Agent trace

Current topic: "Initial Codebase and Baseline Investigation"
Topic summary: Starting investigation into the production two-tower music discovery model. I will inspect the current model training code, model architecture, evaluation script, and data layout to diagnose negative sampling limitations and understand baseline performance.

Strategic Intent: Explore the codebase and understand the current two-tower model and evaluation pipeline.

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Output: /task:
total 40
dr-xr-xr-x 1 root root 4096 Sep 29 05:33 .
drwxr-xr-x 1 root root 4096 Sep 29 05:36 ..
-r--r--r-- 1 root root    0 Sep 29 05:31 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 05:31 current_model
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 data
-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt

/task/current_model:
total 104
dr-xr-xr-x 1 root root  4096 Sep 29 05:31 .
dr-xr-xr-x 1 root root  4096 Sep 29 05:33 ..
-r--r--r-- 1 root root 80058 Sep 29 05:31 train_current_model.py
-r--r--r-- 1 root root 15985 Sep 29 05:31 two_tower_model.py

/task/data:
total 347392
dr-xr-xr-x 1 root root      4096 Sep 29 05:32 .
dr-xr-xr-x 1 root root      4096 Sep 29 05:33 ..
-r--r--r-- 1 root root    349352 Sep 29 05:31 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Sep 29 05:31 target_users.parquet
-r--r--r-- 1 root root   7736341 Sep 29 05:31 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Sep 29 05:32 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Sep 29 05:31 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Sep 29 05:31 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 05:31 train_listens.parquet
-r--r--r-- 1 root root    155988 Sep 29 05:31 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Sep 29 05:31 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Sep 29 05:31 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Sep 29 05:31 val_likes.parquet
-r--r--r-- 1 root root   9947609 Sep 29 05:31 val_listens.parquet
-r--r--r-- 1 root root      7845 Sep 29 05:31 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Sep 29 05:31 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Sep 29 05:31 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Sep 29 05:32 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>

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Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1615
</untrusted_context>

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Output: MemTotal:       15988964 kB
2.8.0+cpu
Process Group PGID: 1625
</untrusted_context>

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

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

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

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

Usage::

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

import json
import sys
from pathlib import Path

import pandas as pd

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


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


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

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

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

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


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

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

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

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

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


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

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

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


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

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


if __name__ == "__main__":
    main()

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

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


def read_ranking(path: Path) -> pd.DataFrame:
    """Read a parquet or CSV ranking into a pandas frame.

    Args:
        path: Input path. A ``.parquet`` suffix selects parquet; every other
            suffix is interpreted as CSV.

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


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

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

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

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

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

    normalized = frame[required].copy()
    for column in required:
        numeric = pd.to_numeric(normalized[column], errors="coerce")
        values = numeric.to_numpy(dtype=float)
        if numeric.isna().any() or not np.isfinite(values).all():
            raise RankingValidationError(
                f"submission {column} must be finite numeric values"
            )
        if not np.equal(numeric, np.floor(numeric)).all():
            raise RankingValidationError(
                f"submission {column} must contain integers"
            )
        normalized[column] = numeric.astype(np.int64)

    submitted_users = set(map(int, normalized.uid.unique()))
    if submitted_users != targets:
        raise RankingValidationError(
            f"submission user set mismatch: missing={len(targets - submitted_users)} "
            f"extra={len(submitted_users - targets)}"
        )
    counts = normalized.groupby("uid").size()
    if not (counts == TOP_N).all():
        raise RankingValidationError(
            f"submission must contain exactly {TOP_N} rows for every target user"
        )
    if normalized.duplicated(["uid", "item_id"]).any():
        raise RankingValidationError(
            "submission contains duplicate (uid, item_id) pairs"
        )
    valid_ranks = set(range(1, TOP_N + 1))
    ranks_are_valid = normalized.groupby("uid")["rank"].apply(
        lambda values: set(map(int, values)) == valid_ranks
    )
    if not ranks_are_valid.all():
        raise RankingValidationError(
            f"each user must have every rank from 1 through {TOP_N} exactly once"
        )
    return normalized


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

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

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


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

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

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

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


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

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

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

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--- FILE CONTENT (truncated) ---
"""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()

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            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,
            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

