Agent trace

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-e68e73d0-c21a-4f8f-a773-e1c42fc9227b",
    "process_id": "1805",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,220p' /task/evaluate_public.py && cat /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,220p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "cat /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 6320
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 25 00:45 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 25 00:45 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 25 00:46 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 25 00:45 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 25 00:45 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 25 00:45 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 25 00:45 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 25 00:45 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 25 00:45 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 25 00:45 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 25 00:45 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 25 00:45 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 25 00:45 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 25 00:45 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 25 00:46 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. 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    ],
    "source": "unified_exec_startup",
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    "command": [
      "/bin/sh",
      "-lc",
      "find / -type d \\( -name 'torch' -o -name 'pyarrow' \\) 2>/dev/null | head -20; find / -type f -name 'libtorch_cpu.so' 2>/dev/null | head"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "find / -type d \\( -name 'torch' -o -name 'pyarrow' \\) 2>/dev/null | head -20; find / -type f -name 'libtorch_cpu.so' 2>/dev/null | head"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
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    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/sklearn/externals/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/pyarrow\n/opt/task-venv/lib/python3.12/site-packages/scipy/_lib/array_api_compat/torch\n/opt/task-venv/lib/python3.12/site-packages/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/include/torch/csrc/api/include/torch\n/opt/task-venv/lib/python3.12/site-packages/torch/lib/libtorch_cpu.so\n"
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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-c2f53801-1cfb-40b9-9bde-6e937205f15c",
    "process_id": "1072",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n m=pq.ParquetFile(p).metadata\n print(p.name, m.num_rows, m.num_row_groups)\nprint('targets',pd.read_parquet(D/'target_users.parquet').head(),pd.read_parquet(D/'target_users.parquet').dtypes)\nprint('catalog',pd.read_parquet(D/'candidate_catalog.parquet').head(),pd.read_parquet(D/'candidate_catalog.parquet').dtypes)\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n print(stage,{k:z[k].shape for k in z.files},'uid first',z['uids'][:3],'items first',z['item_ids'][:5])\n bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(z['item_ids']),bitorder='little')\n print('eligible stats',np.quantile(bits.sum(1),[0,.1,.5,.9,1]),'density',bits.mean())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114 uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\ncatalog    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657.  28038.5 29242.  29852.  30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet 30000 1\ntarget_users.parquet 8456 1\ntest_preference_state.parquet 484776 1\ntrain_dislikes.parquet 101075 1\ntrain_likes.parquet 814831 1\ntrain_listens.parquet 43484159 42\ntrain_undislikes.parquet 19960 1\ntrain_unlikes.parquet 292665 1\nval_dislikes.parquet 3563 1\nval_likes.parquet 37795 1\nval_listens.parquet 1501697 2\nval_undislikes.parquet 606 1\nval_unlikes.parquet 9346 1\nvalidation_preference_state.parquet 469124 1\ntargets    uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114 uid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\ncatalog    item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5 item_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\nvalidation {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24657.  28038.5 29242.  29852.  30000. ] density 0.9689102097130243\ntest {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} uid first [100 200 300] items first [5862961 6901374 5635052 3542184 9378983]\neligible stats [24552. 28001. 29220. 29835. 30000.] density 0.9680439293598234\n",
    "exit_code": 0,
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      "nanos": 266664847
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  "item": {
    "type": "FileChange",
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    "changes": {
      "/app/solution/baseline.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef rank(stage: str, path: Path) -> None:\n    cat = pd.read_parquet(D/'candidate_catalog.parquet')\n    with np.load(D/f'{stage}_eligible_candidates.npz') as z:\n        uids=z['uids'].copy(); items=z['item_ids'].copy(); packed=z['packed_eligible'].copy()\n    rows=[]\n    for r,uid in enumerate(uids):\n        ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n        top=items[ok][:100]\n        rows.extend((int(uid),int(it),j+1) for j,it in enumerate(top))\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n\nif __name__=='__main__':\n    rank('validation',OUT/'validation_pop.parquet')\n    rank('test',OUT/'submission.parquet')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n",
    "stderr": ""
  }
}

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    ],
    "cwd": "file:///app",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n",
    "exit_code": 0,
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    "formatted_output": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"
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      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); tar=pd.read_parquet(D/'target_users.parquet').uid\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); mp=dict(zip(cat.item_id,cat.popularity_rank))\nls=pd.read_parquet(D/'val_listens.parquet'); lk=pd.read_parquet(D/'val_likes.parquet')\nrel=pd.concat([ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[lk.is_organic==1][['uid','item_id']].assign(g=3)])\nrel=rel[rel.uid.isin(set(tar)) & rel.item_id.isin(set(cat.item_id))].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('rel',len(rel),'users',rel.uid.nunique(),'grade',rel.g.value_counts().to_dict())\nrr=rel.item_id.map(mp);print('poprank q',rr.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict(),'top10', (rr<=10).mean(),'top100',(rr<=100).mean(),'top1k',(rr<=1000).mean())\nfor st in ['validation']:\n pref=pd.read_parquet(D/f'{st}_preference_state.parquet')\n x=rel.merge(pref,on=['uid','item_id'],how='left')\n print(x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nprint('read target train')\nt=time.time(); tr=pd.read_parquet(D/'train_listens.parquet',filters=[('uid','in',tar.tolist())]);print(len(tr),tr.memory_usage(deep=True).sum()/1e6,time.time()-t,tr.timestamp.min(),tr.timestamp.max())\nprint(tr.head(),tr.dtypes)\nPY"
