Agent trace

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-63f67241-40ec-46b2-bc29-809ed0b81b9a",
    "process_id": "68868",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,160p' /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lah /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"
      },
      {
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        "cmd": "sed -n '1,160p' /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 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 third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 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 third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4271
    },
    "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 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 third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Package Version\n------- -------\npip     25.0.1\n",
    "stderr": "",
    "aggregated_output": "Package Version\n------- -------\npip     25.0.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 564621861
    },
    "formatted_output": "Package Version\n------- -------\npip     25.0.1\n"
  }
}

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  "thread_id": "01a0d037-6ae8-70e2-a781-e9e98c0008f2",
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e773e807-09f2-4963-960d-b3b4d7502451",
    "process_id": "57270",
    "command": [
      "/bin/sh",
      "-lc",
      "find /opt /usr /venv /root /home -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'libarrow*' 2>/dev/null | head -20; command -v uv parquet-tools duckdb || true; ls -la /opt /opt/codex-home /root/.local /home/agent/.local 2>/dev/null"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "find /opt /usr /venv /root /home -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'libarrow*' 2>/dev/null | head -20; command -v uv parquet-tools duckdb || true; ls -la /opt /opt/codex-home /root/.local /home/agent/.local 2>/dev/null"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n",
    "exit_code": 2,
    "duration": {
      "secs": 0,
      "nanos": 124353057
    },
    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_acero.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_substrait.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_dataset.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_parquet_encryption.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_flight.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_dataset_parquet.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_acero.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_fs.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_cuda.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_substrait.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_feather.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/includes/libarrow_python.pxd\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_flight.so.1801\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python_flight.so\n/opt/task-venv/lib/python3.12/site-packages/pyarrow/libarrow_python.so\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 task-venv\n\n/opt/codex-home:\ntotal 16\ndrwxr-xr-x 4 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:39 ..\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 packages\ndrwxr-xr-x 3 root root 4096 Sep 23 21:39 tmp\n"
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}

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  },
  "thread_token_usage": {
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ac5a9846-a61d-4df0-8f4e-fb393aa98171",
    "process_id": "13718",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog\\n',cat.head(),'\\n',cat.tail(),'\\n',cat.dtypes)\nprint('users',len(users),'\\n',users.head(),'\\n', users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); users=pd.read_parquet(D/'target_users.parquet')\nprint('catalog\\n',cat.head(),'\\n',cat.tail(),'\\n',cat.dtypes)\nprint('users',len(users),'\\n',users.head(),'\\n', users.dtypes)\nfor nm in ['validation','test']:\n z=np.load(D/f'{nm}_eligible_candidates.npz')\n print(nm,{k:(z[k].shape,z[k].dtype) for k in z.files}, 'uidmatch',np.array_equal(z['uids'],users.uid.to_numpy()),'itemshead',z['item_ids'][:10])\n counts=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little').sum(1)\n print('elig counts',np.percentile(counts,[0,1,25,50,75,99,100]), 'mean',counts.mean())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 \n item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\nusers 8456 \n    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114 \n uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24657.   26724.2  28682.75 29242.   29644.   29979.   30000.  ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24552.   26672.7  28657.   29220.   29619.25 29975.   30000.  ] mean 29041.3178807947\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 \n item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\nusers 8456 \n    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114 \n uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24657.   26724.2  28682.75 29242.   29644.   29979.   30000.  ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24552.   26672.7  28657.   29220.   29619.25 29975.   30000.  ] mean 29041.3178807947\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 361815933
