Agent trace

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{
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  "item": {
    "type": "CommandExecution",
    "id": "exec-effaadf0-10f8-4f5d-bb68-ef5f1b2a1c44",
    "process_id": "58485",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lah /task/data && sed -n '1,240p' /task/ranking_evaluation.py && sed -n '1,200p' /task/evaluate_public.py && sed -n '1,200p' /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lah /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,200p' /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      },
      {
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        "cmd": "sed -n '1,200p' /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
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    "stdout": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 7520
    },
    "formatted_output": "total 340M\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:36 .\ndr-xr-xr-x 1 root root 4.0K Sep 23 21:39 ..\n-r--r--r-- 1 root root 342K Sep 23 21:35 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 21:35 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 21:35 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 21:35 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 21:35 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 21:35 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 21:35 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 21:35 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 21:35 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 21:35 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 21:35 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 21:35 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 21:35 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 21:35 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 21:36 validation_preference_state.parquet\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n    \"CandidateSet\",\n    \"QUALITY_K\",\n    \"RankingValidationError\",\n    \"REQUIRED_COLUMNS\",\n    \"TOP_N\",\n    \"ndcg_at_k\",\n    \"ranked_items\",\n    \"read_ranking\",\n    \"score_rankings\",\n    \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n    \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n    \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n    The archive contains aligned ``uids``, ``item_ids``, and\n    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n    copied into memory so they remain valid after the archive is closed.\n    \"\"\"\n\n    def __init__(self, path: Path):\n        \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n        Args:\n            path: Path to a ``.npz`` archive with the candidate-set schema.\n        \"\"\"\n        with np.load(path, allow_pickle=False) as artifact:\n            self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n            self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n            self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n        self.item_column = {\n            int(item): column for column, item in enumerate(self.item_ids)\n        }\n\n    def is_eligible(self, uid: int, item_id: int) -> bool:\n        \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n        IDs absent from the target-user or candidate-item arrays are treated as\n        ineligible rather than raising an exception.\n\n        Args:\n            uid: Target user ID to query.\n            item_id: Candidate item ID to query.\n\n        Returns:\n            ``True`` only when the corresponding packed eligibility bit is set.\n        \"\"\"\n        row = self.uid_row.get(int(uid))\n        column = self.item_column.get(int(item_id))\n        if row is None or column is None:\n            return False\n        byte, bit = divmod(column, 8)\n        return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n    \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n    Args:\n        path: Input path. A ``.parquet`` suffix selects parquet; every other\n            suffix is interpreted as CSV.\n\n    Returns:\n        The ranking frame as loaded by pandas.\n    \"\"\"\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n    frame: pd.DataFrame,\n    targets: set[int],\n) -> pd.DataFrame:\n    \"\"\"Normalize and validate the stage-independent ranking contract.\n\n    Required values must be finite integers. The user set must exactly match\n    ``targets``; every user must have 100 rows, distinct items, and each rank\n    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n    intentionally left to the calling evaluator.\n\n    Args:\n        frame: Raw ranking frame. Extra columns are discarded from an internal\n            copy; the caller's frame is not modified.\n        targets: Exact set of target user IDs the ranking must cover.\n\n    Returns:\n        A normalized frame containing only the required ``int64`` columns.\n\n    Raises:\n        RankingValidationError: If the ranking violates the shared contract.\n    \"\"\"\n    required = list(REQUIRED_COLUMNS)\n    if not all(column in frame for column in required):\n        raise RankingValidationError(f\"submission requires columns {required}\")\n\n    normalized = frame[required].copy()\n    for column in required:\n        numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n        values = numeric.to_numpy(dtype=float)\n        if numeric.isna().any() or not np.isfinite(values).all():\n            raise RankingValidationError(\n                f\"submission {column} must be finite numeric values\"\n            )\n        if not np.equal(numeric, np.floor(numeric)).all():\n            raise RankingValidationError(\n                f\"submission {column} must contain integers\"\n            )\n        normalized[column] = numeric.astype(np.int64)\n\n    submitted_users = set(map(int, normalized.uid.unique()))\n    if submitted_users != targets:\n        raise RankingValidationError(\n            f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n            f\"extra={len(submitted_users - targets)}\"\n        )\n    counts = normalized.groupby(\"uid\").size()\n    if not (counts == TOP_N).all():\n        raise RankingValidationError(\n            f\"submission must contain exactly {TOP_N} rows for every target user\"\n        )\n    if normalized.duplicated([\"uid\", \"item_id\"]).any():\n        raise RankingValidationError(\n            \"submission contains duplicate (uid, item_id) pairs\"\n        )\n    valid_ranks = set(range(1, TOP_N + 1))\n    ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n        lambda values: set(map(int, values)) == valid_ranks\n    )\n    if not ranks_are_valid.all():\n        raise RankingValidationError(\n            f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n        )\n    return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n    \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n    Args:\n        frame: Frame that has passed :func:`validate_ranking`.