Agent trace

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  "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d",
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    "cached_input_tokens": 12160,
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    "output_tokens": 104,
    "reasoning_output_tokens": 0,
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-8de8b08d-4d3e-4def-9b6e-22f66d604018",
    "process_id": "89756",
    "command": [
      "/bin/sh",
      "-lc",
      "ls -lh /task/data && cat /task/requirements.txt && sed -n '1,240p' /task/ranking_evaluation.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "list_files",
        "cmd": "ls -lh /task/data",
        "path": "data"
      },
      {
        "type": "read",
        "cmd": "cat /task/requirements.txt",
        "name": "requirements.txt",
        "path": "/task/requirements.txt"
      },
      {
        "type": "read",
        "cmd": "sed -n '1,240p' /task/ranking_evaluation.py",
        "name": "ranking_evaluation.py",
        "path": "/task/ranking_evaluation.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\"\"\"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",
    "stderr": "",
    "aggregated_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\"\"\"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",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3640
    },
    "formatted_output": "total 340M\n-r--r--r-- 1 root root 342K Sep 23 05:14 candidate_catalog.parquet\n-r--r--r-- 1 root root  83K Sep 23 05:14 target_users.parquet\n-r--r--r-- 1 root root 7.4M Sep 23 05:14 test_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 test_preference_state.parquet\n-r--r--r-- 1 root root 838K Sep 23 05:14 train_dislikes.parquet\n-r--r--r-- 1 root root 5.2M Sep 23 05:14 train_likes.parquet\n-r--r--r-- 1 root root 305M Sep 23 05:14 train_listens.parquet\n-r--r--r-- 1 root root 153K Sep 23 05:14 train_undislikes.parquet\n-r--r--r-- 1 root root 1.8M Sep 23 05:14 train_unlikes.parquet\n-r--r--r-- 1 root root  36K Sep 23 05:14 val_dislikes.parquet\n-r--r--r-- 1 root root 292K Sep 23 05:14 val_likes.parquet\n-r--r--r-- 1 root root 9.5M Sep 23 05:14 val_listens.parquet\n-r--r--r-- 1 root root 7.7K Sep 23 05:14 val_undislikes.parquet\n-r--r--r-- 1 root root  71K Sep 23 05:14 val_unlikes.parquet\n-r--r--r-- 1 root root 7.3M Sep 23 05:14 validation_eligible_candidates.npz\n-r--r--r-- 1 root root 1.1M Sep 23 05:15 validation_preference_state.parquet\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\"\"\"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"
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-95e9af6a-3b17-440b-bd82-39f409cf1dac",
    "process_id": "30470",
    "command": [
      "/bin/sh",
      "-lc",
      "mkdir -p /app/solution && ls -la /app && sed -n '1,240p' /task/evaluate_public.py && python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "mkdir -p /app/solution && ls -la /app && sed -n '1,240p' /task/evaluate_public.py && python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
    "stderr": "",
    "aggregated_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 3790
    },
    "formatted_output": "total 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:20 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 solution\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()\nTraceback (most recent call last):\n  File \"<stdin>\", line 1, in <module>\nModuleNotFoundError: No module named 'pyarrow'\n"
  }
}

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      "-lc",
      "which python python3; ls -la /opt /app/solution; printenv | sort"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "which python python3; ls -la /opt /app/solution; printenv | sort"
      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\n/app/solution:\ntotal 8\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:18 .\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:18 ..\n\n/opt:\ntotal 16\ndrwxr-xr-x 1 root root 4096 Sep 23 05:18 .\ndrwxr-xr-x 1 root root 4096 Sep 23 05:20 ..\ndrwxr-xr-x 4 root root 4096 Sep 23 05:18 codex-home\ndr-xr-xr-x 1 root root 4096 Sep 23 05:18 task-venv\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCODEX_THREAD_ID=01a0ccb5-7d89-7be3-86b0-95a8d1553f3d\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=e8152c5dadea\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n",
    "stderr": "",
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    "exit_code": 0,
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      "nanos": 4690
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    "process_id": "18627",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq,numpy as np,os\nfor name in ['train_listens','train_likes','train_dislikes','val_listens','target_users','candidate_catalog','validation_preference_state']:\n f=pq.ParquetFile('/task/data/'+name+'.parquet'); print(name,f.metadata.num_rows,f.schema);print(f.read_row_group(0).to_pandas().head())\nz=np.load('/task/data/validation_eligible_candidates.npz');print([(k,z[k].shape,z[k].dtype) for k in z.files]);print('cpus',os.cpu_count());print(open('/proc/meminfo').read()[:300])\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x761b3034e040>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x761b39e91f80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\ntrain_dislikes 101075 <pyarrow._parquet.ParquetSchema object at 0x761b108fdd00>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100    3087560  9170134           1\n1  100    3936560  8661238           1\n2  100   20432825  4927727           1\n3  400    2980800   399811           1\n4  400    3157520  1575108           1\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x761b10748240>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fb900>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\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\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fbd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\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\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x761b3019fd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal:       15988968 kB\nMemFree:         5620740 kB\nMemAvailable:   14824416 kB\nBuffers:          176408 kB\nCached:          8945184 kB\nSwapCached:            0 kB\nActive:           709972 kB\nInactive:        8893384 kB\nActive(anon):     508204 kB\nInactive(anon):        0 kB\nActive(file):     20\n",
    "stderr": "",
    "aggregated_output": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x761b3034e040>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x761b39e91f80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\ntrain_dislikes 101075 <pyarrow._parquet.ParquetSchema object at 0x761b108fdd00>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100    3087560  9170134           1\n1  100    3936560  8661238           1\n2  100   20432825  4927727           1\n3  400    2980800   399811           1\n4  400    3157520  1575108           1\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x761b10748240>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fb900>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\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\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fbd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\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\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x761b3019fd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal:       15988968 kB\nMemFree:         5620740 kB\nMemAvailable:   14824416 kB\nBuffers:          176408 kB\nCached:          8945184 kB\nSwapCached:            0 kB\nActive:           709972 kB\nInactive:        8893384 kB\nActive(anon):     508204 kB\nInactive(anon):        0 kB\nActive(file):     20\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 896016804
    },
