Agent trace

- [in_progress] 1: Explore production model, data, and evaluation
- [pending] 2: Diagnose negative-sampling limitations
- [pending] 3: Implement improved training objective
- [pending] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission

- /task/
  - current_model/
    - train_current_model.py
    - two_tower_model.py
  - data/
    - candidate_catalog.parquet
    - target_users.parquet
    - test_eligible_candidates.npz
    - test_preference_state.parquet
    - train_dislikes.parquet
    - train_likes.parquet
    - train_listens.parquet
    - train_undislikes.parquet
    - train_unlikes.parquet
    - val_dislikes.parquet
    - val_likes.parquet
    - val_listens.parquet
    - val_undislikes.parquet
    - val_unlikes.parquet
    - validation_eligible_candidates.npz
    - validation_preference_state.parquet
  - evaluate_public.py
  - ranking_evaluation.py
  - requirements.txt

- /app/
  - solution/

1→#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
10→into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

20→import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
30→import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


40→@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
50→    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
60→    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
70→    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)
class CandidateSet:
    uids: np.ndarray
    item_ids: np.ndarray
    packed: np.ndarray
80→
    @classmethod
    def read(cls, path: Path) -> "CandidateSet":
        with np.load(path, allow_pickle=False) as artifact:
            result = cls(
                uids=artifact["uids"].astype(np.int64, copy=True),
                item_ids=artifact["item_ids"].astype(np.int64, copy=True),
                packed=artifact["packed_eligible"].astype(np.uint8, copy=True),
            )
        expected = (len(result.uids), (len(result.item_ids) + 7) // 8)
90→        if result.packed.shape != expected:
            raise ValueError(
                f"invalid candidate artifact shape {result.packed.shape}; "
                f"expected {expected}"
            )
        return result


@dataclass(frozen=True)
class SequenceData:
100→    item_ids: np.ndarray
    item_features: np.ndarray
    user_ids: np.ndarray
    offsets: np.ndarray
    sequence_timestamps: np.ndarray
    sequence_items: np.ndarray
    event_features: np.ndarray
    activity_features: np.ndarray
    state_ids: np.ndarray
    liked_states: np.ndarray
110→    disliked_states: np.ndarray
    state_counts: np.ndarray
    inference_user_rows: np.ndarray
    inference_liked_items: np.ndarray
    inference_disliked_items: np.ndarray
    inference_user_features: np.ndarray

    @property
    def lengths(self) -> np.ndarray:
        return np.diff(self.offsets)
120→
    @property
    def target_count(self) -> int:
        return int(np.maximum(self.lengths - 1, 0).sum())


@dataclass(frozen=True)
class TargetPartition:
    """A contiguous recent target window and its chronological BCE split."""

130→    window_starts: np.ndarray
    training_counts: np.ndarray
    validation_counts: np.ndarray
    history_training_counts: np.ndarray

    @property
    def selected_count(self) -> int:
        return int(self.training_counts.sum() + self.validation_counts.sum())


140→def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def seed_everything(seed: int) -> None:
    random.seed(seed)
    np.random.seed(seed)
    torch.manual_seed(seed)


150→def model_config(config: Config) -> base.TowerConfig:
    """Map the dense trainer configuration onto the unchanged tower."""
    return base.TowerConfig(
        sequence_length=config.bptt_steps,
        embedding_dim=config.embedding_dim,
        hidden_dim=config.hidden_dim,
        user_hidden_1=config.user_hidden_1,
        user_hidden_2=config.user_hidden_2,
        item_hidden_1=config.item_hidden_1,
        item_hidden_2=config.item_hidden_2,
160→        dropout=config.dropout,
        epochs=config.epochs,
        learning_rate=config.learning_rate,
        weight_decay=config.weight_decay,
        gradient_clip=config.gradient_clip,
        uniform_negatives=config.uniform_negatives,
        inbatch_negatives=config.inbatch_negatives,
        inbatch_correction_power=config.inbatch_correction_power,
        inbatch_correction_min=config.inbatch_correction_min,
        inbatch_correction_max=config.inbatch_correction_max,
170→        seed=config.seed,
    )


def append_state_snapshot(
    active_likes: dict[int, int],
    active_dislikes: dict[int, int],
    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
180→    state_counts: list[tuple[int, int]],
) -> int:
    liked_states.append(base.pack_active_items(active_likes, base.LIKE_ITEMS, n_items))
    disliked_states.append(
        base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
    )
    state_counts.append((len(active_likes), len(active_dislikes)))
    return len(liked_states) - 1


190→def causal_activity_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    organic: np.ndarray,
    completion: np.ndarray,
) -> np.ndarray:
    """Compute the first four reviewed user features at every event cutoff."""
    length = len(items)
    result = np.zeros((length, 4), dtype=np.float16)
    if not length:
200→        return result
    left_rows = np.searchsorted(
        timestamps,
        timestamps - base.PROFILE_LOOKBACK_DAYS * base.DAY,
        side="left",
    )
    rows = np.arange(length, dtype=np.int64)
    counts = rows - left_rows + 1
    organic_sum = np.cumsum(organic.astype(np.float64))
    completion_sum = np.cumsum(np.minimum(completion, 100).astype(np.float64))
210→    before_organic = np.where(
        left_rows > 0, organic_sum[np.maximum(left_rows - 1, 0)], 0.0
    )
    before_completion = np.where(
        left_rows > 0, completion_sum[np.maximum(left_rows - 1, 0)], 0.0
    )

    unique_counts = np.empty(length, dtype=np.int32)
    frequencies: dict[int, int] = {}
    left = 0
220→    for right, raw_item in enumerate(items):
        item = int(raw_item)
        frequencies[item] = frequencies.get(item, 0) + 1
        required_left = int(left_rows[right])
        while left < required_left:
            old_item = int(items[left])
            remaining = frequencies[old_item] - 1
            if remaining:
                frequencies[old_item] = remaining
            else:
230→                del frequencies[old_item]
            left += 1
        unique_counts[right] = len(frequencies)

    result[:, 0] = (np.log1p(counts) / 8.0).astype(np.float16)
    result[:, 1] = (np.log1p(unique_counts) / 6.0).astype(np.float16)
    result[:, 2] = (
        (organic_sum - before_organic) / np.maximum(counts, 1)
    ).astype(np.float16)
    result[:, 3] = (
240→        (completion_sum - before_completion)
        / np.maximum(counts, 1)
        / 100.0
    ).astype(np.float16)
    return result


def build_sequence_parts(
    listens: base.ListenEvents,
    explicit: base.ExplicitEvents,
250→    n_items: int,
    liked_states: list[np.ndarray],
    disliked_states: list[np.ndarray],
    state_counts: list[tuple[int, int]],
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray] | None:
    qualifying = listens.completion_pct >= base.LISTEN_POSITIVE_THRESHOLD
    timestamps = listens.timestamps[qualifying].astype(np.int64, copy=False)
    items = listens.items[qualifying].astype(np.uint16, copy=False)
    organic = listens.organic[qualifying].astype(np.float32, copy=False)
    completion = listens.completion_pct[qualifying].astype(np.float32, copy=False)
260→    if not len(items):
        return None

    event_features = np.zeros((len(items), 3), dtype=np.float16)
    event_features[:, 0] = organic.astype(np.float16)
    event_features[:, 1] = (
        np.minimum(completion, 100.0) / 100.0
    ).astype(np.float16)
    if len(items) > 1:
        gaps = np.maximum(0, timestamps[1:] - timestamps[:-1])
270→        event_features[1:, 2] = np.exp(
            -gaps.astype(np.float64) / (60.0 * base.DAY)
        ).astype(np.float16)

    activity = causal_activity_features(
        timestamps, items, organic, completion
    )
    states = np.zeros(len(items), dtype=np.uint32)
    active_likes: dict[int, int] = {}
    active_dislikes: dict[int, int] = {}
280→    explicit_position = 0
    current_state = 0
    for row, timestamp in enumerate(timestamps):
        changed = False
        while (
            explicit_position < len(explicit.timestamps)
            and int(explicit.timestamps[explicit_position]) < int(timestamp)
        ):
            item = int(explicit.items[explicit_position])
            event_timestamp = int(explicit.timestamps[explicit_position])
290→            destination = (
                active_likes
                if int(explicit.channel[explicit_position]) == 0
                else active_dislikes
            )
            if int(explicit.active[explicit_position]) == 1:
                destination[item] = event_timestamp
            else:
                destination.pop(item, None)
            explicit_position += 1
300→            changed = True
        if changed:
            current_state = append_state_snapshot(
                active_likes,
                active_dislikes,
                n_items,
                liked_states,
                disliked_states,
                state_counts,
            )
310→        states[row] = np.uint32(current_state)
    return timestamps, items.copy(), event_features, activity, states


def final_user_features(
    timestamps: np.ndarray,
    items: np.ndarray,
    event_features: np.ndarray,
    history_end: int,
    like_count: int,
320→    dislike_count: int,
) -> np.ndarray:
    start = int(
        np.searchsorted(
            timestamps,
            history_end - base.PROFILE_LOOKBACK_DAYS * base.DAY,
            side="left",
        )
    )
    result = np.zeros(6, dtype=np.float32)
330→    recent_items = items[start:]
    if len(recent_items):
        result[0] = np.log1p(len(recent_items)) / 8.0
        result[1] = np.log1p(len(np.unique(recent_items))) / 6.0
        result[2] = float(np.mean(event_features[start:, 0]))
        result[3] = float(np.mean(event_features[start:, 1]))
    result[4] = np.log1p(like_count) / 6.0
    result[5] = np.log1p(dislike_count) / 4.0
    return result

340→
def prepare_data(
    public: Path, stage: str, config: Config
) -> tuple[SequenceData, CandidateSet]:
    data = public / "data"
    catalog = pd.read_parquet(data / "candidate_catalog.parquet").sort_values(
        "popularity_rank"
    )
    item_ids = catalog.item_id.to_numpy(dtype=np.int64, copy=True)
    item_index = pd.Index(item_ids)
350→    n_items = len(item_ids)
    if n_items >= np.iinfo(np.uint16).max:
        raise RuntimeError("uint16 item storage is too small for this catalog")

    if stage == "validation":
        prefixes = ("train",)
        history_end = base.TRAIN_END
        candidate_name = "validation_eligible_candidates.npz"
        state_name = "validation_preference_state.parquet"
    else:
360→        prefixes = ("train", "val")
        history_end = base.VALIDATION_END
        candidate_name = "test_eligible_candidates.npz"
        state_name = "test_preference_state.parquet"
    eligibility = CandidateSet.read(data / candidate_name)
    if not np.array_equal(eligibility.item_ids, item_ids):
        raise RuntimeError("catalog and candidate artifact item order differ")

    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
    inference_likes, inference_dislikes, like_counts, dislike_counts = (
370→        base.load_inference_state(
            data / state_name,
            item_index,
            eligibility.uids,
            n_items,
        )
    )
    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)
380→
    # Snapshot zero is the shared empty explicit state.
    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
    disliked_states = [
        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
    ]
    state_counts: list[tuple[int, int]] = [(0, 0)]
    user_ids: list[int] = []
    offsets = [0]
    timestamp_parts: list[np.ndarray] = []
390→    item_parts: list[np.ndarray] = []
    event_parts: list[np.ndarray] = []
    activity_parts: list[np.ndarray] = []
    state_parts: list[np.ndarray] = []

    listen_paths = [data / f"{prefix}_listens.parquet" for prefix in prefixes]
    started = time.monotonic()
    for seen_users, (uid, listens) in enumerate(
        base.merge_listen_streams(listen_paths, item_index), start=1
    ):
400→        parts = build_sequence_parts(
            listens,
            explicit_by_user.get(uid, base.empty_explicit_events()),
            n_items,
            liked_states,
            disliked_states,
            state_counts,
        )
        if parts is None:
            continue
410→        sequence_row = len(user_ids)
        user_ids.append(uid)
        timestamp_parts.append(parts[0].astype(np.uint32, copy=False))
        item_parts.append(parts[1])
        event_parts.append(parts[2])
        activity_parts.append(parts[3])
        state_parts.append(parts[4])
        offsets.append(offsets[-1] + len(parts[1]))
        inference_row = inference_rows.get(uid)
        if inference_row is not None:
420→            inference_sequence_rows[inference_row] = sequence_row
            inference_user_features[inference_row] = final_user_features(
                parts[0],
                parts[1],
                parts[2],
                history_end,
                int(like_counts[inference_row]),
                int(dislike_counts[inference_row]),
            )
        if seen_users % 1_000 == 0:
430→            log(
                f"prepared users={seen_users:,} tokens={offsets[-1]:,} "
                f"states={len(liked_states):,} "
                f"elapsed={time.monotonic() - started:.1f}s"
            )

    missing = np.flatnonzero(inference_sequence_rows < 0)
    if len(missing):
        inference_user_features[missing, 4] = np.log1p(like_counts[missing]) / 6.0
        inference_user_features[missing, 5] = (
440→            np.log1p(dislike_counts[missing]) / 4.0
        )
        log(f"target users without qualifying catalog sequence={len(missing):,}")

    prepared = SequenceData(
        item_ids=item_ids,
        item_features=base.build_item_features(catalog),
        user_ids=np.asarray(user_ids, dtype=np.int64),
        offsets=np.asarray(offsets, dtype=np.int64),
        sequence_timestamps=np.concatenate(timestamp_parts),
450→        sequence_items=np.concatenate(item_parts),
        event_features=np.concatenate(event_parts),
        activity_features=np.concatenate(activity_parts),
        state_ids=np.concatenate(state_parts),
        liked_states=np.stack(liked_states),
        disliked_states=np.stack(disliked_states),
        state_counts=np.asarray(state_counts, dtype=np.int32),
        inference_user_rows=inference_sequence_rows,
        inference_liked_items=inference_likes,
        inference_disliked_items=inference_dislikes,
460→        inference_user_features=inference_user_features,
    )
    log(
        f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
        f"tokens={len(prepared.sequence_items):,} "
        f"next_item_targets={prepared.target_count:,} "
        f"states={len(prepared.liked_states):,}"
    )
    return prepared, eligibility

470→
def cache_metadata(stage: str, config: Config) -> dict[str, object]:
    return {
        "cache_version": CACHE_VERSION,
        "stage": stage,
        "positive_listen_threshold": base.LISTEN_POSITIVE_THRESHOLD,
        "profile_lookback_days": base.PROFILE_LOOKBACK_DAYS,
        "event_time_feature": "exp-negative-gap-over-60-days",
    }

480→
def save_cache(
    path: Path, prepared: SequenceData, stage: str, config: Config
) -> None:
    path.parent.mkdir(parents=True, exist_ok=True)
    np.savez(
        path,
        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
        **prepared.__dict__,
    )
490→    log(f"saved public-derived full-sequence cache: {path}")


def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
    with np.load(path, allow_pickle=False) as artifact:
        actual = json.loads(str(artifact["metadata"].item()))
        expected = cache_metadata(stage, config)
        if actual != expected:
            raise RuntimeError(f"sequence cache differs: {actual} != {expected}")
        values = {
500→            field: artifact[field].copy()
            for field in SequenceData.__dataclass_fields__
        }
    prepared = SequenceData(**values)
    log(
        f"loaded full-sequence cache: tokens={len(prepared.sequence_items):,} "
        f"targets={prepared.target_count:,}"
    )
    return prepared

510→
def load_or_prepare(
    public: Path,
    stage: str,
    config: Config,
    cache: Path | None,
) -> tuple[SequenceData, CandidateSet]:
    candidate_name = (
        "validation_eligible_candidates.npz"
        if stage == "validation"
520→        else "test_eligible_candidates.npz"
    )
    eligibility = CandidateSet.read(public / "data" / candidate_name)
    if cache is not None and cache.is_file():
        prepared = load_cache(cache, stage, config)
        if not np.array_equal(prepared.item_ids, eligibility.item_ids):
            raise RuntimeError("cached catalog differs from candidate artifact")
        return prepared, eligibility
    prepared, built_eligibility = prepare_data(public, stage, config)
    if cache is not None:
530→        save_cache(cache, prepared, stage, config)
    return prepared, built_eligibility


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
540→    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
550→                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
560→    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
570→    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray
580→) -> np.ndarray:
    """Pack each user's complete public-training positive-item set."""
    width = (len(prepared.item_ids) + 7) // 8
    packed = np.zeros((len(prepared.user_ids), width), dtype=np.uint8)
    retained = 0
    for user_row, target_count in enumerate(training_counts):
        if not target_count:
            continue
        offset = int(prepared.offsets[user_row])
        # T adjacent targets consume sequence positions 0 through T.  The
590→        # chronological BCE suffix starts at T + 1 and is intentionally absent.
        items = np.unique(
            prepared.sequence_items[
                offset : offset + int(target_count) + 1
            ].astype(np.int64)
        )
        byte_columns = items >> 3
        bit_values = np.left_shift(
            np.uint8(1), (items & 7).astype(np.uint8)
        )
600→        np.bitwise_or.at(packed[user_row], byte_columns, bit_values)
        retained += len(items)
    log(
        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
        f"unique_user_items={retained:,} bytes={packed.nbytes:,}"
    )
    return packed


def invalid_full_history_negatives(
610→    negatives: np.ndarray,
    targets: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray,
    liked_items: np.ndarray,
) -> np.ndarray:
    """Return row-level invalid negatives using the complete training prefix."""
    byte_columns = negatives >> 3
    bit_columns = negatives & 7
    known = known_positive_packed[user_rows[:, None], byte_columns]
620→    invalid = ((known >> bit_columns) & 1).astype(bool)
    invalid |= negatives == targets[:, None]
    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= (
            negatives[:, column, None] == negatives[:, :column]
        ).any(axis=1)
    return invalid


