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{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-81ab353f-287f-4315-ad64-286977e96ffd",
    "process_id": "29080",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,240p' /task/evaluate_public.py && sed -n '1,200p' /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 '1,240p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,200p' /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3110
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 24 22:42 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 24 22:42 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 24 22:42 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 24 22:42 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 24 22:42 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 24 22:42 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 24 22:42 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 24 22:42 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 24 22:42 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 24 22:42 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 24 22:42 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 24 22:42 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 24 22:42 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 24 22:42 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 24 22:43 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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    "stdout": "\n candidate_catalog (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n\n target_users (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\n\n validation_preference_state (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n\n test_preference_state (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntrain listens (43484159, 6) users 9238 items 845395 ts 0 24786795 organic 0.517\ntrain likes (814831, 4) users 8144 items 172303 ts 80 24786790 organic 0.567\ntrain dislikes (101075, 4) users 5732 items 50876 ts 75 24786580 organic 0.517\ntrain unlikes (292665, 4) users 6197 items 113072 ts 250 24786570 organic 0.947\ntrain undislikes (19960, 4) users 2769 items 14725 ts 15950 24785395 organic 0.889\nval listens (1501697, 6) users 8196 items 191747 ts 24788600 25393395 organic 0.517\nval likes (37795, 4) users 4114 items 22902 ts 24788815 25393390 organic 0.666\nval dislikes (3563, 4) users 955 items 3135 ts 24789920 25393280 organic 0.515\nval unlikes (9346, 4) users 1313 items 8080 ts 24789005 25393385 organic 0.953\nval undislikes (606, 4) users 205 items 579 ts 24810715 25393035 organic 0.87\nvalidation 8456 30000 eligible [24657.   28682.75 29242.   29644.   30000.  ] uid aligned? True\ntest 8456 30000 eligible [24552.   28657.   29220.   29619.25 30000.  ] uid aligned? True\n",
    "stderr": "",
    "aggregated_output": "\n candidate_catalog (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n\n target_users (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\n\n validation_preference_state (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n\n test_preference_state (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntrain listens (43484159, 6) users 9238 items 845395 ts 0 24786795 organic 0.517\ntrain likes (814831, 4) users 8144 items 172303 ts 80 24786790 organic 0.567\ntrain dislikes (101075, 4) users 5732 items 50876 ts 75 24786580 organic 0.517\ntrain unlikes (292665, 4) users 6197 items 113072 ts 250 24786570 organic 0.947\ntrain undislikes (19960, 4) users 2769 items 14725 ts 15950 24785395 organic 0.889\nval listens (1501697, 6) users 8196 items 191747 ts 24788600 25393395 organic 0.517\nval likes (37795, 4) users 4114 items 22902 ts 24788815 25393390 organic 0.666\nval dislikes (3563, 4) users 955 items 3135 ts 24789920 25393280 organic 0.515\nval unlikes (9346, 4) users 1313 items 8080 ts 24789005 25393385 organic 0.953\nval undislikes (606, 4) users 205 items 579 ts 24810715 25393035 organic 0.87\nvalidation 8456 30000 eligible [24657.   28682.75 29242.   29644.   30000.  ] uid aligned? True\ntest 8456 30000 eligible [24552.   28657.   29220.   29619.25 30000.  ] uid aligned? True\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 268286628
