Agent trace

Current topic: "Exploring Baseline and Environment"
Topic summary: Starting investigation of the two-tower music discovery model. We will inspect the directory structure, scripts in /task/, examine train_current_model.py and two_tower_model.py, run the public baseline evaluation, and diagnose negative sampling limitations.

Strategic Intent: Explore the workspace and codebase to understand the baseline model, data, and evaluation pipeline.

<untrusted_context>
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
Process Group PGID: 1612
</untrusted_context>

<untrusted_context>
Output: Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                5299.99
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               128 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)
L2 cache:                                2 MiB (4 instances)
L3 cache:                                16 MiB (1 instance)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-7
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Mitigation; Clear CPU buffers
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1621
</untrusted_context>

<untrusted_context>
Output: MemTotal:       15988968 kB
MemAvailable:   14506048 kB
Process Group PGID: 1625
</untrusted_context>

<untrusted_context>
Output: 2.8.0+cpu 4
Process Group PGID: 1629
</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()

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"""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"

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        )
    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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"""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

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                pending = []
            pending.append(part)
    if pending_uid is not None:
        yield pending_uid, _combine_listen_chunks(pending)


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


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


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


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


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


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

    def __init__(self, item_features: np.ndarray, config: TowerConfig) -> None:
        super().__init__()
        n_items, n_item_features = item_features.shape
        dim = config.embedding_dim
        self.n_items = n_items
        self.context_embedding = nn.Embedding(
            n_items + 1, dim, padding_idx=n_items
        )
        self.event_projection = nn.Sequential(
            nn.Linear(dim + 3, config.hidden_dim),
            nn.GELU(),
        )
        self.sequence_encoder = nn.GRU(
            input_size=config.hidden_dim,

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            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 58 matches for pattern "^(class|def) " in path "/task/current_model":
---
File: train_current_model.py
L41: class Config:
L76: class CandidateSet:
L99: class SequenceData:
L127: class TargetPartition:
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
L27: class TowerConfig:
L49: class ListenEvents:
L57: class ExplicitEvents:
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(
L284: class SequentialTwoTower(nn.Module):
L346: def invalid_negative_mask(
L363: def draw_filtered_mixed_negatives(
---

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Status: Showing lines 41-100 of 2165 total lines.
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--- FILE CONTENT (truncated) ---
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

<untrusted_context>
Output: (empty)
Process Group PGID: 1641
</untrusted_context>

<untrusted_context>
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.8s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
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=198.1s
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=211.1 validation_seconds=15.6
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": 360.854,
  "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": 0.539838046260986,
      "positive_logit": 0.7427799582740985,
      "negative_logit": -0.6222247031655147,
      "pair_accuracy": 0.7547733612041563,
      "targets": 1052953.0,
      "target_weight_sum": 632551.0,
      "batches": 266.0,
      "seconds": 211.101639062,
      "validation_loss": 0.496636221768594,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 78769.25,
      "validation_seconds": 15.603404805000082
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.603016972541809,
    "query_norm_std": 0.7697497010231018,
    "item_norm_mean": 1.7570281028747559,
    "item_norm_std": 0.8279485702514648
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}

real	6m5.168s
user	30m39.551s
sys	1m39.579s
Process Group PGID: 1644
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/production_validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.02128,
    "quality_users": 4263
  }
}
Process Group PGID: 1705
</untrusted_context>

Current topic: "Diagnosing Negative Sampling and Training Objective"
Topic summary: Baseline reproduction completed with quality_ndcg@10 = 0.02128 in 6 minutes. Now we transition into a deep code audit of train_current_model.py and two_tower_model.py, specifically focusing on negative sampling, negative filtering, loss formulation, and candidate eligibility.

Strategic Intent: Analyze the training objective, negative sampling strategy, and loss formulation in train_current_model.py.

<untrusted_context>
Output: (empty)
Process Group PGID: 1725
</untrusted_context>

IMPORTANT: The file content has been truncated.
Status: Showing lines 525-760 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: 761.

