Agent trace

Current topic: "Initial Research & Environment Setup"
Topic summary: Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.

Strategic Intent: Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.

<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 02:09 .
drwxr-xr-x 1 root root 4096 Sep 29 02:12 ..
-r--r--r-- 1 root root    0 Sep 29 01:55 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:56 data
-r--r--r-- 1 root root 5774 Sep 29 01:55 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:55 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:55 requirements.txt
total 347392
dr-xr-xr-x 1 root root      4096 Sep 29 01:56 .
dr-xr-xr-x 1 root root      4096 Sep 29 02:09 ..
-r--r--r-- 1 root root    349352 Sep 29 01:55 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Sep 29 01:55 target_users.parquet
-r--r--r-- 1 root root   7736341 Sep 29 01:55 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Sep 29 01:56 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Sep 29 01:55 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Sep 29 01:55 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:55 train_listens.parquet
-r--r--r-- 1 root root    155988 Sep 29 01:55 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Sep 29 01:55 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Sep 29 01:55 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Sep 29 01:55 val_likes.parquet
-r--r--r-- 1 root root   9947609 Sep 29 01:55 val_listens.parquet
-r--r--r-- 1 root root      7845 Sep 29 01:55 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Sep 29 01:55 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Sep 29 01:55 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Sep 29 01:56 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>

Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app

Path not in workspace: Attempted path "/task/ranking_evaluation.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app

<untrusted_context>
Output: #!/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()
Process Group PGID: 1616
</untrusted_context>

<untrusted_context>
Output: """Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
Process Group PGID: 1619
</untrusted_context>

<untrusted_context>
Output: # Exact distribution inventory for the Yambda two-tower task family.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. Python itself is pinned by task.toml and both
# Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Process Group PGID: 1622
</untrusted_context>

<untrusted_context>
Output: 8
/usr/bin/bash: line 4: free: command not found
Filesystem      Size  Used Avail Use% Mounted on
overlay          96G   14G   83G  15% /
tmpfs            64M     0   64M   0% /dev
shm              64M     0   64M   0% /dev/shm
/dev/root        96G   14G   83G  15% /etc/hosts
tmpfs           7.7G     0  7.7G   0% /proc/acpi
tmpfs           7.7G     0  7.7G   0% /proc/scsi
tmpfs           7.7G     0  7.7G   0% /sys/firmware
Process Group PGID: 1625
</untrusted_context>

<untrusted_context>
Output: MemTotal:       15988968 kB
MemFree:         1284324 kB
MemAvailable:   14408784 kB
Buffers:          248460 kB
Cached:         12547192 kB
Process Group PGID: 1630
</untrusted_context>

<untrusted_context>
Output: --- TARGET USERS ---
target_users: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
   recent_positive_listens: min=20.0, max=19592.0
   history_positive_listens: min=20, max=26597
--- CANDIDATE CATALOG ---
candidate_catalog: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   positive_listens: min=163, max=26615
   popularity_rank: min=1, max=30000
--- PREFERENCE STATES ---
validation_preference_state: shape=(469124, 3), cols=['uid', 'item_id', 'state']
test_preference_state: shape=(484776, 3), cols=['uid', 'item_id', 'state']
--- TRAIN EVENTS ---
train_likes: len=814831
train_dislikes: len=101075
train_unlikes: len=292665
train_undislikes: len=19960
val_likes: len=37795
val_dislikes: len=3563
val_unlikes: len=9346
val_undislikes: len=606
Process Group PGID: 1634
</untrusted_context>

<untrusted_context>
Output: Reading val_listens...
val_listens: (1501697, 6) Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',
       'track_length_seconds'],
      dtype='object')
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100   24943430  9321269           0               100                   195
1  100   24943605  5452713           0               100                   175
2  100   24943765  6448777           0               100                   160
Reading train_listens metadata...
train_listens num_rows: 43484159 num_row_groups: 42
schema: <pyarrow._parquet.ParquetSchema object at 0x7bcb6b6fe280>
required group field_id=-1 schema {
  optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));
  optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));
  optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));
  optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));
  optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));
  optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));
}

validation_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
test_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
Process Group PGID: 1651
</untrusted_context>

<untrusted_context>
Output: cc item_id equals vec item_ids? True
vec item_ids equals tec item_ids? True
vec uids equals tec uids? True
Val eligible counts per user: min=24657, max=30000, mean=29067.3, median=29242.0
Test eligible counts per user: min=24552, max=30000, mean=29041.3, median=29220.0
Process Group PGID: 1671
</untrusted_context>

<untrusted_context>
Output: Validation preference state values:
state
liked       418435
disliked     50689
Name: count, dtype: int64
   uid  item_id     state
0  100   410868     liked
1  100   732449     liked
2  100   982903     liked
3  100  1730273     liked
4  100  2621343     liked
5  100  3535902     liked
6  100  3633039     liked
7  100  4099299     liked
8  100  4221787     liked
9  100  4927727  disliked
Test preference state values:
state
liked       432659
disliked     52117
Name: count, dtype: int64
Process Group PGID: 1688
</untrusted_context>

<untrusted_context>
Output: val_listens ts min: 24788600 max: 25393395 delta: 604795
val_likes ts min: 24788815 max: 25393390
train_likes ts min: 80 max: 24786790
target_users count: 8456
Process Group PGID: 1706
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1726
</untrusted_context>

<untrusted_context>
Output: Number of users with at least 1 relevant item in val: 4263
Relevant items per user: min, median, mean, max: 1 4.0 8.859723199624677 442
Total positives: 37769, grade 3 (likes): 4417, grade 1 (strong listens): 33352
Process Group PGID: 1737
</untrusted_context>

<untrusted_context>
Output: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1757
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 11, in <module>
NameError: name 'np' is not defined
Validation preference state:
   uid  item_id     state
0  100   410868     liked
1  100   732449     liked
2  100   982903     liked
3  100  1730273     liked
4  100  2621343     liked
5  100  3535902     liked
6  100  3633039     liked
7  100  4099299     liked
8  100  4221787     liked
9  100  4927727  disliked
state
disliked     50689
liked       418435
dtype: int64
Exit Code: 1
Process Group PGID: 1777
</untrusted_context>

<untrusted_context>
Output: Are disliked items eligible?: 0 out of 100
Are liked items eligible?: 0 out of 100
Total disliked items eligible: 0 out of 50689
Liked sample (5000) eligible: 0 out of 5000
Process Group PGID: 1794
</untrusted_context>

<untrusted_context>
Output: VPS items in candidate catalog: 469124 out of 469124
Process Group PGID: 1811
</untrusted_context>

<untrusted_context>
Output: Ineligible count stats: min, mean, max: 0 932.6937086092715 5343
VPS count stats: min, mean, max: 0 55.478240302743615 1847
Process Group PGID: 1828
</untrusted_context>

<untrusted_context>
Output: Target users count: 8456
Total rows in train_listens: 43484159
Unique uids in train_listens: 9238
Target uids present in train_listens: 8456
Process Group PGID: 1845
</untrusted_context>

<untrusted_context>
Output: Timestamp min: 0 max: 24786795
Max ts in days: 286.88420138888887 Min ts in days: 0.0
Distance from max to cutoff: 5
90 days before cutoff timestamp: 17010800
Rows in last 90 days: 16769274 (38.6%)
Rows in last 30 days: 6126103 (14.1%)
Rows in last 14 days: 2920227 (6.7%)
Process Group PGID: 1866
</untrusted_context>

<untrusted_context>
Output: Catalog items count: 30000
Unique items in train_listens: 845395
Items in catalog: 30000 (3.5%)
Fraction of all listens in catalog: 71.86%
Process Group PGID: 1886
</untrusted_context>

<untrusted_context>
Output: Train likes in catalog: 0.6809068383505291
Val likes in catalog: 0.5570842704061384
Val listens in catalog: 0.6737517621730615
Process Group PGID: 1908
</untrusted_context>

<untrusted_context>
Output: Total unique gold items in val: 14124
Are all gold items in candidate catalog?: True
Process Group PGID: 1928
</untrusted_context>

<untrusted_context>
Output: Read 43M rows in 1.37 seconds
Process Group PGID: 1948
</untrusted_context>

<untrusted_context>
Output: Validation preference state count: 469124
Test preference state count: 484776
Target users count in VPS: 7038 in TPS: 7092
Process Group PGID: 1970
</untrusted_context>

<untrusted_context>
Output: uid  recent_positive_listens  history_positive_listens
0   100                    844.0                      2604
1   200                     50.0                       286
2   300                    168.0                       359
3   600                   1985.0                      2423
4   700                   1776.0                      6114
5   800                   1703.0                      7281
6   900                     42.0                       136
7  1000                    525.0                       597
8  1100                   1976.0                      3653
9  1400                    782.0                     10713
                  uid  recent_positive_listens  history_positive_listens
count     8456.000000              8456.000000               8456.000000
mean    502289.108325              1251.076750               3236.440752
std     287953.395912              1414.340904               3697.575286
min        100.000000                20.000000                 20.000000
25%     254375.000000               281.000000                630.000000
50%     503850.000000               784.500000               1920.500000
75%     751725.000000              1706.250000               4494.250000
max    1000000.000000             19592.000000              26597.000000
Process Group PGID: 1988
</untrusted_context>

<untrusted_context>
Output: Total graded items in val: 37769, previously listened in train: 0 (0.0%)
Never listened before (true discovery): 37769 (100.0%)
Process Group PGID: 2004
</untrusted_context>

<untrusted_context>
Output: Train listens eligible: 0 out of 10000
Process Group PGID: 2028
</untrusted_context>

Switching to Plan mode.

Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md. Here is the updated code:
# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker

## Objective
Design, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the "Made for You" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).

## Key Architectural Invariants & Constraints
1. **Strict Two-Tower Decomposition**:
   - `ItemTower(item_id, item_features) -> v \in R^d`
   - `UserTower(user_interaction_history) -> u \in R^d`
   - Relevance score: `s(u, v) = u^T v` (dot product)
   - Item embeddings are precomputed offline into a candidate matrix $V \in R^{30000 \times d}$.
   - User embedding $u \in R^d$ is computed online from interaction history.
   - User tower does NOT use user ID lookup (must derive representation from interaction history).
   - No construction or decomposition of a global user-item interaction matrix.
2. **Platform Candidate Boundary**:
   - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).
   - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.
3. **Temporal Split & Retraining**:
   - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.
   - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.

---

## Technical Design

### 1. Item Tower Architecture
- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).
- **Inputs**:
  - `item_id`: mapped to item index in `[0, 30000)`.
  - Item metadata features from `candidate_catalog.parquet`:
    - $\log(1 + \text{positive\_listens})$ (standardized)
    - $\log(1 + \text{organic\_positive\_listens})$ (standardized)
    - Organic ratio: $\frac{\text{organic\_positive\_listens}}{\text{positive\_listens} + 1}$
    - Normalized popularity rank: $\frac{\text{popularity\_rank}}{30000}$
- **Layers**:
  - Item Embedding table: `nn.Embedding(30001, d_item_embed)`
  - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`
  - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`
  - $L_2$ Normalization to ensure stable dot product space.

### 2. User Tower Architecture (History-Derived)
- **Inputs**:
  - Sequence of recent positive listens (item IDs, time decay weights $e^{-\lambda \Delta t}$, played ratio, organic flag).
  - Set of explicit liked items (from likes & preference state).
  - Set of explicit disliked items (from dislikes & preference state).
  - User activity features (listen count, organic listen fraction, like count, dislike count).
- **Pooling & Layers**:
  - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.
  - Likes pooling: Mean pooling of liked item embeddings.
  - Dislikes pooling: Mean pooling of disliked item embeddings.
  - User MLP:
    - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`
    - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`
    - $L_2$ Normalization to unit sphere.

### 3. Training Scheme & Loss Function
- **Dataset Construction**:
  - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\ge 80\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).
  - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).
- **Loss Function**:
  - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:
    $$S_{i, j} = \frac{u_i^T v_j}{\tau} - \log P(v_j)$$
    $$\mathcal{L}_{InfoNCE} = - \sum_i w_i \log \frac{\exp(S_{i, i})}{\sum_j \exp(S_{i, j})}$$
  - Dislike penalty / Margin Ranking loss on explicitly disliked items:
    $$\mathcal{L}_{dislike} = \sum_i \max(0, \gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$
  - Combined loss: $\mathcal{L} = \mathcal{L}_{InfoNCE} + \alpha \mathcal{L}_{dislike}$.

---

## Phased Implementation Plan

### Phase 1: Data Preprocessing & Feature Extraction
- Build fast extraction scripts in `/app/solution/`:
  - `data_prep.py`:
    - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.
    - Build user history representations (recent listens with decay, likes, dislikes, summary stats).
    - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.
    - Extract item metadata features for the 30,000 catalog tracks.

### Phase 2: PyTorch Model Definition
- `model.py`:
  - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.
  - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.
  - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.

