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Basic recommender gemini-3-8-flash-high-03

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

This recording includes 55 readable reasoning summaries, shown in full below.

Agent
gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation
29:47 · 51 steps
Private reward
0.009092 · Failed
Attempt cost
$0.53
  • Thinking
  • Model text
  • Tool call
  • Tool response
01

User context

+00:00
User context

<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app

  • Workspace Directories:
  • /app
  • /task
  • Directory Structure:

Showing up to 200 items (files + folders).

/app/ └───solution/ Showing up to 200 items (files + folders).

/task/ ├───evaluate_public.py ├───ranking_evaluation.py ├───requirements.txt └───data/ ├───candidate_catalog.parquet ├───target_users.parquet ├───test_eligible_candidates.npz ├───test_preference_state.parquet ├───train_dislikes.parquet ├───train_likes.parquet ├───train_listens.parquet ├───train_undislikes.parquet ├───train_unlikes.parquet ├───val_dislikes.parquet ├───val_likes.parquet ├───val_listens.parquet ├───val_undislikes.parquet ├───val_unlikes.parquet ├───validation_eligible_candidates.npz └───validation_preference_state.parquet

</session_context>

02

Task instructions

+00:00
Task instructions

Build a music-discovery ranker

Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.

Data

All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.

For each event family there is a train_*.parquet and val_*.parquet file:

FamilyMeaningColumns
listensPlayback eventsuid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds
likesExplicit positive actionsuid, timestamp, item_id, is_organic
dislikesExplicit negative actionsuid, timestamp, item_id, is_organic
unlikesReversal of a prior likeuid, timestamp, item_id, is_organic
undislikesReversal of a prior dislikeuid, timestamp, item_id, is_organic

is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.

The two public windows have different roles:

  1. Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
  2. After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.

Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.

Additional files under /task/data/:

  • candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
  • target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
  • validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
  • test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
  • validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").

The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.

Discovery objective

Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:

  • grade 3: the user organically likes the track;
  • grade 1: the user organically listens to at least 80% of the track;
  • grade 0: otherwise.

If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.

The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.

The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.

Public tools

/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:

python /task/evaluate_public.py solution/validation.parquet

Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.

The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above.

Deliverable

Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.

Leave the code that builds the artifact and a concise /app/solution/README.md in /app/solution/. Keep the best valid submission saved throughout the attempt so it survives the deadline.

Constraints

  • Write deliverables and intermediate artifacts only under /app/solution/.
  • Treat /task/ and the installed Python environment as read-only.
  • Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
  • Do not install packages or use network access except for the model provider connection managed by Harbor.
  • You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
03

update_topic command

+00:11
Recorded reasoning summary 36 words

Analyzing Task Details

I'm currently dissecting the prompt, paying close attention to the machine learning task, competitive aspects, and specific constraints. My focus is on understanding the requirements and the provided workspace setup before proceeding further.

Recorded reasoning summary 44 words

Investigating Workspace Constraints

I'm now focusing on the strict environment limitations: read-only directories, no new packages, and a tight time budget. My priority is to understand the evaluation script and available tools to quickly establish a baseline and ensure valid submissions are always accessible.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "strategic_intent": "Explore data files, requirements, and evaluation script to understand the problem and setup.",
  "summary": "Starting initial exploration of the task environment. Inspecting /task directory, dataset shapes, evaluation scripts, and installed packages.",
  "title": "Initial Exploration and Setup"
}

Tool response 

Current topic: "Initial Exploration and Setup"
Topic summary: Starting initial exploration of the task environment. Inspecting /task directory, dataset shapes, evaluation scripts, and installed packages.

Strategic Intent: Explore data files, requirements, and evaluation script to understand the problem and setup.
Tool callread_file/task/evaluate_public.py Returned

Tool call · Input 

{
  "start_line": 1,
  "file_path": "/task/evaluate_public.py"
}

Tool response 

#!/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()
Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input 

{
  "start_line": 1,
  "file_path": "/task/ranking_evaluation.py"
}

Tool response 

"""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),
    }
Tool callread_file/task/requirements.txt Returned

Tool call · Input 

{
  "start_line": 1,
  "file_path": "/task/requirements.txt"
}

Tool response 

# 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
Tool callrun_shell_commandCheck sizes of dataset files in /task/data/ Returned

Tool call · Input 

{
  "description": "Check sizes of dataset files in /task/data/",
  "command": "ls -lh /task/data/"
}

Tool response 

<untrusted_context>
Output: total 340M
-r--r--r-- 1 root root 342K Sep 29 04:13 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 29 04:13 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 04:13 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 04:13 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 04:13 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 04:13 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 04:13 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 04:13 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 29 04:13 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 04:13 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 04:13 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 04:13 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 29 04:13 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 04:13 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>
04

Check CPU cores and available memory

+00:21
Tool callrun_shell_commandCheck CPU cores and available memory Returned

Tool call · Input 

{
  "description": "Check CPU cores and available memory",
  "command": "nproc && free -h"
}

Tool response 

<untrusted_context>
Output: 8
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1615
</untrusted_context>
05

Check system memory

+00:25
Tool callrun_shell_commandCheck system memory Returned

Tool call · Input 

{
  "command": "grep -i MemTotal /proc/meminfo && grep -i MemAvailable /proc/meminfo",
  "description": "Check system memory"
}

Tool response 

<untrusted_context>
Output: MemTotal:       15988972 kB
MemAvailable:   14687508 kB
Process Group PGID: 1619
</untrusted_context>
06

Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz

+00:30
Recorded reasoning summary 43 words

Exploring Data Structures

I'm now focusing on understanding the data itself, checking the shapes and types of relevant structures like candidate sets and preference states. I plan to use a quick Python script or a one-liner to get a feel for the data.