--- FILE CONTENT (truncated) ---
        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,
    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):,}"

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    )
    return result


def build_target_weights(
    public: Path,
    stage: str,
    prepared: SequenceData,
    training_counts: np.ndarray,
    config: Config,
) -> np.ndarray:
    """Build nonzero per-token weights without dropping adjacent targets."""
    if config.target_weighting == "uniform":
        weights = np.ones(len(prepared.sequence_items), dtype=np.float32)
    else:
        weights = np.full(
            len(prepared.sequence_items),
            config.ordinary_target_weight,
            dtype=np.float32,
        )
        organic_strong = (
            (prepared.event_features[:, 0] > 0.5)
            & (prepared.event_features[:, 1] >= 0.8)
        )
        weights[organic_strong] = config.organic_strong_target_weight
        if config.target_weighting == "discovery-graded":
            like_outcomes = load_organic_like_outcomes(
                public, stage, prepared.item_ids
            )
            history_end = (
                base.TRAIN_END if stage == "validation" else base.VALIDATION_END
            )
            horizon = config.discovery_horizon_days * base.DAY
            grade_three = 0
            for user_row, uid in enumerate(prepared.user_ids):
                start = int(prepared.offsets[user_row])
                end = int(prepared.offsets[user_row + 1])
                if end - start < 2:
                    continue
                last_training_target = start + int(training_counts[user_row])
                first_validation_target = last_training_target + 1
                split_timestamp = (
                    int(prepared.sequence_timestamps[first_validation_target])
                    if first_validation_target < end
                    else history_end
                )
                user_likes = like_outcomes.get(int(uid), {})
                seen = {int(prepared.sequence_items[start])}
                for position in range(start + 1, end):
                    item = int(prepared.sequence_items[position])
                    novel = item not in seen
                    seen.add(item)
                    if not novel:
                        # Repeats remain training examples, but they cannot be
                        # eligible discovery outcomes at the corresponding
                        # cutoff and therefore keep only the ordinary weight.
                        weights[position] = config.ordinary_target_weight
                        continue
                    timestamps = user_likes.get(item)
                    if timestamps is None:
                        continue
                    timestamp = int(prepared.sequence_timestamps[position])
                    upper = min(
                        timestamp + horizon,
                        split_timestamp
                        if position <= last_training_target
                        else history_end,
                    )
                    outcome = int(np.searchsorted(timestamps, timestamp, side="left"))
                    if outcome < len(timestamps) and int(timestamps[outcome]) < upper:
                        weights[position] = config.organic_like_target_weight
                        grade_three += 1
            log(
                f"grade-3 first-encounter targets={grade_three:,} "
                f"horizon_days={config.discovery_horizon_days}"
            )
    log(
        f"target weighting={config.target_weighting} "
        f"min={weights.min():.3f} mean={weights.mean():.3f} "
        f"max={weights.max():.3f}"
    )
    return weights


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

    lengths = prepared.lengths
    for row, user_row in enumerate(users):
        count = min(steps, max(int(process_counts[row]) - start, 0))
        if not count:
            continue
        offset = int(prepared.offsets[user_row])
        window_start = int(window_starts[row])
        absolute_start = window_start + start
        positions = slice(
            offset + absolute_start,
            offset + absolute_start + count,
        )
        target_positions = slice(
            offset + absolute_start + 1,
            offset + absolute_start + count + 1,
        )
        input_items[row, :count] = prepared.sequence_items[positions]
        event_features[row, :count] = prepared.event_features[positions]
        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]

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


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

    for group_number, users in enumerate(groups, start=1):
        group_totals = totals[users]
        group_starts = training_counts[users]
        hidden = torch.zeros(
            1, len(users), config.embedding_dim, device=device
        )
        max_targets = int(group_totals.max())
        for start in range(0, max_targets, config.bptt_steps):
            steps = min(config.bptt_steps, max_targets - start)
            block = build_dense_block(
                prepared,
                target_weights,
                users,
                window_starts[users],
                group_totals,
                group_starts,
                start,
                steps,
                config,
            )
            sequence_outputs, _ = event_sequence(
                model,
                torch.from_numpy(block[0].astype(np.int64)).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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@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"),