### Phase 3: Training & Validation Loop
- `train_val.py`:
  - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.
  - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).
  - Precompute all candidate item embeddings $V \in R^{30000 \times d}$.
  - Compute user embeddings for all 8,456 target users at the validation cutoff.
  - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.
  - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.

### Phase 4: Tuning & Optimization
- Experiment with:
  - Embedding dimensions ($d=64, 128$)
  - InfoNCE temperature $\tau \in \{0.05, 0.07, 0.1\}$
  - Recency half-life (14 days vs 30 days vs 60 days)
  - Incorporating dislike embeddings into user representation
  - Validate score progression against the popularity baseline (`0.01071`).

### Phase 5: Refitting on Full Data (Train + Val) & Test Submission
- `refit_submission.py`:
  - Combine `train_*` and `val_*` data (listens, likes, dislikes).
  - Update user histories up to validation cutoff with all observed events.
  - Use `test_preference_state.parquet` for active explicit preferences.
  - Train final model on full combined dataset.
  - Precompute item embeddings for all 30,000 catalog items.
  - Compute user embeddings for all 8,456 target users.
  - Apply `test_eligible_candidates.npz` mask.
  - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.
  - Validate submission structure with `ranking_evaluation.validate_ranking`.

### Phase 6: Documentation & Deliverables Verification
- Ensure `/app/solution/README.md` documents:
  - Model architecture (User Tower, Item Tower, History aggregation)
  - Training objective (InfoNCE with popularity correction and dislike penalty)
  - Public validation NDCG@10 score
  - Final submission command
- Verify `/app/solution/submission.parquet` meets all schema requirements:
  - Columns: `uid`, `item_id`, `rank` (1..100)
  - Exactly 8,456 * 100 = 845,600 rows
  - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.

Plan approved. Switching to YOLO mode (all tool calls auto-approved).

The approved implementation plan is stored at: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md
Read and follow the plan strictly during implementation.

Current topic: "Data Preprocessing & Feature Extraction"
Topic summary: Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages.

Strategic Intent: Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.

<untrusted_context>
Output: Total listens for target users in train: 43222297
Catalog listens for target users: 31062540 (71.9%)
Positive listens (>=50%): 27367343, in catalog: 19959853 (72.9%)
Process Group PGID: 2051
</untrusted_context>

<untrusted_context>
Output: VPS liked in train_likes: 418435 out of 418435
VPS liked in train_unlikes: 5417 out of 418435
Process Group PGID: 2073
</untrusted_context>

<untrusted_context>
Output: Total organic likes in train: 461839
Strong organic listens in last 35 days of train: 2024728
Window 0 [24182000..24786800]: organic likes=17201, strong listens=428612, unique users with targets=6853
Window 1 [23575400..24180200]: organic likes=14981, strong listens=406037, unique users with targets=6657
Window 2 [22968800..23573600]: organic likes=14715, strong listens=400103, unique users with targets=6545
Window 3 [22362200..22967000]: organic likes=16990, strong listens=399933, unique users with targets=6412
Process Group PGID: 2091
</untrusted_context>

<untrusted_context>
Output: Catalog organic likes in window: 11210 discovery fraction: 0.7895628902765388
Catalog strong listens in window: 305188 discovery fraction: 0.2222695518827739
Discovery organic likes count: 8851
Discovery strong listens count: 67834
Process Group PGID: 2113
</untrusted_context>

<untrusted_context>
Output: MIPS for all 8456 target users over 30000 candidate items took: 0.495 seconds!
Process Group PGID: 2135
</untrusted_context>

<untrusted_context>
Output: total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 02:09 .
drwxr-xr-x 3 agent agent 4096 Sep 29 02:09 ..
Process Group PGID: 2148
</untrusted_context>

<untrusted_context>
Output: All items eligible in test candidate set?: True
Shape: (845600, 3)
Successfully saved initial baseline submission to /app/solution/submission.parquet!
Process Group PGID: 2151
</untrusted_context>

<untrusted_context>
Output: -rw-r--r-- 1 agent agent 719K Sep 29 02:18 /app/solution/submission.parquet
Process Group PGID: 2168
</untrusted_context>

<untrusted_context>
Output: Window 0: c_cut=24180200, target_likes=7774, target_listens=304885
Window 1: c_cut=23573600, target_likes=7063, target_listens=289032
Window 2: c_cut=22967000, target_likes=7518, target_listens=287032
Window 3: c_cut=22360400, target_likes=7572, target_listens=289103
Total potential target pairs across 4 windows: 1199979
Process Group PGID: 2171
</untrusted_context>

<untrusted_context>
Output: Catalog items: 30000
Target users: 8456
Init maps in 0.04s
Process Group PGID: 2193
</untrusted_context>

<untrusted_context>
Output: Read filtered pyarrow table in 0.35s, rows=5683546
Converted to pandas in 0.36s
Filtered to target users in 0.81s, rows=5681249
Target users covered in last 45d: 8330 out of 8456
Min listens in last 45d: 1 median: 425.0 max: 11435
Process Group PGID: 2210
</untrusted_context>

<untrusted_context>
Output: Target users in last 90d: 8456 out of 8456
Process Group PGID: 2232
</untrusted_context>

<untrusted_context>
Output: Read and filtered in 1.22s, len=10579105
Sorted and took tail(100) in 1.54s, len=808180
Users in recent: 8456
Process Group PGID: 2256
</untrusted_context>

<untrusted_context>
Output: <string>:43: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Populated listen arrays in 0.45s!
Shape items: (8456, 100) Shape weights: (8456, 100)
Non-zero weights per user min: 20
Process Group PGID: 2278
</untrusted_context>

<untrusted_context>
Output: Liked counts: min, median, 90th percentile, max: 1 28.0 158.0 1327
Disliked counts: min, median, 90th percentile, max: 1 3.0 20.0 1703
Process Group PGID: 2300
</untrusted_context>

<untrusted_context>
Output: Cutoff w=1 (24180200): active likes=583825
Cutoff w=2 (23573600): active likes=563988
Cutoff w=3 (22967000): active likes=543365
Process Group PGID: 2318
</untrusted_context>

Successfully created and wrote to new file: /app/solution/data_prep.py. Here is the updated code:
"""Data preparation and feature extraction for the two-tower music ranker."""
from __future__ import annotations

import time
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple

import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch


def load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:
    """Load candidate catalog items, normalized features, and popularity.

    Returns:
        catalog_features: FloatTensor of shape (30000, 4).
        item_to_idx: mapping from catalog item_id to index [0, 29999].
        catalog_item_ids: int64 ndarray of shape (30000,).
        item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.
    """
    catalog_df = pd.read_parquet(data_dir / "candidate_catalog.parquet")
    catalog_item_ids = catalog_df["item_id"].to_numpy(dtype=np.int64)
    item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}

    pos_listens = catalog_df["positive_listens"].to_numpy(dtype=np.float32)
    org_listens = catalog_df["organic_positive_listens"].to_numpy(dtype=np.float32)
    pop_rank = catalog_df["popularity_rank"].to_numpy(dtype=np.float32)

    log_pos = np.log1p(pos_listens)
    log_org = np.log1p(org_listens)
    org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)
    norm_rank = pop_rank / float(len(catalog_df))

    # Standardize features
    features = np.column_stack([
        (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),
        (log_org - log_org.mean()) / (log_org.std() + 1e-6),
        org_ratio,
        norm_rank,
    ]).astype(np.float32)

    # LogQ for popularity debiasing
    prob = pos_listens / pos_listens.sum()
    item_logq = np.log(prob + 1e-12).astype(np.float32)

    return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq


def extract_user_histories(
    data_dir: Path,
    cutoff: int,
    target_uids: np.ndarray,
    item_to_idx: Dict[int, int],
    pref_state_path: Optional[Path] = None,
    lookback_days: int = 90,
    max_listens: int = 100,
    max_likes: int = 60,
    max_dislikes: int = 30,
    decay_half_life_days: float = 14.0,
    include_val: bool = False,
) -> Dict[str, torch.Tensor]:
    """Extract user history tensors at a given cutoff timestamp.

    Args:
        data_dir: Path to data directory.
        cutoff: Timestamp cutoff. Events must have timestamp < cutoff.
        target_uids: Ordered array of target user IDs.
        item_to_idx: Catalog mapping.
        pref_state_path: Optional path to preference state parquet.
        lookback_days: Max days to look back for listens.
        max_listens: Max recent listens per user.
        max_likes: Max liked items per user.
        max_dislikes: Max disliked items per user.
        decay_half_life_days: Exponential decay half life.
        include_val: Whether to include val_* files in history (for final test refit).

    Returns:
        Dictionary of tensors:
            - listen_items: LongTensor (N, max_listens)
            - listen_weights: FloatTensor (N, max_listens)
            - like_items: LongTensor (N, max_likes)
            - like_mask: FloatTensor (N, max_likes)
            - dislike_items: LongTensor (N, max_dislikes)
            - dislike_mask: FloatTensor (N, max_dislikes)
            - user_features: FloatTensor (N, 6)
    """
    n_users = len(target_uids)
    uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
    target_uids_set = set(uid_to_row.keys())

    # --- 1. Process Listens ---
    lookback_ts = cutoff - int(lookback_days * 86400)
    filters = [("timestamp", ">=", lookback_ts), ("timestamp", "<", cutoff), ("played_ratio_pct", ">=", 50)]
    
    train_tbl = pq.read_table(
        data_dir / "train_listens.parquet",
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
        filters=filters,
    )
    listens_df = train_tbl.to_pandas()

    if include_val and (data_dir / "val_listens.parquet").exists():
        val_tbl = pq.read_table(
            data_dir / "val_listens.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
            filters=filters,
        )
        val_df = val_tbl.to_pandas()
        listens_df = pd.concat([listens_df, val_df], ignore_index=True)

    listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values("timestamp")
    recent_listens = listens_df.groupby("uid").tail(max_listens).copy()

    listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)
    listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)

    decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)
    time_diff = (cutoff - recent_listens["timestamp"].to_numpy()).astype(np.float32)
    raw_weights = (
        np.exp(-decay_lambda * time_diff)
        * (recent_listens["played_ratio_pct"].to_numpy() / 100.0)
        * (1.0 + 0.5 * recent_listens["is_organic"].to_numpy())
    ).astype(np.float32)

    recent_uids = recent_listens["uid"].to_numpy(dtype=np.int64)
    recent_items = recent_listens["item_id"].to_numpy(dtype=np.int64)
    recent_listens["pos"] = recent_listens.groupby("uid").cumcount()
    positions = recent_listens["pos"].to_numpy()

    row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)
    mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)

    listen_items[row_indices, positions] = mapped_item_indices
    listen_weights[row_indices, positions] = raw_weights

    # Normalize weights row-wise
    w_sum = listen_weights.sum(axis=1, keepdims=True)
    mask = w_sum > 0
    listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)

    # --- 2. Process Preference State (Likes & Dislikes) ---
    like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)
    like_mask = np.zeros((n_users, max_likes), dtype=np.float32)
    dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)
    dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)

    if pref_state_path and pref_state_path.exists():
        pref_df = pd.read_parquet(pref_state_path)
        pref_df = pref_df[pref_df.uid.isin(target_uids_set)]
        liked_df = pref_df[pref_df.state == "liked"]
        disliked_df = pref_df[pref_df.state == "disliked"]
    else:
        # Reconstruct active preferences from events
        likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
        unlikes_df = pd.read_parquet(data_dir / "train_unlikes.parquet")
        dislikes_df = pd.read_parquet(data_dir / "train_dislikes.parquet")
        undislikes_df = pd.read_parquet(data_dir / "train_undislikes.parquet")

        if include_val and (data_dir / "val_likes.parquet").exists():
            likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
            unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / "val_unlikes.parquet")], ignore_index=True)
            dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / "val_dislikes.parquet")], ignore_index=True)
            undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / "val_undislikes.parquet")], ignore_index=True)

        likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]
        unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]
        dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]
        undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]

        # Set difference for active likes
        active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))
        liked_df = pd.DataFrame(list(active_likes), columns=["uid", "item_id"])

        active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))
        disliked_df = pd.DataFrame(list(active_dislikes), columns=["uid", "item_id"])

    # Populate likes
    if len(liked_df) > 0:
        liked_tail = liked_df.groupby("uid").tail(max_likes).copy()
        liked_tail["pos"] = liked_tail.groupby("uid").cumcount()
        l_rows = np.array([uid_to_row[int(u)] for u in liked_tail["uid"].values], dtype=np.int64)
        l_pos = liked_tail["pos"].values
        l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail["item_id"].values], dtype=np.int64)
        like_items[l_rows, l_pos] = l_items
        like_mask[l_rows, l_pos] = 1.0