    "formatted_output": "train_listens 43484159 <pyarrow._parquet.ParquetSchema object at 0x761b3034e040>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100      39420  8326270           0               100                   170\n1  100      39420  1441281           0               100                   105\n2  100      39625   286361           0               100                   185\n3  100      40110   732449           0               100                   240\n4  100      40360  3397170           0                46                   130\ntrain_likes 814831 <pyarrow._parquet.ParquetSchema object at 0x761b39e91f80>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100      44755   732449           1\n1  100    1155860  6568592           0\n2  100    1259125  5411243           1\n3  100    1260005  7371186           0\n4  100    1263935  4943655           0\ntrain_dislikes 101075 <pyarrow._parquet.ParquetSchema object at 0x761b108fdd00>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic\n0  100    3087560  9170134           1\n1  100    3936560  8661238           1\n2  100   20432825  4927727           1\n3  400    2980800   399811           1\n4  400    3157520  1575108           1\nval_listens 1501697 <pyarrow._parquet.ParquetSchema object at 0x761b10748240>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));\n  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));\n  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));\n}\n\n   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds\n0  100   24943430  9321269           0               100                   195\n1  100   24943605  5452713           0               100                   175\n2  100   24943765  6448777           0               100                   160\n3  100   24951960  6008056           0                 8                   175\n4  100   25246895  7968587           0                45                   565\ntarget_users 8456 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fb900>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional double field_id=-1 recent_positive_listens;\n  optional int64 field_id=-1 history_positive_listens;\n}\n\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\ncandidate_catalog 30000 <pyarrow._parquet.ParquetSchema object at 0x761b0f7fbd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional int64 field_id=-1 positive_listens;\n  optional int64 field_id=-1 organic_positive_listens (Int(bitWidth=64, isSigned=false));\n  optional int32 field_id=-1 popularity_rank;\n}\n\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\nvalidation_preference_state 469124 <pyarrow._parquet.ParquetSchema object at 0x761b3019fd40>\nrequired group field_id=-1 schema {\n  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));\n  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));\n  optional binary field_id=-1 state (String);\n}\n\n   uid  item_id  state\n0  100   410868  liked\n1  100   732449  liked\n2  100   982903  liked\n3  100  1730273  liked\n4  100  2621343  liked\n[('uids', (8456,), dtype('uint32')), ('item_ids', (30000,), dtype('uint32')), ('packed_eligible', (8456, 3750), dtype('uint8'))]\ncpus 8\nMemTotal:       15988968 kB\nMemFree:         5620740 kB\nMemAvailable:   14824416 kB\nBuffers:          176408 kB\nCached:          8945184 kB\nSwapCached:            0 kB\nActive:           709972 kB\nInactive:        8893384 kB\nActive(anon):     508204 kB\nInactive(anon):        0 kB\nActive(file):     20\n"
  }
}

{
  "type": "token_usage_record",
  "thread_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d",
  "turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994",
  "session_id": "01a0ccb5-7d89-7be3-86b0-95a8d1553f3d",
  "root_turn_id": "01a0ccb5-7d9c-7932-849e-5c113cbbd994",
  "response_id": "resp_0b6a1f754489cb4e016ab361c54a4487d29a54e2ecc6a1ece0",
  "usage": {
    "input_tokens": 23309,
    "cached_input_tokens": 21120,
    "cache_write_input_tokens": 0,
    "output_tokens": 741,
    "reasoning_output_tokens": 72,
    "total_tokens": 24050
  },
  "turn_token_usage": {
    "input_tokens": 99411,
    "cached_input_tokens": 87552,
    "cache_write_input_tokens": 0,
    "output_tokens": 1311,
    "reasoning_output_tokens": 98,
    "total_tokens": 100722
  },
  "thread_token_usage": {
    "input_tokens": 99411,
    "cached_input_tokens": 87552,
    "cache_write_input_tokens": 0,
    "output_tokens": 1311,
    "reasoning_output_tokens": 98,
    "total_tokens": 100722
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-cab017c8-fbb5-4d7e-be2c-b32531711ee2",
    "process_id": "35239",
    "command": [
      "/bin/sh",
      "-lc",
      "cat > /app/solution/inspect.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nz=np.load('/task/data/validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little');print('eligible',m.sum(1).min(),np.percentile(m.sum(1),[25,50,75,100]),flush=True)\nui=pd.Index(u);ii=pd.Index(it)\nseen=np.zeros(m.shape,np.uint8); pos=seen.copy();recent=seen.copy();last=np.zeros(m.shape,np.int32)\nfor batch in pq.ParquetFile('/task/data/train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];v=d.played_ratio_pct.values[ok]>=50;t=d.timestamp.values[ok];seen[r,c]=1;pos[r[v],c[v]]=1;v&=t>24786800-90*86400;recent[r[v],c[v]]=1;np.maximum.at(last,(r,c),t)\nprint('seen eligible',np.sum(seen*m),'positive eligible',np.sum(pos*m),'recent eligible',np.sum(recent*m),flush=True)\nfor name in ['validation_preference_state','test_preference_state']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);d=d[ok];print(name,d.groupby('state').size(), 'eligible',d[m[r[ok],c[ok]]>0].groupby('state').size(),flush=True)\nfor name in ['val_listens','val_likes']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');d=d[d.is_organic==1];\n if 'played_ratio_pct' in d:d=d[d.played_ratio_pct>=80]\n d=d.drop_duplicates(['uid','item_id']);r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=m[r,c]>0;r=r[ok];c=c[ok];print(name,'relevant',len(r),'seen',seen[r,c].mean(),'positive',pos[r,c].mean(),'recent',recent[r,c].mean(),'age',np.percentile((24786800-last[r,c])/86400,[0,25,50,75,100]),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat > /app/solution/inspect.py <<'PY'\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nz=np.load('/task/data/validation_eligible_candidates.npz');u=z['uids'];it=z['item_ids'];m=np.unpackbits(z['packed_eligible'],axis=1,count=len(it),bitorder='little');print('eligible',m.sum(1).min(),np.percentile(m.sum(1),[25,50,75,100]),flush=True)\nui=pd.Index(u);ii=pd.Index(it)\nseen=np.zeros(m.shape,np.uint8); pos=seen.copy();recent=seen.copy();last=np.zeros(m.shape,np.int32)\nfor batch in pq.ParquetFile('/task/data/train_listens.parquet').iter_batches(batch_size=1000000):\n d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];v=d.played_ratio_pct.values[ok]>=50;t=d.timestamp.values[ok];seen[r,c]=1;pos[r[v],c[v]]=1;v&=t>24786800-90*86400;recent[r[v],c[v]]=1;np.maximum.at(last,(r,c),t)\nprint('seen eligible',np.sum(seen*m),'positive eligible',np.sum(pos*m),'recent eligible',np.sum(recent*m),flush=True)\nfor name in ['validation_preference_state','test_preference_state']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);d=d[ok];print(name,d.groupby('state').size(), 'eligible',d[m[r[ok],c[ok]]>0].groupby('state').size(),flush=True)\nfor name in ['val_listens','val_likes']:\n d=pd.read_parquet('/task/data/'+name+'.parquet');d=d[d.is_organic==1];\n if 'played_ratio_pct' in d:d=d[d.played_ratio_pct>=80]\n d=d.drop_duplicates(['uid','item_id']);r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=m[r,c]>0;r=r[ok];c=c[ok];print(name,'relevant',len(r),'seen',seen[r,c].mean(),'positive',pos[r,c].mean(),'recent',recent[r,c].mean(),'age',np.percentile((24786800-last[r,c])/86400,[0,25,50,75,100]),flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/inspect.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "eligible 24657 [28682.75 29242.   29644.   30000.  ]\nTraceback (most recent call last):\n  File \"/app/solution/inspect.py\", line 1, in <module>\n    import numpy as np,pandas as pd,pyarrow.parquet as pq\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in <module>\n    from pandas._config import (\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in <module>\n    from pandas._config import config\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in <module>\n    from pandas.util._exceptions import find_stack_level\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in <module>\n    import inspect\n  File \"/app/solution/inspect.py\", line 3, in <module>\n    ui=pd.Index(u);ii=pd.Index(it)\n       ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n",
    "stderr": "",
    "aggregated_output": "eligible 24657 [28682.75 29242.   29644.   30000.  ]\nTraceback (most recent call last):\n  File \"/app/solution/inspect.py\", line 1, in <module>\n    import numpy as np,pandas as pd,pyarrow.parquet as pq\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in <module>\n    from pandas._config import (\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in <module>\n    from pandas._config import config\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in <module>\n    from pandas.util._exceptions import find_stack_level\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in <module>\n    import inspect\n  File \"/app/solution/inspect.py\", line 3, in <module>\n    ui=pd.Index(u);ii=pd.Index(it)\n       ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n",
    "exit_code": 1,
    "duration": {
      "secs": 0,
      "nanos": 441294574
    },
