Agent trace

Script completed
Wall time 0.1 seconds
Output:

/app
/task/ranking_evaluation.py
/task/requirements.txt
/task/current_model/two_tower_model.py
/task/current_model/train_current_model.py
/task/evaluate_public.py
/task/current_model/two_tower_model.py
/task/current_model/train_current_model.py
/task/data/validation_preference_state.parquet
/task/data/test_eligible_candidates.npz
/task/data/val_dislikes.parquet
/task/data/val_unlikes.parquet
/task/data/train_dislikes.parquet
/task/data/validation_eligible_candidates.npz
/task/data/train_listens.parquet
/task/data/train_likes.parquet
/task/data/train_undislikes.parquet
/task/data/val_undislikes.parquet
/task/data/val_listens.parquet
/task/data/val_likes.parquet
/task/data/train_unlikes.parquet
/task/data/candidate_catalog.parquet
/task/data/test_preference_state.parquet
/task/data/target_users.parquet
340M	/task/data
#!/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
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

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
import torch
from torch import nn
from torch.nn import functional as F

import two_tower_model as base


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
    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
    inbatch_negatives: int = 8
    inbatch_correction_power: float = 0.5
    inbatch_correction_min: float = 0.25
    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
    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

    @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)
        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:
    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
    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)

    @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."""

    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())


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)


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,
        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,
        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],
    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


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:
        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))
    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
    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:
                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] = (
        (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,
    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)
    if not len(items):
"""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
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
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
    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
    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:
    timestamps: np.ndarray
    items: np.ndarray
    organic: np.ndarray
    completion_pct: np.ndarray


@dataclass(frozen=True)
class ExplicitEvents:
    timestamps: np.ndarray
    items: np.ndarray
    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:
    """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),
        ]
    )
    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


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],
        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."""
    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))
        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")
        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(),
                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
                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]]:
    """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:
                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]:
    """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:
            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
            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:
        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),
            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(
        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
) -> 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


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))
    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),
        ("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]
            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
        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(
            input_size=config.hidden_dim,

Script completed
Wall time 0.2 seconds
Output:

{"chunk_id":"45541b","wall_time_seconds":0.039557918,"exit_code":1,"original_token_count":45,"output":"Traceback (most recent call last):\n  File \"/task/current_model/train_current_model.py\", line 28, in <module>\n    import numpy as np\nModuleNotFoundError: No module named 'numpy'\n"}

Script completed
Wall time 0.1 seconds
Output:

/usr/local/bin/python
/usr/local/bin/python3
Python 3.12.13
Python 3.12.13
/opt:
total 16
drwxr-xr-x 1 root root 4096 Aug 31 18:58 .
drwxr-xr-x 1 root root 4096 Aug 31 19:01 ..
drwxr-xr-x 4 root root 4096 Aug 31 18:58 codex-home
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 task-venv

/usr/local/bin:
total 48
drwxr-xr-x 1 root root  4096 Aug 31 18:58 .
drwxr-xr-x 1 root root  4096 Aug  3 00:00 ..
lrwxrwxrwx 1 root root     9 Aug  5 01:13 2to3 -> 2to3-3.12
-rwxr-xr-x 1 root root   102 Aug  5 01:13 2to3-3.12
lrwxrwxrwx 1 root root    53 Aug 31 18:58 codex -> /opt/codex-home/packages/standalone/current/bin/codex
lrwxrwxrwx 1 root root     5 Aug  5 01:14 idle -> idle3
lrwxrwxrwx 1 root root     8 Aug  5 01:13 idle3 -> idle3.12
-rwxr-xr-x 1 root root   100 Aug  5 01:13 idle3.12
lrwxrwxrwx 1 root root     4 Aug  5 01:14 pip -> pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3
-rwxr-xr-x 1 root root   230 Aug  5 01:13 pip3.12
lrwxrwxrwx 1 root root     6 Aug  5 01:14 pydoc -> pydoc3
lrwxrwxrwx 1 root root     9 Aug  5 01:13 pydoc3 -> pydoc3.12
-rwxr-xr-x 1 root root    85 Aug  5 01:13 pydoc3.12
lrwxrwxrwx 1 root root     7 Aug  5 01:14 python -> python3
lrwxrwxrwx 1 root root    14 Aug  5 01:14 python-config -> python3-config
lrwxrwxrwx 1 root root    10 Aug  5 01:13 python3 -> python3.12
lrwxrwxrwx 1 root root    17 Aug  5 01:13 python3-config -> python3.12-config
-rwxr-xr-x 1 root root 14480 Aug  5 01:13 python3.12
-rwxr-xr-x 1 root root  3018 Aug  5 01:13 python3.12-config
# 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
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
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
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
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
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
urllib3==2.7.0
webencodings==0.6.1

Script completed
Wall time 0.0 seconds
Output:

/opt/task-venv/pyvenv.cfg
/opt/task-venv/.lock
/opt/task-venv/bin/tqdm
/opt/task-venv/bin/f2py
/opt/task-venv/bin/torchrun
/opt/task-venv/bin/Activate.ps1
/opt/task-venv/bin/activate.fish
/opt/task-venv/bin/markdown-it
/opt/task-venv/bin/pip3
/opt/task-venv/bin/jupyter
/opt/task-venv/bin/dotenv
/opt/task-venv/bin/jupytext-config
/opt/task-venv/bin/python
/opt/task-venv/bin/activate.csh
/opt/task-venv/bin/python3
/opt/task-venv/bin/jupytext
/opt/task-venv/bin/isympy
/opt/task-venv/bin/pip3.12
/opt/task-venv/bin/jupyter-trust
/opt/task-venv/bin/kaggle
/opt/task-venv/bin/numpy-config
/opt/task-venv/bin/idna
/opt/task-venv/bin/torchfrtrace
/opt/task-venv/bin/jsonschema
/opt/task-venv/bin/normalizer
/opt/task-venv/bin/jupyter-migrate
/opt/task-venv/bin/python3.12
/opt/task-venv/bin/pip
/opt/task-venv/bin/jupyter-troubleshoot
/opt/task-venv/bin/chardetect
/opt/task-venv/bin/slugify
/opt/task-venv/bin/activate
/opt/task-venv/lib64
/opt/task-venv:
total 32
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 .
drwxr-xr-x 1 root root 4096 Aug 31 18:58 ..
-r--r--r-- 1 root root    0 Aug 31 18:58 .lock
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 bin
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 etc
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 include
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 lib
lrwxrwxrwx 1 root root    3 Aug 31 18:58 lib64 -> lib
-r--r--r-- 1 root root  171 Aug 31 18:58 pyvenv.cfg
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 share