Tool callrun_shell_commandInspect metadata and shapes of catalog, targets, preference states, and eligibility npz Returned

Tool call · Input 

{
  "description": "Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz",
  "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"catalog:\", catalog.shape, catalog.columns)\nprint(catalog.head())\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(\"targets:\", targets.shape, targets.columns)\nprint(targets.head())\n\nval_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(\"val_pref:\", val_pref.shape, val_pref.columns)\nprint(val_pref.head())\n\ntest_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(\"test_pref:\", test_pref.shape, test_pref.columns)\nprint(test_pref.head())\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"val_npz:\", val_npz[\"uids\"].shape, val_npz[\"item_ids\"].shape, val_npz[\"packed_eligible\"].shape)\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nprint(\"test_npz:\", test_npz[\"uids\"].shape, test_npz[\"item_ids\"].shape, test_npz[\"packed_eligible\"].shape)\n'"
}

Tool response 

<untrusted_context>
Output: catalog: (30000, 4) Index(['item_id', 'positive_listens', 'organic_positive_listens',
       'popularity_rank'],
      dtype='object')
   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
targets: (8456, 3) Index(['uid', 'recent_positive_listens', 'history_positive_listens'], dtype='object')
   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
val_pref: (469124, 3) Index(['uid', 'item_id', 'state'], dtype='object')
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
test_pref: (484776, 3) Index(['uid', 'item_id', 'state'], dtype='object')
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
val_npz: (8456,) (30000,) (8456, 3750)
test_npz: (8456,) (30000,) (8456, 3750)
Process Group PGID: 1623
</untrusted_context>
07

Check distribution of eligible items per user

+00:36
Recorded reasoning summary 42 words

Verifying Data Integrity

I'm currently cross-referencing the user and item counts between the val_npz and test_npz datasets. My next step is to confirm the exact match of uids between the two and then analyze the average number of eligible items per user.

Tool callrun_shell_commandCheck distribution of eligible items per user Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\ncounts = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"val eligible count per user: min=\", counts.min(), \"max=\", counts.max(), \"mean=\", counts.mean(), \"median=\", np.median(counts))\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\npacked_test = test_npz[\"packed_eligible\"]\ncounts_test = np.unpackbits(packed_test, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"test eligible count per user: min=\", counts_test.min(), \"max=\", counts_test.max(), \"mean=\", counts_test.mean(), \"median=\", np.median(counts_test))\n'",
  "description": "Check distribution of eligible items per user"
}

Tool response 

<untrusted_context>
Output: val eligible count per user: min= 24657 max= 30000 mean= 29067.306291390727 median= 29242.0
test eligible count per user: min= 24552 max= 30000 mean= 29041.3178807947 median= 29220.0
Process Group PGID: 1640
</untrusted_context>
08

Check why items are ineligible

+00:49
Recorded reasoning summary 48 words

Investigating Eligibility Logic

I'm currently analyzing the eligibility of catalog items for users. It appears a significant majority, around 29,000 out of 30,000, are typically eligible. My next step is to delve into the specific criteria that determine this eligibility, likely involving user listening history and engagement signals.

Tool callrun_shell_commandCheck why items are ineligible Returned

Tool call · Input 

{
  "description": "Check why items are ineligible",
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\nu0 = uids[0]\nmask0 = np.unpackbits(packed[0], count=len(item_ids), bitorder=\"little\")\nineligible_items = set(item_ids[mask0 == 0])\nprint(f\"User {u0} has {len(ineligible_items)} ineligible items out of {len(item_ids)}\")\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nu0_pref = pref[pref.uid == u0]\nprint(f\"User {u0} pref items: {len(u0_pref)}, in ineligible: {len(set(u0_pref.item_id).intersection(ineligible_items))}\")\n'"
}

Tool response 

<untrusted_context>
Output: User 100 has 682 ineligible items out of 30000
User 100 pref items: 15, in ineligible: 15
Process Group PGID: 1651
</untrusted_context>
09

Check validation labels statistics

+00:57
Recorded reasoning summary 53 words

Refining Model Logic

I'm currently focusing on the core problem: building the most robust ranking model for a music streaming service's "Made for You" shelf. My current efforts are directed towards ensuring that previously interacted-with items are correctly excluded from recommendations, as organic behavior in the immediate subsequent window is paramount for relevance.

Recorded reasoning summary 52 words

Evaluating Ranking Precision

My focus has shifted to precisely implementing the grading system for our ranking model's validation. I'm ensuring that "organic likes" and substantial track listening (over 80%) are correctly mapped to the highest grade, with a keen eye on accurate DCG calculations and per-user NDCG averaging for robust metric assessment.