    },
    "formatted_output": "\n candidate_catalog (30000, 4) {'item_id': dtype('uint32'), 'positive_listens': dtype('int64'), 'organic_positive_listens': dtype('uint64'), 'popularity_rank': dtype('int32')}\n   item_id  positive_listens  organic_positive_listens  popularity_rank\n0  5862961             26615                     20620                1\n1  6901374             25536                     17347                2\n2  5635052             24691                     17194                3\n3  3542184             24075                     17699                4\n4  9378983             24036                     18195                5\n\n target_users (8456, 3) {'uid': dtype('uint32'), 'recent_positive_listens': dtype('float64'), 'history_positive_listens': dtype('int64')}\n   uid  recent_positive_listens  history_positive_listens\n0  100                    844.0                      2604\n1  200                     50.0                       286\n2  300                    168.0                       359\n3  600                   1985.0                      2423\n4  700                   1776.0                      6114\n\n validation_preference_state (469124, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n\n test_preference_state (484776, 3) {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'state': dtype('O')}\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\ntrain listens (43484159, 6) users 9238 items 845395 ts 0 24786795 organic 0.517\ntrain likes (814831, 4) users 8144 items 172303 ts 80 24786790 organic 0.567\ntrain dislikes (101075, 4) users 5732 items 50876 ts 75 24786580 organic 0.517\ntrain unlikes (292665, 4) users 6197 items 113072 ts 250 24786570 organic 0.947\ntrain undislikes (19960, 4) users 2769 items 14725 ts 15950 24785395 organic 0.889\nval listens (1501697, 6) users 8196 items 191747 ts 24788600 25393395 organic 0.517\nval likes (37795, 4) users 4114 items 22902 ts 24788815 25393390 organic 0.666\nval dislikes (3563, 4) users 955 items 3135 ts 24789920 25393280 organic 0.515\nval unlikes (9346, 4) users 1313 items 8080 ts 24789005 25393385 organic 0.953\nval undislikes (606, 4) users 205 items 579 ts 24810715 25393035 organic 0.87\nvalidation 8456 30000 eligible [24657.   28682.75 29242.   29644.   30000.  ] uid aligned? True\ntest 8456 30000 eligible [24552.   28657.   29220.   29619.25 30000.  ] uid aligned? True\n"
  }
}

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  "item": {
    "type": "FileChange",
    "id": "exec-5a68db80-43cd-4514-9a09-02b96f7f26ed",
    "changes": {
      "/app/solution/experiment.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'); O.mkdir(exist_ok=True)\n\ndef load_elig(stage):\n    z=np.load(D/f'{stage}_eligible_candidates.npz')\n    return z['uids'].astype(np.int64),z['item_ids'].astype(np.int64),np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].astype(bool)\n\ndef make_pop(stage,out):\n    uids,items,elig=load_elig(stage)\n    rows=[]\n    for r,u in enumerate(uids):\n        ix=np.flatnonzero(elig[r])[:100]\n        rows.append(pd.DataFrame({'uid':u,'item_id':items[ix],'rank':np.arange(1,101)}))\n    pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\n\nif __name__=='__main__':\n    make_pop('validation',O/'validation_pop.parquet')\n    make_pop('test',O/'submission.parquet')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/experiment.py\n",