630→def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
640→) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
650→    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
660→                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
670→            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)
680→
    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
690→            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
700→        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
710→        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
720→        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
730→    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
740→            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
750→    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
760→    )


def event_sequence(
    model: base.SequentialTwoTower,
    items: torch.Tensor,
    features: torch.Tensor,
    hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    inputs = torch.cat([model.context_embedding(items), features], dim=-1)
770→    projected = model.event_projection(inputs)
    return model.sequence_encoder(projected, hidden)


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
780→    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
790→    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_rows],
        torch.from_numpy(counts.astype(np.float32)).to(device),
    )


def user_queries(
    model: base.SequentialTwoTower,
800→    sequence_vectors: torch.Tensor,
    liked_vectors: torch.Tensor,
    disliked_vectors: torch.Tensor,
    user_features: torch.Tensor,
) -> torch.Tensor:
    return model.user_tower(
        torch.cat(
            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
            dim=1,
        )
810→    )


def candidate_vectors(
    model: base.SequentialTwoTower,
    positives: torch.Tensor,
    negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    all_items = torch.cat([positives, negatives.reshape(-1)])
    unique_items, inverse = torch.unique(all_items, return_inverse=True)
820→    encoded = model.encode_items(unique_items)
    positive_vectors = encoded[inverse[: len(positives)]]
    negative_vectors = encoded[inverse[len(positives) :]].reshape(
        len(positives), negatives.shape[1], -1
    )
    return positive_vectors, negative_vectors


def history_windows(
    sequence: np.ndarray,
830→    start: int,
    count: int,
    width: int,
    padding_item: int,
) -> np.ndarray:
    base_start = max(0, start - width + 1)
    segment = sequence[base_start : start + count]
    padded = np.concatenate(
        [np.full(width - 1, padding_item, dtype=np.uint16), segment]
    )
840→    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
850→    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
860→        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,
870→    training_counts: np.ndarray,
) -> np.ndarray:
    """Return the discarded prefix before the retained chronological suffix."""
    training_region_ends = total_targets - validation_counts
    window_starts = training_region_ends - training_counts
    if (
        np.any(window_starts < 0)
        or np.any(training_counts < 0)
        or np.any(validation_counts < 0)
    ):
880→        raise RuntimeError("chronological target partition produced negative counts")
    if not np.array_equal(
        window_starts + training_counts + validation_counts, total_targets
    ):
        raise RuntimeError("chronological target window is not a final contiguous suffix")
    return window_starts


def chronological_target_partition(
    prepared: SequenceData,
890→    validation_fraction: float,
    max_train_targets_per_user: int,
    max_validation_targets_per_user: int,
) -> TargetPartition:
    """Select a recent contiguous window with a final out-of-time BCE suffix."""
    totals = np.maximum(prepared.lengths - 1, 0).astype(np.int64)
    validation = np.floor(totals * validation_fraction).astype(np.int64)
    validation[(totals >= 2) & (validation == 0)] = 1
    if max_validation_targets_per_user:
        validation = np.minimum(validation, max_validation_targets_per_user)
900→
    history_training = totals - validation
    training = history_training.copy()
    if max_train_targets_per_user:
        training = np.minimum(training, max_train_targets_per_user)
    window_starts = retained_target_window_starts(totals, validation, training)
    return TargetPartition(
        window_starts=window_starts,
        training_counts=training,
        validation_counts=validation,
910→        history_training_counts=history_training,
    )


def load_organic_like_outcomes(
    public: Path, stage: str, item_ids: np.ndarray
) -> dict[int, dict[int, np.ndarray]]:
    """Load mapped organic-like timestamps from public fitting history."""
    prefixes = ("train",) if stage == "validation" else ("train", "val")
    frames = [
920→        pd.read_parquet(
            public / "data" / f"{prefix}_likes.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic"],
        )
        for prefix in prefixes
    ]
    likes = pd.concat(frames, ignore_index=True)
    likes = likes[likes.is_organic == 1].copy()
    mapped = pd.Index(item_ids).get_indexer(
        likes.item_id.to_numpy(dtype=np.int64)
930→    )
    likes = likes.loc[mapped >= 0, ["uid", "timestamp"]].copy()
    likes["item"] = mapped[mapped >= 0].astype(np.int32, copy=False)
    likes = likes.sort_values(["uid", "item", "timestamp"], kind="stable")
    nested: dict[int, dict[int, list[int]]] = {}
    for raw_uid, raw_timestamp, raw_item in likes.itertuples(
        index=False, name=None
    ):
        nested.setdefault(int(raw_uid), {}).setdefault(int(raw_item), []).append(
            int(raw_timestamp)
940→        )
    result = {
        uid: {
            item: np.asarray(timestamps, dtype=np.int64)
            for item, timestamps in items.items()
        }
        for uid, items in nested.items()
    }
    log(
        f"loaded organic-like outcomes: rows={len(likes):,} "
950→        f"users={len(result):,}"
    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
960→    config: Config,
) -> np.ndarray:
    """Build nonzero per-token weights without dropping adjacent targets."""
    if config.target_weighting == "uniform":
        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
    else:
        weights = np.full(
            len(prepared.sequence_items),
            config.ordinary_target_weight,
            dtype=np.float32,
970→        )
        organic_strong = (
            (prepared.event_features[:, 0] > 0.5)
            & (prepared.event_features[:, 1] >= 0.8)
        )
        weights[organic_strong] = config.organic_strong_target_weight
        if config.target_weighting == "discovery-graded":
            like_outcomes = load_organic_like_outcomes(
                public, stage, prepared.item_ids
            )
980→            history_end = (
                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
            )
            horizon = config.discovery_horizon_days * base.DAY
            grade_three = 0
            for user_row, uid in enumerate(prepared.user_ids):
                start = int(prepared.offsets[user_row])
                end = int(prepared.offsets[user_row + 1])
                if end - start < 2:
                    continue
990→                last_training_target = start + int(training_counts[user_row])
                first_validation_target = last_training_target + 1
                split_timestamp = (
                    int(prepared.sequence_timestamps[first_validation_target])
                    if first_validation_target < end
                    else history_end
                )
                user_likes = like_outcomes.get(int(uid), {})
                seen = {int(prepared.sequence_items[start])}
                for position in range(start + 1, end):
1000→                    item = int(prepared.sequence_items[position])

1→"""Shared public-data primitives for the Yambda two-tower baseline."""
from __future__ import annotations

import sys
from dataclasses import dataclass
from pathlib import Path
from typing import Iterator

import numpy as np
10→import pandas as pd
import pyarrow.parquet as pq
import torch
from torch import nn


DAY = 86_400
TRAIN_END = 24_786_800
VALIDATION_END = 25_393_400
LISTEN_POSITIVE_THRESHOLD = 50
20→PROFILE_LOOKBACK_DAYS = 120
LIKE_ITEMS = 48
DISLIKE_ITEMS = 24
TOP_N = 100


@dataclass(frozen=True)
class TowerConfig:
    sequence_length: int = 100
    embedding_dim: int = 192
30→    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
40→    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    seed: int = 29


@dataclass(frozen=True)
class ListenEvents:
50→    timestamps: np.ndarray
    items: np.ndarray
    organic: np.ndarray
    completion_pct: np.ndarray


@dataclass(frozen=True)
class ExplicitEvents:
    timestamps: np.ndarray
    items: np.ndarray
60→    channel: np.ndarray
    active: np.ndarray
    organic_like: np.ndarray


def log(message: str) -> None:
    print(message, file=sys.stderr, flush=True)


def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
70→    """Return four standardized features built only from the public catalog."""
    positives = catalog.positive_listens.to_numpy(dtype=np.float64, copy=False)
    organic = catalog.organic_positive_listens.to_numpy(dtype=np.float64, copy=False)
    popularity_rank = catalog.popularity_rank.to_numpy(dtype=np.float64, copy=False)
    features = np.column_stack(
        [
            np.log1p(positives),
            np.log1p(organic),
            organic / np.maximum(positives, 1.0),
            1.0 - (popularity_rank - 1.0) / max(len(catalog) - 1, 1),
80→        ]
    )
    means = features.mean(axis=0, keepdims=True)
    scales = np.maximum(features.std(axis=0, keepdims=True), 1.0e-6)
    standardized = ((features - means) / scales).astype(np.float32)
    if not np.isfinite(standardized).all():
        raise RuntimeError("catalog features contain non-finite values")
    return standardized


90→def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
    if len(parts) == 1:
        return parts[0]
    timestamps = np.concatenate([part.timestamps for part in parts])
    items = np.concatenate([part.items for part in parts])
    organic = np.concatenate([part.organic for part in parts])
    completion = np.concatenate([part.completion_pct for part in parts])
    order = np.argsort(timestamps, kind="stable")
    return ListenEvents(
        timestamps=timestamps[order],
100→        items=items[order],
        organic=organic[order],
        completion_pct=completion[order],
    )


def iter_listen_users(
    path: Path, item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
    """Stream one uid-sorted public listen file one user at a time."""
110→    parquet = pq.ParquetFile(path)
    pending_uid: int | None = None
    pending: list[ListenEvents] = []
    previous_uid = -1
    for batch in parquet.iter_batches(
        batch_size=750_000,
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
    ):
        frame = batch.to_pandas()
        mapped = item_index.get_indexer(frame.item_id.to_numpy(dtype=np.int64))
120→        keep = mapped >= 0
        if not np.any(keep):
            continue
        uids = frame.uid.to_numpy(dtype=np.int64, copy=False)[keep]
        timestamps = frame.timestamp.to_numpy(dtype=np.int64, copy=False)[keep]
        items = mapped[keep].astype(np.int32, copy=False)
        organic = frame.is_organic.to_numpy(dtype=np.uint8, copy=False)[keep]
        completion = frame.played_ratio_pct.to_numpy(dtype=np.uint16, copy=False)[keep]
        if np.any(uids[1:] < uids[:-1]):
            raise RuntimeError(f"{path.name} is not sorted by uid")
130→        boundaries = np.r_[
            0,
            np.flatnonzero(uids[1:] != uids[:-1]) + 1,
            len(uids),
        ]
        for start, end in zip(boundaries[:-1], boundaries[1:]):
            uid = int(uids[start])
            part = ListenEvents(
                timestamps[start:end].copy(),
                items[start:end].copy(),
140→                organic[start:end].copy(),
                completion[start:end].copy(),
            )
            if pending_uid is None:
                pending_uid = uid
            elif uid != pending_uid:
                if pending_uid < previous_uid:
                    raise RuntimeError(f"{path.name} user order regressed")
                yield pending_uid, _combine_listen_chunks(pending)
                previous_uid = pending_uid
150→                pending_uid = uid
                pending = []
            pending.append(part)
    if pending_uid is not None:
        yield pending_uid, _combine_listen_chunks(pending)


def merge_listen_streams(
    paths: list[Path], item_index: pd.Index
) -> Iterator[tuple[int, ListenEvents]]:
160→    """Merge uid-sorted public listen files without materializing them."""
    streams = [iter(iter_listen_users(path, item_index)) for path in paths]
    heads: list[tuple[int, ListenEvents] | None] = [
        next(stream, None) for stream in streams
    ]
    while any(head is not None for head in heads):
        uid = min(head[0] for head in heads if head is not None)
        parts: list[ListenEvents] = []
        for position, head in enumerate(heads):
            if head is not None and head[0] == uid:
170→                parts.append(head[1])
                heads[position] = next(streams[position], None)
        yield uid, _combine_listen_chunks(parts)


def load_explicit_events(
    data: Path,
    prefixes: tuple[str, ...],
    item_index: pd.Index,
) -> dict[int, ExplicitEvents]:
180→    """Load public explicit actions as chronological state changes."""
    specifications = (
        ("likes", 0, 1, 0),
        ("unlikes", 0, 0, 1),
        ("dislikes", 1, 1, 0),
        ("undislikes", 1, 0, 1),
    )
    parts: list[pd.DataFrame] = []
    for family, channel, active, tie_priority in specifications:
        for prefix in prefixes:
190→            frame = pd.read_parquet(
                data / f"{prefix}_{family}.parquet",
                columns=["uid", "timestamp", "item_id", "is_organic"],
            )
            mapped = item_index.get_indexer(
                frame.item_id.to_numpy(dtype=np.int64, copy=False)
            )
            keep = mapped >= 0
            if not np.any(keep):
                continue
200→            retained = frame.loc[keep, ["uid", "timestamp", "is_organic"]].copy()
            retained["item"] = mapped[keep].astype(np.int32, copy=False)
            retained["channel"] = np.int8(channel)
            retained["active"] = np.int8(active)
            retained["tie_priority"] = np.int8(tie_priority)
            retained["organic_like"] = np.int8(
                family == "likes"
            ) * retained.is_organic.astype(np.int8)
            parts.append(retained.drop(columns="is_organic"))
    if not parts:
210→        return {}
    events = pd.concat(parts, ignore_index=True)
    events = events.sort_values(
        ["uid", "timestamp", "tie_priority"], kind="mergesort"
    ).reset_index(drop=True)
    result: dict[int, ExplicitEvents] = {}
    for uid, rows in events.groupby("uid", sort=False):
        result[int(uid)] = ExplicitEvents(
            timestamps=rows.timestamp.to_numpy(dtype=np.int64, copy=True),
            items=rows.item.to_numpy(dtype=np.int32, copy=True),
220→            channel=rows.channel.to_numpy(dtype=np.int8, copy=True),
            active=rows.active.to_numpy(dtype=np.int8, copy=True),
            organic_like=rows.organic_like.to_numpy(dtype=np.int8, copy=True),
        )
    log(f"loaded explicit events: rows={len(events):,} users={len(result):,}")
    return result


def empty_explicit_events() -> ExplicitEvents:
    return ExplicitEvents(
230→        timestamps=np.empty(0, dtype=np.int64),
        items=np.empty(0, dtype=np.int32),
        channel=np.empty(0, dtype=np.int8),
        active=np.empty(0, dtype=np.int8),
        organic_like=np.empty(0, dtype=np.int8),
    )


def pack_active_items(
    active: dict[int, int], width: int, padding_item: int
240→) -> np.ndarray:
    packed = np.full(width, padding_item, dtype=np.uint16)
    if not active:
        return packed
    ordered = sorted(active.items(), key=lambda pair: (-pair[1], pair[0]))[:width]
    packed[: len(ordered)] = np.fromiter(
        (item for item, _ in ordered), dtype=np.uint16, count=len(ordered)
    )
    return packed

250→
def load_inference_state(
    path: Path,
    item_index: pd.Index,
    uids: np.ndarray,
    n_items: int,
) -> tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
    """Pack the task-provided active explicit state."""
    state = pd.read_parquet(path, columns=["uid", "item_id", "state"])
    mapped = item_index.get_indexer(state.item_id.to_numpy(dtype=np.int64))
260→    keep = mapped >= 0
    state = state.loc[keep, ["uid", "state"]].copy()
    state["item"] = mapped[keep].astype(np.int32, copy=False)
    user_rows = pd.Index(uids)
    liked = np.full((len(uids), LIKE_ITEMS), n_items, dtype=np.uint16)
    disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
    like_counts = np.zeros(len(uids), dtype=np.int32)
    dislike_counts = np.zeros(len(uids), dtype=np.int32)
    for value, width, destination, counts in (
        ("liked", LIKE_ITEMS, liked, like_counts),
270→        ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
    ):
        selected = state[state.state == value]
        for uid, rows in selected.groupby("uid", sort=False):
            row = int(user_rows.get_indexer([int(uid)])[0])
            if row < 0:
                continue
            items = np.sort(rows.item.unique())
            counts[row] = len(items)
            retained = items[:width]
280→            destination[row, : len(retained)] = retained.astype(np.uint16)
    return liked, disliked, like_counts, dislike_counts


class SequentialTwoTower(nn.Module):
    """Order-sensitive user tower and independent catalog-aware item tower."""