\n\n    Returns:\n        A ``uid -> item IDs`` mapping ordered by ascending rank.\n    \"\"\"\n    ordered = frame.sort_values([\"uid\", \"rank\"])\n    return {\n        int(uid): list(map(int, values))\n        for uid, values in ordered.groupby(\"uid\").item_id\n    }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n    \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n    Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n    Args:\n        ranked: Item IDs in predicted rank order.\n        gold: Mapping from relevant item IDs to integer relevance grades.\n        k: Maximum number of predicted positions to score.\n\n    Returns:\n        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n        ``0.0`` when the ideal gain is zero.\n    \"\"\"\n    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n    return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Relevant item grades keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    quality = float(\n        np.mean(\n            [\n                ndcg_at_k(ranked[uid], gold, QUALITY_K)\n                for uid, gold in graded.items()\n            ]\n        )\n    )\n    return {\n        \"quality_ndcg@10\": round(quality, 6),\n        \"quality_users\": len(graded),\n    }\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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  "item": {
    "type": "CommandExecution",
    "id": "exec-5d1f9c04-4564-41a7-93f5-11419fb0d7e4",
    "process_id": "32814",
    "command": [
      "/bin/sh",
      "-lc",
      "find / -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'pyarrow*.so' 2>/dev/null | head; ls -la /task"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "find / -type d -name 'pandas' 2>/dev/null | head -20; find / -type f -name 'pyarrow*.so' 2>/dev/null | head; ls -la /task"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n",
    "stderr": "",
    "aggregated_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 191544790
    },
    "formatted_output": "/opt/task-venv/lib/python3.12/site-packages/pandas\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:39 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:41 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:36 data\n-r--r--r-- 1 root root 5774 Sep 23 21:35 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:35 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:35 requirements.txt\n"
  }
}

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  "item": {
    "type": "CommandExecution",
    "id": "exec-34e55ae9-8071-473c-94a4-e3515ed51a3b",
    "process_id": "37909",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -la /opt/task-venv/bin | head -30; /opt/task-venv/bin/python -c 'import pandas; print(pandas.__version__)'"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "ls -la /opt/task-venv/bin | head -30; /opt/task-venv/bin/python -c 'import pandas; print(pandas.__version__)'"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "2.3.3\n",
    "stderr": "",
    "aggregated_output": "2.3.3\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 545560787
    },
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  }
}

{
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-5bb0c9c2-ba29-4b68-8e5f-11aee5b16074",
    "process_id": "27873",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n  x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n  print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), round(x.is_organic.mean(),3), round((x.played_ratio_pct>=50).mean(),3),round((x.played_ratio_pct>=80).mean(),3))\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd, pyarrow.parquet as pq, numpy as np\nfrom pathlib import Path\nD=Path('/task/data')\nfor p in sorted(D.glob('*.parquet')):\n t=pq.ParquetFile(p)\n print(p.name, t.metadata.num_rows, t.schema.names)\n if p.name in ['train_listens.parquet','val_listens.parquet']:\n  x=pd.read_parquet(p,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n  print(' stats',x.uid.nunique(),x.item_id.nunique(),x.timestamp.min(),x.timestamp.max(), round(x.is_organic.mean(),3), round((x.played_ratio_pct>=50).mean(),3),round((x.played_ratio_pct>=80).mean(),3))\nfor f in ['candidate_catalog.parquet','target_users.parquet','validation_preference_state.parquet','test_preference_state.parquet']:\n x=pd.read_parquet(D/f); print('\\n',f,x.shape,x.head(),x.dtypes,sep='\\n')\nfor f in ['validation_eligible_candidates.npz','test_eligible_candidates.npz']:\n z=np.load(D/f); print(f,{k:z[k].shape for k in z.files},z['uids'][:3],z['item_ids'][:3],np.unpackbits(z['packed_eligible'],bitorder='little',axis=1,count=len(z['item_ids'])).sum(axis=1)[:10])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\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\nuid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n",
    "stderr": "",
    "aggregated_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\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\nuid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n",
    "exit_code": 0,
    "duration": {
      "secs": 2,
      "nanos": 266041720
    },
    "formatted_output": "candidate_catalog.parquet 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']\ntarget_users.parquet 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']\ntest_preference_state.parquet 484776 ['uid', 'item_id', 'state']\ntrain_dislikes.parquet 101075 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_likes.parquet 814831 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_listens.parquet 43484159 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 9238 845395 0 24786795 0.517 0.632 0.592\ntrain_undislikes.parquet 19960 ['uid', 'timestamp', 'item_id', 'is_organic']\ntrain_unlikes.parquet 292665 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_dislikes.parquet 3563 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_likes.parquet 37795 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_listens.parquet 1501697 ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']\n stats 8196 191747 24788600 25393395 0.517 0.643 0.602\nval_undislikes.parquet 606 ['uid', 'timestamp', 'item_id', 'is_organic']\nval_unlikes.parquet 9346 ['uid', 'timestamp', 'item_id', 'is_organic']\nvalidation_preference_state.parquet 469124 ['uid', 'item_id', 'state']\n\n\ncandidate_catalog.parquet\n(30000, 4)\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\nitem_id                     uint32\npositive_listens             int64\norganic_positive_listens    uint64\npopularity_rank              int32\ndtype: object\n\n\ntarget_users.parquet\n(8456, 3)\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\nuid                          uint32\nrecent_positive_listens     float64\nhistory_positive_listens      int64\ndtype: object\n\n\nvalidation_preference_state.parquet\n(469124, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\n\n\ntest_preference_state.parquet\n(484776, 3)\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\nuid        uint32\nitem_id    uint32\nstate      object\ndtype: object\nvalidation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736]\ntest_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} [100 200 300] [5862961 6901374 5635052] [29294 29817 29931 29300 28328 27406 29654 29369 28508 28665]\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-bef2e667-4e30-4345-9362-0b01d139a442",