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    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np,time\nfrom pathlib import Path\nD=Path('/task/data'); tar=pd.read_parquet(D/'target_users.parquet').uid\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); mp=dict(zip(cat.item_id,cat.popularity_rank))\nls=pd.read_parquet(D/'val_listens.parquet'); lk=pd.read_parquet(D/'val_likes.parquet')\nrel=pd.concat([ls[(ls.is_organic==1)&(ls.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),lk[lk.is_organic==1][['uid','item_id']].assign(g=3)])\nrel=rel[rel.uid.isin(set(tar)) & rel.item_id.isin(set(cat.item_id))].sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('rel',len(rel),'users',rel.uid.nunique(),'grade',rel.g.value_counts().to_dict())\nrr=rel.item_id.map(mp);print('poprank q',rr.quantile([0,.1,.25,.5,.75,.9,.99,1]).to_dict(),'top10', (rr<=10).mean(),'top100',(rr<=100).mean(),'top1k',(rr<=1000).mean())\nfor st in ['validation']:\n pref=pd.read_parquet(D/f'{st}_preference_state.parquet')\n x=rel.merge(pref,on=['uid','item_id'],how='left')\n print(x.state.value_counts(dropna=False),pd.crosstab(x.g,x.state,dropna=False))\nprint('read target train')\nt=time.time(); tr=pd.read_parquet(D/'train_listens.parquet',filters=[('uid','in',tar.tolist())]);print(len(tr),tr.memory_usage(deep=True).sum()/1e6,time.time()-t,tr.timestamp.min(),tr.timestamp.max())\nprint(tr.head(),tr.dtypes)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN         119421\nliked        55992\ndisliked       436\nName: count, dtype: int64 state  disliked  liked     NaN\ng                             \n1           420  55320  111580\n3            16    672    7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130 uid                     uint32\ntimestamp               uint32\nitem_id                 uint32\nis_organic               uint8\nplayed_ratio_pct        uint16\ntrack_length_seconds    uint32\ndtype: object\n",
    "stderr": "",
    "aggregated_output": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN         119421\nliked        55992\ndisliked       436\nName: count, dtype: int64 state  disliked  liked     NaN\ng                             \n1           420  55320  111580\n3            16    672    7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130 uid                     uint32\ntimestamp               uint32\nitem_id                 uint32\nis_organic               uint8\nplayed_ratio_pct        uint16\ntrack_length_seconds    uint32\ndtype: object\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 527894144
    },
    "formatted_output": "rel 175729 users 6186 grade {1: 167201, 3: 8528}\npoprank q {0.0: 1.0, 0.1: 212.0, 0.25: 1011.0, 0.5: 4132.0, 0.75: 11338.0, 0.9: 20151.0, 0.99: 28791.440000000002, 1.0: 30000.0} top10 0.013691536399797415 top100 0.06378571550512437 top1k 0.24838245252633315\nstate\nNaN         119421\nliked        55992\ndisliked       436\nName: count, dtype: int64 state  disliked  liked     NaN\ng                             \n1           420  55320  111580\n3            16    672    7841\nread target train\n43222297 821.223775 0.6774373054504395 5 24786795\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130 uid                     uint32\ntimestamp               uint32\nitem_id                 uint32\nis_organic               uint8\nplayed_ratio_pct        uint16\ntrack_length_seconds    uint32\ndtype: object\n"
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}

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      "/app/solution/explore_rank.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nD=Path('/task/data'); O=Path('/app/solution'); CUTOFF=24786800\n\ndef main():\n    cat=pd.read_parquet(D/'candidate_catalog.parquet')\n    items=cat.item_id.to_numpy(np.int64); itemset=set(map(int,items))\n    pop={int(x): i for i,x in enumerate(items)}\n    targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n    tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n    tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\n    age=(CUTOFF-tr.timestamp.to_numpy())/86400.\n    tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\n    tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\n    tr['w90']=np.exp(-age/90)*(1+1.5*tr.is_organic.to_numpy())\n    tr['org']=tr.is_organic.astype(np.float32)\n    tr['strongorg']=((tr.is_organic==1)&(tr.played_ratio_pct>=80)).astype(np.float32)\n    ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n    del tr\n    pref=pd.read_parquet(D/'validation_preference_state.parquet')\n    liked=set(zip(pref.loc[pref.state=='liked','uid'].astype(int),pref.loc[pref.state=='liked','item_id'].astype(int)))\n    ag['liked']=[(int(u),int(i)) in liked for u,i in ag[['uid','item_id']].itertuples(index=False,name=None)]\n    # Also include liked catalog items without qualifying listens.\n    lp=pref[(pref.state=='liked')&pref.item_id.isin(itemset)][['uid','item_id']].copy()\n    ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n    ag['popidx']=ag.item_id.map(pop)\n    methods={\n      'count':'np.log1p(n)+.5*np.log1p(org)+2*liked',\n      'recent':'w30+.2*w90+2*liked',\n      'liked':'10*liked+w30+.2*w90',\n      'organic':'np.log1p(strong)+.2*np.log1p(n)+3*liked',\n      'last':'last/1e7+3*liked',\n    }\n    z=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; assert np.array_equal(items,z['item_ids'])\n    groups={int(u):g for u,g in ag.groupby('uid',sort=False)}\n    for name,expr in methods.items():\n      rows=[]\n      for r,u in enumerate(targets):\n        ok=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n        g=groups.get(int(u))\n        chosen=[]\n        if g is not None:\n          n=g.n.to_numpy(); org=g.org.to_numpy(); strong=g.strong.to_numpy(); last=g['last'].to_numpy(); w30=g.w30.to_numpy(); w90=g.w90.to_numpy(); likedv=g.liked.to_numpy(); liked=likedv\n          score=eval(expr)\n          order=np.lexsort((g.popidx.to_numpy(),-score))\n          for j in order:\n            ii=int(g.item_id.iloc[j]); pi=pop[ii]\n            if ok[pi] and score[j]>0: chosen.append(ii)\n            if len(chosen)>=100: break\n        seen=set(chosen)\n        if len(chosen)<100:\n          for ii in items[ok]:\n            ii=int(ii)\n            if ii not in seen: chosen.append(ii)\n            if len(chosen)==100: break\n        rows.extend((int(u),ii,k+1) for k,ii in enumerate(chosen))\n      pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(O/f'validation_{name}.parquet',index=False)\n\nif __name__=='__main__': main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/explore_rank.py\n",