/opt/task-venv/bin:
total 124
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 .
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 ..
-r--r--r-- 1 root root 9033 Aug 31 18:58 Activate.ps1
-r--r--r-- 1 root root 2158 Aug 31 18:58 activate
-r--r--r-- 1 root root  920 Aug 31 18:58 activate.csh
-r--r--r-- 1 root root 2195 Aug 31 18:58 activate.fish
-r-xr-xr-x 1 root root  302 Aug 31 18:58 chardetect
-r-xr-xr-x 1 root root  304 Aug 31 18:58 dotenv
-r-xr-xr-x 1 root root  308 Aug 31 18:58 f2py
-r-xr-xr-x 1 root root  299 Aug 31 18:58 idna
-r-xr-xr-x 1 root root  297 Aug 31 18:58 isympy
-r-xr-xr-x 1 root root  305 Aug 31 18:58 jsonschema
-r-xr-xr-x 1 root root  311 Aug 31 18:58 jupyter
-r-xr-xr-x 1 root root  311 Aug 31 18:58 jupyter-migrate
-r-xr-xr-x 1 root root  316 Aug 31 18:58 jupyter-troubleshoot
-r-xr-xr-x 1 root root  344 Aug 31 18:58 jupyter-trust
-r-xr-xr-x 1 root root  311 Aug 31 18:58 jupytext
-r-xr-xr-x 1 root root  315 Aug 31 18:58 jupytext-config
-r-xr-xr-x 1 root root  301 Aug 31 18:58 kaggle
-r-xr-xr-x 1 root root  312 Aug 31 18:58 markdown-it
-r-xr-xr-x 1 root root  325 Aug 31 18:58 normalizer
-r-xr-xr-x 1 root root  308 Aug 31 18:58 numpy-config
-r-xr-xr-x 1 root root  313 Aug 31 18:58 pip
-r-xr-xr-x 1 root root  313 Aug 31 18:58 pip3
-r-xr-xr-x 1 root root  313 Aug 31 18:58 pip3.12
lrwxrwxrwx 1 root root   21 Aug 31 18:58 python -> /usr/local/bin/python
lrwxrwxrwx 1 root root    6 Aug 31 18:58 python3 -> python
lrwxrwxrwx 1 root root    6 Aug 31 18:58 python3.12 -> python
-r-xr-xr-x 1 root root  307 Aug 31 18:58 slugify
-r-xr-xr-x 1 root root  321 Aug 31 18:58 torchfrtrace
-r-xr-xr-x 1 root root  312 Aug 31 18:58 torchrun
-r-xr-xr-x 1 root root  299 Aug 31 18:58 tqdm

/opt/task-venv/lib:
total 12
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 .
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 ..
dr-xr-xr-x 1 root root 4096 Aug 31 18:58 python3.12

Script running with cell ID 5
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"7a235a","wall_time_seconds":30.001703563,"session_id":34385,"original_token_count":71,"output":"/opt/task-venv 2.2.6 2.8.0+cpu 2.3.3\nloaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=14.9s\n"}

Script running with cell ID 6
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.1 seconds
Output:

{"chunk_id":"5cf952","wall_time_seconds":30.001188643,"session_id":34385,"original_token_count":229,"output":"prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.3s\nprepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.1s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=29.8s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.2s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.5s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.2s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Script running with cell ID 7
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.2 seconds
Output:

{"chunk_id":"f14292","wall_time_seconds":30.001669427,"session_id":34385,"original_token_count":0,"output":""}

Script running with cell ID 8
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"e52be2","wall_time_seconds":30.001226317,"session_id":34385,"original_token_count":0,"output":""}

Script running with cell ID 9
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"50d510","wall_time_seconds":30.00095638,"session_id":34385,"original_token_count":0,"output":""}

Script running with cell ID 10
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"8e6e46","wall_time_seconds":30.00190771,"session_id":34385,"original_token_count":0,"output":""}

Script running with cell ID 11
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"b4842c","wall_time_seconds":30.002003233,"session_id":34385,"original_token_count":0,"output":""}

Script completed
Wall time 0.0 seconds
Output:

6:loss.  The fast default retains each user's most recent 128 training targets and
9:computes all of its timestep losses and carries the detached GRU hidden state
13:normalization, raw dot-product retrieval, sample-weighted binary cross entropy,
14:and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
55:    loss_microbatch_size: int = 65_536
56:    uniform_negatives: int = 24
57:    inbatch_negatives: int = 8
61:    negative_filter_scope: str = "full-training-history"
165:        uniform_negatives=config.uniform_negatives,
166:        inbatch_negatives=config.inbatch_negatives,
342:    public: Path, stage: str, config: Config
346:        "popularity_rank"
354:    if stage == "validation":
463:        f"prepared stage={stage} sequence_users={len(prepared.user_ids):,} "
471:def cache_metadata(stage: str, config: Config) -> dict[str, object]:
474:        "stage": stage,
477:        "event_time_feature": "exp-negative-gap-over-60-days",
482:    path: Path, prepared: SequenceData, stage: str, config: Config
487:        metadata=np.asarray(json.dumps(cache_metadata(stage, config), sort_keys=True)),
493:def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
496:        expected = cache_metadata(stage, config)
513:    stage: str,
519:        if stage == "validation"
524:        prepared = load_cache(cache, stage, config)
528:    prepared, built_eligibility = prepare_data(public, stage, config)
530:        save_cache(cache, prepared, stage, config)
603:        f"full-history false-negative filter: users={len(prepared.user_ids):,} "
609:def invalid_full_history_negatives(
610:    negatives: np.ndarray,
616:    """Return row-level invalid negatives using the complete training prefix."""
617:    byte_columns = negatives >> 3
618:    bit_columns = negatives & 7
621:    invalid |= negatives == targets[:, None]
622:    invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
623:    for column in range(1, negatives.shape[1]):
625:            negatives[:, column, None] == negatives[:, :column]
630:def draw_full_history_filtered_negatives(
641:    """Draw mixed negatives and reject all known public-training positives."""
647:        (batch_size, config.uniform_negatives),
653:    if config.inbatch_negatives:
656:            config.inbatch_negatives, max(len(unique_targets) - 1, 0)
672:        if corrected_count < config.inbatch_negatives:
675:                (batch_size, config.inbatch_negatives - corrected_count),
681:    negatives = torch.cat([uniform, inbatch], dim=1).numpy()
682:    correction = np.ones_like(negatives, dtype=np.float32)
685:            :, config.uniform_negatives : config.uniform_negatives + corrected_count
688:        invalid = invalid_full_history_negatives(
689:            negatives,
697:                torch.from_numpy(negatives).to(device),
706:        negatives[invalid] = replacements
709:    invalid = invalid_full_history_negatives(
710:        negatives,
717:        raise RuntimeError("full-history negative resampling failed")
719:        torch.from_numpy(negatives).to(device),
724:def draw_training_negatives(
736:    if config.negative_filter_scope == "full-training-history":
738:            raise RuntimeError("full-history negative filter was not built")
739:        return draw_full_history_filtered_negatives(
750:    return base.draw_filtered_mixed_negatives(
755:        config.uniform_negatives,
756:        config.inbatch_negatives,
816:    negatives: torch.Tensor,
818:    all_items = torch.cat([positives, negatives.reshape(-1)])
822:    negative_vectors = encoded[inverse[len(positives) :]].reshape(
823:        len(positives), negatives.shape[1], -1
825:    return positive_vectors, negative_vectors
845:def corrected_negative_loss(
851:    total = config.uniform_negatives + config.inbatch_negatives
853:    if config.uniform_negatives:
854:        result += config.uniform_negatives * raw[
855:            :, : config.uniform_negatives
857:    if config.inbatch_negatives:
858:        selected = slice(config.uniform_negatives, total)
860:        result += config.inbatch_negatives * (
880:        raise RuntimeError("chronological target partition produced negative counts")
915:    public: Path, stage: str, item_ids: np.ndarray
918:    prefixes = ("train",) if stage == "validation" else ("train", "val")
957:    stage: str,
978:                public, stage, prepared.item_ids
981:                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
1119:    negatives: torch.Tensor,
1120:    negative_weights: torch.Tensor,
1121:    sample_weights: np.ndarray,
1127:    total_weight = float(sample_weights.sum())
1129:        raise RuntimeError("target block has no positive sample weight")
1132:        "loss_sum": 0.0,
1134:        "negative_sum": 0.0,
1138:    for begin in range(0, target_count, config.loss_microbatch_size):
1139:        end = min(begin + config.loss_microbatch_size, target_count)
1162:        positive_vectors, negative_vectors = candidate_vectors(
1165:            negatives[begin:end],
1168:        negative_logits = torch.einsum(
1169:            "bd,bkd->bk", query_vectors, negative_vectors
1171:        positive_loss = F.softplus(-positive_logits)
1172:        negative_loss = corrected_negative_loss(
1173:            negative_logits, negative_weights[begin:end], config
1176:            sample_weights[begin:end].astype(np.float32, copy=False)
1178:        loss_sum = (
1179:            selected_weights * (positive_loss + negative_loss) / 2.0
1181:        if not bool(torch.isfinite(loss_sum)):
1182:            raise RuntimeError("dense next-item training produced non-finite loss")
1184:            (loss_sum / total_weight).backward()
1188:        statistics["loss_sum"] += float(loss_sum.detach().cpu())
1190:        statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
1192:            (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
1227:    negative_count = config.uniform_negatives + config.inbatch_negatives
1228:    total_loss = 0.0
1232:    total_negative = 0.0
1293:            negatives, negative_weights = draw_training_negatives(
1314:                negatives,
1315:                negative_weights,
1327:            total_loss += statistics["loss_sum"]
1331:            total_negative += statistics["negative_sum"]
1337:                batch_bce = statistics["loss_sum"] / max(
1340:                running_bce = total_loss / max(total_weight, 1.0e-12)
1351:        "loss": total_loss / max(total_weight, 1.0e-12),
1353:        "negative_logit": total_negative / max(total_targets * negative_count, 1),
1355:        / max(total_targets * negative_count, 1),
1388:    total_loss = 0.0
1449:            negatives, negative_weights = draw_training_negatives(
1469:                negatives,
1470:                negative_weights,
1475:            total_loss += statistics["loss_sum"]
1481:        "validation_loss": total_loss / max(total_weight, 1.0e-12),
1523:    stage: str,
1560:        stage,
1567:        if config.negative_filter_scope == "full-training-history"
1571:    best_loss = float("inf")
1601:            "negative_filter_scope", "last-100"
1629:            stored_filter_scope != config.negative_filter_scope
1657:            prior.pop("validation_loss", None)
1660:                f"{stored_filter_scope}->{config.negative_filter_scope}, "
1666:        if "validation_loss" not in prior:
1682:        best_loss = float(prior["validation_loss"])
1687:            f"loss={best_loss:.6f}"
1731:            diagnostics["validation_loss"]
1732:            < best_loss - config.early_stopping_min_delta
1735:            best_loss = diagnostics["validation_loss"]
1754:            f"epoch={epoch}/{config.epochs} train_loss={diagnostics['loss']:.5f} "
1755:            f"validation_loss={diagnostics['validation_loss']:.5f} "
1766:                f"best_epoch={best_epoch} best_validation_loss={best_loss:.6f}"
1776:        f"loss={best_loss:.6f}"
1861:def rank_candidates(
1867:    ranking_batch_size: int,
1882:    for start in range(0, len(eligibility.uids), ranking_batch_size):
1883:        end = min(start + ranking_batch_size, len(eligibility.uids))
1896:                (uid, int(prepared.item_ids[column]), rank)
1897:                for rank, column in enumerate(columns, start=1)
1900:            log(f"ranked users={end:,}/{len(eligibility.uids):,}")
1909:    return pd.DataFrame(rows, columns=["uid", "item_id", "rank"]), diagnostics
1912:def write_ranking(frame: pd.DataFrame, output: Path) -> None:
1923:    parser = argparse.ArgumentParser(description=__doc__)
1924:    parser.add_argument(
1925:        "--stage", choices=("validation", "submission"), default="validation"
1927:    parser.add_argument("--output", type=Path, required=True)
1928:    parser.add_argument("--public-dir", type=Path, required=True)
1929:    parser.add_argument("--prepared-cache", type=Path)
1930:    parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
1931:    parser.add_argument("--threads", type=int, default=8)
1932:    parser.add_argument("--bptt-steps", type=int, default=Config.bptt_steps)
1933:    parser.add_argument(
1936:    parser.add_argument("--epochs", type=int, default=Config.epochs)
1937:    parser.add_argument("--learning-rate", type=float, default=Config.learning_rate)
1938:    parser.add_argument(
1939:        "--loss-microbatch-size",
1941:        default=Config.loss_microbatch_size,
1943:    parser.add_argument(
1944:        "--negative-filter-scope",
1946:        default=Config.negative_filter_scope,
1948:    parser.add_argument(
1953:    parser.add_argument(
1958:    parser.add_argument(
1963:    parser.add_argument(
1968:    parser.add_argument(
1973:    parser.add_argument(
1978:    parser.add_argument(
1984:    parser.add_argument(
1990:    parser.add_argument(
1995:    parser.add_argument(
2000:    parser.add_argument("--checkpoint-dir", type=Path)
2001:    parser.add_argument("--resume-checkpoint", type=Path)
2002:    parser.add_argument("--seed", type=int, default=Config.seed)
2003:    parser.add_argument("--ranking-batch-size", type=int, default=64)
2004:    parser.add_argument("--prepare-only", action="store_true")
2012:        or args.loss_microbatch_size < 1
2033:        loss_microbatch_size=args.loss_microbatch_size,
2034:        negative_filter_scope=args.negative_filter_scope,
2057:        args.stage,
2071:                    "stage": args.stage,
2095:        args.stage,
2102:    ranking, embedding_diagnostics = rank_candidates(
2108:        args.ranking_batch_size,
2110:    write_ranking(ranking, args.output.resolve())
2115:                "stage": args.stage,
2117:                "rows": len(ranking),
2118:                "users": int(ranking.uid.nunique()),
2126:                    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
2129:                    "loss_execution": "gradient-accumulated-microbatches",
2135:                    "objective": "sample-weighted-BCE-at-every-timestep",
2136:                    "negative_sampling": (
2139:                    "false_negative_filter": config.negative_filter_scope,
    )
    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(
                (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
            ).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)