    "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'); O=Path('/app/solution')\n\ndef make(npz, out, order=None):\n    z=np.load(D/npz)\n    uids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\n    if order is None: order=np.arange(len(items))\n    rows=[]\n    for r,u in enumerate(uids):\n        bits=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little')\n        pick=order[bits[order].astype(bool)][:100]\n        rows.append(pd.DataFrame({'uid':u,'item_id':items[pick],'rank':np.arange(1,101)}))\n    pd.concat(rows,ignore_index=True).to_parquet(out,index=False)\n\nmake('validation_eligible_candidates.npz',O/'validation_pop.parquet')\nmake('test_eligible_candidates.npz',O/'submission.parquet')\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/baseline.py\n",
    "stderr": ""
  }
}

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  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-1fe1e794-cd01-48e5-9feb-39f65bdfdd00",
    "process_id": "99913",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /tmp/analyze.py <<'PY'\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\ntarg=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int)); E=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\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'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[rel.uid.isin(targ)]; rel=rel[[E.is_eligible(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nrr=np.array([rank[int(i)] for i in rel.item_id]); print('rel',len(rel),rel.uid.nunique(),'likes',sum(rel.g==3)); print('pop pct',np.quantile(rr,[0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1])); print('top rates',[(k,(rr<=k).mean()) for k in [10,100,500,1000,3000,10000,30000]])\n# Catalog positive historical pairs eligible check and future prior overlap using batch build set only target-related\nuids=pd.Index(sorted(targ)); items=pd.Index(cat.item_id.astype('int64'))\npairs=set(); counts=0\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n x=b.to_pandas(); x=x[(x.played_ratio_pct>=50)&x.uid.isin(targ)&x.item_id.isin(items)]\n pairs.update(zip(x.uid.astype(int),x.item_id.astype(int))); counts+=len(x)\nprint('hist catalog events/pairs',counts,len(pairs))\nprint('future rel seen hist',np.mean([(int(u),int(i)) in pairs for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]))\nss=list(pairs)[:200000];print('hist pairs eligible sample',np.mean([E.is_eligible(u,i) for u,i in ss]))\nPY\n/opt/task-venv/bin/python /tmp/analyze.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /tmp/analyze.py <<'PY'\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import CandidateSet\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet'); rank=dict(zip(cat.item_id.astype(int),cat.popularity_rank.astype(int)))\ntarg=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int)); E=CandidateSet(__import__('pathlib').Path(D+'validation_eligible_candidates.npz'))\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'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nrel=rel[rel.uid.isin(targ)]; rel=rel[[E.is_eligible(u,i) for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]]\nrr=np.array([rank[int(i)] for i in rel.item_id]); print('rel',len(rel),rel.uid.nunique(),'likes',sum(rel.g==3)); print('pop pct',np.quantile(rr,[0,.01,.05,.1,.25,.5,.75,.9,.95,.99,1])); print('top rates',[(k,(rr<=k).mean()) for k in [10,100,500,1000,3000,10000,30000]])\n# Catalog positive historical pairs eligible check and future prior overlap using batch build set only target-related\nuids=pd.Index(sorted(targ)); items=pd.Index(cat.item_id.astype('int64'))\npairs=set(); counts=0\npf=pq.ParquetFile(D+'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','item_id','played_ratio_pct']):\n x=b.to_pandas(); x=x[(x.played_ratio_pct>=50)&x.uid.isin(targ)&x.item_id.isin(items)]\n pairs.update(zip(x.uid.astype(int),x.item_id.astype(int))); counts+=len(x)\nprint('hist catalog events/pairs',counts,len(pairs))\nprint('future rel seen hist',np.mean([(int(u),int(i)) in pairs for u,i in rel[['uid','item_id']].itertuples(index=False,name=None)]))\nss=list(pairs)[:200000];print('hist pairs eligible sample',np.mean([E.is_eligible(u,i) for u,i in ss]))\nPY\n/opt/task-venv/bin/python /tmp/analyze.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "rel 37769 4263 likes 4417\npop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04]\ntop rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))]\nhist catalog events/pairs 19959853 5514505\nfuture rel seen hist 0.0\nhist pairs eligible sample 0.0\n",
    "stderr": "",
    "aggregated_output": "rel 37769 4263 likes 4417\npop pct [1.00000e+00 2.20000e+01 1.74000e+02 4.28000e+02 1.73300e+03 5.62200e+03\n 1.33740e+04 2.16802e+04 2.56936e+04 2.91010e+04 3.00000e+04]\ntop rates [(10, np.float64(0.005030580634912229)), (100, np.float64(0.031877995181233285)), (500, np.float64(0.11130821573247902)), (1000, np.float64(0.17535545023696683)), (3000, np.float64(0.3511610050570574)), (10000, np.float64(0.6592708305753395)), (30000, np.float64(1.0))]\nhist catalog events/pairs 19959853 5514505\nfuture rel seen hist 0.0\nhist pairs eligible sample 0.0\n",
    "exit_code": 0,
    "duration": {
      "secs": 13,
      "nanos": 771150998