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

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

<untrusted_context>
Output: <class 'pandas.core.frame.DataFrame'>
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
 #   Column                    Non-Null Count  Dtype 
---  ------                    --------------  ----- 
 0   item_id                   30000 non-null  uint32
 1   positive_listens          30000 non-null  int64 
 2   organic_positive_listens  30000 non-null  uint64
 3   popularity_rank           30000 non-null  int32 
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
   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
5  5463340             22746                     17398                6
6  8213481             22505                     17071                7
7   906358             22238                     17183                8
8  2859641             18380                     13704                9
9  3033749             16682                     12267               10
            item_id  ...  popularity_rank
count  3.000000e+04  ...     30000.000000
mean   4.698278e+06  ...     15000.500000
std    2.700274e+06  ...      8660.398374
min    2.060000e+02  ...         1.000000
25%    2.382368e+06  ...      7500.750000
50%    4.687992e+06  ...     15000.500000
75%    7.040711e+06  ...     22500.250000
max    9.390294e+06  ...     30000.000000

[8 rows x 4 columns]
Process Group PGID: 1734
</untrusted_context>

<untrusted_context>
Output: Users: 8456 Items: 30000 Packed shape: (8456, 3750)
Eligible count per user (first 100): min 25547 max 29971 mean 29098.94
Process Group PGID: 1751
</untrusted_context>

<untrusted_context>
Output: candidate_catalog.parquet           rows:     30,000 cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet                rows:      8,456 cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet       rows:    484,776 cols: ['uid', 'item_id', 'state']
train_dislikes.parquet              rows:    101,075 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet                 rows:    814,831 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet               rows: 43,484,159 cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet            rows:     19,960 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet               rows:    292,665 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet                rows:      3,563 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet                   rows:     37,795 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet                 rows:  1,501,697 cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet              rows:        606 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet                 rows:      9,346 cols: ['uid', 'timestamp', 'item_id', 'is_organic']
validation_preference_state.parquet rows:    469,124 cols: ['uid', 'item_id', 'state']
Process Group PGID: 1761
</untrusted_context>

IMPORTANT: The file content has been truncated.
Status: Showing lines 340-470 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

IMPORTANT: The file content has been truncated.
Status: Showing lines 247-314 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: 315.

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

Found 40 matches for pattern "dislike" in path "/app/solution":
---
File: 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: 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
---

<untrusted_context>
Output: Eligible dislikes found in validation candidates: 0
Process Group PGID: 1789
</untrusted_context>

