Agent trace

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-06c3dedb-46a9-4b3a-9f11-ac128d2b65f0",
    "process_id": "48122",
    "command": [
      "/bin/sh",
      "-lc",
      "pwd && ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "pwd && ls -la /app && ls -la /task/current_model && cat /task/current_model/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root  4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n    )\n    ordered = np.argsort(lengths, kind=\"stable\")\n    result = torch.zeros(\n        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n    )\n    for group_start in range(0, len(ordered), config.user_batch_size):\n        output_rows = ordered[group_start : group_start + config.user_batch_size]\n        rows = sequence_rows[output_rows]\n        group_lengths = lengths[output_rows]\n        hidden = torch.zeros(\n            1, len(output_rows), config.embedding_dim, device=device\n        )\n        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n            steps = min(\n                config.bptt_steps,\n                int(group_lengths.max(initial=0)) - start,\n            )\n            items = np.full(\n                (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
    "stderr": "",
    "aggregated_output": "/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root  4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\n    model: base.SequentialTwoTower,\n    items: torch.Tensor,\n    features: torch.Tensor,\n    hidden: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    inputs = torch.cat([model.context_embedding(items), features], dim=-1)\n    projected = model.event_projection(inputs)\n    return model.sequence_encoder(projected, hidden)\n\n\ndef pool_explicit_states(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    state_ids: np.ndarray,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:\n    unique_states, inverse = np.unique(state_ids, return_inverse=True)\n    liked_items = torch.from_numpy(\n        prepared.liked_states[unique_states].astype(np.int64)\n    ).to(device)\n    disliked_items = torch.from_numpy(\n        prepared.disliked_states[unique_states].astype(np.int64)\n    ).to(device)\n    liked_vectors = model._mean_context_channel(liked_items)\n    disliked_vectors = model._mean_context_channel(disliked_items)\n    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)\n    counts = prepared.state_counts[state_ids]\n    return (\n        liked_vectors[inverse_rows],\n        disliked_vectors[inverse_rows],\n        torch.from_numpy(counts.astype(np.float32)).to(device),\n    )\n\n\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\n\ndef train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n    )\n    ordered = np.argsort(lengths, kind=\"stable\")\n    result = torch.zeros(\n        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n    )\n    for group_start in range(0, len(ordered), config.user_batch_size):\n        output_rows = ordered[group_start : group_start + config.user_batch_size]\n        rows = sequence_rows[output_rows]\n        group_lengths = lengths[output_rows]\n        hidden = torch.zeros(\n            1, len(output_rows), config.embedding_dim, device=device\n        )\n        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n            steps = min(\n                config.bptt_steps,\n                int(group_lengths.max(initial=0)) - start,\n            )\n            items = np.full(\n                (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3140
    },
    "formatted_output": "Warning: truncated output (original token count: 20110)\nTotal output lines: 2174\n\n/app\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 05:16 .\ndrwxr-xr-x 1 root  root  4096 Sep 23 05:19 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 05:16 solution\ntotal 104\ndr-xr-xr-x 1 root root  4096 Sep 23 05:14 .\ndr-xr-xr-x 1 root root  4096 Sep 23 05:16 ..\n-r--r--r-- 1 root root 80058 Sep 23 05:14 train_current_model.py\n-r--r--r-- 1 root root 15985 Sep 23 05:14 two_tower_model.py\n#!/usr/bin/env python3\n\"\"\"Train the Yambda windowed two-tower baseline.\n\nEvery catalog listen with at least 50 percent completion is a sequence token.\nWithin each selected chronological window, every adjacent target contributes a\nloss.  The fast default retains each user's most recent 128 training targets and\n16 held-out validation targets; passing zero for either cap restores the\nuncapped behavior for that side of the split.  A 100-step truncated-BPTT block\ncomputes all of its timestep losses and carries the detached GRU hidden state\ninto the next block.\n\nThe model is a pure two-tower retriever: no user-ID embedding, no output\nnormalization, raw dot-product retrieval, sample-weighted binary cross entropy,\nand 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\nselected adjacent target retains a nonzero weight. Only files under the supplied\npublic directory are read.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport random\nimport sys\nimport time\nfrom dataclasses import asdict, dataclass\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nimport two_tower_model as base\n\n\nCACHE_VERSION = 2\n\n\n@dataclass(frozen=True)\nclass Config:\n    bptt_steps: int = 100\n    user_batch_size: int = 64\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    loss_microbatch_size: int = 65_536\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    negative_filter_scope: str = \"full-training-history\"\n    target_weighting: str = \"organic-strong\"\n    ordinary_target_weight: float = 0.25\n    organic_strong_target_weight: float = 1.0\n    organic_like_target_weight: float = 3.0\n    discovery_horizon_days: int = 7\n    validation_fraction: float = 0.10\n    max_train_targets_per_user: int = 128\n    max_validation_targets_per_user: int = 16\n    early_stopping_patience: int = 1\n    early_stopping_min_delta: float = 1.0e-4\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass CandidateSet:\n    uids: np.ndarray\n    item_ids: np.ndarray\n    packed: np.ndarray\n\n    @classmethod\n    def read(cls, path: Path) -> \"CandidateSet\":\n        with np.load(path, allow_pickle=False) as artifact:\n            result = cls(\n                uids=artifact[\"uids\"].astype(np.int64, copy=True),\n                item_ids=artifact[\"item_ids\"].astype(np.int64, copy=True),\n                packed=artifact[\"packed_eligible\"].astype(np.uint8, copy=True),\n            )\n        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)\n        if result.packed.shape != expected:\n            raise ValueError(\n                f\"invalid candidate artifact shape {result.packed.shape}; \"\n                f\"expected {expected}\"\n            )\n        return result\n\n\n@dataclass(frozen=True)\nclass SequenceData:\n    item_ids: np.ndarray\n    item_features: np.ndarray\n    user_ids: np.ndarray\n    offsets: np.ndarray\n    sequence_timestamps: np.ndarray\n    sequence_items: np.ndarray\n    event_features: np.ndarray\n    activity_features: np.ndarray\n    state_ids: np.ndarray\n    liked_states: np.ndarray\n    disliked_states: np.ndarray\n    state_counts: np.ndarray\n    inference_user_rows: np.ndarray\n    inference_liked_items: np.ndarray\n    inference_disliked_items: np.ndarray\n    inference_user_features: np.ndarray\n\n    @property\n    def lengths(self) -> np.ndarray:\n        return np.diff(self.offsets)\n\n    @property\n    def target_count(self) -> int:\n        return int(np.maximum(self.lengths - 1, 0).sum())\n\n\n@dataclass(frozen=True)\nclass TargetPartition:\n    \"\"\"A contiguous recent target window and its chronological BCE split.\"\"\"\n\n    window_starts: np.ndarray\n    training_counts: np.ndarray\n    validation_counts: np.ndarray\n    history_training_counts: np.ndarray\n\n    @property\n    def selected_count(self) -> int:\n        return int(self.training_counts.sum() + self.validation_counts.sum())\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef seed_everything(seed: int) -> None:\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n\n\ndef model_config(config: Config) -> base.TowerConfig:\n    \"\"\"Map the dense trainer configuration onto the unchanged tower.\"\"\"\n    return base.TowerConfig(\n        sequence_length=config.bptt_steps,\n        embedding_dim=config.embedding_dim,\n        hidden_dim=config.hidden_dim,\n        user_hidden_1=config.user_hidden_1,\n        user_hidden_2=config.user_hidden_2,\n        item_hidden_1=config.item_hidden_1,\n        item_hidden_2=config.item_hidden_2,\n        dropout=config.dropout,\n        epochs=config.epochs,\n        learning_rate=config.learning_rate,\n        weight_decay=config.weight_decay,\n        gradient_clip=config.gradient_clip,\n        uniform_negatives=config.uniform_negatives,\n        inbatch_negatives=config.inbatch_negatives,\n        inbatch_correction_power=config.inbatch_correction_power,\n        inbatch_correction_min=config.inbatch_correction_min,\n        inbatch_correction_max=config.inbatch_correction_max,\n        seed=config.seed,\n    )\n\n\ndef append_state_snapshot(\n    active_likes: dict[int, int],\n    active_dislikes: dict[int, int],\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> int:\n    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))\n    disliked_states.append(\n        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)\n    )\n    state_counts.append((len(active_likes), len(active_dislikes)))\n    return len(liked_states) - 1\n\n\ndef causal_activity_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    organic: np.ndarray,\n    completion: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Compute the first four reviewed user features at every event cutoff.\"\"\"\n    length = len(items)\n    result = np.zeros((length, 4), dtype=np.float16)\n    if not length:\n        return result\n    left_rows = np.searchsorted(\n        timestamps,\n        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n        side=\"left\",\n    )\n    rows = np.arange(length, dtype=np.int64)\n    counts = rows - left_rows + 1\n    organic_sum = np.cumsum(organic.astype(np.float64))\n    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))\n    before_organic = np.where(\n        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n    before_completion = np.where(\n        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0\n    )\n\n    unique_counts = np.empty(length, dtype=np.int32)\n    frequencies: dict[int, int] = {}\n    left = 0\n    for right, raw_item in enumerate(items):\n        item = int(raw_item)\n        frequencies[item] = frequencies.get(item, 0) + 1\n        required_left = int(left_rows[right])\n        while left < required_left:\n            old_item = int(items[left])\n            remaining = frequencies[old_item] - 1\n            if remaining:\n                frequencies[old_item] = remaining\n            else:\n                del frequencies[old_item]\n            left += 1\n        unique_counts[right] = len(frequencies)\n\n    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)\n    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)\n    result[:, 2] = (\n        (organic_sum - before_organic) / np.maximum(counts, 1)\n    ).astype(np.float16)\n    result[:, 3] = (\n        (completion_sum - before_completion)\n        / np.maximum(counts, 1)\n        / 100.0\n    ).astype(np.float16)\n    return result\n\n\ndef build_sequence_parts(\n    listens: base.ListenEvents,\n    explicit: base.ExplicitEvents,\n    n_items: int,\n    liked_states: list[np.ndarray],\n    disliked_states: list[np.ndarray],\n    state_counts: list[tuple[int, int]],\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:\n    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD\n    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)\n    items = listens.items[qualifying].astype(np.uint16, copy=False)\n    organic = listens.organic[qualifying].astype(np.float32, copy=False)\n    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)\n    if not len(items):\n        return None\n\n    event_features = np.zeros((len(items), 3), dtype=np.float16)\n    event_features[:, 0] = organic.astype(np.float16)\n    event_features[:, 1] = (\n        np.minimum(completion, 100.0) / 100.0\n    ).astype(np.float16)\n    if len(items) > 1:\n        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])\n        event_features[1:, 2] = np.exp(\n            -gaps.astype(np.float64) / (60.0 * base.DAY)\n        ).astype(np.float16)\n\n    activity = causal_activity_features(\n        timestamps, items, organic, completion\n    )\n    states = np.zeros(len(items), dtype=np.uint32)\n    active_likes: dict[int, int] = {}\n    active_dislikes: dict[int, int] = {}\n    explicit_position = 0\n    current_state = 0\n    for row, timestamp in enumerate(timestamps):\n        changed = False\n        while (\n            explicit_position < len(explicit.timestamps)\n            and int(explicit.timestamps[explicit_position]) < int(timestamp)\n        ):\n            item = int(explicit.items[explicit_position])\n            event_timestamp = int(explicit.timestamps[explicit_position])\n            destination = (\n                active_likes\n                if int(explicit.channel[explicit_position]) == 0\n                else active_dislikes\n            )\n            if int(explicit.active[explicit_position]) == 1:\n                destination[item] = event_timestamp\n            else:\n                destination.pop(item, None)\n            explicit_position += 1\n            changed = True\n        if changed:\n            current_state = append_state_snapshot(\n                active_likes,\n                active_dislikes,\n                n_items,\n                liked_states,\n                disliked_states,\n                state_counts,\n            )\n        states[row] = np.uint32(current_state)\n    return timestamps, items.copy(), event_features, activity, states\n\n\ndef final_user_features(\n    timestamps: np.ndarray,\n    items: np.ndarray,\n    event_features: np.ndarray,\n    history_end: int,\n    like_count: int,\n    dislike_count: int,\n) -> np.ndarray:\n    start = int(\n        np.searchsorted(\n            timestamps,\n            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,\n            side=\"left\",\n        )\n    )\n    result = np.zeros(6, dtype=np.float32)\n    recent_items = items[start:]\n    if len(recent_items):\n        result[0] = np.log1p(len(recent_items)) / 8.0\n        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0\n        result[2] = float(np.mean(event_features[start:, 0]))\n        result[3] = float(np.mean(event_features[start:, 1]))\n    result[4] = np.log1p(like_count) / 6.0\n    result[5] = np.log1p(dislike_count) / 4.0\n    return result\n\n\ndef prepare_data(\n    public: Path, stage: str, config: Config\n) -> tuple[SequenceData, CandidateSet]:\n    data = public / \"data\"\n    catalog = pd.read_parquet(data / \"candidate_catalog.parquet\").sort_values(\n        \"popularity_rank\"\n    )\n    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)\n    item_index = pd.Index(item_ids)\n    n_items = len(item_ids)\n    if n_items >= np.iinfo(np.uint16).max:\n        raise RuntimeError(\"uint16 item storage is too small for this catalog\")\n\n    if stage == \"validation\":\n        prefixes = (\"train\",)\n        history_end = base.TRAIN_END\n        candidate_name = \"validation_eligible_candidates.npz\"\n        state_name = \"validation_preference_state.parquet\"\n    else:\n        prefixes = (\"train\", \"val\")\n        history_end = base.VALIDATION_END\n        candidate_name = \"test_eligible_candidates.npz\"\n        state_name = \"test_preference_state.parquet\"\n    eligibility = CandidateSet.read(data / candidate_name)\n    if not np.array_equal(eligibility.item_ids, item_ids):\n        raise RuntimeError(\"catalog and candidate artifact item order differ\")\n\n    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)\n    inference_likes, inference_dislikes, like_counts, dislike_counts = (\n        base.load_inference_state(\n            data / state_name,\n            item_index,\n            eligibility.uids,\n            n_items,\n        )\n    )\n    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}\n    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)\n    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)\n\n    # Snapshot zero is the shared empty explicit state.\n    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]\n    disliked_states = [\n        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)\n    ]\n    state_counts: list[tuple[int, int]] = [(0, 0)]\n    user_ids: list[int] = []\n    offsets = [0]\n    timestamp_parts: list[np.ndarray] = []\n    item_parts: list[np.ndarray] = []\n    event_parts: list[np.ndarray] = []\n    activity_parts: list[np.ndarray] = []\n    state_parts: list[np.ndarray] = []\n\n    listen_paths = [data / f\"{prefix}_listens.parquet\" for prefix in prefixes]\n    started = time.monotonic()\n    for seen_users, (uid, listens) in enumerate(\n        base.merge_listen_streams(listen_paths, item_index), start=1\n    ):\n        parts = build_sequence_parts(\n            listens,\n            explicit_by_user.get(uid, base.empty_explicit_events()),\n            n_items,\n            liked_states,\n            disliked_states,\n            state_counts,\n        )\n        if parts is None:\n            continue\n        sequence_row = len(user_ids)\n        user_ids.append(uid)\n        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))\n        item_parts.append(parts[1])\n        event_parts.append(parts[2])\n        activity_parts.append(parts[3])\n        state_parts.append(parts[4])\n        offsets.append(offsets[-1] + len(parts[1]))\n        inference_row = inference_rows.get(uid)\n        if inference_row is not None:\n            inference_sequence_rows[inference_row] = sequence_row\n            inference_user_features[inference_row] = final_user_features(\n                parts[0],\n                parts[1],\n                parts[2],\n                history_end,\n                int(like_counts[inference_row]),\n                int(dislike_counts[inference_row]),\n            )\n        if seen_users % 1_000 == 0:\n            log(\n                f\"prepared users={seen_users:,} tokens={offsets[-1]:,} \"\n                f\"states={len(liked_states):,} \"\n                f\"elapsed={time.monotonic() - started:.1f}s\"\n            )\n\n    missing = np.flatnonzero(inference_sequence_rows < 0)\n    if len(missing):\n        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0\n        inference_user_features[missing, 5] = (\n            np.log1p(dislike_counts[missing]) / 4.0\n        )\n        log(f\"target users without qualifying catalog sequence={len(missing):,}\")\n\n    prepared = SequenceData(\n        item_ids=item_ids,\n        item_features=base.build_item_features(catalog),\n        user_ids=np.asarray(user_ids, dtype=np.int64),\n        offsets=np.asarray(offsets, dtype=np.int64),\n        sequence_timestamps=np.concatenate(timestamp_parts),\n        sequence_items=np.concatenate(item_parts),\n        event_features=np.concatenate(event_parts),\n        activity_features=np.concatenate(activity_parts),\n        state_ids=np.concatenate(state_parts),\n        liked_states=np.stack(liked_states),\n        disliked_states=np.stack(disliked_states),\n        state_counts=np.asarray(state_counts, dtype=np.int32),\n        inference_user_rows=inference_sequence_rows,\n        inference_liked_items=inference_likes,\n        inference_disliked_items=inference_dislikes,\n        inference_user_features=inference_user_features,\n    )\n    log(\n        f\"prepared stage={stage} sequence_users={len(prepared.user_ids):,} \"\n        f\"tokens={len(prepared.sequence_items):,} \"\n        f\"next_item_targets={prepared.target_count:,} \"\n        f\"states={len(prepared.liked_states):,}\"\n    )\n    return prepared, eligibility\n\n\ndef cache_metadata(stage: str, config: Config) -> dict[str, object]:\n    return {\n        \"cache_version\": CACHE_VERSION,\n        \"stage\": stage,\n        \"positive_listen_threshold\": base.LISTEN_POSITIVE_THRESHOLD,\n        \"profile_lookback_days\": base.PROFILE_LOOKBACK_DAYS,\n        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n    }\n\n\ndef save_cache(\n    path: Path, prepared: SequenceData, stage: str, config: Config\n) -> None:\n    path.parent.mkdir(parents=True, exist_ok=True)\n    np.savez(\n        path,\n        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),\n        **prepared.