    # Populate dislikes
    if len(disliked_df) > 0:
        disliked_tail = disliked_df.groupby("uid").tail(max_dislikes).copy()
        disliked_tail["pos"] = disliked_tail.groupby("uid").cumcount()
        d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail["uid"].values], dtype=np.int64)
        d_pos = disliked_tail["pos"].values
        d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail["item_id"].values], dtype=np.int64)
        dislike_items[d_rows, d_pos] = d_items
        dislike_mask[d_rows, d_pos] = 1.0

    # --- 3. User Summary Features ---
    tu_df = pd.read_parquet(data_dir / "target_users.parquet")
    tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}

    user_feats = np.zeros((n_users, 6), dtype=np.float32)
    listen_counts = recent_listens.groupby("uid").size().to_dict()
    organic_counts = recent_listens[recent_listens.is_organic == 1].groupby("uid").size().to_dict()
    like_counts = liked_df.groupby("uid").size().to_dict() if len(liked_df) > 0 else {}
    dislike_counts = disliked_df.groupby("uid").size().to_dict() if len(disliked_df) > 0 else {}

    for uid, r in uid_to_row.items():
        rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))
        n_rec = float(listen_counts.get(uid, 0))
        n_org = float(organic_counts.get(uid, 0))
        n_like = float(like_counts.get(uid, 0))
        n_dis = float(dislike_counts.get(uid, 0))

        user_feats[r, 0] = np.log1p(hist_listens)
        user_feats[r, 1] = np.log1p(rec_listens)
        user_feats[r, 2] = np.log1p(n_rec)
        user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)
        user_feats[r, 4] = np.log1p(n_like)
        user_feats[r, 5] = np.log1p(n_dis)

    # Standardize user features
    feat_mean = user_feats.mean(axis=0, keepdims=True)
    feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6
    user_feats = (user_feats - feat_mean) / feat_std

    return {
        "listen_items": torch.from_numpy(listen_items),
        "listen_weights": torch.from_numpy(listen_weights),
        "like_items": torch.from_numpy(like_items),
        "like_mask": torch.from_numpy(like_mask),
        "dislike_items": torch.from_numpy(dislike_items),
        "dislike_mask": torch.from_numpy(dislike_mask),
        "user_features": torch.from_numpy(user_feats),
    }


def build_training_pairs(
    data_dir: Path,
    cutoffs: List[int],
    item_to_idx: Dict[int, int],
    target_uids: np.ndarray,
    week_seconds: int = 7 * 86400,
    gap_seconds: int = 1800,
    listen_strong_threshold: int = 80,
    sample_listens_per_user: int = 30,
    include_val: bool = False,
) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:
    """Build multi-window training samples for the two-tower model.

    For each cutoff C:
      - History extracted up to C
      - Targets extracted from [C + gap, C + gap + week]
      - Only target items in candidate catalog that user had NOT interacted with before C (discovery).

    Returns:
        histories_list: list of user history dicts (one per cutoff).
        user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.
        target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].
        pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).
    """
    uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}
    target_uids_set = set(uid_to_row.keys())

    # Load likes
    likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
    if include_val and (data_dir / "val_likes.parquet").exists():
        likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
    org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]

    # Load strong organic listens
    min_cutoff = min(cutoffs)
    table = pq.read_table(
        data_dir / "train_listens.parquet",
        columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
        filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
    )
    listens_df = table.to_pandas()
    if include_val and (data_dir / "val_listens.parquet").exists():
        val_tbl = pq.read_table(
            data_dir / "val_listens.parquet",
            columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
            filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
        )
        listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)
    strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]

    histories_list: List[Dict[str, torch.Tensor]] = []
    pairs_list: List[Tuple[int, int, int, float]] = []  # (window_idx, user_row, item_idx, weight)

    for w_idx, cutoff in enumerate(cutoffs):
        t_start = cutoff + gap_seconds
        t_end = t_start + week_seconds

        # 1. Extract history up to cutoff
        print(f"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...")
        hist = extract_user_histories(
            data_dir=data_dir,
            cutoff=cutoff,
            target_uids=target_uids,
            item_to_idx=item_to_idx,
            include_val=include_val,
        )
        histories_list.append(hist)

        # 2. Get past listens before cutoff to guarantee DISCOVERY filter
        # Read past items from the history listen_items and likes
        past_items_per_user: Dict[int, Set[int]] = {}
        for r, uid in enumerate(target_uids):
            # Items in listens and likes
            l_itms = set(hist["listen_items"][r].numpy())
            lk_itms = set(hist["like_items"][r][hist["like_mask"][r] > 0].numpy())
            past = l_itms.union(lk_itms)
            past.discard(30000)
            past_items_per_user[r] = past

        # 3. Targets in [t_start, t_end]
        w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]
        w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]

        # Map and filter discovery likes (grade 3, weight 3.0)
        user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}

        for uid, item in zip(w_likes.uid, w_likes.item_id):
            r = uid_to_row[int(uid)]
            item_idx = item_to_idx.get(int(item))
            if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:
                pairs_list.append((w_idx, r, item_idx, 3.0))
                user_seen_targets[r].add(item_idx)

        # Map and filter discovery listens (grade 1, weight 1.0)
        # To balance, subsample if a user has too many listens
        grouped_listens = w_listens.groupby("uid")
        for uid, group in grouped_listens:
            r = uid_to_row[int(uid)]
            items = group["item_id"].to_numpy()
            if len(items) > sample_listens_per_user:
                items = np.random.choice(items, size=sample_listens_per_user, replace=False)
            for itm in items:
                item_idx = item_to_idx.get(int(itm))
                if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:
                    pairs_list.append((w_idx, r, item_idx, 1.0))
                    user_seen_targets[r].add(item_idx)

    np_pairs = np.array(pairs_list, dtype=np.float32)
    np.random.shuffle(np_pairs)

    user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))
    target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))
    weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))

    print(f"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.")
    return histories_list, user_win, target_items, weights

<untrusted_context>
Output: Catalog loaded in 0.03s: feats shape=torch.Size([30000, 4]), logq shape=(30000,)
Extracted validation user histories in 3.42s:
  listen_items: shape=torch.Size([8456, 100]), dtype=torch.int64
  listen_weights: shape=torch.Size([8456, 100]), dtype=torch.float32
  like_items: shape=torch.Size([8456, 60]), dtype=torch.int64
  like_mask: shape=torch.Size([8456, 60]), dtype=torch.float32
  dislike_items: shape=torch.Size([8456, 30]), dtype=torch.int64
  dislike_mask: shape=torch.Size([8456, 30]), dtype=torch.float32
  user_features: shape=torch.Size([8456, 6]), dtype=torch.float32
Process Group PGID: 2337
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 17, in <module>
  File "/app/solution/data_prep.py", line 263, in build_training_pairs
    uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}
                      ^
NameError: name 'u' is not defined
Exit Code: 1
Process Group PGID: 2359
</untrusted_context>

Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
        histories_list: list of user history dicts (one per cutoff).
        user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.
        target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].
        pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).
    """
    uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
    target_uids_set = set(uid_to_row.keys())

    # Load likes
    likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
    if include_val and (data_dir / "val_likes.parquet").exists():
...

<untrusted_context>
Output: Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Generated 76517 discovery training pairs across 2 windows.
Total time: 11.34s
Pairs: user_win shape=torch.Size([76517, 2]), target_items shape=torch.Size([76517]), weights=torch.Size([76517])
Process Group PGID: 2376
</untrusted_context>

Current topic: "PyTorch Two-Tower Architecture"
Topic summary: Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval.

Strategic Intent: Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.

Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval."""
from __future__ import annotations

from typing import Dict, Optional, Tuple

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


class ItemTower(nn.Module):
    """Item Tower computing independently precomputable item embeddings."""

    def __init__(
        self,
        item_embed: nn.Embedding,
        feat_dim: int = 4,
        embed_dim: int = 64,
        hidden_dim: int = 64,
        out_dim: int = 64,
    ):
        super().__init__()
        self.item_embed = item_embed
        self.feat_mlp = nn.Sequential(
            nn.Linear(feat_dim, 32),
            nn.LayerNorm(32),
            nn.GELU(),
        )
        self.out_mlp = nn.Sequential(
            nn.Linear(embed_dim + 32, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, out_dim),
        )

    def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:
        """Compute normalized item embeddings.

        Args:
            item_indices: LongTensor of shape (B,) with indices in [0, 30000].
            item_features: FloatTensor of shape (B, 4).

        Returns:
            L2-normalized FloatTensor of shape (B, out_dim).
        """
        emb = self.item_embed(item_indices)
        feat = self.feat_mlp(item_features)
        combined = torch.cat([emb, feat], dim=-1)
        out = self.out_mlp(combined)
        return F.normalize(out, p=2, dim=-1)


class UserTower(nn.Module):
    """User Tower deriving user embedding strictly from interaction history."""

    def __init__(
        self,
        item_embed: nn.Embedding,
        embed_dim: int = 64,
        user_feat_dim: int = 6,
        hidden_dim: int = 128,
        out_dim: int = 64,
        dropout: float = 0.1,
    ):
        super().__init__()
        self.item_embed = item_embed
        self.user_feat_mlp = nn.Sequential(
            nn.Linear(user_feat_dim, 32),
            nn.LayerNorm(32),
            nn.GELU(),
        )
        in_dim = embed_dim * 3 + 32
        self.mlp = nn.Sequential(
            nn.Linear(in_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
            nn.Linear(hidden_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Linear(hidden_dim, out_dim),
        )

    def forward(
        self,
        listen_items: torch.Tensor,
        listen_weights: torch.Tensor,
        like_items: torch.Tensor,
        like_mask: torch.Tensor,
        dislike_items: torch.Tensor,
        dislike_mask: torch.Tensor,
        user_features: torch.Tensor,
    ) -> torch.Tensor:
        """Compute normalized user embeddings from interaction history.

        Args:
            listen_items: LongTensor (B, max_listens)
            listen_weights: FloatTensor (B, max_listens)
            like_items: LongTensor (B, max_likes)
            like_mask: FloatTensor (B, max_likes)
            dislike_items: LongTensor (B, max_dislikes)
            dislike_mask: FloatTensor (B, max_dislikes)
            user_features: FloatTensor (B, user_feat_dim)

        Returns:
            L2-normalized FloatTensor of shape (B, out_dim).
        """
        # 1. Weighted listens pooling
        l_emb = self.item_embed(listen_items)  # (B, K, D)
        pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1)  # (B, D)

        # 2. Likes pooling
        lk_emb = self.item_embed(like_items)  # (B, L, D)
        lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6
        pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom
        has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()
        pooled_likes = pooled_likes * has_likes

        # 3. Dislikes pooling
        dk_emb = self.item_embed(dislike_items)  # (B, M, D)
        dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6
        pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom
        has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()
        pooled_dislikes = pooled_dislikes * has_dislikes

        # 4. User features projection
        user_feat_proj = self.user_feat_mlp(user_features)

        # 5. Combined MLP
        combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)
        out = self.mlp(combined)
        return F.normalize(out, p=2, dim=-1)


class TwoTowerRanker(nn.Module):
    """Complete Two-Tower Neural Network Ranker."""

    def __init__(
        self,
        num_catalog_items: int = 30000,
        embed_dim: int = 64,
        hidden_dim: int = 128,
        out_dim: int = 64,
        dropout: float = 0.1,
        temperature: float = 0.07,
    ):
        super().__init__()
        # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
        self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
        nn.init.normal_(self.item_embed.weight, std=0.05)
        with torch.no_grad():
            self.item_embed.weight[num_catalog_items].fill_(0.0)

        self.item_tower = ItemTower(
            item_embed=self.item_embed,
            feat_dim=4,
            embed_dim=embed_dim,
            hidden_dim=hidden_dim,
            out_dim=out_dim,
        )
        self.user_tower = UserTower(
            item_embed=self.item_embed,
            embed_dim=embed_dim,
            user_feat_dim=6,
            hidden_dim=hidden_dim,
            out_dim=out_dim,
            dropout=dropout,
        )
        self.temperature = temperature

    def precompute_candidate_embeddings(
        self,
        catalog_features: torch.Tensor,
        batch_size: int = 4096,
    ) -> torch.Tensor:
        """Precompute offline item embeddings for all 30,000 catalog items.

        Args:
            catalog_features: FloatTensor of shape (30000, 4).

        Returns:
            FloatTensor of shape (30000, out_dim).
        """
        self.eval()
        n_items = len(catalog_features)
        embeddings = []
        with torch.no_grad():
            for i in range(0, n_items, batch_size):
                end = min(i + batch_size, n_items)
                idx = torch.arange(i, end, dtype=torch.int64)
                feat = catalog_features[i:end]
                emb = self.item_tower(idx, feat)
                embeddings.append(emb)
        return torch.cat(embeddings, dim=0)

    def compute_user_embeddings(
        self,
        user_history: Dict[str, torch.Tensor],
        batch_size: int = 1024,
    ) -> torch.Tensor:
        """Compute user embeddings from interaction history.

        Args:
            user_history: Dictionary containing history tensors.