Found 1 match for pattern "yambda-windowed-sequence-two-tower-base" in path "/app/solution":
---
File: train_current_model.py
L2064-         config.max_train_targets_per_user,
L2065-         config.max_validation_targets_per_user,
L2066-     )
L2067-     if args.prepare_only:
L2068-         print(
L2069-             json.dumps(
L2070-                 {
L2071-                     "stage": args.stage,
L2072-                     "sequence_users": len(prepared.user_ids),
L2073-                     "sequence_tokens": len(prepared.sequence_items),
L2074-                     "next_item_targets": prepared.target_count,
L2075-                     "selected_training_targets": int(
L2076-                         target_partition.training_counts.sum()
L2077-                     ),
L2078-                     "selected_validation_targets": int(
L2079-                         target_partition.validation_counts.sum()
L2080-                     ),
L2081-                     "discarded_old_targets": int(
L2082-                         target_partition.window_starts.sum()
L2083-                     ),
L2084-                     "target_users": len(eligibility.uids),
L2085-                     "runtime_seconds": round(time.monotonic() - started, 3),
L2086-                     "config": asdict(config),
L2087-                 },
L2088-                 indent=2,
L2089-             )
L2090-         )
L2091-         return 0
L2092- 
L2093-     model, training_history = train_model(
L2094-         public,
L2095-         args.stage,
L2096-         prepared,
L2097-         config,
L2098-         device,
L2099-         args.checkpoint_dir.resolve() if args.checkpoint_dir else None,
L2100-         args.resume_checkpoint.resolve() if args.resume_checkpoint else None,
L2101-     )
L2102-     ranking, embedding_diagnostics = rank_candidates(
L2103-         model,
L2104-         prepared,
L2105-         eligibility,
L2106-         config,
L2107-         device,
L2108-         args.ranking_batch_size,
L2109-     )
L2110-     write_ranking(ranking, args.output.resolve())
L2111-     print(
L2112-         json.dumps(
L2113-             {
L2114:                 "baseline": "yambda-windowed-sequence-two-tower-base",
L2115-                 "stage": args.stage,
L2116-                 "output": str(args.output.resolve()),
L2117-                 "rows": len(ranking),
L2118-                 "users": int(ranking.uid.nunique()),
L2119-                 "runtime_seconds": round(time.monotonic() - started, 3),
L2120-                 "public_inputs_only": True,
L2121-                 "architecture": {
L2122-                     "family": "pure-windowed-sequence-two-tower",
L2123-                     "sequence_training": (
L2124-                         "every-adjacent-target-within-recent-chronological-window"
L2125-                     ),
L2126-                     "sequence_encoder": "one-layer-GRU-dense-timestep-loss",
L2127-                     "bptt_hidden_state": "carried-and-detached",
L2128-                     "model_selection": "chronological-validation-BCE-early-stopping",
L2129-                     "loss_execution": "gradient-accumulated-microbatches",
L2130-                     "user_id_embedding": False,
L2131-                     "user_mlp": "582-512-256-192",
L2132-                     "item_mlp": "196-512-256-192",
L2133-                     "normalization": "none",
L2134-                     "score": "raw-dot-product",
L2135-                     "objective": "sample-weighted-BCE-at-every-timestep",
L2136-                     "negative_sampling": (
L2137-                         "24-uniform-plus-8-propensity-corrected-inbatch"
L2138-                     ),
L2139-                     "false_negative_filter": config.negative_filter_scope,
L2140-                     "target_weighting": config.target_weighting,
L2141-                 },
L2142-                 "config": asdict(config),
L2143-                 "sequence_tokens": len(prepared.sequence_items),
L2144-                 "all_next_item_targets": prepared.target_count,
L2145-                 "selected_training_targets": int(
L2146-                     target_partition.training_counts.sum()
L2147-                 ),
L2148-                 "selected_validation_targets": int(
L2149-                     target_partition.validation_counts.sum()
L2150-                 ),
L2151-                 "discarded_old_targets": int(target_partition.window_starts.sum()),
L2152-                 "training_history": training_history,
L2153-                 "embedding_diagnostics": embedding_diagnostics,
L2154-                 "torch_version": torch.__version__,
L2155-                 "device": str(device),
L2156-             },
L2157-             indent=2,
L2158-         )
L2159-     )
L2160-     return 0
L2161- 
L2162- 
L2163- if __name__ == "__main__":
L2164-     raise SystemExit(main())
---

IMPORTANT: The file content has been truncated.
Status: Showing lines 471-525 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: 526.

--- 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):

<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: 1814
</untrusted_context>

<untrusted_context>
Output: loaded explicit events: rows=720,270 users=8,220
prepared users=1,000 tokens=2,090,746 states=35,727 elapsed=4.8s
prepared users=2,000 tokens=4,301,266 states=71,242 elapsed=9.8s
prepared users=3,000 tokens=6,486,146 states=109,471 elapsed=15.0s
prepared users=4,000 tokens=8,793,043 states=148,210 elapsed=20.4s
prepared users=5,000 tokens=10,992,205 states=182,596 elapsed=25.2s
prepared users=6,000 tokens=13,061,727 states=217,148 elapsed=30.0s
prepared users=7,000 tokens=15,334,418 states=256,914 elapsed=35.5s
prepared users=8,000 tokens=17,462,672 states=296,703 elapsed=40.7s
prepared users=9,000 tokens=19,568,405 states=331,551 elapsed=45.5s
target users without qualifying catalog sequence=7
prepared stage=validation sequence_users=9,135 tokens=20,056,329 next_item_targets=20,047,194 states=339,133
saved public-derived full-sequence cache: /app/solution/validation_cache.npz
{
  "stage": "validation",
  "sequence_users": 9135,
  "sequence_tokens": 20056329,
  "next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "target_users": 8456,
  "runtime_seconds": 50.558,
  "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
  }
}
Process Group PGID: 1825
</untrusted_context>

