, 0.1: 428.0, 0.25: 1733.0, 0.5: 5622.0, 0.75: 13374.0, 0.9: 21680.200000000004, 1.0: 30000.0}\nTraceback (most recent call last):\n  File \"/app/solution/train_ranker.py\", line 176, in <module>\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',choices=['validation','final'],required=True);build(ap.parse_args().stage)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/train_ranker.py\", line 126, in build\n    model=TwoTower(len(item_ids)); opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adamw.py\", line 37, in __init__\n    super().__init__(\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/adam.py\", line 101, in __init__\n    super().__init__(params, defaults)\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/optim/optimizer.py\", line 400, in __init__\n    self.add_param_group(cast(dict, param_group))\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_compile.py\", line 46, in inner\n    import torch._dynamo\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/__init__.py\", line 13, in <module>\n    from . import config, convert_frame, eval_frame, resume_execution\n  File \"/opt/task-venv/lib/python3.12/site-packages/torch/_dynamo/convert_frame.py\", line 26, in <module>\n    import cProfile\n  File \"/usr/local/lib/python3.12/cProfile.py\", line 24, in <module>\n    run.__doc__ = _pyprofile.run.__doc__\n                  ^^^^^^^^^^^^^^\nAttributeError: module 'profile' has no attribute 'run'\n"
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}

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  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-f89d39a5-e72c-40a9-bdd6-23364d56b798",
    "changes": {
      "/app/solution/profile.py": {
        "type": "delete",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\ntargets = set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\ncat = pd.read_parquet(D/'candidate_catalog.parquet')\nitems = set(cat.item_id.astype(int))\nz=np.load(D/'validation_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); its=z['item_ids'].astype(np.int64); packed=z['packed_eligible'].copy()\nur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(its)}\ndef eligible(u,i):\n r=ur.get(int(u)); c=ic.get(int(i)); return r is not None and c is not None and bool((packed[r,c//8]>>(c%8))&1)\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nva=vl[(vl.uid.isin(targets))&(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nlk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nla=lk[(lk.uid.isin(targets))&(lk.is_organic==1)][['uid','item_id']].drop_duplicates()\ngold=pd.concat([va.assign(g=1),la.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\ngold=gold[[eligible(u,i) for u,i in gold[['uid','item_id']].itertuples(index=False,name=None)]]\nprint('gold',len(gold),'users',gold.uid.nunique(),'likes',sum(gold.g==3))\nimport pyarrow.parquet as pq\nkeys=(gold.uid.to_numpy(np.int64)<<32)|gold.item_id.to_numpy(np.int64)\nfor fam in ['positive','strongorg','like']:\n seen=set()\n path=D/('train_likes.parquet' if fam=='like' else 'train_listens.parquet')\n cols=['uid','item_id'] if fam=='like' else ['uid','item_id','is_organic','played_ratio_pct']\n for b in pq.ParquetFile(path).iter_batches(batch_size=500_000,columns=cols):\n  x=b.to_pandas()\n  if fam!='like':\n   x=x[x.played_ratio_pct >= (80 if fam=='strongorg' else 50)]\n   if fam=='strongorg': x=x[x.is_organic==1]\n  kk=(x.uid.to_numpy(np.int64)<<32)|x.item_id.to_numpy(np.int64)\n  seen.update(set(kk[np.isin(kk,keys)]))\n m=np.isin(keys,np.fromiter(seen,dtype=np.int64))\n print(fam,'repeat',m.mean(), 'like repeat',m[gold.g.eq(3).to_numpy()].mean())\nprint('gold popularity quantiles',gold.merge(cat,on='item_id').popularity_rank.quantile([0,.1,.25,.5,.75,.9,1]).to_dict())\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nD /app/solution/profile.py\n",
    "stderr": ""
  }
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    "stdout": "scenario 2 cutoff 23573600 samples 185191\nscenario 1 cutoff 24180200 samples 191483\n/app/solution/train_ranker.py:145: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 5.7904\nepoch 2 loss 5.446\nepoch 3 loss 5.1992\nepoch 4 loss 4.9805\nepoch 5 loss 4.7962\nepoch 6 loss 4.6268\nepoch 7 loss 4.4707\nepoch 8 loss 4.334\nwrote validation.parquet 845600\n",