__dict__,\n    )\n    log(f\"saved public-derived full-sequence cache: {path}\")\n\n\ndef load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n    with np.load(path, allow_pickle=False) as artifact:\n        actual = json.loads(str(artifact[\"metadata\"].item()))\n        expected = cache_metadata(stage, config)\n        if actual != expected:\n            raise RuntimeError(f\"sequence cache differs: {actual} != {expected}\")\n        values = {\n            field: artifact[field].copy()\n            for field in SequenceData.__dataclass_fields__\n        }\n    prepared = SequenceData(**values)\n    log(\n        f\"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} \"\n        f\"targets={prepared.target_count:,}\"\n    )\n    return prepared\n\n\ndef load_or_prepare(\n    public: Path,\n    stage: str,\n    config: Config,\n    cache: Path | None,\n) -> tuple[SequenceData, CandidateSet]:\n    candidate_name = (\n        \"validation_eligible_candidates.npz\"\n        if stage == \"validation\"\n        else \"test_eligible_candidates.npz\"\n    )\n    eligibility = CandidateSet.read(public / \"data\" / candidate_name)\n    if cache is not None and cache.is_file():\n        prepared = load_cache(cache, stage, config)\n        if not np.array_equal(prepared.item_ids, eligibility.item_ids):\n            raise RuntimeError(\"cached catalog differs from candidate artifact\")\n        return prepared, eligibility\n    prepared, built_eligibility = prepare_data(public, stage, config)\n    if cache is not None:\n        save_cache(cache, prepared, stage, config)\n    return prepared, built_eligibility\n\n\ndef build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n           …10110 tokens truncated…  )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\n        history.append(prior)\n        best_loss = float(prior[\"validation_loss\"])\n        best_epoch = start_epoch\n        best_state = snapshot_model(model)\n        log(\n            f\"resumed validation-BCE training: epoch={start_epoch} \"\n            f\"loss={best_loss:.6f}\"\n        )\n\n    for epoch in range(start_epoch + 1, config.epochs + 1):\n        diagnostics = run_training_epoch(\n            model,\n            optimizer,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            item_correction,\n            known_positive_packed,\n            generator,\n            device,\n            epoch,\n        )\n        validation = evaluate_validation_bce(\n            model,\n            prepared,\n            config,\n            target_weights,\n            partition.window_starts,\n            training_counts,\n            validation_counts,\n            item_correction,\n            known_positive_packed,\n            device,\n        )\n        diagnostics.update(validation)\n        history.append(diagnostics)\n        current_state = snapshot_model(model)\n        if checkpoint_dir is not None:\n            save_epoch_checkpoint(\n                checkpoint_dir,\n                epoch,\n                current_state,\n                config,\n                diagnostics,\n                optimizer,\n                generator,\n            )\n        improved = (\n            diagnostics[\"validation_loss\"]\n            < best_loss - config.early_stopping_min_delta\n        )\n        if improved:\n            best_loss = diagnostics[\"validation_loss\"]\n            best_epoch = epoch\n            best_state = current_state\n            epochs_without_improvement = 0\n            if checkpoint_dir is not None:\n                torch.save(\n                    {\n                        \"epoch\": epoch,\n                        \"config\": asdict(config),\n                        \"diagnostics\": diagnostics,\n                        \"model_state\": current_state,\n                        \"optimizer_state\": optimizer.state_dict(),\n                        \"generator_state\": generator.get_state(),\n                    },\n                    checkpoint_dir / \"best.pt\",\n                )\n        else:\n            epochs_without_improvement += 1\n        log(\n            f\"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} \"\n            f\"validation_loss={diagnostics['validation_loss']:.5f} \"\n            f\"pair_accuracy={diagnostics['pair_accuracy']:.3f} \"\n            f\"train_targets={int(diagnostics['targets']):,} \"\n            f\"validation_targets={int(diagnostics['validation_targets']):,} \"\n            f\"train_seconds={diagnostics['seconds']:.1f} \"\n            f\"validation_seconds={diagnostics['validation_seconds']:.1f}\"\n        )\n        release_accelerator_cache(device)\n        if epochs_without_improvement >= config.early_stopping_patience:\n            log(\n                f\"early stopping after epoch={epoch}; \"\n                f\"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}\"\n            )\n            break\n\n    if best_state is None:\n        raise RuntimeError(\"training did not produce a valid checkpoint\")\n    model.load_state_dict(best_state)\n    model.to(device).eval()\n    log(\n        f\"restored best validation-BCE checkpoint: epoch={best_epoch} \"\n        f\"loss={best_loss:.6f}\"\n    )\n    return model, history\n\n\[email protected]_mode()\ndef inference_queries(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n) -> torch.Tensor:\n    sequence_rows = prepared.inference_user_rows\n    lengths = np.where(\n        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0\n    )\n    ordered = np.argsort(lengths, kind=\"stable\")\n    result = torch.zeros(\n        len(sequence_rows), config.embedding_dim, dtype=torch.float32\n    )\n    for group_start in range(0, len(ordered), config.user_batch_size):\n        output_rows = ordered[group_start : group_start + config.user_batch_size]\n        rows = sequence_rows[output_rows]\n        group_lengths = lengths[output_rows]\n        hidden = torch.zeros(\n            1, len(output_rows), config.embedding_dim, device=device\n        )\n        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):\n            steps = min(\n                config.bptt_steps,\n                int(group_lengths.max(initial=0)) - start,\n            )\n            items = np.full(\n                (len(output_rows), steps),\n                len(prepared.item_ids),\n                dtype=np.uint16,\n            )\n            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)\n            counts = np.zeros(len(output_rows), dtype=np.int32)\n            for local_row, sequence_row in enumerate(rows):\n                if sequence_row < 0:\n                    continue\n                count = min(steps, max(int(group_lengths[local_row]) - start, 0))\n                if not count:\n                    continue\n                offset = int(prepared.offsets[sequence_row])\n                selected = slice(offset + start, offset + start + count)\n                items[local_row, :count] = prepared.sequence_items[selected]\n                features[local_row, :count] = prepared.event_features[selected]\n                counts[local_row] = count\n            outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(items.astype(np.int64)).to(device),\n                torch.from_numpy(features.astype(np.float32)).to(device),\n                hidden,\n            )\n            next_rows: list[torch.Tensor] = []\n            for local_row, count in enumerate(counts):\n                next_rows.append(\n                    outputs[local_row, count - 1]\n                    if count\n                    else hidden[0, local_row]\n                )\n            hidden = torch.stack(next_rows).unsqueeze(0)\n\n        liked_items = torch.from_numpy(\n            prepared.inference_liked_items[output_rows].astype(np.int64)\n        ).to(device)\n        disliked_items = torch.from_numpy(\n            prepared.inference_disliked_items[output_rows].astype(np.int64)\n        ).to(device)\n        queries = user_queries(\n            model,\n            hidden.squeeze(0),\n            model._mean_context_channel(liked_items),\n            model._mean_context_channel(disliked_items),\n            torch.from_numpy(\n                prepared.inference_user_features[output_rows].astype(np.float32)\n            ).to(device),\n        )\n        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()\n    return result\n\n\[email protected]_mode()\ndef rank_candidates(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    eligibility: CandidateSet,\n    config: Config,\n    device: torch.device,\n    ranking_batch_size: int,\n) -> tuple[pd.DataFrame, dict[str, float]]:\n    model.eval()\n    queries = inference_queries(model, prepared, config, device)\n    item_parts: list[torch.Tensor] = []\n    for start in range(0, len(prepared.item_ids), 2_048):\n        rows = torch.arange(\n            start,\n            min(start + 2_048, len(prepared.item_ids)),\n            dtype=torch.long,\n            device=device,\n        )\n        item_parts.append(model.encode_items(rows).cpu())\n    items = torch.cat(item_parts)\n    rows: list[tuple[int, int, int]] = []\n    for start in range(0, len(eligibility.uids), ranking_batch_size):\n        end = min(start + ranking_batch_size, len(eligibility.uids))\n        scores = queries[start:end] @ items.T\n        eligible = np.unpackbits(\n            eligibility.packed[start:end],\n            axis=1,\n            count=len(prepared.item_ids),\n            bitorder=\"little\",\n        ).astype(bool, copy=False)\n        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)\n        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()\n        for local_row, columns in enumerate(selected):\n            uid = int(eligibility.uids[start + local_row])\n            rows.extend(\n                (uid, int(prepared.item_ids[column]), rank)\n                for rank, column in enumerate(columns, start=1)\n            )\n        if end == len(eligibility.uids) or end % 1_024 == 0:\n            log(f\"ranked users={end:,}/{len(eligibility.uids):,}\")\n    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()\n    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()\n    diagnostics = {\n        \"query_norm_mean\": float(query_norm.mean()),\n        \"query_norm_std\": float(query_norm.std()),\n        \"item_norm_mean\": float(item_norm.mean()),\n        \"item_norm_std\": float(item_norm.std()),\n    }\n    return pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"]), diagnostics\n\n\ndef write_ranking(frame: pd.DataFrame, output: Path) -> None:\n    output.parent.mkdir(parents=True, exist_ok=True)\n    if output.suffix == \".parquet\":\n        frame.to_parquet(output, index=False, compression=\"zstd\")\n    elif output.suffix == \".csv\":\n        frame.to_csv(output, index=False)\n    else:\n        raise ValueError(\"--output must end in .parquet or .csv\")\n\n\ndef main() -> int:\n    parser = argparse.ArgumentParser(description=__doc__)\n    parser.add_argument(\n        \"--stage\", choices=(\"validation\", \"submission\"), default=\"validation\"\n    )\n    parser.add_argument(\"--output\", type=Path, required=True)\n    parser.add_argument(\"--public-dir\", type=Path, required=True)\n    parser.add_argument(\"--prepared-cache\", type=Path)\n    parser.add_argument(\"--device\", choices=(\"cpu\", \"mps\"), default=\"cpu\")\n    parser.add_argument(\"--threads\", type=int, default=8)\n    parser.add_argument(\"--bptt-steps\", type=int, default=Config.bptt_steps)\n    parser.add_argument(\n        \"--user-batch-size\", type=int, default=Config.user_batch_size\n    )\n    parser.add_argument(\"--epochs\", type=int, default=Config.epochs)\n    parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n    parser.add_argument(\n        \"--loss-microbatch-size\",\n        type=int,\n        default=Config.loss_microbatch_size,\n    )\n    parser.add_argument(\n        \"--negative-filter-scope\",\n        choices=(\"full-training-history\", \"last-100\"),\n        default=Config.negative_filter_scope,\n    )\n    parser.add_argument(\n        \"--target-weighting\",\n        choices=(\"discovery-graded\", \"organic-strong\", \"uniform\"),\n        default=Config.target_weighting,\n    )\n    parser.add_argument(\n        \"--ordinary-target-weight\",\n        type=float,\n        default=Config.ordinary_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-strong-target-weight\",\n        type=float,\n        default=Config.organic_strong_target_weight,\n    )\n    parser.add_argument(\n        \"--organic-like-target-weight\",\n        type=float,\n        default=Config.organic_like_target_weight,\n    )\n    parser.add_argument(\n        \"--discovery-horizon-days\",\n        type=int,\n        default=Config.discovery_horizon_days,\n    )\n    parser.add_argument(\n        \"--validation-fraction\",\n        type=float,\n        default=Config.validation_fraction,\n    )\n    parser.add_argument(\n        \"--max-train-targets-per-user\",\n        type=int,\n        default=Config.max_train_targets_per_user,\n        help=\"retain this many recent pre-validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--max-validation-targets-per-user\",\n        type=int,\n        default=Config.max_validation_targets_per_user,\n        help=\"retain this many final validation targets per user; 0 is uncapped\",\n    )\n    parser.add_argument(\n        \"--early-stopping-patience\",\n        type=int,\n        default=Config.early_stopping_patience,\n    )\n    parser.add_argument(\n        \"--early-stopping-min-delta\",\n        type=float,\n        default=Config.early_stopping_min_delta,\n    )\n    parser.add_argument(\"--checkpoint-dir\", type=Path)\n    parser.add_argument(\"--resume-checkpoint\", type=Path)\n    parser.add_argument(\"--seed\", type=int, default=Config.seed)\n    parser.add_argument(\"--ranking-batch-size\", type=int, default=64)\n    parser.add_argument(\"--prepare-only\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:\n        parser.error(\"threads, BPTT steps, and user batch size must be positive\")\n    if (\n        args.epochs < 1\n        or args.learning_rate <= 0\n        or args.loss_microbatch_size < 1\n        or args.early_stopping_patience < 1\n        or args.early_stopping_min_delta < 0\n        or args.ordinary_target_weight <= 0\n        or args.organic_strong_target_weight <= 0\n        or args.organic_like_target_weight <= 0\n        or args.discovery_horizon_days < 1\n        or args.max_train_targets_per_user < 0\n        or args.max_validation_targets_per_user < 0\n    ):\n        parser.error(\"training counts, learning rate, and patience are invalid\")\n    if not 0.0 < args.validation_fraction < 1.0:\n        parser.error(\"validation fraction must be strictly between zero and one\")\n    public = args.public_dir.resolve()\n    if not (public / \"data\" / \"candidate_catalog.parquet\").is_file():\n        parser.error(f\"public task data not found under {public}\")\n    config = Config(\n        bptt_steps=args.bptt_steps,\n        user_batch_size=args.user_batch_size,\n        epochs=args.epochs,\n        learning_rate=args.learning_rate,\n        loss_microbatch_size=args.loss_microbatch_size,\n        negative_filter_scope=args.negative_filter_scope,\n        target_weighting=args.target_weighting,\n        ordinary_target_weight=args.ordinary_target_weight,\n        organic_strong_target_weight=args.organic_strong_target_weight,\n        organic_like_target_weight=args.organic_like_target_weight,\n        discovery_horizon_days=args.discovery_horizon_days,\n        validation_fraction=args.validation_fraction,\n        max_train_targets_per_user=args.max_train_targets_per_user,\n        max_validation_targets_per_user=args.max_validation_targets_per_user,\n        early_stopping_patience=args.early_stopping_patience,\n        early_stopping_min_delta=args.early_stopping_min_delta,\n        seed=args.seed,\n    )\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    seed_everything(config.seed)\n    device = torch.device(args.device)\n    if device.type == \"mps\" and not torch.backends.mps.is_available():\n        parser.error(\"MPS was requested but is unavailable\")\n\n    started = time.monotonic()\n    prepared, eligibility = load_or_prepare(\n        public,\n        args.stage,\n        config,\n        args.prepared_cache.resolve() if args.prepared_cache else None,\n    )\n    target_partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    if args.prepare_only:\n        print(\n            json.dumps(\n                {\n                    \"stage\": args.stage,\n                    \"sequence_users\": len(prepared.user_ids),\n                    \"sequence_tokens\": len(prepared.sequence_items),\n                    \"next_item_targets\": prepared.target_count,\n                    \"selected_training_targets\": int(\n                        target_partition.training_counts.sum()\n                    ),\n                    \"selected_validation_targets\": int(\n                        target_partition.validation_counts.sum()\n                    ),\n                    \"discarded_old_targets\": int(\n                        target_partition.window_starts.sum()\n                    ),\n                    \"target_users\": len(eligibility.uids),\n                    \"runtime_seconds\": round(time.monotonic() - started, 3),\n                    \"config\": asdict(config),\n                },\n                indent=2,\n            )\n        )\n        return 0\n\n    model, training_history = train_model(\n        public,\n        args.stage,\n        prepared,\n        config,\n        device,\n        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,\n        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,\n    )\n    ranking, embedding_diagnostics = rank_candidates(\n        model,\n        prepared,\n        eligibility,\n        config,\n        device,\n        args.ranking_batch_size,\n    )\n    write_ranking(ranking, args.output.resolve())\n    print(\n        json.dumps(\n            {\n                \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n                \"stage\": args.stage,\n                \"output\": str(args.output.resolve()),\n                \"rows\": len(ranking),\n                \"users\": int(ranking.uid.nunique()),\n                \"runtime_seconds\": round(time.monotonic() - started, 3),\n                \"public_inputs_only\": True,\n                \"architecture\": {\n                    \"family\": \"pure-windowed-sequence-two-tower\",\n                    \"sequence_training\": (\n                        \"every-adjacent-target-within-recent-chronological-window\"\n                    ),\n                    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n                    \"bptt_hidden_state\": \"carried-and-detached\",\n                    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n                    \"loss_execution\": \"gradient-accumulated-microbatches\",\n                    \"user_id_embedding\": False,\n                    \"user_mlp\": \"582-512-256-192\",\n                    \"item_mlp\": \"196-512-256-192\",\n                    \"normalization\": \"none\",\n                    \"score\": \"raw-dot-product\",\n                    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n                    \"negative_sampling\": (\n                        \"24-uniform-plus-8-propensity-corrected-inbatch\"\n                    ),\n                    \"false_negative_filter\": config.negative_filter_scope,\n                    \"target_weighting\": config.target_weighting,\n                },\n                \"config\": asdict(config),\n                \"sequence_tokens\": len(prepared.sequence_items),\n                \"all_next_item_targets\": prepared.target_count,\n                \"selected_training_targets\": int(\n                    target_partition.training_counts.sum()\n                ),\n                \"selected_validation_targets\": int(\n                    target_partition.validation_counts.sum()\n                ),\n                \"discarded_old_targets\": int(target_partition.window_starts.sum()),\n                \"training_history\": training_history,\n                \"embedding_diagnostics\": embedding_diagnostics,\n                \"torch_version\": torch.__version__,\n                \"device\": str(device),\n            },\n            indent=2,\n        )\n    )\n    return 0\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-20d55b42-852b-4303-ac2c-4a659afbdbbb",
    "process_id": "45871",
    "command": [
      "/bin/sh",
      "-lc",
      "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && rg -n '^def |^class |negative|binary_cross' /task/current_model/train_current_model.py"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cp /task/current_model/train_current_model.py /task/current_model/two_tower_model.py /app/solution/ && rg -n '^def |^class |negative|binary_cross' /task/current_model/train_current_model.py"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1629:            stored_filter_scope != config.negative_filter_scope\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944:        \"--negative-filter-scope\",\n1946:        default=Config.negative_filter_scope,\n2034:        negative_filter_scope=args.negative_filter_scope,\n2136:                    \"negative_sampling\": (\n2139:                    \"false_negative_filter\": config.negative_filter_scope,\n",
    "stderr": "",
    "aggregated_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1629:            stored_filter_scope != config.negative_filter_scope\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944:        \"--negative-filter-scope\",\n1946:        default=Config.negative_filter_scope,\n2034:        negative_filter_scope=args.negative_filter_scope,\n2136:                    \"negative_sampling\": (\n2139:                    \"false_negative_filter\": config.negative_filter_scope,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4360
    },
    "formatted_output": "14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n41:class Config:\n56:    uniform_negatives: int = 24\n57:    inbatch_negatives: int = 8\n61:    negative_filter_scope: str = \"full-training-history\"\n76:class CandidateSet:\n99:class SequenceData:\n127:class TargetPartition:\n140:def log(message: str) -> None:\n144:def seed_everything(seed: int) -> None:\n150:def model_config(config: Config) -> base.TowerConfig:\n165:        uniform_negatives=config.uniform_negatives,\n166:        inbatch_negatives=config.inbatch_negatives,\n174:def append_state_snapshot(\n190:def causal_activity_features(\n247:def build_sequence_parts(\n314:def final_user_features(\n341:def prepare_data(\n471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:\n477:        \"event_time_feature\": \"exp-negative-gap-over-60-days\",\n481:def save_cache(\n493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:\n511:def load_or_prepare(\n534:def build_item_correction(\n578:def build_known_positive_filter(\n603:        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n609:def invalid_full_history_negatives(\n610:    negatives: np.ndarray,\n616:    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n617:    byte_columns = negatives >> 3\n618:    bit_columns = negatives & 7\n621:    invalid |= negatives == targets[:, None]\n622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n623:    for column in range(1, negatives.shape[1]):\n625:            negatives[:, column, None] == negatives[:, :column]\n630:def draw_full_history_filtered_negatives(\n641:    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n647:        (batch_size, config.uniform_negatives),\n653:    if config.inbatch_negatives:\n656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n672:        if corrected_count < config.inbatch_negatives:\n675:                (batch_size, config.inbatch_negatives - corrected_count),\n681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n682:    correction = np.ones_like(negatives, dtype=np.float32)\n685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n688:        invalid = invalid_full_history_negatives(\n689:            negatives,\n697:                torch.from_numpy(negatives).to(device),\n706:        negatives[invalid] = replacements\n709:    invalid = invalid_full_history_negatives(\n710:        negatives,\n717:        raise RuntimeError(\"full-history negative resampling failed\")\n719:        torch.from_numpy(negatives).to(device),\n724:def draw_training_negatives(\n736:    if config.negative_filter_scope == \"full-training-history\":\n738:            raise RuntimeError(\"full-history negative filter was not built\")\n739:        return draw_full_history_filtered_negatives(\n750:    return base.draw_filtered_mixed_negatives(\n755:        config.uniform_negatives,\n756:        config.inbatch_negatives,\n763:def event_sequence(\n774:def pool_explicit_states(\n798:def user_queries(\n813:def candidate_vectors(\n816:    negatives: torch.Tensor,\n818:    all_items = torch.cat([positives, negatives.reshape(-1)])\n822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n823:        len(positives), negatives.shape[1], -1\n825:    return positive_vectors, negative_vectors\n828:def history_windows(\n845:def corrected_negative_loss(\n851:    total = config.uniform_negatives + config.inbatch_negatives\n853:    if config.uniform_negatives:\n854:        result += config.uniform_negatives * raw[\n855:            :, : config.uniform_negatives\n857:    if config.inbatch_negatives:\n858:        selected = slice(config.uniform_negatives, total)\n860:        result += config.inbatch_negatives * (\n867:def retained_target_window_starts(\n880:        raise RuntimeError(\"chronological target partition produced negative counts\")\n888:def chronological_target_partition(\n914:def load_organic_like_outcomes(\n955:def build_target_weights(\n1035:def build_dense_block(\n1111:def score_target_microbatches(\n1119:    negatives: torch.Tensor,\n1120:    negative_weights: torch.Tensor,\n1134:        \"negative_sum\": 0.0,\n1162:        positive_vectors, negative_vectors = candidate_vectors(\n1165:            negatives[begin:end],\n1168:        negative_logits = torch.einsum(\n1169:            \"bd,bkd->bk\", query_vectors, negative_vectors\n1172:        negative_loss = corrected_negative_loss(\n1173:            negative_logits, negative_weights[begin:end], config\n1179:            selected_weights * (positive_loss + negative_loss) / 2.0\n1190:        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n1198:def release_accelerator_cache(device: torch.device) -> None:\n1203:def run_training_epoch(\n1227:    negative_count = config.uniform_negatives + config.inbatch_negatives\n1232:    total_negative = 0.0\n1293:            negatives, negative_weights = draw_training_negatives(\n1314:                negatives,\n1315:                negative_weights,\n1331:            total_negative += statistics[\"negative_sum\"]\n1353:        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n1355:        / max(total_targets * negative_count, 1),\n1364:def evaluate_validation_bce(\n1449:            negatives, negative_weights = draw_training_negatives(\n1469:                negatives,\n1470:                negative_weights,\n1488:def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n1495:def save_epoch_checkpoint(\n1521:def train_model(\n1567:        if config.negative_filter_scope == \"full-training-history\"\n1601:            \"negative_filter_scope\", \"last-100\"\n1629:            stored_filter_scope != config.negative_filter_scope\n1660:                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n1782:def inference_queries(\n1861:def rank_candidates(\n1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:\n1922:def main() -> int:\n1944:        \"--negative-filter-scope\",\n1946:        default=Config.negative_filter_scope,\n2034:        negative_filter_scope=args.negative_filter_scope,\n2136:                    \"negative_sampling\": (\n2139:                    \"false_negative_filter\": config.negative_filter_scope,\n"
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-872226f8-7d77-4fd1-bfb2-2e5e0a4c3966",
    "process_id": "8597",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /task/current_model/two_tower_model.py && cat /task/evaluate_public.py"
    ],
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    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "cat /task/current_model/two_tower_model.py",
        "name": "two_tower_model.py",
        "path": "/task/current_model/two_tower_model.py"
      },
      {
        "type": "read",
        "cmd": "cat /task/evaluate_public.py",
        "name": "evaluate_public.py",
        "path": "/task/evaluate_public.py"
      }
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    "status": "completed",
    "stdout": "\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "stderr": "",
    "aggregated_output": "\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2830
    },
    "formatted_output": "\"\"\"Shared public-data primitives for the Yambda two-tower baseline.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Iterator\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nfrom torch import nn\n\n\nDAY = 86_400\nTRAIN_END = 24_786_800\nVALIDATION_END = 25_393_400\nLISTEN_POSITIVE_THRESHOLD = 50\nPROFILE_LOOKBACK_DAYS = 120\nLIKE_ITEMS = 48\nDISLIKE_ITEMS = 24\nTOP_N = 100\n\n\n@dataclass(frozen=True)\nclass TowerConfig:\n    sequence_length: int = 100\n    embedding_dim: int = 192\n    hidden_dim: int = 192\n    user_hidden_1: int = 512\n    user_hidden_2: int = 256\n    item_hidden_1: int = 512\n    item_hidden_2: int = 256\n    dropout: float = 0.05\n    epochs: int = 1\n    learning_rate: float = 1.0e-3\n    weight_decay: float = 1.0e-5\n    gradient_clip: float = 2.0\n    uniform_negatives: int = 24\n    inbatch_negatives: int = 8\n    inbatch_correction_power: float = 0.5\n    inbatch_correction_min: float = 0.25\n    inbatch_correction_max: float = 4.0\n    seed: int = 29\n\n\n@dataclass(frozen=True)\nclass ListenEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    organic: np.ndarray\n    completion_pct: np.ndarray\n\n\n@dataclass(frozen=True)\nclass ExplicitEvents:\n    timestamps: np.ndarray\n    items: np.ndarray\n    channel: np.ndarray\n    active: np.ndarray\n    organic_like: np.ndarray\n\n\ndef log(message: str) -> None:\n    print(message, file=sys.stderr, flush=True)\n\n\ndef build_item_features(catalog: pd.DataFrame) -> np.ndarray:\n    \"\"\"Return four standardized features built only from the public catalog.\"\"\"\n    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)\n    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)\n    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)\n    features = np.column_stack(\n        [\n            np.log1p(positives),\n            np.log1p(organic),\n            organic / np.maximum(positives, 1.0),\n            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),\n        ]\n    )\n    means = features.mean(axis=0, keepdims=True)\n    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)\n    standardized = ((features - means) / scales).astype(np.float32)\n    if not np.isfinite(standardized).all():\n        raise RuntimeError(\"catalog features contain non-finite values\")\n    return standardized\n\n\ndef _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:\n    if len(parts) == 1:\n        return parts[0]\n    timestamps = np.concatenate([part.timestamps for part in parts])\n    items = np.concatenate([part.items for part in parts])\n    organic = np.concatenate([part.organic for part in parts])\n    completion = np.concatenate([part.completion_pct for part in parts])\n    order = np.argsort(timestamps, kind=\"stable\")\n    return ListenEvents(\n        timestamps=timestamps[order],\n        items=items[order],\n        organic=organic[order],\n        completion_pct=completion[order],\n    )\n\n\ndef iter_listen_users(\n    path: Path, item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Stream one uid-sorted public listen file one user at a time.\"\"\"\n    parquet = pq.ParquetFile(path)\n    pending_uid: int | None = None\n    pending: list[ListenEvents] = []\n    previous_uid = -1\n    for batch in parquet.iter_batches(\n        batch_size=750_000,\n        columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n    ):\n        frame = batch.to_pandas()\n        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))\n        keep = mapped >= 0\n        if not np.any(keep):\n            continue\n        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]\n        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]\n        items = mapped[keep].astype(np.int32, copy=False)\n        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]\n        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]\n        if np.any(uids[1:] < uids[:-1]):\n            raise RuntimeError(f\"{path.name} is not sorted by uid\")\n        boundaries = np.r_[\n            0,\n            np.flatnonzero(uids[1:] != uids[:-1]) + 1,\n            len(uids),\n        ]\n        for start, end in zip(boundaries[:-1], boundaries[1:]):\n            uid = int(uids[start])\n            part = ListenEvents(\n                timestamps[start:end].copy(),\n                items[start:end].copy(),\n                organic[start:end].copy(),\n                completion[start:end].copy(),\n            )\n            if pending_uid is None:\n                pending_uid = uid\n            elif uid != pending_uid:\n                if pending_uid < previous_uid:\n                    raise RuntimeError(f\"{path.name} user order regressed\")\n                yield pending_uid, _combine_listen_chunks(pending)\n                previous_uid = pending_uid\n                pending_uid = uid\n                pending = []\n            pending.append(part)\n    if pending_uid is not None:\n        yield pending_uid, _combine_listen_chunks(pending)\n\n\ndef merge_listen_streams(\n    paths: list[Path], item_index: pd.Index\n) -> Iterator[tuple[int, ListenEvents]]:\n    \"\"\"Merge uid-sorted public listen files without materializing them.\"\"\"\n    streams = [iter(iter_listen_users(path, item_index)) for path in paths]\n    heads: list[tuple[int, ListenEvents] | None] = [\n        next(stream, None) for stream in streams\n    ]\n    while any(head is not None for head in heads):\n        uid = min(head[0] for head in heads if head is not None)\n        parts: list[ListenEvents] = []\n        for position, head in enumerate(heads):\n            if head is not None and head[0] == uid:\n                parts.append(head[1])\n                heads[position] = next(streams[position], None)\n        yield uid, _combine_listen_chunks(parts)\n\n\ndef load_explicit_events(\n    data: Path,\n    prefixes: tuple[str, ...],\n    item_index: pd.Index,\n) -> dict[int, ExplicitEvents]:\n    \"\"\"Load public explicit actions as chronological state changes.