    "stderr": ""
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5f29f8dd-01d8-48a3-afff-d1790cf38e2d",
    "process_id": "1556",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python solution/experiment.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D+'/validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; mask=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)]; ur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(items)}\nli=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=li[(li.uid.isin(targets))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=la[(la.uid.isin(targets))&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\ndef eligible(df): return np.array([int(i) in ic and mask[ur[int(u)],ic[int(i)]] for u,i in df.itertuples(index=False)],bool)\nstrong=strong[eligible(strong)];likes=likes[eligible(likes)]; rel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('truth',rel.shape,'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\n# Train interactions target/cat and overlap\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(targets)&tr.item_id.isin(cat)]\nfor desc,q in [('any',tr),('pos50',tr[tr.played_ratio_pct>=50]),('strong80',tr[tr.played_ratio_pct>=80]),('orgstrong',tr[(tr.played_ratio_pct>=80)&(tr.is_organic==1)])]:\n seen=set(zip(q.uid.astype(int),q.item_id.astype(int))); hit=np.array([(int(u),int(i)) in seen for u,i in rel[['uid','item_id']].itertuples(index=False)])\n print(desc,'history pairs',len(seen),'relevant repeat',hit.mean(),'grade3',hit[rel.g.values==3].mean(),'grade1',hit[rel.g.values==1].mean())\nprint('train filtered',tr.shape)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python solution/experiment.py && /opt/task-venv/bin/python /task/evaluate_public.py solution/validation_pop.parquet && /opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data'; targets=set(pd.read_parquet(D+'/target_users.parquet').uid.astype(int)); cat=set(pd.read_parquet(D+'/candidate_catalog.parquet').item_id.astype(int))\nz=np.load(D+'/validation_eligible_candidates.npz'); uids=z['uids']; items=z['item_ids']; mask=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)]; ur={int(x):i for i,x in enumerate(uids)}; ic={int(x):i for i,x in enumerate(items)}\nli=pd.read_parquet(D+'/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); la=pd.read_parquet(D+'/val_likes.parquet',columns=['uid','item_id','is_organic'])\nstrong=li[(li.uid.isin(targets))&(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); likes=la[(la.uid.isin(targets))&(la.is_organic==1)][['uid','item_id']].drop_duplicates()\ndef eligible(df): return np.array([int(i) in ic and mask[ur[int(u)],ic[int(i)]] for u,i in df.itertuples(index=False)],bool)\nstrong=strong[eligible(strong)];likes=likes[eligible(likes)]; rel=pd.concat([strong.assign(g=1),likes.assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nprint('truth',rel.shape,'users',rel.uid.nunique(),'grades',rel.g.value_counts().to_dict())\n# Train interactions target/cat and overlap\ntr=pd.read_parquet(D+'/train_listens.parquet',columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']); tr=tr[tr.uid.isin(targets)&tr.item_id.isin(cat)]\nfor desc,q in [('any',tr),('pos50',tr[tr.played_ratio_pct>=50]),('strong80',tr[tr.played_ratio_pct>=80]),('orgstrong',tr[(tr.played_ratio_pct>=80)&(tr.is_organic==1)])]:\n seen=set(zip(q.uid.astype(int),q.item_id.astype(int))); hit=np.array([(int(u),int(i)) in seen for u,i in rel[['uid','item_id']].itertuples(index=False)])\n print(desc,'history pairs',len(seen),'relevant repeat',hit.mean(),'grade3',hit[rel.g.values==3].mean(),'grade1',hit[rel.g.values==1].mean())\nprint('train filtered',tr.shape)\nPY"
      }
    ],