    },
    "formatted_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\ncatalog\n    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 \n        item_id  positive_listens  organic_positive_listens  popularity_rank\n29995  1275692               163                        81            29996\n29996  1280011               163                        76            29997\n29997  1309246               163                       138            29998\n29998  1634958               163                        46            29999\n29999  1670524               163                        73            30000 \n item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\nusers 8456 \n    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114 \n uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\nvalidation {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24657.   26724.2  28682.75 29242.   29644.   29979.   30000.  ] mean 29067.306291390727\ntest {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} uidmatch True itemshead [5862961 6901374 5635052 3542184 9378983 5463340 8213481  906358 2859641\n 3033749]\nelig counts [24552.   26672.7  28657.   29220.   29619.25 29975.   30000.  ] mean 29041.3178807947\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e378b243-d9fc-4874-88e0-63404b6f321b",
    "process_id": "91310",
    "command": [
      "/bin/sh",
      "-lc",
      "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor f in ['train_listens.parquet','val_listens.parquet','train_likes.parquet','val_likes.parquet']:\n p=D/f; pf=pq.ParquetFile(p)\n print(f,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','is_organic','played_ratio_pct']:\n  if c in pf.schema.names:\n   i=pf.schema.names.index(c); stats=[]\n   for rg in range(pf.num_row_groups):\n    s=pf.metadata.row_group(rg).column(i).statistics\n    stats.append((s.min,s.max))\n   print(c,min(x[0] for x in stats),max(x[1] for x in stats))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "free -h; /opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nD=Path('/task/data')\nfor f in ['train_listens.parquet','val_listens.parquet','train_likes.parquet','val_likes.parquet']:\n p=D/f; pf=pq.ParquetFile(p)\n print(f,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','is_organic','played_ratio_pct']:\n  if c in pf.schema.names:\n   i=pf.schema.names.index(c); stats=[]\n   for rg in range(pf.num_row_groups):\n    s=pf.metadata.row_group(rg).column(i).statistics\n    stats.append((s.min,s.max))\n   print(c,min(x[0] for x in stats),max(x[1] for x in stats))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n",
    "stderr": "",
    "aggregated_output": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 220013655
    },
    "formatted_output": "train_listens.parquet rowgroups 42\nuid 100 1000000\ntimestamp 0 24786795\nitem_id 22 9390623\nis_organic 0 1\nplayed_ratio_pct 0 159\nval_listens.parquet rowgroups 2\nuid 100 1000000\ntimestamp 24788600 25393395\nitem_id 50 9390541\nis_organic 0 1\nplayed_ratio_pct 0 159\ntrain_likes.parquet rowgroups 1\nuid 100 1000000\ntimestamp 80 24786790\nitem_id 26 9390522\nis_organic 0 1\nval_likes.parquet rowgroups 1\nuid 100 999900\ntimestamp 24788815 25393390\nitem_id 209 9390461\nis_organic 0 1\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-0d0cec73-2f82-4e60-8e35-632c92d4a7c4",
    "process_id": "83201",
    "command": [
      "/bin/sh",
      "-lc",
      "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nfor split in ['train','val']:\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n print(split,'shape',x.shape,'memMB',x.memory_usage(deep=True).sum()/1e6,'users',x.uid.nunique(),'items',x.item_id.nunique(),'positive', (x.played_ratio_pct>=50).sum(),'strong organic',((x.played_ratio_pct>=80)&(x.is_organic==1)&x.uid.isin(target)).sum())\n print('organic',x.is_organic.mean(),'ratio percentiles',np.percentile(x.played_ratio_pct,[0,25,50,75,80,90,99,100]))\n del x\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "awk '/MemTotal|MemAvailable/ {print}' /proc/meminfo; /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\ntarget=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nfor split in ['train','val']:\n x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','item_id','timestamp','is_organic','played_ratio_pct'])\n print(split,'shape',x.shape,'memMB',x.memory_usage(deep=True).sum()/1e6,'users',x.uid.nunique(),'items',x.item_id.nunique(),'positive', (x.played_ratio_pct>=50).sum(),'strong organic',((x.played_ratio_pct>=80)&(x.is_organic==1)&x.uid.isin(target)).sum())\n print('organic',x.is_organic.mean(),'ratio percentiles',np.percentile(x.played_ratio_pct,[0,25,50,75,80,90,99,100]))\n del x\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [  0.   7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [  0.   9. 100. 100. 100. 100. 100. 159.]\n",
    "stderr": "",
    "aggregated_output": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [  0.   7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [  0.   9. 100. 100. 100. 100. 100. 159.]\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 901599081