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
290→        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
300→            input_size=config.hidden_dim,
            hidden_size=dim,
            num_layers=1,
            batch_first=True,
        )
        self.user_tower = nn.Sequential(
            nn.Linear(dim * 3 + 6, config.user_hidden_1),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_1, config.user_hidden_2),
310→            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.user_hidden_2, dim),
        )
        self.candidate_embedding = nn.Embedding(n_items, dim)
        self.register_buffer(
            "item_features", torch.from_numpy(item_features), persistent=True
        )
        self.item_tower = nn.Sequential(
            nn.Linear(dim + n_item_features, config.item_hidden_1),
320→            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_1, config.item_hidden_2),
            nn.GELU(),
            nn.Dropout(config.dropout),
            nn.Linear(config.item_hidden_2, dim),
        )
        nn.init.normal_(self.context_embedding.weight, std=0.02)
        nn.init.normal_(self.candidate_embedding.weight, std=0.02)
        with torch.no_grad():
330→            self.context_embedding.weight[n_items].zero_()

    def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
        mask = items.ne(self.n_items)
        vectors = self.context_embedding(items)
        return (vectors * mask.unsqueeze(-1)).sum(dim=1) / mask.sum(
            dim=1, keepdim=True
        ).clamp_min(1)

    def encode_items(self, items: torch.Tensor) -> torch.Tensor:
340→        inputs = torch.cat(
            [self.candidate_embedding(items), self.item_features[items]], dim=1
        )
        return self.item_tower(inputs)


def invalid_negative_mask(
    negatives: torch.Tensor,
    targets: torch.Tensor,
    context_items: torch.Tensor,
350→    liked_items: torch.Tensor,
) -> torch.Tensor:
    """Identify targets, known positives, active likes, and row duplicates."""
    invalid = negatives.eq(targets[:, None])
    invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
    invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
    for column in range(1, negatives.shape[1]):
        invalid[:, column] |= negatives[:, column, None].eq(
            negatives[:, :column]
        ).any(dim=1)
360→    return invalid


def draw_filtered_mixed_negatives(
    targets: torch.Tensor,
    context_items: torch.Tensor,
    liked_items: torch.Tensor,
    n_items: int,
    uniform_count: int,
    inbatch_count: int,
370→    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw uniform and unique-target in-batch negatives."""
    batch_size = len(targets)
    uniform = torch.randint(
        n_items,
        (batch_size, uniform_count),
        generator=generator,
380→        device="cpu",
    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long, device="cpu")
    corrected_count = 0
    if inbatch_count:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(inbatch_count, max(len(unique_targets) - 1, 0))
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
390→            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(targets.detach().cpu()[:, None]),
                -1.0,
            )
            columns = torch.topk(priorities, k=corrected_count, dim=1).indices
            inbatch = unique_targets[columns]
        if corrected_count < inbatch_count:
            filler = torch.randint(
                n_items,
400→                (batch_size, inbatch_count - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives_cpu = torch.cat([uniform, inbatch], dim=1)
    correction_cpu = torch.ones_like(negatives_cpu, dtype=torch.float32)
    if corrected_count:
        correction_cpu[
410→            :, uniform_count : uniform_count + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]]
    negatives = negatives_cpu.to(device)
    correction = correction_cpu.to(device)
    for _ in range(32):
        invalid = invalid_negative_mask(
            negatives, targets, context_items, liked_items
        )
        if not bool(invalid.any()):
            return negatives, correction
420→        replacements = torch.randint(
            n_items,
            (int(invalid.sum().detach().cpu()),),
            generator=generator,
            device="cpu",
        ).to(device)
        negatives[invalid] = replacements
        correction[invalid] = 1.0
    invalid = invalid_negative_mask(
        negatives, targets, context_items, liked_items
430→    )
    if bool(invalid.any()):
        raise RuntimeError("mixed negative resampling failed false-negative filter")
    return negatives, correction

1→#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
10→drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

20→The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

30→import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
40→    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80

50→
def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.
60→
    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
70→    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

80→    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded
90→

def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
100→    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

110→    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
120→        )
    return ranked_items(frame)


def score(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with public relevance labels.

130→    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


140→def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
150→        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()

1→"""Shared public primitives for Yambda discovery-ranking evaluation.

Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.

The module deliberately contains no event-derived labels, private artifact
10→paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations

from pathlib import Path

import numpy as np
import pandas as pd

20→
__all__ = [
    "CandidateSet",
    "QUALITY_K",
    "RankingValidationError",
    "REQUIRED_COLUMNS",
    "TOP_N",
    "ndcg_at_k",
    "ranked_items",
    "read_ranking",
30→    "score_rankings",
    "validate_ranking",
]

TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")


class RankingValidationError(ValueError):
40→    """Report a ranking-schema or structural-contract violation."""


class CandidateSet:
    """Provide membership lookups over one packed candidate-set archive.

    The archive contains aligned ``uids``, ``item_ids``, and
    ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
    ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
    copied into memory so they remain valid after the archive is closed.
50→    """

    def __init__(self, path: Path):
        """Load an eligibility archive and build ID-to-position indexes.

        Args:
            path: Path to a ``.npz`` archive with the candidate-set schema.
        """
        with np.load(path, allow_pickle=False) as artifact:
            self.uids = artifact["uids"].astype(np.int64, copy=True)
60→            self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
            self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
        self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
        self.item_column = {
            int(item): column for column, item in enumerate(self.item_ids)
        }

    def is_eligible(self, uid: int, item_id: int) -> bool:
        """Return whether ``item_id`` is eligible for ``uid``.

70→        IDs absent from the target-user or candidate-item arrays are treated as
        ineligible rather than raising an exception.

        Args:
            uid: Target user ID to query.
            item_id: Candidate item ID to query.

        Returns:
            ``True`` only when the corresponding packed eligibility bit is set.
        """
80→        row = self.uid_row.get(int(uid))
        column = self.item_column.get(int(item_id))
        if row is None or column is None:
            return False
        byte, bit = divmod(column, 8)
        return bool((int(self.packed[row, byte]) >> bit) & 1)


def read_ranking(path: Path) -> pd.DataFrame:
    """Read a parquet or CSV ranking into a pandas frame.
90→
    Args:
        path: Input path. A ``.parquet`` suffix selects parquet; every other
            suffix is interpreted as CSV.

    Returns:
        The ranking frame as loaded by pandas.
    """
    return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)

100→
def validate_ranking(
    frame: pd.DataFrame,
    targets: set[int],
) -> pd.DataFrame:
    """Normalize and validate the stage-independent ranking contract.

    Required values must be finite integers. The user set must exactly match
    ``targets``; every user must have 100 rows, distinct items, and each rank
    from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
110→    intentionally left to the calling evaluator.

    Args:
        frame: Raw ranking frame. Extra columns are discarded from an internal
            copy; the caller's frame is not modified.
        targets: Exact set of target user IDs the ranking must cover.

    Returns:
        A normalized frame containing only the required ``int64`` columns.

120→    Raises:
        RankingValidationError: If the ranking violates the shared contract.
    """
    required = list(REQUIRED_COLUMNS)
    if not all(column in frame for column in required):
        raise RankingValidationError(f"submission requires columns {required}")

    normalized = frame[required].copy()
    for column in required:
        numeric = pd.to_numeric(normalized[column], errors="coerce")
130→        values = numeric.to_numpy(dtype=float)
        if numeric.isna().any() or not np.isfinite(values).all():
            raise RankingValidationError(
                f"submission {column} must be finite numeric values"
            )
        if not np.equal(numeric, np.floor(numeric)).all():
            raise RankingValidationError(
                f"submission {column} must contain integers"
            )
        normalized[column] = numeric.astype(np.int64)
140→
    submitted_users = set(map(int, normalized.uid.unique()))
    if submitted_users != targets:
        raise RankingValidationError(
            f"submission user set mismatch: missing={len(targets - submitted_users)} "
            f"extra={len(submitted_users - targets)}"
        )
    counts = normalized.groupby("uid").size()
    if not (counts == TOP_N).all():
        raise RankingValidationError(
150→            f"submission must contain exactly {TOP_N} rows for every target user"
        )
    if normalized.duplicated(["uid", "item_id"]).any():
        raise RankingValidationError(
            "submission contains duplicate (uid, item_id) pairs"
        )
    valid_ranks = set(range(1, TOP_N + 1))
    ranks_are_valid = normalized.groupby("uid")["rank"].apply(
        lambda values: set(map(int, values)) == valid_ranks
    )
160→    if not ranks_are_valid.all():
        raise RankingValidationError(
            f"each user must have every rank from 1 through {TOP_N} exactly once"
        )
    return normalized


def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
    """Convert a validated ranking frame to ordered items keyed by user.

170→    Args:
        frame: Frame that has passed :func:`validate_ranking`.

    Returns:
        A ``uid -> item IDs`` mapping ordered by ascending rank.
    """
    ordered = frame.sort_values(["uid", "rank"])
    return {
        int(uid): list(map(int, values))
        for uid, values in ordered.groupby("uid").item_id
180→    }


def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
    """Compute graded normalized discounted cumulative gain at ``k``.

    Relevance gain is ``2**grade - 1`` and the discount at one-based rank
    ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.

    Args:
190→        ranked: Item IDs in predicted rank order.
        gold: Mapping from relevant item IDs to integer relevance grades.
        k: Maximum number of predicted positions to score.

    Returns:
        For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
        ``0.0`` when the ideal gain is zero.
    """
    gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
    discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
200→    dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
    ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
    ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
    idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
    return dcg / idcg if idcg else 0.0


def score_rankings(
    ranked: dict[int, list[int]],
    graded: dict[int, dict[int, int]],
210→) -> dict[str, float | int]:
    """Aggregate per-user NDCG@10 over users with relevance labels.

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Relevant item grades keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
220→    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
230→        "quality_users": len(graded),
    }

1→# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
10→chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
20→jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
30→networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
40→python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
50→setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
60→urllib3==2.7.0
webencodings==0.6.1

- /app/solution/

1000→                    item = int(prepared.sequence_items[position])
                    novel = item not in seen
                    seen.add(item)
                    if not novel:
                        # Repeats remain training examples, but they cannot be
                        # eligible discovery outcomes at the corresponding
                        # cutoff and therefore keep only the ordinary weight.
                        weights[position] = config.ordinary_target_weight
                        continue
                    timestamps = user_likes.get(item)
1010→                    if timestamps is None:
                        continue
                    timestamp = int(prepared.sequence_timestamps[position])
                    upper = min(
                        timestamp + horizon,
                        split_timestamp
                        if position <= last_training_target
                        else history_end,
                    )
                    outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
1020→                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
                        weights[position] = config.organic_like_target_weight
                        grade_three += 1
            log(
                f"grade-3 first-encounter targets={grade_three:,} "
                f"horizon_days={config.discovery_horizon_days}"
            )
    log(
        f"target weighting={config.target_weighting} "
        f"min={weights.min():.3f} mean={weights.mean():.3f} "
1030→        f"max={weights.max():.3f}"
    )
    return weights


def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
    window_starts: np.ndarray,
1040→    process_counts: np.ndarray,
    score_starts: np.ndarray,
    start: int,
    steps: int,
    config: Config,
) -> tuple[np.ndarray, ...]:
    """Build one causal sequence block and mark the target rows to score."""
    batch_size = len(users)
    padding_item = len(prepared.item_ids)
    input_items = np.full((batch_size, steps), padding_item, dtype=np.uint16)
1050→    event_features = np.zeros((batch_size, steps, 3), dtype=np.float16)
    activity_features = np.zeros((batch_size, steps, 4), dtype=np.float16)
    state_ids = np.zeros((batch_size, steps), dtype=np.uint32)
    targets = np.zeros((batch_size, steps), dtype=np.uint16)
    weights = np.ones((batch_size, steps), dtype=np.float32)
    scored = np.zeros((batch_size, steps), dtype=bool)
    histories = np.full(
        (batch_size, steps, config.bptt_steps),
        padding_item,
        dtype=np.uint16,
1060→    )
    processed = np.zeros(batch_size, dtype=np.int32)

    lengths = prepared.lengths
    for row, user_row in enumerate(users):
        count = min(steps, max(int(process_counts[row]) - start, 0))
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        window_start = int(window_starts[row])
1070→        absolute_start = window_start + start
        positions = slice(
            offset + absolute_start,
            offset + absolute_start + count,
        )
        target_positions = slice(
            offset + absolute_start + 1,
            offset + absolute_start + count + 1,
        )
        input_items[row, :count] = prepared.sequence_items[positions]
1080→        event_features[row, :count] = prepared.event_features[positions]
        activity_features[row, :count] = prepared.activity_features[positions]
        state_ids[row, :count] = prepared.state_ids[positions]
        targets[row, :count] = prepared.sequence_items[target_positions]
        weights[row, :count] = target_weights[target_positions]
        score_from = min(count, max(int(score_starts[row]) - start, 0))
        scored[row, score_from:count] = True
        processed[row] = count
        user_sequence = prepared.sequence_items[
            offset : offset + int(lengths[user_row])
1090→        ]
        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
            padding_item,
        )
    return (
        input_items,
1100→        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
        processed,
        weights,
    )

1110→
def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    negatives: torch.Tensor,
1120→    negative_weights: torch.Tensor,
    sample_weights: np.ndarray,
    device: torch.device,
    backward: bool,
) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compute one logical block's exact BCE in bounded-memory slices."""
    target_count = len(positive_items)
    total_weight = float(sample_weights.sum())
    if total_weight <= 0:
        raise RuntimeError("target block has no positive sample weight")
1130→    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
    statistics = {
        "loss_sum": 0.0,
        "positive_sum": 0.0,
        "negative_sum": 0.0,
        "pair_correct": 0.0,
        "weight_sum": 0.0,
    }
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
1140→        selected_states = flat_states[begin:end]
        if backward:
            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
1150→        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
1160→            torch.from_numpy(numeric).to(device),
        )
        positive_vectors, negative_vectors = candidate_vectors(
            model,
            positive_items[begin:end],
            negatives[begin:end],
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
1170→        )
        positive_loss = F.softplus(-positive_logits)
        negative_loss = corrected_negative_loss(
            negative_logits, negative_weights[begin:end], config
        )
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        loss_sum = (
            selected_weights * (positive_loss + negative_loss) / 2.0
1180→        ).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
            sequence_gradient[begin:end] = sequence_input.grad
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
1190→        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
        statistics["pair_correct"] += float(
            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
        )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
1200→        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
1210→    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
1220→        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
1230→    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
1240→        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
1250→                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
1260→                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
1270→            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
1280→                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
1290→            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
1300→                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                model,
                prepared,
                config,
1310→                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=True,
            )
1320→            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()

            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
1330→            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
1340→                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
1350→        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
1360→    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
1370→    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
1380→    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
1390→    total_weight = 0.0
    started = time.monotonic()

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
1400→        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
1410→                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
1420→                [
                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
1430→            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
1440→                    np.int64
                )
            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
1450→                positive_items,
                known_history,
                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
1460→            )
            statistics, _ = score_target_microbatches(
                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
1470→                negative_weights,
                block[8].reshape(-1)[flat_scored],
                device,
                backward=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
1480→    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
1490→        name: value.detach().cpu().clone()
        for name, value in model.state_dict().items()
    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
1500→    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
1510→            "diagnostics": diagnostics,
            "model_state": model_state,
            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path

1520→
def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
1530→    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
1540→        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
1550→    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
1560→        stage,
        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
1570→    history: list[dict[str, float]] = []
    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
1580→        )
        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
1590→        )
        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
1600→        stored_filter_scope = stored_config.get(
            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
1610→            stored_config.get("discovery_horizon_days", 7),
        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
1620→            stored_config.get("max_train_targets_per_user", 0),
            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
1630→            or stored_target_weighting != config.target_weighting
            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
1640→            log(f"restored optimizer state from {resume_checkpoint}")
        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
1650→                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
1660→                f"{stored_filter_scope}->{config.negative_filter_scope}, "
                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
1670→                    prepared,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
1680→            )
        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

1690→    for epoch in range(start_epoch + 1, config.epochs + 1):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
1700→            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
1710→            partition.window_starts,
            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
1720→        if checkpoint_dir is not None:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
1730→        improved = (
            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
1740→                torch.save(
                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,
                        "model_state": current_state,
                        "optimizer_state": optimizer.state_dict(),
                        "generator_state": generator.get_state(),
                    },
                    checkpoint_dir / "best.pt",
1750→                )
        else:
            epochs_without_improvement += 1
        log(
            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
            f"validation_loss={diagnostics['validation_loss']:.5f} "
            f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
            f"train_targets={int(diagnostics['targets']):,} "
            f"validation_targets={int(diagnostics['validation_targets']):,} "
            f"train_seconds={diagnostics['seconds']:.1f} "
1760→            f"validation_seconds={diagnostics['validation_seconds']:.1f}"
        )
        release_accelerator_cache(device)
        if epochs_without_improvement >= config.early_stopping_patience:
            log(
                f"early stopping after epoch={epoch}; "
                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
            )
            break

1770→    if best_state is None:
        raise RuntimeError("training did not produce a valid checkpoint")
    model.load_state_dict(best_state)
    model.to(device).eval()
    log(
        f"restored best validation-BCE checkpoint: epoch={best_epoch} "
        f"loss={best_loss:.6f}"
    )
    return model, history

1780→
@torch.inference_mode()
def inference_queries(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
) -> torch.Tensor:
    sequence_rows = prepared.inference_user_rows
    lengths = np.where(
1790→        sequence_rows >= 0, prepared.lengths[np.maximum(sequence_rows, 0)], 0
    )
    ordered = np.argsort(lengths, kind="stable")
    result = torch.zeros(
        len(sequence_rows), config.embedding_dim, dtype=torch.float32
    )
    for group_start in range(0, len(ordered), config.user_batch_size):
        output_rows = ordered[group_start : group_start + config.user_batch_size]
        rows = sequence_rows[output_rows]
        group_lengths = lengths[output_rows]
1800→        hidden = torch.zeros(
            1, len(output_rows), config.embedding_dim, device=device
        )
        for start in range(0, int(group_lengths.max(initial=0)), config.bptt_steps):
            steps = min(
                config.bptt_steps,
                int(group_lengths.max(initial=0)) - start,
            )
            items = np.full(
                (len(output_rows), steps),
1810→                len(prepared.item_ids),
                dtype=np.uint16,
            )
            features = np.zeros((len(output_rows), steps, 3), dtype=np.float16)
            counts = np.zeros(len(output_rows), dtype=np.int32)
            for local_row, sequence_row in enumerate(rows):
                if sequence_row < 0:
                    continue
                count = min(steps, max(int(group_lengths[local_row]) - start, 0))
                if not count:
1820→                    continue
                offset = int(prepared.offsets[sequence_row])
                selected = slice(offset + start, offset + start + count)
                items[local_row, :count] = prepared.sequence_items[selected]
                features[local_row, :count] = prepared.event_features[selected]
                counts[local_row] = count
            outputs, _ = event_sequence(
                model,
                torch.from_numpy(items.astype(np.int64)).to(device),
                torch.from_numpy(features.astype(np.float32)).to(device),
1830→                hidden,
            )
            next_rows: list[torch.Tensor] = []
            for local_row, count in enumerate(counts):
                next_rows.append(
                    outputs[local_row, count - 1]
                    if count
                    else hidden[0, local_row]
                )
            hidden = torch.stack(next_rows).unsqueeze(0)
1840→
        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
1850→            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