\"\"\"\n    specifications = (\n        (\"likes\", 0, 1, 0),\n        (\"unlikes\", 0, 0, 1),\n        (\"dislikes\", 1, 1, 0),\n        (\"undislikes\", 1, 0, 1),\n    )\n    parts: list[pd.DataFrame] = []\n    for family, channel, active, tie_priority in specifications:\n        for prefix in prefixes:\n            frame = pd.read_parquet(\n                data / f\"{prefix}_{family}.parquet\",\n                columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n            )\n            mapped = item_index.get_indexer(\n                frame.item_id.to_numpy(dtype=np.int64, copy=False)\n            )\n            keep = mapped >= 0\n            if not np.any(keep):\n                continue\n            retained = frame.loc[keep, [\"uid\", \"timestamp\", \"is_organic\"]].copy()\n            retained[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n            retained[\"channel\"] = np.int8(channel)\n            retained[\"active\"] = np.int8(active)\n            retained[\"tie_priority\"] = np.int8(tie_priority)\n            retained[\"organic_like\"] = np.int8(\n                family == \"likes\"\n            ) * retained.is_organic.astype(np.int8)\n            parts.append(retained.drop(columns=\"is_organic\"))\n    if not parts:\n        return {}\n    events = pd.concat(parts, ignore_index=True)\n    events = events.sort_values(\n        [\"uid\", \"timestamp\", \"tie_priority\"], kind=\"mergesort\"\n    ).reset_index(drop=True)\n    result: dict[int, ExplicitEvents] = {}\n    for uid, rows in events.groupby(\"uid\", sort=False):\n        result[int(uid)] = ExplicitEvents(\n            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),\n            items=rows.item.to_numpy(dtype=np.int32, copy=True),\n            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),\n            active=rows.active.to_numpy(dtype=np.int8, copy=True),\n            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),\n        )\n    log(f\"loaded explicit events: rows={len(events):,} users={len(result):,}\")\n    return result\n\n\ndef empty_explicit_events() -> ExplicitEvents:\n    return ExplicitEvents(\n        timestamps=np.empty(0, dtype=np.int64),\n        items=np.empty(0, dtype=np.int32),\n        channel=np.empty(0, dtype=np.int8),\n        active=np.empty(0, dtype=np.int8),\n        organic_like=np.empty(0, dtype=np.int8),\n    )\n\n\ndef pack_active_items(\n    active: dict[int, int], width: int, padding_item: int\n) -> np.ndarray:\n    packed = np.full(width, padding_item, dtype=np.uint16)\n    if not active:\n        return packed\n    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]\n    packed[: len(ordered)] = np.fromiter(\n        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)\n    )\n    return packed\n\n\ndef load_inference_state(\n    path: Path,\n    item_index: pd.Index,\n    uids: np.ndarray,\n    n_items: int,\n) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:\n    \"\"\"Pack the task-provided active explicit state.\"\"\"\n    state = pd.read_parquet(path, columns=[\"uid\", \"item_id\", \"state\"])\n    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))\n    keep = mapped >= 0\n    state = state.loc[keep, [\"uid\", \"state\"]].copy()\n    state[\"item\"] = mapped[keep].astype(np.int32, copy=False)\n    user_rows = pd.Index(uids)\n    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)\n    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)\n    like_counts = np.zeros(len(uids), dtype=np.int32)\n    dislike_counts = np.zeros(len(uids), dtype=np.int32)\n    for value, width, destination, counts in (\n        (\"liked\", LIKE_ITEMS, liked, like_counts),\n        (\"disliked\", DISLIKE_ITEMS, disliked, dislike_counts),\n    ):\n        selected = state[state.state == value]\n        for uid, rows in selected.groupby(\"uid\", sort=False):\n            row = int(user_rows.get_indexer([int(uid)])[0])\n            if row < 0:\n                continue\n            items = np.sort(rows.item.unique())\n            counts[row] = len(items)\n            retained = items[:width]\n            destination[row, : len(retained)] = retained.astype(np.uint16)\n    return liked, disliked, like_counts, dislike_counts\n\n\nclass SequentialTwoTower(nn.Module):\n    \"\"\"Order-sensitive user tower and independent catalog-aware item tower.\"\"\"\n\n    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:\n        super().__init__()\n        n_items, n_item_features = item_features.shape\n        dim = config.embedding_dim\n        self.n_items = n_items\n        self.context_embedding = nn.Embedding(\n            n_items + 1, dim, padding_idx=n_items\n        )\n        self.event_projection = nn.Sequential(\n            nn.Linear(dim + 3, config.hidden_dim),\n            nn.GELU(),\n        )\n        self.sequence_encoder = nn.GRU(\n            input_size=config.hidden_dim,\n            hidden_size=dim,\n            num_layers=1,\n            batch_first=True,\n        )\n        self.user_tower = nn.Sequential(\n            nn.Linear(dim * 3 + 6, config.user_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_1, config.user_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.user_hidden_2, dim),\n        )\n        self.candidate_embedding = nn.Embedding(n_items, dim)\n        self.register_buffer(\n            \"item_features\", torch.from_numpy(item_features), persistent=True\n        )\n        self.item_tower = nn.Sequential(\n            nn.Linear(dim + n_item_features, config.item_hidden_1),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_1, config.item_hidden_2),\n            nn.GELU(),\n            nn.Dropout(config.dropout),\n            nn.Linear(config.item_hidden_2, dim),\n        )\n        nn.init.normal_(self.context_embedding.weight, std=0.02)\n        nn.init.normal_(self.candidate_embedding.weight, std=0.02)\n        with torch.no_grad():\n            self.context_embedding.weight[n_items].zero_()\n\n    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:\n        mask = items.ne(self.n_items)\n        vectors = self.context_embedding(items)\n        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(\n            dim=1, keepdim=True\n        ).clamp_min(1)\n\n    def encode_items(self, items: torch.Tensor) -> torch.Tensor:\n        inputs = torch.cat(\n            [self.candidate_embedding(items), self.item_features[items]], dim=1\n        )\n        return self.item_tower(inputs)\n\n\ndef invalid_negative_mask(\n    negatives: torch.Tensor,\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n) -> torch.Tensor:\n    \"\"\"Identify targets, known positives, active likes, and row duplicates.\"\"\"\n    invalid = negatives.eq(targets[:, None])\n    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)\n    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= negatives[:, column, None].eq(\n            negatives[:, :column]\n        ).any(dim=1)\n    return invalid\n\n\ndef draw_filtered_mixed_negatives(\n    targets: torch.Tensor,\n    context_items: torch.Tensor,\n    liked_items: torch.Tensor,\n    n_items: int,\n    uniform_count: int,\n    inbatch_count: int,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw uniform and unique-target in-batch negatives.\"\"\"\n    batch_size = len(targets)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, uniform_count),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device=\"cpu\")\n    corrected_count = 0\n    if inbatch_count:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),\n                -1.0,\n            )\n            columns = torch.topk(priorities, k=corrected_count, dim=1).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < inbatch_count:\n            filler = torch.randint(\n                n_items,\n                (batch_size, inbatch_count - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives_cpu = torch.cat([uniform, inbatch], dim=1)\n    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)\n    if corrected_count:\n        correction_cpu[\n            :, uniform_count : uniform_count + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]]\n    negatives = negatives_cpu.to(device)\n    correction = correction_cpu.to(device)\n    for _ in range(32):\n        invalid = invalid_negative_mask(\n            negatives, targets, context_items, liked_items\n        )\n        if not bool(invalid.any()):\n            return negatives, correction\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum().detach().cpu()),),\n            generator=generator,\n            device=\"cpu\",\n        ).to(device)\n        negatives[invalid] = replacements\n        correction[invalid] = 1.0\n    invalid = invalid_negative_mask(\n        negatives, targets, context_items, liked_items\n    )\n    if bool(invalid.any()):\n        raise RuntimeError(\"mixed negative resampling failed false-negative filter\")\n    return negatives, correction\n#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n    python evaluate_public.py <validation-ranking.parquet|csv>\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n    CandidateSet,\n    RankingValidationError,\n    ranked_items,\n    read_ranking,\n    score_rankings,\n    validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n    eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n    \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n    Only target-user events for eligible candidates are retained. Organic\n    listens completed to at least 80 percent receive grade 1, and organic likes\n    receive grade 3. Repeated user-item events are collapsed, and processing\n    likes second ensures that grade 3 wins over grade 1.\n\n    Args:\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Complete set of target user IDs.\n\n    Returns:\n        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n        relevant event are omitted.\n    \"\"\"\n    listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n    likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n    strong = listens[\n        listens.uid.isin(targets)\n        & (listens.is_organic == 1)\n        & (listens.played_ratio_pct >= LISTEN_STRONG)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n    organic_likes = likes[\n        likes.uid.isin(targets) & (likes.is_organic == 1)\n    ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n    graded: dict[int, dict[int, int]] = {}\n    for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 1\n    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n        uid, item = int(raw_uid), int(raw_item)\n        if eligibility.is_eligible(uid, item):\n            graded.setdefault(uid, {})[item] = 3\n    return graded\n\n\ndef load_submission(\n    path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n    \"\"\"Load and validate a public validation ranking.\n\n    Files ending in ``.parquet`` are read as parquet; every other suffix is\n    read as CSV. The required columns must be integer-valued, cover exactly the\n    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n    duplicate user-item pairs, and contain only cutoff-eligible items.\n\n    Args:\n        path: Ranking file to read.\n        eligibility: Validation-cutoff candidate membership service.\n        targets: Exact set of users the ranking must cover.\n\n    Returns:\n        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n    Raises:\n        ValueError: If the ranking violates the required schema or invariants.\n    \"\"\"\n    frame = validate_ranking(read_ranking(path), targets)\n    if not all(\n        eligibility.is_eligible(uid, item)\n        for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n    ):\n        raise RankingValidationError(\n            \"submission contains an item rejected by eligibility service\"\n        )\n    return ranked_items(frame)\n\n\ndef score(\n    ranked: dict[int, list[int]],\n    graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n    \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n    Args:\n        ranked: Ranked item IDs for every target user.\n        graded: Eligible validation relevance keyed by user and item.\n\n    Returns:\n        The six-decimal ``quality_ndcg@10`` value and number of scored users.\n    \"\"\"\n    return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n    \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n\n    if len(sys.argv) != 2:\n        raise SystemExit(\n            \"usage: evaluate_public.py <validation-ranking.parquet|csv>\"\n        )\n    path = Path(sys.argv[1])\n    ranked = load_submission(path, eligibility, targets)\n    print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n    main()\n"
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    "stdout": "def build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\n",
    "stderr": "",
    "aggregated_output": "def build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2711
    },
    "formatted_output": "def build_item_correction(\n    prepared: SequenceData,\n    config: Config,\n    target_starts: np.ndarray,\n    target_counts: np.ndarray,\n) -> torch.Tensor:\n    \"\"\"Estimate in-batch propensities from the selected training targets.\"\"\"\n    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)\n    for user_row, count in enumerate(target_counts):\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        target_start = int(target_starts[user_row])\n        counts += np.bincount(\n            prepared.sequence_items[\n                offset\n                + target_start\n                + 1 : offset\n                + target_start\n                + int(count)\n                + 1\n            ].astype(np.int64),\n            minlength=len(prepared.item_ids),\n        )\n    total = float(counts.sum())\n    probabilities = (counts + 1.0) / (total + len(counts))\n    raw = np.power(\n        (1.0 / len(counts)) / probabilities,\n        config.inbatch_correction_power,\n    )\n    normalization = float(np.sum(counts * raw) / max(total, 1.0))\n    correction = np.clip(\n        raw / max(normalization, 1.0e-12),\n        config.inbatch_correction_min,\n        config.inbatch_correction_max,\n    ).astype(np.float32)\n    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))\n    log(\n        f\"inbatch correction: min={correction.min():.3f} \"\n        f\"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}\"\n    )\n    return torch.from_numpy(correction)\n\n\ndef build_known_positive_filter(\n    prepared: SequenceData, training_counts: np.ndarray\n) -> np.ndarray:\n    \"\"\"Pack each user's complete public-training positive-item set.\"\"\"\n    width = (len(prepared.item_ids) + 7) // 8\n    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)\n    retained = 0\n    for user_row, target_count in enumerate(training_counts):\n        if not target_count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        # T adjacent targets consume sequence positions 0 through T.  The\n        # chronological BCE suffix starts at T + 1 and is intentionally absent.\n        items = np.unique(\n            prepared.sequence_items[\n                offset : offset + int(target_count) + 1\n            ].astype(np.int64)\n        )\n        byte_columns = items >> 3\n        bit_values = np.left_shift(\n            np.uint8(1), (items & 7).astype(np.uint8)\n        )\n        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)\n        retained += len(items)\n    log(\n        f\"full-history false-negative filter: users={len(prepared.user_ids):,} \"\n        f\"unique_user_items={retained:,} bytes={packed.nbytes:,}\"\n    )\n    return packed\n\n\ndef invalid_full_history_negatives(\n    negatives: np.ndarray,\n    targets: np.ndarray,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray,\n    liked_items: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return row-level invalid negatives using the complete training prefix.\"\"\"\n    byte_columns = negatives >> 3\n    bit_columns = negatives & 7\n    known = known_positive_packed[user_rows[:, None], byte_columns]\n    invalid = ((known >> bit_columns) & 1).astype(bool)\n    invalid |= negatives == targets[:, None]\n    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)\n    for column in range(1, negatives.shape[1]):\n        invalid[:, column] |= (\n            negatives[:, column, None] == negatives[:, :column]\n        ).any(axis=1)\n    return invalid\n\n\ndef draw_full_history_filtered_negatives(\n    targets: torch.Tensor,\n    user_rows: np.ndarray,\n    liked_items: torch.Tensor,\n    known_positive_packed: np.ndarray,\n    n_items: int,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    \"\"\"Draw mixed negatives and reject all known public-training positives.\"\"\"\n    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)\n    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)\n    batch_size = len(targets_cpu)\n    uniform = torch.randint(\n        n_items,\n        (batch_size, config.uniform_negatives),\n        generator=generator,\n        device=\"cpu\",\n    )\n    inbatch = torch.empty((batch_size, 0), dtype=torch.long)\n    corrected_count = 0\n    if config.inbatch_negatives:\n        unique_targets = torch.unique(targets.detach().cpu())\n        corrected_count = min(\n            config.inbatch_negatives, max(len(unique_targets) - 1, 0)\n        )\n        if corrected_count:\n            priorities = torch.rand(\n                (batch_size, len(unique_targets)), generator=generator\n            )\n            priorities.masked_fill_(\n                unique_targets[None, :].eq(\n                    torch.from_numpy(targets_cpu)[:, None]\n                ),\n                -1.0,\n            )\n            columns = torch.topk(\n                priorities, k=corrected_count, dim=1\n            ).indices\n            inbatch = unique_targets[columns]\n        if corrected_count < config.inbatch_negatives:\n            filler = torch.randint(\n                n_items,\n                (batch_size, config.inbatch_negatives - corrected_count),\n                generator=generator,\n                device=\"cpu\",\n            )\n            inbatch = torch.cat([inbatch, filler], dim=1)\n\n    negatives = torch.cat([uniform, inbatch], dim=1).numpy()\n    correction = np.ones_like(negatives, dtype=np.float32)\n    if corrected_count:\n        correction[\n            :, config.uniform_negatives : config.uniform_negatives + corrected_count\n        ] = item_correction[inbatch[:, :corrected_count]].numpy()\n    for _ in range(32):\n        invalid = invalid_full_history_negatives(\n            negatives,\n            targets_cpu,\n            user_rows,\n            known_positive_packed,\n            liked_cpu,\n        )\n        if not np.any(invalid):\n            return (\n                torch.from_numpy(negatives).to(device),\n                torch.from_numpy(correction).to(device),\n            )\n        replacements = torch.randint(\n            n_items,\n            (int(invalid.sum()),),\n            generator=generator,\n            device=\"cpu\",\n        ).numpy()\n        negatives[invalid] = replacements\n        # Rejected in-batch items become ordinary uniform draws.