    },
    "formatted_output": "train shape (43484159, 5) memMB 652.262517 users 9238 items 845395 positive 27500755 strong organic 12123010\norganic 0.5169151828370419 ratio percentiles [  0.   7. 100. 100. 100. 100. 100. 159.]\nval shape (1501697, 5) memMB 22.525587 users 8196 items 191747 positive 964959 strong organic 421615\norganic 0.517072352145606 ratio percentiles [  0.   9. 100. 100. 100. 100. 100. 159.]\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-cf608b89-616a-46fa-9e5b-a6a2a7a41610",
    "changes": {
      "/app/solution/explore.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\ncat = pd.read_parquet(D/'candidate_catalog.parquet')\nitems = cat.item_id.to_numpy(np.int64)\nimap = pd.Series(np.arange(len(items), dtype=np.int32), index=items)\ntargets = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nz=np.load(D/'validation_eligible_candidates.npz')\nelig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nrow={int(u):i for i,u in enumerate(z['uids'])}\n\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic','timestamp'])\nx=x[x.uid.isin(targets) & x.item_id.isin(items)]\nx['r']=x.item_id.map(imap)\nx['ur']=x.uid.map(row)\npos=x[x.played_ratio_pct>=50]\nuniq=pos[['ur','r']].drop_duplicates().to_numpy()\nprint('target candidate events',len(x),'pos',len(pos),'unique pos',len(uniq),'unique/user',len(uniq)/len(targets))\nprint('historical positives eligible fraction',elig[uniq[:,0],uniq[:,1]].mean())\nanyx=x[['ur','r']].drop_duplicates().to_numpy()\nprint('any listened eligible fraction',elig[anyx[:,0],anyx[:,1]].mean(),'unique any/user',len(anyx)/len(targets))\n\n# public truth basic properties\nvl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic'])\nvl=vl[vl.uid.isin(targets)&vl.item_id.isin(items)&(vl.played_ratio_pct>=80)&(vl.is_organic==1)]\nvk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nvk=vk[vk.uid.isin(targets)&vk.item_id.isin(items)&(vk.is_organic==1)]\ntruth=pd.concat([vl.assign(g=1)[['uid','item_id','g']],vk.assign(g=3)[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\ntruth['ur']=truth.uid.map(row); truth['r']=truth.item_id.map(imap)\ntruth=truth[elig[truth.ur,truth.r]]\nprint('truth rows/users/mean',len(truth),truth.uid.nunique(),len(truth)/truth.uid.nunique(),'grades',truth.g.value_counts().to_dict())\nhist=set(map(tuple,uniq.tolist()))\nprint('truth seen positive fraction',np.mean([(int(a),int(b)) in hist for a,b in truth[['ur','r']].to_numpy()]))\nprint('truth pop ranks',np.percentile(truth.r,[0,10,25,50,75,90,99,100]))\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/explore.py\n",
    "stderr": ""
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c796dce0-0b6e-4ad7-aea7-198aec4bb006",
    "process_id": "44466",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/explore.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/explore.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [    0.    427.   1732.   5621.  13373.  21679.2 29100.  29999. ]\n",
    "stderr": "",
    "aggregated_output": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [    0.    427.   1732.   5621.  13373.  21679.2 29100.  29999. ]\n",
    "exit_code": 0,
    "duration": {
      "secs": 11,
      "nanos": 788090572
    },
    "formatted_output": "target candidate events 31062540 pos 19959853 unique pos 5514505 unique/user 652.1410832544939\nhistorical positives eligible fraction 0.0\nany listened eligible fraction 0.0 unique any/user 930.5421002838222\ntruth rows/users/mean 37769 4263 8.859723199624677 grades {1: 33352, 3: 4417}\ntruth seen positive fraction 0.0\ntruth pop ranks [    0.    427.   1732.   5621.  13373.  21679.2 29100.  29999. ]\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-7792e802-e859-44c3-b6e5-d5844b185a25",
    "changes": {
      "/app/solution/build_ranker.py": {
        "type": "add",
        "content": "#!/opt/task-venv/bin/python\n\"\"\"Build validation experiments or the final Made-for-You ranking.\"\"\"\nfrom __future__ import annotations\n\nimport argparse, json, time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.neighbors import NearestNeighbors\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\nCUTOFF = {'validation': 24786800, 'test': 25395200}\n\ndef timer(msg):\n    print(f'[{time.strftime(\"%H:%M:%S\")}] {msg}', flush=True)\n\ndef data(stage):\n    cat = pd.read_parquet(D/'candidate_catalog.parquet')\n    items = cat.item_id.to_numpy(np.int64)\n    item_map = pd.Series(np.arange(len(items),dtype=np.int32),index=items)\n    targ = pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n    z=np.load(D/f'{\"validation\" if stage==\"validation\" else \"test\"}_eligible_candidates.npz')\n    elig=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\n    assert np.array_equal(items,z['item_ids']) and np.array_equal(targ,z['uids'])\n    return cat,items,item_map,targ,elig\n\ndef load_events(stage,item_map):\n    parts=[]\n    names=['train'] + (['val'] if stage=='test' else [])\n    for split in names:\n        timer(f'reading {split} listens')\n        x=pd.read_parquet(D/f'{split}_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n        x=x[(x.played_ratio_pct>=50)&x.item_id.isin(item_map.index)]\n        x=x[['uid','timestamp','item_id','is_organic']]\n        parts.append(x)\n    x=pd.concat(parts,ignore_index=True)\n    x['col']=x.item_id.map(item_map).astype(np.int32)\n    x['row']=(x.uid//100).astype(np.int32)\n    # Positive events, with organic intent slightly upweighted and exponential recency.