    "formatted_output": "eligible 24657 [28682.75 29242.   29644.   30000.  ]\nTraceback (most recent call last):\n  File \"/app/solution/inspect.py\", line 1, in <module>\n    import numpy as np,pandas as pd,pyarrow.parquet as pq\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/__init__.py\", line 37, in <module>\n    from pandas._config import (\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/__init__.py\", line 20, in <module>\n    from pandas._config import config\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/_config/config.py\", line 72, in <module>\n    from pandas.util._exceptions import find_stack_level\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/util/_exceptions.py\", line 4, in <module>\n    import inspect\n  File \"/app/solution/inspect.py\", line 3, in <module>\n    ui=pd.Index(u);ii=pd.Index(it)\n       ^^^^^^^^\nAttributeError: partially initialized module 'pandas' has no attribute 'Index' (most likely due to a circular import)\n"
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      "cat > /app/solution/model.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,time,json,sys\nfrom scipy import sparse,linalg\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');ids=np.load(P/'ids.npz');uids=ids['uids'];items=ids['items'];targets=ids['targets'];ui=pd.Index(uids);ii=pd.Index(items);tr=ui.get_indexer(targets);N=len(uids);M=len(items)\ndef load(k,stage='validation'):\n x=sparse.load_npz(P/f'train_{k}.npz')\n if stage=='test':\n  if k in ['recent','short','strong','skip']:x*=np.exp(-(7+1800/86400)/{'recent':60,'short':14,'strong':90,'skip':60}[k])\n  x+=sparse.load_npz(P/f'val_{k}.npz')\n return x\nclass Eval:\n def __init__(self,stage='validation'):\n  self.stage=stage;z=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(z['uids'],targets);self.packed=z['packed_eligible'];self.discount=1/np.log2(np.arange(2,12));self.gold=sparse.csr_matrix((len(targets),M),dtype=np.float32);self.idcg=np.ones(len(targets));self.active=np.zeros(len(targets),bool)\n  if stage=='validation':\n   gg=[]\n   for name in ['listens','likes']:\n    d=pd.read_parquet(D/f'val_{name}.parquet');d=d[d.is_organic==1]\n    if name=='listens':d=d[d.played_ratio_pct>=80]\n    r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=((self.packed[r,c//8]>>(c%8))&1)>0;r=r[ok];c=c[ok];gg.append(pd.DataFrame({'r':r,'c':c,'g':1 if name=='listens' else 7}))\n   g=pd.concat(gg).groupby(['r','c']).g.max().reset_index();self.gold=sparse.csr_matrix((g.g,(g.r,g.c)),shape=(len(targets),M),dtype=np.float32)\n   for r in range(len(targets)):\n    v=self.gold.data[self.gold.indptr[r]:self.gold.indptr[r+1]]\n    if len(v):self.active[r]=True;self.idcg[r]=(np.sort(v)[::-1][:10]*self.discount[:min(10,len(v))]).sum()\n def mask(self,start,end):return np.unpackbits(self.packed[start:end],axis=1,count=M,bitorder='little').astype(bool)\n def run(self,scores,name,save=True):\n  ranks=[];metric=[]\n  for st in range(0,len(targets),128):\n   en=min(st+128,len(targets));s=np.asarray(scores[st:en],dtype=np.float32).copy();s[~self.mask(st,en)]=-np.inf;ix=np.argpartition(s,-100,axis=1)[:,-100:];v=np.take_along_axis(s,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1);ranks.append(ix)\n   g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\n   metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n  result=float(np.asarray(metric)[self.active].mean()) if self.active.any() else 0.;print(name,'NDCG',result,flush=True)\n  if save:\n   ranks=np.concatenate(ranks);pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranks.ravel()],'rank':np.tile(np.arange(1,101),len(targets))}).to_parquet(P/f'{name}.parquet',index=False)\n  return result\n\ndef ease(stage,variant,reg):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='log':x=load('count',stage);x.data=np.log1p(x.data)\n if variant=='recent':x=load('recent',stage);x.data=np.sqrt(x.data)\n if variant=='strong':x=load('strong',stage);x.data=np.log1p(x.data)\n if variant=='hybrid':\n  x.data[:]=0.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=load('likes',stage);l.data[:]=1;x+=l\n if variant=='liked':\n  l=load('likes',stage);l.data[:]=1;x+=2*l\n print('matrix',variant,x.shape,x.nnz,'time',time.time()-t,flush=True)\n # A user-space Woodbury solve yields the exact EASE unseen-item scores.\n k=([email protected]).toarray(order='F');k.flat[::N+1]+=reg;print('kernel',time.time()-t,flush=True)\n chol=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False);print('chol',time.time()-t,flush=True)\n name=f'{stage}_ease_{variant}_{reg}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n  en=min(M,st+1000);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(chol,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':ease(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\ncat /app/solution/prepare.log"
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        "cmd": "cat > /app/solution/model.py <<'PY'\nimport os\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,time,json,sys\nfrom scipy import sparse,linalg\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data');ids=np.load(P/'ids.npz');uids=ids['uids'];items=ids['items'];targets=ids['targets'];ui=pd.Index(uids);ii=pd.Index(items);tr=ui.get_indexer(targets);N=len(uids);M=len(items)\ndef load(k,stage='validation'):\n x=sparse.load_npz(P/f'train_{k}.npz')\n if stage=='test':\n  if k in ['recent','short','strong','skip']:x*=np.exp(-(7+1800/86400)/{'recent':60,'short':14,'strong':90,'skip':60}[k])\n  x+=sparse.load_npz(P/f'val_{k}.npz')\n return x\nclass Eval:\n def __init__(self,stage='validation'):\n  self.stage=stage;z=np.load(D/f'{stage}_eligible_candidates.npz');assert np.array_equal(z['uids'],targets);self.packed=z['packed_eligible'];self.discount=1/np.log2(np.arange(2,12));self.gold=sparse.csr_matrix((len(targets),M),dtype=np.float32);self.idcg=np.ones(len(targets));self.active=np.zeros(len(targets),bool)\n  if stage=='validation':\n   gg=[]\n   for name in ['listens','likes']:\n    d=pd.read_parquet(D/f'val_{name}.parquet');d=d[d.is_organic==1]\n    if name=='listens':d=d[d.played_ratio_pct>=80]\n    r=pd.Index(targets).get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];ok=((self.packed[r,c//8]>>(c%8))&1)>0;r=r[ok];c=c[ok];gg.append(pd.DataFrame({'r':r,'c':c,'g':1 if name=='listens' else 7}))\n   g=pd.concat(gg).groupby(['r','c']).g.max().reset_index();self.gold=sparse.csr_matrix((g.g,(g.r,g.c)),shape=(len(targets),M),dtype=np.float32)\n   for r in range(len(targets)):\n    v=self.gold.data[self.gold.indptr[r]:self.gold.indptr[r+1]]\n    if len(v):self.active[r]=True;self.idcg[r]=(np.sort(v)[::-1][:10]*self.discount[:min(10,len(v))]).sum()\n def mask(self,start,end):return np.unpackbits(self.packed[start:end],axis=1,count=M,bitorder='little').astype(bool)\n def run(self,scores,name,save=True):\n  ranks=[];metric=[]\n  for st in range(0,len(targets),128):\n   en=min(st+128,len(targets));s=np.asarray(scores[st:en],dtype=np.float32).copy();s[~self.mask(st,en)]=-np.inf;ix=np.argpartition(s,-100,axis=1)[:,-100:];v=np.take_along_axis(s,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-v,axis=1),axis=1);ranks.append(ix)\n   g=self.gold[np.arange(st,en)[:,None],ix[:,:10]].toarray() if False else np.asarray(self.gold[np.arange(st,en)[:,None],ix[:,:10]])\n   metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n  result=float(np.asarray(metric)[self.active].mean()) if self.active.any() else 0.;print(name,'NDCG',result,flush=True)\n  if save:\n   ranks=np.concatenate(ranks);pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranks.ravel()],'rank':np.tile(np.arange(1,101),len(targets))}).to_parquet(P/f'{name}.parquet',index=False)\n  return result\n\ndef ease(stage,variant,reg):\n t=time.time();x=load('count',stage);x.data[:]=1\n if variant=='log':x=load('count',stage);x.data=np.log1p(x.data)\n if variant=='recent':x=load('recent',stage);x.data=np.sqrt(x.data)\n if variant=='strong':x=load('strong',stage);x.data=np.log1p(x.data)\n if variant=='hybrid':\n  x.data[:]=0.5;r=load('recent',stage);r.data=np.minimum(r.data,1);x+=r;l=load('likes',stage);l.data[:]=1;x+=l\n if variant=='liked':\n  l=load('likes',stage);l.data[:]=1;x+=2*l\n print('matrix',variant,x.shape,x.nnz,'time',time.time()-t,flush=True)\n # A user-space Woodbury solve yields the exact EASE unseen-item scores.\n k=([email protected]).toarray(order='F');k.flat[::N+1]+=reg;print('kernel',time.time()-t,flush=True)\n chol=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False);print('chol',time.time()-t,flush=True)\n name=f'{stage}_ease_{variant}_{reg}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n  en=min(M,st+1000);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(chol,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':ease(sys.argv[1],sys.argv[2],float(sys.argv[3]))\nPY\ncat /app/solution/prepare.log"
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    "stdout": "users 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n",
    "stderr": "",
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    "exit_code": 0,
    "duration": {
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      "nanos": 2840
    },