    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,
            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),
            )
        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(
        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),
        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,
    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(
            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
    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,
    )


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)
    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]:
    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)
    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,
    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,
        )
    )


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)
    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,
    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]
    )
    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:
    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]
        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,
    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)
    ):
        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,
    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)

    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,
        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 = [
        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)
    )
    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)
        )
    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):,} "
        f"users={len(result):,}"
    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
    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,
        )
        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
            )
            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
                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):
                    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)
                    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"))
                    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} "
        f"max={weights.max():.3f}"
    )
    return weights


def build_dense_block(
    prepared: SequenceData,
    target_weights: np.ndarray,
    users: np.ndarray,
    window_starts: np.ndarray,
    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)
    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,
    )
    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])
        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]
        event_features[row, :count] = prepared.event_features[positions]
            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),
            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),
            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():
            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:
        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,
    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)
    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,
    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,
        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
            )
            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,
                (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[
            :, 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
        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
    )
    if bool(invalid.any()):
        raise RuntimeError("mixed negative resampling failed false-negative filter")
    return negatives, correction

Script running with cell ID 13
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"40d2e6","wall_time_seconds":30.001070634,"session_id":34385,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=199.2s\n"}

Script running with cell ID 14
Wall time 11.0 seconds
Output:

Script completed
Wall time 13.9 seconds
Output:

{"chunk_id":"ddecf6","wall_time_seconds":30.0017543,"session_id":34385,"original_token_count":58,"output":"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=212.3 validation_seconds=15.5\nrestored best validation-BCE checkpoint: epoch=1 loss=0.496636\n"}

Script running with cell ID 15
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"af05c0","wall_time_seconds":30.001933297,"session_id":34385,"original_token_count":0,"output":""}

Script running with cell ID 16
Wall time 11.0 seconds
Output:

Script completed
Wall time 5.6 seconds
Output:

{"chunk_id":"a6e217","wall_time_seconds":19.13857311,"exit_code":0,"original_token_count":802,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/production_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 361.405,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5398380488240146,\n      \"positive_logit\": 0.7427799616596575,\n      \"negative_logit\": -0.6222247061344452,\n      \"pair_accuracy\": 0.754773301847281,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 212.262626608,\n      \"validation_loss\": 0.4966362179911506,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 15.493451819000029\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.6030172109603882,\n    \"query_norm_std\": 0.7697497606277466,\n    \"item_norm_mean\": 1.7570278644561768,\n    \"item_norm_std\": 0.8279486298561096\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script completed
Wall time 3.4 seconds
Output:

{"chunk_id":"0ced7c","wall_time_seconds":3.184462268,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/production_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.02128,\n    \"quality_users\": 4263\n  }\n}\n"}

Script completed
Wall time 0.1 seconds
Output:

Warning: truncated output (original token count: 11193)
Total output lines: 1199

    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)
    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
) -> 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
        # 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)
        )
        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(
    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]
    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


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,
) -> 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",
    )
    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(
                (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
            ).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)

    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,
            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),
            )
        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(
        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),
        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,
    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(
            positive_items,
            user_rows,
            liked_items,
            known_positive_packed,
            len(prepared.item_ids),
            config,
            item_correction,
            generator,
            device,
        )
    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,
    )


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)
    projected = model.event_projection(inputs)
        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])
        ]
        histories[row, :count] = history_windows(
            user_sequence,
            absolute_start,
            count,
            config.bptt_steps,
            padding_item,
        )
    return (
        input_items,
        event_features,
        activity_features,
        state_ids,
        targets,
        scored,
        histories,
        processed,
        weights,
    )


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,
    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")
    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)
        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
        )
        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,
            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
        )
        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
        ).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())
        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":
        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,
    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[
        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
    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]
        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,
                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),
                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])
                ]
            ).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(
                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)
            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,
                config,
                item_correction,
                generator,
                device,
            )
            optimizer.zero_grad(set_to_none=True)
            statistics, sequence_gradient = score_target_microbatches(
                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],
                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)
            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:
                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"
                )

    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),
        "target_weight_sum": total_weight,
        "batches": float(batches),
        "seconds": time.monotonic() - started,
    }


@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,
    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")
    ]
    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()

    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)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).t…1193 tokens truncated…ed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        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
    )
    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
        )
        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",
        )
        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(
            "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),
        )
        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),
            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
            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}")
        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}"
            )
        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}, "
                "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,
                    config,
                    target_weights,
                    partition.window_starts,
                    training_counts,
                    validation_counts,
                    item_correction,
                    known_positive_packed,
                    device,
                )
            )
        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):
        diagnostics = run_training_epoch(
            model,
            optimizer,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            training_counts,
            item_correction,
            known_positive_packed,
            generator,
            device,
            epoch,
        )
        validation = evaluate_validation_bce(
            model,
            prepared,
            config,
            target_weights,
            partition.window_starts,
            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:
            save_epoch_checkpoint(
                checkpoint_dir,
                epoch,
                current_state,
                config,
                diagnostics,
                optimizer,
                generator,
            )
        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:
                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",
                )
        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} "
            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

    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


@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(
        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]
        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),
                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:
                    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


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)
    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",
        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",
        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",
        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",
        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",
        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",
    )
    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,
    )
    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 (
        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
        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,
        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,
        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)
    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,
    )
    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(
                {
                    "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()
                    ),
                    "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,
            )
        )
        return 0

    model, training_history = train_model(
        public,
        args.stage,
        prepared,
        config,
        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,
        args.ranking_batch_size,
    )
    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),
                "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": config.negative_filter_scope,
                    "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()
                ),
                "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,
        )
    )
    return 0

Script failed
Wall time 0.0 seconds
Output:

Script error:
Exit code: 1
Wall time: 0 seconds
Output:
Failed to write file /app/solution/train_current_model.py

Script completed
Wall time 0.0 seconds
Output:

total 1312
-rw-r--r-- 1 agent agent 1244231 Aug 31 19:08 production_validation.parquet
-r--r--r-- 1 agent agent   80058 Aug 31 19:02 train_current_model.py
-r--r--r-- 1 agent agent   15985 Aug 31 19:02 two_tower_model.py