Found 57 matches for pattern "def " in path "/task/current_model":
---
File: train_current_model.py
L82:     def read(cls, path: Path) -> "CandidateSet":
L118:     def lengths(self) -> np.ndarray:
L122:     def target_count(self) -> int:
L136:     def selected_count(self) -> int:
L140: def log(message: str) -> None:
L144: def seed_everything(seed: int) -> None:
L150: def model_config(config: Config) -> base.TowerConfig:
L174: def append_state_snapshot(
L190: def causal_activity_features(
L247: def build_sequence_parts(
L314: def final_user_features(
L341: def prepare_data(
L471: def cache_metadata(stage: str, config: Config) -> dict[str, object]:
L481: def save_cache(
L493: def load_cache(path: Path, stage: str, config: Config) -> SequenceData:
L511: def load_or_prepare(
L534: def build_item_correction(
L578: def build_known_positive_filter(
L609: def invalid_full_history_negatives(
L630: def draw_full_history_filtered_negatives(
L724: def draw_training_negatives(
L763: def event_sequence(
L774: def pool_explicit_states(
L798: def user_queries(
L813: def candidate_vectors(
L828: def history_windows(
L845: def corrected_negative_loss(
L867: def retained_target_window_starts(
L888: def chronological_target_partition(
L914: def load_organic_like_outcomes(
L955: def build_target_weights(
L1035: def build_dense_block(
L1111: def score_target_microbatches(
L1198: def release_accelerator_cache(device: torch.device) -> None:
L1203: def run_training_epoch(
L1364: def evaluate_validation_bce(
L1488: def snapshot_model(model: nn.Module) -> dict[str, torch.Tensor]:
L1495: def save_epoch_checkpoint(
L1521: def train_model(
L1782: def inference_queries(
L1861: def rank_candidates(
L1912: def write_ranking(frame: pd.DataFrame, output: Path) -> None:
L1922: def main() -> int:
---
File: two_tower_model.py
L65: def log(message: str) -> None:
L69: def build_item_features(catalog: pd.DataFrame) -> np.ndarray:
L90: def _combine_listen_chunks(parts: list[ListenEvents]) -> ListenEvents:
L106: def iter_listen_users(
L157: def merge_listen_streams(
L175: def load_explicit_events(
L228: def empty_explicit_events() -> ExplicitEvents:
L238: def pack_active_items(
L251: def load_inference_state(
L287:     def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
L332:     def _mean_context_channel(self, items: torch.Tensor) -> torch.Tensor:
L339:     def encode_items(self, items: torch.Tensor) -> torch.Tensor:
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---

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--- FILE CONTENT (truncated) ---
#!/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,

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


def build_item_correction(
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
    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,
    )

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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])

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                    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]
        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),

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        "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)).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)
            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,
            )
            statistics, _ = 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=False,
            )
            total_loss += statistics["loss_sum"]
            total_targets += len(positive_items)
            total_weight += statistics["weight_sum"]
        if group_number % 25 == 0:
            release_accelerator_cache(device)
    return {
        "validation_loss": total_loss / max(total_weight, 1.0e-12),
        "validation_targets": float(total_targets),
        "validation_target_weight_sum": total_weight,
        "validation_seconds": time.monotonic() - started,
    }


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


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


def train_model(
    public: Path,
    stage: str,
    prepared: SequenceData,
    config: Config,
    device: torch.device,
    checkpoint_dir: Path | None,
    resume_checkpoint: Path | None,
) -> tuple[base.SequentialTwoTower, list[dict[str, float]]]:
    partition = chronological_target_partition(
        prepared,
        config.validation_fraction,
        config.max_train_targets_per_user,
        config.max_validation_targets_per_user,
    )
    training_counts = partition.training_counts
    validation_counts = partition.validation_counts
    log(
        f"chronological BCE window: train={int(training_counts.sum()):,} "
        f"validation={int(validation_counts.sum()):,} "
        f"selected={partition.selected_count:,} "
        f"discarded_old_targets={int(partition.window_starts.sum()):,} "
        f"all={prepared.target_count:,}"
    )
    model = base.SequentialTwoTower(prepared.item_features, model_config(config)).to(
        device
    )
    optimizer = torch.optim.AdamW(
        model.parameters(), lr=config.learning_rate, weight_decay=config.weight_decay
    )
    generator = torch.Generator(device="cpu").manual_seed(config.seed + 10_000)
    item_correction = build_item_correction(
        prepared,
        config,
        partition.window_starts,
        training_counts,
    )
    target_weights = build_target_weights(
        public,
        stage,
        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(

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            "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
            outputs, _ = event_sequence(
                model,
                torch.from_numpy(items.astype(np.int64)).to(device),
                torch.from_numpy(features.astype(np.float32)).to(device),
                hidden,
            )
            next_rows: list[torch.Tensor] = []
            for local_row, count in enumerate(counts):
                next_rows.append(
                    outputs[local_row, count - 1]
                    if count
                    else hidden[0, local_row]
                )
            hidden = torch.stack(next_rows).unsqueeze(0)

        liked_items = torch.from_numpy(
            prepared.inference_liked_items[output_rows].astype(np.int64)
        ).to(device)
        disliked_items = torch.from_numpy(
            prepared.inference_disliked_items[output_rows].astype(np.int64)
        ).to(device)
        queries = user_queries(
            model,
            hidden.squeeze(0),
            model._mean_context_channel(liked_items),
            model._mean_context_channel(disliked_items),
            torch.from_numpy(
                prepared.inference_user_features[output_rows].astype(np.float32)
            ).to(device),
        )
        result[torch.from_numpy(output_rows.astype(np.int64))] = queries.cpu()
    return result


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


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


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)

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    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