    "formatted_output": "users 9238\ntrain done 23.728557109832764 [('count', 5564926), ('organic', 2060124), ('recent', 5564926), ('short', 5564926), ('strong', 5337972), ('skip', 4567895)]\nval done 25.296415090560913 [('count', 485745), ('organic', 184980), ('recent', 485745), ('short', 485745), ('strong', 459775), ('skip', 276778)]\n"
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    "parsed_cmd": [
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        "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation.parquet"
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    "stdout": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n",
    "exit_code": 1,
    "duration": {
      "secs": 1,
      "nanos": 247554139
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/evaluate_public.py\", line 157, in <module>\n    main()\n  File \"/task/evaluate_public.py\", line 152, in main\n    ranked = load_submission(path, eligibility, targets)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/task/evaluate_public.py\", line 113, in load_submission\n    frame = validate_ranking(read_ranking(path), targets)\n                             ^^^^^^^^^^^^^^^^^^\n  File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n    return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n           ^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n    return impl.read(\n           ^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n    path_or_handle, handles, filesystem = _get_path_or_handle(\n                                          ^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n    handles = get_handle(\n              ^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n    handle = open(handle, ioargs.mode)\n             ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation.parquet'\n"
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      "cat > /app/solution/prepare.py <<'PY'\nimport os,time\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data'); t=time.time()\nz=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; targets=z['uids']\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());np.savez(P/'ids.npz',uids=uids,items=items,targets=targets);print('users',len(uids),flush=True)\nui=pd.Index(uids);ii=pd.Index(items);shape=(len(uids),len(items))\nfor stage in ['train','val']:\n mats={k:sparse.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','short','strong','skip']}\n cutoff=24786800 if stage=='train' else 24786800+1800+7*86400\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n  d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];d=d[ok];age=(cutoff-d.timestamp.to_numpy(dtype=np.float64))/86400;pos=d.played_ratio_pct.values>=50;org=d.is_organic.values==1;strong=d.played_ratio_pct.values>=80\n  vals={'count':pos,'organic':pos*org,'recent':pos*np.exp(-age/60),'short':pos*np.exp(-age/14),'strong':strong*(0.3+0.7*org)*np.exp(-age/90),'skip':(~pos)*np.exp(-age/60)}\n  for k,v in vals.items():\n   v=v.astype(np.float32);nz=v>0;mats[k]+=sparse.csr_matrix((v[nz],(r[nz],c[nz])),shape=shape)\n for k,x in mats.items():sparse.save_npz(P/f'{stage}_{k}.npz',x)\n d=pd.read_parquet(D/f'{stage}_likes.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=shape);sparse.save_npz(P/f'{stage}_likes.npz',x)\n print(stage,'done',time.time()-t,[(k,x.nnz) for k,x in mats.items()],flush=True)\n# Always keep an eligible final baseline, using observed train + val organic popularity.\npop=np.asarray((sparse.load_npz(P/'train_organic.npz')+4*sparse.load_npz(P/'val_organic.npz')).sum(axis=0)).ravel()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz');rows=[];scores=pop if stage=='test' else np.asarray(sparse.load_npz(P/'train_organic.npz').sum(axis=0)).ravel()\n for r,u in enumerate(z['uids']):\n  eligible=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(eligible,scores,-np.inf); ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix],kind='stable')];rows.append(ix)\n out=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[np.asarray(rows).ravel()],'rank':np.tile(np.arange(1,101),len(rows))});out.to_parquet(P/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('baseline saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
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        "cmd": "cat > /app/solution/prepare.py <<'PY'\nimport os,time\nos.environ.setdefault('OPENBLAS_NUM_THREADS','4')\nimport numpy as np,pandas as pd,pyarrow.parquet as pq\nfrom scipy import sparse\nfrom pathlib import Path\nP=Path('/app/solution');D=Path('/task/data'); t=time.time()\nz=np.load(D/'validation_eligible_candidates.npz');items=z['item_ids']; targets=z['uids']\nuids=np.sort(pd.read_parquet(D/'train_listens.parquet',columns=['uid']).uid.unique());np.savez(P/'ids.npz',uids=uids,items=items,targets=targets);print('users',len(uids),flush=True)\nui=pd.Index(uids);ii=pd.Index(items);shape=(len(uids),len(items))\nfor stage in ['train','val']:\n mats={k:sparse.csr_matrix(shape,dtype=np.float32) for k in ['count','organic','recent','short','strong','skip']}\n cutoff=24786800 if stage=='train' else 24786800+1800+7*86400\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n  d=batch.to_pandas();r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);r=r[ok];c=c[ok];d=d[ok];age=(cutoff-d.timestamp.to_numpy(dtype=np.float64))/86400;pos=d.played_ratio_pct.values>=50;org=d.is_organic.values==1;strong=d.played_ratio_pct.values>=80\n  vals={'count':pos,'organic':pos*org,'recent':pos*np.exp(-age/60),'short':pos*np.exp(-age/14),'strong':strong*(0.3+0.7*org)*np.exp(-age/90),'skip':(~pos)*np.exp(-age/60)}\n  for k,v in vals.items():\n   v=v.astype(np.float32);nz=v>0;mats[k]+=sparse.csr_matrix((v[nz],(r[nz],c[nz])),shape=shape)\n for k,x in mats.items():sparse.save_npz(P/f'{stage}_{k}.npz',x)\n d=pd.read_parquet(D/f'{stage}_likes.parquet');r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(r[ok],c[ok])),shape=shape);sparse.save_npz(P/f'{stage}_likes.npz',x)\n print(stage,'done',time.time()-t,[(k,x.nnz) for k,x in mats.items()],flush=True)\n# Always keep an eligible final baseline, using observed train + val organic popularity.\npop=np.asarray((sparse.load_npz(P/'train_organic.npz')+4*sparse.load_npz(P/'val_organic.npz')).sum(axis=0)).ravel()\nfor stage in ['validation','test']:\n z=np.load(D/f'{stage}_eligible_candidates.npz');rows=[];scores=pop if stage=='test' else np.asarray(sparse.load_npz(P/'train_organic.npz').sum(axis=0)).ravel()\n for r,u in enumerate(z['uids']):\n  eligible=np.unpackbits(z['packed_eligible'][r],count=len(items),bitorder='little').astype(bool);s=np.where(eligible,scores,-np.inf); ix=np.argpartition(s,-100)[-100:];ix=ix[np.argsort(-s[ix],kind='stable')];rows.append(ix)\n out=pd.DataFrame({'uid':np.repeat(z['uids'],100),'item_id':items[np.asarray(rows).ravel()],'rank':np.tile(np.arange(1,101),len(rows))});out.to_parquet(P/('submission.parquet' if stage=='test' else 'validation.parquet'),index=False)\nprint('baseline saved',time.time()-t,flush=True)\nPY\nOPENBLAS_NUM_THREADS=4 /opt/task-venv/bin/python /app/solution/prepare.py > /app/solution/prepare.log 2>&1"
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    "exit_code": 143,
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    "stdout": "  File \"/app/solution/model.py\", line 54, in ease\n    out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\n                                                         ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 32, in run\n    metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n                  ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n    return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\n",
    "stderr": "",
    "aggregated_output": "  File \"/app/solution/model.py\", line 54, in ease\n    out.flush();print('solved',time.time()-t,flush=True);Eval(stage).run(out,name)\n                                                         ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/app/solution/model.py\", line 32, in run\n    metric.extend((g*self.discount).sum(1)/self.idcg[st:en])\n                  ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/_core/_methods.py\", line 52, in _sum\n    return umr_sum(a, axis, dtype, out, keepdims, initial, where)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nnumpy.exceptions.AxisError: axis 1 is out of bounds for array of dimension 1\n-rw-r--r-- 1 agent agent 692K Sep 23 05:24 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent 685K Sep 23 05:24 /app/solution/validation.parquet\n",
    "exit_code": 0,