Script completed
Wall time 0.0 seconds
Output:

{}

Script completed
Wall time 0.0 seconds
Output:

{}

Script running with cell ID 23
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.9 seconds
Output:

{"chunk_id":"0d035a","wall_time_seconds":30.00175434,"session_id":32809,"original_token_count":79,"output":"loaded explicit events: rows=720,270 users=8,220\nprepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s\nprepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s\nprepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s\nprepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s\n"}

Script running with cell ID 24
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"8fb073","wall_time_seconds":30.001364928,"session_id":32809,"original_token_count":232,"output":"prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s\nprepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s\nprepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s\nprepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s\nprepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s\ntarget users without qualifying catalog sequence=7\nprepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133\nsaved public-derived full-sequence cache: /app/solution/validation_prepared.npz\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Script running with cell ID 25
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"e7bdc1","wall_time_seconds":30.001154015,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 26
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"00aad3","wall_time_seconds":30.00101027,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 27
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.7 seconds
Output:

{"chunk_id":"c2ef1f","wall_time_seconds":30.000477147,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 28
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"7f2749","wall_time_seconds":30.001228637,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 29
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"7dc8d4","wall_time_seconds":30.00195128,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 30
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.5 seconds
Output:

{"chunk_id":"086ae1","wall_time_seconds":30.000921178,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 31
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"179d03","wall_time_seconds":30.000548437,"session_id":32809,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.649828 running_bce=0.632990 elapsed=230.3s\n"}

Script running with cell ID 32
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"7edf3a","wall_time_seconds":30.001458772,"session_id":32809,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/hard48_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.63205 validation_loss=0.60409 pair_accuracy=0.609 train_targets=1,052,953 validation_targets=130,103 train_seconds=245.6 validation_seconds=20.6\nrestored best validation-BCE checkpoint: epoch=1 loss=0.604093\n"}

Script running with cell ID 33
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.0 seconds
Output:

{"chunk_id":"7711bc","wall_time_seconds":30.001882205,"session_id":32809,"original_token_count":0,"output":""}

Script running with cell ID 34
Wall time 11.0 seconds
Output:

Script completed
Wall time 14.1 seconds
Output:

{"chunk_id":"7126ec","wall_time_seconds":27.649319675,"exit_code":0,"original_token_count":809,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/hard48_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 401.579,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"sampled-hard-pool-with-full-history-filter\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negative_pool_size\": 48,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.6320480839785293,\n      \"positive_logit\": 0.2749283446064721,\n      \"negative_logit\": -0.21619851451581898,\n      \"pair_accuracy\": 0.6088086612127986,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 245.624033005,\n      \"validation_loss\": 0.6040925726377234,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 20.57991937099996\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5106288194656372,\n    \"query_norm_std\": 1.0394723415374756,\n    \"item_norm_mean\": 1.2617090940475464,\n    \"item_norm_std\": 0.25714924931526184\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script completed
Wall time 3.4 seconds
Output:

{"chunk_id":"e3d8b5","wall_time_seconds":3.203490952,"exit_code":0,"original_token_count":34,"output":"{\n  \"model\": \"/app/solution/hard48_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.019374,\n    \"quality_users\": 4263\n  }\n}\n"}

Script completed
Wall time 0.0 seconds
Output:

{}

Script completed
Wall time 0.0 seconds
Output:

{}

Script running with cell ID 38
Wall time 11.0 seconds
Output:

Script completed
Wall time 14.7 seconds
Output:

{"chunk_id":"3571ad","wall_time_seconds":30.001729939,"session_id":27972,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Script completed
Wall time 8.2 seconds
Output:

{"chunk_id":"f4bfd3","wall_time_seconds":8.003511864,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/popularity_diagnostic.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01071,\n    \"quality_users\": 4263\n  }\n}\n"}

Script running with cell ID 40
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"f62855","wall_time_seconds":30.001052335,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 41
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"8773b6","wall_time_seconds":30.00121818,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 42
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"b29a35","wall_time_seconds":30.001268725,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 43
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"e42b0f","wall_time_seconds":30.001806786,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 44
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"21aa29","wall_time_seconds":30.001979474,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 45
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"c34f7d","wall_time_seconds":30.001910921,"session_id":27972,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.623926 running_bce=0.620101 elapsed=234.2s\n"}

Script running with cell ID 46
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"ffa0bd","wall_time_seconds":30.001943572,"session_id":27972,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/blended_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.61885 validation_loss=0.58598 pair_accuracy=0.754 train_targets=1,052,953 validation_targets=130,103 train_seconds=249.5 validation_seconds=20.2\nrestored best validation-BCE checkpoint: epoch=1 loss=0.585976\n"}

Script running with cell ID 47
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"26f36d","wall_time_seconds":30.001921575,"session_id":27972,"original_token_count":0,"output":""}

Script running with cell ID 48
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"369477","wall_time_seconds":30.000480142,"session_id":27972,"original_token_count":57,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n"}

Script completed
Wall time 0.0 seconds
Output:

{"chunk_id":"9950cd","wall_time_seconds":0.00000232,"exit_code":0,"original_token_count":770,"output":"{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/blended_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 354.035,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"sampled-hard-pool-with-full-history-filter\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negative_pool_size\": 48,\n    \"hard_negative_count\": 8,\n    \"hard_negative_weight\": 0.25,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.6188478072126893,\n      \"positive_logit\": 0.29570962463799333,\n      \"negative_logit\": -0.5569627711488554,\n      \"pair_accuracy\": 0.7536614288576983,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 249.50920362500005,\n      \"validation_loss\": 0.5859764186777909,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 20.203965118000042\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.195103406906128,\n    \"query_norm_std\": 0.6614366769790649,\n    \"item_norm_mean\": 1.912940263748169,\n    \"item_norm_std\": 1.0607719421386719\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script completed
Wall time 3.4 seconds
Output:

{"chunk_id":"d4f857","wall_time_seconds":3.221938694,"exit_code":0,"original_token_count":34,"output":"{\n  \"model\": \"/app/solution/blended_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.01577,\n    \"quality_users\": 4263\n  }\n}\n"}

Script completed
Wall time 0.0 seconds
Output:

{}

Script running with cell ID 52
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.6 seconds
Output:

{"chunk_id":"262672","wall_time_seconds":30.000404581,"session_id":7731,"original_token_count":103,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=1.000 mean_on_targets=1.000 max=1.000\ntarget weighting=organic-strong min=0.250 mean=0.575 max=1.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Script running with cell ID 53
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.0 seconds
Output:

{"chunk_id":"94c3f6","wall_time_seconds":30.001549668,"session_id":7731,"original_token_count":0,"output":""}

Script running with cell ID 54
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"7ccd75","wall_time_seconds":30.001279986,"session_id":7731,"original_token_count":0,"output":""}