        Returns:
            FloatTensor of shape (N_users, out_dim).
        """
        self.eval()
        n_users = len(user_history["listen_items"])
        user_embeddings = []
        with torch.no_grad():
            for i in range(0, n_users, batch_size):
                end = min(i + batch_size, n_users)
                u_emb = self.user_tower(
                    listen_items=user_history["listen_items"][i:end],
                    listen_weights=user_history["listen_weights"][i:end],
                    like_items=user_history["like_items"][i:end],
                    like_mask=user_history["like_mask"][i:end],
                    dislike_items=user_history["dislike_items"][i:end],
                    dislike_mask=user_history["dislike_mask"][i:end],
                    user_features=user_history["user_features"][i:end],
                )
                user_embeddings.append(u_emb)
        return torch.cat(user_embeddings, dim=0)

    def forward_loss(
        self,
        user_history_batch: Dict[str, torch.Tensor],
        target_item_indices: torch.Tensor,
        target_item_features: torch.Tensor,
        target_item_logq: torch.Tensor,
        sample_weights: torch.Tensor,
        dislike_item_indices: Optional[torch.Tensor] = None,
        dislike_item_features: Optional[torch.Tensor] = None,
        dislike_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.

        Args:
            user_history_batch: Dict of user history tensors of batch size B.
            target_item_indices: LongTensor of positive item indices (B,).
            target_item_features: FloatTensor of positive item features (B, 4).
            target_item_logq: FloatTensor of positive item logq (B,).
            sample_weights: FloatTensor of positive sample weights (B,).
            dislike_item_indices: Optional LongTensor of hard negative items (B,).
            dislike_item_features: Optional FloatTensor (B, 4).
            dislike_mask: Optional FloatTensor (B,).

        Returns:
            Scalar loss tensor.
        """
        # User vectors (B, D)
        u = self.user_tower(
            listen_items=user_history_batch["listen_items"],
            listen_weights=user_history_batch["listen_weights"],
            like_items=user_history_batch["like_items"],
            like_mask=user_history_batch["like_mask"],
            dislike_items=user_history_batch["dislike_items"],
            dislike_mask=user_history_batch["dislike_mask"],
            user_features=user_history_batch["user_features"],
        )

        # Positive candidate vectors (B, D)
        v_pos = self.item_tower(target_item_indices, target_item_features)

        # Dot product matrix (B, B)
        sim = torch.matmul(u, v_pos.t())  # Cosine similarities since both L2 normalized

        # Popularity correction and temperature scaling
        # sim / tau - logq
        logits = sim / self.temperature - target_item_logq.unsqueeze(0)

        # Cross entropy with in-batch negatives
        batch_size = len(target_item_indices)
        targets = torch.arange(batch_size, device=target_item_indices.device)
        per_sample_loss = F.cross_entropy(logits, targets, reduction="none")

        # Weight by grade importance (likes 3.0, listens 1.0)
        norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
        infonce_loss = (per_sample_loss * norm_weights).mean()

        # Dislike penalty
        if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:
            v_neg = self.item_tower(dislike_item_indices, dislike_item_features)
            dislike_sim = (u * v_neg).sum(dim=-1)
            # Penalize similarity to disliked tracks
            dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)
            return infonce_loss + 0.2 * dislike_loss

        return infonce_loss

<untrusted_context>
Output: Precomputed candidate embeddings shape: torch.Size([30000, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
User embeddings shape: torch.Size([16, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
Scores shape: torch.Size([16, 30000]) min: -0.33630895614624023 max: 0.42073172330856323
Loss: 3.4881701469421387
Backward succeeded!
Process Group PGID: 2398
</untrusted_context>

Current topic: "Training & Public Validation"
Topic summary: Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py.

Strategic Intent: Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.

Successfully created and wrote to new file: /app/solution/train_val.py. Here is the updated code:
"""Training and validation script for the two-tower music ranker."""
from __future__ import annotations

import argparse
import json
import subprocess
import time
from pathlib import Path

import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, TensorDataset

from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker


def train_epoch(
    model: TwoTowerRanker,
    optimizer: torch.optim.Optimizer,
    scheduler: torch.optim.lr_scheduler.LRScheduler,
    histories_stacked: dict[str, torch.Tensor],
    global_uids: torch.Tensor,
    target_items: torch.Tensor,
    sample_weights: torch.Tensor,
    catalog_features: torch.Tensor,
    item_logq: torch.Tensor,
    batch_size: int = 512,
) -> float:
    model.train()
    total_loss = 0.0
    num_batches = 0

    n_samples = len(target_items)
    perm = torch.randperm(n_samples)

    for i in range(0, n_samples, batch_size):
        idx = perm[i : min(i + batch_size, n_samples)]
        b_uids = global_uids[idx]
        b_items = target_items[idx]
        b_weights = sample_weights[idx]

        b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
        b_feats = catalog_features[b_items]
        b_logq = item_logq[b_items]

        optimizer.zero_grad()
        loss = model.forward_loss(
            user_history_batch=b_hist,
            target_item_indices=b_items,
            target_item_features=b_feats,
            target_item_logq=b_logq,
            sample_weights=b_weights,
        )
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()

        total_loss += loss.item()
        num_batches += 1

    return total_loss / max(num_batches, 1)


def evaluate_on_validation(
    model: TwoTowerRanker,
    val_user_history: dict[str, torch.Tensor],
    catalog_features: torch.Tensor,
    catalog_item_ids: np.ndarray,
    target_uids: np.ndarray,
    data_dir: Path,
    output_path: Path,
) -> float:
    print("Precomputing candidate item embeddings offline...")
    t0 = time.time()
    cand_embeddings = model.precompute_candidate_embeddings(catalog_features)
    print(f"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s")

    print("Computing user embeddings for validation targets...")
    t0 = time.time()
    user_embeddings = model.compute_user_embeddings(val_user_history)
    print(f"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s")

    print("Running MIPS candidate retrieval and applying eligibility masks...")
    t0 = time.time()
    scores = torch.matmul(user_embeddings, cand_embeddings.t())  # (8456, 30000)

    # Load validation eligibility bitmask
    vec = np.load(data_dir / "validation_eligible_candidates.npz")
    unpacked_eligible = np.unpackbits(vec["packed_eligible"], axis=1, count=30000, bitorder="little")
    ineligible_mask = unpacked_eligible == 0
    scores[torch.from_numpy(ineligible_mask)] = -1e9

    # Top 100 retrieval
    top100 = torch.topk(scores, 100, dim=1).indices.numpy()
    print(f"MIPS retrieval completed in {time.time() - t0:.2f}s")

    # Build validation dataframe
    rows = []
    for u_idx, uid in enumerate(target_uids):
        top_items = catalog_item_ids[top100[u_idx]]
        for r, item_id in enumerate(top_items, 1):
            rows.append((int(uid), int(item_id), r))

    val_df = pd.DataFrame(rows, columns=["uid", "item_id", "rank"])
    output_path.parent.mkdir(parents=True, exist_ok=True)
    val_df.to_parquet(output_path, index=False)
    print(f"Saved validation ranking to {output_path}")

    # Run public evaluator
    cmd = ["python3", "/task/evaluate_public.py", str(output_path)]
    env = {"PYTHONPATH": "/task"}
    result = subprocess.run(cmd, capture_output=True, text=True, env=env)
    print("Evaluator Output:\n", result.stdout)
    if result.returncode != 0:
        print("Evaluator Error:\n", result.stderr)
        return 0.0

    try:
        report = json.loads(result.stdout)
        score = report.get("scores", {}).get("quality_ndcg@10", 0.0)
        return score
    except Exception as e:
        print(f"Failed to parse evaluator JSON: {e}")
        return 0.0


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data_dir", type=str, default="/task/data")
    parser.add_argument("--output_path", type=str, default="/app/solution/validation.parquet")
    parser.add_argument("--epochs", type=int, default=5)
    parser.add_argument("--batch_size", type=int, default=512)
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--embed_dim", type=int, default=64)
    parser.add_argument("--hidden_dim", type=int, default=128)
    parser.add_argument("--out_dim", type=int, default=64)
    parser.add_argument("--temperature", type=float, default=0.07)
    parser.add_argument("--num_windows", type=int, default=3)
    parser.add_argument("--sample_listens", type=int, default=30)
    args = parser.parse_args()

    data_dir = Path(args.data_dir)
    torch.manual_seed(42)
    np.random.seed(42)

    # Set intraop parallelism
    torch.set_num_threads(8)

    print("=== Loading Catalog and Users ===")
    cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)
    target_users_df = pd.read_parquet(data_dir / "target_users.parquet")
    target_uids = target_users_df["uid"].to_numpy(dtype=np.int64)

    val_cutoff = 24786800
    week_sec = 7 * 86400
    gap_sec = 1800

    cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]
    print(f"Training cutoffs: {cutoffs}")

    print("=== Building Training Pairs ===")
    t0 = time.time()
    histories_list, user_win, target_items, sample_weights = build_training_pairs(
        data_dir=data_dir,
        cutoffs=cutoffs,
        item_to_idx=item_to_idx,
        target_uids=target_uids,
        sample_listens_per_user=args.sample_listens_per_user,
    )
    print(f"Data preparation completed in {time.time() - t0:.2f}s")

    # Stack user histories across windows
    keys = list(histories_list[0].keys())
    histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}
    n_users = len(target_uids)
    global_uids = user_win[:, 0] * n_users + user_win[:, 1]
    item_logq_t = torch.from_numpy(item_logq)

    print("=== Extracting Validation Cutoff History ===")
    t0 = time.time()
    val_history = extract_user_histories(
        data_dir=data_dir,
        cutoff=val_cutoff,
        target_uids=target_uids,
        item_to_idx=item_to_idx,
        pref_state_path=data_dir / "validation_preference_state.parquet",
    )
    print(f"Validation history ready in {time.time() - t0:.2f}s")

    print("=== Initializing Two-Tower Ranker ===")
    model = TwoTowerRanker(
        num_catalog_items=30000,
        embed_dim=args.embed_dim,
        hidden_dim=args.hidden_dim,
        out_dim=args.out_dim,
        temperature=args.temperature,
    )

    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
    total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)

    print(f"=== Starting Training for {args.epochs} Epochs ===")
    best_score = 0.0
    for epoch in range(1, args.epochs + 1):
        t_ep = time.time()
        avg_loss = train_epoch(
            model=model,
            optimizer=optimizer,
            scheduler=scheduler,
            histories_stacked=histories_stacked,
            global_uids=global_uids,
            target_items=target_items,
            sample_weights=sample_weights,
            catalog_features=cat_feats,
            item_logq=item_logq_t,
            batch_size=args.batch_size,
        )
        ep_duration = time.time() - t_ep
        print(f"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)")

        # Validate
        score = evaluate_on_validation(
            model=model,
            val_user_history=val_history,
            catalog_features=cat_feats,
            catalog_item_ids=cat_items,
            target_uids=target_uids,
            data_dir=data_dir,
            output_path=Path(args.output_path),
        )
        print(f"Epoch {epoch} Public NDCG@10: {score:.6f}")
        if score > best_score:
            best_score = score
            torch.save(model.state_dict(), "/app/solution/best_val_model.pt")

    print(f"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===")


if __name__ == "__main__":
    main()

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600]
=== Building Training Pairs ===
Traceback (most recent call last):
  File "/app/solution/train_val.py", line 244, in <module>
    main()
  File "/app/solution/train_val.py", line 171, in main
    sample_listens_per_user=args.sample_listens_per_user,
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Namespace' object has no attribute 'sample_listens_per_user'
Exit Code: 1
Process Group PGID: 2411
</untrusted_context>

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
    histories_list, user_win, target_items, sample_weights = build_training_pairs(
        data_dir=data_dir,
        cutoffs=cutoffs,
        item_to_idx=item_to_idx,
        target_uids=target_uids,
        sample_listens_per_user=args.sample_listens,
    )
    print(f"Data preparation completed in {time.time() - t0:.2f}s")

    # Stack user histories across windows
    keys = list(histories_list[0].keys())
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Generated 76229 discovery training pairs across 2 windows.
Data preparation completed in 11.35s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.32s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 3 Epochs ===
Epoch 1/3: Loss=6.2061 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Traceback (most recent call last):
  File "/app/solution/train_val.py", line 244, in <module>
    main()
  File "/app/solution/train_val.py", line 226, in main
    score = evaluate_on_validation(
            ^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/train_val.py", line 115, in evaluate_on_validation
    result = subprocess.run(cmd, capture_output=True, text=True, env=env)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/subprocess.py", line 548, in run
    with Popen(*popenargs, **kwargs) as process:
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/usr/local/lib/python3.12/subprocess.py", line 1026, in __init__
    self._execute_child(args, executable, preexec_fn, close_fds,
  File "/usr/local/lib/python3.12/subprocess.py", line 1955, in _execute_child
    raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'python3'
Exit Code: 1
Process Group PGID: 2435
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.009969,
    "quality_users": 4263
  }
}
Process Group PGID: 2472
</untrusted_context>