    "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}\ntruth (37769, 3) users 4263 grades {1: 33352, 3: 4417}\nany history pairs 7868664 relevant repeat 0.0 grade3 0.0 grade1 0.0\npos50 history pairs 5514505 relevant repeat 0.0 grade3 0.0 grade1 0.0\nstrong80 history pairs 5290619 relevant repeat 0.0 grade3 0.0 grade1 0.0\norgstrong history pairs 1935328 relevant repeat 0.0 grade3 0.0 grade1 0.0\ntrain filtered (31062540, 5)\n",
    "stderr": "",
    "aggregated_output": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\ntruth (37769, 3) users 4263 grades {1: 33352, 3: 4417}\nany history pairs 7868664 relevant repeat 0.0 grade3 0.0 grade1 0.0\npos50 history pairs 5514505 relevant repeat 0.0 grade3 0.0 grade1 0.0\nstrong80 history pairs 5290619 relevant repeat 0.0 grade3 0.0 grade1 0.0\norgstrong history pairs 1935328 relevant repeat 0.0 grade3 0.0 grade1 0.0\ntrain filtered (31062540, 5)\n",
    "exit_code": 0,
    "duration": {
      "secs": 45,
      "nanos": 11159210
    },
    "formatted_output": "{\n  \"model\": \"solution/validation_pop.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\ntruth (37769, 3) users 4263 grades {1: 33352, 3: 4417}\nany history pairs 7868664 relevant repeat 0.0 grade3 0.0 grade1 0.0\npos50 history pairs 5514505 relevant repeat 0.0 grade3 0.0 grade1 0.0\nstrong80 history pairs 5290619 relevant repeat 0.0 grade3 0.0 grade1 0.0\norgstrong history pairs 1935328 relevant repeat 0.0 grade3 0.0 grade1 0.0\ntrain filtered (31062540, 5)\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-84a0f6bd-4108-47aa-9eeb-89aabc0c9ce9",
    "process_id": "74336",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, pandas as pd,numpy as np\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); uids=z['uids'].astype('int64'); items=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)]\nur={int(x):i for i,x in enumerate(uids)}; ir={int(x):i for i,x in enumerate(items)}\nfor batch in pq.ParquetFile(D+'/train_listens.parquet').iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n d=batch.to_pandas(); d=d[d.uid.isin(ur)&d.item_id.isin(ir)&(d.played_ratio_pct>=50)].head(200000)\n ok=np.fromiter((mask[ur[int(u)],ir[int(i)]] for u,i in d[['uid','item_id']].itertuples(index=False,name=None)),bool,len(d))\n print('sample positive eligible',ok.mean(),len(d));break\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\nD='/task/data'; z=np.load(D+'/validation_eligible_candidates.npz'); uids=z['uids'].astype('int64'); items=z['item_ids'].astype('int64'); mask=np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(items)]\nur={int(x):i for i,x in enumerate(uids)}; ir={int(x):i for i,x in enumerate(items)}\nfor batch in pq.ParquetFile(D+'/train_listens.parquet').iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n d=batch.to_pandas(); d=d[d.uid.isin(ur)&d.item_id.isin(ir)&(d.played_ratio_pct>=50)].head(200000)\n ok=np.fromiter((mask[ur[int(u)],ir[int(i)]] for u,i in d[['uid','item_id']].itertuples(index=False,name=None)),bool,len(d))\n print('sample positive eligible',ok.mean(),len(d));break\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "sample positive eligible 0.0 200000\n",
    "stderr": "",
    "aggregated_output": "sample positive eligible 0.0 200000\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 975163533
    },
    "formatted_output": "sample positive eligible 0.0 200000\n"
  }
}

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  "item": {
    "type": "FileChange",
    "id": "exec-6d9af1b2-60cb-4f10-ae85-9f5eb3356fd2",
    "changes": {
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        "type": "add",