Script running with cell ID 55
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"b382c5","wall_time_seconds":30.001452541,"session_id":7731,"original_token_count":0,"output":""}

Script running with cell ID 56
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"860edd","wall_time_seconds":30.000538459,"session_id":7731,"original_token_count":0,"output":""}

Script running with cell ID 57
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.3 seconds
Output:

{"chunk_id":"14f562","wall_time_seconds":30.001055745,"session_id":7731,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.564065 running_bce=0.586965 elapsed=190.3s\n"}

Script running with cell ID 58
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"351bbd","wall_time_seconds":30.001543581,"session_id":7731,"original_token_count":76,"output":"saved epoch checkpoint: /app/solution/inbatch16_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.58454 validation_loss=0.54140 pair_accuracy=0.728 train_targets=1,052,953 validation_targets=130,103 train_seconds=203.0 validation_seconds=14.1\nrestored best validation-BCE checkpoint: epoch=1 loss=0.541397\n"}

Script running with cell ID 59
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.0 seconds
Output:

{"chunk_id":"c8d368","wall_time_seconds":30.001639457,"session_id":7731,"original_token_count":0,"output":""}

Script running with cell ID 60
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.6 seconds
Output:

{"chunk_id":"9f96a0","wall_time_seconds":29.342123552,"exit_code":0,"original_token_count":824,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/inbatch16_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 301.348,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"16-uniform-plus-16-uncorrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 16,\n    \"inbatch_negatives\": 16,\n    \"hard_negative_pool_size\": 32,\n    \"hard_negative_count\": 8,\n    \"hard_negative_weight\": 0.25,\n    \"inbatch_correction_power\": 0.0,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"organic-strong\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5845398030558726,\n      \"positive_logit\": 0.4497601119500438,\n      \"negative_logit\": -0.514334340422768,\n      \"pair_accuracy\": 0.7281032783514554,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 632551.0,\n      \"batches\": 266.0,\n      \"seconds\": 202.991531957,\n      \"validation_loss\": 0.54139749728081,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 78769.25,\n      \"validation_seconds\": 14.09756859700019\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.5723634958267212,\n    \"query_norm_std\": 0.8727400898933411,\n    \"item_norm_mean\": 1.7504078149795532,\n    \"item_norm_std\": 0.7237846851348877\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script completed
Wall time 3.3 seconds
Output:

{"chunk_id":"9cc654","wall_time_seconds":3.172107859,"exit_code":0,"original_token_count":35,"output":"{\n  \"model\": \"/app/solution/inbatch16_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.014744,\n    \"quality_users\": 4263\n  }\n}\n"}

Script completed
Wall time 0.0 seconds
Output:

{}

Script running with cell ID 63
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"de5190","wall_time_seconds":30.001676182,"session_id":35704,"original_token_count":131,"output":"loaded full-sequence cache: tokens=20,056,329 targets=20,047,194\nchronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=301,527 users=7,526\ngrade-3 first-encounter targets=46,417 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250\n"}

Script running with cell ID 64
Wall time 11.0 seconds
Output:

Script completed
Wall time 14.7 seconds
Output:

{"chunk_id":"6d987d","wall_time_seconds":30.001470629,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 65
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.1 seconds
Output:

{"chunk_id":"f8b529","wall_time_seconds":30.003785754,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 66
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.3 seconds
Output:

{"chunk_id":"7b78f5","wall_time_seconds":30.001353778,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 67
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.8 seconds
Output:

{"chunk_id":"ed9f4e","wall_time_seconds":30.001909899,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 68
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.7 seconds
Output:

{"chunk_id":"c89898","wall_time_seconds":30.001693789,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 69
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.8 seconds
Output:

{"chunk_id":"424955","wall_time_seconds":30.000682628,"session_id":35704,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=197.1s\n"}

Script running with cell ID 70
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.1 seconds
Output:

{"chunk_id":"937c7e","wall_time_seconds":30.001842521,"session_id":35704,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/graded_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.54212 validation_loss=0.50483 pair_accuracy=0.760 train_targets=1,052,953 validation_targets=130,103 train_seconds=210.2 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.504831\n"}

Script running with cell ID 71
Wall time 11.0 seconds
Output:

Script completed
Wall time 14.4 seconds
Output:

{"chunk_id":"3ac12e","wall_time_seconds":30.001956858,"session_id":35704,"original_token_count":0,"output":""}

Script running with cell ID 72
Wall time 11.0 seconds
Output:

Script completed
Wall time 4.3 seconds
Output:

{"chunk_id":"ba7e7f","wall_time_seconds":19.606326709,"exit_code":0,"original_token_count":828,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"validation\",\n  \"output\": \"/app/solution/graded_validation.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 318.957,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"discovery-graded\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negative_pool_size\": 32,\n    \"hard_negative_count\": 8,\n    \"hard_negative_weight\": 0.25,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"discovery-graded\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20056329,\n  \"all_next_item_targets\": 20047194,\n  \"selected_training_targets\": 1052953,\n  \"selected_validation_targets\": 130103,\n  \"discarded_old_targets\": 18864138,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5421154203829986,\n      \"positive_logit\": 0.7774311310299945,\n      \"negative_logit\": -0.6407562001908027,\n      \"pair_accuracy\": 0.7602795423917307,\n      \"targets\": 1052953.0,\n      \"target_weight_sum\": 348780.0,\n      \"batches\": 266.0,\n      \"seconds\": 210.21664959500004,\n      \"validation_loss\": 0.5048305744945647,\n      \"validation_targets\": 130103.0,\n      \"validation_target_weight_sum\": 42129.5,\n      \"validation_seconds\": 15.439250831999743\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.7521849870681763,\n    \"query_norm_std\": 0.8638168573379517,\n    \"item_norm_mean\": 1.8543912172317505,\n    \"item_norm_std\": 0.8459439873695374\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script completed
Wall time 3.4 seconds
Output:

{"chunk_id":"ec933d","wall_time_seconds":3.181827419,"exit_code":0,"original_token_count":34,"output":"{\n  \"model\": \"/app/solution/graded_validation.parquet\",\n  \"scores\": {\n    \"quality_ndcg@10\": 0.023522,\n    \"quality_users\": 4263\n  }\n}\n"}

Script running with cell ID 74
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.8 seconds
Output:

{"chunk_id":"14a93d","wall_time_seconds":30.001286372,"session_id":64619,"original_token_count":79,"output":"loaded explicit events: rows=746,504 users=8,307\nprepared users=1,000 tokens=2,167,338 states=37,073 elapsed=5.1s\nprepared users=2,000 tokens=4,450,246 states=73,784 elapsed=10.2s\nprepared users=3,000 tokens=6,703,970 states=113,190 elapsed=15.5s\nprepared users=4,000 tokens=9,087,187 states=153,283 elapsed=21.1s\n"}