\n    age=(CUTOFF[stage]-x.timestamp.to_numpy())/86400.0\n    x['wlong']=(1.0+0.35*x.is_organic.to_numpy()) * (0.35+0.65*np.exp(-np.maximum(age,0)/120.0))\n    x['wrecent']=(1.0+0.50*x.is_organic.to_numpy()) * np.exp(-np.maximum(age,0)/35.0)\n    # Add explicit likes as high-confidence positives.\n    likes=[]\n    for split in names:\n        q=pd.read_parquet(D/f'{split}_likes.parquet',columns=['uid','timestamp','item_id','is_organic'])\n        q=q[q.item_id.isin(item_map.index)].copy()\n        q['col']=q.item_id.map(item_map).astype(np.int32); q['row']=(q.uid//100).astype(np.int32)\n        ageq=(CUTOFF[stage]-q.timestamp.to_numpy())/86400.0\n        q['wlong']=2.5*(1+.25*q.is_organic.to_numpy())*(.35+.65*np.exp(-np.maximum(ageq,0)/180.))\n        q['wrecent']=3.0*(1+.25*q.is_organic.to_numpy())*np.exp(-np.maximum(ageq,0)/60.)\n        likes.append(q[['row','col','wlong','wrecent']])\n    y=pd.concat([x[['row','col','wlong','wrecent']]]+likes,ignore_index=True)\n    del x,likes\n    shape=(10001,len(item_map))\n    mats={}\n    for col in ['wlong','wrecent']:\n        mats[col]=sparse.coo_matrix((y[col].to_numpy(np.float32),(y.row,y.col)),shape=shape).tocsr()\n        mats[col].sum_duplicates()\n        mats[col].data=np.log1p(mats[col].data).astype(np.float32)\n        timer(f'{col} matrix nnz={mats[col].nnz}')\n    return mats\n\ndef bm25(X,K1=1.6,B=.75):\n    X=X.tocsr(copy=True)\n    rows=np.repeat(np.arange(X.shape[0]),np.diff(X.indptr))\n    rl=np.asarray(X.sum(axis=1)).ravel(); avg=max(rl.mean(),1e-6)\n    df=np.bincount(X.indices,minlength=X.shape[1])\n    idf=np.log(X.shape[0]/(1.0+df)).clip(0)\n    den=K1*((1-B)+B*rl[rows]/avg)\n    X.data *= idf[X.indices]*(K1+1)/(X.data+den)\n    return X\n\ndef top_frame(scores,targ,items,elig,n=100):\n    rows=[]\n    for a in range(0,len(targ),256):\n        s=np.asarray(scores(a,min(a+256,len(targ))),dtype=np.float32)\n        s[~elig[a:a+len(s)]]=-np.inf\n        ix=np.argpartition(s,-n,axis=1)[:,-n:]\n        sv=np.take_along_axis(s,ix,axis=1)\n        order=np.argsort(-sv,axis=1)\n        ix=np.take_along_axis(ix,order,axis=1)\n        for j,u in enumerate(targ[a:a+len(s)]):\n            rows.append(pd.DataFrame({'uid':u,'item_id':items[ix[j]],'rank':np.arange(1,n+1)}))\n    return pd.concat(rows,ignore_index=True).astype({'uid':'int64','item_id':'int64','rank':'int64'})\n\ndef public_score(frame,items,targ,elig):\n    im={int(v):i for i,v in enumerate(items)}; ur={int(v):i for i,v in enumerate(targ)}\n    vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n    vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n    lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n    lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n    g=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n    g=g[[elig[ur[int(u)],im[int(i)]] for u,i in g[['uid','item_id']].itertuples(index=False,name=None)]]\n    gold={int(u):dict(zip(q.item_id.astype(int),q.g.astype(int))) for u,q in g.groupby('uid')}\n    dc=[]\n    for u,q in frame[frame['rank']<=10].sort_values(['uid','rank']).groupby('uid'):\n        if int(u) not in gold: continue\n        gg=gold[int(u)]; vals=np.array([gg.get(int(i),0) for i in q.item_id]); ideal=np.sort(list(gg.values()))[::-1][:10]\n        disc=1/np.log2(np.arange(2,12)); dc.append(((2**vals-1)*disc[:len(vals)]).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n    return float(np.mean(dc)),len(dc)\n\ndef main():\n    ap=argparse.ArgumentParser(); ap.add_argument('--stage',choices=['validation','test'],default='validation'); ap.add_argument('--mode',choices=['all','best'],default='all'); args=ap.parse_args()\n    stage=args.stage\n    cat,items,imap,targ,elig=data(stage); mats=load_events(stage,imap)\n    rows=(targ//100).astype(int); pop=np.log1p(cat.positive_listens.to_numpy(np.float32))\n    outputs={}\n    outputs['pop']=top_frame(lambda a,b:np.broadcast_to(pop,(b-a,len(pop))).copy(),targ,items,elig)\n    timer('fitting BM25 SVD')\n    X=bm25(mats['wlong']); svd=TruncatedSVD(n_components=96,n_iter=5,random_state=42)\n    U=svd.fit_transform(X).astype(np.float32); V=svd.components_.astype(np.float32)\n    timer(f'SVD explained={svd.explained_variance_ratio_.sum():.3f}')\n    # Normalize latent preference score per user; blend with log popularity.\n    def latent(a,b):\n        s=U[rows[a:b]]@V\n        s=(s-s.mean(1,keepdims=True))/(s.std(1,keepdims=True)+1e-6)\n        return s\n    for alpha in ([0.0,0.08,0.16,0.28,0.45] if args.mode=='all' else [0.16]):\n        def score(a,b,al=alpha): return latent(a,b)+al*((pop-pop.mean())/pop.std())\n        outputs[f'svd_p{alpha}']=top_frame(score,targ,items,elig)\n    # Cosine KNN on a recency-aware profile, with similarity-weighted neighbors.