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

<untrusted_context>
Output: usage: train_current_model.py [-h] [--stage {validation,submission}] --output
                              OUTPUT --public-dir PUBLIC_DIR
                              [--prepared-cache PREPARED_CACHE]
                              [--device {cpu,mps}] [--threads THREADS]
                              [--bptt-steps BPTT_STEPS]
                              [--user-batch-size USER_BATCH_SIZE]
                              [--epochs EPOCHS]
                              [--learning-rate LEARNING_RATE]
                              [--loss-microbatch-size LOSS_MICROBATCH_SIZE]
                              [--negative-filter-scope {full-training-history,last-100}]
                              [--target-weighting {discovery-graded,organic-strong,uniform}]
                              [--ordinary-target-weight ORDINARY_TARGET_WEIGHT]
                              [--organic-strong-target-weight ORGANIC_STRONG_TARGET_WEIGHT]
                              [--organic-like-target-weight ORGANIC_LIKE_TARGET_WEIGHT]
                              [--discovery-horizon-days DISCOVERY_HORIZON_DAYS]
                              [--validation-fraction VALIDATION_FRACTION]
                              [--max-train-targets-per-user MAX_TRAIN_TARGETS_PER_USER]
                              [--max-validation-targets-per-user MAX_VALIDATION_TARGETS_PER_USER]
                              [--early-stopping-patience EARLY_STOPPING_PATIENCE]
                              [--early-stopping-min-delta EARLY_STOPPING_MIN_DELTA]
                              [--checkpoint-dir CHECKPOINT_DIR]
                              [--resume-checkpoint RESUME_CHECKPOINT]
                              [--seed SEED]
                              [--ranking-batch-size RANKING_BATCH_SIZE]
                              [--prepare-only]
train_current_model.py: error: the following arguments are required: --output
Exit Code: 2
Process Group PGID: 1639
</untrusted_context>

IMPORTANT: The file content has been truncated.
Status: Showing lines 470-535 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 536.

--- FILE CONTENT (truncated) ---

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


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


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


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


def build_item_correction(
    prepared: SequenceData,

<untrusted_context>
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.9s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.9s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.1s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.6s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.5s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.2s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.7s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=41.1s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.8s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
target weighting=organic-strong min=0.250 mean=0.575 max=1.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.547529 running_bce=0.541614 elapsed=198.4s
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=211.5 validation_seconds=15.4
restored best validation-BCE checkpoint: epoch=1 loss=0.496636
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/production_validation.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 361.702,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "organic-strong"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "organic-strong",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5398380482933169,
      "positive_logit": 0.7427799585458127,
      "negative_logit": -0.6222247023150492,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 211.50029177400006,
      "validation_loss": 0.49663621852386697,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 15.381582812999909
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.603016972541809,
    "query_norm_std": 0.7697497010231018,
    "item_norm_mean": 1.7570278644561768,
    "item_norm_std": 0.8279485106468201
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
Process Group PGID: 1650
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
Process Group PGID: 1710
</untrusted_context>

<untrusted_context>
Output: total 512M
-rw-r--r-- 1 agent agent 1.2M Sep 29 05:43 production_validation.parquet
-rw-r--r-- 1 agent agent 511M Sep 29 05:38 validation_cache.npz
Process Group PGID: 1730
</untrusted_context>

<untrusted_context>
Output: Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   15G   82G  15% /
Process Group PGID: 1733
</untrusted_context>

Current topic: "Diagnosing Negative Sampling and Training Objective"
Topic summary: Baseline model reproduced with validation NDCG@10 of 0.02128. Sequence cache is saved. Now analyzing the negative sampling implementation, candidate generation, loss function, and filtering logic to diagnose bottlenecks and limitations.

Strategic Intent: Analyze the current negative sampling strategy and loss objective in detail to identify limitations.

IMPORTANT: The file content has been truncated.
Status: Showing lines 535-725 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 726.

--- FILE CONTENT (truncated) ---
    prepared: SequenceData,
    config: Config,
    target_starts: np.ndarray,
    target_counts: np.ndarray,
) -> torch.Tensor:
    """Estimate in-batch propensities from the selected training targets."""
    counts = np.zeros(len(prepared.item_ids), dtype=np.float64)
    for user_row, count in enumerate(target_counts):
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        target_start = int(target_starts[user_row])
        counts += np.bincount(
            prepared.sequence_items[
                offset
                + target_start
                + 1 : offset
                + target_start
                + int(count)
                + 1
            ].astype(np.int64),
            minlength=len(prepared.item_ids),
        )
    total = float(counts.sum())
    probabilities = (counts + 1.0) / (total + len(counts))
    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,

Found 40 matches for pattern "dislike" in path "/task":
---
File: current_model/train_current_model.py
L110:     disliked_states: np.ndarray
L114:     inference_disliked_items: np.ndarray
L176:     active_dislikes: dict[int, int],
L179:     disliked_states: list[np.ndarray],
L183:     disliked_states.append(
L184:         base.pack_active_items(active_dislikes, base.DISLIKE_ITEMS, n_items)
L186:     state_counts.append((len(active_likes), len(active_dislikes)))
L252:     disliked_states: list[np.ndarray],
L279:     active_dislikes: dict[int, int] = {}
L293:                 else active_dislikes
L304:                 active_dislikes,
L307:                 disliked_states,
L320:     dislike_count: int,
L337:     result[5] = np.log1p(dislike_count) / 4.0
L369:     inference_likes, inference_dislikes, like_counts, dislike_counts = (
L383:     disliked_states = [
L384:         np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
L405:             disliked_states,
L427:                 int(dislike_counts[inference_row]),
L440:             np.log1p(dislike_counts[missing]) / 4.0
L455:         disliked_states=np.stack(disliked_states),
L459:         inference_disliked_items=inference_dislikes,
L784:     disliked_items = torch.from_numpy(
L785:         prepared.disliked_states[unique_states].astype(np.int64)
L788:     disliked_vectors = model._mean_context_channel(disliked_items)
L793:         disliked_vectors[inverse_rows],
L802:     disliked_vectors: torch.Tensor,
L807:             [sequence_vectors, liked_vectors, disliked_vectors, user_features],
L1147:         liked_vectors, disliked_vectors, _ = pool_explicit_states(
L1159:             disliked_vectors,
L1844:         disliked_items = torch.from_numpy(
L1845:             prepared.inference_disliked_items[output_rows].astype(np.int64)
L1851:             model._mean_context_channel(disliked_items),
---
File: current_model/two_tower_model.py
L22: DISLIKE_ITEMS = 24
L184:         ("dislikes", 1, 1, 0),
L185:         ("undislikes", 1, 0, 1),
L265:     disliked = np.full((len(uids), DISLIKE_ITEMS), n_items, dtype=np.uint16)
L267:     dislike_counts = np.zeros(len(uids), dtype=np.int32)
L270:         ("disliked", DISLIKE_ITEMS, disliked, dislike_counts),
L281:     return liked, disliked, like_counts, dislike_counts
---