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      "cat > /app/solution/rerank.py <<'PY'\nfrom model import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\n\ndef features(stage,names,K=500):\n e=Eval(stage);models=[np.load(P/(stage+'_'+n+'.npy'),mmap_mode='r') for n in names]\n # Retrieval union: 200 from the strongest organic model, 100 from other models.\n lists=[];stats=[];us=[]\n for k in ['count','organic','recent','short','strong','likes','skip']:\n  x=load(k,stage);stats.append(np.asarray(x.sum(0)).ravel());us.append(np.asarray(x[tr].sum(1)).ravel())\n ip=np.stack(stats,axis=1);up=np.stack(us,axis=1)\n itemfeat=np.concatenate([np.log1p(ip),np.log((ip[:,[1,2,3,4,5,6]]+1)/(ip[:,[0,0,2,0,0,0]]+1))],axis=1)\n userfeat=np.concatenate([np.log1p(up),np.log((up[:,[1,2,3,4,5,6]]+1)/(up[:,[0,0,2,0,0,0]]+1))],axis=1)\n popscore=ip[:,3]+.05*ip[:,1];po=np.argsort(-popscore)\n F=len(names)*3+itemfeat.shape[1]+userfeat.shape[1]\n X=np.lib.format.open_memmap(P/f'{stage}_features.npy',mode='w+',dtype=np.float32,shape=(len(targets),K,F));pool=np.empty((len(targets),K),np.int32);Y=np.empty((len(targets),K),np.float32)\n for st in range(0,len(targets),64):\n  en=min(st+64,len(targets));mask=e.mask(st,en);ss=[];cand=[]\n  for j,mo in enumerate(models):\n   s=np.array(mo[st:en]);s[~mask]=-np.inf;ix=np.argpartition(s,-K,axis=1)[:,-K:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1);cand.append(ix);ss.append(s)\n  for row in range(en-st):\n   v=np.concatenate([cand[0][row,:200]]+[a[row,:100] for a in cand[1:]]+[po[mask[row,po]][:100],cand[0][row]])\n   _,ix=np.unique(v,return_index=True);pool[st+row]=v[np.sort(ix)[:K]]\n  q=pool[st:en];f=[]\n  for s in ss:\n   v=np.take_along_axis(s,q,axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n  X[st:en]=np.concatenate([np.stack(f,axis=2),itemfeat[q],np.broadcast_to(userfeat[st:en,None,:],(en-st,K,userfeat.shape[1]))],axis=2)\n  Y[st:en]=e.gold[np.arange(st,en)[:,None],q].toarray()\n X.flush();np.savez(P/f'{stage}_pool.npz',pool=pool,Y=Y,names=names);print('features',stage,X.shape,'retrieved positives',np.sum(Y>0),flush=True);return X,pool,Y,e\n\ndef metric(pred,Y,e,rows):\n ix=np.argsort(-pred,axis=1)[:,:10];scores=(np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg;return scores[rows&e.active].mean()\n\ndef train(names):\n X,pool,Y,e=features('validation',names);rng=np.random.default_rng(2026);fold=rng.random(len(targets))<.5;K=X.shape[1];sample=fold&e.active;hold=~fold\n # Report the unchanged retrieval model on exactly the held-out users.\n print('baseline holdout',metric(X[:,:,1],Y,e,hold),flush=True)\n w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\n best=(-1,None)\n for loss in ['poisson','squared_error']:\n  model=HistGradientBoostingRegressor(loss=loss,learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n  t=time.time();model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape)\n  for blend in [0,.25,.5,.75,1]:\n   # Scale baseline into the same positive prediction range per user.\n   base=np.maximum(X[:,:,1],0);base/=np.maximum(base.max(1,keepdims=True),1e-6);pp=np.maximum(pred,0);pp/=np.maximum(pp.max(1,keepdims=True),1e-6);score=metric(blend*pp+(1-blend)*base,Y,e,hold)\n   print('rerank',loss,'blend',blend,'holdout',score,'seconds',time.time()-t,flush=True)\n   if score>best[0]:best=(score,dict(loss=loss,blend=blend))\n  joblib.dump(model,P/f'rerank_{loss}_holdout.joblib')\n print('best',best,flush=True);json.dump({'names':names,'config':best[1],'holdout_ndcg':best[0]},open(P/'rerank_config.json','w'))\nif __name__=='__main__':train(sys.argv[1:])\nPY\ntail /app/solution/asym.log; tail /app/solution/sweep.log; tail /app/solution/strong_sweep.log"
    ],
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    "parsed_cmd": [
      {
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        "cmd": "cat > /app/solution/rerank.py <<'PY'\nfrom model import *\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nimport joblib\n\ndef features(stage,names,K=500):\n e=Eval(stage);models=[np.load(P/(stage+'_'+n+'.npy'),mmap_mode='r') for n in names]\n # Retrieval union: 200 from the strongest organic model, 100 from other models.\n lists=[];stats=[];us=[]\n for k in ['count','organic','recent','short','strong','likes','skip']:\n  x=load(k,stage);stats.append(np.asarray(x.sum(0)).ravel());us.append(np.asarray(x[tr].sum(1)).ravel())\n ip=np.stack(stats,axis=1);up=np.stack(us,axis=1)\n itemfeat=np.concatenate([np.log1p(ip),np.log((ip[:,[1,2,3,4,5,6]]+1)/(ip[:,[0,0,2,0,0,0]]+1))],axis=1)\n userfeat=np.concatenate([np.log1p(up),np.log((up[:,[1,2,3,4,5,6]]+1)/(up[:,[0,0,2,0,0,0]]+1))],axis=1)\n popscore=ip[:,3]+.05*ip[:,1];po=np.argsort(-popscore)\n F=len(names)*3+itemfeat.shape[1]+userfeat.shape[1]\n X=np.lib.format.open_memmap(P/f'{stage}_features.npy',mode='w+',dtype=np.float32,shape=(len(targets),K,F));pool=np.empty((len(targets),K),np.int32);Y=np.empty((len(targets),K),np.float32)\n for st in range(0,len(targets),64):\n  en=min(st+64,len(targets));mask=e.mask(st,en);ss=[];cand=[]\n  for j,mo in enumerate(models):\n   s=np.array(mo[st:en]);s[~mask]=-np.inf;ix=np.argpartition(s,-K,axis=1)[:,-K:];ix=np.take_along_axis(ix,np.argsort(-np.take_along_axis(s,ix,axis=1),axis=1),axis=1);cand.append(ix);ss.append(s)\n  for row in range(en-st):\n   v=np.concatenate([cand[0][row,:200]]+[a[row,:100] for a in cand[1:]]+[po[mask[row,po]][:100],cand[0][row]])\n   _,ix=np.unique(v,return_index=True);pool[st+row]=v[np.sort(ix)[:K]]\n  q=pool[st:en];f=[]\n  for s in ss:\n   v=np.take_along_axis(s,q,axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n  X[st:en]=np.concatenate([np.stack(f,axis=2),itemfeat[q],np.broadcast_to(userfeat[st:en,None,:],(en-st,K,userfeat.shape[1]))],axis=2)\n  Y[st:en]=e.gold[np.arange(st,en)[:,None],q].toarray()\n X.flush();np.savez(P/f'{stage}_pool.npz',pool=pool,Y=Y,names=names);print('features',stage,X.shape,'retrieved positives',np.sum(Y>0),flush=True);return X,pool,Y,e\n\ndef metric(pred,Y,e,rows):\n ix=np.argsort(-pred,axis=1)[:,:10];scores=(np.take_along_axis(Y,ix,axis=1)*e.discount).sum(1)/e.idcg;return scores[rows&e.active].mean()\n\ndef train(names):\n X,pool,Y,e=features('validation',names);rng=np.random.default_rng(2026);fold=rng.random(len(targets))<.5;K=X.shape[1];sample=fold&e.active;hold=~fold\n # Report the unchanged retrieval model on exactly the held-out users.\n print('baseline holdout',metric(X[:,:,1],Y,e,hold),flush=True)\n w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\n best=(-1,None)\n for loss in ['poisson','squared_error']:\n  model=HistGradientBoostingRegressor(loss=loss,learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n  t=time.time();model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape)\n  for blend in [0,.25,.5,.75,1]:\n   # Scale baseline into the same positive prediction range per user.\n   base=np.maximum(X[:,:,1],0);base/=np.maximum(base.max(1,keepdims=True),1e-6);pp=np.maximum(pred,0);pp/=np.maximum(pp.max(1,keepdims=True),1e-6);score=metric(blend*pp+(1-blend)*base,Y,e,hold)\n   print('rerank',loss,'blend',blend,'holdout',score,'seconds',time.time()-t,flush=True)\n   if score>best[0]:best=(score,dict(loss=loss,blend=blend))\n  joblib.dump(model,P/f'rerank_{loss}_holdout.joblib')\n print('best',best,flush=True);json.dump({'names':names,'config':best[1],'holdout_ndcg':best[0]},open(P/'rerank_config.json','w'))\nif __name__=='__main__':train(sys.argv[1:])\nPY\ntail /app/solution/asym.log; tail /app/solution/sweep.log; tail /app/solution/strong_sweep.log"
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    "stdout": "validation_asym_organic_300.0_2.0 72.85397028923035\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\n",
    "stderr": "",
    "aggregated_output": "validation_asym_organic_300.0_2.0 72.85397028923035\nvalidation_asym_organic_300.0_2.0 NDCG 0.023519793824858255\nsolved 44.20782208442688\nvalidation_ease_hybrid_1000 NDCG 0.027989974709693704\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\n",
    "exit_code": 0,
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    "stdout": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n",
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    "aggregated_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n",