    "stderr": ""
  }
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    "stdout": "/bin/sh: 1: /usr/bin/time: not found\n",
    "stderr": "",
    "aggregated_output": "/bin/sh: 1: /usr/bin/time: not found\n",
    "exit_code": 127,
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    "command": [
      "/bin/sh",
      "-lc",
      "for f in solution/validation_{count,recent,liked,organic,last}.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "for f in solution/validation_{count,recent,liked,organic,last}.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 1,
      "nanos": 958534024
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_{count,recent,liked,organic,last}.parquet'\n"
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    "reasoning_output_tokens": 11,
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  "turn_token_usage": {
    "input_tokens": 353331,
    "cached_input_tokens": 332416,
    "cache_write_input_tokens": 0,
    "output_tokens": 5034,
    "reasoning_output_tokens": 1285,
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  "thread_token_usage": {
    "input_tokens": 353331,
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}

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-86f49ad2-26ea-4030-b2b6-6554ab723acd",
    "process_id": "61143",
    "command": [
      "/bin/sh",
      "-lc",
      "for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 10,
      "nanos": 45404879
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_count.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-ce8165ee-aa19-4a09-8ae5-03e7610fd732",
    "process_id": "84798",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh solution; dmesg | tail -5 2>/dev/null || true"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -lh solution; dmesg | tail -5 2>/dev/null || true"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 1.5M\n-rw-r--r-- 1 agent agent  805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n",
    "stderr": "",
    "aggregated_output": "total 1.5M\n-rw-r--r-- 1 agent agent  805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4510
    },
    "formatted_output": "total 1.5M\n-rw-r--r-- 1 agent agent  805 Sep 25 01:04 baseline.py\n-rw-r--r-- 1 agent agent 3.3K Sep 25 01:05 explore_rank.py\n-rw-r--r-- 1 agent agent 719K Sep 25 01:04 submission.parquet\n-rw-r--r-- 1 agent agent 712K Sep 25 01:04 validation_pop.parquet\ndmesg: read kernel buffer failed: Operation not permitted\n"
  }
}

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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.dataset as ds\nD=ds.dataset('/task/data/train_listens.parquet',format='parquet')\nfor days in [7,30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=50)\n print(days,D.count_rows(filter=f))\nfor days in [30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=80)&(ds.field('is_organic')==1)\n print('org',days,D.count_rows(filter=f))\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.dataset as ds\nD=ds.dataset('/task/data/train_listens.parquet',format='parquet')\nfor days in [7,30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=50)\n print(days,D.count_rows(filter=f))\nfor days in [30,90,180]:\n f=(ds.field('timestamp')>=24786800-days*86400)&(ds.field('played_ratio_pct')>=80)&(ds.field('is_organic')==1)\n print('org',days,D.count_rows(filter=f))\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n",
    "stderr": "",
    "aggregated_output": "7 950150\n30 3883797\n90 10582861\n180 19038702\norg 30 1745899\norg 90 4791628\norg 180 8566731\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 840682296
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  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-296b608d-9bef-406c-9f64-7205e64a595c",
    "changes": {
      "/app/solution/explore_rank.py": {
        "type": "update",
        "unified_diff": "@@ -11,6 +11,5 @@\n     targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n-    tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n-    tr=tr[(tr.played_ratio_pct>=50)&tr.item_id.isin(itemset)].copy()\n+    tr=pd.read_parquet(D/'train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',CUTOFF-30*86400),('played_ratio_pct','>=',50)])\n+    tr=tr[tr.item_id.isin(itemset)].copy()\n     age=(CUTOFF-tr.timestamp.to_numpy())/86400.\n-    tr['w7']=np.exp(-age/7)*(1+1.5*tr.is_organic.to_numpy())\n     tr['w30']=np.exp(-age/30)*(1+1.5*tr.is_organic.to_numpy())\n@@ -19,3 +18,3 @@\n     tr['strongorg']=((tr.is_organic==1)&(tr.played_ratio_pct>=80)).astype(np.float32)\n-    ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w7=('w7','sum'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n+    ag=tr.groupby(['uid','item_id'],sort=False).agg(n=('timestamp','size'),org=('org','sum'),strong=('strongorg','sum'),last=('timestamp','max'),w30=('w30','sum'),w90=('w90','sum')).reset_index()\n     del tr\n@@ -26,3 +25,3 @@\n     lp=pref[(pref.state=='liked')&pref.item_id.isin(itemset)][['uid','item_id']].copy()\n-    ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w7':0,'w30':0,'w90':0,'liked':True})\n+    ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n     ag['popidx']=ag.item_id.map(pop)\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/explore_rank.py\n",
    "stderr": ""
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9215924c-003a-4983-85ed-ad77ed16cbf3",
    "process_id": "59770",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh solution/validation_*.parquet && for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -lh solution/validation_*.parquet && for f in solution/validation_count.parquet solution/validation_recent.parquet solution/validation_liked.parquet solution/validation_organic.parquet solution/validation_last.parquet; do /opt/task-venv/bin/python /task/evaluate_public.py \"$f\"; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "{\n  \"model\": \"solution/validation_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 11,
      "nanos": 755510714
    },