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      "/app/solution/experiment.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport numpy as np, pandas as pd, pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nimport sys, time\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import ndcg_at_k\n\nD=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items)\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\niz=pd.Series(np.arange(ni,dtype=np.int32),index=items)\nz=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64)\n\n# labels for rapid local iteration\nli=pd.read_parquet(D/'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nla=pd.read_parquet(D/'val_likes.parquet',columns=['uid','item_id','is_organic'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last')\nurow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i in enumerate(items)}\ngold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=urow.get(int(u)); c=icol.get(int(i))\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\n\ndef evaluate_order(order,name):\n vals=[]\n for u,g in gold.items():\n  r=urow[u]; bits=np.unpackbits(packed[r],count=ni,bitorder='little')\n  pick=order[bits[order].astype(bool)][:10]\n  vals.append(ndcg_at_k(items[pick].tolist(),g,10))\n print(name,round(float(np.mean(vals)),6),flush=True)\n\n# temporal/global counts\nnames=[]; arr=[]\nfor w in [7,14,30,60,90,180,1000]:\n for org in [0,1]:\n  names.append(f'p{w}_o{org}'); arr.append(np.zeros(ni,np.float32))\nnames += ['decay14_org','decay30_org','strong30_org']; arr += [np.zeros(ni,np.float32) for _ in range(3)]\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=750000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); pos=x.played_ratio_pct.to_numpy()>=50; ii=iz.reindex(x.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(np.int32); age=(CUT-x.timestamp.to_numpy())/DAY; organic=x.is_organic.to_numpy()==1\n k=0\n for w in [7,14,30,60,90,180,1000]:\n  for org in [0,1]:\n   m=ok&pos&(age<=w)&(organic if org else True); arr[k]+=np.bincount(ii[m],minlength=ni); k+=1\n for hl,k in [(14,14),(30,15)]:\n  m=ok&pos&organic; arr[k]+=np.bincount(ii[m],weights=np.exp(-age[m]/hl),minlength=ni)\n m=ok&organic&(x.played_ratio_pct.to_numpy()>=80)&(age<=30); arr[16]+=np.bincount(ii[m],minlength=ni)\nfor n,a in zip(names,arr): evaluate_order(np.argsort(-a,kind='stable'),n)\n# Likes popularity\nlikes=pd.read_parquet(D/'train_likes.parquet'); ii=iz.reindex(likes.item_id).to_numpy(); ok=~pd.isna(ii); ii=np.where(ok,ii,0).astype(int); age=(CUT-likes.timestamp.to_numpy())/DAY\nfor w in [7,30,90,1000]:\n for org in [0,1]:\n  m=ok&(age<=w)&((likes.is_organic.to_numpy()==1) if org else True); a=np.bincount(ii[m],minlength=ni); evaluate_order(np.argsort(-a,kind='stable'),f'like{w}_o{org}')\n"
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/experiment.py\n",
    "stderr": ""
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      "/app/solution/dev_svd.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport sys,time\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\nD=Path('/task/data'); O=Path('/app/solution'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); item_index=pd.Series(np.arange(ni,dtype=np.int32),index=items)\n# all history users\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),pd.read_parquet(D/'target_users.parquet').uid.to_numpy()])).astype(np.int64)\nui=pd.Series(np.arange(len(users),dtype=np.int32),index=users); targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64); target_rows=ui.loc[targets].to_numpy()\nrows=[]; cols=[]; vals=[]; rec=[]\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=1000000,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); m=x.played_ratio_pct.to_numpy()>=50; x=x[m]; cc=item_index.reindex(x.item_id).to_numpy(); ok=~pd.isna(cc); x=x[ok]; cc=cc[ok].astype(np.int32)\n rows.append(ui.loc[x.uid].to_numpy(np.int32)); cols.append(cc); vals.append(np.ones(len(x),np.float32)); age=(CUT-x.timestamp.to_numpy())/DAY; rec.append((np.exp(-age/45)*(1+0.35*x.is_organic.to_numpy())).astype(np.float32))\n print('read',sum(map(len,rows)),flush=True)\nr=np.concatenate(rows); c=np.concatenate(cols)\nX=sparse.coo_matrix((np.concatenate(vals),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); X.data=np.log1p(X.data)\nR=sparse.coo_matrix((np.concatenate(rec),(r,c)),shape=(len(users),ni),dtype=np.float32).tocsr(); R.data=np.log1p(R.data)\ndel rows,cols,vals,rec,r,c\nsparse.save_npz(O/'train_catalog_counts.npz',X); sparse.save_npz(O/'train_catalog_recent.npz',R)\n# truth and eligibility\nz=np.load(D/'validation_eligible_candidates.npz'); packed=z['packed_eligible']; eu=z['uids'].astype(np.int64); urow={int(u):r for r,u in enumerate(eu)}; icol={int(i):c for c,i 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'])\nrel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n rr=urow.get(int(u)); cc=icol.get(int(i));\n if rr is not None and cc is not None and ((packed[rr,cc//8]>>(cc%8))&1): gold.setdefault(int(u),{})[int(i)]=int(g)\n# recent 7 popularity used for blend\nrecent=np.asarray((R>0).sum(axis=0)).ravel().astype(np.float32) # unique listeners recent-weight presence approximation\n# exact event pop from rows already unavailable: aggregate R sum\nrecent_sum=np.asarray(R.sum(axis=0)).ravel().astype(np.float32)\n\ndef score_model(M,label,ncomp=64):\n print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n # evaluate blends after per-row standardization. pop transformed to z-scale\n pop=np.log1p(recent_sum); pop=(pop-pop.mean())/(pop.std()+1e-6)\n results={a:[] for a in [0,.1,.2,.35,.5,.75,1,1.5,2]}\n pred_by_u={a:{} for a in results}\n for s in range(0,len(targets),256):\n  ids=np.arange(s,min(s+256,len(targets))); sc=U[target_rows[ids]]@V\n  mu=sc.mean(1,keepdims=True); sd=sc.std(1,keepdims=True)+1e-7; sc=(sc-mu)/sd\n  for j,k in enumerate(ids):\n   u=int(targets[k]); er=urow[u]; bits=np.unpackbits(packed[er],count=ni,bitorder='little').astype(bool)\n   for a in results:\n    ss=sc[j]+a*pop; ss[~bits]=-np.inf; top=np.argpartition(ss,-10)[-10:]; top=top[np.argsort(-ss[top])]; pred_by_u[a][u]=items[top].tolist()\n for a in results:\n  results[a]=float(np.mean([ndcg_at_k(pred_by_u[a][u],g,10) for u,g in gold.items()]))\n print(label,results,flush=True)\n best=max(results,key=results.get); return sv,best,results[best]\n\nfor M,label in [(X,'count'),(R,'recent')]:\n # normalize rows for taste-focused SVD\n for norm in [False,True]:\n  A=M.copy()\n  if norm:\n   rs=np.sqrt(np.asarray(A.multiply(A).sum(axis=1)).ravel())+1e-8; A=sparse.diags(1/rs)@A\n  for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/dev_svd.py\n",
    "stderr": ""
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-aee69d87-9a3c-4fa7-b62b-0cd68cb29239",
    "process_id": "46778",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/dev_svd.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n  File \"/app/solution/dev_svd.py\", line 59, in <module>\n    for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/dev_svd.py\", line 36, in score_model\n    print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n    U, Sigma, VT = _randomized_svd(\n                   ^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n    Q = _randomized_range_finder(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n    Q, _ = normalizer(A @ Q)\n           ^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n    lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n",
    "stderr": "",
    "aggregated_output": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n  File \"/app/solution/dev_svd.py\", line 59, in <module>\n    for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/dev_svd.py\", line 36, in score_model\n    print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n    U, Sigma, VT = _randomized_svd(\n                   ^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n    Q = _randomized_range_finder(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n    Q, _ = normalizer(A @ Q)\n           ^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n    lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n",