<untrusted_context>
Output: -rw-r--r-- 1 agent agent 511M Sep 29 05:47 /app/solution/validation_cache.npz
Process Group PGID: 1858
</untrusted_context>

<untrusted_context>
Output: loaded full-sequence cache: tokens=20,056,329 targets=20,047,194
{
  "stage": "validation",
  "sequence_users": 9135,
  "sequence_tokens": 20056329,
  "next_item_targets": 20047194,
  "selected_training_targets": 1052953,
  "selected_validation_targets": 130103,
  "discarded_old_targets": 18864138,
  "target_users": 8456,
  "runtime_seconds": 0.412,
  "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
  }
}
Process Group PGID: 1861
</untrusted_context>

<untrusted_context>
Output: Unique items recommended: 2575
count    845600.000000
mean       2991.802935
std        5853.839534
min           1.000000
25%         102.000000
50%         413.000000
75%        2627.000000
max       29992.000000
Name: pop_rank, dtype: float64
Most frequently recommended items:
Item 6896222: count 5089 (pop rank 818)
Item 7565403: count 4970 (pop rank 2062)
Item 2055777: count 4647 (pop rank 1170)
Item 8722394: count 4627 (pop rank 634)
Item 5010168: count 4572 (pop rank 2947)
Item 5134208: count 4397 (pop rank 306)
Item 3968387: count 4160 (pop rank 318)
Item 8823268: count 4157 (pop rank 76)
Item 6414220: count 4155 (pop rank 3207)
Item 8350060: count 4132 (pop rank 1529)
Item 7227266: count 4091 (pop rank 919)
Item 8729910: count 4036 (pop rank 564)
Item 3375935: count 4003 (pop rank 282)
Item 6625048: count 3992 (pop rank 128)
Item 6006390: count 3988 (pop rank 75)
Item 6813373: count 3960 (pop rank 126)
Item 2408923: count 3865 (pop rank 111)
Item 2705460: count 3815 (pop rank 3016)
Item 1034521: count 3747 (pop rank 189)
Item 781438: count 3710 (pop rank 86)
Process Group PGID: 1879
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1898
</untrusted_context>

<untrusted_context>
Output: Total graded interactions: 37769
Unique relevant items: 14124
Popularity rank of relevant items:
count    37769.000000
mean      8463.087744
std       8117.953262
min          1.000000
25%       1733.000000
50%       5622.000000
75%      13374.000000
max      30000.000000
Name: pop_rank, dtype: float64
Grade distribution:
grade
1    33352
3     4417
Name: count, dtype: int64
Process Group PGID: 1909
</untrusted_context>

<untrusted_context>
Output: Total listens by target users: 43222297
Low completion listens (<50%): 15854954
Eligible found among low completion listens: 0
Process Group PGID: 1935
</untrusted_context>

Found 4 matches for pattern "choices=" in path "/app/solution":
---
File: train_current_model.py
L1925:         "--stage", choices=("validation", "submission"), default="validation"
L1930:     parser.add_argument("--device", choices=("cpu", "mps"), default="cpu")
L1945:         choices=("full-training-history", "last-100"),
L1950:         choices=("discovery-graded", "organic-strong", "uniform"),
---

Current topic: "Testing Hypotheses on Negative Sampling and Loss"
Topic summary: We have completed the code audit and identified several major limitations: 1) point-wise BCE loss dividing positive vs negative weights unequally and failing to enforce pairwise/listwise ranking order; 2) negative sampling dominated by uniform random tail items, with in-batch selection not sampling proportional to popularity; 3) inbatch propensity correction erroneously downscaling negative gradients for popular items; 4) target weighting not aligning with discovery-graded objective. We are now running targeted experiments to test these hypotheses.

Strategic Intent: Formulate hypotheses on negative sampling and loss objectives, then run controlled experiments.