    "formatted_output": "{\n  \"model\": \"solution/validation_count.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_recent.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_liked.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_organic.parquet'\nTraceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'solution/validation_last.parquet'\n"
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    "aggregated_output": "/app/solution/explore_rank.py:26: FutureWarning: Downcasting object dtype arrays on .fillna, .ffill, .bfill is deprecated and will change in a future version. Call result.infer_objects(copy=False) instead. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n  ag=lp.merge(ag,on=['uid','item_id'],how='outer').fillna({'n':0,'org':0,'strong':0,'last':0,'w30':0,'w90':0,'liked':True})\n",
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        "content": "#!/usr/bin/env python3\n\"\"\"Train and apply a scalable history-based neural two-tower retriever.\"\"\"\nfrom __future__ import annotations\nimport argparse, json, random\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, TensorDataset\n\nDATA=Path('/task/data'); OUT=Path('/app/solution')\nTRAIN_CUTOFF=24786800\n\nclass UserTower(nn.Module):\n    \"\"\"History encoder: recency-weighted embedding pooling followed by an MLP.\"\"\"\n    def __init__(self,n_items:int,dim:int,max_hist:int):\n        super().__init__(); self.max_hist=max_hist\n        self.history_embedding=nn.Embedding(n_items+1,dim,padding_idx=0)\n        self.net=nn.Sequential(nn.Linear(dim,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\n        w=torch.exp((torch.arange(max_hist)-max_hist+1)/48.0)\n        self.register_buffer('recency_weight',w)\n    def forward(self,hist):\n        mask=hist.ne(0); w=self.recency_weight[None,:]*mask\n        pooled=(self.history_embedding(hist)*w[...,None]).sum(1)/w.sum(1,keepdim=True).clamp_min(1e-6)\n        return self.net(pooled)\n\nclass ItemTower(nn.Module):\n    \"\"\"Independently executable catalog encoder with ID and popularity inputs.\"\"\"\n    def __init__(self,n_items:int,dim:int,pop_features:torch.Tensor):\n        super().__init__(); self.item_embedding=nn.Embedding(n_items,dim)\n        self.register_buffer('pop_features',pop_features)\n        self.net=nn.Sequential(nn.Linear(dim+3,dim*2),nn.GELU(),nn.Linear(dim*2,dim),nn.LayerNorm(dim))\n    def forward(self,item_idx):\n        x=torch.cat([self.item_embedding(item_idx),self.pop_features[item_idx]],dim=-1)\n        return self.net(x)\n\nclass TwoTower(nn.Module):\n    def __init__(self,n_items,dim,max_hist,pop_features):\n        super().__init__(); self.user_tower=UserTower(n_items,dim,max_hist); self.item_tower=ItemTower(n_items,dim,pop_features)\n        self.logit_scale=nn.Parameter(torch.tensor(2.3))\n    def loss(self,hist,target):\n        u=nn.functional.normalize(self.user_tower(hist),dim=1)\n        v=nn.functional.normalize(self.item_tower(target),dim=1)\n        [email protected]*self.logit_scale.exp().clamp(max=50)\n        # Duplicate items in a batch are alternative positives, not false negatives.\n        positive=target[:,None].eq(target[None,:])\n        return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\n\ndef load_events(stage,cutoff,item_lookup):\n    paths=[DATA/'train_listens.parquet']+([DATA/'val_listens.parquet'] if stage=='test' else [])\n    frames=[]\n    since=cutoff-120*86400\n    for p in paths:\n        x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'],filters=[('timestamp','>=',since),('played_ratio_pct','>=',50)])\n        raw=x.item_id.to_numpy(np.int64); valid=raw<len(item_lookup); idx=np.full(len(x),-1,np.int32); idx[valid]=item_lookup[raw[valid]]\n        keep=idx>=0; x=x.loc[keep,['uid','timestamp','is_organic','played_ratio_pct']].copy(); x['idx']=idx[keep]; frames.append(x)\n    x=pd.concat(frames,ignore_index=True).sort_values(['uid','timestamp'],kind='stable')\n    return x\n\ndef build_examples(events,targets,max_hist,n_per_user=14):\n    histories=[]; labels=[]\n    for uid,g in events.groupby('uid',sort=False):\n        # First encounter within the long lookback approximates a discovery event.\n        seq=g.drop_duplicates('idx',keep='first')\n        ids=seq.idx.to_numpy(np.int32)\n        organic=((seq.is_organic.to_numpy()==1)&(seq.played_ratio_pct.to_numpy()>=80))\n        positions=np.flatnonzero(organic & (np.arange(len(ids))>=12))\n        if not len(positions): continue\n        choose=positions[np.linspace(0,len(positions)-1,min(n_per_user,len(positions))).astype(int)]\n        for p in np.unique(choose):\n            h=ids[max(0,p-max_hist):p]+1\n            row=np.zeros(max_hist,np.int32); row[-len(h):]=h\n            histories.append(row);labels.append(ids[p])\n    return torch.from_numpy(np.asarray(histories,np.int32)).long(),torch.from_numpy(np.asarray(labels,np.int64))\n\ndef build_inference_histories(events,uids,max_hist,pref_path,item_lookup):\n    by={}\n    for uid,g in events.groupby('uid',sort=False):\n        ids=g.drop_duplicates('idx',keep='last').idx.to_numpy(np.int32)\n        by[int(uid)]=list(ids[-max_hist:])\n    # Active likes are user-side cutoff state. Append them as most-recent taste evidence.\n    pref=pd.read_parquet(pref_path)\n    pref=pref[pref.state.eq('liked')]\n    for uid,g in pref.groupby('uid',sort=False):\n        raw=g.item_id.to_numpy(np.int64); ok=raw<len(item_lookup); liked=item_lookup[raw[ok]]; liked=liked[liked>=0]\n        if not len(liked): continue\n        base=by.get(int(uid),[]); seen=set(map(int,liked)); base=[x for x in base if int(x) not in seen]+list(map(int,liked))\n        by[int(uid)]=base[-max_hist:]\n    out=np.zeros((len(uids),max_hist),np.int32)\n    for r,u in enumerate(uids):\n        h=np.asarray(by.get(int(u),[]),np.int32)+1\n        if len(h): out[r,-len(h):]=h\n    return torch.from_numpy(out).long()\n\ndef train_and_rank(stage,dim,epochs,seed):\n    random.seed(seed);np.random.seed(seed);torch.manual_seed(seed);torch.set_num_threads(min(16,torch.get_num_threads()))\n    cat=pd.read_parquet(DATA/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); n=len(items)\n    lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\n    cutoff=TRAIN_CUTOFF if stage=='validation' else 25393395\n    events=load_events(stage,cutoff,lookup)\n    max_hist=128; histories,labels=build_examples(events,None,max_hist)\n    print(f'{stage}: {len(events):,} positive events, {len(histories):,} training examples')\n    rank=cat.popularity_rank.to_numpy(np.float32)\n    pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\n    model=TwoTower(n,dim,max_hist,torch.from_numpy(pf))\n    loader=DataLoader(TensorDataset(histories,labels),batch_size=512,shuffle=True,num_workers=0,drop_last=True)\n    opt=torch.optim.AdamW(model.parameters(),lr=2e-3,weight_decay=2e-5)\n    