1860→@torch.inference_mode()
def rank_candidates(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    eligibility: CandidateSet,
    config: Config,
    device: torch.device,
    ranking_batch_size: int,
) -> tuple[pd.DataFrame, dict[str, float]]:
    model.eval()
1870→    queries = inference_queries(model, prepared, config, device)
    item_parts: list[torch.Tensor] = []
    for start in range(0, len(prepared.item_ids), 2_048):
        rows = torch.arange(
            start,
            min(start + 2_048, len(prepared.item_ids)),
            dtype=torch.long,
            device=device,
        )
        item_parts.append(model.encode_items(rows).cpu())
1880→    items = torch.cat(item_parts)
    rows: list[tuple[int, int, int]] = []
    for start in range(0, len(eligibility.uids), ranking_batch_size):
        end = min(start + ranking_batch_size, len(eligibility.uids))
        scores = queries[start:end] @ items.T
        eligible = np.unpackbits(
            eligibility.packed[start:end],
            axis=1,
            count=len(prepared.item_ids),
            bitorder="little",
1890→        ).astype(bool, copy=False)
        scores.masked_fill_(~torch.from_numpy(eligible), -torch.inf)
        selected = torch.topk(scores, k=base.TOP_N, dim=1).indices.numpy()
        for local_row, columns in enumerate(selected):
            uid = int(eligibility.uids[start + local_row])
            rows.extend(
                (uid, int(prepared.item_ids[column]), rank)
                for rank, column in enumerate(columns, start=1)
            )
        if end == len(eligibility.uids) or end % 1_024 == 0:
1900→            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
    query_norm = torch.linalg.vector_norm(queries, dim=1).numpy()
    item_norm = torch.linalg.vector_norm(items, dim=1).numpy()
    diagnostics = {
        "query_norm_mean": float(query_norm.mean()),
        "query_norm_std": float(query_norm.std()),
        "item_norm_mean": float(item_norm.mean()),
        "item_norm_std": float(item_norm.std()),
    }
    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
1910→

def write_ranking(frame: pd.DataFrame, output: Path) -> None:
    output.parent.mkdir(parents=True, exist_ok=True)
    if output.suffix == ".parquet":
        frame.to_parquet(output, index=False, compression="zstd")
    elif output.suffix == ".csv":
        frame.to_csv(output, index=False)
    else:
        raise ValueError("--output must end in .parquet or .csv")
1920→

def main() -> int:
    parser = argparse.ArgumentParser(description=__doc__)
    parser.add_argument(
        "--stage", choices=("validation", "submission"), default="validation"
    )
    parser.add_argument("--output", type=Path, required=True)
    parser.add_argument("--public-dir", type=Path, required=True)
    parser.add_argument("--prepared-cache", type=Path)
1930→    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
    parser.add_argument("--threads", type=int, default=8)
    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
    parser.add_argument(
        "--user-batch-size", type=int, default=Config.user_batch_size
    )
    parser.add_argument("--epochs", type=int, default=Config.epochs)
    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
    parser.add_argument(
        "--loss-microbatch-size",
1940→        type=int,
        default=Config.loss_microbatch_size,
    )
    parser.add_argument(
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--target-weighting",
1950→        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
        "--organic-strong-target-weight",
1960→        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
    parser.add_argument(
        "--discovery-horizon-days",
1970→        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
    parser.add_argument(
        "--max-train-targets-per-user",
1980→        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
        help="retain this many final validation targets per user; 0 is uncapped",
    )
1990→    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )

1990→    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
        default=Config.early_stopping_min_delta,
    )
2000→    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
2010→        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
2020→        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
    config = Config(
        bptt_steps=args.bptt_steps,
2030→        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
2040→        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
2050→    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
2060→    )
    target_partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
        print(
            json.dumps(
2070→                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
2080→                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
                indent=2,
            )
2090→        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
2100→        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,
        args.ranking_batch_size,
    )
2110→    write_ranking(ranking, args.output.resolve())
    print(
        json.dumps(
            {
                "baseline": "yambda-windowed-sequence-two-tower-base",
                "stage": args.stage,
                "output": str(args.output.resolve()),
                "rows": len(ranking),
                "users": int(ranking.uid.nunique()),
                "runtime_seconds": round(time.monotonic() - started, 3),
2120→                "public_inputs_only": True,
                "architecture": {
                    "family": "pure-windowed-sequence-two-tower",
                    "sequence_training": (
                        "every-adjacent-target-within-recent-chronological-window"
                    ),
                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
                    "bptt_hidden_state": "carried-and-detached",
                    "model_selection": "chronological-validation-BCE-early-stopping",
                    "loss_execution": "gradient-accumulated-microbatches",
2130→                    "user_id_embedding": False,
                    "user_mlp": "582-512-256-192",
                    "item_mlp": "196-512-256-192",
                    "normalization": "none",
                    "score": "raw-dot-product",
                    "objective": "sample-weighted-BCE-at-every-timestep",
                    "negative_sampling": (
                        "24-uniform-plus-8-propensity-corrected-inbatch"
                    ),
                    "false_negative_filter": config.negative_filter_scope,
2140→                    "target_weighting": config.target_weighting,
                },
                "config": asdict(config),
                "sequence_tokens": len(prepared.sequence_items),
                "all_next_item_targets": prepared.target_count,
                "selected_training_targets": int(
                    target_partition.training_counts.sum()
                ),
                "selected_validation_targets": int(
                    target_partition.validation_counts.sum()
2150→                ),
                "discarded_old_targets": int(target_partition.window_starts.sum()),
                "training_history": training_history,
                "embedding_diagnostics": embedding_diagnostics,
                "torch_version": torch.__version__,
                "device": str(device),
            },
            indent=2,
        )
    )
2160→    return 0


if __name__ == "__main__":
    raise SystemExit(main())

exit: 1
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
ModuleNotFoundError: No module named 'pandas'

exit: 0
/usr/local/bin/python
/usr/local/bin/python3
ls: cannot access '/usr/bin/python*': No such file or directory
task-venv
---
# Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
/usr/local/bin/python3 ['', '/usr/local/lib/python312.zip', '/usr/local/lib/python3.12', '/usr/local/lib/python3.12/lib-dynload', '/usr/local/lib/python3.12/site-packages']

exit: 0
Activate.ps1
activate
activate.csh
activate.fish
chardetect
dotenv
f2py
idna
isympy
jsonschema
2.8.0+cpu 2.3.3 2.2.6

exit: 0
candidate_catalog.parquet: rows=30,000 cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2

target_users.parquet: rows=8,456 cols=['uid', 'recent_positive_listens', 'history_positive_listens']
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286

train_listens.parquet: rows=43,484,159 cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100      39420  8326270           0               100                   170
1  100      39420  1441281           0               100                   105

train_likes.parquet: rows=814,831 cols=['uid', 'timestamp', 'item_id', 'is_organic']
   uid  timestamp  item_id  is_organic
0  100      44755   732449           1
1  100    1155860  6568592           0

train_dislikes.parquet: rows=101,075 cols=['uid', 'timestamp', 'item_id', 'is_organic']
   uid  timestamp  item_id  is_organic
0  100    3087560  9170134           1
1  100    3936560  8661238           1

train_unlikes.parquet: rows=292,665 cols=['uid', 'timestamp', 'item_id', 'is_organic']
   uid  timestamp  item_id  is_organic
0  100    3087555  5038504           1
1  100    3087555  9170134           1

train_undislikes.parquet: rows=19,960 cols=['uid', 'timestamp', 'item_id', 'is_organic']
   uid  timestamp  item_id  is_organic
0  100    7895900  9170134           1
1  100    7895900  5038504           1

val_listens.parquet: rows=1,501,697 cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100   24943430  9321269           0               100                   195
1  100   24943605  5452713           0               100                   175

val_likes.parquet: rows=37,795 cols=['uid', 'timestamp', 'item_id', 'is_organic']
   uid  timestamp  item_id  is_organic
0  100   25249855  3526521           1
1  300   25046245  4867327           1

val_dislikes.parquet: rows=3,563 cols=['uid', 'timestamp', 'item_id', 'is_organic']
    uid  timestamp  item_id  is_organic
0  1600   25163525  6747257           0
1  1900   24877510  5832822           1

validation_preference_state.parquet: rows=469,124 cols=['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked

test_preference_state.parquet: rows=484,776 cols=['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked

catalog dtypes
 item_id                     uint32
positive_listens             int64
organic_positive_listens    uint64
popularity_rank              int32
dtype: object
catalog describe
             item_id  ...  popularity_rank
count  3.000000e+04  ...     30000.000000
mean   4.698278e+06  ...     15000.500000
std    2.700274e+06  ...      8660.398374
min    2.060000e+02  ...         1.000000
25%    2.382368e+06  ...      7500.750000
50%    4.687992e+06  ...     15000.500000
75%    7.040711e+06  ...     22500.250000
max    9.390294e+06  ...     30000.000000

[8 rows x 4 columns]
n unique items 30000
target users (8456, 3)
validation_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
  n_users 8456 n_items 30000 eligible mean/min/max/p50 29067.306291390727 24657 30000 29242.0
test_eligible_candidates.npz {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}
  n_users 8456 n_items 30000 eligible mean/min/max/p50 29041.3178807947 24552 30000 29220.0

exit: 0
train_listens.parquet num_row_groups 42 metadata rows 43484159
val_listens.parquet num_row_groups 2 metadata rows 1501697
pop stats count    30000.000000
mean       668.544300
std       1053.389553
min        163.000000
50%        354.000000
90%       1367.000000
99%       5052.160000
max      26615.000000
Name: positive_listens, dtype: float64
organic ratio count    30000.000000
mean         0.459444
std          0.217957
min          0.000000
25%          0.301042
50%          0.456960
75%          0.610599
max          1.000000
dtype: float64
likes organic fraction 0.5667911505576003 n 814831
listens organic 0.5169151828370419
listens >=50 0.6324315712303417
listens organic>=80 0.2800464187429726
listens rec>=50 0.3288134191579973
                  uid  recent_positive_listens  history_positive_listens
count     8456.000000              8456.000000               8456.000000
mean    502289.108325              1251.076750               3236.440752
std     287953.395912              1414.340904               3697.575286
min        100.000000                20.000000                 20.000000
25%     254375.000000               281.000000                630.000000
50%     503850.000000               784.500000               1920.500000
75%     751725.000000              1706.250000               4494.250000
max    1000000.000000             19592.000000              26597.000000
val listens target users 0.9872517558468852 organic>=80 target 421615
val likes target organic 20702

exit: 0
batches 0 rows 1000000 unique so far 218
batches 10 rows 11000000 unique so far 2315
batches 20 rows 21000000 unique so far 4388
batches 30 rows 31000000 unique so far 6566
batches 40 rows 41000000 unique so far 8700
TOTAL unique train listen users 9238 rows 43484159
target users 8456 in train listens 8456

exit: 0
8
               total        used        free      shared  buff/cache   available
Mem:            15Gi       1.0Gi       2.1Gi       3.0Mi        12Gi        14Gi
Swap:             0B          0B          0B
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   14G   83G  14% /
overlay          96G   14G   83G  14% /

exit: 0
total 108
drwxr-xr-x 1 agent agent  4096 Sep 29 16:43 .
drwxr-xr-x 1 agent agent  4096 Sep 29 16:34 ..
-r--r--r-- 1 agent agent 80058 Sep 29 16:43 train_current_model.py
-r--r--r-- 1 agent agent 15985 Sep 29 16:43 two_tower_model.py

- [completed] 1: Explore production model, data, and evaluation
- [in_progress] 2: Diagnose negative-sampling limitations
- [pending] 3: Implement improved training objective
- [pending] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission

exit: 0

<task-id>01a0ee0d-4059-72c3-99b7-f247fa770bf1</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-0a0d7874-a1da-40d6-b574-adcd40038656-21.log</output-file>
<status>running</status>
<summary>Background task 01a0ee0d-4059-72c3-99b7-f247fa770bf1 started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee0d-4059-72c3-99b7-f247fa770bf1"] when you need the output.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

<workspace_result workspace_path="/app">
Found 25 matching lines
/app/solution/train_current_model.py
545:def build_item_correction(
780:    item_correction: torch.Tensor,
829:        ] = item_correction[inbatch[:, :corrected_count]].numpy()
867:def draw_training_negatives(
875:    item_correction: torch.Tensor,
889:            item_correction,
900:        item_correction,
1376:def run_training_epoch(
1384:    item_correction: torch.Tensor,
1400:    negative_count = config.uniform_negatives + config.inbatch_negatives
1466:            negatives, negative_weights = draw_training_negatives(
1474:                item_correction,
1526:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
1527:        "pair_accuracy": total_pair_correct
1528:        / max(total_targets * negative_count, 1),
1537:def evaluate_validation_bce(
1545:    item_correction: torch.Tensor,
1622:            negatives, negative_weights = draw_training_negatives(
1630:                item_correction,
1694:def train_model(
1725:    item_correction = build_item_correction(
1849:                    item_correction,
1872:            item_correction,
1886:            item_correction,
1929:            f"pair_accuracy={diagnostics['pair_accuracy']:.3f} "
</workspace_result>

1370→
def release_accelerator_cache(device: torch.device) -> None:
    if device.type == "mps":
        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
1380→    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
) -> dict[str, float]:
1390→    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
    group_order = torch.randperm(len(groups), generator=generator).numpy()
1400→    negative_count = config.uniform_negatives + config.inbatch_negatives
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    batches = 0
    started = time.monotonic()

1410→    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
1420→                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
                config,
            )
1430→            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
                    if count
1440→                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
1450→            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
            ).to(device)
1460→            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
                liked_items,
1470→                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
1480→                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
1490→                device,
                backward=True,
            )
            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()

            count = len(positive_items)
1500→            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
1510→                batch_bce = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
                running_bce = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
                    f"targets={total_targets:,} "
                    f"batch_bce={batch_bce:.6f} "
                    f"running_bce={running_bce:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
1520→                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_targets * negative_count, 1),
        "pair_accuracy": total_pair_correct
        / max(total_targets * negative_count, 1),
        "targets": float(total_targets),
1530→        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
1540→    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

1376→def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
1380→    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    target_counts: np.ndarray,
    popularity_probs: torch.Tensor,
    log_item_q: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    generator: torch.Generator,
    device: torch.device,
    epoch: int,
1390→) -> dict[str, float]:
    model.train()
    train_users = np.flatnonzero(target_counts > 0)
    sorted_users = train_users[
        np.argsort(target_counts[train_users], kind="stable")
    ]
    groups = [
        sorted_users[start : start + config.user_batch_size]
        for start in range(0, len(sorted_users), config.user_batch_size)
    ]
1400→    group_order = torch.randperm(len(groups), generator=generator).numpy()
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    total_positive = 0.0
    total_negative = 0.0
    total_pair_correct = 0.0
    total_pair_total = 0.0
    total_shared = 0.0
    batches = 0
1410→    started = time.monotonic()

    for group_number in group_order:
        users = groups[int(group_number)]
        group_counts = target_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_counts.max())
        for start in range(0, max_targets, config.bptt_steps):
1420→            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_counts,
                np.zeros(len(users), dtype=np.int64),
                start,
                steps,
1430→                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
1440→                    sequence_outputs[row, count - 1]
                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0).detach()
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
1450→            sequence_vectors = sequence_outputs.reshape(
                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
1460→            shared_items = build_shared_softmax_items(
                positive_items,
                flat_states,
                prepared,
                config,
                popularity_probs,
                generator,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
1470→                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                shared_items,
                block[8].reshape(-1)[flat_scored],
                flat_user_rows,
1480→                known_positive_packed,
                log_item_q,
                device,
                backward=True,
            )
            if sequence_gradient is None:
                raise RuntimeError("training block omitted its sequence gradient")
            sequence_vectors.backward(sequence_gradient)
            nn.utils.clip_grad_norm_(model.parameters(), config.gradient_clip)
            optimizer.step()
1490→
            count = len(positive_items)
            total_loss += statistics["loss_sum"]
            total_targets += count
            total_weight += statistics["weight_sum"]
            total_positive += statistics["positive_sum"]
            total_negative += statistics["negative_sum"]
            total_pair_correct += statistics["pair_correct"]
            total_pair_total += statistics["pair_total"]
            total_shared += statistics["shared_items"]
1500→            batches += 1
            if batches % 25 == 0:
                release_accelerator_cache(device)
            if batches % 250 == 0:
                batch_loss = statistics["loss_sum"] / max(
                    statistics["weight_sum"], 1.0e-12
                )
                running_loss = total_loss / max(total_weight, 1.0e-12)
                log(
                    f"epoch={epoch}/{config.epochs} batches={batches:,} "
1510→                    f"targets={total_targets:,} "
                    f"shared_items={int(statistics['shared_items']):,} "
                    f"batch_loss={batch_loss:.6f} "
                    f"running_loss={running_loss:.6f} "
                    f"elapsed={time.monotonic() - started:.1f}s"
                )

    return {
        "epoch": float(epoch),
        "loss": total_loss / max(total_weight, 1.0e-12),
1520→        "positive_logit": total_positive / max(total_targets, 1),
        "negative_logit": total_negative / max(total_pair_total, 1),
        "pair_accuracy": total_pair_correct / max(total_pair_total, 1),
        "targets": float(total_targets),
        "target_weight_sum": total_weight,
        "shared_items_mean": total_shared / max(batches, 1),
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }

1530→
@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
1540→    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()