Tool callrun_shell_commandCheck validation labels statistics Returned

Tool call · Input 

{
  "command": "python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nimport numpy as np\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
  "description": "Check validation labels statistics"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1669
</untrusted_context>
10

Check validation labels statistics with PYTHONPATH

+01:00
Tool callrun_shell_commandCheck validation labels statistics with PYTHONPATH Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'",
  "description": "Check validation labels statistics with PYTHONPATH"
}

Tool response 

<untrusted_context>
Output: Total target users: 8456
Users with relevant items in val: 4263
Items per relevant user: mean=8.86, median=4.0, max=442
Grade 3 items: 4417, Grade 1 items: 33352
Process Group PGID: 1672
</untrusted_context>
11

Compute popularity baseline NDCG@10

+01:19
Recorded reasoning summary 40 words

Testing Popularity Baseline

I'm assessing a simple popularity baseline against our validation set. My focus is on the NDCG@10 metric for users with relevant items, currently standing at 4263 out of 8456 total target users. I'm now calculating its performance.

Tool callrun_shell_commandCompute popularity baseline NDCG@10 Returned

Tool call · Input 

{
  "description": "Compute popularity baseline NDCG@10",
  "command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings, ndcg_at_k\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Catalog order\ncat_items = catalog[\"item_id\"].to_numpy()\n# Map item_id to index in val_npz item_ids\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\n# For each user, get top 100 eligible items by catalog popularity\nranked = {}\nfor r, uid in enumerate(uids):\n    uid = int(uid)\n    mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n    # which items in cat_cols are eligible?\n    eligible_mask = mask[cat_cols] == 1\n    top100 = cat_items[eligible_mask][:100]\n    ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Popularity NDCG@10:\", res)\n'"
}

Tool response 

<untrusted_context>
Output: Popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1692
</untrusted_context>
12

Create initial baseline submission and validate against test_eligible_candidates

+01:29
Recorded reasoning summary 44 words

Establishing Baseline Metric

My initial thought process has focused on establishing a baseline performance metric. I've identified NDCG@10 at 0.01071 as a promising starting point. My next immediate step is to confirm the exact file format and row count requirements for a valid submission.

Tool callrun_shell_commandCreate initial baseline submission and validate against test_eligible_candidates Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nuids = test_npz[\"uids\"]\nitem_ids = test_npz[\"item_ids\"]\npacked = test_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\ncat_items = catalog[\"item_id\"].to_numpy()\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n    uid = int(uid)\n    mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n    eligible_mask = mask[cat_cols] == 1\n    top100 = cat_items[eligible_mask][:100]\n    for rank, item in enumerate(top100, 1):\n        records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\nPath(\"/app/solution\").mkdir(parents=True, exist_ok=True)\ndf.to_parquet(\"/app/solution/submission.parquet\", index=False)\n\n# Validate\nvalidated = validate_ranking(df, targets)\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n    if not eligibility.is_eligible(uid, item):\n        raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Baseline submission successfully created and validated!\")\n'",
  "description": "Create initial baseline submission and validate against test_eligible_candidates"
}

Tool response 

<untrusted_context>
Output: Baseline submission successfully created and validated!
Process Group PGID: 1712
</untrusted_context>
13

Verify submission.parquet format and row count

+01:34
Tool callrun_shell_commandVerify submission.parquet format and row count Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(df.info())\nprint(df.head())\n'",
  "description": "Verify submission.parquet format and row count"
}

Tool response 

<untrusted_context>
Output: <class 'pandas.core.frame.DataFrame'>
RangeIndex: 845600 entries, 0 to 845599
Data columns (total 3 columns):
 #   Column   Non-Null Count   Dtype 
---  ------   --------------   ----- 
 0   uid      845600 non-null  int64 
 1   item_id  845600 non-null  uint32
 2   rank     845600 non-null  int64 
dtypes: int64(2), uint32(1)
memory usage: 16.1 MB
None
   uid  item_id  rank
0  100  5862961     1
1  100  6901374     2
2  100  5635052     3
3  100  3542184     4
4  100  8213481     5
Process Group PGID: 1729
</untrusted_context>
14

Test evaluate_public.py on val_baseline.parquet

+01:43
Recorded reasoning summary 43 words

Ensuring Data Type Compliance

I'm focusing on ensuring type consistency. Specifically, I'm reviewing the item_id's uint32 type against the int64 cast in validate_ranking and plan to ensure it's int64 for df.to_parquet compliance. Next, I'll run python3 /task/evaluate_public.py with a popularity baseline validation parquet.

Tool callrun_shell_commandTest evaluate_public.py on val_baseline.parquet Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\n\ncat_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n    uid = int(uid)\n    mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n    eligible_mask = mask[cat_cols] == 1\n    top100 = cat_items[eligible_mask][:100]\n    for rank, item in enumerate(top100, 1):\n        records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_baseline.parquet\", index=False)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_baseline.parquet",
  "description": "Test evaluate_public.py on val_baseline.parquet"
}

Tool response 

<untrusted_context>
Output: {
  "model": "/app/solution/val_baseline.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Process Group PGID: 1748
</untrusted_context>
15

Inspect train_listens and train_likes

+01:55
Recorded reasoning summary 47 words

Exploring Dataset Potential

My current focus is on a deep dive into the dataset after confirming the successful execution of evaluate_public.py, which yielded a score of 0.01071. I'm now examining the available data files, specifically train_listens.parquet (305MB) and train_likes.parquet, to understand their structure and content more thoroughly.