\n    timer('fitting cosine neighbors')\n    R=bm25(mats['wrecent'],K1=1.2,B=.7)\n    nn=NearestNeighbors(n_neighbors=181,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n    dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\n    # exclude self; CSR neighbor-to-user projection\n    mask=ind!=rows[:,None]\n    nr=np.repeat(np.arange(len(rows)),mask.sum(1)); nc=ind[mask]\n    for power in ([1.,2.,4.] if args.mode=='all' else [2.]):\n        nw=(sim[mask]**power).astype(np.float32)\n        S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n        def knn(a,b):\n            z=(S[a:b]@mats['wlong']).toarray().astype(np.float32)\n            z=(z-z.mean(1,keepdims=True))/(z.std(1,keepdims=True)+1e-6)\n            return z\n        for alpha in ([0.,.12,.25] if args.mode=='all' else [.12]):\n            def score(a,b,al=alpha): return knn(a,b)+al*((pop-pop.mean())/pop.std())\n            outputs[f'knn{power}_p{alpha}']=top_frame(score,targ,items,elig)\n    OUT.mkdir(exist_ok=True)\n    report={}\n    for name,frame in outputs.items():\n        path=OUT/(('validation_'+name) if stage=='validation' else ('candidate_'+name))\n        frame.to_parquet(path.with_suffix('.parquet'),index=False)\n        if stage=='validation':\n            report[name]=public_score(frame,items,targ,elig); print(name,report[name],flush=True)\n    (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\n    timer('done')\n\nif __name__=='__main__': main()\n"
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/build_ranker.py\n",
    "stderr": ""
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{
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    "command": [
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    "stdout": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n",
    "stderr": "",
    "aggregated_output": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n",
    "exit_code": 0,
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      "secs": 135,
      "nanos": 554149196
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    "formatted_output": "[21:46:19] reading train listens\n[21:46:23] wlong matrix nnz=5687487\n[21:46:24] wrecent matrix nnz=5687487\n[21:46:26] fitting BM25 SVD\n[21:46:28] SVD explained=0.213\n[21:46:52] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.0 (0.02155647891496469, 4263)\nsvd_p0.08 (0.021574427027172473, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nsvd_p0.28 (0.02164517360685791, 4263)\nsvd_p0.45 (0.02206570395970459, 4263)\nknn1.0_p0.0 (0.020528399158166784, 4263)\nknn1.0_p0.12 (0.02014221503503751, 4263)\nknn1.0_p0.25 (0.01997109552560057, 4263)\nknn2.0_p0.0 (0.021384785009514424, 4263)\nknn2.0_p0.12 (0.02091249141006109, 4263)\nknn2.0_p0.25 (0.02057379737132925, 4263)\nknn4.0_p0.0 (0.022873399121303224, 4263)\nknn4.0_p0.12 (0.022518863077981285, 4263)\nknn4.0_p0.25 (0.02188574748325066, 4263)\n[21:48:33] done\n"
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      "/app/solution/tune_ranker.py": {
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        "content": "#!/opt/task-venv/bin/python\n\"\"\"Validation-only hyperparameter search; never used as validation features.\"\"\"\nimport numpy as np, pandas as pd\nfrom scipy import sparse\nfrom sklearn.neighbors import NearestNeighbors\nfrom sklearn.decomposition import TruncatedSVD\nfrom build_ranker import data, load_events, bm25, top_frame, public_score, timer, OUT, D\n\ncat,items,imap,targ,elig=data('validation')\nmats=load_events('validation',imap); rows=(targ//100).astype(int)\n\n# Add cutoff-correct active likes as profile evidence (and compare independently).\nstate=pd.read_parquet(D/'validation_preference_state.parquet')\nstate=state[state.item_id.isin(items)]\nsm=[]\nfor name,sign in [('liked',1.),('disliked',-1.)]:\n    q=state[state.state==name]\n    sm.append(sparse.coo_matrix((np.full(len(q),sign,np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=mats['wlong'].shape).tocsr())\nliked,disliked=sm\n\ndef gold_data():\n    ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\n    vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n    vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n    lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n    lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n    q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n    q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\n    return {ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}\ngold=gold_data(); disc=1/np.log2(np.arange(2,12))\n\ndef metric(scorefn):\n    vals=[]\n    for a in range(0,len(targ),256):\n        b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\n        ix=np.argpartition(s,-10,axis=1)[:,-10:]\n        ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n        for j in range(b-a):\n            gg=gold.get(a+j)\n            if not gg: continue\n            grades=np.array([gg.get(int(i),0) for i in ix[j]])\n            ideal=np.sort(list(gg.values()))[::-1][:10]\n            vals.append(((2**grades-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n    return np.mean(vals)\n\n# Broad-neighbor retrieval, then tune effective neighborhood and popularity shrinkage.