1531→@torch.no_grad()
def evaluate_validation_bce(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
    window_starts: np.ndarray,
    training_counts: np.ndarray,
    validation_counts: np.ndarray,
1540→    item_correction: torch.Tensor,
    known_positive_packed: np.ndarray | None,
    device: torch.device,
) -> dict[str, float]:
    """Measure deterministic BCE on each user's chronological target suffix."""
    model.eval()
    totals = training_counts + validation_counts
    users_with_validation = np.flatnonzero(validation_counts > 0)
    ordered = users_with_validation[
        np.argsort(totals[users_with_validation], kind="stable")
1550→    ]
    groups = [
        ordered[start : start + config.user_batch_size]
        for start in range(0, len(ordered), config.user_batch_size)
    ]
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 20_000)
    total_loss = 0.0
    total_targets = 0
    total_weight = 0.0
    started = time.monotonic()
1560→
    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
1570→            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
1580→            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).to(device),
                torch.from_numpy(block[1].astype(np.float32)).to(device),
                hidden,
            )
            hidden = torch.stack(
                [
                    sequence_outputs[row, count - 1]
1590→                    if count
                    else hidden[0, row]
                    for row, count in enumerate(block[7])
                ]
            ).unsqueeze(0)
            flat_scored = block[5].reshape(-1)
            if not np.any(flat_scored):
                continue
            mask = torch.from_numpy(flat_scored).to(device)
            sequence_vectors = sequence_outputs.reshape(
1600→                -1, config.embedding_dim
            )[mask]
            flat_states = block[3].reshape(-1)[flat_scored]
            positive_items = torch.from_numpy(
                block[4].reshape(-1)[flat_scored].astype(np.int64)
            ).to(device)
            known_history = torch.from_numpy(
                block[6].reshape(-1, config.bptt_steps)[flat_scored].astype(
                    np.int64
                )
1610→            ).to(device)
            liked_items = torch.from_numpy(
                prepared.liked_states[flat_states].astype(np.int64)
            ).to(device)
            flat_user_rows = np.broadcast_to(
                users[:, None], block[5].shape
            ).reshape(-1)[flat_scored]
            negatives, negative_weights = draw_training_negatives(
                positive_items,
                known_history,
1620→                liked_items,
                flat_user_rows,
                known_positive_packed,
                prepared,
                config,
                item_correction,
                generator,
                device,
            )
            statistics, _ = score_target_microbatches(
1630→                model,
                prepared,
                config,
                sequence_vectors,
                flat_states,
                block[2].reshape(-1, 4)[flat_scored].astype(np.float32),
                positive_items,
                negatives,
                negative_weights,
                block[8].reshape(-1)[flat_scored],
1640→                device,
                backward=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
1650→        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
    return {
        name: value.detach().cpu().clone()
        for name, value in model.state_dict().items()
1660→    }


def save_epoch_checkpoint(
    checkpoint_dir: Path,
    epoch: int,
    model_state: dict[str, torch.Tensor],
    config: Config,
    diagnostics: dict[str, float],
    optimizer: torch.optim.Optimizer,
1670→    generator: torch.Generator,
) -> Path:
    checkpoint_dir.mkdir(parents=True, exist_ok=True)
    path = checkpoint_dir / f"epoch-{epoch:02d}.pt"
    torch.save(
        {
            "epoch": epoch,
            "config": asdict(config),
            "diagnostics": diagnostics,
            "model_state": model_state,
1680→            "optimizer_state": optimizer.state_dict(),
            "generator_state": generator.get_state(),
        },
        path,
    )
    log(f"saved epoch checkpoint: {path}")
    return path


def train_model(
1690→    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
1700→        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
1710→        f"discarded_old_targets={int(partition.window_starts.sum()):,} "

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

1685→    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
1690→        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
1700→        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
1710→    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
1720→        prepared,
        partition.history_training_counts,
        config,
    )
    known_positive_packed = (
        build_known_positive_filter(prepared, partition.history_training_counts)
        if config.negative_filter_scope == "full-training-history"
        else None
    )
    history: list[dict[str, float]] = []
1730→    best_loss = float("inf")
    best_epoch = 0
    best_state: dict[str, torch.Tensor] | None = None
    epochs_without_improvement = 0
    start_epoch = 0

    if resume_checkpoint is not None:
        payload = torch.load(
            resume_checkpoint, map_location=device, weights_only=False
        )
1740→        stored_config = payload.get("config", {})
        architecture_fields = (
            "bptt_steps",
            "embedding_dim",
            "hidden_dim",
            "user_hidden_1",
            "user_hidden_2",
            "item_hidden_1",
            "item_hidden_2",
        )
1750→        mismatches = {
            name: (stored_config.get(name), getattr(config, name))
            for name in architecture_fields
            if stored_config.get(name) != getattr(config, name)
        }
        if mismatches:
            raise RuntimeError(
                f"resume checkpoint architecture differs: {mismatches}"
            )
        stored_filter_scope = stored_config.get(
1760→            "negative_filter_scope", "last-100"
        )
        stored_target_weighting = stored_config.get(
            "target_weighting", "uniform"
        )
        stored_weight_values = (
            stored_config.get("ordinary_target_weight", 1.0),
            stored_config.get("organic_strong_target_weight", 1.0),
            stored_config.get("organic_like_target_weight", 3.0),
            stored_config.get("discovery_horizon_days", 7),
1770→        )
        current_weight_values = (
            config.ordinary_target_weight,
            config.organic_strong_target_weight,
            config.organic_like_target_weight,
            config.discovery_horizon_days,
        )
        stored_window_values = (
            stored_config.get("validation_fraction", 0.10),
            stored_config.get("max_train_targets_per_user", 0),
1780→            stored_config.get("max_validation_targets_per_user", 0),
        )
        current_window_values = (
            config.validation_fraction,
            config.max_train_targets_per_user,
            config.max_validation_targets_per_user,
        )
        objective_changed = (
            stored_filter_scope != config.negative_filter_scope
            or stored_target_weighting != config.target_weighting
1790→            or stored_weight_values != current_weight_values
            or stored_window_values != current_window_values
        )
        model.load_state_dict(payload["model_state"])
        if "optimizer_state" in payload and not objective_changed:
            optimizer.load_state_dict(payload["optimizer_state"])
            for group in optimizer.param_groups:
                group["lr"] = config.learning_rate
                group["weight_decay"] = config.weight_decay
            log(f"restored optimizer state from {resume_checkpoint}")
1800→        else:
            log(
                f"using a fresh AdamW optimizer after loading {resume_checkpoint}"
            )
        if "generator_state" in payload and not objective_changed:
            generator.set_state(payload["generator_state"])
        start_epoch = int(payload["epoch"])
        if start_epoch >= config.epochs:
            raise RuntimeError(
                f"resume epoch {start_epoch} must be below --epochs {config.epochs}"
1810→            )
        prior = {
            str(name): float(value)
            for name, value in payload.get("diagnostics", {}).items()
        }
        if objective_changed:
            prior.pop("validation_loss", None)
            log(
                "BCE objective changed: filter "
                f"{stored_filter_scope}->{config.negative_filter_scope}, "
1820→                "target weighting "
                f"{stored_target_weighting}->{config.target_weighting}, "
                f"target window {stored_window_values}->{current_window_values}; "
                "recomputing the resume checkpoint's validation BCE"
            )
        if "validation_loss" not in prior:
            prior.update(
                evaluate_validation_bce(
                    model,
                    prepared,
1830→                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
1840→        history.append(prior)
        best_loss = float(prior["validation_loss"])
        best_epoch = start_epoch
        best_state = snapshot_model(model)
        log(
            f"resumed validation-BCE training: epoch={start_epoch} "
            f"loss={best_loss:.6f}"
        )

    for epoch in range(start_epoch + 1, config.epochs + 1):
1850→        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
1860→            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
1870→            training_counts,
            validation_counts,
            item_correction,
            known_positive_packed,
            device,
        )
        diagnostics.update(validation)
        history.append(diagnostics)
        current_state = snapshot_model(model)
        if checkpoint_dir is not None:
1880→            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
        improved = (
1890→            diagnostics["validation_loss"]
            < best_loss - config.early_stopping_min_delta
        )
        if improved:
            best_loss = diagnostics["validation_loss"]
            best_epoch = epoch
            best_state = current_state
            epochs_without_improvement = 0
            if checkpoint_dir is not None:
                torch.save(
1900→                    {
                        "epoch": epoch,
                        "config": asdict(config),
                        "diagnostics": diagnostics,
                        "model_state": current_state,

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

2140→    parser.add_argument(
        "--organic-strong-target-weight",
        type=float,
        default=Config.organic_strong_target_weight,
    )
    parser.add_argument(
        "--organic-like-target-weight",
        type=float,
        default=Config.organic_like_target_weight,
    )
2150→    parser.add_argument(
        "--discovery-horizon-days",
        type=int,
        default=Config.discovery_horizon_days,
    )
    parser.add_argument(
        "--validation-fraction",
        type=float,
        default=Config.validation_fraction,
    )
2160→    parser.add_argument(
        "--max-train-targets-per-user",
        type=int,
        default=Config.max_train_targets_per_user,
        help="retain this many recent pre-validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--max-validation-targets-per-user",
        type=int,
        default=Config.max_validation_targets_per_user,
2170→        help="retain this many final validation targets per user; 0 is uncapped",
    )
    parser.add_argument(
        "--early-stopping-patience",
        type=int,
        default=Config.early_stopping_patience,
    )
    parser.add_argument(
        "--early-stopping-min-delta",
        type=float,
2180→        default=Config.early_stopping_min_delta,
    )
    parser.add_argument("--checkpoint-dir", type=Path)
    parser.add_argument("--resume-checkpoint", type=Path)
    parser.add_argument("--seed", type=int, default=Config.seed)
    parser.add_argument("--ranking-batch-size", type=int, default=64)
    parser.add_argument("--prepare-only", action="store_true")
    args = parser.parse_args()

    if args.threads < 1 or args.bptt_steps < 2 or args.user_batch_size < 1:
2190→        parser.error("threads, BPTT steps, and user batch size must be positive")
    if (
        args.epochs < 1
        or args.learning_rate <= 0
        or args.loss_microbatch_size < 1
        or args.early_stopping_patience < 1
        or args.early_stopping_min_delta < 0
        or args.ordinary_target_weight <= 0
        or args.organic_strong_target_weight <= 0
        or args.organic_like_target_weight <= 0
2200→        or args.discovery_horizon_days < 1
        or args.max_train_targets_per_user < 0
        or args.max_validation_targets_per_user < 0
    ):
        parser.error("training counts, learning rate, and patience are invalid")
    if not 0.0 < args.validation_fraction < 1.0:
        parser.error("validation fraction must be strictly between zero and one")
    public = args.public_dir.resolve()
    if not (public / "data" / "candidate_catalog.parquet").is_file():
        parser.error(f"public task data not found under {public}")
2210→    config = Config(
        bptt_steps=args.bptt_steps,
        user_batch_size=args.user_batch_size,
        epochs=args.epochs,
        learning_rate=args.learning_rate,
        loss_microbatch_size=args.loss_microbatch_size,
        negative_filter_scope=args.negative_filter_scope,
        target_weighting=args.target_weighting,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
2220→        organic_like_target_weight=args.organic_like_target_weight,
        discovery_horizon_days=args.discovery_horizon_days,
        validation_fraction=args.validation_fraction,
        max_train_targets_per_user=args.max_train_targets_per_user,
        max_validation_targets_per_user=args.max_validation_targets_per_user,
        early_stopping_patience=args.early_stopping_patience,
        early_stopping_min_delta=args.early_stopping_min_delta,
        seed=args.seed,
    )
    torch.set_num_threads(args.threads)
2230→    torch.set_num_interop_threads(1)
    seed_everything(config.seed)
    device = torch.device(args.device)
    if device.type == "mps" and not torch.backends.mps.is_available():
        parser.error("MPS was requested but is unavailable")

    started = time.monotonic()
    prepared, eligibility = load_or_prepare(
        public,
        args.stage,
2240→        config,
        args.prepared_cache.resolve() if args.prepared_cache else None,
    )
    target_partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    if args.prepare_only:
2250→        print(
            json.dumps(
                {
                    "stage": args.stage,
                    "sequence_users": len(prepared.user_ids),
                    "sequence_tokens": len(prepared.sequence_items),
                    "next_item_targets": prepared.target_count,
                    "selected_training_targets": int(
                        target_partition.training_counts.sum()
                    ),
2260→                    "selected_validation_targets": int(
                        target_partition.validation_counts.sum()
                    ),
                    "discarded_old_targets": int(
                        target_partition.window_starts.sum()
                    ),
                    "target_users": len(eligibility.uids),
                    "runtime_seconds": round(time.monotonic() - started, 3),
                    "config": asdict(config),
                },
2270→                indent=2,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
2280→        device,
        args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
        args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
    )
    ranking, embedding_diagnostics = rank_candidates(
        model,
        prepared,
        eligibility,
        config,
        device,

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

=== Task 01a0ee0d-4059-72c3-99b7-f247fa770bf1 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --prepare-only \
  --threads 8
Status: completed
Duration: 54.72s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-0a0d7874-a1da-40d6-b574-adcd40038656-21.log

=== Output ===
loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.9s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.4s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
{
  "stage": "validation",
  "sequence_users": 9135,
  "sequence_tokens": 20056329,
  "next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "target_users": 8456,
  "runtime_seconds": 50.635,
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  }
}

exit: 0
COMPILE_OK

exit: 0
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
loaded 9135 users 20056329 tokens
probs 1.0 0.0010235466609455676
sample users [0 1 2 3 4 5 6 7] counts [128 128  63  15 128 128 128  51]
positives 127 shared 433 unique pos 104
invalid frac 0.06171919041297667 rows with all invalid except maybe target 0
mask keeps all targets
stats {'loss_sum': 762.9369, 'positive_sum': 0.7923, 'negative_sum': 399.1449, 'pair_correct': 25180.0, 'pair_total': 51470.0, 'weight_sum': 127.0, 'shared_items': 433.0}
UNIT_OK

exit: 0
-rw-r--r-- 1 agent agent 511M Sep 29 16:44 /app/solution/validation_cache.npz

<task-id>01a0ee12-786d-7a93-8df7-8982ea725f09</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-fcb2f742-615c-4869-ba63-6b4a7ebc27f0-53.log</output-file>
<status>running</status>
<summary>Background task 01a0ee12-786d-7a93-8df7-8982ea725f09 started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee12-786d-7a93-8df7-8982ea725f09"] when you need the output.

- [completed] 1: Explore production model, data, and evaluation
- [completed] 2: Diagnose negative-sampling limitations
- [completed] 3: Implement improved training objective
- [in_progress] 4: Validate on public backtest
- [pending] 5: Refit on train+val and produce submission

=== Task 01a0ee12-786d-7a93-8df7-8982ea725f09 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints \
  --epochs 1 \
  --threads 8
Status: running
Duration: 61.76s
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-fcb2f742-615c-4869-ba63-6b4a7ebc27f0-53.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250


Waited the requested 60s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

The file /app/solution/test_negative_sampling.py has been created.

exit: 0
...
----------------------------------------------------------------------
Ran 3 tests in 0.014s

OK

=== Task 01a0ee12-786d-7a93-8df7-8982ea725f09 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints \
  --epochs 1 \
  --threads 8
Status: running
Duration: 267.41s
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-fcb2f742-615c-4869-ba63-6b4a7ebc27f0-53.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 shared_items=2,005 batch_loss=7.217205 running_loss=7.770816 elapsed=199.8s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-01.pt
epoch=1/1 train_loss=7.76250 validation_loss=6.59010 pair_accuracy=0.673 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.5 validation_seconds=10.1
restored best validation-softmax checkpoint: epoch=1 loss=6.590104


Waited the requested 180s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Task 01a0ee12-786d-7a93-8df7-8982ea725f09 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints \
  --epochs 1 \
  --threads 8
Status: completed
Duration: 319.33s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-fcb2f742-615c-4869-ba63-6b4a7ebc27f0-53.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 shared_items=2,005 batch_loss=7.217205 running_loss=7.770816 elapsed=199.8s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-01.pt
epoch=1/1 train_loss=7.76250 validation_loss=6.59010 pair_accuracy=0.673 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.5 validation_seconds=10.1
restored best validation-softmax checkpoint: epoch=1 loss=6.590104
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-sampled-softmax",
  "stage": "validation",
  "output": "/app/solution/validation_ranking.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 314.951,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-softmax-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-sampled-softmax-at-every-timestep",
    "negative_sampling": "shared-inbatch-plus-dislikes-plus-popularity-plus-uniform",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "shared_popularity_negatives": 256,
    "shared_uniform_negatives": 64,
    "popularity_sampling_power": 0.75,
    "softmax_temperature": 1.0,
    "logit_correction_power": 0.0,
    "use_batch_items_as_negatives": true,
    "use_dislike_negatives": true,
    "loss_type": "sampled-softmax",
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 7.762499441031795,
      "positive_logit": 0.5902611491725098,
      "negative_logit": -0.07198980824945261,
      "pair_accuracy": 0.6726512298112624,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 2973.5563909774437,
      "batches": 266.0,
      "seconds": 212.470825181,
      "validation_loss": 6.590103978798988,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 10.086177119000013
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.2648407220840454,
    "query_norm_std": 0.4572801887989044,
    "item_norm_mean": 1.6884385347366333,
    "item_norm_std": 0.9687454700469971
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/validation_ranking_epoch1.parquet",
  "scores": {
    "quality_ndcg@10": 0.012796,
    "quality_users": 4263
  }
}