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
    output_path.parent.mkdir(parents=True, exist_ok=True)
    val_df.to_parquet(output_path, index=False)
    print(f"Saved validation ranking to {output_path}")

    # Run public evaluator
    import os
    import sys
    cmd = [sys.executable, "/task/evaluate_public.py", str(output_path)]
    env = os.environ.copy()
    env["PYTHONPATH"] = f"/task:{env.get('PYTHONPATH', '')}"
    result = subprocess.run(cmd, capture_output=True, text=True, env=env)
    print("Evaluator Output:\n", result.stdout)
    if result.returncode != 0:
        print("Evaluator Error:\n", result.stderr)
        return 0.0
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 192471 discovery training pairs across 4 windows.
Data preparation completed in 21.84s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.35s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.0946 (7.8s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011541,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.011541
Epoch 2/5: Loss=5.7236 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.06s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011515,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.011515
Epoch 3/5: Loss=5.4151 (7.8s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.14s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011372,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.011372
Epoch 4/5: Loss=5.1981 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.06s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.010071,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.010071
Epoch 5/5: Loss=5.0703 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.009783,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.009783
=== Training Complete! Best Validation NDCG@10: 0.011541 ===
Process Group PGID: 2492
</untrusted_context>

<untrusted_context>
Output: logq min, mean, max: -11.720305 -10.745149 -6.624825
Range of logq: 5.0954804
Process Group PGID: 2618
</untrusted_context>

<untrusted_context>
Output: (empty)
Process Group PGID: 2635
</untrusted_context>

Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:
...
        embed_dim: int = 64,
        hidden_dim: int = 128,
        out_dim: int = 64,
        dropout: float = 0.1,
        temperature: float = 0.07,
        logq_weight: float = 0.0,
    ):
        super().__init__()
        # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
        self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
        nn.init.normal_(self.item_embed.weight, std=0.05)
...
            hidden_dim=hidden_dim,
            out_dim=out_dim,
            dropout=dropout,
        )
        self.temperature = temperature
        self.logq_weight = logq_weight

    def precompute_candidate_embeddings(
        self,
        catalog_features: torch.Tensor,
        batch_size: int = 4096,
...

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

        # Dot product matrix (B, B)
        sim = torch.matmul(u, v_pos.t())  # Cosine similarities since both L2 normalized

        # Popularity correction and temperature scaling
        logits = sim / self.temperature
        if self.logq_weight > 0.0:
            logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)

        # Cross entropy with in-batch negatives
        batch_size = len(target_item_indices)
        targets = torch.arange(batch_size, device=target_item_indices.device)
        per_sample_loss = F.cross_entropy(logits, targets, reduction="none")
...

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
    parser.add_argument("--lr", type=float, default=1e-3)
    parser.add_argument("--embed_dim", type=int, default=64)
    parser.add_argument("--hidden_dim", type=int, default=128)
    parser.add_argument("--out_dim", type=int, default=64)
    parser.add_argument("--temperature", type=float, default=0.07)
    parser.add_argument("--logq_weight", type=float, default=0.0)
    parser.add_argument("--num_windows", type=int, default=3)
    parser.add_argument("--sample_listens", type=int, default=30)
    args = parser.parse_args()

    data_dir = Path(args.data_dir)
...

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
        num_catalog_items=30000,
        embed_dim=args.embed_dim,
        hidden_dim=args.hidden_dim,
        out_dim=args.out_dim,
        temperature=args.temperature,
        logq_weight=args.logq_weight,
    )

    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
    total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 192471 discovery training pairs across 4 windows.
Data preparation completed in 21.74s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.33s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.0840 (8.4s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.00425,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.004250
Epoch 2/5: Loss=5.7201 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.53s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.005138,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.005138
Epoch 3/5: Loss=5.4037 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.13s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.004426,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.004426
Epoch 4/5: Loss=5.1761 (8.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.13s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.004201,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.004201
Epoch 5/5: Loss=5.0458 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.002985,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.002985
=== Training Complete! Best Validation NDCG@10: 0.005138 ===
Process Group PGID: 2646
</untrusted_context>

<untrusted_context>
Output: Total past listen pairs before cutoff: 12039859
User 0 tail listens count: 85
User 0 FULL past listens count: 781
Process Group PGID: 2772
</untrusted_context>

<untrusted_context>
Output: Read and filtered in 3.53s, rows=43222297
Built full past sets for all 8359 users in 6.11s
Process Group PGID: 2794
</untrusted_context>

<untrusted_context>
Output: Window 0 True Discovery Likes: 5420 out of 7774
Window 0 True Discovery Strong Listens: 67576 out of 304885
Process Group PGID: 2816
</untrusted_context>

Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
            filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
        )
        listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)
    strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]

    # Load full target user listens to guarantee 100% true discovery filtering
    full_table = pq.read_table(
        data_dir / "train_listens.parquet",
        columns=["uid", "timestamp", "item_id"],
    )
    all_listens_df = full_table.to_pandas()
    if include_val and (data_dir / "val_listens.parquet").exists():
        val_tbl_full = pq.read_table(
            data_dir / "val_listens.parquet",
            columns=["uid", "timestamp", "item_id"],
        )
        all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)
    all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]

    histories_list: List[Dict[str, torch.Tensor]] = []
    pairs_list: List[Tuple[int, int, int, float]] = []  # (window_idx, user_row, item_idx, weight)

    for w_idx, cutoff in enumerate(cutoffs):
        t_start = cutoff + gap_seconds
...
            item_to_idx=item_to_idx,
            include_val=include_val,
        )
        histories_list.append(hist)

        # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter
        df_prior = all_listens_df[all_listens_df.timestamp < cutoff]
        past_items_per_user = df_prior.groupby("uid")["item_id"].apply(set).to_dict()

        # Add liked items before cutoff as well
        likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]
        for uid, itm in zip(likes_prior.uid, likes_prior.item_id):
            past_items_per_user.setdefault(int(uid), set()).add(int(itm))

        # 3. Targets in [t_start, t_end]
        w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]
        w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]

        # Map and filter discovery likes (grade 3, weight 5.0)
        user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}

        for uid, item in zip(w_likes.uid, w_likes.item_id):
            r = uid_to_row[int(uid)]
            item_int = int(item)
            if item_int not in past_items_per_user.get(int(uid), set()):
                item_idx = item_to_idx.get(item_int)
                if item_idx is not None and item_idx not in user_seen_targets[r]:
                    pairs_list.append((w_idx, r, item_idx, 5.0))
                    user_seen_targets[r].add(item_idx)

        # Map and filter discovery listens (grade 1, weight 1.0)
        # To balance, subsample if a user has too many listens
        grouped_listens = w_listens.groupby("uid")
        for uid, group in grouped_listens:
            r = uid_to_row[int(uid)]
            user_past = past_items_per_user.get(int(uid), set())
            items = [int(it) for it in group["item_id"].to_numpy() if int(it) not in user_past]
            if len(items) > sample_listens_per_user:
                items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))
            for itm in items:
                item_idx = item_to_idx.get(itm)
                if item_idx is not None and item_idx not in user_seen_targets[r]:
                    pairs_list.append((w_idx, r, item_idx, 1.0))
                    user_seen_targets[r].add(item_idx)

    np_pairs = np.array(pairs_list, dtype=np.float32)
    np.random.shuffle(np_pairs)
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Generated 76663 discovery training pairs across 3 windows.
Data preparation completed in 39.79s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.39s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.1895 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007081,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.007081
Epoch 2/5: Loss=5.9163 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007211,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.007211
Epoch 3/5: Loss=5.5643 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.005033,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.005033
Epoch 4/5: Loss=5.2741 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.003885,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.003885
Epoch 5/5: Loss=5.1058 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.004044,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.004044
=== Training Complete! Best Validation NDCG@10: 0.007211 ===
Process Group PGID: 2838
</untrusted_context>

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Generated 76663 discovery training pairs across 3 windows.
Data preparation completed in 39.44s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.41s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.1998 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.009107,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.009107
Epoch 2/5: Loss=5.9321 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.00915,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.009150
Epoch 3/5: Loss=5.5913 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.10s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007496,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.007496
Epoch 4/5: Loss=5.3021 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.005305,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.005305
Epoch 5/5: Loss=5.1320 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.00526,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.005260
=== Training Complete! Best Validation NDCG@10: 0.009150 ===
Process Group PGID: 2964
</untrusted_context>

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 8, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 3090
</untrusted_context>

<untrusted_context>
Output: /usr/bin/bash: line 67:  3103 Killed                  PYTHONPATH=/app/solution:/task python3 -c "
import sys
sys.path.extend(['/app/solution', '/task'])
import torch
import numpy as np
import pandas as pd
from pathlib import Path
from model import TwoTowerRanker
from data_prep import load_catalog_features, extract_user_histories
import evaluate_public
from ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking

DATA = Path('/task/data')
cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)
tu = pd.read_parquet(DATA / 'target_users.parquet')
target_uids = tu.uid.values
targets_set = set(target_uids)

model = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)
model.load_state_dict(torch.load('/app/solution/best_val_model.pt'))
model.eval()

# Precompute items
cand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)

# User histories
val_hist = extract_user_histories(
    DATA, 24786800, target_uids, item_to_idx,
    pref_state_path=DATA / 'validation_preference_state.parquet'
)
user_emb = model.compute_user_embeddings(val_hist) # (8456, 64)

# Dot product scores
base_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)

cc = pd.read_parquet(DATA / 'candidate_catalog.parquet')
log_pop = np.log1p(cc.positive_listens.values.astype(np.float32))
log_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

# Validation eligibility
vec = np.load(DATA / 'validation_eligible_candidates.npz')
unpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')
eligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = evaluate_public.validation_truth(eligibility, targets_set)

for beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:
    scores = base_scores + beta * log_pop_norm
    scores[unpacked_eligible == 0] = -1e9
    
    top100 = np.argsort(-scores, axis=1)[:, :100]
    
    rows = []
    for u_idx, uid in enumerate(target_uids):
        top_items = cat_items[top100[u_idx]]
        for r, item_id in enumerate(top_items, 1):
            rows.append((int(uid), int(item_id), r))
    
    val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])
    validated = validate_ranking(val_df, targets_set)
    ranked = ranked_items(validated)
    res = score_rankings(ranked, graded)
    print(f'Beta = {beta:.2f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}')
"
Exit Code: 137
Process Group PGID: 3101
</untrusted_context>

<untrusted_context>
Output: Beta = 0.00: NDCG@10 = 0.009150
Beta = 0.05: NDCG@10 = 0.009835
Beta = 0.10: NDCG@10 = 0.010169
Beta = 0.15: NDCG@10 = 0.010700
Beta = 0.20: NDCG@10 = 0.010400
Beta = 0.30: NDCG@10 = 0.010395
Process Group PGID: 3126
</untrusted_context>

<untrusted_context>
Output: VPS liked merged with train_likes: 428358 out of 418435
Process Group PGID: 3151
</untrusted_context>

Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
    def __init__(
        self,
        item_embed: nn.Embedding,
        feat_dim: int = 4,
        embed_dim: int = 64,
        hidden_dim: int = 128,
        out_dim: int = 64,
    ):
        super().__init__()
        self.item_embed = item_embed
        self.feat_mlp = nn.Sequential(
...
        item_embed: nn.Embedding,
        embed_dim: int = 64,
        user_feat_dim: int = 6,
        hidden_dim: int = 128,
        out_dim: int = 64,
        dropout: float = 0.2,
        short_window: int = 20,
    ):
        super().__init__()
        self.item_embed = item_embed
        self.short_window = short_window
        self.user_feat_mlp = nn.Sequential(
            nn.Linear(user_feat_dim, 32),
            nn.LayerNorm(32),
            nn.GELU(),
        )
        # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)
        in_dim = embed_dim * 4 + 32
        self.mlp = nn.Sequential(
            nn.Linear(in_dim, hidden_dim),
            nn.LayerNorm(hidden_dim),
            nn.GELU(),
            nn.Dropout(dropout),
...
        like_mask: torch.Tensor,
        dislike_items: torch.Tensor,
        dislike_mask: torch.Tensor,
        user_features: torch.Tensor,
    ) -> torch.Tensor:
        """Compute normalized user embeddings from interaction history."""
        # 1. Full listen history pooling (long-term)
        l_emb = self.item_embed(listen_items)  # (B, K, D)
        pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1)  # (B, D)

        # 2. Short-term listens pooling (most recent short_window items)
        s_items = listen_items[:, -self.short_window :]
        s_weights = listen_weights[:, -self.short_window :]
        s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7
        s_norm_weights = s_weights / s_sum
        s_emb = self.item_embed(s_items)
        pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1)  # (B, D)

        # 3. Likes pooling
        lk_emb = self.item_embed(like_items)  # (B, L, D)
        lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6
        pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom
        has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()
        pooled_likes = pooled_likes * has_likes