Script running with cell ID 75
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"5df01d","wall_time_seconds":30.001970817,"session_id":64619,"original_token_count":207,"output":"prepared users=5,000 tokens=11,354,844 states=188,776 elapsed=26.1s\nprepared users=6,000 tokens=13,485,786 states=224,384 elapsed=31.0s\nprepared users=7,000 tokens=15,826,114 states=265,318 elapsed=36.7s\nprepared users=8,000 tokens=18,028,622 states=306,474 elapsed=42.1s\nprepared users=9,000 tokens=20,195,749 states=342,272 elapsed=47.0s\ntarget users without qualifying catalog sequence=6\nprepared stage=submission sequence_users=9,153 tokens=20,712,971 next_item_targets=20,703,818 states=350,322\nsaved public-derived full-sequence cache: /app/solution/submission_prepared.npz\nchronological BCE window: train=1,067,606 validation=131,997 selected=1,199,603 discarded_old_targets=19,504,215 all=20,703,818\ninbatch correction: min=0.250 mean_on_targets=1.001 max=4.000\nloaded organic-like outcomes: rows=314,586 users=7,613\n"}

Script running with cell ID 76
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"803b26","wall_time_seconds":30.001933442,"session_id":64619,"original_token_count":53,"output":"grade-3 first-encounter targets=47,773 horizon_days=7\ntarget weighting=discovery-graded min=0.250 mean=0.310 max=3.000\nfull-history false-negative filter: users=9,153 unique_user_items=5,682,910 bytes=34,323,750\n"}

Script running with cell ID 77
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.0 seconds
Output:

{"chunk_id":"cd246f","wall_time_seconds":30.001944957,"session_id":64619,"original_token_count":0,"output":""}

Script running with cell ID 78
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.5 seconds
Output:

{"chunk_id":"196273","wall_time_seconds":30.00195649,"session_id":64619,"original_token_count":0,"output":""}

Script running with cell ID 79
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.2 seconds
Output:

{"chunk_id":"de8f3d","wall_time_seconds":30.001216189,"session_id":64619,"original_token_count":0,"output":""}

Script running with cell ID 80
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.5 seconds
Output:

{"chunk_id":"1e3443","wall_time_seconds":30.001030494,"session_id":64619,"original_token_count":0,"output":""}

Script running with cell ID 81
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.2 seconds
Output:

{"chunk_id":"dd044d","wall_time_seconds":30.001481266,"session_id":64619,"original_token_count":24,"output":"epoch=1/1 batches=250 targets=994,228 batch_bce=0.455749 running_bce=0.544431 elapsed=200.0s\n"}

Script running with cell ID 82
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.2 seconds
Output:

{"chunk_id":"38fce5","wall_time_seconds":30.001274486,"session_id":64619,"original_token_count":75,"output":"saved epoch checkpoint: /app/solution/final_checkpoints/epoch-01.pt\nepoch=1/1 train_loss=0.54085 validation_loss=0.51262 pair_accuracy=0.762 train_targets=1,067,606 validation_targets=131,997 train_seconds=214.4 validation_seconds=15.4\nrestored best validation-BCE checkpoint: epoch=1 loss=0.512617\n"}

Script running with cell ID 83
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.0 seconds
Output:

{"chunk_id":"7e258d","wall_time_seconds":30.001469987,"session_id":64619,"original_token_count":0,"output":""}

Script running with cell ID 84
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.1 seconds
Output:

{"chunk_id":"24fd14","wall_time_seconds":30.001992019,"session_id":64619,"original_token_count":0,"output":""}

Script completed
Wall time 7.9 seconds
Output:

{"chunk_id":"2664cf","wall_time_seconds":7.890011691,"exit_code":0,"original_token_count":826,"output":"ranked users=1,024/8,456\nranked users=2,048/8,456\nranked users=3,072/8,456\nranked users=4,096/8,456\nranked users=5,120/8,456\nranked users=6,144/8,456\nranked users=7,168/8,456\nranked users=8,192/8,456\nranked users=8,456/8,456\n{\n  \"baseline\": \"yambda-windowed-sequence-two-tower-base\",\n  \"stage\": \"submission\",\n  \"output\": \"/app/solution/submission.parquet\",\n  \"rows\": 845600,\n  \"users\": 8456,\n  \"runtime_seconds\": 375.873,\n  \"public_inputs_only\": true,\n  \"architecture\": {\n    \"family\": \"pure-windowed-sequence-two-tower\",\n    \"sequence_training\": \"every-adjacent-target-within-recent-chronological-window\",\n    \"sequence_encoder\": \"one-layer-GRU-dense-timestep-loss\",\n    \"bptt_hidden_state\": \"carried-and-detached\",\n    \"model_selection\": \"chronological-validation-BCE-early-stopping\",\n    \"loss_execution\": \"gradient-accumulated-microbatches\",\n    \"user_id_embedding\": false,\n    \"user_mlp\": \"582-512-256-192\",\n    \"item_mlp\": \"196-512-256-192\",\n    \"normalization\": \"none\",\n    \"score\": \"raw-dot-product\",\n    \"objective\": \"sample-weighted-BCE-at-every-timestep\",\n    \"negative_sampling\": \"24-uniform-plus-8-propensity-corrected-inbatch\",\n    \"false_negative_filter\": \"full-training-history\",\n    \"target_weighting\": \"discovery-graded\"\n  },\n  \"config\": {\n    \"bptt_steps\": 100,\n    \"user_batch_size\": 64,\n    \"embedding_dim\": 192,\n    \"hidden_dim\": 192,\n    \"user_hidden_1\": 512,\n    \"user_hidden_2\": 256,\n    \"item_hidden_1\": 512,\n    \"item_hidden_2\": 256,\n    \"dropout\": 0.05,\n    \"epochs\": 1,\n    \"learning_rate\": 0.001,\n    \"weight_decay\": 1e-05,\n    \"gradient_clip\": 2.0,\n    \"loss_microbatch_size\": 65536,\n    \"uniform_negatives\": 24,\n    \"inbatch_negatives\": 8,\n    \"hard_negative_pool_size\": 32,\n    \"hard_negative_count\": 8,\n    \"hard_negative_weight\": 0.25,\n    \"inbatch_correction_power\": 0.5,\n    \"inbatch_correction_min\": 0.25,\n    \"inbatch_correction_max\": 4.0,\n    \"negative_filter_scope\": \"full-training-history\",\n    \"target_weighting\": \"discovery-graded\",\n    \"ordinary_target_weight\": 0.25,\n    \"organic_strong_target_weight\": 1.0,\n    \"organic_like_target_weight\": 3.0,\n    \"discovery_horizon_days\": 7,\n    \"validation_fraction\": 0.1,\n    \"max_train_targets_per_user\": 128,\n    \"max_validation_targets_per_user\": 16,\n    \"early_stopping_patience\": 1,\n    \"early_stopping_min_delta\": 0.0001,\n    \"seed\": 29\n  },\n  \"sequence_tokens\": 20712971,\n  \"all_next_item_targets\": 20703818,\n  \"selected_training_targets\": 1067606,\n  \"selected_validation_targets\": 131997,\n  \"discarded_old_targets\": 19504215,\n  \"training_history\": [\n    {\n      \"epoch\": 1.0,\n      \"loss\": 0.5408499574223266,\n      \"positive_logit\": 0.7880318116306771,\n      \"negative_logit\": -0.656281838298863,\n      \"pair_accuracy\": 0.7620188592514467,\n      \"targets\": 1067606.0,\n      \"target_weight_sum\": 353472.75,\n      \"batches\": 270.0,\n      \"seconds\": 214.4452345550003,\n      \"validation_loss\": 0.5126167220076836,\n      \"validation_targets\": 131997.0,\n      \"validation_target_weight_sum\": 43182.0,\n      \"validation_seconds\": 15.432067502999871\n    }\n  ],\n  \"embedding_diagnostics\": {\n    \"query_norm_mean\": 1.4391510486602783,\n    \"query_norm_std\": 0.6248915791511536,\n    \"item_norm_mean\": 1.8983010053634644,\n    \"item_norm_std\": 0.8495252728462219\n  },\n  \"torch_version\": \"2.8.0+cpu\",\n  \"device\": \"cpu\"\n}\n"}