<task-id>01a0ee17-71f6-7890-9bd2-6d9c92c3f218</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-988b1008-b54b-4cda-b31d-8757b40a61b1-61.log</output-file>
<status>running</status>
<summary>Background task 01a0ee17-71f6-7890-9bd2-6d9c92c3f218 started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee17-71f6-7890-9bd2-6d9c92c3f218"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "01a0ee17-71ed-7033-999a-37bd14933918" (running for 0s): Evaluate 1-epoch validation ranking NDCG
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

=== Task 01a0ee17-71f6-7890-9bd2-6d9c92c3f218 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints \
  --resume-checkpoint /app/solution/val_checkpoints/epoch-01.pt \
  --epochs 4 \
  --early-stopping-patience 4 \
  --threads 8
Status: running
Duration: 258.02s
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-988b1008-b54b-4cda-b31d-8757b40a61b1-61.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
restored optimizer state from /app/solution/val_checkpoints/epoch-01.pt
resumed validation-softmax training: epoch=1 loss=6.590104
epoch=2/4 batches=250 targets=989,419 shared_items=1,936 batch_loss=6.583493 running_loss=7.303529 elapsed=196.2s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-02.pt
epoch=2/4 train_loss=7.29483 validation_loss=6.18851 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.6 validation_seconds=11.6


Waited the requested 240s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Task 01a0ee17-71f6-7890-9bd2-6d9c92c3f218 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints \
  --resume-checkpoint /app/solution/val_checkpoints/epoch-01.pt \
  --epochs 4 \
  --early-stopping-patience 4 \
  --threads 8
Status: completed
Duration: 762.94s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-988b1008-b54b-4cda-b31d-8757b40a61b1-61.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
restored optimizer state from /app/solution/val_checkpoints/epoch-01.pt
resumed validation-softmax training: epoch=1 loss=6.590104
epoch=2/4 batches=250 targets=989,419 shared_items=1,936 batch_loss=6.583493 running_loss=7.303529 elapsed=196.2s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-02.pt
epoch=2/4 train_loss=7.29483 validation_loss=6.18851 pair_accuracy=0.791 train_targets=1,052,953 validation_targets=130,103 train_seconds=208.6 validation_seconds=11.6
epoch=3/4 batches=250 targets=990,306 shared_items=1,868 batch_loss=6.284878 running_loss=6.938137 elapsed=200.6s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-03.pt
epoch=3/4 train_loss=6.93358 validation_loss=5.94431 pair_accuracy=0.841 train_targets=1,052,953 validation_targets=130,103 train_seconds=213.5 validation_seconds=10.3
epoch=4/4 batches=250 targets=992,557 shared_items=4,164 batch_loss=7.021966 running_loss=6.679199 elapsed=201.5s
saved epoch checkpoint: /app/solution/val_checkpoints/epoch-04.pt
epoch=4/4 train_loss=6.67286 validation_loss=5.72496 pair_accuracy=0.869 train_targets=1,052,953 validation_targets=130,103 train_seconds=212.8 validation_seconds=10.3
restored best validation-softmax checkpoint: epoch=4 loss=5.724961
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-sampled-softmax",
  "stage": "validation",
  "output": "/app/solution/validation_ranking.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 758.402,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-softmax-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-sampled-softmax-at-every-timestep",
    "negative_sampling": "shared-inbatch-plus-dislikes-plus-popularity-plus-uniform",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 4,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "shared_popularity_negatives": 256,
    "shared_uniform_negatives": 64,
    "popularity_sampling_power": 0.75,
    "softmax_temperature": 1.0,
    "logit_correction_power": 0.0,
    "use_batch_items_as_negatives": true,
    "use_dislike_negatives": true,
    "loss_type": "sampled-softmax",
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 4,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 7.762499441031795,
      "positive_logit": 0.5902611491725098,
      "negative_logit": -0.07198980824945261,
      "pair_accuracy": 0.6726512298112624,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 2973.5563909774437,
      "batches": 266.0,
      "seconds": 212.470825181,
      "validation_loss": 6.590103978798988,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 10.086177119000013
    },
    {
      "epoch": 2.0,
      "loss": 7.294829816944926,
      "positive_logit": 1.3261520239128821,
      "negative_logit": -0.6787766086590322,
      "pair_accuracy": 0.7911617978260643,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 2973.657894736842,
      "batches": 266.0,
      "seconds": 208.5868024240001,
      "validation_loss": 6.188505907290901,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 11.572802217000117
    },
    {
      "epoch": 3.0,
      "loss": 6.933580038541489,
      "positive_logit": 1.656641832774383,
      "negative_logit": -1.5280880443075635,
      "pair_accuracy": 0.8414602700486775,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 2973.1015037593984,
      "batches": 266.0,
      "seconds": 213.49752619000014,
      "validation_loss": 5.94430934717061,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 10.279035137999927
    },
    {
      "epoch": 4.0,
      "loss": 6.672856509323206,
      "positive_logit": 1.4524826894100449,
      "negative_logit": -2.5076853997125923,
      "pair_accuracy": 0.8690104846297628,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 2973.718045112782,
      "batches": 266.0,
      "seconds": 212.75261702000012,
      "validation_loss": 5.724961338913054,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 10.266722509999909
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 2.0529134273529053,
    "query_norm_std": 0.7588235139846802,
    "item_norm_mean": 5.278851509094238,
    "item_norm_std": 2.0562691688537598
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/validation_ranking_epoch4.parquet",
  "scores": {
    "quality_ndcg@10": 0.015553,
    "quality_users": 4263
  }
}

The file /app/solution/two_tower_model.py has been updated successfully.

The file /app/solution/two_tower_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

40→CACHE_VERSION = 2


@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
50→    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
60→    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    inbatch_correction_max: float = 4.0
    shared_popularity_negatives: int = 256
    shared_uniform_negatives: int = 64
    shared_inbatch_negatives: int = 128
    popularity_sampling_power: float = 0.75
    softmax_temperature: float = 0.07
    logit_correction_power: float = 0.0
70→    use_batch_items_as_negatives: bool = False
    use_dislike_negatives: bool = True
    normalize_towers: bool = True
    loss_type: str = "sampled-softmax"
    epochs: int = 4
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
80→    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)

800→        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
810→                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
820→                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)

    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

<workspace_result workspace_path="/app">
Found 1 matching lines
/app/solution/train_current_model.py
943:def user_queries(
944-    model: base.SequentialTwoTower,
945-    sequence_vectors: torch.Tensor,
946-    liked_vectors: torch.Tensor,
947-    disliked_vectors: torch.Tensor,
948-    user_features: torch.Tensor,
949-) -> torch.Tensor:
950-    raw = model.user_tower(
951-        torch.cat(
952-            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
953-            dim=1,
954-        )
955-    )
956-    if config.normalize_towers:
957-        return F.normalize(raw, dim=-1)
958-    return raw
959-
960-
961-def candidate_vectors(
962-    model: base.SequentialTwoTower,
963-    positives: torch.Tensor,
</workspace_result>

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

1245→        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
1250→            padding_item,
        )
    return (
        input_items,
        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
1260→        processed,
        weights,
    )


def score_target_microbatches(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    config: Config,
    sequence_vectors: torch.Tensor,
1270→    flat_states: np.ndarray,
    flat_activity: np.ndarray,
    positive_items: torch.Tensor,
    shared_items: torch.Tensor,
    sample_weights: np.ndarray,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
    log_item_q: torch.Tensor | None,
    device: torch.device,
    backward: bool,
1280→) -> tuple[dict[str, float], torch.Tensor | None]:
    """Compute one logical block's sampled softmax in bounded-memory slices."""
    target_count = len(positive_items)
    total_weight = float(sample_weights.sum())
    if total_weight <= 0:
        raise RuntimeError("target block has no positive sample weight")
    if shared_items.ndim != 1 or not len(shared_items):
        raise RuntimeError("sampled-softmax shared item set must be a 1-D catalog list")
    sequence_gradient = torch.zeros_like(sequence_vectors) if backward else None
    statistics = {
1290→        "loss_sum": 0.0,
        "positive_sum": 0.0,
        "negative_sum": 0.0,
        "pair_correct": 0.0,
        "pair_total": 0.0,
        "weight_sum": 0.0,
        "shared_items": float(len(shared_items)),
    }
    shared_cpu = shared_items.detach().cpu()
    shared_on_device = shared_items.to(device)
1300→    temperature = max(float(config.softmax_temperature), 1.0e-6)
    log_q_slice = None
    if log_item_q is not None and config.logit_correction_power:
        log_q_slice = (
            config.logit_correction_power * log_item_q[shared_cpu].to(device)
        )
    for begin in range(0, target_count, config.loss_microbatch_size):
        end = min(begin + config.loss_microbatch_size, target_count)
        selected_states = flat_states[begin:end]
        if backward:
1310→            sequence_input = (
                sequence_vectors[begin:end].detach().requires_grad_(True)
            )
        else:
            sequence_input = sequence_vectors[begin:end]
        liked_vectors, disliked_vectors, _ = pool_explicit_states(
            model, prepared, selected_states, device
        )
        counts = prepared.state_counts[selected_states]
        numeric = np.zeros((end - begin, 6), dtype=np.float32)
1320→        numeric[:, :4] = flat_activity[begin:end]
        numeric[:, 4] = np.log1p(counts[:, 0]) / 6.0
        numeric[:, 5] = np.log1p(counts[:, 1]) / 4.0
        query_vectors = user_queries(
            model,
            sequence_input,
            liked_vectors,
            disliked_vectors,
            torch.from_numpy(numeric).to(device),
        )
1330→        encoded_items = model.encode_items(shared_on_device)
        scores = query_vectors @ encoded_items.T
        targets_cpu = (
            positive_items[begin:end].detach().cpu().numpy().astype(np.int64, copy=False)
        )
        shared_np = shared_cpu.numpy().astype(np.int64, copy=False)
        positives_cpu = positive_items[begin:end].detach().cpu()
        target_index = torch.searchsorted(shared_cpu, positives_cpu)
        if (
            not len(shared_cpu)
1340→            or int(target_index.max()) >= len(shared_cpu)
            or torch.any(shared_cpu[target_index] != positives_cpu)
        ):
            raise RuntimeError("positive item missing from sampled-softmax class set")
        target_index = target_index.to(device)
        invalid = shared_false_negative_mask(
            shared_np,
            targets_cpu,
            user_rows[begin:end],
            selected_states,
1350→            prepared,
            known_positive_packed,
        )
        logits = scores / temperature
        if log_q_slice is not None:
            logits = logits - log_q_slice.unsqueeze(0)
        logits = logits.masked_fill(
            torch.from_numpy(invalid).to(device), -1.0e9
        )
        selected_weights = torch.from_numpy(
1360→            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        per_row = F.cross_entropy(logits, target_index, reduction="none")
        loss_sum = (selected_weights * per_row).sum()
        if not bool(torch.isfinite(loss_sum)):
            raise RuntimeError("dense next-item training produced non-finite loss")
        if backward:
            (loss_sum / total_weight).backward()
            if sequence_input.grad is None:
                raise RuntimeError("sequence microbatch did not produce a gradient")
1370→            sequence_gradient[begin:end] = sequence_input.grad
        positive_logits = scores.gather(1, target_index.unsqueeze(1)).squeeze(1)
        target_hit = torch.from_numpy(
            shared_np[None, :] == targets_cpu[:, None]
        ).to(device)
        comparable = ~torch.from_numpy(invalid).to(device) & ~target_hit
        statistics["loss_sum"] += float(loss_sum.detach().cpu())
        statistics["positive_sum"] += float(positive_logits.detach().sum().cpu())
        if bool(comparable.any()):
            negative_scores = scores.detach().masked_select(comparable)
1380→            statistics["negative_sum"] += float(negative_scores.sum().cpu())
            statistics["pair_total"] += float(comparable.sum().detach().cpu())
            statistics["pair_correct"] += float(
                (positive_logits.detach()[:, None] > scores.detach())[comparable].sum().cpu()
            )
        statistics["weight_sum"] += float(selected_weights.sum().detach().cpu())
    return statistics, sequence_gradient


def release_accelerator_cache(device: torch.device) -> None:
1390→    if device.type == "mps":
        torch.mps.empty_cache()


def run_training_epoch(
    model: base.SequentialTwoTower,
    optimizer: torch.optim.Optimizer,
    prepared: SequenceData,
    config: Config,
    target_weights: np.ndarray,
1400→    window_starts: np.ndarray,
    target_counts: np.ndarray,
    popularity_probs: torch.Tensor,
    log_item_q: torch.Tensor,
    known_positive_packed: np.ndarray | None,

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/test_negative_sampling.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

exit: 0
...
----------------------------------------------------------------------
Ran 3 tests in 0.014s

OK

exit: 0
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
config 4 0.07 128 True
positives 127 shared 437 unique pos 104
stats {'loss_sum': 766.2101, 'positive_sum': -3.066, 'negative_sum': -1310.4995, 'pair_correct': 26945.0, 'pair_total': 52010.0, 'weight_sum': 127.0, 'shared_items': 437.0}
mean pos logit -0.024141643929669238 loss mean 6.033150259904035
OK

<task-id>01a0ee26-76ae-7fb3-8180-66fe2011eb5b</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-dca7253d-3462-483e-b681-f5a5fabdef19-87.log</output-file>
<status>running</status>
<summary>Background task 01a0ee26-76ae-7fb3-8180-66fe2011eb5b started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee26-76ae-7fb3-8180-66fe2011eb5b"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "01a0ee26-76b2-7131-ba00-fa9727a43896" (running for 0s): Prepare hidden-test sequence cache in parallel
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

<task-id>01a0ee26-76b2-7131-ba00-fa9727a43896</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-dca7253d-3462-483e-b681-f5a5fabdef19-88.log</output-file>
<status>running</status>
<summary>Background task 01a0ee26-76b2-7131-ba00-fa9727a43896 started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee26-76b2-7131-ba00-fa9727a43896"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "01a0ee26-76ae-7fb3-8180-66fe2011eb5b" (running for 0s): Train cosine sampled-softmax model for 4 epochs
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

exit: 0
popularity {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
random {'quality_ndcg@10': 0.000241, 'quality_users': 4263}

=== Task 01a0ee26-76b2-7131-ba00-fa9727a43896 ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage submission \
  --public-dir /task \
  --output /app/solution/submission.parquet \
  --prepared-cache /app/solution/test_cache.npz \
  --prepare-only \
  --threads 2
Status: completed
Duration: 86.21s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-dca7253d-3462-483e-b681-f5a5fabdef19-88.log

=== Output ===
loaded explicit events: rows=746,504 users=8,307
prepared users=1,000 tokens=2,167,338 states=37,073 elapsed=7.3s
prepared users=2,000 tokens=4,450,246 states=73,784 elapsed=16.5s
prepared users=3,000 tokens=6,703,970 states=113,190 elapsed=24.9s
prepared users=4,000 tokens=9,087,187 states=153,283 elapsed=33.6s
prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=41.0s
prepared users=6,000 tokens=13,485,786 states=224,384 elapsed=49.2s
prepared users=7,000 tokens=15,826,114 states=265,318 elapsed=58.1s
prepared users=8,000 tokens=18,028,622 states=306,474 elapsed=66.6s
prepared users=9,000 tokens=20,195,749 states=342,272 elapsed=74.9s
target users without qualifying catalog sequence=6
prepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322
saved public-derived full-sequence cache: /app/solution/test_cache.npz
{
  "stage": "submission",
  "sequence_users": 9153,
  "sequence_tokens": 20712971,
  "next_item_targets": 20703818,
  "selected_training_targets": 1067606,
  "selected_validation_targets": 131997,
  "discarded_old_targets": 19504215,
  "target_users": 8456,
  "runtime_seconds": 81.895,
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 4,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "shared_popularity_negatives": 256,
    "shared_uniform_negatives": 64,
    "shared_inbatch_negatives": 128,
    "popularity_sampling_power": 0.75,
    "softmax_temperature": 0.07,
    "logit_correction_power": 0.0,
    "use_batch_items_as_negatives": false,
    "use_dislike_negatives": true,
    "normalize_towers": true,
    "loss_type": "sampled-softmax",
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 4,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  }
}

=== Task 01a0ee26-76ae-7fb3-8180-66fe2011eb5b ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints_v2 \
  --epochs 4 \
  --threads 8
Status: running
Duration: 328.27s
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-dca7253d-3462-483e-b681-f5a5fabdef19-87.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/4 batches=250 targets=987,417 shared_items=686 batch_loss=6.034254 running_loss=6.011657 elapsed=142.5s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-01.pt
epoch=1/4 train_loss=5.99733 validation_loss=5.64895 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=147.7 validation_seconds=9.0
epoch=2/4 batches=250 targets=990,241 shared_items=530 batch_loss=5.112707 running_loss=5.504172 elapsed=81.2s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-02.pt
epoch=2/4 train_loss=5.49540 validation_loss=5.29456 pair_accuracy=0.817 train_targets=1,052,953 validation_targets=130,103 train_seconds=86.3 validation_seconds=9.2