IMPORTANT: The file content has been truncated.
Status: Showing lines 240-340 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 341.

--- FILE CONTENT (truncated) ---
        (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):
        return None

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

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


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

IMPORTANT: The file content has been truncated.
Status: Showing lines 341-470 of 2165 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 471.

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

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

    explicit_by_user = base.load_explicit_events(data, prefixes, item_index)
    inference_likes, inference_dislikes, like_counts, dislike_counts = (
        base.load_inference_state(
            data / state_name,
            item_index,
            eligibility.uids,
            n_items,
        )
    )
    inference_rows = {int(uid): row for row, uid in enumerate(eligibility.uids)}
    inference_user_features = np.zeros((len(eligibility.uids), 6), dtype=np.float32)
    inference_sequence_rows = np.full(len(eligibility.uids), -1, dtype=np.int32)

    # Snapshot zero is the shared empty explicit state.
    liked_states = [np.full(base.LIKE_ITEMS, n_items, dtype=np.uint16)]
    disliked_states = [
        np.full(base.DISLIKE_ITEMS, n_items, dtype=np.uint16)
    ]
    state_counts: list[tuple[int, int]] = [(0, 0)]
    user_ids: list[int] = []
    offsets = [0]
    timestamp_parts: list[np.ndarray] = []
    item_parts: list[np.ndarray] = []
    event_parts: list[np.ndarray] = []
    activity_parts: list[np.ndarray] = []
    state_parts: list[np.ndarray] = []