    "exit_code": 130,
    "duration": {
      "secs": 106,
      "nanos": 732728853
    },
    "formatted_output": "read 446673\nread 908896\nread 1340365\nread 1785737\nread 2234580\nread 2709445\nread 3185874\nread 3652603\nread 4090504\nread 4527184\nread 5004134\nread 5470718\nread 5944315\nread 6418602\nread 6909729\nread 7394955\nread 7832967\nread 8248997\nread 8723144\nread 9196517\nread 9674330\nread 10155336\nread 10631815\nread 11064652\nread 11522531\nread 11970245\nread 12443394\nread 12909457\nread 13330236\nread 13800518\nread 14274349\nread 14730189\nread 15204898\nread 15667516\nread 16133636\nread 16586586\nread 17040408\nread 17502704\nread 17981413\nread 18436772\nread 18903433\nread 19367547\nread 19850665\nread 20056329\nfit count 32\nvariance 0.27032307\ncount {0: 0.015069708378442051, 0.1: 0.0151962219319369, 0.2: 0.015346427853058094, 0.35: 0.015588916849881026, 0.5: 0.015644867969879454, 0.75: 0.015986121072956227, 1: 0.016240402216572705, 1.5: 0.01676304884840745, 2: 0.017538452916403345}\nfit count 64\nvariance 0.32269448\ncount {0: 0.018016355262563045, 0.1: 0.018246667713089113, 0.2: 0.01845391634743531, 0.35: 0.018373933371208663, 0.5: 0.018445216144724795, 0.75: 0.018514560052943153, 1: 0.0191944953607997, 1.5: 0.01950374331757431, 2: 0.01950947915367972}\nfit count 128\nvariance 0.38011488\ncount {0: 0.019065681598316273, 0.1: 0.018979976844124113, 0.2: 0.019072462103960415, 0.35: 0.019430970221175754, 0.5: 0.019857979887910958, 0.75: 0.020025740080260743, 1: 0.0203391335731335, 1.5: 0.020557194910255316, 2: 0.02072602631946557}\nfit count_norm 32\nvariance 0.16401763\ncount_norm {0: 0.017100787389841294, 0.1: 0.01695861782265323, 0.2: 0.017181264677926078, 0.35: 0.017147181485357783, 0.5: 0.017426565993863864, 0.75: 0.0171470658783238, 1: 0.01710584839236254, 1.5: 0.01733126780696215, 2: 0.01776865969307444}\nfit count_norm 64\nvariance 0.2067266\ncount_norm {0: 0.019907668298057457, 0.1: 0.01989388766413997, 0.2: 0.01986408692769773, 0.35: 0.01986990423231424, 0.5: 0.01997715692079431, 0.75: 0.01976652227203293, 1: 0.019854832834343028, 1.5: 0.020191713909334937, 2: 0.02021094364373062}\nfit count_norm 128\nTraceback (most recent call last):\n  File \"/app/solution/dev_svd.py\", line 59, in <module>\n    for k in [32,64,128]: score_model(A,label+('_norm' if norm else ''),k)\n                          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/dev_svd.py\", line 36, in score_model\n    print('fit',label,ncomp,flush=True); sv=TruncatedSVD(n_components=ncomp,n_iter=5,random_state=42); U=sv.fit_transform(M).astype(np.float32); V=sv.components_.astype(np.float32); print('variance',sv.explained_variance_ratio_.sum(),flush=True)\n                                                                                                         ^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py\", line 316, in wrapped\n    data_to_wrap = f(self, X, *args, **kwargs)\n                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py\", line 1365, in wrapper\n    return fit_method(estimator, *args, **kwargs)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/decomposition/_truncated_svd.py\", line 244, in fit_transform\n    U, Sigma, VT = _randomized_svd(\n                   ^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 568, in _randomized_svd\n    Q = _randomized_range_finder(\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/extmath.py\", line 350, in _randomized_range_finder\n    Q, _ = normalizer(A @ Q)\n           ^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/scipy/linalg/_decomp_lu.py\", line 355, in lu\n    lu_dispatcher(a1, u, p, permute_l)\nKeyboardInterrupt\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-69ed39a1-c0fa-4b21-a385-5a68016ad448",
    "changes": {
      "/app/solution/tune_global.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task'); from ranking_evaluation import ndcg_at_k\nD=Path('/task/data'); CUT=24786800; DAY=86400\ncat=pd.read_parquet(D/'candidate_catalog.parquet'); items=cat.item_id.to_numpy(np.int64); ni=len(items); ix=pd.Series(np.arange(ni),index=items)\nz=np.load(D/'validation_eligible_candidates.npz'); us=z['uids'].astype(int); packed=z['packed_eligible']; ur={u:r for r,u in enumerate(us)}; ic={int(i):c for c,i 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']); rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last'); gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\ndef ev(score,name):\n order=np.argsort(-score,kind='stable'); v=[]\n for u,g in gold.items():\n  b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little'); p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\n q=np.mean(v); print(name,round(q,6));return q\nW=[1,2,3,5,7,10,14,21,30]; A={}\npf=pq.ParquetFile(D/'train_listens.parquet')\nfor b in pf.iter_batches(batch_size=500000,columns=['timestamp','item_id','is_organic','played_ratio_pct']):\n x=b.to_pandas(); age=(CUT-x.timestamp.to_numpy())/DAY; jj=ix.reindex(x.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int); org=x.is_organic.to_numpy()==1; ratio=x.played_ratio_pct.to_numpy()\n for w in W:\n  for typ,m0 in [('all',ratio>=50),('org',(ratio>=50)&org),('rec',(ratio>=50)&~org),('strong',(ratio>=80)),('skip',ratio<20)]:\n   k=f'{typ}{w}';m=ok&m0&(age<=w);A[k]=A.get(k,0)+np.bincount(jj[m],minlength=ni)\nlikes=pd.read_parquet(D/'train_likes.parquet');age=(CUT-likes.timestamp.to_numpy())/DAY;jj=ix.reindex(likes.item_id).to_numpy();ok=~pd.isna(jj);jj=np.where(ok,jj,0).astype(int)\nfor w in W:A[f'like{w}']=np.bincount(jj[ok&(age<=w)],minlength=ni)\nbest=(0,None)\nfor k,a in A.items():\n q=ev(a,k)\n if q>best[0]:best=q,k\n# standardized log score blends around best individual horizons\nkeys=['all3','all5','all7','all10','all14','org3','org5','org7','rec7','like7','like5','strong7']\nZ={k:(np.log1p(A[k])-np.log1p(A[k]).mean())/(np.log1p(A[k]).std()+1e-9) for k in keys}\nfor a in [0,.1,.2,.3,.5,.75,1,1.5,2]:\n for b in [0,.1,.25,.5,1]:\n  q=ev(Z['all7']+a*Z['like7']+b*Z['all3'],f'blend like={a} d3={b}')\n  if q>best[0]:best=q,('blend',a,b)\nprint('BEST',best)\nnp.savez('/app/solution/global_signals.npz',**A)\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/tune_global.py\n",
    "stderr": ""
  }