        # 4. Dislikes pooling
        dk_emb = self.item_embed(dislike_items)  # (B, M, D)
        dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6
        pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom
        has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()
        pooled_dislikes = pooled_dislikes * has_dislikes

        # 5. User features projection
        user_feat_proj = self.user_feat_mlp(user_features)

        # 6. Combined MLP
        combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)
        out = self.mlp(combined)
        return F.normalize(out, p=2, dim=-1)


class TwoTowerRanker(nn.Module):
...
        self,
        num_catalog_items: int = 30000,
        embed_dim: int = 64,
        hidden_dim: int = 128,
        out_dim: int = 64,
        dropout: float = 0.2,
        temperature: float = 0.07,
        logq_weight: float = 1.0,
    ):
        super().__init__()
        # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
        self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
        nn.init.normal_(self.item_embed.weight, std=0.05)
...
    def precompute_candidate_embeddings(
        self,
        catalog_features: torch.Tensor,
        batch_size: int = 4096,
    ) -> torch.Tensor:
        """Precompute offline item embeddings for all 30,000 catalog items."""
        self.eval()
        n_items = len(catalog_features)
        embeddings = []
        with torch.no_grad():
            for i in range(0, n_items, batch_size):
...
    def compute_user_embeddings(
        self,
        user_history: Dict[str, torch.Tensor],
        batch_size: int = 1024,
    ) -> torch.Tensor:
        """Compute user embeddings from interaction history."""
        self.eval()
        n_users = len(user_history["listen_items"])
        user_embeddings = []
        with torch.no_grad():
            for i in range(0, n_users, batch_size):
...
        sample_weights: torch.Tensor,
        dislike_item_indices: Optional[torch.Tensor] = None,
        dislike_item_features: Optional[torch.Tensor] = None,
        dislike_mask: Optional[torch.Tensor] = None,
    ) -> torch.Tensor:
        """Compute InfoNCE sampled softmax loss with popularity debiasing."""
        # User vectors (B, D)
        u = self.user_tower(
            listen_items=user_history_batch["listen_items"],
            listen_weights=user_history_batch["listen_weights"],
            like_items=user_history_batch["like_items"],
...

        # Positive candidate vectors (B, D)
        v_pos = self.item_tower(target_item_indices, target_item_features)

        # Dot product matrix (B, B)
        sim = torch.matmul(u, v_pos.t())

        # Popularity correction and temperature scaling
        logits = sim / self.temperature
        if self.logq_weight > 0.0:
            logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)
...
        # Cross entropy with in-batch negatives
        batch_size = len(target_item_indices)
        targets = torch.arange(batch_size, device=target_item_indices.device)
        per_sample_loss = F.cross_entropy(logits, targets, reduction="none")

        # Weight by grade importance (likes 5.0, listens 1.0)
        norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
        infonce_loss = (per_sample_loss * norm_weights).mean()

        return infonce_loss

Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
    if pref_state_path and pref_state_path.exists():
        pref_df = pd.read_parquet(pref_state_path)
        pref_df = pref_df[pref_df.uid.isin(target_uids_set)]
        liked_df = pref_df[pref_df.state == "liked"]
        disliked_df = pref_df[pref_df.state == "disliked"]

        # Merge with likes to get timestamps for chronological ordering
        likes_ts = pd.read_parquet(data_dir / "train_likes.parquet")
        if include_val and (data_dir / "val_likes.parquet").exists():
            likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
        likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][["uid", "item_id", "timestamp"]].drop_duplicates(["uid", "item_id"])
        liked_df = pd.merge(liked_df, likes_ts, on=["uid", "item_id"], how="left").sort_values("timestamp")
    else:
        # Reconstruct active preferences from events
        likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
        unlikes_df = pd.read_parquet(data_dir / "train_unlikes.parquet")
        dislikes_df = pd.read_parquet(data_dir / "train_dislikes.parquet")
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.82s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.60s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.2144 (5.6s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.010692,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.010692
Epoch 2/5: Loss=6.0222 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.008403,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.008403
Epoch 3/5: Loss=5.7904 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007303,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.007303
Epoch 4/5: Loss=5.6115 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007151,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.007151
Epoch 5/5: Loss=5.5162 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.007181,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.007181
=== Training Complete! Best Validation NDCG@10: 0.010692 ===
Process Group PGID: 3169
</untrusted_context>

<untrusted_context>
Output: Gold items popularity ranks: min=1, 25%=1733, median=5622, 75%=13374, max=30000
Process Group PGID: 3295
</untrusted_context>

<untrusted_context>
Output: Predicted items popularity ranks: min=1, 25%=162, median=651, 75%=4160, max=29987
Process Group PGID: 3315
</untrusted_context>

<untrusted_context>
Output: sim shape: torch.Size([8, 24])
loss: 3.8542702198028564
Process Group PGID: 3332
</untrusted_context>

Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:
...
    def forward_loss(
        self,
        user_history_batch: Dict[str, torch.Tensor],
        target_item_indices: torch.Tensor,
        target_item_features: torch.Tensor,
        catalog_features: torch.Tensor,
        item_logq: torch.Tensor,
        sample_weights: torch.Tensor,
        num_uniform_negatives: int = 256,
    ) -> torch.Tensor:
        """Compute Mixed Negative Sampling InfoNCE loss.

        Args:
            user_history_batch: Dict of user history tensors of batch size B.
            target_item_indices: LongTensor of positive item indices (B,).
            target_item_features: FloatTensor of positive item features (B, 4).
            catalog_features: All catalog features (30000, 4).
            item_logq: Precomputed log probabilities of catalog items (30000,).
            sample_weights: FloatTensor of sample weights (B,).
            num_uniform_negatives: Number of uniform random catalog negatives.

        Returns:
            Scalar loss tensor.
        """
        batch_size = len(target_item_indices)

        # 1. User vectors (B, D)
        u = self.user_tower(
            listen_items=user_history_batch["listen_items"],
            listen_weights=user_history_batch["listen_weights"],
            like_items=user_history_batch["like_items"],
            like_mask=user_history_batch["like_mask"],
            dislike_items=user_history_batch["dislike_items"],
            dislike_mask=user_history_batch["dislike_mask"],
            user_features=user_history_batch["user_features"],
        )

        # 2. Positive candidate vectors (B, D)
        v_pos = self.item_tower(target_item_indices, target_item_features)

        # 3. Uniform catalog negatives (M, D)
        if num_uniform_negatives > 0:
            neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)
            neg_features = catalog_features[neg_indices]
            v_neg = self.item_tower(neg_indices, neg_features)
            v_all = torch.cat([v_pos, v_neg], dim=0)  # (B + M, D)
            all_indices = torch.cat([target_item_indices, neg_indices], dim=0)
        else:
            v_all = v_pos
            all_indices = target_item_indices

        # 4. Dot product matrix (B, B + M)
        sim = torch.matmul(u, v_all.t())

        # 5. Temperature scaling
        logits = sim / self.temperature

        # 6. Mixed sampling logq correction
        if self.logq_weight > 0.0:
            # For in-batch items: logq from popularity
            # For uniform items: log(1 / N)
            total_items = float(len(catalog_features))
            uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))
            pos_logq = item_logq[target_item_indices]
            if num_uniform_negatives > 0:
                neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)
                cand_logq = torch.cat([pos_logq, neg_logq], dim=0)
            else:
                cand_logq = pos_logq
            logits = logits - self.logq_weight * cand_logq.unsqueeze(0)

        # 7. Cross entropy with targets = 0..B-1
        targets = torch.arange(batch_size, device=target_item_indices.device)
        per_sample_loss = F.cross_entropy(logits, targets, reduction="none")

        # Weight by sample importance
        norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
        return (per_sample_loss * norm_weights).mean()

Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
        optimizer.zero_grad()
        loss = model.forward_loss(
            user_history_batch=b_hist,
            target_item_indices=b_items,
            target_item_features=b_feats,
            catalog_features=catalog_features,
            item_logq=item_logq,
            sample_weights=b_weights,
            num_uniform_negatives=256,
        )
        loss.backward()
        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
        optimizer.step()
        scheduler.step()
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.52s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.56s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.5575 (6.0s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014781,
    "quality_users": 4263
  }
}

Epoch 1 Public NDCG@10: 0.014781
Epoch 2/5: Loss=6.2701 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011245,
    "quality_users": 4263
  }
}

Epoch 2 Public NDCG@10: 0.011245
Epoch 3/5: Loss=5.9364 (6.0s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.010538,
    "quality_users": 4263
  }
}

Epoch 3 Public NDCG@10: 0.010538
Epoch 4/5: Loss=5.6442 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.00954,
    "quality_users": 4263
  }
}

Epoch 4 Public NDCG@10: 0.009540
Epoch 5/5: Loss=5.4715 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.00938,
    "quality_users": 4263
  }
}

Epoch 5 Public NDCG@10: 0.009380
=== Training Complete! Best Validation NDCG@10: 0.014781 ===
Process Group PGID: 3345
</untrusted_context>

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

from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker


def train_and_evaluate(
    model: TwoTowerRanker,
    optimizer: torch.optim.Optimizer,
    scheduler: torch.optim.lr_scheduler.LRScheduler,
    histories_stacked: dict[str, torch.Tensor],
    global_uids: torch.Tensor,
    target_items: torch.Tensor,
    sample_weights: torch.Tensor,
    catalog_features: torch.Tensor,
    item_logq: torch.Tensor,
    val_user_history: dict[str, torch.Tensor],
    catalog_item_ids: np.ndarray,
    target_uids: np.ndarray,
    data_dir: Path,
    output_path: Path,
    epochs: int = 3,
    batch_size: int = 512,
    num_uniform_negatives: int = 512,
    eval_every_steps: int = 60,
) -> float:
    best_score = 0.0
    global_step = 0
    n_samples = len(target_items)
    item_logq_t = torch.from_numpy(item_logq)

    for epoch in range(1, epochs + 1):
        model.train()
        perm = torch.randperm(n_samples)
        ep_loss = 0.0
        n_batches = 0

        for i in range(0, n_samples, batch_size):
            idx = perm[i : min(i + batch_size, n_samples)]
            b_uids = global_uids[idx]
            b_items = target_items[idx]
            b_weights = sample_weights[idx]

            b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
            b_feats = catalog_features[b_items]

            optimizer.zero_grad()
            loss = model.forward_loss(
                user_history_batch=b_hist,
                target_item_indices=b_items,
                target_item_features=b_feats,
                catalog_features=catalog_features,
                item_logq=item_logq_t,
                sample_weights=b_weights,
                num_uniform_negatives=num_uniform_negatives,
            )
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            optimizer.step()
            scheduler.step()

            ep_loss += loss.item()
            n_batches += 1
            global_step += 1

            if global_step % eval_every_steps == 0:
                avg_l = ep_loss / n_batches
                score = evaluate_on_validation(
                    model=model,
                    val_user_history=val_user_history,
                    catalog_features=catalog_features,
                    catalog_item_ids=catalog_item_ids,
                    target_uids=target_uids,
                    data_dir=data_dir,
                    output_path=output_path,
                )
                print(f"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}")
                if score > best_score:
                    best_score = score
                    print(f"*** New best score: {best_score:.6f}! Saving model checkpoint...")
                    torch.save(model.state_dict(), "/app/solution/best_val_model.pt")
                model.train()

        # End of epoch evaluation
        score = evaluate_on_validation(
            model=model,
            val_user_history=val_user_history,
            catalog_features=catalog_features,
            catalog_item_ids=catalog_item_ids,
            target_uids=target_uids,
            data_dir=data_dir,
            output_path=output_path,
        )
        print(f"[End of Epoch {epoch}] Val NDCG@10={score:.6f}")
        if score > best_score:
            best_score = score
            print(f"*** New best score: {best_score:.6f}! Saving model checkpoint...")
            torch.save(model.state_dict(), "/app/solution/best_val_model.pt")

    return best_score


def evaluate_on_validation(
    model: TwoTowerRanker,
    val_user_history: dict[str, torch.Tensor],
    catalog_features: torch.Tensor,
    catalog_item_ids: np.ndarray,
...

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

    optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
    total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)

    print(f"=== Starting Training with Intra-Epoch Validation ===")
    best_score = train_and_evaluate(
        model=model,
        optimizer=optimizer,
        scheduler=scheduler,
        histories_stacked=histories_stacked,
        global_uids=global_uids,
        target_items=target_items,
        sample_weights=sample_weights,
        catalog_features=cat_feats,
        item_logq=item_logq,
        val_user_history=val_history,
        catalog_item_ids=cat_items,
        target_uids=target_uids,
        data_dir=data_dir,
        output_path=Path(args.output_path),
        epochs=args.epochs,
        batch_size=args.batch_size,
        num_uniform_negatives=512,
        eval_every_steps=40,
    )

    print(f"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===")


if __name__ == "__main__":
    main()
...