model.train()\n    for ep in range(epochs):\n        total=0.;count=0\n        for h,y in loader:\n            opt.zero_grad(set_to_none=True);loss=model.loss(h,y);loss.backward();nn.utils.clip_grad_norm_(model.parameters(),5);opt.step()\n            total+=float(loss)*len(h);count+=len(h)\n        print(f'epoch {ep+1}/{epochs} loss={total/count:.4f}')\n    ckpt=OUT/f'model_{stage}.pt';torch.save({'state_dict':model.state_dict(),'dim':dim,'max_hist':max_hist,'stage':stage,'seed':seed},ckpt)\n    elig=np.load(DATA/f'{stage}_eligible_candidates.npz');uids=elig['uids'].astype(np.int64);packed=elig['packed_eligible']\n    pref=DATA/f'{stage}_preference_state.parquet'\n    h=build_inference_histories(events,uids,max_hist,pref,lookup)\n    model.eval(); rows=[]\n    with torch.no_grad():\n        ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\n        for start in range(0,len(uids),128):\n            stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\n            scores=([email protected]).numpy()\n            for j,r in enumerate(range(start,stop)):\n                ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool)\n                scores[j,~ok]=-np.inf\n                top=np.argpartition(scores[j],-100)[-100:];top=top[np.argsort(scores[j,top])[::-1]]\n                rows.extend((int(uids[r]),int(items[k]),q+1) for q,k in enumerate(top))\n    path=OUT/('validation.parquet' if stage=='validation' else 'submission.parquet')\n    pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(path,index=False)\n    (OUT/f'metrics_{stage}.json').write_text(json.dumps({'examples':len(histories),'epochs':epochs,'dim':dim,'seed':seed},indent=2))\n    print('wrote',path)\n\nif __name__=='__main__':\n    p=argparse.ArgumentParser();p.add_argument('--stage',choices=['validation','test'],required=True);p.add_argument('--dim',type=int,default=64);p.add_argument('--epochs',type=int,default=12);p.add_argument('--seed',type=int,default=2026)\n    a=p.parse_args();train_and_rank(a.stage,a.dim,a.epochs,a.seed)\n"
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/train_two_tower.py\n",
    "stderr": ""
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        "content": "#!/usr/bin/env python3\n\"\"\"Calibration sweep: popularity is an extra coordinate in the exact dot product.\"\"\"\nfrom pathlib import Path\nimport numpy as np, pandas as pd, torch\nfrom torch import nn\nfrom train_two_tower import DATA, OUT, TwoTower, load_events, build_inference_histories, TRAIN_CUTOFF\n\ndef main():\n    cat=pd.read_parquet(DATA/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);n=len(items)\n    lookup=np.full(int(items.max())+1,-1,np.int32);lookup[items]=np.arange(n,dtype=np.int32)\n    ck=torch.load(OUT/'model_validation.pt',map_location='cpu',weights_only=True)\n    rank=cat.popularity_rank.to_numpy(np.float32)\n    pf=np.stack([np.log1p(cat.positive_listens.to_numpy())/12,np.log1p(cat.organic_positive_listens.to_numpy())/12,-np.log(rank)/12],1).astype('float32')\n    m=TwoTower(n,ck['dim'],ck['max_hist'],torch.from_numpy(pf));m.load_state_dict(ck['state_dict']);m.eval()\n    ev=load_events('validation',TRAIN_CUTOFF,lookup)\n    z=np.load(DATA/'validation_eligible_candidates.npz');uids=z['uids'];packed=z['packed_eligible']\n    h=build_inference_histories(ev,uids,ck['max_hist'],DATA/'validation_preference_state.parquet',lookup)\n    with torch.no_grad():\n        iv=nn.functional.normalize(m.item_tower(torch.arange(n)),dim=1)\n        uv=nn.functional.normalize(m.user_tower(h),dim=1)\n        neural=([email protected]).numpy()\n    pop=-np.log(rank)[None,:]\n    for alpha in [0.25,0.5,1,2,4,8,16]:\n        rows=[]\n        for r,u in enumerate(uids):\n            s=pop[0]+alpha*neural[r]\n            ok=np.unpackbits(packed[r],count=n,bitorder='little').astype(bool);s=s.copy();s[~ok]=-np.inf\n            top=np.argpartition(s,-100)[-100:];top=top[np.argsort(s[top])[::-1]]\n            rows.extend((int(u),int(items[k]),q+1) for q,k in enumerate(top))\n        pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(OUT/f'validation_blend_{alpha}.parquet',index=False)\n\nif __name__=='__main__':main()\n"
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      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N);bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')[:1000]\n for r in range(N): out_i[r*100:(r+1)*100]=items[order[bits[r,order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | rg quality_ndcg; done"
    ],
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nfrom pathlib import Path\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');z=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids'];N=len(z['uids']);out_u=np.repeat(z['uids'].astype('int64'),100);out_i=np.empty(N*100,'int64');out_r=np.tile(np.arange(1,101,dtype='int64'),N);bits=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nscores={'organic':cat.organic_positive_listens.to_numpy(),'ratio':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+100)**.5,'rate':cat.organic_positive_listens.to_numpy()/(cat.positive_listens.to_numpy()+1000),'orglog':np.log1p(cat.organic_positive_listens.to_numpy())-.2*np.log1p(cat.positive_listens.to_numpy())}\nfor name,s in scores.items():\n order=np.argsort(-s,kind='stable')[:1000]\n for r in range(N): out_i[r*100:(r+1)*100]=items[order[bits[r,order]][:100]]\n pd.DataFrame({'uid':out_u,'item_id':out_i,'rank':out_r}).to_parquet(O/f'validation_global_{name}.parquet',index=False)\nPY\nfor n in organic ratio rate orglog; do /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_global_$n.parquet | rg quality_ndcg; done"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_ndcg@10\": 0.010205,\n    \"quality_ndcg@10\": 0.004558,\n    \"quality_ndcg@10\": 0.010563,\n",
    "stderr": "",
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    "exit_code": 0,
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    },
    "formatted_output": "    \"quality_ndcg@10\": 3.2e-05,\n    \"quality_ndcg@10\": 0.010205,\n    \"quality_ndcg@10\": 0.004558,\n    \"quality_ndcg@10\": 0.010563,\n"
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      "/app/solution/train_two_tower.py": {
        "type": "update",