Script running with cell ID 86
Wall time 11.0 seconds
Output:

Script completed
Wall time 16.4 seconds
Output:

{"chunk_id":"154dbb","wall_time_seconds":30.00433669,"session_id":62531,"original_token_count":0,"output":""}

Script running with cell ID 87
Wall time 11.0 seconds
Output:

Script completed
Wall time 15.5 seconds
Output:

{"chunk_id":"87a354","wall_time_seconds":30.001086273,"session_id":62531,"original_token_count":0,"output":""}

Script completed
Wall time 0.1 seconds
Output:

{"chunk_id":"31229b","wall_time_seconds":0.058292173,"exit_code":130,"original_token_count":236,"output":"Traceback (most recent call last):\n  File \"<stdin>\", line 21, in <module>\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n    return format.read_array(bytes,\n           ^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n    data = _read_bytes(fp, read_size, \"array data\")\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n    r = fp.read(size - len(data))\n        ^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n    data = self._read1(n)\n           ^^^^^^^^^^^^^^\n  File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n    data = self._decompressor.decompress(data, n)\n           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n"}

Script completed
Wall time 1.8 seconds
Output:

{"chunk_id":"06b381","wall_time_seconds":1.6197288250000001,"exit_code":0,"original_token_count":119,"output":"{'file': '/app/solution/submission.parquet', 'regular': True, 'rows': 845600, 'users': 8456, 'per_user': 100, 'ranks': '1..100', 'distinct_items': True, 'test_eligible': True, 'dtypes': {'uid': 'int64', 'item_id': 'int64', 'rank': 'int64'}}\n-rw-r--r-- 1 agent agent 1.2M Aug 31 19:44 /app/solution/submission.parquet\n-rw-r--r-- 1 agent agent  82K Aug 31 19:32 /app/solution/train_current_model.py\n-rw-r--r-- 1 agent agent  16K Aug 31 19:02 /app/solution/two_tower_model.py\n"}

Script completed
Wall time 0.0 seconds
Output:

{}

Script completed
Wall time 0.2 seconds
Output:

README.md 2302 bytes
blended_validation.parquet 1254039 bytes
graded_validation.parquet 1249069 bytes
hard48_validation.parquet 1208798 bytes
inbatch16_validation.parquet 1282732 bytes
popularity_diagnostic.parquet 728599 bytes
production_validation.parquet 1244231 bytes
submission.parquet 1200186 bytes
submission_prepared.npz 552686232 bytes
train_current_model.py 83886 bytes
two_tower_model.py 15985 bytes
validation_prepared.npz 535225808 bytes
51089f5db60c7c08e7be996437485b2c6067c10df8ed67fa5070260480fb7399  /app/solution/submission.parquet
524461294c95ab652c3cebb97946693b357b482687b54dbc7661a5df56f3376b  /app/solution/train_current_model.py
944adf51f9fc61fcbb8392c5c62243b9cc74b5e2ca2f403b5722967f14773f16  /app/solution/two_tower_model.py
465af08ef5d339eaf1696b18a1e8cb9b16f77c0b79757026ca21a819f1b53472  /app/solution/README.md
# Improved Yambda two-tower

## Result

Public validation NDCG@10 improved from **0.021280** to **0.023522**
(+0.002242 absolute, +10.54% relative; 4,263 scored users).

The final artifact is `submission.parquet`. It has 845,600 rows, exactly 100
distinct eligible items and ranks 1--100 for each of the 8,456 target users.

## Diagnosis and model change

The production sampler uses 24 uniform and eight propensity-corrected in-batch
negatives, with a full-training-history false-negative filter. I tested three
more aggressive alternatives under the same chronological protocol:

| Variant | Public NDCG@10 |
| --- | ---: |
| Production objective | 0.021280 |
| Replace loss with top-32 from a 48-item hard pool | 0.019374 |
| Production loss plus a 25% top-8 hard term | 0.015770 |
| 16 uniform + 16 uncorrected in-batch | 0.014744 |
| **Selected: discovery-graded weighting, production sampler** | **0.023522** |

The negative tests showed that popularity-skewed and aggressively mined
negatives over-concentrate a one-epoch CPU fit. The platform eligibility rule
already removes prior encounters, while the production uniform component,
propensity correction, and full-history filter approximate that discovery
candidate boundary well.

The selected change fixes the remaining loss mismatch without changing the
towers or serving score. Adjacent targets remain nonzero-weight examples;
organic >=80% listens receive grade-1 emphasis, and cutoff-safe first encounters
followed by an organic like within seven days receive grade-3 emphasis. On the
validation stage, only outcomes inside `train_*` are used to construct these
weights. The final fit analogously uses `train_* + val_*` as observed history.

The model remains a single sequential user tower and independent precomputable
item tower with raw dot-product retrieval. It has no user-ID embedding or
per-candidate sequence computation.

## Reproduce the final artifact

```sh
/opt/task-venv/bin/python /app/solution/train_current_model.py \
  --stage submission \
  --public-dir /task \
  --prepared-cache /app/solution/submission_prepared.npz \
  --checkpoint-dir /app/solution/final_checkpoints \
  --output /app/solution/submission.parquet
```

The prepared cache and checkpoint flags are optional and only reduce rerun or
recovery cost.

Script completed
Wall time 0.0 seconds
Output:

{}