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    "formatted_output": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nbest 0.03109444468385386 {'validation_ease_strong_300.0': np.float64(1.0), 'validation_ease_recent_300': np.float64(0.0), 'validation_ease_hybrid_1000': np.float64(0.0), 'validation_asym_organic_300.0_2.0': np.float64(0.0)}\nbest 0.03135854710380908 {'validation_ease_strong_300.0': np.float64(0.5213151337496114), 'validation_ease_recent_300': np.float64(0.004566857812913324), 'validation_ease_hybrid_1000': np.float64(0.4354648733549479), 'validation_asym_organic_300.0_2.0': np.float64(0.038653135082527365)}\nbest 0.03138977064271955 {'validation_ease_strong_300.0': np.float64(0.8876951962581665), 'validation_ease_recent_300': np.float64(0.001173582618003885), 'validation_ease_hybrid_1000': np.float64(0.0022135015541043454), 'validation_asym_organic_300.0_2.0': np.float64(0.10891771956972521)}\nbest 0.03171507399553618 {'validation_ease_strong_300.0': np.float64(0.6610446794913707), 'validation_ease_recent_300': np.float64(0.008573553252556407), 'validation_ease_hybrid_1000': np.float64(0.19462317006675262), 'validation_asym_organic_300.0_2.0': np.float64(0.13575859718932026)}\nbest 0.03180092594537532 {'validation_ease_strong_300.0': np.float64(0.6368850670430746), 'validation_ease_recent_300': np.float64(0.0571924424128944), 'validation_ease_hybrid_1000': np.float64(0.13604944778341566), 'validation_asym_organic_300.0_2.0': np.float64(0.16987304276061532)}\nbest 0.03189775711854503 {'validation_ease_strong_300.0': np.float64(0.623355815310758), 'validation_ease_recent_300': np.float64(0.031658839550060505), 'validation_ease_hybrid_1000': np.float64(0.3444135521840138), 'validation_asym_organic_300.0_2.0': np.float64(0.0005717929551677022)}\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nmatrix recent (9238, 30000) 5564926 time 0.38924431800842285\nkernel 7.929508447647095\nchol 9.285109043121338\nsolved 44.72099852561951\nvalidation_ease_recent_300 NDCG 0.02825181915291134\nmatrix liked (9238, 30000) 5618006 time 0.1580970287322998\nkernel 8.21726393699646\nchol 9.574827194213867\nsolved 42.9216411113739\nvalidation_ease_liked_300 NDCG 0.02145577970503426\n"
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      "-lc",
      "cat > /app/solution/extra_prepare.py <<'PY'\nfrom model import *\nimport pyarrow.parquet as pq\nfor stage in ['train','val']:\n t=time.time();cut=24786800 if stage=='train' else 25393400;parts=[];outside=sparse.csr_matrix((N,10000001),dtype=np.float32)\n for batch in pq.ParquetFile(D/f'{stage}_listens.parquet').iter_batches(batch_size=2000000):\n  d=batch.to_pandas();d=d[d.played_ratio_pct>=80];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ts=d.timestamp.to_numpy(dtype=np.float64);age=(cut-ts)/86400;v=((.3+.7*d.is_organic.values)*np.exp(-age/90)).astype(np.float32)\n  ok=(r>=0)&(c<0);outside+=sparse.csr_matrix((v[ok],(r[ok],d.item_id.values[ok])),shape=outside.shape)\n  ok=(r>=0)&(c>=0);key=r[ok].astype(np.int64)*10000+(ts[ok]//7200).astype(np.int64);_,sess=np.unique(key,return_inverse=True);x=sparse.csr_matrix((np.ones(ok.sum(),np.float32),(sess,c[ok])),shape=(sess.max()+1,M));x.data[:]=1;parts.append(x)\n outside.data=np.log1p(outside.data);sparse.save_npz(P/f'{stage}_outside.npz',outside);sparse.save_npz(P/f'{stage}_sessions.npz',sparse.vstack(parts,format='csr'));print(stage,'done',time.time()-t,'outside',outside.nnz,'sessions',sum(x.shape[0] for x in parts),flush=True)\nPY\nOPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python /app/solution/extra_prepare.py > /app/solution/extra_prepare.log 2>&1"
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      "-lc",
      "cat > /app/solution/outside.py <<'PY'\nfrom model import *\ndef fit(stage,reg=1000,weight=.5):\n t=time.time();x=load('strong',stage);x.data=np.log1p(x.data);o=sparse.load_npz(P/'train_outside.npz')\n if stage=='test':\n  # Outside-cache values are logarithmic; decay and merge in count space.\n  o.data=np.expm1(o.data)*np.exp(-(7+1800/86400)/90);v=sparse.load_npz(P/'val_outside.npz');v.data=np.expm1(v.data);o+=v;o.data=np.log1p(o.data)\n k=([email protected]+weight*([email protected])).toarray(order='F');del o;k.flat[::N+1]+=reg;cf=linalg.cho_factor(k,lower=True,overwrite_a=True,check_finite=False)\n name=f'{stage}_outside_{reg}_{weight}';out=np.lib.format.open_memmap(P/f'{name}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n for st in range(0,M,1000):\n  en=min(st+1000,M);v=x[:,st:en].toarray(order='F');sol=linalg.cho_solve(cf,v,overwrite_b=True,check_finite=False);denom=1-np.asarray(x[:,st:en].multiply(sol).sum(0)).ravel();out[:,st:en]=-reg*sol[tr]/np.maximum(denom,1e-5)\n out.flush();print(name,'time',time.time()-t,flush=True);Eval(stage).run(out,name)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]),float(sys.argv[3]))\nPY\ncat > /app/solution/session_knn.py <<'PY'\nfrom model import *\ndef fit(stage,power=.6,top=200):\n t=time.time();x=sparse.load_npz(P/'train_sessions.npz')\n if stage=='test':x=sparse.vstack([x,sparse.load_npz(P/'val_sessions.npz')],format='csr')\n degree=np.asarray(x.sum(1)).ravel();x=sparse.diags(1/np.sqrt(np.maximum(degree,1)))@x\n norm=np.asarray(x.power(2).sum(0)).ravel();scale=np.maximum(norm,1)**power;xt=x.T.tocsr();rows=[];cols=[];vals=[]\n for st in range(0,M,300):\n  en=min(st+300,M);co=(xt[st:en]@x).toarray();co/=scale[None,:];co[np.arange(en-st),np.arange(st,en)]=0;ix=np.argpartition(co,-top,axis=1)[:,-top:];v=np.take_along_axis(co,ix,axis=1);v/=scale[st:en,None];rows.append(np.repeat(np.arange(st,en),top));cols.append(ix.ravel());vals.append(v.ravel())\n sim=sparse.csr_matrix((np.concatenate(vals),(np.concatenate(rows),np.concatenate(cols))),shape=(M,M));sparse.save_npz(P/f'{stage}_session_sim_{power}.npz',sim);print('sim built',time.time()-t,flush=True)\n for profile in ['short','strong']:\n  h=load(profile,stage)[tr];h.data=np.log1p(h.data);out=np.lib.format.open_memmap(P/f'{stage}_session_{power}_{profile}.npy',mode='w+',dtype=np.float32,shape=(len(targets),M))\n  for st in range(0,len(targets),128):out[st:st+128]=(h[st:st+128]@sim).toarray()\n  out.flush();Eval(stage).run(out,f'{stage}_session_{power}_{profile}')\n print('done',time.time()-t,flush=True)\nif __name__=='__main__':fit(sys.argv[1],float(sys.argv[2]))\nPY\ntail /app/solution/rerank.log; tail /app/solution/extra_prepare.log; tail /app/solution/strong_sweep.log; tail /app/solution/final_strong.log"
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    "stdout": "features validation (8456, 500, 38) retrieved positives 11540\nbaseline holdout 0.03196263974640137\nrerank poisson blend 0 holdout 0.03196263974640137 seconds 40.55131530761719\nrerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\ntrain done 19.82935070991516 outside 2764264 sessions 2334996\nval done 0.8569111824035645 outside 223136 sessions 76366\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\n",
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    "stdout": "rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\n",
    "stderr": "",
    "aggregated_output": "rerank poisson blend 0.25 holdout 0.03845269238050031 seconds 40.67441010475159\nrerank poisson blend 0.5 holdout 0.04849349422808413 seconds 40.804423809051514\nrerank poisson blend 0.75 holdout 0.055083363463257255 seconds 40.93277072906494\nrerank poisson blend 1 holdout 0.055597262296575266 seconds 41.05809712409973\nrerank squared_error blend 0 holdout 0.03196263974640137 seconds 47.25633454322815\nrerank squared_error blend 0.25 holdout 0.036485161165373474 seconds 47.38992381095886\nrerank squared_error blend 0.5 holdout 0.047192198993316385 seconds 47.52120661735535\nrerank squared_error blend 0.75 holdout 0.05195035019369132 seconds 47.65059733390808\nrerank squared_error blend 1 holdout 0.05347194270391868 seconds 47.77997589111328\nbest (np.float64(0.055597262296575266), {'loss': 'poisson', 'blend': 1})\nvalidation_strong_100_0_0 time 42.17256259918213\nvalidation_strong_100_0_0 NDCG 0.02992938081142049\nvalidation_strong_1000_0_0 time 43.76785898208618\nvalidation_strong_1000_0_0 NDCG 0.03183120606237439\nvalidation_strong_300_1_0 time 44.70142436027527\nvalidation_strong_300_1_0 NDCG 0.029180014149125157\nvalidation_strong_300_2_0.25 time 63.89295029640198\nvalidation_strong_300_2_0.25 NDCG 0.028353624625109014\n",
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      "cat > /app/solution/blend.py <<'PY'\nfrom model import *\ndef tune(names):\n e=Eval();a=[np.load(P/(n+'.npy'),mmap_mode='r') for n in names]; rr=[];ff=[];gg=[]\n for st in range(0,len(targets),64):\n  en=min(st+64,len(targets));mask=e.mask(st,en);cand=[];full=[]\n  for s in a:\n   b=np.asarray(s[st:en]).copy();b[~mask]=-np.inf;ix=np.argpartition(b,-100,axis=1)[:,-100:];cand.append(ix);full.append(b)\n  pool=np.concatenate(cand,axis=1)\n  # duplicate candidates get -infinity in all features after their first occurrence\n  for row in range(len(pool)):\n   _,ix=np.unique(pool[row],return_index=True);du=np.ones(pool.shape[1],bool);du[ix]=False;pool[row,du]=-1\n  f=[]\n  for b in full:\n   scale=np.partition(b,-10,axis=1)[:,-10:].mean(1);v=np.take_along_axis(b,np.maximum(pool,0),axis=1)/np.maximum(scale[:,None],1e-6);v[pool<0]=-1e6;f.append(v)\n  rr.append(pool);ff.append(np.stack(f,axis=2));gg.append(e.gold[np.arange(st,en)[:,None],np.maximum(pool,0)].toarray())\n pool=np.concatenate(rr);feat=np.concatenate(ff);gold=np.concatenate(gg);gold[pool<0]=0\n def score(w):\n  s=feat@w;ix=np.argsort(-s,axis=1)[:,:10];dcg=(np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg;return dcg[e.active].mean()\n best=(0,None)\n rng=np.random.default_rng(42)\n weights=[np.eye(len(a))[i] for i in range(len(a))]\n weights += [rng.dirichlet(np.ones(len(a))*.35) for _ in range(150)]\n for w in weights:\n  scoreval=score(w)\n  if scoreval>best[0]:best=(scoreval,w);print('best',scoreval,dict(zip(names,w)),flush=True)\n np.savez(P/'blend_tuning.npz',pool=pool,feat=feat,gold=gold,names=np.array(names),weights=best[1]);return best\nif __name__=='__main__':tune(sys.argv[1:])\nPY\nOPENBLAS_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/strong_sweep.py > /app/solution/strong_sweep.log 2>&1"