Tool callrun_shell_commandInspect train_listens and train_likes Returned

Tool call · Input 

{
  "description": "Inspect train_listens and train_likes",
  "command": "python3 -c '\nimport pandas as pd\n\ndf_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\nprint(\"train_listens shape:\", df_listens.shape)\nprint(df_listens.head())\nprint(\"min timestamp:\", df_listens.timestamp.min(), \"max timestamp:\", df_listens.timestamp.max())\nprint(\"is_organic distribution:\\n\", df_listens.is_organic.value_counts())\nprint(\"played_ratio_pct distribution:\\n\", df_listens.played_ratio_pct.describe())\n\ndf_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\nprint(\"train_likes shape:\", df_likes.shape)\nprint(df_likes.head())\nprint(\"min timestamp:\", df_likes.timestamp.min(), \"max timestamp:\", df_likes.timestamp.max())\n'"
}

Tool response 

<untrusted_context>
Output: train_listens shape: (43484159, 6)
   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
min timestamp: 0 max timestamp: 24786795
is_organic distribution:
 is_organic
1    22477622
0    21006537
Name: count, dtype: int64
played_ratio_pct distribution:
 count    4.348416e+07
mean     6.465021e+01
std      4.412183e+01
min      0.000000e+00
25%      7.000000e+00
50%      1.000000e+02
75%      1.000000e+02
max      1.590000e+02
Name: played_ratio_pct, dtype: float64
train_likes shape: (814831, 4)
   uid  timestamp  item_id  is_organic
0  100      44755   732449           1
1  100    1155860  6568592           0
2  100    1259125  5411243           1
3  100    1260005  7371186           0
4  100    1263935  4943655           0
min timestamp: 80 max timestamp: 24786790
Process Group PGID: 1789
</untrusted_context>
16

Check unique users in train_listens vs target users

+02:01
Recorded reasoning summary 58 words

Processing Massive Datasets

I'm currently grappling with the sheer scale of the train_listens dataset, which clocks in at an impressive 43.4 million rows. Despite its size, reading it into memory was surprisingly swift thanks to the efficient parquet format, occupying a manageable 305MB. My next step is to investigate the distribution of target users within this substantial data.

Tool callrun_shell_commandCheck unique users in train_listens vs target users Returned

Tool call · Input 

{
  "description": "Check unique users in train_listens vs target users",
  "command": "python3 -c '\nimport pandas as pd\ndf_targets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(df_targets.uid)\nprint(f\"Number of target users: {len(target_uids)}\")\n\n# Sample or check total unique users in train_listens\n# Using pyarrow to read only uid column\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nunique_uids = set(table[\"uid\"].to_pylist())\nprint(f\"Total unique uids in train_listens: {len(unique_uids)}\")\nprint(f\"Are all target uids in train_listens? {len(target_uids.intersection(unique_uids))}\")\n'"
}

Tool response 

<untrusted_context>
Output: Number of target users: 8456
Total unique uids in train_listens: 9238
Are all target uids in train_listens? 8456
Process Group PGID: 1809
</untrusted_context>
17

Check items overlap between catalog and train_listens

+02:22
Recorded reasoning summary 46 words

Analyzing User Data

I'm currently examining the unique items within my training data, train_listens, and comparing them against the 30,000 items in the catalog. My focus is on identifying how many unique items are present in train_listens and which specific items are candidates for further analysis.

Tool callrun_shell_commandCheck items overlap between catalog and train_listens Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\nprint(\"Catalog items:\", len(cat_items))\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"item_id\"])\ntrain_items = set(table[\"item_id\"].to_pylist())\nprint(\"Train items:\", len(train_items))\nprint(\"Catalog items in train items:\", len(cat_items.intersection(train_items)))\n'",
  "description": "Check items overlap between catalog and train_listens"
}

Tool response 

<untrusted_context>
Output: Catalog items: 30000
Train items: 845395
Catalog items in train items: 30000
Process Group PGID: 1830
</untrusted_context>
18

Verify timestamps and units of val_listens

+02:45
Recorded reasoning summary 42 words

Verifying Timestamp Boundaries

All 30,000 catalog items are now incorporated into the training set. My current focus is on scrutinizing the date range and timestamps, specifically noting that the training data concludes at timestamp 24786800. I'm investigating the units of this timestamp.

Tool callrun_shell_commandVerify timestamps and units of val_listens Returned

Tool call · Input 

{
  "description": "Verify timestamps and units of val_listens",
  "command": "python3 -c '\nimport pandas as pd\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(\"val_listens shape:\", val_listens.shape)\nprint(\"val_listens min ts:\", val_listens.timestamp.min(), \"max ts:\", val_listens.timestamp.max())\nprint(\"difference:\", val_listens.timestamp.max() - val_listens.timestamp.min())\nprint(\"gap from train end (24786800):\", val_listens.timestamp.min() - 24786800)\n'"
}

Tool response 

<untrusted_context>
Output: val_listens shape: (1501697, 6)
val_listens min ts: 24788600 max ts: 25393395
difference: 604795
gap from train end (24786800): 1800
Process Group PGID: 1857
</untrusted_context>
19

Check values in validation preference state

+02:51
Recorded reasoning summary 50 words

Analyzing Data Gaps

I'm currently examining the val_listens dataset, noting its substantial size over seven days and its specific start and end times. I'm also paying close attention to the defined gaps between training and testing windows. The instruction regarding distinct roles for public windows is also a key focus.

Tool callrun_shell_commandCheck values in validation preference state Returned

Tool call · Input 

{
  "description": "Check values in validation preference state",
  "command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(pref[\"state\"].value_counts())\n'"
}

Tool response 

<untrusted_context>
Output: state
liked       418435
disliked     50689
Name: count, dtype: int64
Process Group PGID: 1877
</untrusted_context>
20

Check if graded items appeared in user train_listens

+03:00
Recorded reasoning summary 37 words

Analyzing User Interaction Data

I'm currently investigating user interactions within the validation phase, specifically focusing on whether these interactions overlap with tracks previously engaged during training. My next step is to verify the extent of this overlap.