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.54s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.57s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.010606,
    "quality_users": 4263
  }
}

[Step 40 | Ep 1] Loss=6.9659 | Val NDCG@10=0.010606
*** New best score: 0.010606! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011484,
    "quality_users": 4263
  }
}

[Step 80 | Ep 1] Loss=6.9158 | Val NDCG@10=0.011484
*** New best score: 0.011484! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012202,
    "quality_users": 4263
  }
}

[Step 120 | Ep 1] Loss=6.8938 | Val NDCG@10=0.012202
*** New best score: 0.012202! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014921,
    "quality_users": 4263
  }
}

[Step 160 | Ep 1] Loss=6.8727 | Val NDCG@10=0.014921
*** New best score: 0.014921! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01712,
    "quality_users": 4263
  }
}

[Step 200 | Ep 1] Loss=6.8518 | Val NDCG@10=0.017120
*** New best score: 0.017120! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017548,
    "quality_users": 4263
  }
}

[End of Epoch 1] Val NDCG@10=0.017548
*** New best score: 0.017548! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017774,
    "quality_users": 4263
  }
}

[Step 240 | Ep 2] Loss=6.7210 | Val NDCG@10=0.017774
*** New best score: 0.017774! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016264,
    "quality_users": 4263
  }
}

[Step 280 | Ep 2] Loss=6.5972 | Val NDCG@10=0.016264
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015541,
    "quality_users": 4263
  }
}

[Step 320 | Ep 2] Loss=6.5819 | Val NDCG@10=0.015541
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014918,
    "quality_users": 4263
  }
}

[Step 360 | Ep 2] Loss=6.5692 | Val NDCG@10=0.014918
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014588,
    "quality_users": 4263
  }
}

[Step 400 | Ep 2] Loss=6.5617 | Val NDCG@10=0.014588
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014989,
    "quality_users": 4263
  }
}

[Step 440 | Ep 2] Loss=6.5526 | Val NDCG@10=0.014989
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014672,
    "quality_users": 4263
  }
}

[End of Epoch 2] Val NDCG@10=0.014672
=== Training Complete! Best Validation NDCG@10: 0.017774 ===
Process Group PGID: 3471
</untrusted_context>

<untrusted_context>
Output: Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017774,
    "quality_users": 4263
  }
}

Verified saved best model score: 0.017774
Process Group PGID: 3741
</untrusted_context>

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 61.25s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.59s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01299,
    "quality_users": 4263
  }
}

[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990
*** New best score: 0.012990! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012391,
    "quality_users": 4263
  }
}

[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01336,
    "quality_users": 4263
  }
}

[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360
*** New best score: 0.013360! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014308,
    "quality_users": 4263
  }
}

[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308
*** New best score: 0.014308! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016339,
    "quality_users": 4263
  }
}

[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339
*** New best score: 0.016339! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018645,
    "quality_users": 4263
  }
}

[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645
*** New best score: 0.018645! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018652,
    "quality_users": 4263
  }
}

[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652
*** New best score: 0.018652! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019376,
    "quality_users": 4263
  }
}

[End of Epoch 1] Val NDCG@10=0.019376
*** New best score: 0.019376! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018836,
    "quality_users": 4263
  }
}

[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.21s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018001,
    "quality_users": 4263
  }
}

[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017624,
    "quality_users": 4263
  }
}

[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017974,
    "quality_users": 4263
  }
}

[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017418,
    "quality_users": 4263
  }
}

[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017469,
    "quality_users": 4263
  }
}

[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017564,
    "quality_users": 4263
  }
}

[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017218,
    "quality_users": 4263
  }
}

[End of Epoch 2] Val NDCG@10=0.017218
=== Training Complete! Best Validation NDCG@10: 0.019376 ===
Process Group PGID: 3784
</untrusted_context>

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 65.83s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.62s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011977,
    "quality_users": 4263
  }
}

[Step 40 | Ep 1] Loss=6.9523 | Val NDCG@10=0.011977
*** New best score: 0.011977! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.28s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012458,
    "quality_users": 4263
  }
}

[Step 80 | Ep 1] Loss=6.9107 | Val NDCG@10=0.012458
*** New best score: 0.012458! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012316,
    "quality_users": 4263
  }
}

[Step 120 | Ep 1] Loss=6.8925 | Val NDCG@10=0.012316
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012337,
    "quality_users": 4263
  }
}

[Step 160 | Ep 1] Loss=6.8767 | Val NDCG@10=0.012337
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.25s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015118,
    "quality_users": 4263
  }
}

[Step 200 | Ep 1] Loss=6.8610 | Val NDCG@10=0.015118
*** New best score: 0.015118! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016676,
    "quality_users": 4263
  }
}

[Step 240 | Ep 1] Loss=6.8429 | Val NDCG@10=0.016676
*** New best score: 0.016676! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.30s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018768,
    "quality_users": 4263
  }
}

[Step 280 | Ep 1] Loss=6.8254 | Val NDCG@10=0.018768
*** New best score: 0.018768! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018617,
    "quality_users": 4263
  }
}

[End of Epoch 1] Val NDCG@10=0.018617
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017452,
    "quality_users": 4263
  }
}

[Step 320 | Ep 2] Loss=6.5896 | Val NDCG@10=0.017452
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017485,
    "quality_users": 4263
  }
}

[Step 360 | Ep 2] Loss=6.5827 | Val NDCG@10=0.017485
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.29s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016044,
    "quality_users": 4263
  }
}

[Step 400 | Ep 2] Loss=6.5715 | Val NDCG@10=0.016044
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015823,
    "quality_users": 4263
  }
}

[Step 440 | Ep 2] Loss=6.5632 | Val NDCG@10=0.015823
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.31s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016519,
    "quality_users": 4263
  }
}

[Step 480 | Ep 2] Loss=6.5536 | Val NDCG@10=0.016519
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.04s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.0163,
    "quality_users": 4263
  }
}

[Step 520 | Ep 2] Loss=6.5480 | Val NDCG@10=0.016300
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016186,
    "quality_users": 4263
  }
}

[Step 560 | Ep 2] Loss=6.5458 | Val NDCG@10=0.016186
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016426,
    "quality_users": 4263
  }
}

[End of Epoch 2] Val NDCG@10=0.016426
=== Training Complete! Best Validation NDCG@10: 0.018768 ===
Process Group PGID: 4108
</untrusted_context>

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800, 21147200]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Extracting user histories for training cutoff 5: timestamp=21147200...
Generated 178230 discovery training pairs across 6 windows.
Data preparation completed in 71.28s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.62s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.011399,
    "quality_users": 4263
  }
}

[Step 40 | Ep 1] Loss=6.9946 | Val NDCG@10=0.011399
*** New best score: 0.011399! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012145,
    "quality_users": 4263
  }
}

[Step 80 | Ep 1] Loss=6.9303 | Val NDCG@10=0.012145
*** New best score: 0.012145! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.013402,
    "quality_users": 4263
  }
}

[Step 120 | Ep 1] Loss=6.9087 | Val NDCG@10=0.013402
*** New best score: 0.013402! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.013911,
    "quality_users": 4263
  }
}

[Step 160 | Ep 1] Loss=6.8934 | Val NDCG@10=0.013911
*** New best score: 0.013911! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014808,
    "quality_users": 4263
  }
}

[Step 200 | Ep 1] Loss=6.8799 | Val NDCG@10=0.014808
*** New best score: 0.014808! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016187,
    "quality_users": 4263
  }
}

[Step 240 | Ep 1] Loss=6.8634 | Val NDCG@10=0.016187
*** New best score: 0.016187! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017724,
    "quality_users": 4263
  }
}

[Step 280 | Ep 1] Loss=6.8476 | Val NDCG@10=0.017724
*** New best score: 0.017724! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018013,
    "quality_users": 4263
  }
}

[Step 320 | Ep 1] Loss=6.8328 | Val NDCG@10=0.018013
*** New best score: 0.018013! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017491,
    "quality_users": 4263
  }
}

[End of Epoch 1] Val NDCG@10=0.017491
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018002,
    "quality_users": 4263
  }
}

[Step 360 | Ep 2] Loss=6.6126 | Val NDCG@10=0.018002
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016227,
    "quality_users": 4263
  }
}

[Step 400 | Ep 2] Loss=6.6077 | Val NDCG@10=0.016227
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016475,
    "quality_users": 4263
  }
}

[Step 440 | Ep 2] Loss=6.5923 | Val NDCG@10=0.016475
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015871,
    "quality_users": 4263
  }
}

[Step 480 | Ep 2] Loss=6.5801 | Val NDCG@10=0.015871
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015568,
    "quality_users": 4263
  }
}

[Step 520 | Ep 2] Loss=6.5679 | Val NDCG@10=0.015568
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015686,
    "quality_users": 4263
  }
}

[Step 560 | Ep 2] Loss=6.5607 | Val NDCG@10=0.015686
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015682,
    "quality_users": 4263
  }
}

[Step 600 | Ep 2] Loss=6.5577 | Val NDCG@10=0.015682
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015152,
    "quality_users": 4263
  }
}

[Step 640 | Ep 2] Loss=6.5543 | Val NDCG@10=0.015152
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.015548,
    "quality_users": 4263
  }
}

[Step 680 | Ep 2] Loss=6.5490 | Val NDCG@10=0.015548
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016087,
    "quality_users": 4263
  }
}

[End of Epoch 2] Val NDCG@10=0.016087
=== Training Complete! Best Validation NDCG@10: 0.018013 ===
Process Group PGID: 4432
</untrusted_context>

<untrusted_context>
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 61.24s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.64s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.21s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.53s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01299,
    "quality_users": 4263
  }
}

[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990
*** New best score: 0.012990! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.012391,
    "quality_users": 4263
  }
}

[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.01336,
    "quality_users": 4263
  }
}

[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360
*** New best score: 0.013360! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.014308,
    "quality_users": 4263
  }
}

[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308
*** New best score: 0.014308! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.016339,
    "quality_users": 4263
  }
}

[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339
*** New best score: 0.016339! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018645,
    "quality_users": 4263
  }
}

[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645
*** New best score: 0.018645! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018652,
    "quality_users": 4263
  }
}

[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652
*** New best score: 0.018652! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019376,
    "quality_users": 4263
  }
}

[End of Epoch 1] Val NDCG@10=0.019376
*** New best score: 0.019376! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018836,
    "quality_users": 4263
  }
}

[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.018001,
    "quality_users": 4263
  }
}

[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017624,
    "quality_users": 4263
  }
}

[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017974,
    "quality_users": 4263
  }
}

[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017418,
    "quality_users": 4263
  }
}

[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017469,
    "quality_users": 4263
  }
}

[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.24s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017564,
    "quality_users": 4263
  }
}

[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017218,
    "quality_users": 4263
  }
}

[End of Epoch 2] Val NDCG@10=0.017218
=== Training Complete! Best Validation NDCG@10: 0.019376 ===
Process Group PGID: 4810
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.017218,
    "quality_users": 4263
  }
}
Process Group PGID: 5134
</untrusted_context>

<untrusted_context>
Output: Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.60s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
 {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019376,
    "quality_users": 4263
  }
}

Verified saved best model score: 0.019376
Process Group PGID: 5154
</untrusted_context>

Current topic: "Full Data Refit & Submission Generation"
Topic summary: Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.

Strategic Intent: Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.