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

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

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

Found 100 matches for pattern "negative" in path "/task" (results limited to 100 matches for performance):
---
File: current_model/train_current_model.py
L14: and 24 uniform plus eight propensity-corrected unique in-batch negatives. Every
L56:     uniform_negatives: int = 24
L57:     inbatch_negatives: int = 8
L61:     negative_filter_scope: str = "full-training-history"
L165:         uniform_negatives=config.uniform_negatives,
L166:         inbatch_negatives=config.inbatch_negatives,
L477:         "event_time_feature": "exp-negative-gap-over-60-days",
L603:         f"full-history false-negative filter: users={len(prepared.user_ids):,} "
L609: def invalid_full_history_negatives(
L610:     negatives: np.ndarray,
L616:     """Return row-level invalid negatives using the complete training prefix."""
L617:     byte_columns = negatives >> 3
L618:     bit_columns = negatives & 7
L621:     invalid |= negatives == targets[:, None]
L622:     invalid |= (negatives[:, :, None] == liked_items[:, None, :]).any(axis=2)
L623:     for column in range(1, negatives.shape[1]):
L625:             negatives[:, column, None] == negatives[:, :column]
L630: def draw_full_history_filtered_negatives(
L641:     """Draw mixed negatives and reject all known public-training positives."""
L647:         (batch_size, config.uniform_negatives),
L653:     if config.inbatch_negatives:
L656:             config.inbatch_negatives, max(len(unique_targets) - 1, 0)
L672:         if corrected_count < config.inbatch_negatives:
L675:                 (batch_size, config.inbatch_negatives - corrected_count),
L681:     negatives = torch.cat([uniform, inbatch], dim=1).numpy()
L682:     correction = np.ones_like(negatives, dtype=np.float32)
L685:             :, config.uniform_negatives : config.uniform_negatives + corrected_count
L688:         invalid = invalid_full_history_negatives(
L689:             negatives,
L697:                 torch.from_numpy(negatives).to(device),
L706:         negatives[invalid] = replacements
L709:     invalid = invalid_full_history_negatives(
L710:         negatives,
L717:         raise RuntimeError("full-history negative resampling failed")
L719:         torch.from_numpy(negatives).to(device),
L724: def draw_training_negatives(
L736:     if config.negative_filter_scope == "full-training-history":
L738:             raise RuntimeError("full-history negative filter was not built")
L739:         return draw_full_history_filtered_negatives(
L750:     return base.draw_filtered_mixed_negatives(
L755:         config.uniform_negatives,
L756:         config.inbatch_negatives,
L816:     negatives: torch.Tensor,
L818:     all_items = torch.cat([positives, negatives.reshape(-1)])
L822:     negative_vectors = encoded[inverse[len(positives) :]].reshape(
L823:         len(positives), negatives.shape[1], -1
L825:     return positive_vectors, negative_vectors
L845: def corrected_negative_loss(
L851:     total = config.uniform_negatives + config.inbatch_negatives
L853:     if config.uniform_negatives:
L854:         result += config.uniform_negatives * raw[
L855:             :, : config.uniform_negatives
L857:     if config.inbatch_negatives:
L858:         selected = slice(config.uniform_negatives, total)
L860:         result += config.inbatch_negatives * (
L880:         raise RuntimeError("chronological target partition produced negative counts")
L1119:     negatives: torch.Tensor,
L1120:     negative_weights: torch.Tensor,
L1134:         "negative_sum": 0.0,
L1162:         positive_vectors, negative_vectors = candidate_vectors(
L1165:             negatives[begin:end],
L1168:         negative_logits = torch.einsum(
L1169:             "bd,bkd->bk", query_vectors, negative_vectors
L1172:         negative_loss = corrected_negative_loss(
L1173:             negative_logits, negative_weights[begin:end], config
L1179:             selected_weights * (positive_loss + negative_loss) / 2.0
L1190:         statistics["negative_sum"] += float(negative_logits.detach().sum().cpu())
L1192:             (positive_logits.detach()[:, None] > negative_logits.detach()).sum().cpu()
L1227:     negative_count = config.uniform_negatives + config.inbatch_negatives
L1232:     total_negative = 0.0
L1293:             negatives, negative_weights = draw_training_negatives(
L1314:                 negatives,
L1315:                 negative_weights,
L1331:             total_negative += statistics["negative_sum"]
L1353:         "negative_logit": total_negative / max(total_targets * negative_count, 1),
L1355:         / max(total_targets * negative_count, 1),
L1449:             negatives, negative_weights = draw_training_negatives(
L1469:                 negatives,
L1470:                 negative_weights,
L1567:         if config.negative_filter_scope == "full-training-history"
L1601:             "negative_filter_scope", "last-100"
L1629:             stored_filter_scope != config.negative_filter_scope
L1660:                 f"{stored_filter_scope}->{config.negative_filter_scope}, "
L1944:         "--negative-filter-scope",
L1946:         default=Config.negative_filter_scope,
L2034:         negative_filter_scope=args.negative_filter_scope,
L2136:                     "negative_sampling": (
L2139:                     "false_negative_filter": config.negative_filter_scope,
---
File: current_model/two_tower_model.py
L40:     uniform_negatives: int = 24
L41:     inbatch_negatives: int = 8
L346: def invalid_negative_mask(
L347:     negatives: torch.Tensor,
L353:     invalid = negatives.eq(targets[:, None])
L354:     invalid |= negatives[:, :, None].eq(context_items[:, None, :]).any(dim=2)
L355:     invalid |= negatives[:, :, None].eq(liked_items[:, None, :]).any(dim=2)
L356:     for column in range(1, negatives.shape[1]):
L357:         invalid[:, column] |= negatives[:, column, None].eq(
L358:             negatives[:, :column]
L363: def draw_filtered_mixed_negatives(
L374:     """Draw uniform and unique-target in-batch negatives."""
---

<untrusted_context>
Output: Number of rows: 845600
Unique items predicted: 2575
Average popularity rank of top-100 predictions: 2991.8002459791865
Min pop rank: 1 Max pop rank: 29992
Average popularity rank of top-10 predictions: 2597.0539025543994
Top 10 most frequently predicted items:
item_id
6896222    5089
7565403    4970
2055777    4647
8722394    4627
5010168    4572
5134208    4397
3968387    4160
8823268    4157
6414220    4155
8350060    4132
Name: count, dtype: int64
Process Group PGID: 1758
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1778
</untrusted_context>

<untrusted_context>
Output: Total true positive instances in validation: 37769
Unique true positive items: 14124
Mean pop rank of true positives: 8463.087743916969
Median pop rank of true positives: 5622.0
Quantiles of pop rank: 25%: 1733.0 75%: 13374.0
Top 10 most frequent true positive items:
item
6399259    173
6252104    109
8250570     88
2055777     83
1265696     77
3134183     60
7579671     51
1005397     46
4948220     45
6625048     43
Name: count, dtype: int64
Process Group PGID: 1789
</untrusted_context>

Found 12 matches for pattern "uniform_negatives" in path "/task/current_model":
---
File: train_current_model.py
L56:     uniform_negatives: int = 24
L165:         uniform_negatives=config.uniform_negatives,
L647:         (batch_size, config.uniform_negatives),
L685:             :, config.uniform_negatives : config.uniform_negatives + corrected_count
L755:         config.uniform_negatives,
L851:     total = config.uniform_negatives + config.inbatch_negatives
L853:     if config.uniform_negatives:
L854:         result += config.uniform_negatives * raw[
L855:             :, : config.uniform_negatives
L858:         selected = slice(config.uniform_negatives, total)
L1227:     negative_count = config.uniform_negatives + config.inbatch_negatives
---
File: two_tower_model.py
L40:     uniform_negatives: int = 24
---

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
Scored targets in batch: 5644
Unique targets in batch: 3484
Negatives shape: torch.Size([5644, 32])
Correction shape: torch.Size([5644, 32])
Correction for inbatch negatives (cols 24..31):
Fraction of inbatch corrections equal to 1.0 (meaning replaced by uniform or default): 0.06400602409638555
Min inbatch corr: 0.25 Max inbatch corr: 4.0
Process Group PGID: 1817
</untrusted_context>