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        "type": "update",
        "unified_diff": "@@ -127,6 +127,6 @@\n     ds=TensorDataset(torch.from_numpy(pairs),torch.from_numpy(labels))\n-    loader=DataLoader(ds,batch_size=384,shuffle=True,drop_last=True)\n+    loader=DataLoader(ds,batch_size=512,shuffle=True,drop_last=True)\n     ft=torch.from_numpy(item_feat)\n     model.train()\n-    for epoch in range(8):\n+    for epoch in range(6):\n         total=0.; n=0\n@@ -141,4 +141,8 @@\n             for mask,val in hs:u[mask]=val\n-            v=model.item_tower(y,ft[y]); logits=([email protected])/7.0\n-            loss=nn.functional.cross_entropy(logits,torch.arange(len(y)))\n+            v=model.item_tower(y,ft[y]); pos=(u*v).sum(1)/7.0\n+            neg_id=torch.randint(0,len(item_ids),(len(y),6))\n+            nv=model.item_tower(neg_id.reshape(-1),ft[neg_id.reshape(-1)]).reshape(len(y),6,DIM)\n+            neg=(u[:,None,:]*nv).sum(2)/7.0\n+            # Uniform negatives preserve useful catalog-popularity information.\n+            loss=nn.functional.softplus(-pos).mean()+nn.functional.softplus(neg).mean()\n             opt.zero_grad();loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n@@ -159,3 +163,4 @@\n     ecol=np.array([item_to_col[int(x)] for x in ei],np.int64)\n-    rows=[]\n+    alphas=[0.,1.,2.,4.,8.] if stage=='validation' else [4.]\n+    outrows={alpha:[] for alpha in alphas}\n     with torch.no_grad():\n@@ -163,12 +168,14 @@\n             b=min(a+128,len(targets)); U=model.user_tower(torch.from_numpy(infer_ids[a:b]),torch.from_numpy(infer_w[a:b]),torch.from_numpy(infer_f[a:b]))\n-            # Last coordinate is fixed to one, making item bias a dot-product component.\n-            U[:,-1]=1.; scores=([email protected]).numpy()\n+            U[:,-1]=1.; base=([email protected]).numpy()\n             for j,u in enumerate(targets[a:b]):\n-                mask=np.unpackbits(packed[er[int(u)]],count=len(ei),bitorder='little').astype(bool)\n-                allowed=ecol[mask]; s=scores[j,allowed]\n-                top=allowed[np.argpartition(s,-100)[-100:]]; top=top[np.argsort(scores[j,top])[::-1]]\n-                rows.extend((int(u),int(item_ids[k]),r+1) for r,k in enumerate(top))\n-    name='validation.parquet' if stage=='validation' else 'submission.parquet'\n-    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/name,index=False)\n-    print('wrote',name,len(rows),flush=True)\n+                mask=np.unpackbits(packed[er[int(u)]],count=len(ei),bitorder='little').astype(bool); allowed=ecol[mask]\n+                for alpha in alphas:\n+                    scores=base[j]+alpha*item_feat[:,0]; s=scores[allowed]\n+                    top=allowed[np.argpartition(s,-100)[-100:]]; top=top[np.argsort(scores[top])[::-1]]\n+                    outrows[alpha].extend((int(u),int(item_ids[k]),r+1) for r,k in enumerate(top))\n+    for alpha,rows in outrows.items():\n+        name=(f'validation_a{int(alpha)}.parquet' if stage=='validation' else 'submission.parquet')\n+        pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/name,index=False)\n+        if stage=='validation' and alpha==0: pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/'validation.parquet',index=False)\n+        print('wrote',name,len(rows),flush=True)\n \n",
        "move_path": null
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_ranker.py\n",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -u solution/train_ranker.py --stage final"
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    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:149: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0801\nepoch 2 loss 0.9692\nepoch 3 loss 0.9016\nepoch 4 loss 0.8263\nepoch 5 loss 0.7637\nepoch 6 loss 0.7162\nwrote submission.parquet 845600\n",
    "stderr": "",
    "aggregated_output": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:149: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0801\nepoch 2 loss 0.9692\nepoch 3 loss 0.9016\nepoch 4 loss 0.8263\nepoch 5 loss 0.7637\nepoch 6 loss 0.7162\nwrote submission.parquet 845600\n",