        "content": "from pathlib import Path\nimport argparse, json\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\n\nD=Path('/task/data'); O=Path('/app/solution'); O.mkdir(exist_ok=True)\nCUTOFFS={'validation':24786800,'test':25393395}\n\ndef elig(stage):\n    z=np.load(D/f'{stage}_eligible_candidates.npz')\n    return z['uids'].astype(np.int64),z['item_ids'].astype(np.int64),np.unpackbits(z['packed_eligible'],axis=1,bitorder='little')[:,:len(z['item_ids'])].astype(bool)\n\ndef build_matrix(stage):\n    cache=O/f'matrix_{stage}.npz'; meta=O/f'matrix_{stage}_meta.npz'\n    if cache.exists():\n        m=np.load(meta); return sparse.load_npz(cache),m['users'],m['items'],m['pop']\n    cutoff=CUTOFFS[stage]\n    eu,items,_=elig(stage)\n    # Include every listener to improve item representations.\n    files=[D/'train_listens.parquet']+([D/'val_listens.parquet'] if stage=='test' else [])\n    us=[]\n    for f in files:\n        us.append(pq.read_table(f,columns=['uid']).column(0).to_numpy())\n    users=np.unique(np.concatenate(us)).astype(np.int64)\n    umax=int(users.max()); imax=int(items.max())\n    umap=np.full(umax+1,-1,np.int32); umap[users]=np.arange(len(users),dtype=np.int32)\n    imap=np.full(imax+1,-1,np.int32); imap[items]=np.arange(len(items),dtype=np.int32)\n    X=sparse.csr_matrix((len(users),len(items)),dtype=np.float32)\n    pop=np.zeros((4,len(items)),np.float64) # long organic, 90d, 30d, 7d\n    day=86400.\n    for f in files:\n      pf=pq.ParquetFile(f)\n      for b in pf.iter_batches(batch_size=1000000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n        q=b.to_pandas(); u=q.uid.to_numpy(np.int64); it=q.item_id.to_numpy(np.int64)\n        good=(u<=umax)&(it<=imax)&(q.played_ratio_pct.to_numpy()>=50)\n        u=u[good]; it=it[good]; ts=q.timestamp.to_numpy(np.int64)[good]; org=q.is_organic.to_numpy(np.float32)[good]; rat=q.played_ratio_pct.to_numpy(np.float32)[good]\n        rr=umap[u]; cc=imap[it]; good=(rr>=0)&(cc>=0); rr=rr[good];cc=cc[good];ts=ts[good];org=org[good];rat=rat[good]\n        # Interest evidence: recent behavior matters, organic and deep plays are stronger.\n        w=np.exp(np.maximum((ts-cutoff)/(180*day),-3.0)).astype(np.float32)*(0.65+0.35*np.minimum(rat,100)/100)*(1+0.35*org)\n        X += sparse.coo_matrix((w,(rr,cc)),shape=X.shape,dtype=np.float32).tocsr()\n        strong=(org>0)&(rat>=80)\n        for k,age in enumerate([1e9,90,30,7]):\n            s=strong & ((cutoff-ts)<=age*day)\n            if s.any(): pop[k]+=np.bincount(cc[s],weights=np.exp(np.maximum((ts[s]-cutoff)/(max(age,30)*day),-5)),minlength=len(items))\n    # Likes are high-confidence interest evidence.\n    likefiles=[D/'train_likes.parquet']+([D/'val_likes.parquet'] if stage=='test' else [])\n    for f in likefiles:\n      for b in pq.ParquetFile(f).iter_batches(batch_size=500000,columns=['uid','timestamp','item_id','is_organic']):\n        q=b.to_pandas();u=q.uid.to_numpy(np.int64);it=q.item_id.to_numpy(np.int64); good=(u<=umax)&(it<=imax);u=u[good];it=it[good];ts=q.timestamp.to_numpy(np.int64)[good];org=q.is_organic.to_numpy(np.float32)[good]\n        rr=umap[u];cc=imap[it];good=(rr>=0)&(cc>=0);rr=rr[good];cc=cc[good];ts=ts[good];org=org[good]\n        w=(2.5+org)*np.exp(np.maximum((ts-cutoff)/(240*day),-3.0))\n        X += sparse.coo_matrix((w.astype(np.float32),(rr,cc)),shape=X.shape).tocsr()\n    X.sum_duplicates(); X.data=np.log1p(X.data)\n    sparse.save_npz(cache,X);np.savez(meta,users=users,items=items,pop=pop)\n    return X,users,items,pop\n\ndef write_rank(stage, X, users, items, pop, factors, blend, name):\n    eu,ei,em=elig(stage); assert np.array_equal(ei,items)\n    ur={int(u):i for i,u in enumerate(users)}; rows=np.array([ur[int(u)] for u in eu])\n    # TF-IDF-like weighting, retaining signal from prolific listeners.\n    df=np.asarray((X>0).sum(axis=0)).ravel(); idf=np.log((len(users)+1)/(df+5)).clip(0.15,5)\n    A=X.multiply(idf).tocsr(); A=normalize(A,norm='l2',axis=1)\n    svd=TruncatedSVD(n_components=factors,n_iter=5,random_state=42)\n    U=svd.fit_transform(A); V=svd.components_\n    # Collaborative score and several global discovery priors.