Recorded reasoning summary 55 words

Investigating Validation Item Eligibility

I'm digging into the implications of an item being "eligible" in validation. This means I need to clarify what triggers eligibility and how it relates to past interactions. My focus is on understanding the criteria for an item's inclusion in the validation set, especially if it was already encountered during training.

Tool callrun_shell_commandCheck if graded items appeared in user train_listens Returned

Tool call · Input 

{
  "description": "Check if graded items appeared in user train_listens",
  "command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Let us check train_listens for these (uid, item) pairs\ntrain_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\n# sample 1000 graded pairs\npairs = []\nfor u, items in list(graded.items())[:500]:\n    for it in items:\n        pairs.append((u, it))\nsample_pairs = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total sample graded pairs: {len(sample_pairs)}\")\nmerged = sample_pairs.merge(train_listens[[\"uid\", \"item_id\", \"timestamp\"]], on=[\"uid\", \"item_id\"])\nprint(f\"Sample pairs present in train_listens: {len(merged)}\")\nif len(merged) > 0:\n    print(merged.head())\n'"
}

Tool response 

<untrusted_context>
Output: Total sample graded pairs: 5013
Sample pairs present in train_listens: 0
Process Group PGID: 1894
</untrusted_context>
21

Check overlap across all graded pairs and train_listens

+03:10
Tool callrun_shell_commandCheck overlap across all graded pairs and train_listens Returned

Tool call · Input 

{
  "description": "Check overlap across all graded pairs and train_listens",
  "command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\nimport pyarrow.parquet as pq\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Collect all (uid, item_id) in graded\npairs = []\nfor u, items in graded.items():\n    for it in items:\n        pairs.append((u, it))\ngraded_df = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total graded pairs: {len(graded_df)}\")\n\n# Check train_listens\nt_listens = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntrain_pairs = set(zip(t_listens[\"uid\"].to_pylist(), t_listens[\"item_id\"].to_pylist()))\noverlap = sum(1 for row in graded_df.itertuples(index=False) if (row.uid, row.item_id) in train_pairs)\nprint(f\"Overlap between graded pairs and train_listens: {overlap}\")\n'"
}

Tool response 

<untrusted_context>
Output: Total graded pairs: 37769
Overlap between graded pairs and train_listens: 0
Process Group PGID: 1914
</untrusted_context>
22

update_topic command

+04:08
Recorded reasoning summary 31 words

Refining Discovery Strategy

I've confirmed a crucial detail: only items the user hasn't encountered during training are considered for discovery. This ensures a truly fresh and exploratory experience, prioritizing novel recommendations.

Recorded reasoning summary 42 words

Filtering Candidate Items

My focus has shifted to refining how candidate items are filtered. I've confirmed that the system now excludes items already consumed or explicitly liked by the user. This ensures the discovery process prioritizes genuinely new content for each user.

Recorded reasoning summary 49 words

Exploring Item-Item Similarity

I'm investigating the potential of item-item collaborative filtering for discovery, specifically how to identify items with the highest similarity that the user hasn't encountered. I'm also considering efficient ways to handle a large item catalog, possibly through sparse representations or low-rank factorization to manage memory constraints.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "title": "Researching Collaborative Filtering & Recency Models",
  "summary": "Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.",
  "strategic_intent": "Analyze user interaction patterns, recency, and test collaborative filtering methods."
}

Tool response 

Current topic: "Researching Collaborative Filtering & Recency Models"
Topic summary: Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.

Strategic Intent: Analyze user interaction patterns, recency, and test collaborative filtering methods.
23

Analyze rows by time window and target users

+04:14
Recorded reasoning summary 42 words

Analyzing Recent Interactions

I'm currently exploring recent interactions, specifically aiming to script a Python test. My focus is on analyzing the temporal distribution of events within train_listens and train_likes. I'll also investigate processing speed by filtering train_listens by target users and timestamps.

Tool callrun_shell_commandAnalyze rows by time window and target users Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets_frame.uid.astype(int))\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\ndays30 = cutoff - 30 * 86400\ndays14 = cutoff - 14 * 86400\ndays7 = cutoff - 7 * 86400\n\nprint(f\"Total rows: {len(listens)}\")\nt_in_target = listens.uid.isin(target_uids)\nprint(f\"Rows for target users: {t_in_target.sum()} ({t_in_target.mean():.1%})\")\nprint(f\"Rows in last 90 days: {(listens.timestamp >= days90).sum()}\")\nprint(f\"Rows in last 30 days: {(listens.timestamp >= days30).sum()}\")\nprint(f\"Rows in last 14 days: {(listens.timestamp >= days14).sum()}\")\nprint(f\"Rows in last 7 days: {(listens.timestamp >= days7).sum()}\")\n'",
  "description": "Analyze rows by time window and target users"
}

Tool response 

<untrusted_context>
Output: Total rows: 43484159
Rows for target users: 43222297 (99.4%)
Rows in last 90 days: 16769274
Rows in last 30 days: 6126103
Rows in last 14 days: 2920227
Rows in last 7 days: 1491699
Process Group PGID: 1944
</untrusted_context>
24

Check fraction of recent listens in catalog

+05:01
Recorded reasoning summary 46 words

Analyzing Listen Data

My current focus is on understanding user listening habits. I'm reviewing recent data, particularly the last 90 and 30 days, noting the significant volume of activity. I'm investigating which specific items users are engaging with and if these align with our training data.