Waited the requested 240s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Task 01a0ee26-76ae-7fb3-8180-66fe2011eb5b ===
Command: /opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/validation_ranking.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --checkpoint-dir /app/solution/val_checkpoints_v2 \
  --epochs 4 \
  --threads 8
Status: completed
Duration: 546.48s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-dca7253d-3462-483e-b681-f5a5fabdef19-87.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological target window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
popularity sampler: power=0.750 min_p=2.565e-06 max_p=1.024e-03 entropy=9.9
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/4 batches=250 targets=987,417 shared_items=686 batch_loss=6.034254 running_loss=6.011657 elapsed=142.5s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-01.pt
epoch=1/4 train_loss=5.99733 validation_loss=5.64895 pair_accuracy=0.703 train_targets=1,052,953 validation_targets=130,103 train_seconds=147.7 validation_seconds=9.0
epoch=2/4 batches=250 targets=990,241 shared_items=530 batch_loss=5.112707 running_loss=5.504172 elapsed=81.2s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-02.pt
epoch=2/4 train_loss=5.49540 validation_loss=5.29456 pair_accuracy=0.817 train_targets=1,052,953 validation_targets=130,103 train_seconds=86.3 validation_seconds=9.2
epoch=3/4 batches=250 targets=988,144 shared_items=462 batch_loss=4.592500 running_loss=5.147560 elapsed=84.9s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-03.pt
epoch=3/4 train_loss=5.14250 validation_loss=5.11356 pair_accuracy=0.860 train_targets=1,052,953 validation_targets=130,103 train_seconds=90.2 validation_seconds=9.2
epoch=4/4 batches=250 targets=993,112 shared_items=663 batch_loss=5.006621 running_loss=4.943336 elapsed=81.1s
saved epoch checkpoint: /app/solution/val_checkpoints_v2/epoch-04.pt
epoch=4/4 train_loss=4.94110 validation_loss=4.94181 pair_accuracy=0.876 train_targets=1,052,953 validation_targets=130,103 train_seconds=86.0 validation_seconds=9.3
restored best validation-softmax checkpoint: epoch=4 loss=4.941813
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-sampled-softmax",
  "stage": "validation",
  "output": "/app/solution/validation_ranking.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 541.947,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-softmax-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-sampled-softmax-at-every-timestep",
    "negative_sampling": "shared-inbatch-plus-dislikes-plus-popularity-plus-uniform",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 4,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "shared_popularity_negatives": 256,
    "shared_uniform_negatives": 64,
    "shared_inbatch_negatives": 128,
    "popularity_sampling_power": 0.75,
    "softmax_temperature": 0.07,
    "logit_correction_power": 0.0,
    "use_batch_items_as_negatives": false,
    "use_dislike_negatives": true,
    "normalize_towers": true,
    "loss_type": "sampled-softmax",
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 4,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 5.997333782672968,
      "positive_logit": 0.4013544784834452,
      "negative_logit": 0.3363762068790562,
      "pair_accuracy": 0.7033531991877809,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 630.7857142857143,
      "batches": 266.0,
      "seconds": 147.70364849599991,
      "validation_loss": 5.6489511030424495,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 8.970216299000185
    },
    {
      "epoch": 2.0,
      "loss": 5.495397190775754,
      "positive_logit": 0.6553476331368371,
      "negative_logit": 0.49800194643717693,
      "pair_accuracy": 0.8169562911870893,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 630.6503759398496,
      "batches": 266.0,
      "seconds": 86.27923798399979,
      "validation_loss": 5.294558658025428,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 9.19563953800025
    },
    {
      "epoch": 3.0,
      "loss": 5.142502513580172,
      "positive_logit": 0.6588379137833834,
      "negative_logit": 0.42455062401679006,
      "pair_accuracy": 0.8597615111407672,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 630.8308270676691,
      "batches": 266.0,
      "seconds": 90.20486343799985,
      "validation_loss": 5.113561912054476,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 9.230200715000137
    },
    {
      "epoch": 4.0,
      "loss": 4.941101042090432,
      "positive_logit": 0.5727759574895144,
      "negative_logit": 0.2949956641881039,
      "pair_accuracy": 0.8762716302522324,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "shared_items_mean": 630.6766917293234,
      "batches": 266.0,
      "seconds": 85.95637205799994,
      "validation_loss": 4.941812552854643,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 9.319979656999749
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.0,
    "query_norm_std": 4.510347650921176e-08,
    "item_norm_mean": 1.0,
    "item_norm_std": 4.538310705015647e-08
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/validation_ranking_v2_epoch4.parquet",
  "scores": {
    "quality_ndcg@10": 0.014299,
    "quality_users": 4263
  }
}

<task-id>call-b16f2d73-3606-4a21-963a-ddc37487533a-94</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-b16f2d73-3606-4a21-963a-ddc37487533a-94.log</output-file>
<status>running</status>
<summary>Command "Sweep popularity-blend weights on cosine retrieval" is still running after 30s (block_until_ms) and has been moved to the background as task call-b16f2d73-3606-4a21-963a-ddc37487533a-94.</summary>
Use get_command_or_subagent_output with task_ids=["call-b16f2d73-3606-4a21-963a-ddc37487533a-94"] when you need the output.

=== Task call-b16f2d73-3606-4a21-963a-ddc37487533a-94 ===
Command: cd /app/solution && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch

sys.path.insert(0, '/app/solution')
sys.path.insert(0, '/task')
import train_current_model as tcm
import two_tower_model as base
from ranking_evaluation import ranked_items, score_rankings, validate_ranking
from evaluate_public import validation_truth

config = tcm.Config()
device = torch.device('cpu')
prepared, eligibility = tcm.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/validation_cache.npz'))
model = base.SequentialTwoTower(prepared.item_features, tcm.model_config(config))
ckpt = torch.load('/app/solution/val_checkpoints_v2/epoch-04.pt', map_location='cpu', weights_only=False)
model.load_state_dict(ckpt['model_state'])
model.eval()

print('computing embeddings...')
with torch.inference_mode():
    queries = tcm.inference_queries(model, prepared, config, device)
    item_parts = []
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)

catalog = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
# item order in prepared.item_ids matches catalog sorted by popularity_rank
assert np.array_equal(prepared.item_ids, catalog.item_id.to_numpy())
pop = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float64))
pop = (pop - pop.mean()) / max(pop.std(), 1e-6)
organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float64))
organic = (organic - organic.mean()) / max(organic.std(), 1e-6)
inv_rank = 1.0 - (catalog.popularity_rank.to_numpy(dtype=np.float64) - 1) / max(len(catalog)-1, 1)
inv_rank = (inv_rank - inv_rank.mean()) / max(inv_rank.std(), 1e-6)

targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
from ranking_evaluation import CandidateSet as EvalCS
graded = validation_truth(EvalCS(Path('/task/data/validation_eligible_candidates.npz')), targets)

def evaluate_scores(user_item_scores, tag):
    rows = []
    for start in range(0, len(eligibility.uids), 64):
        end = min(start+64, len(eligibility.uids))
        scores = user_item_scores[start:end]
        eligible = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores = scores.copy()
        scores[~eligible] = -np.inf
        selected = np.argpartition(-scores, kth=99, axis=1)[:, :100]
        # order the 100
        for i, cols in enumerate(selected):
            ordered = cols[np.argsort(-scores[i, cols])]
            uid = int(eligibility.uids[start+i])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r, c in enumerate(ordered, 1))
    frame = pd.DataFrame(rows, columns=['uid','item_id','rank'])
    frame = validate_ranking(frame, targets)
    scores_out = score_rankings(ranked_items(frame), graded)
    print(tag, scores_out)
    return frame, scores_out

# precompute cosine
print('matmul...')
cosine = (queries @ items.T).numpy()
print('cosine stats', cosine.mean(), cosine.std(), cosine.min(), cosine.max())

results = []
for alpha in [0.0, 0.02, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5]:
    blended = cosine + alpha * pop[None, :]
    _, s = evaluate_scores(blended, f'cosine+{alpha}*logpop')
    results.append((s['quality_ndcg@10'], alpha, 'pop'))

for alpha in [0.05, 0.1, 0.2]:
    blended = cosine + alpha * organic[None, :]
    _, s = evaluate_scores(blended, f'cosine+{alpha}*organic')
    results.append((s['quality_ndcg@10'], alpha, 'org'))

# multiplicative
for beta in [0.1, 0.25, 0.5]:
    blended = cosine * (1.0 + beta * (pop - pop.min()) / (pop.max()-pop.min()+1e-6))[None,:]
    _, s = evaluate_scores(blended, f'cosine*(1+{beta}*pop01)')
    results.append((s['quality_ndcg@10'], beta, 'mult'))

print('BEST', max(results))
PY
Status: completed
Duration: 136.10s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-b16f2d73-3606-4a21-963a-ddc37487533a-94.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
computing embeddings...
matmul...
cosine stats 0.22454382 0.25464734 -0.91197336 0.9612747
cosine+0.0*logpop {'quality_ndcg@10': 0.014299, 'quality_users': 4263}
cosine+0.02*logpop {'quality_ndcg@10': 0.016308, 'quality_users': 4263}
cosine+0.05*logpop {'quality_ndcg@10': 0.015278, 'quality_users': 4263}
cosine+0.1*logpop {'quality_ndcg@10': 0.014523, 'quality_users': 4263}
cosine+0.15*logpop {'quality_ndcg@10': 0.013916, 'quality_users': 4263}
cosine+0.2*logpop {'quality_ndcg@10': 0.012665, 'quality_users': 4263}
cosine+0.3*logpop {'quality_ndcg@10': 0.012081, 'quality_users': 4263}
cosine+0.5*logpop {'quality_ndcg@10': 0.01194, 'quality_users': 4263}
cosine+0.05*organic {'quality_ndcg@10': 0.017327, 'quality_users': 4263}
cosine+0.1*organic {'quality_ndcg@10': 0.017224, 'quality_users': 4263}
cosine+0.2*organic {'quality_ndcg@10': 0.015219, 'quality_users': 4263}
cosine*(1+0.1*pop01) {'quality_ndcg@10': 0.015931, 'quality_users': 4263}
cosine*(1+0.25*pop01) {'quality_ndcg@10': 0.016354, 'quality_users': 4263}
cosine*(1+0.5*pop01) {'quality_ndcg@10': 0.015931, 'quality_users': 4263}
BEST (0.017327, 0.05, 'org')

<task-id>01a0ee32-1684-7c10-acae-5e67d4aa21cf</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-421739e0-b26b-43bf-9500-4d53aa9fce85-96.log</output-file>
<status>running</status>
<summary>Background task 01a0ee32-1684-7c10-acae-5e67d4aa21cf started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee32-1684-7c10-acae-5e67d4aa21cf"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "01a0ee32-1688-7933-a405-19a0e9aaf3df" (running for 0s): Reproduce production 1-epoch validation ranking
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

<task-id>01a0ee32-1688-7933-a405-19a0e9aaf3df</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-421739e0-b26b-43bf-9500-4d53aa9fce85-97.log</output-file>
<status>running</status>
<summary>Background task 01a0ee32-1688-7933-a405-19a0e9aaf3df started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee32-1688-7933-a405-19a0e9aaf3df"] when you need the output.

<system-reminder>
Note: 1 other background task is still running:
- "01a0ee32-1684-7c10-acae-5e67d4aa21cf" (running for 0s): Blend organic popularity into unnormalized v1 scores
Consider killing duplicate tasks with kill_command_or_subagent before launching new ones.
</system-reminder>

=== Task 01a0ee32-1684-7c10-acae-5e67d4aa21cf ===
Command: cd /app && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch

sys.path.insert(0, '/app/solution')
sys.path.insert(0, '/task')
import train_current_model as tcm
# unnormalized item tower from production primitives
sys.path.insert(0, '/task/current_model')
import two_tower_model as orig

config = tcm.Config()
device = torch.device('cpu')
prepared, eligibility = tcm.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/validation_cache.npz'))
model = orig.SequentialTwoTower(prepared.item_features, tcm.model_config(config))
ckpt = torch.load('/app/solution/val_checkpoints/epoch-04.pt', map_location='cpu', weights_only=False)
model.load_state_dict(ckpt['model_state'])
model.eval()
print('loaded v1 unnormalized')

# unnormalized user queries: bypass F.normalize by using orig user_tower directly
from torch.nn import functional as F

def unnorm_queries(model, prepared, config, device):
    # monkeypatch user_queries temporarily
    def uq(model, sequence_vectors, liked_vectors, disliked_vectors, user_features):
        return model.user_tower(torch.cat([sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1))
    orig_uq = tcm.user_queries
    tcm.user_queries = uq
    try:
        with torch.inference_mode():
            q = tcm.inference_queries(model, prepared, config, device)
    finally:
        tcm.user_queries = orig_uq
    return q

queries = unnorm_queries(model, prepared, config, device)
with torch.inference_mode():
    item_parts = []
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('q norm', float(torch.linalg.vector_norm(queries, dim=1).mean()), 'i norm', float(torch.linalg.vector_norm(items, dim=1).mean()))
dots = (queries @ items.T).numpy()
print('dot stats', dots.mean(), dots.std(), dots.min(), dots.max())

catalog = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
pop = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float64))
pop = (pop - pop.mean()) / max(pop.std(), 1e-6)
organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float64))
organic = (organic - organic.mean()) / max(organic.std(), 1e-6)

from ranking_evaluation import ranked_items, score_rankings, validate_ranking, CandidateSet as EvalCS
from evaluate_public import validation_truth
targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
graded = validation_truth(EvalCS(Path('/task/data/validation_eligible_candidates.npz')), targets)

def evaluate_scores(user_item_scores, tag):
    rows = []
    for start in range(0, len(eligibility.uids), 64):
        end = min(start+64, len(eligibility.uids))
        scores = user_item_scores[start:end].copy()
        eligible = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores[~eligible] = -np.inf
        selected = np.argpartition(-scores, kth=99, axis=1)[:, :100]
        for i, cols in enumerate(selected):
            ordered = cols[np.argsort(-scores[i, cols])]
            uid = int(eligibility.uids[start+i])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r, c in enumerate(ordered, 1))
    frame = pd.DataFrame(rows, columns=['uid','item_id','rank'])
    s = score_rankings(ranked_items(validate_ranking(frame, targets)), graded)
    print(tag, s, flush=True)
    return s

results=[]
for alpha, feat, name in [(0.0, pop, 'none')] + [(a, pop, 'pop') for a in [0.05,0.1,0.2,0.5,1,2]] + [(a, organic, 'org') for a in [0.05,0.1,0.2,0.5,1,2]]:
    s = evaluate_scores(dots + alpha * feat[None,:], f'v1+{alpha}*{name}')
    results.append((s['quality_ndcg@10'], alpha, name))
print('BEST', max(results))
PY
Status: running
Duration: 189.75s
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-421739e0-b26b-43bf-9500-4d53aa9fce85-96.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
loaded v1 unnormalized


Waited the requested 180s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

=== Multi-wait (wait_all) ===
--- Task 01a0ee32-1684-7c10-acae-5e67d4aa21cf [failed] ---
Command: cd /app && /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch

sys.path.insert(0, '/app/solution')
sys.path.insert(0, '/task')
import train_current_model as tcm
# unnormalized item tower from production primitives
sys.path.insert(0, '/task/current_model')
import two_tower_model as orig

config = tcm.Config()
device = torch.device('cpu')
prepared, eligibility = tcm.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/validation_cache.npz'))
model = orig.SequentialTwoTower(prepared.item_features, tcm.model_config(config))
ckpt = torch.load('/app/solution/val_checkpoints/epoch-04.pt', map_location='cpu', weights_only=False)
model.load_state_dict(ckpt['model_state'])
model.eval()
print('loaded v1 unnormalized')

# unnormalized user queries: bypass F.normalize by using orig user_tower directly
from torch.nn import functional as F

def unnorm_queries(model, prepared, config, device):
    # monkeypatch user_queries temporarily
    def uq(model, sequence_vectors, liked_vectors, disliked_vectors, user_features):
        return model.user_tower(torch.cat([sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1))
    orig_uq = tcm.user_queries
    tcm.user_queries = uq
    try:
        with torch.inference_mode():
            q = tcm.inference_queries(model, prepared, config, device)
    finally:
        tcm.user_queries = orig_uq
    return q

queries = unnorm_queries(model, prepared, config, device)
with torch.inference_mode():
    item_parts = []
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('q norm', float(torch.linalg.vector_norm(queries, dim=1).mean()), 'i norm', float(torch.linalg.vector_norm(items, dim=1).mean()))
dots = (queries @ items.T).numpy()
print('dot stats', dots.mean(), dots.std(), dots.min(), dots.max())

catalog = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
pop = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float64))
pop = (pop - pop.mean()) / max(pop.std(), 1e-6)
organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float64))
organic = (organic - organic.mean()) / max(organic.std(), 1e-6)

from ranking_evaluation import ranked_items, score_rankings, validate_ranking, CandidateSet as EvalCS
from evaluate_public import validation_truth
targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
graded = validation_truth(EvalCS(Path('/task/data/validation_eligible_candidates.npz')), targets)

def evaluate_scores(user_item_scores, tag):
    rows = []
    for start in range(0, len(eligibility.uids), 64):
        end = min(start+64, len(eligibility.uids))
        scores = user_item_scores[start:end].copy()
        eligible = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores[~eligible] = -np.inf
        selected = np.argpartition(-scores, kth=99, axis=1)[:, :100]
        for i, cols in enumerate(selected):
            ordered = cols[np.argsort(-scores[i, cols])]
            uid = int(eligibility.uids[start+i])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r, c in enumerate(ordered, 1))
    frame = pd.DataFrame(rows, columns=['uid','item_id','rank'])
    s = score_rankings(ranked_items(validate_ranking(frame, targets)), graded)
    print(tag, s, flush=True)
    return s

results=[]
for alpha, feat, name in [(0.0, pop, 'none')] + [(a, pop, 'pop') for a in [0.05,0.1,0.2,0.5,1,2]] + [(a, organic, 'org') for a in [0.05,0.1,0.2,0.5,1,2]]:
    s = evaluate_scores(dots + alpha * feat[None,:], f'v1+{alpha}*{name}')
    results.append((s['quality_ndcg@10'], alpha, name))
print('BEST', max(results))
PY
Duration: 399.11s
Exit Code: 137
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
loaded v1 unnormalized
q norm 2.0529136657714844 i norm 1.0
dot stats -0.57759917 0.6861227 -7.497898 3.7218776
v1+0.0*none {'quality_ndcg@10': 0.01332, 'quality_users': 4263}
v1+0.05*pop {'quality_ndcg@10': 0.014231, 'quality_users': 4263}
v1+0.1*pop {'quality_ndcg@10': 0.014198, 'quality_users': 4263}
v1+0.2*pop {'quality_ndcg@10': 0.013971, 'quality_users': 4263}
v1+0.5*pop {'quality_ndcg@10': 0.013105, 'quality_users': 4263}
v1+1*pop {'quality_ndcg@10': 0.012155, 'quality_users': 4263}
v1+2*pop {'quality_ndcg@10': 0.011488, 'quality_users': 4263}
v1+0.05*org {'quality_ndcg@10': 0.014352, 'quality_users': 4263}
v1+0.1*org {'quality_ndcg@10': 0.014488, 'quality_users': 4263}
v1+0.2*org {'quality_ndcg@10': 0.014446, 'quality_users': 4263}
v1+0.5*org {'quality_ndcg@10': 0.014056, 'quality_users': 4263}
v1+1*org {'quality_ndcg@10': 0.013477, 'quality_users': 4263}
--: line 84:  9227 Killed                  /opt/task-venv/bin/python - <<'PY'
import sys
from pathlib import Path
import numpy as np
import pandas as pd
import torch

sys.path.insert(0, '/app/solution')
sys.path.insert(0, '/task')
import train_current_model as tcm
# unnormalized item tower from production primitives
sys.path.insert(0, '/task/current_model')
import two_tower_model as orig

config = tcm.Config()
device = torch.device('cpu')
prepared, eligibility = tcm.load_or_prepare(Path('/task'), 'validation', config, Path('/app/solution/validation_cache.npz'))
model = orig.SequentialTwoTower(prepared.item_features, tcm.model_config(config))
ckpt = torch.load('/app/solution/val_checkpoints/epoch-04.pt', map_location='cpu', weights_only=False)
model.load_state_dict(ckpt['model_state'])
model.eval()
print('loaded v1 unnormalized')