\n    gp=np.log1p(pop)\n    gp=(gp-gp.mean(axis=1,keepdims=True))/(gp.std(axis=1,keepdims=True)+1e-8)\n    base=blend[0]*gp[0]+blend[1]*gp[1]+blend[2]*gp[2]+blend[3]*gp[3]\n    out=[]\n    for a in range(0,len(eu),256):\n        b=min(a+256,len(eu)); sc=(U[rows[a:b]]@V).astype(np.float32)\n        # Per-user standardization makes blend coefficients stable.\n        sc/=sc.std(axis=1,keepdims=True)+1e-8\n        sc+=blend[4]*base\n        sc[~em[a:b]]=-np.inf\n        ix=np.argpartition(sc,-100,axis=1)[:,-100:]\n        vals=np.take_along_axis(sc,ix,axis=1); order=np.argsort(-vals,axis=1);ix=np.take_along_axis(ix,order,axis=1)\n        for j,u in enumerate(eu[a:b]): out.append(pd.DataFrame({'uid':u,'item_id':items[ix[j]],'rank':np.arange(1,101)}))\n    pd.concat(out,ignore_index=True).to_parquet(O/name,index=False)\n    print(json.dumps({'stage':stage,'factors':factors,'blend':blend,'explained':float(svd.explained_variance_ratio_.sum())}))\n\nif __name__=='__main__':\n    ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--factors',type=int,default=128);ap.add_argument('--blend',default='0,0,0,0,0.15');ap.add_argument('--name',default='validation.parquet');a=ap.parse_args()\n    X,u,i,p=build_matrix(a.stage);write_rank(a.stage,X,u,i,p,a.factors,[float(x) for x in a.blend.split(',')],a.name)\n"
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      "/app/solution/knn_model.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport argparse, json\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.preprocessing import normalize\nfrom build_model import build_matrix, elig, O\n\ndef run(stage,k,power,trend,name):\n    X,users,items,pop=build_matrix(stage);eu,ei,em=elig(stage)\n    ur={int(u):i for i,u in enumerate(users)}; rows=np.array([ur[int(u)] for u in eu])\n    # Downweight ubiquitous tracks when matching listeners.\n    df=np.asarray((X>0).sum(axis=0)).ravel();idf=np.log((len(users)+1)/(df+5)).clip(.15,5)\n    A=normalize(X.multiply(idf),axis=1).tocsr()\n    gp=np.log1p(pop[1]+pop[2]);gp=(gp-gp.mean())/(gp.std()+1e-8)\n    out=[]\n    for a in range(0,len(eu),128):\n      b=min(a+128,len(eu)); S=(A[rows[a:b]]@A.T).toarray(); S[np.arange(b-a),rows[a:b]]=0\n      ix=np.argpartition(S,-k,axis=1)[:,-k:]; sv=np.take_along_axis(S,ix,axis=1)\n      sv=np.maximum(sv,0)**power\n      rr=np.repeat(np.arange(b-a),k); W=sparse.csr_matrix((sv.ravel(),(rr,ix.ravel())),shape=(b-a,len(users)))\n      sc=(W@X).toarray().astype(np.float32);sc/=sc.std(axis=1,keepdims=True)+1e-8;sc+=trend*gp\n      sc[~em[a:b]]=-np.inf\n      top=np.argpartition(sc,-100,axis=1)[:,-100:]; vals=np.take_along_axis(sc,top,axis=1);top=np.take_along_axis(top,np.argsort(-vals,axis=1),axis=1)\n      for j,u in enumerate(eu[a:b]):out.append(pd.DataFrame({'uid':u,'item_id':items[top[j]],'rank':np.arange(1,101)}))\n    pd.concat(out,ignore_index=True).to_parquet(O/name,index=False)\n    print(json.dumps({'k':k,'power':power,'trend':trend}))\n\nif __name__=='__main__':\n ap=argparse.ArgumentParser();ap.add_argument('--stage',default='validation');ap.add_argument('--k',type=int,default=100);ap.add_argument('--power',type=float,default=2);ap.add_argument('--trend',type=float,default=.2);ap.add_argument('--name',default='validation_knn.parquet');a=ap.parse_args();run(a.stage,a.k,a.power,a.trend,a.name)\n"
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        "move_path": null
      }
    },