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        "cmd": "cat > /app/solution/finalize_reranker.py <<'PY'\nfrom rerank import *\nfrom extra_features import extend\n\ndef export(pred,pool,path):\n ix=np.argsort(-pred,axis=1)[:,:100];ranked=np.take_along_axis(pool,ix,axis=1);frame=pd.DataFrame({'uid':np.repeat(targets,100),'item_id':items[ranked.ravel()],'rank':np.tile(np.arange(1,101),len(targets))});frame.to_parquet(path,index=False)\n\ndef fit_full(kind='base',leaves=15,iters=150,l2=10):\n e=Eval();X=np.load(P/('validation_features_extended.npy' if kind=='extended' else 'validation_features.npy'),mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1]\n config=dict(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42)\n pred=np.zeros(Y.shape,np.float32)\n for trainfold in [fold,~fold]:\n  sample=trainfold&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[~trainfold]=model.predict(np.asarray(X[~trainfold]).reshape(-1,X.shape[-1])).reshape(-1,K)\n  print(kind,'OOF fold score',metric(pred,Y,e,~trainfold),flush=True)\n print(kind,'OOF score',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);export(pred,pool,P/f'validation_rerank_{kind}.parquet');np.save(P/f'oof_{kind}.npy',pred)\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/f'final_reranker_{kind}.joblib');print('full model fitted',kind,flush=True)\n json.dump(dict(kind=kind,names=z['names'].tolist(),config=config),open(P/f'final_config_{kind}.json','w'))\n\ndef predict(kind='base'):\n config=json.load(open(P/f'final_config_{kind}.json'));X,pool,Y,e=features('test',config['names']);\n if kind=='extended':X=extend('test')\n model=joblib.load(P/f'final_reranker_{kind}.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n export(pred,pool,P/f'submission_rerank_{kind}.parquet');np.save(P/f'test_predictions_{kind}.npy',pred);print('prediction saved',kind,flush=True)\nif __name__=='__main__':\n if sys.argv[1]=='fit':fit_full(sys.argv[2],int(sys.argv[3]),int(sys.argv[4]),float(sys.argv[5]))\n else:predict(sys.argv[2])\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/finalize_reranker.py fit base 15 150 10 > /app/solution/final_rerank_base.log 2>&1"
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      "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state=='liked'];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);expected=sparse.csr_matrix((np.ones(ok.sum()),(r[ok],c[ok])),shape=(N,M));actual=sparse.load_npz(P/f'{stage}_active_likes.npz');print(stage,'like state differences',(actual[tr]-expected[tr]).nnz)\nPY\ntail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/pool_test.log; tail /app/solution/augment.log"
    ],
    "cwd": "file:///app",
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      {
        "type": "unknown",
        "cmd": "cd /app/solution && OPENBLAS_NUM_THREADS=1 /opt/task-venv/bin/python - <<'PY'\nfrom model import *\nfor stage in ['validation','test']:\n d=pd.read_parquet(D/f'{stage}_preference_state.parquet');d=d[d.state=='liked'];r=ui.get_indexer(d.uid);c=ii.get_indexer(d.item_id);ok=(r>=0)&(c>=0);expected=sparse.csr_matrix((np.ones(ok.sum()),(r[ok],c[ok])),shape=(N,M));actual=sparse.load_npz(P/f'{stage}_active_likes.npz');print(stage,'like state differences',(actual[tr]-expected[tr]).nnz)\nPY\ntail /app/solution/final_rerank_base.log; tail /app/solution/refit_bases.log; tail /app/solution/tune_rerank.log; tail /app/solution/pool_test.log; tail /app/solution/augment.log"
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    ],
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    "stdout": "validation like state differences 4467\ntest like state differences 4544\nbase OOF fold score 0.055597262296575266\nbase OOF fold score 0.05486647535596832\nbase OOF score 0.05524052581442583\nfull model fitted base\nmatrix recent (9238, 30000) 5724357 time 0.4726097583770752\nkernel 8.522367715835571\nchol 9.85982370376587\nsolved 59.81119728088379\ntest_ease_recent_300 NDCG 0.0\nmatrix hybrid (9238, 30000) 5778549 time 0.6035466194152832\nkernel 11.544252634048462\nchol 13.856557607650757\nsolved 87.70576524734497\ntest_ease_hybrid_1000 NDCG 0.0\nconfig 15 300 10 score 0.05516978200752623 time 66.46084570884705\nconfig 31 250 10 score 0.0518653317863001 time 112.47266101837158\nconfig 31 250 50 score 0.054111808164323615 time 157.16783714294434\n",
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    "stdout": "low 0\nhigh 0\nmax 14474\noom 0\noom_kill 0\noom_group_kill 0\nsock_throttled 4\n",
    "stderr": "",
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      {
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    "type": "CommandExecution",
    "id": "exec-2b21e9a8-1181-4cc4-b73e-4689a1f4c2cc",
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    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n  a=p.read_bytes().split(b'\\0')\n  if len(a)>1 and a[1]==b'/app/solution/tune_extended.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\nPY\ncat > /app/solution/augment.py <<'PY'\nfrom rerank import *\nextra_names=['outside_1000.0_1.0','session_0.6_short','session_0.6_strong']\ndef augment(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];x=np.lib.format.open_memmap(P/f'{stage}_features_augmented.npy',mode='w+',dtype=np.float32,shape=(*old.shape[:2],old.shape[2]+len(extra_names)*3));e=Eval(stage);mods=[np.load(P/f'{stage}_{name}.npy',mmap_mode='r') for name in extra_names]\n for st in range(0,len(targets),64):\n  en=min(st+64,len(targets));f=[];mask=e.mask(st,en)\n  for mod in mods:\n   s=np.array(mod[st:en]);s[~mask]=-np.inf;v=np.take_along_axis(s,pool[st:en],axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n  x[st:en]=np.concatenate([old[st:en],np.stack(f,axis=2)],axis=2)\n x.flush();return x\nif __name__=='__main__':\n X=augment('validation');e=Eval();z=np.load(P/'validation_pool.npz');Y=z['Y'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;K=X.shape[1];model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);print('augmented score',metric(pred,Y,e,~fold),flush=True);joblib.dump(model,P/'rerank_augmented_holdout.joblib');np.save(P/'augmented_holdout_preds.npy',pred)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/augment.py > /app/solution/augment.log 2>&1"
    ],
    "cwd": "file:///app",
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      {
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        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\nimport os,signal\nfor p in Path('/proc').glob('[0-9]*/cmdline'):\n try:\n  a=p.read_bytes().split(b'\\0')\n  if len(a)>1 and a[1]==b'/app/solution/tune_extended.py':os.kill(int(p.parent.name),signal.SIGTERM)\n except (OSError,ValueError):pass\nPY\ncat > /app/solution/augment.py <<'PY'\nfrom rerank import *\nextra_names=['outside_1000.0_1.0','session_0.6_short','session_0.6_strong']\ndef augment(stage):\n old=np.load(P/f'{stage}_features.npy',mmap_mode='r');pool=np.load(P/f'{stage}_pool.npz')['pool'];x=np.lib.format.open_memmap(P/f'{stage}_features_augmented.npy',mode='w+',dtype=np.float32,shape=(*old.shape[:2],old.shape[2]+len(extra_names)*3));e=Eval(stage);mods=[np.load(P/f'{stage}_{name}.npy',mmap_mode='r') for name in extra_names]\n for st in range(0,len(targets),64):\n  en=min(st+64,len(targets));f=[];mask=e.mask(st,en)\n  for mod in mods:\n   s=np.array(mod[st:en]);s[~mask]=-np.inf;v=np.take_along_axis(s,pool[st:en],axis=1);scale=np.partition(s,-10,axis=1)[:,-10:].mean(1);z=v/np.maximum(scale[:,None],1e-6);f.extend([v,z,np.log1p(np.maximum(z,0)*20)])\n  x[st:en]=np.concatenate([old[st:en],np.stack(f,axis=2)],axis=2)\n x.flush();return x\nif __name__=='__main__':\n X=augment('validation');e=Eval();z=np.load(P/'validation_pool.npz');Y=z['Y'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;K=X.shape[1];model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);print('augmented score',metric(pred,Y,e,~fold),flush=True);joblib.dump(model,P/'rerank_augmented_holdout.joblib');np.save(P/'augmented_holdout_preds.npy',pred)\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/augment.py > /app/solution/augment.log 2>&1"
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      "cat > /app/solution/tune_rerank.py <<'PY'\nfrom rerank import *\ne=Eval();X=np.load(P/'validation_features.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;hold=~fold;K=X.shape[1];w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\nfor leaves,iters,l2 in [(15,300,10),(31,250,10),(31,250,50),(7,300,10)]:\n t=time.time();model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42);model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);score=metric(pred,Y,e,hold);print('config',leaves,iters,l2,'score',score,'time',time.time()-t,flush=True);joblib.dump(model,P/f'rerank_tuned_{leaves}_{iters}_{l2}.joblib')\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank.py > /app/solution/tune_rerank.log 2>&1"