<untrusted_context>
Output: Top 5 catalog items by popularity:
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
3  3542184             24075                     17699                4
4  9378983             24036                     18195                5
Bottom 5 catalog items:
       item_id  positive_listens  organic_positive_listens  popularity_rank
29995  1275692               163                        81            29996
29996  1280011               163                        76            29997
29997  1309246               163                       138            29998
29998  1634958               163                        46            29999
29999  1670524               163                        73            30000
Process Group PGID: 1831
</untrusted_context>

<untrusted_context>
Output: item_id  popularity_rank  positive_listens  organic_positive_listens
75    8823268               76              8956                      7255
305   5134208              306              5028                      3633
317   3968387              318              4914                      3402
633   8722394              634              3464                      1695
817   6896222              818              3020                      2017
1169  2055777             1170              2465                      1558
1528  8350060             1529              2115                      1153
2061  7565403             2062              1763                      1152
2946  5010168             2947              1385                      1228
3206  6414220             3207              1305                       612
Process Group PGID: 1848
</untrusted_context>

<untrusted_context>
Output: (empty)
Process Group PGID: 1871
</untrusted_context>

Current topic: "Testing Loss Objectives and Negative Sampling"
Topic summary: Diagnosed the core issue: the current model uses a pointwise BCE loss where negative gradients are diluted (divided by 64), popular negatives are severely downweighted (loss weight 0.25), and there is no direct ranking competition. This causes the model to predict only ~2,500 popular items for all users. We will now test targeted improvements starting with discovery-graded weighting and InfoNCE/ranking loss objectives.

Strategic Intent: Run quick experiments with target weighting and loss functions on the validation backtest.

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.551810 running_bce=0.544138 elapsed=196.1s
epoch=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=209.2 validation_seconds=15.5
restored best validation-BCE checkpoint: epoch=1 loss=0.504831
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/exp1_discovery_graded.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 316.261,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "discovery-graded",
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.542115418840845,
      "positive_logit": 0.7774311445323794,
      "negative_logit": -0.6407562046595957,
      "pair_accuracy": 0.7602795423917307,
      "targets": 1052953.0,
      "target_weight_sum": 348780.0,
      "batches": 266.0,
      "seconds": 209.22850921400004,
      "validation_loss": 0.5048305718913402,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 15.475705923000078
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.7521852254867554,
    "query_norm_std": 0.8638167977333069,
    "item_norm_mean": 1.8543914556503296,
    "item_norm_std": 0.8459439277648926
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
{
  "model": "/app/solution/exp1_discovery_graded.parquet",
  "scores": {
    "quality_ndcg@10": 0.023522,
    "quality_users": 4263
  }
}
Process Group PGID: 1875
</untrusted_context>

<untrusted_context>
Output: Unique items predicted: 2930
Mean pop rank: 3232.9024917218544
Process Group PGID: 1962
</untrusted_context>

<untrusted_context>
Output: K=32: unique_items=29862, time=0.3472s
K=64: unique_items=29999, time=0.3962s
K=128: unique_items=30000, time=0.4619s
Process Group PGID: 1981
</untrusted_context>

<untrusted_context>
Output: Dislikes shape: (101075, 4)
Unique users with dislikes: 5732
Unique items disliked: 50876
   uid  timestamp  item_id  is_organic
0  100    3087560  9170134           1
1  100    3936560  8661238           1
2  100   20432825  4927727           1
3  400    2980800   399811           1
4  400    3157520  1575108           1
Process Group PGID: 2025
</untrusted_context>

IMPORTANT: The file content has been truncated.
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--- FILE CONTENT (truncated) ---
        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,

EACCES: permission denied, open '/app/solution/train_current_model.py'

<untrusted_context>
Output: total 525228
drwxr-xr-x 1 agent agent      4096 Sep 29 05:54 .
drwxr-xr-x 1 agent agent      4096 Sep 29 05:33 ..
-rw-r--r-- 1 agent agent   1249069 Sep 29 05:54 exp1_discovery_graded.parquet
-rw-r--r-- 1 agent agent   1244230 Sep 29 05:43 production_validation.parquet
-r--r--r-- 1 agent agent     80058 Sep 29 05:49 train_current_model.py
-r--r--r-- 1 agent agent     15985 Sep 29 05:49 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:38 validation_cache.npz
Process Group PGID: 2072
</untrusted_context>

<untrusted_context>
Output: (empty)
Process Group PGID: 2075
</untrusted_context>

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
    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"
    loss_type: str = "infonce"
    temperature: float = 0.1
    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
...

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--- FILE CONTENT (truncated) ---
            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()
        )

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--- FILE CONTENT (truncated) ---
    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)

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
        )
        positive_logits = (query_vectors * positive_vectors).sum(dim=1)
        negative_logits = torch.einsum(
            "bd,bkd->bk", query_vectors, negative_vectors
        )
        selected_weights = torch.from_numpy(
            sample_weights[begin:end].astype(np.float32, copy=False)
        ).to(device)
        if config.loss_type == "infonce":
            all_logits = torch.cat([positive_logits.unsqueeze(1), negative_logits], dim=1)
            scaled_logits = all_logits / config.temperature
            targets_idx = torch.zeros(len(positive_logits), dtype=torch.long, device=device)
            sample_loss = F.cross_entropy(scaled_logits, targets_idx, reduction="none")
            loss_sum = (selected_weights * sample_loss).sum()
        else:
            positive_loss = F.softplus(-positive_logits)
            negative_loss = corrected_negative_loss(
                negative_logits, negative_weights[begin:end], config
            )
            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:
...