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        "unified_diff": "@@ -17,3 +17,3 @@\n def build_matrix(stage):\n-    cache=O/f'matrix_v3_{stage}.npz'; meta=O/f'matrix_v3_{stage}_meta.npz'\n+    cache=O/f'matrix_{stage}.npz'; meta=O/f'matrix_{stage}_meta.npz'\n     if cache.exists():\n@@ -56,9 +56,2 @@\n         X += sparse.coo_matrix((w.astype(np.float32),(rr,cc)),shape=X.shape).tocsr()\n-    # Cutoff-correct active likes emphasize durable taste and avoid relying only\n-    # on action logs whose later reversals otherwise remain positive evidence.\n-    pref=pd.read_parquet(D/f'{stage}_preference_state.parquet')\n-    pref=pref[pref.state.eq('liked')]\n-    u=pref.uid.to_numpy(np.int64);it=pref.item_id.to_numpy(np.int64);good=(u<=umax)&(it<=imax)\n-    rr=umap[u[good]];cc=imap[it[good]];good=(rr>=0)&(cc>=0)\n-    X += sparse.coo_matrix((np.full(good.sum(),1.0,np.float32),(rr[good],cc[good])),shape=X.shape).tocsr()\n     X.sum_duplicates(); X.data=np.log1p(X.data)\n@@ -73,3 +66,3 @@\n     A=X.multiply(idf).tocsr(); A=normalize(A,norm='l2',axis=1)\n-    latent=O/f'latent_v3_{stage}_{factors}.npz'\n+    latent=O/f'latent_{stage}_{factors}.npz'\n     if latent.exists():\n",
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/build_model.py\n",
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      "/app/solution/README.md": {
        "type": "add",
        "content": "# Music discovery ranker\n\n`submission.parquet` is the final hidden-test ranking. It contains 100 eligible,\ndistinct tracks for each of the 8,456 target users.\n\nThe model is an implicit collaborative ranker over the 30,000-track retrieval\ncatalog. Positive listens are weighted by completion, organic intent, and an\nexponential recency decay; likes receive additional weight. Log-compressed\ninteraction strengths are popularity-adjusted and row-normalized, then a\n320-component randomized truncated SVD produces user-to-unseen-track scores.\nThe final score blends this personalized estimate with standardized 90-day and\n30-day organic strong-listen trend signals. All ineligible tracks are masked\nbefore top-100 selection.\n\nModel selection used training history only for features and the subsequent\npublic window only for evaluation. The chosen validation model scored\n`0.031166` NDCG@10 (4,263 scored users), versus `0.010710` for catalog\npopularity. The final model is refit on training plus validation interactions.\n\nRebuild from `/app` with:\n\n```sh\n/opt/task-venv/bin/python solution/build_model.py \\\n  --stage test --factors 320 --blend 0,1,1,0,1.0 \\\n  --name submission.parquet\n```\n\nThe `.npz` files in this directory are model caches generated by the script.\n"
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      "cp solution/validation_320_c100.parquet solution/validation.parquet\n/opt/task-venv/bin/python - <<'PY'\nimport sys\nfrom pathlib import Path\nimport pandas as pd\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet,validate_ranking\nD=Path('/task/data');p=Path('/app/solution/submission.parquet')\ntargets=set(pd.read_parquet(D/'target_users.parquet').uid.astype(int))\nf=validate_ranking(pd.read_parquet(p),targets); c=CandidateSet(D/'test_eligible_candidates.npz')\nbad=sum(not c.is_eligible(u,i) for u,i in f[['uid','item_id']].itertuples(index=False,name=None))\nprint({'rows':len(f),'users':f.uid.nunique(),'bad_eligibility':bad,'duplicates':int(f.duplicated(['uid','item_id']).sum()),'size_bytes':p.stat().st_size})\nPY\nls -lh solution/submission.parquet solution/validation.parquet solution/README.md solution/build_model.py"
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      }
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    "stdout": "{'rows': 845600, 'users': 8456, 'bad_eligibility': 0, 'duplicates': 0, 'size_bytes': 1665843}\n-rw-r--r-- 1 agent agent 1.3K Sep 24 23:03 solution/README.md\n-rw-r--r-- 1 agent agent 5.8K Sep 24 23:02 solution/build_model.py\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:03 solution/submission.parquet\n-rw-r--r-- 1 agent agent 1.6M Sep 24 23:03 solution/validation.parquet\n",
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