\nfor profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\n    timer('neighbors '+profile_name)\n    R=bm25(R0,K1=1.2,B=.7)\n    nn=NearestNeighbors(n_neighbors=401,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\n    dist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32)\n    df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n    for k in [40,80,180,400]:\n      ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\n      nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\n      for power in [2.,4.,7.]:\n        nw=(ss[keep]**power).astype(np.float32)\n        S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n        for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\n          for gamma in [0.,.15,.3,.5]:\n            scale=np.power(df+3,gamma)\n            val=metric(lambda a,b,S=S,M=M,scale=scale:(S[a:b]@M).toarray()/scale)\n            print('RESULT',profile_name,k,power,predname,gamma,val,flush=True)\n\n"
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    "stdout": "Success. Updated the following files:\nA /app/solution/tune_ranker.py\n",
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        "unified_diff": "@@ -46,3 +46,3 @@\n # Broad-neighbor retrieval, then tune effective neighborhood and popularity shrinkage.\n-for profile_name,R0 in [('recent',mats['wrecent']),('long',mats['wlong']),('active',mats['wrecent']+3*liked)]:\n+for profile_name,R0 in [('recent',mats['wrecent']),('active',mats['wrecent']+3*liked)]:\n     timer('neighbors '+profile_name)\n@@ -52,10 +52,10 @@\n     df=np.asarray((mats['wlong']>0).sum(axis=0)).ravel().astype(np.float32)\n-    for k in [40,80,180,400]:\n+    for k in [40,100,250]:\n       ii=ind[:,:k+1]; ss=sim[:,:k+1]; keep=ii!=rows[:,None]\n       nr=np.repeat(np.arange(len(rows)),keep.sum(1)); nc=ii[keep]\n-      for power in [2.,4.,7.]:\n+      for power in [4.,7.]:\n         nw=(ss[keep]**power).astype(np.float32)\n         S=sparse.csr_matrix((nw,(nr,nc)),shape=(len(rows),R.shape[0]))\n-        for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32)),('recent',mats['wrecent'])]:\n-          for gamma in [0.,.15,.3,.5]:\n+        for predname,M in [('long',mats['wlong']),('binary',(mats['wlong']>0).astype(np.float32))]:\n+          for gamma in [0.,.15,.3]:\n             scale=np.power(df+3,gamma)\n@@ -63,2 +63 @@\n             print('RESULT',profile_name,k,power,predname,gamma,val,flush=True)\n-\n",
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        "unified_diff": "@@ -5,5 +5,22 @@\n from build_ranker import data,load_events,bm25,D,timer\n-from tune_ranker import metric\n-\n cat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\n+ur={int(v):i for i,v in enumerate(targ)}; im={int(v):i for i,v in enumerate(items)}\n+vl=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\n+vl=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)&vl.uid.isin(targ)&vl.item_id.isin(items)].assign(g=1)\n+lk=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\n+lk=lk[(lk.is_organic==1)&lk.uid.isin(targ)&lk.item_id.isin(items)].assign(g=3)\n+q=pd.concat([vl[['uid','item_id','g']],lk[['uid','item_id','g']]]).groupby(['uid','item_id']).g.max().reset_index()\n+q=q[[elig[ur[int(u)],im[int(i)]] for u,i in q[['uid','item_id']].itertuples(index=False,name=None)]]\n+gold={ur[int(u)]:{im[int(i)]:int(g) for i,g in zip(z.item_id,z.g)} for u,z in q.groupby('uid')}; disc=1/np.log2(np.arange(2,12))\n+def metric(scorefn):\n+ vals=[]\n+ for a in range(0,len(targ),256):\n+  b=min(a+256,len(targ)); s=scorefn(a,b); s[~elig[a:b]]=-np.inf\n+  ix=np.argpartition(s,-10,axis=1)[:,-10:]; ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1)\n+  for j in range(b-a):\n+   gg=gold.get(a+j)\n+   if not gg: continue\n+   gr=np.array([gg.get(int(i),0) for i in ix[j]]); ideal=np.sort(list(gg.values()))[::-1][:10]\n+   vals.append(((2**gr-1)*disc).sum()/((2**ideal-1)*disc[:len(ideal)]).sum())\n+ return np.mean(vals)\n timer('reading all positive listens for neighbor profiles')\n",
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        "content": "#!/opt/task-venv/bin/python\nimport numpy as np,pandas as pd\nfrom scipy import sparse\nfrom sklearn.neighbors import NearestNeighbors\nfrom build_ranker import data,load_events,bm25,top_frame,public_score,D,timer\n\ncat,items,imap,targ,elig=data('validation'); mats=load_events('validation',imap); rows=(targ//100).astype(int)\nR=bm25(mats['wrecent'],K1=1.2,B=.7)\nnn=NearestNeighbors(n_neighbors=251,metric='cosine',algorithm='brute',n_jobs=-1).fit(R)\ndist,ind=nn.kneighbors(R[rows]); sim=np.maximum(0,1-dist).astype(np.float32); keep=ind!=rows[:,None]\nS=sparse.csr_matrix(((sim[keep]**4).astype(np.float32),(np.repeat(np.arange(len(rows)),keep.sum(1)),ind[keep])),shape=(len(rows),R.shape[0]))\n\ntimer('building objective-aligned bonus matrices')\nx=pd.read_parquet(D/'train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nx=x[x.item_id.isin(items)]; x['col']=x.item_id.map(imap).astype(np.int32); x['row']=(x.uid//100).astype(np.int32)\nshape=mats['wlong'].shape\ndef mat(mask):\n q=x[mask]; m=sparse.coo_matrix((np.ones(len(q),np.float32),(q.row,q.col)),shape=shape).tocsr();m.sum_duplicates();m.data=np.log1p(m.data);return m\nfeatures={'org50':mat((x.is_organic==1)&(x.played_ratio_pct>=50)),'org80':mat((x.is_organic==1)&(x.played_ratio_pct>=80)),'all80':mat(x.played_ratio_pct>=80)}\ndel x\nq=pd.read_parquet(D/'train_likes.parquet'); q=q[q.item_id.isin(items)];\nfeatures['likes']=sparse.coo_matrix((np.ones(len(q),np.float32),((q.uid//100).astype(int),q.item_id.map(imap).astype(int))),shape=shape).tocsr();features['likes'].sum_duplicates();features['likes'].data=np.log1p(features['likes'].data)\nfor name,F in features.items():\n for beta in [.5,1.,2.,4.]:\n  M=mats['wlong']+beta*F; df=np.asarray((M>0).sum(axis=0)).ravel().astype(np.float32); scale=np.power(df+3,.15)\n  frame=top_frame(lambda a,b,M=M,scale=scale:(S[a:b]@M).toarray()/scale,targ,items,elig)\n  print('OBJ',name,beta,public_score(frame,items,targ,elig),flush=True)\n"