        "unified_diff": "@@ -62,4 +62,14 @@\n \n-def build_examples(events,targets,max_hist,n_per_user=28):\n+def load_likes(stage,cutoff,item_lookup):\n+    paths=[DATA/'train_likes.parquet']+([DATA/'val_likes.parquet'] if stage=='test' else [])\n+    frames=[]\n+    for p in paths:\n+        x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic'],filters=[('timestamp','>=',cutoff-90*86400),('is_organic','==',1)])\n+        raw=x.item_id.to_numpy(np.int64);valid=raw<len(item_lookup);idx=np.full(len(x),-1,np.int32);idx[valid]=item_lookup[raw[valid]]\n+        keep=idx>=0;x=x.loc[keep,['uid','timestamp']].copy();x['idx']=idx[keep];frames.append(x)\n+    return pd.concat(frames,ignore_index=True)\n+\n+def build_examples(events,likes,max_hist,n_per_user=28):\n     histories=[]; labels=[]\n+    likes_by={int(u):g for u,g in likes.groupby('uid',sort=False)} if likes is not None else {}\n     for uid,g in events.groupby('uid',sort=False):\n@@ -76,2 +86,12 @@\n             histories.append(row);labels.append(ids[p])\n+        lg=likes_by.get(int(uid))\n+        if lg is not None:\n+            take=np.linspace(0,len(lg)-1,min(12,len(lg))).astype(int)\n+            times=g.timestamp.to_numpy(); gids=g.idx.to_numpy(np.int32)\n+            for q in np.unique(take):\n+                target=int(lg.idx.iloc[q]); p=np.searchsorted(times,int(lg.timestamp.iloc[q]),side='left')\n+                hh=gids[:p]; hh=hh[hh!=target]\n+                if len(hh)<8: continue\n+                _,rev=np.unique(hh[::-1],return_index=True); h=hh[np.sort(len(hh)-1-rev)][-max_hist:]+1\n+                row=np.zeros(max_hist,np.int32);row[-len(h):]=h;histories.append(row);labels.append(target)\n     return torch.from_numpy(np.asarray(histories,np.int32)).long(),torch.from_numpy(np.asarray(labels,np.int64))\n@@ -104,3 +124,4 @@\n     events=load_events(stage,cutoff,lookup)\n-    max_hist=128; histories,labels=build_examples(events,None,max_hist)\n+    likes=load_likes(stage,cutoff,lookup)\n+    max_hist=128; histories,labels=build_examples(events,likes,max_hist)\n     print(f'{stage}: {len(events):,} positive events, {len(histories):,} training examples')\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n",
    "stderr": ""
  }
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        "unified_diff": "@@ -48,2 +48,11 @@\n         return -(torch.logsumexp(logits.masked_fill(~positive,-1e9),1)-torch.logsumexp(logits,1)).mean()\n+    def serving_user_vector(self,hist,alpha=24.0):\n+        \"\"\"Augmented request-time vector; its last coordinate calibrates popularity.\"\"\"\n+        u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\n+        return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n+    def serving_item_vector(self,item_idx,alpha=24.0):\n+        \"\"\"Offline vector. Dotting this with serving_user_vector is the final score.\"\"\"\n+        v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\n+        log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\n+        return torch.cat([v,log_pop_rank],dim=1)\n \n@@ -141,9 +150,12 @@\n     elig=np.load(DATA/f'{stage}_eligible_candidates.npz');uids=elig['uids'].astype(np.int64);packed=elig['packed_eligible']\n-    pref=DATA/f'{stage}_preference_state.parquet'\n-    h=build_inference_histories(events,uids,max_hist,pref,lookup)\n+    # Validation selected listening history without the explicit-like append.\n+    # Preference state remains available for experiments and eligibility is\n+    # always platform-owned, but appending unordered likes reduced NDCG.\n+    h=build_inference_histories(events,uids,max_hist,None,lookup)\n     model.eval(); rows=[]\n     with torch.no_grad():\n-        ivec=nn.functional.normalize(model.item_tower(torch.arange(n)),dim=1)\n+        ivec=model.serving_item_vector(torch.arange(n))\n+        np.save(OUT/f'item_vectors_{stage}.npy',ivec.numpy().astype('float32'))\n         for start in range(0,len(uids),128):\n-            stop=min(start+128,len(uids));uvec=nn.functional.normalize(model.user_tower(h[start:stop]),dim=1)\n+            stop=min(start+128,len(uids));uvec=model.serving_user_vector(h[start:stop])\n             scores=([email protected]).numpy()\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n",
    "stderr": ""
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    "stdout": "loaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1303\nepoch 2/10 loss=5.9230\nepoch 3/10 loss=5.6029\nepoch 4/10 loss=5.2651\nepoch 5/10 loss=4.9455\nepoch 6/10 loss=4.6405\nepoch 7/10 loss=4.3525\nepoch 8/10 loss=4.0867\nepoch 9/10 loss=3.8460\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n",
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    "aggregated_output": "loaded train_listens.parquet: 9,878,394\nloaded val_listens.parquet: 964,959\ntest: 7,887,086 positive events, 226,895 training examples\n/app/solution/train_two_tower.py:147: UserWarning: Converting a tensor with requires_grad=True to a scalar may lead to unexpected behavior.\nConsider using tensor.detach() first. (Triggered internally at /pytorch/torch/csrc/autograd/generated/python_variable_methods.cpp:835.)\n  total+=float(loss)*len(h);count+=len(h)\nepoch 1/10 loss=6.1303\nepoch 2/10 loss=5.9230\nepoch 3/10 loss=5.6029\nepoch 4/10 loss=5.2651\nepoch 5/10 loss=4.9455\nepoch 6/10 loss=4.6405\nepoch 7/10 loss=4.3525\nepoch 8/10 loss=4.0867\nepoch 9/10 loss=3.8460\nepoch 10/10 loss=3.6207\nwrote /app/solution/submission.parquet\n",
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        "type": "add",
        "content": "# Made for You two-tower ranker\n\n`train_two_tower.py` is the complete CPU PyTorch training and retrieval program.\nIt never constructs or factorizes a global user-item interaction matrix. Training\nexamples are sampled from per-user event sequences and use in-batch negatives.\n\n## Model\n\n- **User tower:** a trainable history-item embedding table, exponential recency\n  pooling over the latest 128 distinct positive-listen items, then a\n  `Linear(96,192) -> GELU -> Linear(192,96) -> LayerNorm` network. There is no\n  user-ID embedding.\n- **Item tower:** a separate trainable item embedding plus three catalog\n  popularity features, followed by the same 2-layer MLP and LayerNorm.\n- The serving vectors are 97-dimensional. Normalized neural outputs occupy the\n  first 96 coordinates; the last coordinate implements the validation-selected\n  popularity calibration. Thus the complete final score is **exactly one dot\n  product**: `24 * cosine(neural_user, neural_item) - log(popularity_rank)`.\n  `item_vectors_test.npy` contains the independently precomputed catalog side.\n\nThe loss is duplicate-aware in-batch sampled softmax. Positives are later\norganic listens completed at least 80%, plus organic likes (the higher-value\ngrade-3 channel). A 90-day history is used. The final checkpoint is refit on\ntrain plus validation events, treating validation solely as observed history.\nEligibility is applied only after scoring and remains platform-owned.\n\n## Validation and final build\n\nPublic validation NDCG@10: **0.012491** (4,263 scored users). The selected\nvalidation ranking is `validation.parquet`.\n\nReproduce the final refit and submission with:\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_two_tower.py \\\n  --stage test --dim 96 --epochs 10 --seed 2027\n```\n\nThe command writes `submission.parquet`, `model_test.pt`, and the offline item\nvectors, all under `/app/solution/`.\n"