\n        correction[invalid] = 1.0\n    invalid = invalid_full_history_negatives(\n        negatives,\n        targets_cpu,\n        user_rows,\n        known_positive_packed,\n        liked_cpu,\n    )\n    if np.any(invalid):\n        raise RuntimeError(\"full-history negative resampling failed\")\n    return (\n        torch.from_numpy(negatives).to(device),\n        torch.from_numpy(correction).to(device),\n    )\n\n\ndef draw_training_negatives(\n    positive_items: torch.Tensor,\n    known_history: torch.Tensor,\n    liked_items: torch.Tensor,\n    user_rows: np.ndarray,\n    known_positive_packed: np.ndarray | None,\n    prepared: SequenceData,\n    config: Config,\n    item_correction: torch.Tensor,\n    generator: torch.Generator,\n    device: torch.device,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    if config.negative_filter_scope == \"full-training-history\":\n        if known_positive_packed is None:\n            raise RuntimeError(\"full-history negative filter was not built\")\n        return draw_full_history_filtered_negatives(\n            positive_items,\n            user_rows,\n            liked_items,\n            known_positive_packed,\n            len(prepared.item_ids),\n            config,\n            item_correction,\n            generator,\n            device,\n        )\n    return base.draw_filtered_mixed_negatives(\n        positive_items,\n        known_history,\n        liked_items,\n        len(prepared.item_ids),\n        config.uniform_negatives,\n        config.inbatch_negatives,\n        item_correction,\n        generator,\n        device,\n    )\n\n\ndef event_sequence(\ndef user_queries(\n    model: base.SequentialTwoTower,\n    sequence_vectors: torch.Tensor,\n    liked_vectors: torch.Tensor,\n    disliked_vectors: torch.Tensor,\n    user_features: torch.Tensor,\n) -> torch.Tensor:\n    return model.user_tower(\n        torch.cat(\n            [sequence_vectors, liked_vectors, disliked_vectors, user_features],\n            dim=1,\n        )\n    )\n\n\ndef candidate_vectors(\n    model: base.SequentialTwoTower,\n    positives: torch.Tensor,\n    negatives: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n    all_items = torch.cat([positives, negatives.reshape(-1)])\n    unique_items, inverse = torch.unique(all_items, return_inverse=True)\n    encoded = model.encode_items(unique_items)\n    positive_vectors = encoded[inverse[: len(positives)]]\n    negative_vectors = encoded[inverse[len(positives) :]].reshape(\n        len(positives), negatives.shape[1], -1\n    )\n    return positive_vectors, negative_vectors\n\n\ndef history_windows(\n    sequence: np.ndarray,\n    start: int,\n    count: int,\n    width: int,\n    padding_item: int,\n) -> np.ndarray:\n    base_start = max(0, start - width + 1)\n    segment = sequence[base_start : start + count]\n    padded = np.concatenate(\n        [np.full(width - 1, padding_item, dtype=np.uint16), segment]\n    )\n    windows = np.lib.stride_tricks.sliding_window_view(padded, width)\n    offset = start - base_start\n    return windows[offset : offset + count].copy()\n\n\ndef corrected_negative_loss(\n    logits: torch.Tensor,\n    correction: torch.Tensor,\n    config: Config,\n) -> torch.Tensor:\n    raw = F.softplus(logits)\n    total = config.uniform_negatives + config.inbatch_negatives\n    result = torch.zeros(len(logits), device=logits.device)\n    if config.uniform_negatives:\n        result += config.uniform_negatives * raw[\n            :, : config.uniform_negatives\n        ].mean(dim=1)\n    if config.inbatch_negatives:\n        selected = slice(config.uniform_negatives, total)\n        weights = correction[:, selected]\n        result += config.inbatch_negatives * (\n            (raw[:, selected] * weights).sum(dim=1)\n            / weights.sum(dim=1).clamp_min(1.0e-6)\n        )\n    return result / total\n\n\ndef build_target_weights(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    training_counts: np.ndarray,\n    config: Config,\n) -> np.ndarray:\n    \"\"\"Build nonzero per-token weights without dropping adjacent targets.\"\"\"\n    if config.target_weighting == \"uniform\":\n        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)\n    else:\n        weights = np.full(\n            len(prepared.sequence_items),\n            config.ordinary_target_weight,\n            dtype=np.float32,\n        )\n        organic_strong = (\n            (prepared.event_features[:, 0] > 0.5)\n            & (prepared.event_features[:, 1] >= 0.8)\n        )\n        weights[organic_strong] = config.organic_strong_target_weight\n        if config.target_weighting == \"discovery-graded\":\n            like_outcomes = load_organic_like_outcomes(\n                public, stage, prepared.item_ids\n            )\n            history_end = (\n                base.TRAIN_END if stage == \"validation\" else base.VALIDATION_END\n            )\n            horizon = config.discovery_horizon_days * base.DAY\n            grade_three = 0\n            for user_row, uid in enumerate(prepared.user_ids):\n                start = int(prepared.offsets[user_row])\n                end = int(prepared.offsets[user_row + 1])\n                if end - start < 2:\n                    continue\n                last_training_target = start + int(training_counts[user_row])\n                first_validation_target = last_training_target + 1\n                split_timestamp = (\n                    int(prepared.sequence_timestamps[first_validation_target])\n                    if first_validation_target < end\n                    else history_end\n                )\n                user_likes = like_outcomes.get(int(uid), {})\n                seen = {int(prepared.sequence_items[start])}\n                for position in range(start + 1, end):\n                    item = int(prepared.sequence_items[position])\n                    novel = item not in seen\n                    seen.add(item)\n                    if not novel:\n                        # Repeats remain training examples, but they cannot be\n                        # eligible discovery outcomes at the corresponding\n                        # cutoff and therefore keep only the ordinary weight.\n                        weights[position] = config.ordinary_target_weight\n                        continue\n                    timestamps = user_likes.get(item)\n                    if timestamps is None:\n                        continue\n                    timestamp = int(prepared.sequence_timestamps[position])\n                    upper = min(\n                        timestamp + horizon,\n                        split_timestamp\n                        if position <= last_training_target\n                        else history_end,\n                    )\n                    outcome = int(np.searchsorted(timestamps, timestamp, side=\"left\"))\n                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:\n                        weights[position] = config.organic_like_target_weight\n                        grade_three += 1\n            log(\n                f\"grade-3 first-encounter targets={grade_three:,} \"\n                f\"horizon_days={config.discovery_horizon_days}\"\n            )\n    log(\n        f\"target weighting={config.target_weighting} \"\n        f\"min={weights.min():.3f} mean={weights.mean():.3f} \"\n        f\"max={weights.max():.3f}\"\n    )\n    return weights\n\n\ndef build_dense_block(\n    prepared: SequenceData,\n    target_weights: np.ndarray,\n    users: np.ndarray,\n    window_starts: np.ndarray,\n    process_counts: np.ndarray,\n    score_starts: np.ndarray,\n    start: int,\n    steps: int,\n    config: Config,\n) -> tuple[np.ndarray, ...]:\n    \"\"\"Build one causal sequence block and mark the target rows to score.\"\"\"\n    batch_size = len(users)\n    padding_item = len(prepared.item_ids)\n    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)\n    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)\n    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)\n    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)\n    targets = np.zeros((batch_size, steps), dtype=np.uint16)\n    weights = np.ones((batch_size, steps), dtype=np.float32)\n    scored = np.zeros((batch_size, steps), dtype=bool)\n    histories = np.full(\n        (batch_size, steps, config.bptt_steps),\n        padding_item,\n        dtype=np.uint16,\n    )\n    processed = np.zeros(batch_size, dtype=np.int32)\n\n    lengths = prepared.lengths\n    for row, user_row in enumerate(users):\n        count = min(steps, max(int(process_counts[row]) - start, 0))\n        if not count:\n            continue\n        offset = int(prepared.offsets[user_row])\n        window_start = int(window_starts[row])\n        absolute_start = window_start + start\n        positions = slice(\n            offset + absolute_start,\n            offset + absolute_start + count,\n        )\n        target_positions = slice(\n            offset + absolute_start + 1,\n            offset + absolute_start + count + 1,\n        )\n        input_items[row, :count] = prepared.sequence_items[positions]\n        event_features[row, :count] = prepared.event_features[positions]\n        activity_features[row, :count] = prepared.activity_features[positions]\n        state_ids[row, :count] = prepared.state_ids[positions]\n        targets[row, :count] = prepared.sequence_items[target_positions]\n        weights[row, :count] = target_weights[target_positions]\n        score_from = min(count, max(int(score_starts[row]) - start, 0))\n        scored[row, score_from:count] = True\n        processed[row] = count\n        user_sequence = prepared.sequence_items[\n            offset : offset + int(lengths[user_row])\n        ]\n        histories[row, :count] = history_windows(\n            user_sequence,\n            absolute_start,\n            count,\n            config.bptt_steps,\n            padding_item,\n        )\n    return (\n        input_items,\n        event_features,\n        activity_features,\n        state_ids,\n        targets,\n        scored,\n        histories,\n        processed,\n        weights,\n    )\n\n\ndef score_target_microbatches(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    sequence_vectors: torch.Tensor,\n    flat_states: np.ndarray,\n    flat_activity: np.ndarray,\n    positive_items: torch.Tensor,\n    negatives: torch.Tensor,\n    negative_weights: torch.Tensor,\n    sample_weights: np.ndarray,\n    device: torch.device,\n    backward: bool,\n) -> tuple[dict[str, float], torch.Tensor | None]:\n    \"\"\"Compute one logical block's exact BCE in bounded-memory slices.\"\"\"\n    target_count = len(positive_items)\n    total_weight = float(sample_weights.sum())\n    if total_weight <= 0:\n        raise RuntimeError(\"target block has no positive sample weight\")\n    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None\n    statistics = {\n        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            negatives[begin:end],\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits, negative_weights[begin:end], config\n        )\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        loss_sum = (\n            selected_weights * (positive_loss + negative_loss) / 2.0\n        ).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n    epoch: int,\n) -> dict[str, float]:\n    model.train()\n    train_users = np.flatnonzero(target_counts > 0)\n    sorted_users = train_users[\n        np.argsort(target_counts[train_users], kind=\"stable\")\n    ]\n    groups = [\n        sorted_users[start : start + config.user_batch_size]\n        for start in range(0, len(sorted_users), config.user_batch_size)\n    ]\n    group_order = torch.randperm(len(groups), generator=generator).numpy()\n    negative_count = config.uniform_negatives + config.inbatch_negatives\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    total_positive = 0.0\n    total_negative = 0.0\n    total_pair_correct = 0.0\n    batches = 0\n    started = time.monotonic()\n\n    for group_number in group_order:\n        users = groups[int(group_number)]\n        group_counts = target_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_counts.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_counts,\n                np.zeros(len(users), dtype=np.int64),\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0).detach()\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            optimizer.zero_grad(set_to_none=True)\n            statistics, sequence_gradient = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=True,\n            )\n            if sequence_gradient is None:\n                raise RuntimeError(\"training block omitted its sequence gradient\")\n            sequence_vectors.backward(sequence_gradient)\n            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)\n            optimizer.step()\n\n            count = len(positive_items)\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += count\n            total_weight += statistics[\"weight_sum\"]\n            total_positive += statistics[\"positive_sum\"]\n            total_negative += statistics[\"negative_sum\"]\n            total_pair_correct += statistics[\"pair_correct\"]\n            batches += 1\n            if batches % 25 == 0:\n                release_accelerator_cache(device)\n            if batches % 250 == 0:\n                batch_bce = statistics[\"loss_sum\"] / max(\n                    statistics[\"weight_sum\"], 1.0e-12\n                )\n                running_bce = total_loss / max(total_weight, 1.0e-12)\n                log(\n                    f\"epoch={epoch}/{config.epochs} batches={batches:,} \"\n                    f\"targets={total_targets:,} \"\n                    f\"batch_bce={batch_bce:.6f} \"\n                    f\"running_bce={running_bce:.6f} \"\n                    f\"elapsed={time.monotonic() - started:.1f}s\"\n                )\n\n    return {\n        \"epoch\": float(epoch),\n        \"loss\": total_loss / max(total_weight, 1.0e-12),\n        \"positive_logit\": total_positive / max(total_targets, 1),\n        \"negative_logit\": total_negative / max(total_targets * negative_count, 1),\n        \"pair_accuracy\": total_pair_correct\n        / max(total_targets * negative_count, 1),\n        \"targets\": float(total_targets),\n        \"target_weight_sum\": total_weight,\n        \"batches\": float(batches),\n        \"seconds\": time.monotonic() - started,\n    }\n\n\[email protected]_grad()\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-82f213af-a196-4357-a536-dd95ac0d8549",
    "process_id": "44867",
    "command": [
      "/bin/sh",
      "-lc",
      "cat /app/solution/production.log && which python && ls /opt && ls -lh /task/data && cat /task/requirements.txt"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "cat /app/solution/production.log && which python && ls /opt && ls -lh /task/data && cat /task/requirements.txt"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 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 05:14 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 05:14 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "stderr": "",
    "aggregated_output": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 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 05:14 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 05:14 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 3549
    },
    "formatted_output": "Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n/usr/local/bin/python\ncodex-home\ntask-venv\ntotal 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 05:14 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 05:14 validation_preference_state.parquet\n# Exact distribution inventory for the Yambda two-tower task family.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. Python itself is pinned by task.toml and both\n# Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nfilelock==3.32.3\nfsspec==2026.7.0\nidna==3.18\nJinja2==3.1.6\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nMarkupSafe==3.0.3\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nmpmath==1.3.0\nnbformat==5.11.1\nnetworkx==3.6.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsetuptools==84.0.0\nsix==1.17.0\nsympy==1.14.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntorch==2.8.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n"
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-bd5e815b-faba-4450-a9f6-43984bf8fe8a",
    "process_id": "28108",
    "command": [
      "/bin/sh",
      "-lc",
      "sed -n '1521,1680p' /app/solution/train_current_model.py && ls -la /app/solution && nproc && free -h"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "sed -n '1521,1680p' /app/solution/train_current_model.py && ls -la /app/solution && nproc && free -h"
      }
    ],
    "source": "unified_exec_startup",
    "status": "failed",
    "stdout": "def train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\ntotal 108\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n",
    "stderr": "",
    "aggregated_output": "def train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\ntotal 108\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n",
    "exit_code": 127,
    "duration": {
      "secs": 0,
      "nanos": 3710
    },