Tool callrun_shell_commandCheck fraction of recent listens in catalog Returned

Tool call · Input 

{
  "description": "Check fraction of recent listens in catalog",
  "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"timestamp\", \"item_id\"])\nrecent = listens[listens.timestamp >= days90]\nin_cat = recent.item_id.isin(cat_items)\nprint(f\"Recent listens in catalog: {in_cat.sum()} / {len(recent)} ({in_cat.mean():.1%})\")\n'"
}

Tool response 

<untrusted_context>
Output: Recent listens in catalog: 12117965 / 16769274 (72.3%)
Process Group PGID: 1965
</untrusted_context>
25

Check pref items in catalog

+05:08
Recorded reasoning summary 41 words

Analyzing Catalog Performance

My recent analysis reveals a strong focus on the candidate catalog, with over 72% of all listens originating from it. I'm now shifting to examine the train_likes and validation_preference_state datasets, with train_likes already showing a substantial 814,831 rows.

Tool callrun_shell_commandCheck pref items in catalog Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nin_cat = pref.item_id.isin(cat_items)\nprint(\"Pref items in catalog:\", in_cat.sum(), f\"({in_cat.mean():.1%})\")\nprint(\"Unique items in pref:\", pref.item_id.nunique())\n'",
  "description": "Check pref items in catalog"
}

Tool response 

<untrusted_context>
Output: Pref items in catalog: 469124 (100.0%)
Unique items in pref: 28814
Process Group PGID: 1992
</untrusted_context>
26

Check organic popularity baseline NDCG@10

+05:16
Recorded reasoning summary 41 words

Completing Preference State

All active preference state items are now present in the candidate catalog. I'm currently verifying the extent to which catalog items are represented within the preference state and exploring the possibility of users unliking items after initial likes.

Recorded reasoning summary 59 words

Refining Candidate Generation

I've confirmed that validation_preference_state precisely captures users' current explicit likes and dislikes. Coupled with train_listens detailing listening habits, I'm now defining the discovery problem: recommending 100 new catalog tracks per user that they'll organically engage with or like within a week. My current focus is on how item popularity, both globally and recently, influences this prediction.

Tool callrun_shell_commandCheck organic popularity baseline NDCG@10 Returned

Tool call · Input 

{
  "command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Sort catalog by organic_positive_listens descending\ncat_sorted = catalog.sort_values(\"organic_positive_listens\", ascending=False)\ncat_items = cat_sorted[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nranked = {}\nfor r, uid in enumerate(uids):\n    uid = int(uid)\n    mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n    eligible_mask = mask[cat_cols] == 1\n    top100 = cat_items[eligible_mask][:100]\n    ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Organic popularity NDCG@10:\", res)\n'",
  "description": "Check organic popularity baseline NDCG@10"
}

Tool response 

<untrusted_context>
Output: Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 2009
</untrusted_context>
27

Check unique pairs in recent listens

+05:25
Recorded reasoning summary 37 words

Exploring Personalization

I'm now considering the significant impact of personalized collaborative filtering. Given the strong influence of individual user tastes in music, this approach is crucial and likely to yield substantial gains beyond just global popularity metrics.

Recorded reasoning summary 40 words

Evaluating Collaborative Filtering

I'm currently exploring the feasibility of implementing item-item collaborative filtering. My primary concern is the sparsity of the user-item interaction matrix given the scale of users and the catalog, which could impact the effectiveness of similarity calculations.

Tool callrun_shell_commandCheck unique pairs in recent listens Returned

Tool call · Input 

{
  "description": "Check unique pairs in recent listens",
  "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id)}\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid)}\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(uid_to_idx))]\n\nprint(f\"Recent catalog listens for targets: {len(recent)}\")\nprint(f\"Unique (uid, item) pairs: {recent.groupby(['uid', 'item_id']).ngroups}\")\n'"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 20, in <module>
NameError: name 'uid' is not defined. Did you mean: 'id'?
Recent catalog listens for targets: 12099707
Exit Code: 1
Process Group PGID: 2029
</untrusted_context>
28

Check unique pairs in recent listens without error

+05:36
Tool callrun_shell_commandCheck unique pairs in recent listens without error Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport pandas as pd\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(target_uids))]\n\nn_unique = len(recent.drop_duplicates(subset=[\"uid\", \"item_id\"]))\nprint(f\"Unique (uid, item) pairs in last 90 days: {n_unique}\")\n'",
  "description": "Check unique pairs in recent listens without error"
}

Tool response 

<untrusted_context>
Output: Unique (uid, item) pairs in last 90 days: 4306717
Process Group PGID: 2057
</untrusted_context>
29

Benchmark sparse matrix multiplication speed

+05:49
Recorded reasoning summary 46 words

Exploring Item-Item Similarity

I'm currently investigating item-item similarity approaches, specifically cosine and BM25, to understand potential patterns. This analysis stems from the observed user engagement data showing an average of around 500 items per user, with 4.3 million unique pairs across 8,456 users and 30,000 items.