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    "stdout": "/proc/11659 /bin/sh -c cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n",
    "stderr": "",
    "aggregated_output": "/proc/11659 /bin/sh -c cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4220
    },
    "formatted_output": "/proc/11659 /bin/sh -c cp solution/validation_blend_nopref_24.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nD=Path('/task/data');S=Path('/app/solution/submission.parquet')\nx=pd.read_parquet(S); targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nz=np.load(D/'test_eligible_candidates.npz');row={int(u):r for r,u in enumerate(z['uids'])};col={int(i):c for c,i in enumerate(z['item_ids'])}\nbad=0\nfor u,i in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(u)];c=col.get(int(i),-1);bad += c<0 or not ((int(z['packed_eligible'][r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md \n/proc/11683 /bin/sh -c for d in /proc/[0-9]*; do c=$(tr '\u0000' ' ' < \"$d/cmdline\" 2>/dev/null); case \"$c\" in *bad_eligibility*|*evaluate_public.py*solution/validation*) echo \"$d $c\";; esac; done \n/proc/8778 bash -c set -o pipefail; if [ -s ~/.nvm/nvm.sh ]; then . ~/.nvm/nvm.sh; fi; codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- '# Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user'\"'\"'s\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n' 2>&1 </dev/null | tee /logs/agent/codex.txt \n/proc/9004 node /home/agent/.nvm/versions/node/v22.23.3/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n/proc/9012 /home/agent/.nvm/versions/node/v22.23.3/lib/node_modules/@openai/codex/node_modules/@openai/codex-linux-x64/vendor/x86_64-unknown-linux-musl/bin/codex exec --dangerously-bypass-approvals-and-sandbox --skip-git-repo-check --model gpt-5.6-sol --json --enable unified_exec -c model_reasoning_effort=medium -- # Build a latency-efficient PyTorch two-tower neural-network ranker\n\nBuild a two-tower neural network in CPU PyTorch for the \"Made for You\" shelf of\na music streaming service. Although the supplied benchmark is small, it\nrepresents a production system with millions of users and millions of items;\nthe training approach must scale to that setting and therefore must not\nconstruct or decompose a global user-item interaction matrix. The production\nshelf has a strict request-time latency budget. Candidate item embeddings are\ncomputed offline and stored in a nearest-neighbor index. At request time, the\nsystem computes one user embedding and retrieves items by maximum inner-product\nsearch.\n\nThe model must therefore decompose exactly into a user vector and an\nindependently precomputable item vector of the same dimensionality, with the\nfinal relevance score equal to their dot product. Both vectors must be produced\nby separate trainable user and item towers implemented as PyTorch neural\nnetworks. The user tower must derive its representation from interaction\nhistory rather than a user-ID lookup. Per-user candidate eligibility,\ncutoff-specific explicit preference state, and a temporal backtest are\nprovided. Candidate eligibility is platform-owned; all modeling within that\ncandidate boundary is in scope.\n\n## Data\n\nAll inputs are under `/task/data/`. Training data ends at timestamp `24786800`.\nA 30-minute gap precedes a seven-day public validation window. The hidden test\nis the following seven-day window after another 30-minute gap.\n\nFor each event family there is a `train_*.parquet` and `val_*.parquet` file:\n\n| Family | Meaning | Columns |\n| --- | --- | --- |\n| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |\n| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |\n| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |\n\n`is_organic=1` means the listener sought out the track; `0` means the action\ncame through a recommendation surface. A listen is a conventional positive at\n50% completion. The discovery-quality objective below uses the stronger 80%\nthreshold.\n\nThe two public windows have different roles:\n\n1. Build and tune a validation ranker using only `train_*` as history and\n   `val_*` as subsequent outcomes.\n2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test\n   time the validation events are observed history, not recommendable outcomes.\n\nDo not use `val_*` events as features when measuring a validation ranking. They\nmay and should be used when refitting the final hidden-test ranker.\n\nAdditional files under `/task/data/`:\n\n- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by\n  training positive-listen popularity.\n- `target_users.parquet`: users with at least 20 positive listens in the 90 days\n  before the training cutoff, selected without future labels.\n- `validation_preference_state.parquet`: active explicit state at the training\n  cutoff, for validation ranking.\n- `test_preference_state.parquet`: active explicit state after validation, for\n  the final hidden-test ranking. State is `liked` or `disliked`; a pair may have\n  both active channels.\n- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:\n  cutoff-correct candidate sets for every target user. Each contains aligned\n  arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover\n  the Boolean item mask with `np.unpackbits(packed_eligible[r],\n  count=len(item_ids), bitorder=\"little\")`.\n\nThe exact available Python distribution inventory is listed in\n`/task/requirements.txt`. Future test interactions are hidden and used only by\nthe separate Harbor verifier.\n\n## Discovery objective\n\nRank only the candidates returned for the relevant cutoff. Relevance is based\non organic behavior in the immediately following window:\n\n- grade 3: the user organically likes the track;\n- grade 1: the user organically listens to at least 80% of the track;\n- grade 0: otherwise.\n\nIf both positive events occur, grade 3 wins. Ranking quality is measured with\ngraded NDCG@10. DCG uses gain `2^grade - 1` and discount\n`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one\nrelevant item.\n\nThe public evaluator reports this objective for development, but its score is\nnot a separate acceptance stage. Final acceptance is determined on the hidden\ntest using the same objective.\n\nThe Yambda logs contain interactions, not impression propensities. Treat the\norganic objective as a robust offline target, not as a causal estimate of\nonline lift.