    "formatted_output": "def train_model(\n    public: Path,\n    stage: str,\n    prepared: SequenceData,\n    config: Config,\n    device: torch.device,\n    checkpoint_dir: Path | None,\n    resume_checkpoint: Path | None,\n) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:\n    partition = chronological_target_partition(\n        prepared,\n        config.validation_fraction,\n        config.max_train_targets_per_user,\n        config.max_validation_targets_per_user,\n    )\n    training_counts = partition.training_counts\n    validation_counts = partition.validation_counts\n    log(\n        f\"chronological BCE window: train={int(training_counts.sum()):,} \"\n        f\"validation={int(validation_counts.sum()):,} \"\n        f\"selected={partition.selected_count:,} \"\n        f\"discarded_old_targets={int(partition.window_starts.sum()):,} \"\n        f\"all={prepared.target_count:,}\"\n    )\n    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(\n        device\n    )\n    optimizer = torch.optim.AdamW(\n        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay\n    )\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 10_000)\n    item_correction = build_item_correction(\n        prepared,\n        config,\n        partition.window_starts,\n        training_counts,\n    )\n    target_weights = build_target_weights(\n        public,\n        stage,\n        prepared,\n        partition.history_training_counts,\n        config,\n    )\n    known_positive_packed = (\n        build_known_positive_filter(prepared, partition.history_training_counts)\n        if config.negative_filter_scope == \"full-training-history\"\n        else None\n    )\n    history: list[dict[str, float]] = []\n    best_loss = float(\"inf\")\n    best_epoch = 0\n    best_state: dict[str, torch.Tensor] | None = None\n    epochs_without_improvement = 0\n    start_epoch = 0\n\n    if resume_checkpoint is not None:\n        payload = torch.load(\n            resume_checkpoint, map_location=device, weights_only=False\n        )\n        stored_config = payload.get(\"config\", {})\n        architecture_fields = (\n            \"bptt_steps\",\n            \"embedding_dim\",\n            \"hidden_dim\",\n            \"user_hidden_1\",\n            \"user_hidden_2\",\n            \"item_hidden_1\",\n            \"item_hidden_2\",\n        )\n        mismatches = {\n            name: (stored_config.get(name), getattr(config, name))\n            for name in architecture_fields\n            if stored_config.get(name) != getattr(config, name)\n        }\n        if mismatches:\n            raise RuntimeError(\n                f\"resume checkpoint architecture differs: {mismatches}\"\n            )\n        stored_filter_scope = stored_config.get(\n            \"negative_filter_scope\", \"last-100\"\n        )\n        stored_target_weighting = stored_config.get(\n            \"target_weighting\", \"uniform\"\n        )\n        stored_weight_values = (\n            stored_config.get(\"ordinary_target_weight\", 1.0),\n            stored_config.get(\"organic_strong_target_weight\", 1.0),\n            stored_config.get(\"organic_like_target_weight\", 3.0),\n            stored_config.get(\"discovery_horizon_days\", 7),\n        )\n        current_weight_values = (\n            config.ordinary_target_weight,\n            config.organic_strong_target_weight,\n            config.organic_like_target_weight,\n            config.discovery_horizon_days,\n        )\n        stored_window_values = (\n            stored_config.get(\"validation_fraction\", 0.10),\n            stored_config.get(\"max_train_targets_per_user\", 0),\n            stored_config.get(\"max_validation_targets_per_user\", 0),\n        )\n        current_window_values = (\n            config.validation_fraction,\n            config.max_train_targets_per_user,\n            config.max_validation_targets_per_user,\n        )\n        objective_changed = (\n            stored_filter_scope != config.negative_filter_scope\n            or stored_target_weighting != config.target_weighting\n            or stored_weight_values != current_weight_values\n            or stored_window_values != current_window_values\n        )\n        model.load_state_dict(payload[\"model_state\"])\n        if \"optimizer_state\" in payload and not objective_changed:\n            optimizer.load_state_dict(payload[\"optimizer_state\"])\n            for group in optimizer.param_groups:\n                group[\"lr\"] = config.learning_rate\n                group[\"weight_decay\"] = config.weight_decay\n            log(f\"restored optimizer state from {resume_checkpoint}\")\n        else:\n            log(\n                f\"using a fresh AdamW optimizer after loading {resume_checkpoint}\"\n            )\n        if \"generator_state\" in payload and not objective_changed:\n            generator.set_state(payload[\"generator_state\"])\n        start_epoch = int(payload[\"epoch\"])\n        if start_epoch >= config.epochs:\n            raise RuntimeError(\n                f\"resume epoch {start_epoch} must be below --epochs {config.epochs}\"\n            )\n        prior = {\n            str(name): float(value)\n            for name, value in payload.get(\"diagnostics\", {}).items()\n        }\n        if objective_changed:\n            prior.pop(\"validation_loss\", None)\n            log(\n                \"BCE objective changed: filter \"\n                f\"{stored_filter_scope}->{config.negative_filter_scope}, \"\n                \"target weighting \"\n                f\"{stored_target_weighting}->{config.target_weighting}, \"\n                f\"target window {stored_window_values}->{current_window_values}; \"\n                \"recomputing the resume checkpoint's validation BCE\"\n            )\n        if \"validation_loss\" not in prior:\n            prior.update(\n                evaluate_validation_bce(\n                    model,\n                    prepared,\n                    config,\n                    target_weights,\n                    partition.window_starts,\n                    training_counts,\n                    validation_counts,\n                    item_correction,\n                    known_positive_packed,\n                    device,\n                )\n            )\ntotal 108\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:19 .\ndrwxr-xr-x 1 agent agent  4096 Sep 23 05:16 ..\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.json\n-rw-r--r-- 1 agent agent     0 Sep 23 05:19 production.log\n-r--r--r-- 1 agent agent 80058 Sep 23 05:19 train_current_model.py\n-r--r--r-- 1 agent agent 15985 Sep 23 05:19 two_tower_model.py\n8\n/bin/sh: 1: free: not found\n"
  }
}

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  "type": "token_usage_record",
  "thread_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae",
  "turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90",
  "session_id": "01a0ccb4-4865-7d00-b11c-a70fe1949bae",
  "root_turn_id": "01a0ccb4-4879-7f51-8f79-356dfdfcdf90",
  "response_id": "resp_03ec3525bea3ac86016ab3617af58c87d2ac4940d8d2fc56c3",
  "usage": {
    "input_tokens": 43260,
    "cached_input_tokens": 41216,
    "cache_write_input_tokens": 0,
    "output_tokens": 297,
    "reasoning_output_tokens": 129,
    "total_tokens": 43557
  },
  "turn_token_usage": {
    "input_tokens": 200387,
    "cached_input_tokens": 168320,
    "cache_write_input_tokens": 0,
    "output_tokens": 872,
    "reasoning_output_tokens": 129,
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  "thread_token_usage": {
    "input_tokens": 200387,
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-f9df1513-c3f2-445b-ac99-6ed4080493ab",
    "process_id": "45436",
    "command": [
      "/bin/sh",
      "-lc",
      "tail -n 8 /app/solution/production.log && sed -n '1364,1488p' /app/solution/train_current_model.py && sed -n '867,954p' /app/solution/train_current_model.py && cat /sys/fs/cgroup/memory.max && cat /sys/fs/cgroup/cpu.max"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "read",
        "cmd": "tail -n 8 /app/solution/production.log",
        "name": "production.log",
        "path": "/app/solution/production.log"
      },
      {
        "type": "read",
        "cmd": "sed -n '1364,1488p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "sed -n '867,954p' /app/solution/train_current_model.py",
        "name": "train_current_model.py",
        "path": "/app/solution/train_current_model.py"
      },
      {
        "type": "read",
        "cmd": "cat /sys/fs/cgroup/memory.max",
        "name": "memory.max",
        "path": "/sys/fs/cgroup/memory.max"
      },
      {
        "type": "read",
        "cmd": "cat /sys/fs/cgroup/cpu.max",
        "name": "cpu.max",
        "path": "/sys/fs/cgroup/cpu.max"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\n8589934592\n800000 100000\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\n8589934592\n800000 100000\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 4080
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\ndef evaluate_validation_bce(\n    model: base.SequentialTwoTower,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    training_counts: np.ndarray,\n    validation_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    device: torch.device,\n) -> dict[str, float]:\n    \"\"\"Measure deterministic BCE on each user's chronological target suffix.\"\"\"\n    model.eval()\n    totals = training_counts + validation_counts\n    users_with_validation = np.flatnonzero(validation_counts > 0)\n    ordered = users_with_validation[\n        np.argsort(totals[users_with_validation], kind=\"stable\")\n    ]\n    groups = [\n        ordered[start : start + config.user_batch_size]\n        for start in range(0, len(ordered), config.user_batch_size)\n    ]\n    generator = torch.Generator(device=\"cpu\").manual_seed(config.seed + 20_000)\n    total_loss = 0.0\n    total_targets = 0\n    total_weight = 0.0\n    started = time.monotonic()\n\n    for group_number, users in enumerate(groups, start=1):\n        group_totals = totals[users]\n        group_starts = training_counts[users]\n        hidden = torch.zeros(\n            1, len(users), config.embedding_dim, device=device\n        )\n        max_targets = int(group_totals.max())\n        for start in range(0, max_targets, config.bptt_steps):\n            steps = min(config.bptt_steps, max_targets - start)\n            block = build_dense_block(\n                prepared,\n                target_weights,\n                users,\n                window_starts[users],\n                group_totals,\n                group_starts,\n                start,\n                steps,\n                config,\n            )\n            sequence_outputs, _ = event_sequence(\n                model,\n                torch.from_numpy(block[0].astype(np.int64)).to(device),\n                torch.from_numpy(block[1].astype(np.float32)).to(device),\n                hidden,\n            )\n            hidden = torch.stack(\n                [\n                    sequence_outputs[row, count - 1]\n                    if count\n                    else hidden[0, row]\n                    for row, count in enumerate(block[7])\n                ]\n            ).unsqueeze(0)\n            flat_scored = block[5].reshape(-1)\n            if not np.any(flat_scored):\n                continue\n            mask = torch.from_numpy(flat_scored).to(device)\n            sequence_vectors = sequence_outputs.reshape(\n                -1, config.embedding_dim\n            )[mask]\n            flat_states = block[3].reshape(-1)[flat_scored]\n            positive_items = torch.from_numpy(\n                block[4].reshape(-1)[flat_scored].astype(np.int64)\n            ).to(device)\n            known_history = torch.from_numpy(\n                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(\n                    np.int64\n                )\n            ).to(device)\n            liked_items = torch.from_numpy(\n                prepared.liked_states[flat_states].astype(np.int64)\n            ).to(device)\n            flat_user_rows = np.broadcast_to(\n                users[:, None], block[5].shape\n            ).reshape(-1)[flat_scored]\n            negatives, negative_weights = draw_training_negatives(\n                positive_items,\n                known_history,\n                liked_items,\n                flat_user_rows,\n                known_positive_packed,\n                prepared,\n                config,\n                item_correction,\n                generator,\n                device,\n            )\n            statistics, _ = score_target_microbatches(\n                model,\n                prepared,\n                config,\n                sequence_vectors,\n                flat_states,\n                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),\n                positive_items,\n                negatives,\n                negative_weights,\n                block[8].reshape(-1)[flat_scored],\n                device,\n                backward=False,\n            )\n            total_loss += statistics[\"loss_sum\"]\n            total_targets += len(positive_items)\n            total_weight += statistics[\"weight_sum\"]\n        if group_number % 25 == 0:\n            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\ndef retained_target_window_starts(\n    total_targets: np.ndarray,\n    validation_counts: np.ndarray,\n    training_counts: np.ndarray,\n) -> np.ndarray:\n    \"\"\"Return the discarded prefix before the retained chronological suffix.\"\"\"\n    training_region_ends = total_targets - validation_counts\n    window_starts = training_region_ends - training_counts\n    if (\n        np.any(window_starts < 0)\n        or np.any(training_counts < 0)\n        or np.any(validation_counts < 0)\n    ):\n        raise RuntimeError(\"chronological target partition produced negative counts\")\n    if not np.array_equal(\n        window_starts + training_counts + validation_counts, total_targets\n    ):\n        raise RuntimeError(\"chronological target window is not a final contiguous suffix\")\n    return window_starts\n\n\ndef chronological_target_partition(\n    prepared: SequenceData,\n    validation_fraction: float,\n    max_train_targets_per_user: int,\n    max_validation_targets_per_user: int,\n) -> TargetPartition:\n    \"\"\"Select a recent contiguous window with a final out-of-time BCE suffix.\"\"\"\n    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)\n    validation = np.floor(totals * validation_fraction).astype(np.int64)\n    validation[(totals >= 2) & (validation == 0)] = 1\n    if max_validation_targets_per_user:\n        validation = np.minimum(validation, max_validation_targets_per_user)\n\n    history_training = totals - validation\n    training = history_training.copy()\n    if max_train_targets_per_user:\n        training = np.minimum(training, max_train_targets_per_user)\n    window_starts = retained_target_window_starts(totals, validation, training)\n    return TargetPartition(\n        window_starts=window_starts,\n        training_counts=training,\n        validation_counts=validation,\n        history_training_counts=history_training,\n    )\n\n\ndef load_organic_like_outcomes(\n    public: Path, stage: str, item_ids: np.ndarray\n) -> dict[int, dict[int, np.ndarray]]:\n    \"\"\"Load mapped organic-like timestamps from public fitting history.\"\"\"\n    prefixes = (\"train\",) if stage == \"validation\" else (\"train\", \"val\")\n    frames = [\n        pd.read_parquet(\n            public / \"data\" / f\"{prefix}_likes.parquet\",\n            columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\"],\n        )\n        for prefix in prefixes\n    ]\n    likes = pd.concat(frames, ignore_index=True)\n    likes = likes[likes.is_organic == 1].copy()\n    mapped = pd.Index(item_ids).get_indexer(\n        likes.item_id.to_numpy(dtype=np.int64)\n    )\n    likes = likes.loc[mapped >= 0, [\"uid\", \"timestamp\"]].copy()\n    likes[\"item\"] = mapped[mapped >= 0].astype(np.int32, copy=False)\n    likes = likes.sort_values([\"uid\", \"item\", \"timestamp\"], kind=\"stable\")\n    nested: dict[int, dict[int, list[int]]] = {}\n    for raw_uid, raw_timestamp, raw_item in likes.itertuples(\n        index=False, name=None\n    ):\n        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(\n            int(raw_timestamp)\n        )\n    result = {\n        uid: {\n            item: np.asarray(timestamps, dtype=np.int64)\n            for item, timestamps in items.items()\n        }\n        for uid, items in nested.items()\n    }\n    log(\n        f\"loaded organic-like outcomes: rows={len(likes):,} \"\n        f\"users={len(result):,}\"\n    )\n    return result\n\n\n8589934592\n800000 100000\n"
  }
}

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      },
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    "source": "unified_exec_startup",
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    "stdout": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n",
    "stderr": "",
    "aggregated_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n",
    "exit_code": 2,
    "duration": {
      "secs": 0,
      "nanos": 4190
    },
    "formatted_output": "loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s\nls: cannot access '/app/solution/validation_cache.npz': No such file or directory\n"
  }
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{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-4ae9631f-17a1-4161-a66f-e18a7351ff1f",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -60,2 +60,5 @@\n     inbatch_correction_max: float = 4.0\n+    hard_negatives: int = 16\n+    hard_pool_size: int = 1024\n+    hard_negative_weight: float = 0.5\n     negative_filter_scope: str = \"full-training-history\"\n@@ -1123,2 +1126,4 @@\n     backward: bool,\n+    user_rows: np.ndarray | None = None,\n+    known_positive_packed: np.ndarray | None = None,\n ) -> tuple[dict[str, float], torch.Tensor | None]:\n@@ -1161,2 +1166,10 @@\n         )\n+        selected_negatives = negatives[begin:end]\n+        if config.hard_negatives:\n+            hard_items = mine_hard_negatives(\n+                model, query_vectors, positive_items[begin:end],\n+                selected_negatives, prepared.liked_states[selected_states],\n+                user_rows[begin:end], known_positive_packed, config,\n+            )\n+            selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n         positive_vectors, negative_vectors = candidate_vectors(\n@@ -1164,3 +1177,3 @@\n             positive_items[begin:end],\n-            negatives[begin:end],\n+            selected_negatives,\n         )\n@@ -1172,4 +1185,8 @@\n         negative_loss = corrected_negative_loss(\n-            negative_logits, negative_weights[begin:end], config\n+            negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\n         )\n+        if config.hard_negatives:\n+            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n+            negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n+                             + config.hard_negative_weight * hard_loss)\n         selected_weights = torch.from_numpy(\n@@ -1189,5 +1206,6 @@\n         statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n-        statistics[\"negative_sum\"] += float(negative_logits.detach().sum().cpu())\n+        sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n+        statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n         statistics[\"pair_correct\"] += float(\n-            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()\n+            (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\n         )\n@@ -1318,2 +1336,4 @@\n                 backward=True,\n+                user_rows=flat_user_rows,\n+                known_positive_packed=known_positive_packed,\n             )\n@@ -1473,2 +1493,4 @@\n                 backward=False,\n+                user_rows=flat_user_rows,\n+                known_positive_packed=known_positive_packed,\n             )\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
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    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -1113,2 +1113,51 @@\n \[email protected]_grad()\n+def mine_hard_negatives(\n+    model: base.SequentialTwoTower,\n+    queries: torch.Tensor,\n+    positives: torch.Tensor,\n+    random_negatives: torch.Tensor,\n+    liked_items: np.ndarray,\n+    user_rows: np.ndarray,\n+    known_positive_packed: np.ndarray,\n+    config: Config,\n+) -> torch.Tensor:\n+    \"\"\"Mine a shared pool, then backpropagate only through the retained items.\n+\n+    The pool mixes uniform catalog coverage and current positive targets. Its\n+    deterministic seed makes chronological validation repeatable. Mining is a\n+    training operation only; inference remains a single dot product.\n+    \"\"\"\n+    if known_positive_packed is None:\n+        raise ValueError(\"hard mining requires the full-training-history filter\")\n+    seed = config.seed + int(positives.sum()) + int(user_rows[0])\n+    generator = torch.Generator(device=\"cpu\").manual_seed(seed)\n+    uniform = torch.randperm(model.n_items, generator=generator)[:config.hard_pool_size]\n+    batch_pool = torch.unique(positives.cpu())\n+    if len(batch_pool) > config.hard_pool_size:\n+        batch_pool = batch_pool[torch.randperm(len(batch_pool), generator=generator)[:config.hard_pool_size]]\n+    pool = torch.unique(torch.cat([uniform, batch_pool])).numpy()\n+    invalid = ((known_positive_packed[user_rows[:, None], pool[None, :] >> 3]\n+                >> (pool[None, :] & 7)) & 1).astype(bool)\n+    excluded = np.concatenate([\n+        positives.cpu().numpy()[:, None], random_negatives.cpu().numpy(), liked_items\n+    ], axis=1)\n+    columns = np.searchsorted(pool, excluded)\n+    safe_columns = np.minimum(columns, len(pool) - 1)\n+    present = (columns < len(pool)) & (pool[safe_columns] == excluded)\n+    rows = np.broadcast_to(np.arange(len(queries))[:, None], excluded.shape)\n+    invalid[rows[present], safe_columns[present]] = True\n+    if np.any((~invalid).sum(axis=1) < config.hard_negatives):\n+        raise RuntimeError(\"hard pool has insufficient eligible negatives\")\n+    pool_items = torch.from_numpy(pool).to(queries.device)\n+    was_training = model.training\n+    model.eval()\n+    vectors = model.encode_items(pool_items)\n+    model.train(was_training)\n+    scores = queries.detach() @ vectors.T\n+    scores.masked_fill_(torch.from_numpy(invalid).to(scores.device), -torch.inf)\n+    columns = scores.topk(config.hard_negatives, dim=1).indices\n+    return pool_items[columns]\n+\n+\n def score_target_microbatches(\n@@ -1959,2 +2008,5 @@\n     parser.add_argument(\"--learning-rate\", type=float, default=Config.learning_rate)\n+    parser.add_argument(\"--hard-negatives\", type=int, default=Config.hard_negatives)\n+    parser.add_argument(\"--hard-pool-size\", type=int, default=Config.hard_pool_size)\n+    parser.add_argument(\"--hard-negative-weight\", type=float, default=Config.hard_negative_weight)\n     parser.add_argument(\n@@ -2054,2 +2106,5 @@\n         learning_rate=args.learning_rate,\n+        hard_negatives=args.hard_negatives,\n+        hard_pool_size=args.hard_pool_size,\n+        hard_negative_weight=args.hard_negative_weight,\n         loss_microbatch_size=args.loss_microbatch_size,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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        "name": "production.log",