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    "stdout": "all1 0.018312\norg1 0.018231\nrec1 0.009206\nstrong1 0.017614\nskip1 0.028891\nall2 0.02061\norg2 0.021367\nrec2 0.007519\nstrong2 0.020396\nskip2 0.030657\nall3 0.028592\norg3 0.026298\nrec3 0.011398\nstrong3 0.023621\nskip3 0.031376\nall5 0.029501\norg5 0.027385\nrec5 0.013583\nstrong5 0.024779\nskip5 0.032211\nall7 0.030933\norg7 0.028456\nrec7 0.015199\nstrong7 0.030107\nskip7 0.029497\nall10 0.028858\norg10 0.027443\nrec10 0.01385\nstrong10 0.026372\nskip10 0.029402\nall14 0.025124\norg14 0.026849\nrec14 0.013997\nstrong14 0.022238\nskip14 0.024044\nall21 0.020286\norg21 0.018416\nrec21 0.012978\nstrong21 0.020034\nskip21 0.018828\nall30 0.017182\norg30 0.01431\nrec30 0.010585\nstrong30 0.016688\nskip30 0.016514\nlike1 0.01253\nlike2 0.021055\nlike3 0.030269\nlike5 0.025373\nlike7 0.030187\nlike10 0.030801\nlike14 0.020652\nlike21 0.016229\nlike30 0.016812\nblend like=0 d3=0 0.030933\nblend like=0 d3=0.1 0.030575\nblend like=0 d3=0.25 0.030366\nblend like=0 d3=0.5 0.030484\nblend like=0 d3=1 0.029623\nblend like=0.1 d3=0 0.033046\nblend like=0.1 d3=0.1 0.032577\nblend like=0.1 d3=0.25 0.032492\nblend like=0.1 d3=0.5 0.032056\nblend like=0.1 d3=1 0.031225\nblend like=0.2 d3=0 0.032908\nblend like=0.2 d3=0.1 0.033074\nblend like=0.2 d3=0.25 0.032657\nblend like=0.2 d3=0.5 0.032726\nblend like=0.2 d3=1 0.032333\nblend like=0.3 d3=0 0.032865\nblend like=0.3 d3=0.1 0.032945\nblend like=0.3 d3=0.25 0.032885\nblend like=0.3 d3=0.5 0.032684\nblend like=0.3 d3=1 0.032552\nblend like=0.5 d3=0 0.032932\nblend like=0.5 d3=0.1 0.032885\nblend like=0.5 d3=0.25 0.032944\nblend like=0.5 d3=0.5 0.032808\nblend like=0.5 d3=1 0.032615\nblend like=0.75 d3=0 0.032799\nblend like=0.75 d3=0.1 0.033184\nblend like=0.75 d3=0.25 0.033041\nblend like=0.75 d3=0.5 0.032874\nblend like=0.75 d3=1 0.032515\nblend like=1 d3=0 0.031725\nblend like=1 d3=0.1 0.031826\nblend like=1 d3=0.25 0.032763\nblend like=1 d3=0.5 0.033059\nblend like=1 d3=1 0.032846\nblend like=1.5 d3=0 0.031162\nblend like=1.5 d3=0.1 0.031906\nblend like=1.5 d3=0.25 0.031944\nblend like=1.5 d3=0.5 0.032383\nblend like=1.5 d3=1 0.033141\nblend like=2 d3=0 0.031119\nblend like=2 d3=0.1 0.031058\nblend like=2 d3=0.25 0.031617\nblend like=2 d3=0.5 0.032015\nblend like=2 d3=1 0.032392\nBEST (np.float64(0.033184193148729016), ('blend', 0.75, 0.1))\n",
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      "/app/solution/hybrid_dev.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)}\ntargets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\n# user ordering matches sorted all train users in dev_svd\nimport pyarrow.parquet as pq\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32)\n# strengthen explicit current likes; disliked state is omitted from factor fitting\npref=pd.read_parquet(D/'validation_preference_state.parquet'); pref=pref[pref.state.eq('liked')]; rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc); L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsignals=np.load(O/'global_signals.npz');Z={k:(np.log1p(signals[k])-np.log1p(signals[k]).mean())/(np.log1p(signals[k]).std()+1e-9) for k in signals.files}\n# eligibility + truth\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];eus=z['uids'].astype(int);ur={u:r for r,u in enumerate(eus)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\n\ndef eval_global(sc,name):\n order=np.argsort(-sc);v=[]\n for u,g in gold.items():\n  b=np.unpackbits(packed[ur[u]],count=ni,bitorder='little');p=order[b[order].astype(bool)][:10];v.append(ndcg_at_k(items[p].tolist(),g,10))\n print(name,np.mean(v),flush=True)\n\nbase=Z['all7']+.75*Z['like7']+.1*Z['all3']\nfor k in ['skip3','skip5','skip7','org7','strong7','all5','all10']:\n for w in [-.5,-.25,-.1,.1,.25,.5,1]:eval_global(base+w*Z[k],f'base {k} {w}')\n\nfor lw in [0,1,3,6]:\n A=X+lw*L\n # row-normalizing degrades earlier; use raw confidence\n print('FIT likes',lw,flush=True);sv=TruncatedSVD(n_components=128,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32)\n alphas=[0,.025,.05,.075,.1,.15,.2,.3,.5,1]\n vals={a:[] for a in alphas}; ranked={a:{} for a in alphas}\n for s in range(0,len(targets),256):\n  ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n  for j,k0 in enumerate(ids):\n   u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n   for a in alphas:\n    ss=base+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];ranked[a][u]=items[top].tolist()\n for a in alphas: vals[a]=np.mean([ndcg_at_k(ranked[a][u],g,10) for u,g in gold.items()])\n print('HYBRID',lw,vals,flush=True)\n"
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    "stdout": "Success. Updated the following files:\nA /app/solution/hybrid_dev.py\n",
    "stderr": ""
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        "type": "add",
        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32);R=sparse.load_npz(O/'train_catalog_recent.npz').astype(np.float32)\npref=pd.read_parquet(D/'validation_preference_state.parquet');pref=pref[pref.state.eq('liked')];rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc);L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsig=np.load(O/'global_signals.npz');Z={k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9) for k in sig.files};bases={'b1':Z['all7']+.75*Z['like7']+.1*Z['all3'],'b2':Z['all7']+.75*Z['like7']+.1*Z['all3']+Z['all10']}\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nalphas=[.15,.2,.25,.3,.35,.4,.5]\ndef fit_eval(A,name,k):\n print('fit',name,k,flush=True);sv=TruncatedSVD(n_components=k,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32);np.savez_compressed(O/f'factors_{name}_{k}.npz',U=U,V=V)\n vals={(bn,a):[] for bn in bases for a in alphas};pred={(bn,a):{} for bn in bases for a in alphas}\n for s in range(0,len(targets),256):\n  ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n  for j,k0 in enumerate(ids):\n   u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n   for bn,b in bases.items():\n    for a in alphas:\n     ss=b+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(bn,a)][u]=items[top].tolist()\n for q in vals: vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\n print(name,k,vals,flush=True)\nfor A,n,k in [(X+L,'countlikes',128),(X+L,'countlikes',256),(R+L,'recentlikes',128)]:fit_eval(A,n,k)\n"
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/compare_models.py\n",
    "stderr": ""
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  "item": {
    "type": "CommandExecution",
    "id": "exec-d5a93b5b-b703-4669-9dba-0dd6b079a268",
    "process_id": "11167",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python /app/solution/compare_models.py"
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    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "/opt/task-venv/bin/python /app/solution/compare_models.py"
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    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n",
    "stderr": "",
    "aggregated_output": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n",
    "exit_code": 0,
    "duration": {
      "secs": 120,
      "nanos": 360374920
    },