    "exit_code": 0,
    "duration": {
      "secs": 261,
      "nanos": 754592583
    },
    "formatted_output": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:149: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0801\nepoch 2 loss 0.9692\nepoch 3 loss 0.9016\nepoch 4 loss 0.8263\nepoch 5 loss 0.7637\nepoch 6 loss 0.7162\nwrote submission.parquet 845600\n"
  }
}

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  "item": {
    "type": "FileChange",
    "id": "exec-0df7a341-5d77-4dd7-90f4-2776f2bfdb71",
    "changes": {
      "/app/solution/train_ranker.py": {
        "type": "update",
        "unified_diff": "@@ -164,2 +164,7 @@\n     alphas=[0.,1.,2.,4.,8.] if stage=='validation' else [4.]\n+    # Append the selected popularity prior inside the item vector's final\n+    # coordinate. Since the user vector's matching coordinate is 1, the full\n+    # relevance score—including the prior—is exactly one dot product.\n+    vectors={alpha:V.clone() for alpha in alphas}\n+    for alpha in alphas: vectors[alpha][:,-1] += alpha*ft[:,0]\n     outrows={alpha:[] for alpha in alphas}\n@@ -168,3 +173,3 @@\n             b=min(a+128,len(targets)); U=model.user_tower(torch.from_numpy(infer_ids[a:b]),torch.from_numpy(infer_w[a:b]),torch.from_numpy(infer_f[a:b]))\n-            U[:,-1]=1.; base=([email protected]).numpy()\n+            U[:,-1]=1.\n             for j,u in enumerate(targets[a:b]):\n@@ -172,3 +177,3 @@\n                 for alpha in alphas:\n-                    scores=base[j]+alpha*item_feat[:,0]; s=scores[allowed]\n+                    scores=(U[j]@vectors[alpha].T).numpy(); s=scores[allowed]\n                     top=allowed[np.argpartition(s,-100)[-100:]]; top=top[np.argsort(scores[top])[::-1]]\n",
        "move_path": null
      },
      "/app/solution/README.md": {
        "type": "add",
        "content": "# Made for You two-tower ranker\n\n`train_ranker.py` implements a CPU PyTorch two-tower retriever. It never builds\nor factorizes a user-item matrix and has no user-ID embedding. The user tower\nforms a recency/strength-weighted mean of hashed embeddings for the user's last\n160 positive listens and explicit likes, concatenates four activity features,\nand applies a 52→96→64 MLP. The item tower combines a trainable candidate-ID\nembedding with three catalog-popularity features in a 51→96→64 MLP. A learned\nitem bias and the selected popularity prior occupy the final item coordinate;\nthe matching user coordinate is one. Thus the complete serving score is exactly\nthe dot product of independent 64-dimensional tower outputs. Item vectors can\nbe computed offline and indexed for maximum-inner-product retrieval.\n\nTraining uses two cutoff-correct historical backtests. Organic 80%-complete\nlistens are positives and organic likes are sampled three times to reflect the\ngraded objective. A sampled logistic retrieval loss contrasts each positive\nwith six uniform catalog negatives. This is linear in event count and mini-batch\nsize rather than in the number of all users times all items. Public validation\nselected an item-side popularity weight of 4; its NDCG@10 is **0.012347** over\n4,263 scored users (the popularity-only baseline was 0.010710). The final model\nis refit with `train_* + val_*`, treating validation as observed history.\n\nRun from `/app`:\n\n```sh\n/opt/task-venv/bin/python solution/train_ranker.py --stage final\n```\n\nThis writes `solution/model_final.pt` and `solution/submission.parquet`.\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/README.md\nM /app/solution/train_ranker.py\n",
    "stderr": ""
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    ],
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys, numpy as np, pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\np='solution/submission.parquet'; f=pd.read_parquet(p)\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(f,t); c=CandidateSet(__import__('pathlib').Path('/task/data/test_eligible_candidates.npz'))\nbad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':bad,'rank_min':int(f['rank'].min()),'rank_max':int(f['rank'].max())})\nPY\nls -lh solution"