Tool callrun_shell_commandBenchmark sparse matrix multiplication speed Returned

Tool call · Input 

{
  "description": "Benchmark sparse matrix multiplication speed",
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# test sparse dot\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nRtR = R.T @ R\nprint(f\"RtR shape: {RtR.shape}, nnz: {RtR.nnz}, time: {time.time() - t0:.2f}s\")\n'"
}

Tool response 

<untrusted_context>
Output: RtR shape: (30000, 30000), nnz: 817434538, time: 21.02s
Process Group PGID: 2078
</untrusted_context>
30

Benchmark TruncatedSVD speed

+06:16
Recorded reasoning summary 47 words

Exploring Real-World Data

My recent experiments with uniformly random data yielded a high density of nonzeros, as expected due to the lack of inherent structure. I'm now shifting focus to real-world datasets, anticipating sparsity and community-driven clustering. The next step involves investigating the feasibility of applying SVD.

Tool callrun_shell_commandBenchmark TruncatedSVD speed Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"TruncatedSVD k=64 time: {time.time() - t0:.2f}s, U shape: {U.shape}, V shape: {V.shape}\")\n'",
  "description": "Benchmark TruncatedSVD speed"
}

Tool response 

<untrusted_context>
Output: TruncatedSVD k=64 time: 2.12s, U shape: (8456, 64), V shape: (64, 30000)
Process Group PGID: 2101
</untrusted_context>
31

Benchmark score calculation and top100 argpartition

+06:25
Recorded reasoning summary 37 words

Achieving Efficient SVD

I'm pleased to report that a TruncatedSVD with k=64 completed in a swift 2.12 seconds. This yields $U$ (8456x64) and $V$ (64x30000), enabling rapid calculation of all predicted scores ($\hat{Y}$) via $U @ V$.

Tool callrun_shell_commandBenchmark score calculation and top100 argpartition Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\n\nU = np.random.randn(8456, 64).astype(np.float32)\nV = np.random.randn(64, 30000).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V\nprint(f\"Matrix multiply time: {time.time() - t0:.3f}s, scores shape: {scores.shape}\")\n\nt0 = time.time()\n# test argpartition for top 100\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nprint(f\"Top 100 argpartition time: {time.time() - t0:.3f}s\")\n'",
  "description": "Benchmark score calculation and top100 argpartition"
}

Tool response 

<untrusted_context>
Output: Matrix multiply time: 0.204s, scores shape: (8456, 30000)
Top 100 argpartition time: 0.968s
Process Group PGID: 2119
</untrusted_context>
32

Benchmark TruncatedSVD on large sparse matrix

+06:36
Recorded reasoning summary 35 words

Achieving Rapid Iteration

I'm thrilled by the performance gains; the entire evaluation cycle now takes mere seconds. This speed allows for incredibly swift iteration, enabling experimentation with various weighting schemes, recency half-lives, and interaction models.

Recorded reasoning summary 48 words

Exploring Feature Dimensions

I'm now considering a broader spectrum of signal types, including explicit likes and implicit completion rates. I'm also evaluating different dimensionality reduction techniques and normalization strategies, aiming to integrate these with popularity biases for a more robust hybrid approach. Direct evaluation via evaluate_public.py is key.

Tool callrun_shell_commandBenchmark TruncatedSVD on large sparse matrix Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# test 43M nonzeros\nN, M = 8456, 845000\nnnz = 20000000 # test 20M\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nprint(f\"TruncatedSVD 20M nnz time: {time.time() - t0:.2f}s\")\n'",
  "description": "Benchmark TruncatedSVD on large sparse matrix"
}

Tool response 

<untrusted_context>
Output: TruncatedSVD 20M nnz time: 25.17s
Process Group PGID: 2129
</untrusted_context>
33

Verify candidate items alignment

+07:08
Recorded reasoning summary 57 words

Evaluating Candidate Performance

My recent focus has been on understanding the performance implications of candidate item selection. I've noted that processing 20 million non-zero entries took 25 seconds. I'm now analyzing the candidate items, recognizing that our ranking operations are effectively confined to a 30,000-item catalog, as the test and validation sets exclusively draw from this subset.

Tool callrun_shell_commandVerify candidate items alignment Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"cat len:\", len(cat))\nprint(\"val len:\", len(val_npz[\"item_ids\"]))\nprint(\"test len:\", len(test_npz[\"item_ids\"]))\nprint(\"val == test item_ids:\", np.array_equal(val_npz[\"item_ids\"], test_npz[\"item_ids\"]))\nprint(\"cat items == set(val item_ids):\", set(cat.item_id) == set(val_npz[\"item_ids\"]))\n'",
  "description": "Verify candidate items alignment"
}

Tool response 

<untrusted_context>
Output: cat len: 30000
val len: 30000
test len: 30000
val == test item_ids: True
cat items == set(val item_ids): True
Process Group PGID: 2147
</untrusted_context>
34

Verify target uids alignment

+07:13
Recorded reasoning summary 42 words

Confirming Data Integrity

I've confirmed that the item_ids in both val_npz and test_npz perfectly match the 30,000 items listed in candidate_catalog.parquet. Similarly, the uids across val_npz, test_npz, and target_users are identical. My next step is to verify the order of these uids.

Tool callrun_shell_commandVerify target uids alignment Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"val == test uids:\", np.array_equal(val_npz[\"uids\"], test_npz[\"uids\"]))\nprint(\"val == targets uids:\", np.array_equal(val_npz[\"uids\"], targets[\"uid\"].to_numpy()))\n'",
  "description": "Verify target uids alignment"
}

Tool response 

<untrusted_context>
Output: val == test uids: True
val == targets uids: True
Process Group PGID: 2164
</untrusted_context>
35

Check item_ids order between catalog and npz

+07:17
Recorded reasoning summary 46 words

Confirming Data Alignment

I've confirmed that all rows and columns across val_npz, test_npz, target_users, and candidate_catalog are perfectly aligned. The user IDs (uids) count 8456, and item IDs (item_ids) are 30000. Each row index $r$ corresponds to user $r$, and column index $c$ to item $c$.