\n\n## Public tools\n\n`/task/evaluate_public.py` scores a validation-stage ranking against the public\nwindow. Pass the path to a ranking with the same schema as the final\ndeliverable:\n\n```sh\npython /task/evaluate_public.py solution/validation.parquet\n```\n\nStage-independent evaluation helpers are available in\n`/task/ranking_evaluation.py`. The builder installs the same checked-in module\nin the public agent image and private verifier image so scoring mechanics cannot\ndrift. The public copy contains no hidden labels, evaluator-owned data paths, or\npass thresholds.\n\nThe public evaluator uses `validation_eligible_candidates.npz`; it is not a\nvalidator for the final artifact. No model implementation, fitted\nrepresentation, or example submission is supplied. Design and tune the\nrequired PyTorch neural two-tower model, then refit the selected approach as\ndescribed above.\n\n## Deliverable\n\nWrite `/app/solution/submission.parquet` or\n`/app/solution/submission.csv`, with exactly 100 rows for every target user and\ncolumns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties\nor duplicate items. Every submitted item must be present in that user's\n`test_eligible_candidates.npz` candidate set.\n\nLeave the code that defines, trains, and applies both towers, along with a\nconcise `/app/solution/README.md`, in `/app/solution/`. The README should state\nthe model architecture, training objective, public validation score, and final\nsubmission command. Keep the best valid submission saved throughout the\nattempt so it survives the deadline.\n\n## Constraints\n\n- Write deliverables and intermediate artifacts only under `/app/solution/`.\n- Treat `/task/` and the installed Python environment as read-only.\n- Do not invoke nested agents or attempt to access verifier files, credentials,\n  other trials, or host paths.\n- Do not install packages or use network access except for the model provider\n  connection managed by Harbor.\n- The environment includes CPU PyTorch 2.8.0. No accelerator is available.\n- You have up to 60 minutes. Improve the model as much as possible within that\n  limit. Structural validity alone is not sufficient; the final submission is\n  also evaluated for ranking quality.\n \n"
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      "kill 11659 11661 2>/dev/null || true\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');x=pd.read_parquet('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nrow={int(v):r for r,v in enumerate(u)};col={int(v):c for c,v in enumerate(items)};bad=0\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(uid)];c=col.get(int(item),-1);bad += c<0 or not ((int(packed[r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "kill 11659 11661 2>/dev/null || true\n/opt/task-venv/bin/python - <<'PY'\nimport sys,numpy as np,pandas as pd\nfrom pathlib import Path\nsys.path.insert(0,'/task');from ranking_evaluation import validate_ranking\nD=Path('/task/data');x=pd.read_parquet('/app/solution/submission.parquet');targets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int));x=validate_ranking(x,targets)\nwith np.load(D/'test_eligible_candidates.npz') as z:u=z['uids'].copy();items=z['item_ids'].copy();packed=z['packed_eligible'].copy()\nrow={int(v):r for r,v in enumerate(u)};col={int(v):c for c,v in enumerate(items)};bad=0\nfor uid,item in x[['uid','item_id']].itertuples(index=False,name=None):\n r=row[int(uid)];c=col.get(int(item),-1);bad += c<0 or not ((int(packed[r,c//8])>>(c%8))&1)\nvec=np.load('/app/solution/item_vectors_test.npy')\nprint({'rows':len(x),'users':x.uid.nunique(),'bad_eligibility':bad,'item_vectors':vec.shape,'finite_vectors':bool(np.isfinite(vec).all())})\nPY\n/opt/task-venv/bin/python /task/evaluate_public.py solution/validation.parquet\nls -lh solution/submission.parquet solution/model_test.pt solution/item_vectors_test.npy solution/README.md"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012491,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent  12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent  23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n",
    "stderr": "",
    "aggregated_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012491,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent  12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent  23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n",
    "exit_code": 0,
    "duration": {
      "secs": 6,
      "nanos": 49423125
    },
    "formatted_output": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'item_vectors': (30000, 97), 'finite_vectors': True}\n{\n  \"model\": \"solution/validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.012491,\n    \"quality_users\": 4263\n  }\n}\n-rw-r--r-- 1 agent agent 1.9K Sep 25 01:30 solution/README.md\n-rw-r--r-- 1 agent agent  12M Sep 25 01:30 solution/item_vectors_test.npy\n-rw-r--r-- 1 agent agent  23M Sep 25 01:29 solution/model_test.pt\n-rw-r--r-- 1 agent agent 1.8M Sep 25 01:30 solution/submission.parquet\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-1c524d03-5ab6-409d-a90a-a30afa63300d",
    "changes": {
      "/app/solution/train_two_tower.py": {
        "type": "update",
        "unified_diff": "@@ -26,2 +26,5 @@\n         return self.net(pooled)\n+    def serving_vector(self,hist,alpha=24.0):\n+        u=nn.functional.normalize(self(hist),dim=1)*alpha**0.5\n+        return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n \n@@ -36,2 +39,6 @@\n         return self.net(x)\n+    def serving_vector(self,item_idx,alpha=24.0):\n+        v=nn.functional.normalize(self(item_idx),dim=1)*alpha**0.5\n+        log_pop_rank=self.pop_features[item_idx,2:3]*12.0\n+        return torch.cat([v,log_pop_rank],dim=1)\n \n@@ -50,9 +57,6 @@\n         \"\"\"Augmented request-time vector; its last coordinate calibrates popularity.\"\"\"\n-        u=nn.functional.normalize(self.user_tower(hist),dim=1)*alpha**0.5\n-        return torch.cat([u,torch.ones((len(u),1),device=u.device)],dim=1)\n+        return self.user_tower.serving_vector(hist,alpha)\n     def serving_item_vector(self,item_idx,alpha=24.0):\n         \"\"\"Offline vector. Dotting this with serving_user_vector is the final score.\"\"\"\n-        v=nn.functional.normalize(self.item_tower(item_idx),dim=1)*alpha**0.5\n-        log_pop_rank=self.item_tower.pop_features[item_idx,2:3]*12.0\n-        return torch.cat([v,log_pop_rank],dim=1)\n+        return self.item_tower.serving_vector(item_idx,alpha)\n \n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_two_tower.py\n",
    "stderr": ""
  }
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