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_objective.py\n",
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        "content": "# Made for You ranker\n\n`submission.parquet` contains the final 100-track ranking for every target\nuser. It was produced by `build_ranker.py`, using only events available at the\ntest cutoff:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build_ranker.py --stage test --mode best\n```\n\nThe selected model is an implicit collaborative nearest-listener ranker. User\nprofiles combine positive listens, explicit likes, organic intent, and\nexponential recency weighting. BM25 weighting controls activity/popularity\nbias before cosine neighbor retrieval. Candidate scores use 250 neighbors,\nfavor organic historical positives to match the discovery target, and apply a\nsmall listener-frequency shrinkage. Scores are masked by the supplied\nper-user cutoff-correct eligibility set before ranking.\n\nHyperparameters were selected on the temporal validation window with no\nvalidation events used as validation features. The chosen validation model\nscored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\npopularity baseline. The final artifact refits the same approach using the\ntraining and validation events as observed history and uses the test preference\nand eligibility cutoff.\n\n`explore.py` and the `tune_*.py` files document the development experiments;\nonly `build_ranker.py` is needed to reproduce the final artifact.\n"
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    "stdout": "[22:06:41] reading train listens\n[22:06:45] wlong matrix nnz=5687487\n[22:06:46] wrecent matrix nnz=5687487\n[22:06:48] fitting BM25 SVD\n[22:06:51] SVD explained=0.213\n[22:06:55] fitting cosine neighbors\npop (0.010713530758619576, 4263)\nsvd_p0.16 (0.021621107846515508, 4263)\nknn4.0_g0.15 (0.024664004091426703, 4263)\n[22:07:12] done\n",
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        "unified_diff": "@@ -15,4 +15,5 @@\n favor organic historical positives to match the discovery target, and apply a\n-small listener-frequency shrinkage. Scores are masked by the supplied\n-per-user cutoff-correct eligibility set before ranking.\n+small listener-frequency shrinkage. A validation-selected reciprocal-rank\n+fusion adds a complementary latent-SVD ranking. Scores are masked by the\n+supplied per-user cutoff-correct eligibility set before ranking.\n \n@@ -20,6 +21,6 @@\n validation events used as validation features. The chosen validation model\n-scored 0.024664 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\n+scored 0.025977 NDCG@10 (4,263 scored users), versus 0.010714 for the catalog\n popularity baseline. The final artifact refits the same approach using the\n-training and validation events as observed history and uses the test preference\n-and eligibility cutoff.\n+training and validation events as observed history and uses the test\n+eligibility cutoff.\n \n",
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        "unified_diff": "@@ -154,3 +154,10 @@\n     if stage=='test':\n-        outputs['knn4.0_g0.15'].to_parquet(OUT/'submission.parquet',index=False)\n+        # Validation-selected reciprocal-rank fusion adds complementary global\n+        # latent structure while retaining the stronger local-neighbor model.\n+        k=outputs['knn4.0_g0.15'].assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\n+        v=outputs['svd_p0.16'].assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\n+        final=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\n+        final['rank']=final.groupby('uid').score.rank(method='first',ascending=False).astype(np.int64)\n+        final=final[final['rank']<=100][['uid','item_id','rank']].astype(np.int64)\n+        final.to_parquet(OUT/'submission.parquet',index=False)\n     (OUT/f'{stage}_scores.json').write_text(json.dumps(report,indent=2))\n",
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8cebfd67-9076-4d8b-99dc-a6e099bee5ca",