    "formatted_output": "fit countlikes 128\ncountlikes 128 {('b1', 0.15): np.float64(0.03733431409438778), ('b1', 0.2): np.float64(0.038318194226979484), ('b1', 0.25): np.float64(0.03868524751412139), ('b1', 0.3): np.float64(0.03884630768990876), ('b1', 0.35): np.float64(0.03928328400303363), ('b1', 0.4): np.float64(0.03938270502305204), ('b1', 0.5): np.float64(0.038268314571897756), ('b2', 0.15): np.float64(0.03686993222826123), ('b2', 0.2): np.float64(0.03787368396544102), ('b2', 0.25): np.float64(0.03779994566052241), ('b2', 0.3): np.float64(0.03836133443162376), ('b2', 0.35): np.float64(0.03857664502498326), ('b2', 0.4): np.float64(0.03864066088209385), ('b2', 0.5): np.float64(0.038696163350064386)}\nfit countlikes 256\ncountlikes 256 {('b1', 0.15): np.float64(0.03652877485207598), ('b1', 0.2): np.float64(0.037349092962168144), ('b1', 0.25): np.float64(0.03785824508770949), ('b1', 0.3): np.float64(0.038693113397033504), ('b1', 0.35): np.float64(0.03922769502633056), ('b1', 0.4): np.float64(0.03951803891885395), ('b1', 0.5): np.float64(0.03959999614879957), ('b2', 0.15): np.float64(0.036055576456927675), ('b2', 0.2): np.float64(0.03673429140086785), ('b2', 0.25): np.float64(0.03736641908335357), ('b2', 0.3): np.float64(0.03759662455221706), ('b2', 0.35): np.float64(0.03798347087849127), ('b2', 0.4): np.float64(0.038453492223783026), ('b2', 0.5): np.float64(0.03881258780768569)}\nfit recentlikes 128\nrecentlikes 128 {('b1', 0.15): np.float64(0.03915869964400322), ('b1', 0.2): np.float64(0.04121729542884333), ('b1', 0.25): np.float64(0.04177864322422572), ('b1', 0.3): np.float64(0.04242218678150414), ('b1', 0.35): np.float64(0.04278694932290147), ('b1', 0.4): np.float64(0.04254463853508151), ('b1', 0.5): np.float64(0.04255527341377238), ('b2', 0.15): np.float64(0.03778788065877486), ('b2', 0.2): np.float64(0.03931425609118336), ('b2', 0.25): np.float64(0.040443970168485555), ('b2', 0.3): np.float64(0.04096235842085064), ('b2', 0.35): np.float64(0.041835881634086705), ('b2', 0.4): np.float64(0.04179604572431314), ('b2', 0.5): np.float64(0.042208812504189626)}\n"
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      "/app/solution/ensemble_validate.py": {
        "type": "add",
        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nF=[]\nfor f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n q=np.load(O/f);F.append((q['U'],q['V']))\nsig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nweights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\nfor s in range(0,len(targets),256):\n ids=np.arange(s,min(s+256,len(targets)));Ps=[]\n for U,V in F[:2]:\n  P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8);Ps.append(P)\n for j,k0 in enumerate(ids):\n  u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n  for w in weights:\n   taste=(1-w)*Ps[0][j]+w*Ps[1][j]\n   for a in alphas:\n    ss=base+a*taste;ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(w,a)][u]=items[top].tolist()\nfor q in vals:vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\nprint(vals);print('BEST',max(vals,key=vals.get),max(vals.values()))\n"
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/ensemble_validate.py\n",
    "stderr": ""
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    "stdout": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n",
    "stderr": "",
    "aggregated_output": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n",
    "exit_code": 0,
    "duration": {
      "secs": 152,
      "nanos": 596509868
    },
    "formatted_output": "fit recent_l0 128\nrecent_l0 128 {('b1', 0.15): np.float64(0.039329255810957926), ('b1', 0.2): np.float64(0.040987289052751605), ('b1', 0.25): np.float64(0.041662310961473166), ('b1', 0.3): np.float64(0.042077566021905786), ('b1', 0.35): np.float64(0.041996165308023685), ('b1', 0.4): np.float64(0.041955480730998335), ('b1', 0.5): np.float64(0.042918611284117836), ('b2', 0.15): np.float64(0.03777655444131693), ('b2', 0.2): np.float64(0.039631013251331886), ('b2', 0.25): np.float64(0.040376180656570816), ('b2', 0.3): np.float64(0.040454574108418684), ('b2', 0.35): np.float64(0.040916094664041766), ('b2', 0.4): np.float64(0.04164378991634008), ('b2', 0.5): np.float64(0.04149766768254906)}\nfit recent_l05 128\nrecent_l05 128 {('b1', 0.15): np.float64(0.03931854839628444), ('b1', 0.2): np.float64(0.04121979007970184), ('b1', 0.25): np.float64(0.0418214637649455), ('b1', 0.3): np.float64(0.04268276359647525), ('b1', 0.35): np.float64(0.04275588699037706), ('b1', 0.4): np.float64(0.04280163750926149), ('b1', 0.5): np.float64(0.04324971563141847), ('b2', 0.15): np.float64(0.03805744838386286), ('b2', 0.2): np.float64(0.03959807965195016), ('b2', 0.25): np.float64(0.040577236996168504), ('b2', 0.3): np.float64(0.0410211124090298), ('b2', 0.35): np.float64(0.04127101524559316), ('b2', 0.4): np.float64(0.04174487092658602), ('b2', 0.5): np.float64(0.0421788897754844)}\nfit recent_l2 128\nrecent_l2 128 {('b1', 0.15): np.float64(0.03945972663397354), ('b1', 0.2): np.float64(0.041121182119279946), ('b1', 0.25): np.float64(0.0417487225861114), ('b1', 0.3): np.float64(0.042435416125234236), ('b1', 0.35): np.float64(0.0424805288495672), ('b1', 0.4): np.float64(0.04258591776821298), ('b1', 0.5): np.float64(0.04173484371341996), ('b2', 0.15): np.float64(0.037937012266124656), ('b2', 0.2): np.float64(0.03937048308813187), ('b2', 0.25): np.float64(0.04048124193504158), ('b2', 0.3): np.float64(0.041622557696842866), ('b2', 0.35): np.float64(0.04173001741836678), ('b2', 0.4): np.float64(0.04123685174798368), ('b2', 0.5): np.float64(0.04191584999350497)}\nfit recentlikes 256\nrecentlikes 256 {('b1', 0.15): np.float64(0.03829035632215435), ('b1', 0.2): np.float64(0.03978395457786281), ('b1', 0.25): np.float64(0.040817353525777735), ('b1', 0.3): np.float64(0.04176490088070027), ('b1', 0.35): np.float64(0.04252805847350084), ('b1', 0.4): np.float64(0.04310944753411426), ('b1', 0.5): np.float64(0.04261264684517896), ('b2', 0.15): np.float64(0.037046525565684045), ('b2', 0.2): np.float64(0.0387644290584636), ('b2', 0.25): np.float64(0.03955373663008294), ('b2', 0.3): np.float64(0.040880802318979455), ('b2', 0.35): np.float64(0.04130758848011612), ('b2', 0.4): np.float64(0.041867704046888346), ('b2', 0.5): np.float64(0.04228848861398548)}\n"
  }
}

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        "type": "update",
        "unified_diff": "@@ -6,3 +6,3 @@\n F=[]\n-for f in ['factors_recentlikes_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n+for f in ['factors_recent_l05_128.npz','factors_countlikes_256.npz','factors_countlikes_128.npz']:\n  q=np.load(O/f);F.append((q['U'],q['V']))\n@@ -14,3 +14,3 @@\n  if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\n-weights=[0,.15,.3,.5,.7,1];alphas=[.25,.3,.35,.4,.45,.5,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\n+weights=[0,.1,.2,.3,.4,.5];alphas=[.35,.4,.45,.5,.55,.6];vals={(w,a):[] for w in weights for a in alphas};pred={q:{} for q in vals}\n for s in range(0,len(targets),256):\n",
        "move_path": null
      },