Successfully created and wrote to new file: /app/solution/refit_submission.py. Here is the updated code:
"""Refit the two-tower ranker on train + val data and generate the final hidden-test submission."""
from __future__ import annotations

import argparse
import sys
import time
from pathlib import Path

import numpy as np
import pandas as pd
import torch

from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker

# Add /task to path for evaluation validators
sys.path.append("/task")
from ranking_evaluation import CandidateSet, validate_ranking


def refit_and_submit(
    data_dir: Path,
    submission_path: Path,
    epochs: int = 1,
    steps_to_train: int = 280,
    batch_size: int = 512,
    lr: float = 7e-4,
    embed_dim: int = 96,
    hidden_dim: int = 128,
    out_dim: int = 96,
    temperature: float = 0.07,
    logq_weight: float = 1.0,
    num_windows: int = 6,
    sample_listens: int = 35,
    num_uniform_negatives: int = 512,
) -> None:
    torch.manual_seed(42)
    np.random.seed(42)
    torch.set_num_threads(8)

    print("=== Loading Catalog and Users for Full Refit ===")
    cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)
    target_users_df = pd.read_parquet(data_dir / "target_users.parquet")
    target_uids = target_users_df["uid"].to_numpy(dtype=np.int64)
    targets_set = set(target_uids)

    test_cutoff = 25393400
    val_cutoff = 24786800
    week_sec = 7 * 86400
    gap_sec = 1800

    # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!
    cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]
    print(f"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}")

    print("=== Building Full Training Pairs (including Val Window) ===")
    t0 = time.time()
    histories_list, user_win, target_items, sample_weights = build_training_pairs(
        data_dir=data_dir,
        cutoffs=cutoffs,
        item_to_idx=item_to_idx,
        target_uids=target_uids,
        sample_listens_per_user=sample_listens,
        include_val=True,
    )
    print(f"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}")

    # Stack user histories across windows
    keys = list(histories_list[0].keys())
    histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}
    n_users = len(target_uids)
    global_uids = user_win[:, 0] * n_users + user_win[:, 1]
    item_logq_t = torch.from_numpy(item_logq)

    print("=== Initializing Two-Tower Ranker ===")
    model = TwoTowerRanker(
        num_catalog_items=30000,
        embed_dim=embed_dim,
        hidden_dim=hidden_dim,
        out_dim=out_dim,
        temperature=temperature,
        logq_weight=logq_weight,
    )

    optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
    total_steps = steps_to_train
    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)

    print(f"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===")
    model.train()
    n_samples = len(target_items)
    perm = torch.randperm(n_samples)
    global_step = 0
    t_train = time.time()

    while global_step < steps_to_train:
        for i in range(0, n_samples, batch_size):
            if global_step >= steps_to_train:
                break
            idx = perm[i : min(i + batch_size, n_samples)]
            b_uids = global_uids[idx]
            b_items = target_items[idx]
            b_weights = sample_weights[idx]

            b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
            b_feats = cat_feats[b_items]

            optimizer.zero_grad()
            loss = model.forward_loss(
                user_history_batch=b_hist,
                target_item_indices=b_items,
                target_item_features=b_feats,
                catalog_features=cat_feats,
                item_logq=item_logq_t,
                sample_weights=b_weights,
                num_uniform_negatives=num_uniform_negatives,
            )
            loss.backward()
            torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
            optimizer.step()
            scheduler.step()
            global_step += 1

            if global_step % 40 == 0:
                print(f"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}")

    print(f"Refit training completed in {time.time() - t_train:.2f}s")
    torch.save(model.state_dict(), "/app/solution/final_refitted_model.pt")

    # --- Generate Hidden Test Predictions ---
    print("=== Extracting Hidden-Test User Histories ===")
    t0 = time.time()
    test_history = extract_user_histories(
        data_dir=data_dir,
        cutoff=test_cutoff,
        target_uids=target_uids,
        item_to_idx=item_to_idx,
        pref_state_path=data_dir / "test_preference_state.parquet",
        include_val=True,
    )
    print(f"Test user histories ready in {time.time() - t0:.2f}s")

    print("=== Precomputing Candidate Item Embeddings Offline ===")
    t0 = time.time()
    cand_embeddings = model.precompute_candidate_embeddings(cat_feats)
    print(f"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s")

    print("=== Computing Target User Embeddings ===")
    t0 = time.time()
    user_embeddings = model.compute_user_embeddings(test_history)
    print(f"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s")

    print("=== Computing MIPS Retrieval with Test Candidate Mask ===")
    t0 = time.time()
    scores = torch.matmul(user_embeddings, cand_embeddings.t())  # (8456, 30000)

    # Load test eligibility bitmask
    test_eligibility = CandidateSet(data_dir / "test_eligible_candidates.npz")
    unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder="little")
    ineligible_mask = unpacked_test == 0
    scores[torch.from_numpy(ineligible_mask)] = -1e9

    # Top 100 retrieval
    top100 = torch.topk(scores, 100, dim=1).indices.numpy()
    print(f"MIPS retrieval completed in {time.time() - t0:.2f}s")

    print("=== Building Final Submission DataFrame ===")
    rows = []
    for u_idx, uid in enumerate(target_uids):
        top_items = cat_items[top100[u_idx]]
        for r, item_id in enumerate(top_items, 1):
            rows.append((int(uid), int(item_id), r))

    sub_df = pd.DataFrame(rows, columns=["uid", "item_id", "rank"])

    # Strict contract validation
    print("=== Validating Submission Contract ===")
    validated = validate_ranking(sub_df, targets_set)
    print("Contract structure validation passed!")

    # Check candidate eligibility for all rows
    print("Checking candidate eligibility against test_eligible_candidates.npz...")
    is_valid_candidate = [
        test_eligibility.is_eligible(int(u), int(it))
        for u, it in zip(validated["uid"].values, validated["item_id"].values)
    ]
    if not all(is_valid_candidate):
        num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)
        raise ValueError(f"Found {num_invalid} ineligible items in final submission!")
    print("100% of submitted items are verified eligible in test_eligible_candidates.npz!")

    submission_path.parent.mkdir(parents=True, exist_ok=True)
    validated.to_parquet(submission_path, index=False)
    print(f"Successfully saved final submission to {submission_path}")
    print(f"Rows: {len(validated)}, Users: {validated['uid'].nunique()}")


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--data_dir", type=str, default="/task/data")
    parser.add_argument("--submission_path", type=str, default="/app/solution/submission.parquet")
    parser.add_argument("--steps", type=int, default=280)
    parser.add_argument("--batch_size", type=int, default=512)
    parser.add_argument("--lr", type=float, default=7e-4)
    parser.add_argument("--embed_dim", type=int, default=96)
    parser.add_argument("--hidden_dim", type=int, default=128)
    parser.add_argument("--out_dim", type=int, default=96)
    parser.add_argument("--temperature", type=float, default=0.07)
    parser.add_argument("--logq_weight", type=float, default=1.0)
    parser.add_argument("--num_windows", type=int, default=6)
    parser.add_argument("--sample_listens", type=int, default=35)
    args = parser.parse_args()

    refit_and_submit(
        data_dir=Path(args.data_dir),
        submission_path=Path(args.submission_path),
        steps_to_train=args.steps,
        batch_size=args.batch_size,
        lr=args.lr,
        embed_dim=args.embed_dim,
        hidden_dim=args.hidden_dim,
        out_dim=args.out_dim,
        temperature=args.temperature,
        logq_weight=args.logq_weight,
        num_windows=args.num_windows,
        sample_listens=args.sample_listens,
    )


if __name__ == "__main__":
    main()

<untrusted_context>
Output: === Loading Catalog and Users for Full Refit ===
Refit training cutoffs (6 windows): [24786800, 24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Full Training Pairs (including Val Window) ===
Extracting user histories for training cutoff 0: timestamp=24786800...
Extracting user histories for training cutoff 1: timestamp=24180200...
Extracting user histories for training cutoff 2: timestamp=23573600...
Extracting user histories for training cutoff 3: timestamp=22967000...
Extracting user histories for training cutoff 4: timestamp=22360400...
Extracting user histories for training cutoff 5: timestamp=21753800...
Generated 180980 discovery training pairs across 6 windows.
Data preparation completed in 74.25s, pairs=180980
=== Initializing Two-Tower Ranker ===
=== Training Refitted Two-Tower Model for 280 Steps ===
[Step 40/280] Loss: 6.8969
[Step 80/280] Loss: 6.8447
[Step 120/280] Loss: 6.8488
[Step 160/280] Loss: 6.7927
[Step 200/280] Loss: 6.8334
[Step 240/280] Loss: 6.8618
[Step 280/280] Loss: 6.8466
Refit training completed in 10.20s
=== Extracting Hidden-Test User Histories ===
Test user histories ready in 3.69s
=== Precomputing Candidate Item Embeddings Offline ===
Candidate embeddings shape=torch.Size([30000, 96]) computed in 0.02s
=== Computing Target User Embeddings ===
User embeddings shape=torch.Size([8456, 96]) computed in 0.17s
=== Computing MIPS Retrieval with Test Candidate Mask ===
MIPS retrieval completed in 0.52s
=== Building Final Submission DataFrame ===
=== Validating Submission Contract ===
Contract structure validation passed!
Checking candidate eligibility against test_eligible_candidates.npz...
100% of submitted items are verified eligible in test_eligible_candidates.npz!
Successfully saved final submission to /app/solution/submission.parquet
Rows: 845600, Users: 8456
Process Group PGID: 5197
</untrusted_context>

<untrusted_context>
Output: Checking submission file: /app/solution/submission.parquet
Shape: (845600, 3)
Columns: ['uid', 'item_id', 'rank']
Data types:
 uid        int64
item_id    int64
rank       int64
dtype: object
validate_ranking passed!
Validating every single item against test_eligible_candidates.npz...
Total invalid candidates: 0
All 845,600 user-item pairs are 100% verified eligible!
Process Group PGID: 5233
</untrusted_context>

Current topic: "Documentation & Verification"
Topic summary: Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands.

Strategic Intent: Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.

Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:
# Latency-Efficient PyTorch Two-Tower Ranker for "Made for You" Discovery Shelf

## Overview
This solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the "Made for You" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.

---

## 1. Model Architecture

The ranker strictly decomposes into two independent PyTorch neural towers:

### Item Tower (`ItemTower` in `model.py`)
- **Inputs**:
  - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).
  - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.
  - Catalog Metadata Features (4 dimensions):
    - Standardized $\log(1 + \text{positive\_listens})$
    - Standardized $\log(1 + \text{organic\_positive\_listens})$
    - Organic Listen Ratio: $\frac{\text{organic\_listens} + 1e-4}{\text{positive\_listens} + 1}$
    - Normalized Popularity Rank: $\frac{\text{popularity\_rank}}{30000}$
- **Architecture**:
  - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).
  - Concatenation of item embedding (96d) and feature projection (32d).
  - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.
  - $L_2$ Normalization to the unit sphere: $\|v\|_2 = 1$.
- **Offline Precomputation**:
  - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \in \mathbb{R}^{30000 \times 96}$.

### User Tower (`UserTower` in `model.py`)
- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):
  1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\ge 50\%$ played ratio), capturing the user's immediate session context and mood.
  2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\lambda = \frac{\ln 2}{14 \text{ days}}$), played ratio completion weighting, and organic discovery bonus.
  3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.
  4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.
  5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.
- **Architecture**:
  - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.
  - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).
  - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.
  - $L_2$ Normalization: $\|u\|_2 = 1$.

### Relevance Scoring & Latency-Efficient Retrieval
- **Exact Decomposition**:
  $$\text{RelevanceScore}(u, v) = u^T v$$
  The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.
- **MIPS Latency**:
  At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\mu$s per user).

---

## 2. Training Objective

The model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:

1. **Temporal Slicing & True Discovery Filtering**:
   - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).
   - In each target window $[C + 1800, C + 1800 + 7 \text{ days}]$, ground-truth organic likes and strong organic listens ($\ge 80\%$) are matched with histories strictly before $C$.
   - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.
2. **Grade Importance Weighting**:
   - Relevant items are weighted by their contribution to graded NDCG@10:
     - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)
     - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)
3. **Mixed Negative Sampling**:
   - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.
   - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.
4. **LogQ Debiasing**:
   - Logits are corrected by item sampling frequency:
     $$\text{Logit}_{i, j} = \frac{u_i^T v_j}{\tau} - \log q(v_j)$$
     where $\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.

---

## 3. Public Validation Score

The validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:

- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)
- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`
- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)
- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.

Evaluation command:
```sh
python /task/evaluate_public.py /app/solution/validation.parquet
```

---

## 4. Final Submission Command

To reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):

```sh
PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \
  --steps 280 \
  --lr 7e-4 \
  --embed_dim 96 \
  --out_dim 96 \
  --num_windows 6 \
  --sample_listens 35 \
  --submission_path /app/solution/submission.parquet
```

### Artifacts in `/app/solution/`
- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).
- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.
- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.
- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.
- `train_val.py`: Validation training loop and public evaluator integration.
- `refit_submission.py`: Full data refitting and final test submission generation.
- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.
- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.

<untrusted_context>
Output: total 26M
-rw-r--r-- 1 agent agent 7.3K Sep 29 03:03 README.md
-rw-r--r-- 1 agent agent  12M Sep 29 02:58 best_val_model.pt
-rw-r--r-- 1 agent agent  18K Sep 29 02:38 data_prep.py
-rw-r--r-- 1 agent agent  12M Sep 29 03:02 final_refitted_model.pt
-rw-r--r-- 1 agent agent  11K Sep 29 02:41 model.py
-rw-r--r-- 1 agent agent 8.7K Sep 29 03:00 refit_submission.py
-rw-r--r-- 1 agent agent 1.2M Sep 29 03:02 submission.parquet
-rw-r--r-- 1 agent agent  11K Sep 29 02:43 train_val.py
-rw-r--r-- 1 agent agent 1.3M Sep 29 03:00 validation.parquet
Process Group PGID: 5253
</untrusted_context>

<untrusted_context>
Output: {
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.019376,
    "quality_users": 4263
  }
}
Process Group PGID: 5256
</untrusted_context>

<untrusted_context>
Output: ALL SUBMISSION CHECKS PASSED PERFECTLY!
Rows: 845600, Unique UIDs: 8456, Items per UID: exactly 100
Process Group PGID: 5276
</untrusted_context>

Current topic: "Project Completion & Verification"
Topic summary: Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.

Strategic Intent: Final synthesis and deliverable verification of the two-tower neural ranker.