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
        "--loss-type",
        choices=("infonce", "bce"),
        default=Config.loss_type,
    )
    parser.add_argument(
        "--temperature",
        type=float,
        default=Config.temperature,
    )
    parser.add_argument(
        "--ordinary-target-weight",
        type=float,
        default=Config.ordinary_target_weight,
    )
    parser.add_argument(
...

IMPORTANT: The file content has been truncated.
Status: Showing lines 2040-2070 of 2184 total lines.
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--- FILE CONTENT (truncated) ---
    ):
        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():

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
        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,
        loss_type=args.loss_type,
        temperature=args.temperature,
        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,
...

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=2.882234 running_bce=2.888013 elapsed=196.7s
epoch=1/1 train_loss=2.87533 validation_loss=2.66998 pair_accuracy=0.775 train_targets=1,052,953 validation_targets=130,103 train_seconds=209.7 validation_seconds=15.8
restored best validation-BCE checkpoint: epoch=1 loss=2.669981
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/exp2_infonce_01.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 316.706,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "discovery-graded",
    "loss_type": "infonce",
    "temperature": 0.1,
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 2.8753293551465733,
      "positive_logit": 0.013731765006966084,
      "negative_logit": -0.1270103591336814,
      "pair_accuracy": 0.7748257460209526,
      "targets": 1052953.0,
      "target_weight_sum": 348780.0,
      "batches": 266.0,
      "seconds": 209.6856498139996,
      "validation_loss": 2.669981021485753,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 15.780993051999758
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 0.6981536149978638,
    "query_norm_std": 0.19272513687610626,
    "item_norm_mean": 0.8023920655250549,
    "item_norm_std": 0.17788256704807281
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
{
  "model": "/app/solution/exp2_infonce_01.parquet",
  "scores": {
    "quality_ndcg@10": 0.018216,
    "quality_users": 4263
  }
}
Process Group PGID: 2084
</untrusted_context>

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
    logits: torch.Tensor,
    correction: torch.Tensor,
    config: Config,
) -> torch.Tensor:
    raw = F.softplus(logits)
    return raw.mean(dim=1)


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

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.563095 running_bce=0.563300 elapsed=198.1s
epoch=1/1 train_loss=0.56103 validation_loss=0.52015 pair_accuracy=0.763 train_targets=1,052,953 validation_targets=130,103 train_seconds=211.1 validation_seconds=15.2
restored best validation-BCE checkpoint: epoch=1 loss=0.520147
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/exp3_bce_unweighted.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 320.086,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 24,
    "inbatch_negatives": 8,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "discovery-graded",
    "loss_type": "bce",
    "temperature": 0.1,
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5610289522722313,
      "positive_logit": 0.6264490667548376,
      "negative_logit": -0.6609028545055078,
      "pair_accuracy": 0.7632027201119138,
      "targets": 1052953.0,
      "target_weight_sum": 348780.0,
      "batches": 266.0,
      "seconds": 211.13066360600033,
      "validation_loss": 0.5201468392721145,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 15.211160614000164
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.8937625885009766,
    "query_norm_std": 0.849906325340271,
    "item_norm_mean": 1.7925578355789185,
    "item_norm_std": 0.7955362796783447
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
{
  "model": "/app/solution/exp3_bce_unweighted.parquet",
  "scores": {
    "quality_ndcg@10": 0.015049,
    "quality_users": 4263
  }
}
Process Group PGID: 2227
</untrusted_context>

<untrusted_context>
Output: Unique items predicted: 3066
Mean pop rank: 3535.2990444654683
Process Group PGID: 2317
</untrusted_context>

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
    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,
...

IMPORTANT: The file content has been truncated.
Status: Showing lines 1940-2070 of 2186 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2071.

--- FILE CONTENT (truncated) ---
    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(
        "--loss-type",
        choices=("infonce", "bce"),
        default=Config.loss_type,
    )
    parser.add_argument(
        "--temperature",
        type=float,
        default=Config.temperature,
    )
    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,
        loss_type=args.loss_type,
        temperature=args.temperature,
        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)

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
        "--negative-filter-scope",
        choices=("full-training-history", "last-100"),
        default=Config.negative_filter_scope,
    )
    parser.add_argument(
        "--uniform-negatives",
        type=int,
        default=Config.uniform_negatives,
    )
    parser.add_argument(
        "--inbatch-negatives",
        type=int,
        default=Config.inbatch_negatives,
    )
    parser.add_argument(
        "--inbatch-correction-power",
        type=float,
        default=Config.inbatch_correction_power,
    )
    parser.add_argument(
        "--inbatch-correction-min",
        type=float,
        default=Config.inbatch_correction_min,
    )
    parser.add_argument(
        "--inbatch-correction-max",
        type=float,
        default=Config.inbatch_correction_max,
    )
    parser.add_argument(
        "--target-weighting",
        choices=("discovery-graded", "organic-strong", "uniform"),
        default=Config.target_weighting,
    )
    parser.add_argument(
...