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      {
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        "cmd": "cat > /app/solution/tune_rerank.py <<'PY'\nfrom rerank import *\ne=Eval();X=np.load(P/'validation_features.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;sample=fold&e.active;hold=~fold;K=X.shape[1];w=np.repeat(10/e.idcg[sample],K);xt=np.asarray(X[sample]).reshape(-1,X.shape[-1]);yt=Y[sample].ravel()\nfor leaves,iters,l2 in [(15,300,10),(31,250,10),(31,250,50),(7,300,10)]:\n t=time.time();model=HistGradientBoostingRegressor(loss='poisson',learning_rate=.05,max_iter=iters,max_leaf_nodes=leaves,min_samples_leaf=100,l2_regularization=l2,early_stopping=False,random_state=42);model.fit(xt,yt,sample_weight=w);pred=model.predict(np.asarray(X).reshape(-1,X.shape[-1])).reshape(Y.shape);score=metric(pred,Y,e,hold);print('config',leaves,iters,l2,'score',score,'time',time.time()-t,flush=True);joblib.dump(model,P/f'rerank_tuned_{leaves}_{iters}_{l2}.joblib')\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/tune_rerank.py > /app/solution/tune_rerank.log 2>&1"
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      "cat > /app/solution/ensemble_check.py <<'PY'\nfrom rerank import *\ne=Eval();a=np.load(P/'validation_features.npy',mmap_mode='r');b=np.load(P/'validation_features_extended.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];hold=(np.random.default_rng(2026).random(len(targets))>=.5)&e.active;gold=Y[hold];K=Y.shape[1];preds=[]\nfor name,X in [('rerank_poisson_holdout',a),('rerank_extended_15_150_10',b),('rerank_tuned_7_300_10',a)]:\n m=joblib.load(P/(name+'.joblib'));p=m.predict(np.asarray(X[hold]).reshape(-1,X.shape[-1])).reshape(-1,K);p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\np=np.load(P/'augmented_holdout_preds.npy')[hold];p/=np.maximum(p.max(1,keepdims=True),1e-6);preds.append(p)\nfor j in [1,2,3]:\n for weight in [.25,.5,.75]:\n  p=preds[0]*(1-weight)+preds[j]*weight;ix=np.argsort(-p,axis=1)[:,:10];s=((np.take_along_axis(gold,ix,axis=1)*e.discount).sum(1)/e.idcg[hold]).mean();print('blend',j,weight,s,flush=True)\nnp.save(P/'holdout_ensemble_predictions.npy',np.stack(preds))\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/ensemble_check.py > /app/solution/ensemble_check.log 2>&1"
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      "cat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.055241 NDCG@10** across 4,263 relevant users for this cross-fitted ranker. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\nMD\ntail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log"
    ],
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      {
        "type": "unknown",
        "cmd": "cat > /app/solution/README.md <<'MD'\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. A Poisson gradient-boosted reranker combines their scores with item popularity, temporal trends, and user activity statistics. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.055241 NDCG@10** across 4,263 relevant users for this cross-fitted ranker. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`model.py`, `asymmetric.py`, `rerank.py`, and `finalize_reranker.py` implement the selected pipeline. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\nMD\ntail /app/solution/ensemble_check.log; tail /app/solution/predict_base.log"
      }
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      "cat > /app/solution/final_augmented.py <<'PY'\nfrom finalize_reranker import export\nfrom augment import *\ndef train():\n e=Eval();X=np.load(P/'validation_features_augmented.npy',mmap_mode='r');z=np.load(P/'validation_pool.npz');Y=z['Y'];pool=z['pool'];fold=np.random.default_rng(2026).random(len(targets))<.5;K=X.shape[1];config=dict(loss='poisson',learning_rate=.05,max_iter=150,max_leaf_nodes=15,min_samples_leaf=100,l2_regularization=10,early_stopping=False,random_state=42)\n pred=np.load(P/'augmented_holdout_preds.npy');sample=(~fold)&e.active;model=HistGradientBoostingRegressor(**config);model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));pred[fold]=model.predict(np.asarray(X[fold]).reshape(-1,X.shape[-1])).reshape(-1,K);np.save(P/'oof_augmented.npy',pred)\n base=np.load(P/'oof_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);combined=.5*base+.5*pred;print('augmented OOF',metric(pred,Y,e,np.ones(len(targets),bool)),flush=True);print('ensemble OOF',metric(combined,Y,e,np.ones(len(targets),bool)),flush=True);export(combined,pool,P/'validation_ensemble.parquet')\n model=HistGradientBoostingRegressor(**config);sample=e.active;model.fit(np.asarray(X[sample]).reshape(-1,X.shape[-1]),Y[sample].ravel(),sample_weight=np.repeat(10/e.idcg[sample],K));joblib.dump(model,P/'final_reranker_augmented.joblib');print('augmented full fit complete',flush=True)\ndef predict():\n X=augment('test');pool=np.load(P/'test_pool.npz')['pool'];model=joblib.load(P/'final_reranker_augmented.joblib');pred=np.empty(pool.shape,np.float32)\n for st in range(0,len(targets),128):pred[st:st+128]=model.predict(np.asarray(X[st:st+128]).reshape(-1,X.shape[-1])).reshape(-1,pool.shape[1])\n np.save(P/'test_predictions_augmented.npy',pred);base=np.load(P/'test_predictions_base.npy');base/=np.maximum(base.max(1,keepdims=True),1e-6);pred/=np.maximum(pred.max(1,keepdims=True),1e-6);export(.5*base+.5*pred,pool,P/'submission_ensemble.parquet');print('ensemble test ranking saved',flush=True)\nif __name__=='__main__':train() if sys.argv[1]=='train' else predict()\nPY\nOPENBLAS_NUM_THREADS=1 OMP_NUM_THREADS=2 /opt/task-venv/bin/python /app/solution/final_augmented.py train > /app/solution/final_augmented.log 2>&1"
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    "stdout": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n",
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    "aggregated_output": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n",
    "exit_code": 0,
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    "formatted_output": "Build sources parse; final model files: True True\n# Music discovery ranker\n\n`submission.parquet` contains 100 ranked, distinct, test-eligible tracks for each of the 8,456 target users.\n\nThe ranker combines four regularized linear collaborative models: organic completion-weighted history, recent listening, listening plus explicit-action history, and an asymmetric organic/like model. Two Poisson gradient-boosted rerankers combine their scores with item popularity, temporal trends, and user activity statistics. One additionally uses session-neighbor scores and collaborative history outside the retrieval catalog; their normalized predictions are averaged. Its training targets are the specified organic relevance gains, weighted by each user's ideal DCG.\n\nValidation representations and features use only training history. The reranker is evaluated with two user folds: each validation user's ranking is produced by a model fitted on other users' outcomes. The public evaluator reports **0.056862 NDCG@10** across 4,263 relevant users for the cross-fitted ensemble with a 500-item shortlist (`validation_ensemble.parquet`). The final model expands the shortlist to 800 items, including session and outside-catalog retrieval: on the held-out half of users this improved NDCG from 0.057101 to **0.057535**. That larger-shortlist result is a held-out-half estimate, not a full cross-fitted score. Popularity alone scored 0.010577. Validation results guided model selection; they are development estimates.\n\nFor the final ranking, collaborative models and all history features are refitted using train plus validation events. The final reranker uses the observed validation labels. The test candidate mask is applied before retrieval and ranking. Eligibility is authoritative; history signals never expand it.\n\nRebuild from the provided inputs, without installing anything:\n\n```sh\n/opt/task-venv/bin/python /app/solution/build.py\n```\n\nCheck the final artifact:\n\n```sh\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`build.py` orchestrates the selected pipeline; `final_augmented.py` fits the ensemble and `wide_pool.py` creates the final ranking. Other scripts and logs record experiments with neighbors, session co-occurrence, outside-catalog history, regularization, explicit-action statistics, and larger shortlists. All generated files are confined to this directory. The reported objective is an offline discovery measure, not a causal estimate of online lift.\n\n"
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    "stdout": "VALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\nVALID: 845,600 rows; 8,456 users; 100 unique eligible tracks each.\n",
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