    "process_id": "91580",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/app/solution')\nk=pd.read_parquet(p/'validation_knn4.0_g0.parquet').assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\nv=pd.read_parquet(p/'validation_svd_p0.parquet').assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\nf=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\nf['rank']=f.groupby('uid').score.rank(method='first',ascending=False).astype('int64')\nf[f['rank']<=100][['uid','item_id','rank']].to_parquet(p/'validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd\nfrom pathlib import Path\np=Path('/app/solution')\nk=pd.read_parquet(p/'validation_knn4.0_g0.parquet').assign(score=lambda q:1/(10+q['rank']))[['uid','item_id','score']]\nv=pd.read_parquet(p/'validation_svd_p0.parquet').assign(score=lambda q:.7/(10+q['rank']))[['uid','item_id','score']]\nf=pd.concat([k,v]).groupby(['uid','item_id'],as_index=False).score.sum()\nf['rank']=f.groupby('uid').score.rank(method='first',ascending=False).astype('int64')\nf[f['rank']<=100][['uid','item_id','rank']].to_parquet(p/'validation.parquet',index=False)\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.025977,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.025977,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
    "duration": {
      "secs": 4,
      "nanos": 521089720
    },
    "formatted_output": "{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.025977,\n    \"quality_users\": 4263\n  }\n}\n"
  }
}

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{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-3600d11b-b533-4ea3-bc91-75ec63decfc2",
    "process_id": "36219",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/build_ranker.py --stage test --mode best"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n",
    "stderr": "",
    "aggregated_output": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n",
    "exit_code": 0,
    "duration": {
      "secs": 30,
      "nanos": 356828683
    },
    "formatted_output": "[22:08:31] reading train listens\n[22:08:32] reading val listens\n[22:08:35] wlong matrix nnz=5850135\n[22:08:36] wrecent matrix nnz=5850135\n[22:08:38] fitting BM25 SVD\n[22:08:41] SVD explained=0.215\n[22:08:45] fitting cosine neighbors\n[22:09:00] done\n"
  }
}

{
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  "turn_id": "01a0d037-6afa-7393-b70d-1bddc66588d8",
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-10effb5e-06fa-4751-8104-f36c52390e8e",
    "process_id": "1457",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nc=CandidateSet(D/'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(),'duplicate_pairs',f.duplicated(['uid','item_id']).sum(),'bad_eligibility',bad,'rank_counts',f.groupby('uid').size().value_counts().to_dict())\nprint(f.head(12).to_string(index=False))\nPY\nsha256sum solution/submission.parquet\nfind solution -maxdepth 1 -type f -printf '%f %k KB\\n' | sort | tail -20"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport sys\nsys.path.insert(0,'/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data'); p=Path('/app/solution/submission.parquet')\nf=validate_ranking(pd.read_parquet(p),set(pd.read_parquet(D/'target_users.parquet').uid.astype(int)))\nc=CandidateSet(D/'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(),'duplicate_pairs',f.duplicated(['uid','item_id']).sum(),'bad_eligibility',bad,'rank_counts',f.groupby('uid').size().value_counts().to_dict())\nprint(f.head(12).to_string(index=False))\nPY\nsha256sum solution/submission.parquet\nfind solution -maxdepth 1 -type f -printf '%f %k KB\\n' | sort | tail -20"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid  item_id  rank\n 100    75946    23\n 100   196692    78\n 100   237151    52\n 100   388527    27\n 100   489014    81\n 100   507759    47\n 100   517293     8\n 100   549737    43\n 100   841419    84\n 100   994326    38\n 100  1525699    40\n 100  1558292    89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef  solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n",
    "stderr": "",
    "aggregated_output": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid  item_id  rank\n 100    75946    23\n 100   196692    78\n 100   237151    52\n 100   388527    27\n 100   489014    81\n 100   507759    47\n 100   517293     8\n 100   549737    43\n 100   841419    84\n 100   994326    38\n 100  1525699    40\n 100  1558292    89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef  solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n",
    "exit_code": 0,
    "duration": {
      "secs": 1,
      "nanos": 818031459
    },
    "formatted_output": "rows 845600 users 8456 duplicate_pairs 0 bad_eligibility 0 rank_counts {100: 8456}\n uid  item_id  rank\n 100    75946    23\n 100   196692    78\n 100   237151    52\n 100   388527    27\n 100   489014    81\n 100   507759    47\n 100   517293     8\n 100   549737    43\n 100   841419    84\n 100   994326    38\n 100  1525699    40\n 100  1558292    89\n7f7f3f0e39a9594edba60444ccd59ea200316fc02fc2afcc93ba197a6e8a84ef  solution/submission.parquet\ncandidate_pop.parquet 732 KB\ncandidate_svd_p0.parquet 1768 KB\nexplore.py 4 KB\nsubmission.parquet 2476 KB\ntest_scores.json 4 KB\ntune.log 4 KB\ntune_allitems.log 4 KB\ntune_allitems.py 4 KB\ntune_ensemble.py 4 KB\ntune_objective.log 4 KB\ntune_objective.py 4 KB\ntune_ranker.py 4 KB\nvalidation.parquet 2476 KB\nvalidation_knn1.0_p0.parquet 1528 KB\nvalidation_knn2.0_p0.parquet 1620 KB\nvalidation_knn4.0_g0.parquet 1796 KB\nvalidation_knn4.0_p0.parquet 1764 KB\nvalidation_pop.parquet 724 KB\nvalidation_scores.json 4 KB\nvalidation_svd_p0.parquet 1768 KB\n"
  }
}