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    "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'rank_min': 1, 'rank_max': 100}\ntotal 73M\n-rw-r--r-- 1 agent agent 1.6K Sep 24 23:28 README.md\ndrwxr-xr-x 2 agent agent 4.0K Sep 24 23:07 __pycache__\n-rw-r--r-- 1 agent agent  31M Sep 24 23:28 model_final.pt\n-rw-r--r-- 1 agent agent  31M Sep 24 23:23 model_validation.pt\n-rw-r--r-- 1 agent agent  642 Sep 24 23:06 pop_baseline.py\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:28 submission.parquet\n-rw-r--r-- 1 agent agent  11K Sep 24 23:28 train_ranker.py\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:23 validation.parquet\n-rw-r--r-- 1 agent agent 1.8M Sep 24 23:23 validation_a0.parquet\n-rw-r--r-- 1 agent agent 1.7M Sep 24 23:23 validation_a1.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:23 validation_a2.parquet\n-rw-r--r-- 1 agent agent 1.5M Sep 24 23:23 validation_a4.parquet\n-rw-r--r-- 1 agent agent 1.4M Sep 24 23:23 validation_a8.parquet\n-rw-r--r-- 1 agent agent 712K Sep 24 23:06 validation_pop.parquet\n",
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    "exit_code": 0,
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-a6e3ca54-f3bc-4200-b47b-84baa3c5a36c",
    "process_id": "11517",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python -u solution/train_ranker.py --stage final"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python -m py_compile solution/train_ranker.py && /opt/task-venv/bin/python -u solution/train_ranker.py --stage final"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:151: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0937\nepoch 2 loss 0.992\nepoch 3 loss 0.9249\nepoch 4 loss 0.8539\nepoch 5 loss 0.7893\nepoch 6 loss 0.7356\nwrote submission.parquet 845600\n",
    "stderr": "",
    "aggregated_output": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:151: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0937\nepoch 2 loss 0.992\nepoch 3 loss 0.9249\nepoch 4 loss 0.8539\nepoch 5 loss 0.7893\nepoch 6 loss 0.7356\nwrote submission.parquet 845600\n",
    "exit_code": 0,
    "duration": {
      "secs": 256,
      "nanos": 289088238
    },
    "formatted_output": "scenario 2 cutoff 24180200 samples 191483\nscenario 1 cutoff 24786800 samples 192785\n/app/solution/train_ranker.py:151: 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  total+=float(loss)*len(y);n+=len(y)\nepoch 1 loss 1.0937\nepoch 2 loss 0.992\nepoch 3 loss 0.9249\nepoch 4 loss 0.8539\nepoch 5 loss 0.7893\nepoch 6 loss 0.7356\nwrote submission.parquet 845600\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-dafcc6ee-d27a-4167-99f7-f8c2e8d25200",
    "process_id": "39702",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nf=pd.read_parquet('/app/solution/submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(f,t); c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nassert not f.duplicated(['uid','item_id']).any()\nprint(f'PASS rows={len(f)} users={f.uid.nunique()} ranks={f[\"rank\"].min()}..{f[\"rank\"].max()}')\nPY\nsha256sum solution/submission.parquet"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\nf=pd.read_parquet('/app/solution/submission.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(f,t); c=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nassert all(c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nassert not f.duplicated(['uid','item_id']).any()\nprint(f'PASS rows={len(f)} users={f.uid.nunique()} ranks={f[\"rank\"].min()}..{f[\"rank\"].max()}')\nPY\nsha256sum solution/submission.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "PASS rows=845600 users=8456 ranks=1..100\n8008dc2a6671593e6cb3c2fbcc5a7cd9e15cbce9d7f526b77590a96e366ffdc0  solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "PASS rows=845600 users=8456 ranks=1..100\n8008dc2a6671593e6cb3c2fbcc5a7cd9e15cbce9d7f526b77590a96e366ffdc0  solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 684530854
    },
    "formatted_output": "PASS rows=845600 users=8456 ranks=1..100\n8008dc2a6671593e6cb3c2fbcc5a7cd9e15cbce9d7f526b77590a96e366ffdc0  solution/submission.parquet\n"
  }
}