# unnormalized user queries: bypass F.normalize by using orig user_tower directly
from torch.nn import functional as F

def unnorm_queries(model, prepared, config, device):
    # monkeypatch user_queries temporarily
    def uq(model, sequence_vectors, liked_vectors, disliked_vectors, user_features):
        return model.user_tower(torch.cat([sequence_vectors, liked_vectors, disliked_vectors, user_features], dim=1))
    orig_uq = tcm.user_queries
    tcm.user_queries = uq
    try:
        with torch.inference_mode():
            q = tcm.inference_queries(model, prepared, config, device)
    finally:
        tcm.user_queries = orig_uq
    return q

queries = unnorm_queries(model, prepared, config, device)
with torch.inference_mode():
    item_parts = []
    for start in range(0, len(prepared.item_ids), 2048):
        rows = torch.arange(start, min(start+2048, len(prepared.item_ids)))
        item_parts.append(model.encode_items(rows).cpu())
    items = torch.cat(item_parts)
print('q norm', float(torch.linalg.vector_norm(queries, dim=1).mean()), 'i norm', float(torch.linalg.vector_norm(items, dim=1).mean()))
dots = (queries @ items.T).numpy()
print('dot stats', dots.mean(), dots.std(), dots.min(), dots.max())

catalog = pd.read_parquet('/task/data/candidate_catalog.parquet').sort_values('popularity_rank')
pop = np.log1p(catalog.positive_listens.to_numpy(dtype=np.float64))
pop = (pop - pop.mean()) / max(pop.std(), 1e-6)
organic = np.log1p(catalog.organic_positive_listens.to_numpy(dtype=np.float64))
organic = (organic - organic.mean()) / max(organic.std(), 1e-6)

from ranking_evaluation import ranked_items, score_rankings, validate_ranking, CandidateSet as EvalCS
from evaluate_public import validation_truth
targets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))
graded = validation_truth(EvalCS(Path('/task/data/validation_eligible_candidates.npz')), targets)

def evaluate_scores(user_item_scores, tag):
    rows = []
    for start in range(0, len(eligibility.uids), 64):
        end = min(start+64, len(eligibility.uids))
        scores = user_item_scores[start:end].copy()
        eligible = np.unpackbits(eligibility.packed[start:end], axis=1, count=len(prepared.item_ids), bitorder='little').astype(bool)
        scores[~eligible] = -np.inf
        selected = np.argpartition(-scores, kth=99, axis=1)[:, :100]
        for i, cols in enumerate(selected):
            ordered = cols[np.argsort(-scores[i, cols])]
            uid = int(eligibility.uids[start+i])
            rows.extend((uid, int(prepared.item_ids[c]), r) for r, c in enumerate(ordered, 1))
    frame = pd.DataFrame(rows, columns=['uid','item_id','rank'])
    s = score_rankings(ranked_items(validate_ranking(frame, targets)), graded)
    print(tag, s, flush=True)
    return s

results=[]
for alpha, feat, name in [(0.0, pop, 'none')] + [(a, pop, 'pop') for a in [0.05,0.1,0.2,0.5,1,2]] + [(a, organic, 'org') for a in [0.05,0.1,0.2,0.5,1,2]]:
    s = evaluate_scores(dots + alpha * feat[None,:], f'v1+{alpha}*{name}')
    results.append((s['quality_ndcg@10'], alpha, name))
print('BEST', max(results))
PY


--- Task 01a0ee32-1688-7933-a405-19a0e9aaf3df [running] ---
Command: /opt/task-venv/bin/python /task/current_model/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --epochs 1 \
  --threads 8
Duration: 493.88s
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250


Waited the requested 300s. Unless the user specified, do not kill this task just because this wait returned. It is still working. You will be notified automatically when it completes. Do other work, or wait again with a longer timeout_ms.

1/2 tasks completed (wait_all)

The file /app/solution/train_current_model.py has been updated successfully.

The file /app/solution/train_current_model.py has been updated successfully.

=== Task 01a0ee32-1688-7933-a405-19a0e9aaf3df ===
Command: /opt/task-venv/bin/python /task/current_model/train_current_model.py \
  --stage validation \
  --public-dir /task \
  --output /app/solution/production_validation.parquet \
  --prepared-cache /app/solution/validation_cache.npz \
  --epochs 1 \
  --threads 8
Status: completed
Duration: 659.97s
Exit Code: 0
Output File: /home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-421739e0-b26b-43bf-9500-4d53aa9fce85-97.log

=== Output ===
loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=534.5s
epoch=1/1 train_loss=0.53984 validation_loss=0.49664 pair_accuracy=0.755 train_targets=1,052,953 validation_targets=130,103 train_seconds=548.1 validation_seconds=17.7
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 655.401,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5398380480520908,
      "positive_logit": 0.7427799558504077,
      "negative_logit": -0.6222247064224623,
      "pair_accuracy": 0.7547733315257186,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 548.1133763040002,
      "validation_loss": 0.49663621922608403,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 17.718920270000126
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.6030168533325195,
    "query_norm_std": 0.7697497010231018,
    "item_norm_mean": 1.7570281028747559,
    "item_norm_std": 0.8279485106468201
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

exit: 0
{
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}

exit: 0

1→#!/usr/bin/env python3
"""Train the Yambda windowed two-tower baseline.

Every catalog listen with at least 50 percent completion is a sequence token.
Within each selected chronological window, every adjacent target contributes a
loss.  The fast default retains each user's most recent 128 training targets and
16 held-out validation targets; passing zero for either cap restores the
uncapped behavior for that side of the split.  A 100-step truncated-BPTT block
computes all of its timestep losses and carries the detached GRU hidden state
10→into the next block.

The model is a pure two-tower retriever: no user-ID embedding, no output
normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
selected adjacent target retains a nonzero weight. Only files under the supplied
public directory are read.
"""
from __future__ import annotations

20→import argparse
import json
import random
import sys
import time
from dataclasses import asdict, dataclass
from pathlib import Path

import numpy as np
import pandas as pd
30→import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


CACHE_VERSION = 2


40→@dataclass(frozen=True)
class Config:
    bptt_steps: int = 100
    user_batch_size: int = 64
    embedding_dim: int = 192
    hidden_dim: int = 192
    user_hidden_1: int = 512
    user_hidden_2: int = 256
    item_hidden_1: int = 512
    item_hidden_2: int = 256
50→    dropout: float = 0.05
    epochs: int = 1
    learning_rate: float = 1.0e-3
    weight_decay: float = 1.0e-5
    gradient_clip: float = 2.0
    loss_microbatch_size: int = 65_536
    uniform_negatives: int = 24
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
60→    inbatch_correction_max: float = 4.0
    negative_filter_scope: str = "full-training-history"
    target_weighting: str = "organic-strong"
    ordinary_target_weight: float = 0.25
    organic_strong_target_weight: float = 1.0
    organic_like_target_weight: float = 3.0
    discovery_horizon_days: int = 7
    validation_fraction: float = 0.10
    max_train_targets_per_user: int = 128
    max_validation_targets_per_user: int = 16
70→    early_stopping_patience: int = 1
    early_stopping_min_delta: float = 1.0e-4
    seed: int = 29


@dataclass(frozen=True)

530→        save_cache(cache, prepared, stage, config)
    return prepared, built_eligibility


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
540→    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
550→                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
560→    raw = np.power(
        (1.0 / len(counts)) / probabilities,
        config.inbatch_correction_power,
    )
    normalization = float(np.sum(counts * raw) / max(total, 1.0))
    correction = np.clip(
        raw / max(normalization, 1.0e-12),
        config.inbatch_correction_min,
        config.inbatch_correction_max,
    ).astype(np.float32)
570→    mean_on_targets = float(np.sum(counts * correction) / max(total, 1.0))
    log(
        f"inbatch correction: min={correction.min():.3f} "
        f"mean_on_targets={mean_on_targets:.3f} max={correction.max():.3f}"
    )
    return torch.from_numpy(correction)


def build_known_positive_filter(
    prepared: SequenceData, training_counts: np.ndarray

630→def draw_full_history_filtered_negatives(
    targets: torch.Tensor,
    user_rows: np.ndarray,
    liked_items: torch.Tensor,
    known_positive_packed: np.ndarray,
    n_items: int,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
640→) -> tuple[torch.Tensor, torch.Tensor]:
    """Draw mixed negatives and reject all known public-training positives."""
    targets_cpu = targets.detach().cpu().numpy().astype(np.int64, copy=False)
    liked_cpu = liked_items.detach().cpu().numpy().astype(np.int64, copy=False)
    batch_size = len(targets_cpu)
    uniform = torch.randint(
        n_items,
        (batch_size, config.uniform_negatives),
        generator=generator,
        device="cpu",
650→    )
    inbatch = torch.empty((batch_size, 0), dtype=torch.long)
    corrected_count = 0
    if config.inbatch_negatives:
        unique_targets = torch.unique(targets.detach().cpu())
        corrected_count = min(
            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
        )
        if corrected_count:
            priorities = torch.rand(
660→                (batch_size, len(unique_targets)), generator=generator
            )
            priorities.masked_fill_(
                unique_targets[None, :].eq(
                    torch.from_numpy(targets_cpu)[:, None]
                ),
                -1.0,
            )
            columns = torch.topk(
                priorities, k=corrected_count, dim=1
670→            ).indices
            inbatch = unique_targets[columns]
        if corrected_count < config.inbatch_negatives:
            filler = torch.randint(
                n_items,
                (batch_size, config.inbatch_negatives - corrected_count),
                generator=generator,
                device="cpu",
            )
            inbatch = torch.cat([inbatch, filler], dim=1)
680→
    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
    correction = np.ones_like(negatives, dtype=np.float32)
    if corrected_count:
        correction[
            :, config.uniform_negatives : config.uniform_negatives + corrected_count
        ] = item_correction[inbatch[:, :corrected_count]].numpy()
    for _ in range(32):
        invalid = invalid_full_history_negatives(
            negatives,
690→            targets_cpu,
            user_rows,
            known_positive_packed,
            liked_cpu,
        )
        if not np.any(invalid):
            return (
                torch.from_numpy(negatives).to(device),
                torch.from_numpy(correction).to(device),
            )
700→        replacements = torch.randint(
            n_items,
            (int(invalid.sum()),),
            generator=generator,
            device="cpu",
        ).numpy()
        negatives[invalid] = replacements
        # Rejected in-batch items become ordinary uniform draws.
        correction[invalid] = 1.0
    invalid = invalid_full_history_negatives(
710→        negatives,
        targets_cpu,
        user_rows,
        known_positive_packed,
        liked_cpu,
    )
    if np.any(invalid):
        raise RuntimeError("full-history negative resampling failed")
    return (
        torch.from_numpy(negatives).to(device),
720→        torch.from_numpy(correction).to(device),
    )


def draw_training_negatives(
    positive_items: torch.Tensor,
    known_history: torch.Tensor,
    liked_items: torch.Tensor,
    user_rows: np.ndarray,
    known_positive_packed: np.ndarray | None,
730→    prepared: SequenceData,
    config: Config,
    item_correction: torch.Tensor,
    generator: torch.Generator,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor]:
    if config.negative_filter_scope == "full-training-history":
        if known_positive_packed is None:
            raise RuntimeError("full-history negative filter was not built")
        return draw_full_history_filtered_negatives(
740→            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
750→    return base.draw_filtered_mixed_negatives(
        positive_items,
        known_history,
        liked_items,
        len(prepared.item_ids),
        config.uniform_negatives,
        config.inbatch_negatives,
        item_correction,
        generator,
        device,
760→    )


def event_sequence(
    model: base.SequentialTwoTower,
    items: torch.Tensor,
    features: torch.Tensor,
    hidden: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    inputs = torch.cat([model.context_embedding(items), features], dim=-1)
770→    projected = model.event_projection(inputs)
    return model.sequence_encoder(projected, hidden)


def pool_explicit_states(
    model: base.SequentialTwoTower,
    prepared: SequenceData,
    state_ids: np.ndarray,
    device: torch.device,
) -> tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
780→    unique_states, inverse = np.unique(state_ids, return_inverse=True)
    liked_items = torch.from_numpy(
        prepared.liked_states[unique_states].astype(np.int64)
    ).to(device)
    disliked_items = torch.from_numpy(
        prepared.disliked_states[unique_states].astype(np.int64)
    ).to(device)
    liked_vectors = model._mean_context_channel(liked_items)
    disliked_vectors = model._mean_context_channel(disliked_items)
    inverse_rows = torch.from_numpy(inverse.astype(np.int64)).to(device)
790→    counts = prepared.state_counts[state_ids]
    return (
        liked_vectors[inverse_rows],
        disliked_vectors[inverse_rows],
        torch.from_numpy(counts.astype(np.float32)).to(device),
    )


def user_queries(
    model: base.SequentialTwoTower,
800→    sequence_vectors: torch.Tensor,
    liked_vectors: torch.Tensor,
    disliked_vectors: torch.Tensor,
    user_features: torch.Tensor,
) -> torch.Tensor:
    return model.user_tower(
        torch.cat(
            [sequence_vectors, liked_vectors, disliked_vectors, user_features],
            dim=1,
        )
810→    )


def candidate_vectors(
    model: base.SequentialTwoTower,
    positives: torch.Tensor,
    negatives: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
    all_items = torch.cat([positives, negatives.reshape(-1)])
    unique_items, inverse = torch.unique(all_items, return_inverse=True)
820→    encoded = model.encode_items(unique_items)
    positive_vectors = encoded[inverse[: len(positives)]]
    negative_vectors = encoded[inverse[len(positives) :]].reshape(
        len(positives), negatives.shape[1], -1
    )
    return positive_vectors, negative_vectors


def history_windows(
    sequence: np.ndarray,
830→    start: int,
    count: int,
    width: int,
    padding_item: int,
) -> np.ndarray:
    base_start = max(0, start - width + 1)
    segment = sequence[base_start : start + count]
    padded = np.concatenate(
        [np.full(width - 1, padding_item, dtype=np.uint16), segment]
    )
840→    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
850→    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
860→        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,

840→    windows = np.lib.stride_tricks.sliding_window_view(padded, width)
    offset = start - base_start
    return windows[offset : offset + count].copy()


def corrected_negative_loss(
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
850→    raw = F.softplus(logits)
    total = config.uniform_negatives + config.inbatch_negatives
    result = torch.zeros(len(logits), device=logits.device)
    if config.uniform_negatives:
        result += config.uniform_negatives * raw[
            :, : config.uniform_negatives
        ].mean(dim=1)
    if config.inbatch_negatives:
        selected = slice(config.uniform_negatives, total)
        weights = correction[:, selected]
860→        result += config.inbatch_negatives * (
            (raw[:, selected] * weights).sum(dim=1)
            / weights.sum(dim=1).clamp_min(1.0e-6)
        )
    return result / total


def retained_target_window_starts(
    total_targets: np.ndarray,
    validation_counts: np.ndarray,

<task-id>01a0ee3d-e4a6-7b32-b5ec-0a3a7e51b96a</task-id>
<task-type>bash</task-type>
<output-file>/home/agent/.grok/sessions/%2Fapp/4d5581d2-2d82-47ab-9e0b-64e7f282e777/terminal/call-12273ced-7dd4-479c-b23b-59ffc1f8b2ac-109.log</output-file>
<status>running</status>
<summary>Background task 01a0ee3d-e4a6-7b32-b5ec-0a3a7e51b96a started</summary>
Use get_command_or_subagent_output with task_ids=["01a0ee3d-e4a6-7b32-b5ec-0a3a7e51b96a"] when you need the output.