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Status: Showing lines 2070-2100 of 2211 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 2101.

--- FILE CONTENT (truncated) ---
    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,
        loss_type=args.loss_type,
        temperature=args.temperature,
        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()

Successfully modified file: /app/solution/train_current_model.py (1 replacements). Here is the updated code:
...
        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,
        uniform_negatives=args.uniform_negatives,
        inbatch_negatives=args.inbatch_negatives,
        inbatch_correction_power=args.inbatch_correction_power,
        inbatch_correction_min=args.inbatch_correction_min,
        inbatch_correction_max=args.inbatch_correction_max,
        target_weighting=args.target_weighting,
        loss_type=args.loss_type,
        temperature=args.temperature,
        ordinary_target_weight=args.ordinary_target_weight,
        organic_strong_target_weight=args.organic_strong_target_weight,
...

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
chronological BCE window: train=1,052,953 validation=130,103 selected=1,183,056 discarded_old_targets=18,864,138 all=20,047,194
inbatch correction: min=0.250 mean_on_targets=1.001 max=4.000
loaded organic-like outcomes: rows=301,527 users=7,526
grade-3 first-encounter targets=46,417 horizon_days=7
target weighting=discovery-graded min=0.250 mean=0.310 max=3.000
full-history false-negative filter: users=9,135 unique_user_items=5,524,275 bytes=34,256,250
epoch=1/1 batches=250 targets=987,417 batch_bce=0.556066 running_bce=0.556343 elapsed=190.2s
epoch=1/1 train_loss=0.55431 validation_loss=0.52006 pair_accuracy=0.726 train_targets=1,052,953 validation_targets=130,103 train_seconds=203.0 validation_seconds=13.7
restored best validation-BCE checkpoint: epoch=1 loss=0.520058
ranked users=1,024/8,456
ranked users=2,048/8,456
ranked users=3,072/8,456
ranked users=4,096/8,456
ranked users=5,120/8,456
ranked users=6,144/8,456
ranked users=7,168/8,456
ranked users=8,192/8,456
ranked users=8,456/8,456
{
  "baseline": "yambda-windowed-sequence-two-tower-base",
  "stage": "validation",
  "output": "/app/solution/exp4_u16_ib16.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 309.606,
  "public_inputs_only": true,
  "architecture": {
    "family": "pure-windowed-sequence-two-tower",
    "sequence_training": "every-adjacent-target-within-recent-chronological-window",
    "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
    "bptt_hidden_state": "carried-and-detached",
    "model_selection": "chronological-validation-BCE-early-stopping",
    "loss_execution": "gradient-accumulated-microbatches",
    "user_id_embedding": false,
    "user_mlp": "582-512-256-192",
    "item_mlp": "196-512-256-192",
    "normalization": "none",
    "score": "raw-dot-product",
    "objective": "sample-weighted-BCE-at-every-timestep",
    "negative_sampling": "24-uniform-plus-8-propensity-corrected-inbatch",
    "false_negative_filter": "full-training-history",
    "target_weighting": "discovery-graded"
  },
  "config": {
    "bptt_steps": 100,
    "user_batch_size": 64,
    "embedding_dim": 192,
    "hidden_dim": 192,
    "user_hidden_1": 512,
    "user_hidden_2": 256,
    "item_hidden_1": 512,
    "item_hidden_2": 256,
    "dropout": 0.05,
    "epochs": 1,
    "learning_rate": 0.001,
    "weight_decay": 1e-05,
    "gradient_clip": 2.0,
    "loss_microbatch_size": 65536,
    "uniform_negatives": 16,
    "inbatch_negatives": 16,
    "inbatch_correction_power": 0.5,
    "inbatch_correction_min": 0.25,
    "inbatch_correction_max": 4.0,
    "negative_filter_scope": "full-training-history",
    "target_weighting": "discovery-graded",
    "loss_type": "bce",
    "temperature": 0.1,
    "ordinary_target_weight": 0.25,
    "organic_strong_target_weight": 1.0,
    "organic_like_target_weight": 3.0,
    "discovery_horizon_days": 7,
    "validation_fraction": 0.1,
    "max_train_targets_per_user": 128,
    "max_validation_targets_per_user": 16,
    "early_stopping_patience": 1,
    "early_stopping_min_delta": 0.0001,
    "seed": 29
  },
  "sequence_tokens": 20056329,
  "all_next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "training_history": [
    {
      "epoch": 1.0,
      "loss": 0.5543083887019831,
      "positive_logit": 0.6960524660219143,
      "negative_logit": -0.5012892177810132,
      "pair_accuracy": 0.7255772574844271,
      "targets": 1052953.0,
      "target_weight_sum": 348780.0,
      "batches": 266.0,
      "seconds": 202.97917069799996,
      "validation_loss": 0.5200578049660878,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 13.70762099400008
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.7176122665405273,
    "query_norm_std": 0.8431686758995056,
    "item_norm_mean": 1.8370518684387207,
    "item_norm_std": 0.7449614405632019
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
{
  "model": "/app/solution/exp4_u16_ib16.parquet",
  "scores": {
    "quality_ndcg@10": 0.020984,
    "quality_users": 4263
  }
}
Process Group PGID: 2340
</untrusted_context>