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      }
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    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n",
    "stderr": "",
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    "exit_code": 0,
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      "/app/solution/test_mining.py": {
        "type": "add",
        "content": "\"\"\"Small invariant test for the score-aware training sampler.\"\"\"\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nt.seed_everything(3)\nconfig = t.Config(hard_negatives=8, hard_pool_size=100)\nmodel = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config))\nmodel.train()\nqueries = torch.randn(2, 192)\npositives = torch.tensor([1, 2])\nsampled = torch.tensor([[3, 4], [5, 6]])\nliked = np.array([[7, 100], [8, 100]])\nmask = np.zeros((2, 100), dtype=np.uint8)\nmask[0, :20] = 1\nmask[1, 20:40] = 1\npacked = np.packbits(mask, axis=1, bitorder=\"little\")\nmined = t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config)\nassert model.training\nassert mined.shape == (2, 8)\nassert torch.equal(mined, t.mine_hard_negatives(model, queries, positives, sampled, liked, np.arange(2), packed, config))\nfor r in range(2):\n    assert len(set(mined[r].tolist())) == 8\n    assert not mask[r, mined[r].numpy()].any()\n    assert positives[r].item() not in mined[r]\n    assert not set(mined[r].tolist()).intersection(sampled[r].tolist() + liked[r].tolist())\n    model.eval()\n    with torch.no_grad():\n        scores = queries[r] @ model.encode_items(torch.arange(100)).T\n    excluded = mask[r].astype(bool)\n    excluded[positives[r]] = True\n    excluded[sampled[r]] = True\n    excluded[liked[r][liked[r] < 100]] = True\n    scores[torch.from_numpy(excluded)] = -torch.inf\n    assert torch.equal(scores.topk(8).indices, mined[r])\nprint(\"hard-mining filtering, uniqueness, determinism, and top-k checks passed\")\n"
      },
      "/app/solution/rank_checkpoint.py": {
        "type": "add",
        "content": "\"\"\"Rank an existing checkpoint, preserving stage-specific observed history.\"\"\"\nimport argparse\nimport json\nfrom pathlib import Path\n\nimport numpy as np\nimport torch\nimport train_current_model as trainer\n\n\ndef main():\n    p = argparse.ArgumentParser()\n    p.add_argument(\"--checkpoint\", type=Path, required=True)\n    p.add_argument(\"--stage\", choices=[\"validation\", \"submission\"], required=True)\n    p.add_argument(\"--output\", type=Path, required=True)\n    p.add_argument(\"--threads\", type=int, default=8)\n    p.add_argument(\"--save-vectors\", type=Path)\n    args = p.parse_args()\n    torch.set_num_threads(args.threads)\n    torch.set_num_interop_threads(1)\n    payload = torch.load(args.checkpoint, map_location=\"cpu\", weights_only=False)\n    config = trainer.Config(**payload[\"config\"])\n    prepared, eligibility = trainer.load_or_prepare(\n        Path(\"/task\"), args.stage, config,\n        Path(\"/app/solution\") / f\"{args.stage}_cache.npz\",\n    )\n    model = trainer.base.SequentialTwoTower(prepared.item_features, trainer.model_config(config))\n    model.load_state_dict(payload[\"model_state\"])\n    model.eval()\n    ranking, diagnostics = trainer.rank_candidates(model, prepared, eligibility, config, torch.device(\"cpu\"), 64)\n    trainer.write_ranking(ranking, args.output)\n    if args.save_vectors:\n        with torch.inference_mode():\n            queries = trainer.inference_queries(model, prepared, config, torch.device(\"cpu\"))\n            items = torch.cat([model.encode_items(ids) for ids in torch.arange(model.n_items).split(2048)])\n        np.savez(args.save_vectors, queries=queries.numpy(), items=items.numpy(), uids=eligibility.uids, item_ids=eligibility.item_ids)\n    print(json.dumps(diagnostics))\n\n\nif __name__ == \"__main__\":\n    main()\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/rank_checkpoint.py\nA /app/solution/test_mining.py\n",
    "stderr": ""
  }
}

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      }
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    "status": "completed",
    "stdout": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n",
    "stderr": "",
    "aggregated_output": "hard-mining filtering, uniqueness, determinism, and top-k checks passed\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n",
    "exit_code": 0,
    "duration": {
      "secs": 5,
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    },
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    "stdout": "saved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n",
    "stderr": "",
    "aggregated_output": "saved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=14.8s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=22.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=30.6s\n5863890944\n",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 3530
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    "stderr": "",
    "aggregated_output": "            release_accelerator_cache(device)\n    return {\n        \"validation_loss\": total_loss / max(total_weight, 1.0e-12),\n        \"validation_targets\": float(total_targets),\n        \"validation_target_weight_sum\": total_weight,\n        \"validation_seconds\": time.monotonic() - started,\n    }\n\n\ndef snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:\n    return {\n        name: value.detach().cpu().clone()\n        for name, value in model.state_dict().items()\n    }\n\n\ndef save_epoch_checkpoint(\n    checkpoint_dir: Path,\n    epoch: int,\n    model_state: dict[str, torch.Tensor],\n    config: Config,\n    diagnostics: dict[str, float],\n    optimizer: torch.optim.Optimizer,\n    generator: torch.Generator,\n) -> Path:\n    checkpoint_dir.mkdir(parents=True, exist_ok=True)\n    path = checkpoint_dir / f\"epoch-{epoch:02d}.pt\"\n    torch.save(\n        {\n            \"epoch\": epoch,\n            \"config\": asdict(config),\n            \"diagnostics\": diagnostics,\n            \"model_state\": model_state,\n            \"optimizer_state\": optimizer.state_dict(),\n            \"generator_state\": generator.get_state(),\n        },\n        path,\n    )\n    log(f\"saved epoch checkpoint: {path}\")\n    return path\n\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_cache.npz\n",
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      }
    ],
    "source": "unified_exec_startup",
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    "stdout": "prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n5622009856\n",
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    ],
    "cwd": "file:///app",
    "parsed_cmd": [
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        "type": "unknown",
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      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "target_users 8456\ncandidate_catalog 30000\n item_id  positive_listens  organic_positive_listens  popularity_rank\n 5862961             26615                     20620                1\n 6901374             25536                     17347                2\n 5635052             24691                     17194                3\n 3542184             24075                     17699                4\n 9378983             24036                     18195                5\ntrain_listens 43484159\nval_listens 1501697\n",
    "stderr": "",
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    "exit_code": 0,
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    "source": "unified_exec_startup",
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    "stdout": "inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n",
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}

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  "item": {
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      "/app/solution/validate_submission.py": {
        "type": "add",
        "content": "\"\"\"Validate the final artifact against the supplied test candidate boundary.\"\"\"\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\npath = Path(\"/app/solution/submission.parquet\")\nassert path.is_file() and not path.is_symlink()\nframe = pd.read_parquet(path)[[\"uid\", \"item_id\", \"rank\"]]\nfor col in frame:\n    values = frame[col].to_numpy()\n    assert np.isfinite(values).all() and (values == np.floor(values)).all(), col\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\").uid.to_numpy()\nassert set(frame.uid) == set(targets)\ncounts = frame.groupby(\"uid\").agg(rows=(\"rank\", \"size\"), items=(\"item_id\", \"nunique\"), ranks=(\"rank\", \"nunique\"), low=(\"rank\", \"min\"), high=(\"rank\", \"max\"))\nassert (counts[[\"rows\", \"items\", \"ranks\"]] == 100).all().all()\nassert (counts.low == 1).all() and (counts.high == 100).all()\nwith np.load(\"/task/data/test_eligible_candidates.npz\", allow_pickle=False) as eligible:\n    rows = pd.Index(eligible[\"uids\"]).get_indexer(frame.uid)\n    cols = pd.Index(eligible[\"item_ids\"]).get_indexer(frame.item_id)\n    assert (rows >= 0).all() and (cols >= 0).all()\n    allowed = (eligible[\"packed_eligible\"][rows, cols >> 3] >> (cols & 7)) & 1\n    assert allowed.all()\nprint(f\"Validated {len(frame):,} rows: exactly 100 distinct eligible ranked tracks for {len(targets):,} users.\")\n"
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    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/validate_submission.py\n",
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    "stdout": "chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n",
    "stderr": "",
    "aggregated_output": "chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n",
    "exit_code": 0,
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      "secs": 0,
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    "stdout": "saved public-derived full-sequence cache: /app/solution/validation_cache.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\nepoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=252.5s\nls: cannot access '/app/solution/production_ckpt': No such file or directory\n",
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      "nanos": 4580
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    "stdout": "        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        selected_negatives = negatives[begin:end]\n        if config.hard_negatives:\n            hard_items = mine_hard_negatives(\n                model, query_vectors, positive_items[begin:end],\n                selected_negatives, prepared.liked_states[selected_states],\n                user_rows[begin:end], known_positive_packed, config,\n            )\n            selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            selected_negatives,\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\n        )\n        if config.hard_negatives and config.hard_objective == \"bce\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n            negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n                             + config.hard_negative_weight * hard_loss)\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        per_target_loss = (positive_loss + negative_loss) / 2.0\n        if config.hard_negatives and config.hard_objective == \"pairwise\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n                                  - positive_logits[:, None]).mean(dim=1)\n            per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n        loss_sum = (selected_weights * per_target_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n        statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n",
    "stderr": "",
    "aggregated_output": "        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        selected_negatives = negatives[begin:end]\n        if config.hard_negatives:\n            hard_items = mine_hard_negatives(\n                model, query_vectors, positive_items[begin:end],\n                selected_negatives, prepared.liked_states[selected_states],\n                user_rows[begin:end], known_positive_packed, config,\n            )\n            selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            selected_negatives,\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\n        )\n        if config.hard_negatives and config.hard_objective == \"bce\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n            negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n                             + config.hard_negative_weight * hard_loss)\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        per_target_loss = (positive_loss + negative_loss) / 2.0\n        if config.hard_negatives and config.hard_objective == \"pairwise\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n                                  - positive_logits[:, None]).mean(dim=1)\n            per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n        loss_sum = (selected_weights * per_target_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n        statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 5911
    },
    "formatted_output": "        \"loss_sum\": 0.0,\n        \"positive_sum\": 0.0,\n        \"negative_sum\": 0.0,\n        \"pair_correct\": 0.0,\n        \"weight_sum\": 0.0,\n    }\n    for begin in range(0, target_count, config.loss_microbatch_size):\n        end = min(begin + config.loss_microbatch_size, target_count)\n        selected_states = flat_states[begin:end]\n        if backward:\n            sequence_input = (\n                sequence_vectors[begin:end].detach().requires_grad_(True)\n            )\n        else:\n            sequence_input = sequence_vectors[begin:end]\n        liked_vectors, disliked_vectors, _ = pool_explicit_states(\n            model, prepared, selected_states, device\n        )\n        counts = prepared.state_counts[selected_states]\n        numeric = np.zeros((end - begin, 6), dtype=np.float32)\n        numeric[:, :4] = flat_activity[begin:end]\n        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0\n        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0\n        query_vectors = user_queries(\n            model,\n            sequence_input,\n            liked_vectors,\n            disliked_vectors,\n            torch.from_numpy(numeric).to(device),\n        )\n        selected_negatives = negatives[begin:end]\n        if config.hard_negatives:\n            hard_items = mine_hard_negatives(\n                model, query_vectors, positive_items[begin:end],\n                selected_negatives, prepared.liked_states[selected_states],\n                user_rows[begin:end], known_positive_packed, config,\n            )\n            selected_negatives = torch.cat([selected_negatives, hard_items], dim=1)\n        positive_vectors, negative_vectors = candidate_vectors(\n            model,\n            positive_items[begin:end],\n            selected_negatives,\n        )\n        positive_logits = (query_vectors * positive_vectors).sum(dim=1)\n        negative_logits = torch.einsum(\n            \"bd,bkd->bk\", query_vectors, negative_vectors\n        )\n        positive_loss = F.softplus(-positive_logits)\n        negative_loss = corrected_negative_loss(\n            negative_logits[:, :negatives.shape[1]], negative_weights[begin:end], config\n        )\n        if config.hard_negatives and config.hard_objective == \"bce\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]).mean(dim=1)\n            negative_loss = ((1.0 - config.hard_negative_weight) * negative_loss\n                             + config.hard_negative_weight * hard_loss)\n        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n        per_target_loss = (positive_loss + negative_loss) / 2.0\n        if config.hard_negatives and config.hard_objective == \"pairwise\":\n            hard_loss = F.softplus(negative_logits[:, negatives.shape[1]:]\n                                  - positive_logits[:, None]).mean(dim=1)\n            per_target_loss = per_target_loss + config.hard_negative_weight * hard_loss\n        loss_sum = (selected_weights * per_target_loss).sum()\n        if not bool(torch.isfinite(loss_sum)):\n            raise RuntimeError(\"dense next-item training produced non-finite loss\")\n        if backward:\n            (loss_sum / total_weight).backward()\n            if sequence_input.grad is None:\n                raise RuntimeError(\"sequence microbatch did not produce a gradient\")\n            sequence_gradient[begin:end] = sequence_input.grad\n        statistics[\"loss_sum\"] += float(loss_sum.detach().cpu())\n        statistics[\"positive_sum\"] += float(positive_logits.detach().sum().cpu())\n        sampled_logits = negative_logits[:, :negatives.shape[1]].detach()\n        statistics[\"negative_sum\"] += float(sampled_logits.sum().cpu())\n        statistics[\"pair_correct\"] += float(\n            (positive_logits.detach()[:, None] > sampled_logits).sum().cpu()\n        )\n        statistics[\"weight_sum\"] += float(selected_weights.sum().detach().cpu())\n    return statistics, sequence_gradient\n\n\ndef release_accelerator_cache(device: torch.device) -> None:\n    if device.type == \"mps\":\n        torch.mps.empty_cache()\n\n\ndef run_training_epoch(\n    model: base.SequentialTwoTower,\n    optimizer: torch.optim.Optimizer,\n    prepared: SequenceData,\n    config: Config,\n    target_weights: np.ndarray,\n    window_starts: np.ndarray,\n    target_counts: np.ndarray,\n    item_correction: torch.Tensor,\n    known_positive_packed: np.ndarray | None,\n    generator: torch.Generator,\n    device: torch.device,\n"