      "/app/solution/compare_likes.py": {
        "type": "delete",
        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution')\ncat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets])\nX=sparse.load_npz(O/'train_catalog_counts.npz').astype(np.float32);R=sparse.load_npz(O/'train_catalog_recent.npz').astype(np.float32)\npref=pd.read_parquet(D/'validation_preference_state.parquet');pref=pref[pref.state.eq('liked')];rr=pref.uid.map(ui).to_numpy();cc=pref.item_id.map(ic).to_numpy();ok=~pd.isna(rr)&~pd.isna(cc);L=sparse.coo_matrix((np.ones(ok.sum(),np.float32),(rr[ok].astype(int),cc[ok].astype(int))),shape=X.shape).tocsr()\nsig=np.load(O/'global_signals.npz');Z={k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9) for k in sig.files};bases={'b1':Z['all7']+.75*Z['like7']+.1*Z['all3'],'b2':Z['all7']+.75*Z['like7']+.1*Z['all3']+Z['all10']}\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nalphas=[.15,.2,.25,.3,.35,.4,.5]\ndef fit_eval(A,name,k):\n print('fit',name,k,flush=True);sv=TruncatedSVD(n_components=k,n_iter=5,random_state=42);U=sv.fit_transform(A).astype(np.float32);V=sv.components_.astype(np.float32);np.savez_compressed(O/f'factors_{name}_{k}.npz',U=U,V=V)\n vals={(bn,a):[] for bn in bases for a in alphas};pred={(bn,a):{} for bn in bases for a in alphas}\n for s in range(0,len(targets),256):\n  ids=np.arange(s,min(s+256,len(targets)));P=U[tr[ids]]@V;P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n  for j,k0 in enumerate(ids):\n   u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n   for bn,b in bases.items():\n    for a in alphas:\n     ss=b+a*P[j];ss[~bits]=-np.inf;top=np.argpartition(ss,-10)[-10:];top=top[np.argsort(-ss[top])];pred[(bn,a)][u]=items[top].tolist()\n for q in vals: vals[q]=np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()])\n print(name,k,vals,flush=True)\nfor A,n,k in [(R,'recent_l0',128),(R+.5*L,'recent_l05',128),(R+2*L,'recent_l2',128),(R+L,'recentlikes',256)]:fit_eval(A,n,k)\n"
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    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/ensemble_validate.py\nD /app/solution/compare_likes.py\n",
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        "content": "from pathlib import Path\nimport sys,numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nsys.path.insert(0,'/task');from ranking_evaluation import ndcg_at_k\nD=Path('/task/data');O=Path('/app/solution');cat=pd.read_parquet(D/'candidate_catalog.parquet');items=cat.item_id.to_numpy(np.int64);ni=len(items);ic={int(i):c for c,i in enumerate(items)};targets=pd.read_parquet(D/'target_users.parquet').uid.to_numpy(np.int64)\nusers=np.unique(np.concatenate([pq.read_table(D/'train_listens.parquet',columns=['uid']).column(0).to_numpy(),targets])).astype(np.int64);ui={int(u):r for r,u in enumerate(users)};tr=np.array([ui[int(u)] for u in targets]);R=sparse.load_npz(O/'train_catalog_recent.npz')\nf=np.load(O/'factors_recent_l05_128.npz');U=f['U'];Un=U/(np.linalg.norm(U,axis=1,keepdims=True)+1e-8)\nsig=np.load(O/'global_signals.npz');zz=lambda k:(np.log1p(sig[k])-np.log1p(sig[k]).mean())/(np.log1p(sig[k]).std()+1e-9);base=zz('all7')+.75*zz('like7')+.1*zz('all3')\nz=np.load(D/'validation_eligible_candidates.npz');packed=z['packed_eligible'];us=z['uids'].astype(int);ur={u:r for r,u in enumerate(us)}\nli=pd.read_parquet(D/'val_listens.parquet');la=pd.read_parquet(D/'val_likes.parquet');rel=pd.concat([li[(li.is_organic==1)&(li.played_ratio_pct>=80)][['uid','item_id']].assign(g=1),la[la.is_organic==1][['uid','item_id']].assign(g=3)]).sort_values('g').drop_duplicates(['uid','item_id'],keep='last');gold={}\nfor u,i,g in rel.itertuples(index=False,name=None):\n r=ur.get(int(u));c=ic.get(int(i));\n if r is not None and c is not None and ((packed[r,c//8]>>(c%8))&1):gold.setdefault(int(u),{})[int(i)]=int(g)\nKs=[10,25,50,100];alphas=[.05,.1,.2,.3,.4,.5,1];pred={(k,a):{} for k in Ks for a in alphas}\nfor s in range(0,len(targets),128):\n ids=np.arange(s,min(s+128,len(targets)));S=Un[tr[ids]]@Un.T;S[np.arange(len(ids)),tr[ids]]=-np.inf; top=np.argpartition(S,-max(Ks),axis=1)[:,-max(Ks):]\n # order descending so first k are best\n top=np.take_along_axis(top,np.argsort(-np.take_along_axis(S,top,axis=1),axis=1),axis=1)\n for k in Ks:\n  tt=top[:,:k];rr=np.repeat(np.arange(len(ids)),k);cc=tt.ravel();vv=np.maximum(S[np.arange(len(ids))[:,None],tt],0).ravel();W=sparse.coo_matrix((vv,(rr,cc)),shape=(len(ids),len(users))).tocsr();P=(W@R).toarray();P=np.log1p(P);P=(P-P.mean(1,keepdims=True))/(P.std(1,keepdims=True)+1e-8)\n  for j,k0 in enumerate(ids):\n   u=int(targets[k0]);bits=np.unpackbits(packed[ur[u]],count=ni,bitorder='little').astype(bool)\n   for a in alphas:\n    ss=base+a*P[j];ss[~bits]=-np.inf;q=np.argpartition(ss,-10)[-10:];q=q[np.argsort(-ss[q])];pred[(k,a)][u]=items[q].tolist()\nfor q in pred:print(q,np.mean([ndcg_at_k(pred[q][u],g,10) for u,g in gold.items()]))\n"
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    "stdout": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n",
    "stderr": "",
    "aggregated_output": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n",
    "exit_code": 0,
    "duration": {
      "secs": 65,
      "nanos": 995183635
    },
    "formatted_output": "{(0.2, 0): np.float64(0.0390293099501517), (0.2, 0.1): np.float64(0.042780572491624196), (0.2, 0.2): np.float64(0.04450536992608238), (0.2, 0.3): np.float64(0.04524232615619524), (0.2, 0.4): np.float64(0.044956440239257274), (0.2, 0.5): np.float64(0.04439939491229238), (0.3, 0): np.float64(0.04144073439999226), (0.3, 0.1): np.float64(0.04447565650439314), (0.3, 0.2): np.float64(0.045424340350231796), (0.3, 0.3): np.float64(0.04549190640015004), (0.3, 0.4): np.float64(0.045525096951744067), (0.3, 0.5): np.float64(0.04435018324775258), (0.4, 0): np.float64(0.04280358115865442), (0.4, 0.1): np.float64(0.04539736378399722), (0.4, 0.2): np.float64(0.04587003658952277), (0.4, 0.3): np.float64(0.0458792467708756), (0.4, 0.4): np.float64(0.04514746128878674), (0.4, 0.5): np.float64(0.04366079516212517), (0.5, 0): np.float64(0.0438363380901075), (0.5, 0.1): np.float64(0.045168073267709496), (0.5, 0.2): np.float64(0.045762495724853476), (0.5, 0.3): np.float64(0.04553136838745063), (0.5, 0.4): np.float64(0.04467585883367421), (0.5, 0.5): np.float64(0.04334685090543959), (0.6, 0): np.float64(0.044278833874293294), (0.6, 0.1): np.float64(0.045111670097572436), (0.6, 0.2): np.float64(0.045559572371500746), (0.6, 0.3): np.float64(0.045007311718368345), (0.6, 0.4): np.float64(0.04428144433615525), (0.6, 0.5): np.float64(0.043276715193202954), (0.7, 0): np.float64(0.04365293508636747), (0.7, 0.1): np.float64(0.04491569645707642), (0.7, 0.2): np.float64(0.04513178654161386), (0.7, 0.3): np.float64(0.044124402200540966), (0.7, 0.4): np.float64(0.04378392804035195), (0.7, 0.5): np.float64(0.04291311143764874)}\nBEST (0.4, 0.3) 0.0458792467708756\n"
  }
}