<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=197.6s
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=211.0 validation_seconds=15.4
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/test_discovery_graded.parquet",
  "rows": 845600,
  "users": 8456,
  "runtime_seconds": 318.16,
  "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": 0.5421154196502023,
      "positive_logit": 0.7774311393444494,
      "negative_logit": -0.6407561951803926,
      "pair_accuracy": 0.7602795720701684,
      "targets": 1052953.0,
      "target_weight_sum": 348780.0,
      "batches": 266.0,
      "seconds": 210.97680790599998,
      "validation_loss": 0.5048305711443279,
      "validation_targets": 130103.0,
      "validation_target_weight_sum": 42129.5,
      "validation_seconds": 15.413563092999993
    }
  ],
  "embedding_diagnostics": {
    "query_norm_mean": 1.7521849870681763,
    "query_norm_std": 0.8638167977333069,
    "item_norm_mean": 1.8543914556503296,
    "item_norm_std": 0.8459439277648926
  },
  "torch_version": "2.8.0+cpu",
  "device": "cpu"
}
Process Group PGID: 1963
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/test_discovery_graded.parquet",
  "scores": {
    "quality_ndcg@10": 0.023522,
    "quality_users": 4263
  }
}
Process Group PGID: 2050
</untrusted_context>

<untrusted_context>
Output: Batch size   64: 1.18s, df shape: (845600, 3)
Batch size  512: 0.86s, df shape: (845600, 3)
Batch size 1024: 0.88s, df shape: (845600, 3)
Process Group PGID: 2088
</untrusted_context>

Found 13 matches for pattern "inbatch.*correction" in path "/app/solution":
---
File: train_current_model.py
L58:     inbatch_correction_power: float = 0.5
L59:     inbatch_correction_min: float = 0.25
L60:     inbatch_correction_max: float = 4.0
L167:         inbatch_correction_power=config.inbatch_correction_power,
L168:         inbatch_correction_min=config.inbatch_correction_min,
L169:         inbatch_correction_max=config.inbatch_correction_max,
L562:         config.inbatch_correction_power,
L567:         config.inbatch_correction_min,
L568:         config.inbatch_correction_max,
L572:         f"inbatch correction: min={correction.min():.3f} "
---
File: two_tower_model.py
L42:     inbatch_correction_power: float = 0.5
L43:     inbatch_correction_min: float = 0.25
L44:     inbatch_correction_max: float = 4.0
---

Found 12 matches for pattern "uniform_negatives" in path "/app/solution":
---
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
---

IMPORTANT: The file content has been truncated.
Status: Showing lines 1920-1980 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: 1981.

--- FILE CONTENT (truncated) ---


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,

<untrusted_context>
Output: Top 10 items prob sum (unweighted pos): 0.011343252297067924
Top 10 items prob sum (p^0.75): 0.005174187018907085
Top 1000 items prob sum (p^0.75): 0.15994544467164737
Min prob: 1.2763169235110207e-05 Max prob: 0.0005829921103052797 Ratio: 45.67769176808582
Process Group PGID: 2124
</untrusted_context>

<untrusted_context>
Output: (empty)
Process Group PGID: 2141
</untrusted_context>

<untrusted_context>
Output: Average time per multinomial draw: 11.50 ms
Process Group PGID: 2144
</untrusted_context>

IMPORTANT: The file content has been truncated.
Status: Showing lines 645-685 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: 686.

--- FILE CONTENT (truncated) ---
    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

IMPORTANT: The file content has been truncated.
Status: Showing lines 41-75 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: 76.

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

IMPORTANT: The file content has been truncated.
Status: Showing lines 41-75 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: 76.

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

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

<untrusted_context>
Output: (empty)
Process Group PGID: 2160
</untrusted_context>

<untrusted_context>
Output: total 525308
drwxr-xr-x 1 agent agent      4096 Sep 29 06:05 .
drwxr-xr-x 1 agent agent      4096 Sep 29 05:33 ..
-rw-r--r-- 1 agent agent   1244231 Sep 29 05:43 production_validation.parquet
-rw-r--r-- 1 agent agent   1249067 Sep 29 05:55 test_discovery_graded.parquet
-rw-r--r-- 1 agent agent     80058 Sep 29 05:43 train_current_model.py
-rw-r--r-- 1 agent agent     80058 Sep 29 06:05 train_current_model_baseline.py
-rw-r--r-- 1 agent agent     15985 Sep 29 05:43 two_tower_model.py
-rw-r--r-- 1 agent agent 535225808 Sep 29 05:47 validation_cache.npz
Process Group PGID: 2163
</untrusted_context>

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