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-c01cc012-f371-48b8-b6be-6516347b7465",
    "changes": {
      "/app/solution/train_current_model.py": {
        "type": "update",
        "unified_diff": "@@ -66,2 +66,4 @@\n     hard_include_inbatch: bool = False\n+    training_objective: str = \"bce\"\n+    shared_pool_size: int = 4096\n     negative_filter_scope: str = \"full-training-history\"\n@@ -1166,2 +1168,39 @@\n \n+def shared_softmax_loss(\n+    model, queries, positives, liked_items, user_rows, known_positive_packed, config,\n+):\n+    \"\"\"Uniform sampled softmax with a shared, filtered catalog denominator.\n+\n+    Every sampled negative participates. Log-sum-exp concentrates gradients on\n+    confusable tracks without a biased top-k negative proposal. N/K corrects\n+    the sampled denominator to the catalog sum. Item encoding and matrix\n+    multiplication are shared across queries, avoiding B x K x D tensors.\n+    \"\"\"\n+    generator = torch.Generator(device=\"cpu\").manual_seed(\n+        config.seed + int(positives.sum()) + int(user_rows[0]))\n+    pool = torch.randperm(model.n_items, generator=generator)[:config.shared_pool_size]\n+    unique, inverse = torch.unique(torch.cat([positives.cpu(), pool]), return_inverse=True)\n+    encoded = model.encode_items(unique.to(queries.device))\n+    pos_vectors = encoded[inverse[:len(positives)].to(queries.device)]\n+    pool_vectors = encoded[inverse[len(positives):].to(queries.device)]\n+    positive_logits = (queries * pos_vectors).sum(dim=1)\n+    raw_logits = queries @ pool_vectors.T\n+    columns = pool.numpy()\n+    invalid = ((known_positive_packed[user_rows[:, None], columns[None, :] >> 3]\n+                >> (columns[None, :] & 7)) & 1).astype(bool)\n+    # Pool order is random; build an inverse catalog lookup for explicit likes.\n+    lookup = np.full(model.n_items + 1, -1, dtype=np.int64)\n+    lookup[columns] = np.arange(len(columns))\n+    excluded = np.concatenate([positives.cpu().numpy()[:, None], liked_items], axis=1)\n+    mapped = lookup[excluded]\n+    present = mapped >= 0\n+    rows = np.broadcast_to(np.arange(len(queries))[:, None], mapped.shape)\n+    invalid[rows[present], mapped[present]] = True\n+    negative_logits = raw_logits.masked_fill(torch.from_numpy(invalid).to(queries.device), -torch.inf)\n+    log_negative_sum = torch.logsumexp(negative_logits, dim=1) + np.log(model.n_items / len(pool))\n+    loss = torch.logaddexp(positive_logits, log_negative_sum) - positive_logits\n+    # Random-pool diagnostics only; masked entries never enter the loss.\n+    return loss, positive_logits, raw_logits[:, :32]\n+\n+\n def score_target_microbatches(\n@@ -1715,3 +1754,5 @@\n                                          (\"hard_objective\", \"bce\"),\n-                                         (\"hard_include_inbatch\", True)))\n+                                         (\"hard_include_inbatch\", True),\n+                                         (\"training_objective\", \"bce\"),\n+                                         (\"shared_pool_size\", 4096)))\n         )\n@@ -2033,2 +2074,4 @@\n     parser.add_argument(\"--hard-include-inbatch\", action=\"store_true\", default=Config.hard_include_inbatch)\n+    parser.add_argument(\"--training-objective\", choices=(\"bce\", \"shared-softmax\"), default=Config.training_objective)\n+    parser.add_argument(\"--shared-pool-size\", type=int, default=Config.shared_pool_size)\n     parser.add_argument(\n@@ -2143,2 +2186,4 @@\n         hard_include_inbatch=args.hard_include_inbatch,\n+        training_objective=args.training_objective,\n+        shared_pool_size=args.shared_pool_size,\n         loss_microbatch_size=args.loss_microbatch_size,\n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/train_current_model.py\n",
    "stderr": ""
  }
}

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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-161a1488-5770-4eb5-a8b7-b85c88a5e30d",
    "process_id": "25909",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=s.index('        selected_negatives = negatives[begin:end]')\nend=s.index('        loss_sum = (selected_weights * per_target_loss).sum()',start)\nold=s[start:end]\nweights='''        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n'''\nassert weights in old\nold=old.replace(weights,'')\nnew='''        if config.training_objective == \"shared-softmax\":\n            per_target_loss, positive_logits, negative_logits = shared_softmax_loss(\n                model, query_vectors, positive_items[begin:end],\n                prepared.liked_states[selected_states], user_rows[begin:end],\n                known_positive_packed, config,\n            )\n        else:\n'''+''.join('    '+line if line.strip() else line for line in old.splitlines(keepends=True))+weights\ns=s[:start]+new+s[end:]\np.write_text(s)\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nfrom pathlib import Path\np=Path('/app/solution/train_current_model.py')\ns=p.read_text()\nstart=s.index('        selected_negatives = negatives[begin:end]')\nend=s.index('        loss_sum = (selected_weights * per_target_loss).sum()',start)\nold=s[start:end]\nweights='''        selected_weights = torch.from_numpy(\n            sample_weights[begin:end].astype(np.float32, copy=False)\n        ).to(device)\n'''\nassert weights in old\nold=old.replace(weights,'')\nnew='''        if config.training_objective == \"shared-softmax\":\n            per_target_loss, positive_logits, negative_logits = shared_softmax_loss(\n                model, query_vectors, positive_items[begin:end],\n                prepared.liked_states[selected_states], user_rows[begin:end],\n                known_positive_packed, config,\n            )\n        else:\n'''+''.join('    '+line if line.strip() else line for line in old.splitlines(keepends=True))+weights\ns=s[:start]+new+s[end:]\np.write_text(s)\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "",
    "stderr": "",
    "aggregated_output": "",
    "exit_code": 0,
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      "secs": 0,
      "nanos": 1820
    },
    "formatted_output": ""
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-f862af4d-5d9e-432e-a93b-cc7f13cdcf19",
    "changes": {
      "/app/solution/test_shared_softmax.py": {
        "type": "add",
        "content": "\"\"\"Check the sampled denominator against exact masked catalog softmax.\"\"\"\nimport numpy as np\nimport torch\nimport train_current_model as t\n\ntorch.set_num_threads(1)\nt.seed_everything(11)\nconfig = t.Config(training_objective=\"shared-softmax\", shared_pool_size=100)\nmodel = t.base.SequentialTwoTower(np.zeros((100, 4), dtype=np.float32), t.model_config(config)).eval()\nqueries = torch.randn(2, 192, requires_grad=True)\npositives = torch.tensor([1, 2])\nlikes = np.array([[7, 100], [8, 100]])\nknown = np.zeros((2, 100), dtype=np.uint8)\nknown[0, :20] = 1\nknown[1, 20:40] = 1\npacked = np.packbits(known, axis=1, bitorder=\"little\")\nloss, _, _ = t.shared_softmax_loss(model, queries, positives, likes, np.arange(2), packed, config)\nscores = queries @ model.encode_items(torch.arange(100)).T\nmask = known.astype(bool)\nmask[0, 7] = mask[1, 8] = True\nmask[np.arange(2), positives.numpy()] = False\nexpected = torch.nn.functional.cross_entropy(scores.masked_fill(torch.from_numpy(mask), -torch.inf), positives, reduction=\"none\")\nassert torch.allclose(loss, expected, atol=1e-5), (loss, expected)\nloss.mean().backward()\nassert torch.isfinite(queries.grad).all() and queries.grad.abs().sum() > 0\nassert model.candidate_embedding.weight.grad.abs().sum() > 0\nprint(\"shared-softmax matches exact filtered catalog CE and propagates finite tower gradients\")\n"
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nA /app/solution/test_shared_softmax.py\n",
    "stderr": ""
  }
}

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        "type": "update",
        "unified_diff": "@@ -1,3 +1,3 @@\n #!/usr/bin/env python3\n-\"\"\"Train the Yambda windowed two-tower baseline.\n+\"\"\"Train the Yambda windowed two-tower with shared-pool negative training.\n \n@@ -11,7 +11,9 @@\n \n-The model is a pure two-tower retriever: no user-ID embedding, no output\n-normalization, raw dot-product retrieval, sample-weighted binary cross entropy,\n-and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every\n-selected adjacent target retains a nonzero weight. Only files under the supplied\n-public directory are read.\n+The towers, chronological split, eligibility boundary, and raw dot-product\n+retrieval are unchanged. Shared sampled softmax scores a large uniform catalog\n+pool with matrix multiplication and corrects its denominator by N/K. Known\n+positives and active likes are filtered before the loss. The production BCE and\n+experimental hard-negative modes remain available for controlled comparisons.\n+Every selected adjacent target retains a nonzero weight. Only files under the\n+supplied public directory are read.\n \"\"\"\n@@ -24,3 +26,3 @@\n import time\n-from dataclasses import asdict, dataclass, replace\n+from dataclasses import asdict, dataclass\n from pathlib import Path\n@@ -54,3 +56,3 @@\n     gradient_clip: float = 2.0\n-    loss_microbatch_size: int = 65_536\n+    loss_microbatch_size: int = 4096\n     uniform_negatives: int = 24\n@@ -60,9 +62,8 @@\n     inbatch_correction_max: float = 4.0\n-    hard_negatives: int = 16\n+    hard_negatives: int = 0\n     hard_pool_size: int = 1024\n     hard_negative_weight: float = 0.5\n-    hard_warmup_epochs: int = 0\n     hard_objective: str = \"pairwise\"\n     hard_include_inbatch: bool = False\n-    training_objective: str = \"bce\"\n+    training_objective: str = \"shared-softmax\"\n     shared_pool_size: int = 4096\n@@ -1832,4 +1833,2 @@\n     for epoch in range(start_epoch + 1, config.epochs + 1):\n-        training_config = (replace(config, hard_negatives=0)\n-                           if epoch <= config.hard_warmup_epochs else config)\n         diagnostics = run_training_epoch(\n@@ -1838,3 +1837,3 @@\n             prepared,\n-            training_config,\n+            config,\n             target_weights,\n@@ -2088,3 +2087,2 @@\n     parser.add_argument(\"--hard-negative-weight\", type=float, default=Config.hard_negative_weight)\n-    parser.add_argument(\"--hard-warmup-epochs\", type=int, default=Config.hard_warmup_epochs)\n     parser.add_argument(\"--hard-objective\", choices=(\"bce\", \"pairwise\"), default=Config.hard_objective)\n@@ -2204,3 +2202,2 @@\n         hard_negative_weight=args.hard_negative_weight,\n-        hard_warmup_epochs=args.hard_warmup_epochs,\n         hard_objective=args.hard_objective,\n@@ -2316,4 +2313,6 @@\n                     \"score\": \"raw-dot-product\",\n-                    \"objective\": \"sample-weighted-BCE-with-optional-hard-pair-ranking\",\n+                    \"objective\": config.training_objective,\n                     \"negative_sampling\": (\n+                        f\"uniform-shared-pool-{config.shared_pool_size}\"\n+                        if config.training_objective == \"shared-softmax\" else\n                         f\"24-uniform-plus-8-corrected-inbatch-plus-{config.hard_negatives}-mined\"\n",
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        "content": "# Music discovery two-tower\n\nSelected public NDCG@10: **0.022637**, versus production **0.021280**\n(**+6.4% relative**, 4,263 scored users). `submission.parquet` is the\ntest-cutoff ranking. The final fit starts from the production checkpoint and\nrefits the selected objective on **train + val** observed history.\n\nThe original 24 uniform / 8 corrected in-batch negatives give each target few\ncompetitive comparisons. Simply adding aggressively mined negatives was harmful:\nit changed the negative distribution and strongly suppressed useful popular\ntracks. The selected loss instead shares **4,096 uniformly sampled catalog\nitems** across a loss slice, masks training-prefix positives, active likes and\nthe target, and uses sampled softmax. The sampled negative denominator is scaled\nby catalog size / pool size. Log-sum-exp emphasizes confusable negatives without\na top-k proposal. Shared item encoding and matrix multiplication avoid a large\nper-target negative-vector tensor; the softmax training pass took about 141\nseconds, versus 265 seconds for the production pass on this CPU.\n\nThe selected recipe also uses the trainer's existing discovery-graded weighting:\nordinary/repeated targets 0.1, novel organic strong listens 1, and qualifying\norganic-like targets 7. Every selected adjacent target retains nonzero weight.\nThe GRU, both towers, 192-dimensional raw dot product, chronological partitions,\ncutoffs, candidate masks, and request-time serving architecture are unchanged.\nValidation-stage fitting reads only train events. Validation outcomes are used\nonly for model selection; final refitting observes train and val events.\n\n| Public experiment | NDCG@10 |\n|---|---:|\n| Production, one BCE pass | 0.021280 |\n| Aggressive mined-negative BCE, one pass | 0.002453 |\n| Production + second BCE pass | 0.019878 |\n| Production + hard pairwise auxiliary pass | 0.020386 |\n| Production + shared-softmax pass | 0.021570 |\n| Production + discovery-graded shared-softmax pass, selected | **0.022637** |\n| A second graded shared-softmax pass | 0.020540 |\n\nThe supplied environment's Python is `/opt/task-venv/bin/python`. The retained\n`production_ckpt/best.pt` was generated from the unmodified production trainer:\n\n```sh\n/opt/task-venv/bin/python /task/current_model/train_current_model.py \\\n  --stage validation --public-dir /task \\\n  --output /app/solution/production_validation.parquet \\\n  --prepared-cache /app/solution/validation_cache.npz \\\n  --checkpoint-dir /app/solution/production_ckpt\n```\n\nFinal submission command (`--epochs 2` means one new pass after checkpoint epoch 1):\n\n```sh\n/opt/task-venv/bin/python /app/solution/train_current_model.py \\\n  --stage submission --public-dir /task \\\n  --output /app/solution/submission.parquet \\\n  --prepared-cache /app/solution/submission_cache.npz \\\n  --checkpoint-dir /app/solution/final_ckpt \\\n  --resume-checkpoint /app/solution/production_ckpt/best.pt \\\n  --epochs 2 --hard-negatives 0 --training-objective shared-softmax \\\n  --shared-pool-size 4096 --learning-rate 0.0005 \\\n  --target-weighting discovery-graded --organic-like-target-weight 7 \\\n  --ordinary-target-weight 0.1 --threads 8 --loss-microbatch-size 4096\n/opt/task-venv/bin/python /app/solution/validate_submission.py\n```\n\n`test_shared_softmax.py` verifies equality to exact filtered full-catalog\ncross-entropy when the pool covers the catalog, and finite nonzero gradients.\n`test_mining.py` checks the experimental miner's filtering, uniqueness,\ndeterminism and top-k selection. `validate_submission.py` independently checks\nall required schema, coverage, rank, uniqueness and test-eligibility conditions.\nRanking writes are atomic, preserving the previous valid file until completion.\n"
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  "thread_token_usage": {
    "input_tokens": 9780011,
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{
  "type": "codex_item_completed",
  "item": {
    "type": "CommandExecution",
    "id": "exec-9fa56d2a-ae95-42e9-bdb0-fda96ad51c3e",
    "process_id": "85302",
    "command": [
      "/bin/sh",
      "-lc",
      "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\np=Path('/app/solution')\na=json.loads((p/'final.json').read_text())\nprint(json.dumps({k:a[k] for k in ['stage','rows','users','runtime_seconds','embedding_diagnostics']},indent=2))\nprint('Selected checkpoint epoch:', min(a['training_history'],key=lambda r:r['validation_loss'])['epoch'])\nprint('Local implementation:',(p/'train_current_model.py').is_file(),(p/'two_tower_model.py').is_file())\nprint('Regular submission:',(p/'submission.parquet').is_file() and not (p/'submission.parquet').is_symlink())\nPY"
    ],
    "cwd": "file:///app",
    "parsed_cmd": [
      {
        "type": "unknown",
        "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport json\nfrom pathlib import Path\np=Path('/app/solution')\na=json.loads((p/'final.json').read_text())\nprint(json.dumps({k:a[k] for k in ['stage','rows','users','runtime_seconds','embedding_diagnostics']},indent=2))\nprint('Selected checkpoint epoch:', min(a['training_history'],key=lambda r:r['validation_loss'])['epoch'])\nprint('Local implementation:',(p/'train_current_model.py').is_file(),(p/'two_tower_model.py').is_file())\nprint('Regular submission:',(p/'submission.parquet').is_file() and not (p/'submission.parquet').is_symlink())\nPY"
      }
    ],
    "source": "unified_exec_startup",
    "status": "completed",
    "stdout": "{\n  \"stage\": \"submission\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 268.593,\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8813598155975342,\n    \"query_norm_std\": 0.7769436240196228,\n    \"item_norm_mean\": 2.642338991165161,\n    \"item_norm_std\": 1.0539997816085815\n  }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n",
    "stderr": "",
    "aggregated_output": "{\n  \"stage\": \"submission\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 268.593,\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8813598155975342,\n    \"query_norm_std\": 0.7769436240196228,\n    \"item_norm_mean\": 2.642338991165161,\n    \"item_norm_std\": 1.0539997816085815\n  }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n",
    "exit_code": 0,
    "duration": {
      "secs": 0,
      "nanos": 2490
    },
    "formatted_output": "{\n  \"stage\": \"submission\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 268.593,\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.8813598155975342,\n    \"query_norm_std\": 0.7769436240196228,\n    \"item_norm_mean\": 2.642338991165161,\n    \"item_norm_std\": 1.0539997816085815\n  }\n}\nSelected checkpoint epoch: 2.0\nLocal implementation: True True\nRegular submission: True\n"
  }
}

{
  "type": "codex_item_completed",
  "item": {
    "type": "FileChange",
    "id": "exec-25cdf5f9-f319-4828-a6bc-0f68f0f9f05e",
    "changes": {
      "/app/solution/README.md": {
        "type": "update",
        "unified_diff": "@@ -6,2 +6,5 @@\n refits the selected objective on **train + val** observed history.\n+The completed refit and ranking took 268.6 seconds. Independent validation\n+confirmed **845,600 rows**, exactly **100 distinct eligible tracks for each of\n+8,456 target users**, complete ranks 1–100, integer fields, and a regular file.\n \n",
        "move_path": null
      }
    },
    "status": "completed",
    "stdout": "Success. Updated the following files:\nM /app/solution/README.md\n",
    "stderr": ""
  }
}

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