Agent trace

/task/:
total 36
dr-xr-xr-x 1 root root 4096 Sep 24 07:42 .
drwxr-xr-x 1 root root 4096 Sep 24 07:44 ..
dr-xr-xr-x 1 root root 4096 Sep 24 07:39 data
-r--r--r-- 1 root root 5774 Sep 24 07:38 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 07:38 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 24 07:38 requirements.txt

/task/data/:
total 347392
dr-xr-xr-x 1 root root      4096 Sep 24 07:39 .
dr-xr-xr-x 1 root root      4096 Sep 24 07:42 ..
-r--r--r-- 1 root root    349352 Sep 24 07:38 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Sep 24 07:38 target_users.parquet
-r--r--r-- 1 root root   7736341 Sep 24 07:39 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Sep 24 07:39 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Sep 24 07:38 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Sep 24 07:38 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 24 07:38 train_listens.parquet
-r--r--r-- 1 root root    155988 Sep 24 07:38 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Sep 24 07:38 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Sep 24 07:38 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Sep 24 07:38 val_likes.parquet
-r--r--r-- 1 root root   9947609 Sep 24 07:38 val_listens.parquet
-r--r--r-- 1 root root      7845 Sep 24 07:38 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Sep 24 07:38 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Sep 24 07:39 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Sep 24 07:39 validation_preference_state.parquet
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# 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. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. 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
idna==3.18
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
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
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
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
8
               total        used        free      shared  buff/cache   available
Mem:              15           1           4           0           9          14
Swap:              0           0           0

"""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),
    }
====
#!/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()

targets (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']    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
cat (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
3  3542184             24075                     17699                4
4  9378983             24036                     18195                5
prefstate (469124, 3) ['uid', 'item_id', '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
state
liked       418435
disliked     50689
Name: count, dtype: int64
{'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
elig per user mean 29108.8
0.2735106945037842

(43484159, 6) uid                     uint32
timestamp               uint32
item_id                 uint32
is_organic               uint8
played_ratio_pct        uint16
track_length_seconds    uint32
dtype: object 0.6735928058624268
0 24786795
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100      39420  8326270           0               100                   170
1  100      39420  1441281           0               100                   105
2  100      39625   286361           0               100                   185
3  100      40110   732449           0               100                   240
4  100      40360  3397170           0                46                   130
mem 0.826199153

Exit code 1
Traceback (most recent call last):
  File "<string>", line 17, in <module>
IndexError: arrays used as indices must be of integer (or boolean) type
elig 24657 30000 29067.306291390727
nuid 9238
frac listens in catalog 0.7186436099638032

hist items/user 930.5421002838222 hist&elig 0.0 hist&~elig 930.5421002838222 ~elig 932.6937086092715
liked&elig 0 liked&~elig 418435
disl&elig 0 disl&~elig 48979
listen80 n 170791 eligible 0.20842433149287726 | among eligible: inhist 0.0 liked_state 0.0 disl 0.0
like n 8528 eligible 0.5179409005628518 | among eligible: inhist 0.0 liked_state 0.0 disl 0.0
3.381736993789673

File created successfully at: /app/solution/rank.py (file state is current in your context — no need to Read it back)

File created successfully at: /app/solution/tune.py (file state is current in your context — no need to Read it back)

scored users 4263 load 2.5
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.5, 'knn': 300, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02584 (18s)
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.5, 'knn': 100, 'sim_pow': 3.0, 'lam': 0.0} ndcg@10=0.01320 (20s)
{'tau': 4.0, 'pw': 0.5, 'alpha': 0.6, 'knn': 150, 'sim_pow': 3.0, 'lam': 0.0} ndcg@10=0.00994 (18s)

scored users 4263 load 2.3
POP ndcg 0.01057706484365688

Command running in background with ID: bck66upk4. Output is being written to: /tmp/claude-1000/-app/67ea2940-133d-4d2e-ae7b-a3f3880d8e36/tasks/bck66upk4.output. You will be notified when it completes. To check interim output, use Read on that file path.

File created successfully at: /app/solution/build_submission.py (file state is current in your context — no need to Read it back)

(Bash completed with no output)

<system-reminder>Warning: the file exists but the contents are empty.</system-reminder>

scored users 4263 load 2.3
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.5, 'knn': 0, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02287 (19s)
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.5, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02594 (18s)
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.3, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02330 (19s)
{'tau': 8.0, 'pw': 0.5, 'alpha': 0.75, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.01357 (18s)
{'tau': 26.0, 'pw': 0.5, 'alpha': 0.5, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02239 (18s)
{'tau': 8.0, 'pw': 1.0, 'alpha': 0.5, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} ndcg@10=0.02608 (20s)

[exited with code 0]

Command running in background with ID: bvvz1u061. Output is being written to: /tmp/claude-1000/-app/67ea2940-133d-4d2e-ae7b-a3f3880d8e36/tasks/bvvz1u061.output. You will be notified when it completes. To check interim output, use Read on that file path.

Command running in background with ID: bnlun834k. Output is being written to: /tmp/claude-1000/-app/67ea2940-133d-4d2e-ae7b-a3f3880d8e36/tasks/bnlun834k.output. You will be notified when it completes. To check interim output, use Read on that file path.

File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)

[exited with code 0]
===
total 36376
drwxr-xr-x 1 agent agent    4096 Sep 24 07:54 .
drwxr-xr-x 1 agent agent    4096 Sep 24 07:42 ..
-rw-r--r-- 1 agent agent    2450 Sep 24 07:54 README.md
-rw-r--r-- 1 agent agent    1405 Sep 24 07:50 build_submission.py
-rw-r--r-- 1 agent agent    4300 Sep 24 07:48 rank.py
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:50 top_1362332444284304948.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:51 top_1688925918491487567.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:51 top_3011764530292788585.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:49 top_3057232542602329196.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:51 top_3734596689788643276.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:49 top_6636378195443717945.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:48 top_7372356717027704005.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:52 top_8425661309531784997.npy
-rw-r--r-- 1 agent agent 3382528 Sep 24 07:50 top_8615652301355512701.npy
-rw-r--r-- 1 agent agent 6764928 Sep 24 07:49 top_pop.npy
-rw-r--r-- 1 agent agent    2576 Sep 24 07:48 tune.py

Exit code 2
3
ls: cannot access '/app/solution/submission.parquet': No such file or directory

wrote (845600, 3) {'tau': 8.0, 'pw': 1.0, 'alpha': 0.5, 'knn': 1000, 'sim_pow': 1.0, 'lam': 0.0} 26.0 s

[exited with code 0]
-rw-r--r-- 1 agent agent 1823918 Sep 24 07:54 /app/solution/submission.parquet

ok
{
  "model": "/app/solution/validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.026081,
    "quality_users": 4263
  }
}

rows 845600 users 8456 all eligible True
total 3600
drwxr-xr-x 1 agent agent    4096 Sep 24 07:56 .
drwxr-xr-x 1 agent agent    4096 Sep 24 07:42 ..
-rw-r--r-- 1 agent agent    2450 Sep 24 07:54 README.md
-rw-r--r-- 1 agent agent    1405 Sep 24 07:50 build_submission.py
-rw-r--r-- 1 agent agent    4300 Sep 24 07:48 rank.py
-rw-r--r-- 1 agent agent 1823918 Sep 24 07:54 submission.parquet
-rw-r--r-- 1 agent agent    2576 Sep 24 07:48 tune.py
-rw-r--r-- 1 agent agent 1824761 Sep 24 07:56 validation.parquet

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