Tool callrun_shell_commandCheck item_ids order between catalog and npz Returned

Tool call · Input 

{
  "description": "Check item_ids order between catalog and npz",
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"Are item_ids in exact same order as catalog?\", np.array_equal(cat[\"item_id\"].to_numpy(), val_npz[\"item_ids\"]))\n'"
}

Tool response 

<untrusted_context>
Output: Are item_ids in exact same order as catalog? True
Process Group PGID: 2180
</untrusted_context>
36

Initial SVD model experiment on recent listens

+07:32
Recorded reasoning summary 32 words

Confirming Index Alignment

I've confirmed that the item_ids in both validation and test eligible candidate files perfectly align with the candidate_catalog.parquet. This direct correspondence simplifies the mapping process significantly for subsequent steps.

Recorded reasoning summary 47 words

Leveraging User-Item Matrix

My current focus is on constructing a comprehensive user-item matrix using a combination of train_listens, train_likes, and validation_preference_state. This unified data source will allow me to evaluate explicit positive and negative signals from validation_preference_state, alongside the quality signals from organic train_likes for my experiments.

Recorded reasoning summary 39 words

Exploring Factorization Methods

I'm now exploring low-rank matrix factorization techniques like TruncatedSVD, SVD, and iALS. The goal is to leverage the constructed user-item matrix, incorporating factors like listen completion, organic engagement, and recency, to generate predictions for eligible candidates.

Tool callrun_shell_commandInitial SVD model experiment on recent listens Returned

Tool call · Input 

{
  "description": "Initial SVD model experiment on recent listens",
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\n# Group by uid, item_id and compute count of listens with played_ratio_pct >= 50\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = [uid_to_row[u] for u in grouped.uid]\ncols = [item_to_col[it] for it in grouped.item_id]\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (len(target_uids), len(cat_items))), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\n# Run TruncatedSVD\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\n# Predict scores\nscores = U @ V\n\n# Generate top 100 for each user\nranked = {}\nfor r in range(len(target_uids)):\n    uid = int(target_uids[r])\n    mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n    user_scores = scores[r].copy()\n    user_scores[~mask_eligible] = -1e9\n    # top 100 indices\n    top100_idx = np.argpartition(-user_scores, 100)[:100]\n    # sort top 100\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'"
}

Tool response 

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 49, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py", line 56, in __init__
    coo = self._coo_container(arg1, shape=shape, dtype=dtype)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 101, in __init__
    self._check()
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 219, in _check
    if self.nnz > 0:
       ^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py", line 349, in nnz
    return self._getnnz()
           ^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 168, in _getnnz
    if any(len(idx) != nnz for idx in self.coords):
       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 168, in <genexpr>
    if any(len(idx) != nnz for idx in self.coords):
           ^^^^^^^^
TypeError: len() of unsized object
Loading train listens (last 90 days)... [0.7s]
Recent listens: 16769274 [1.3s]
Filtered listens: 12099707 [5.1s]
Exit Code: 1
Process Group PGID: 2197
</untrusted_context>
37

Run initial SVD experiment with fixed sparse matrix creation

+07:49
Recorded reasoning summary 47 words

Fixing SciPy Array Types

I'm addressing a recent discovery in SciPy version 1.15.3 regarding array types. It seems rows and cols are now expected to be NumPy arrays, not lists. I'm updating the code to reflect this, ensuring rows and cols are correctly converted to np.int32 arrays.

Tool callrun_shell_commandRun initial SVD experiment with fixed sparse matrix creation Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = np.array([uid_to_row[u] for u in grouped.uid], dtype=np.int32)\ncols = np.array([item_to_col[it] for it in grouped.item_id], dtype=np.int32)\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (rows, cols)), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD fitted. Explained var: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\nscores = U @ V\nprint(f\"Scores computed. [{time.time()-t0:.1f}s]\")\n\nranked = {}\nfor r in range(len(target_uids)):\n    uid = int(target_uids[r])\n    mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n    user_scores = scores[r].copy()\n    user_scores[~mask_eligible] = -1e9\n    top100_idx = np.argpartition(-user_scores, 100)[:100]\n    top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n    ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'",
  "description": "Run initial SVD experiment with fixed sparse matrix creation"
}

Tool response 

<untrusted_context>
Output: Loading train listens (last 90 days)... [0.7s]
Recent listens: 16769274 [1.3s]
Filtered listens: 12099707 [5.0s]
R shape: (8456, 30000), nnz: 3000810 [7.2s]
SVD fitted. Explained var: 0.2716 [9.0s]
Scores computed. [9.2s]
TruncatedSVD k=64 NDCG@10: {'quality_ndcg@10': 0.02495, 'quality_users': 4263} [10.5s]
Process Group PGID: 2233
</untrusted_context>
38

Benchmark Item-Item CF via associative multiply

+08:12
Recorded reasoning summary 47 words

Improving NDCG Performance

My recent experiments show a significant jump in NDCG@10 from a mere 0.01071 (using popularity) to an impressive 0.02495 with a basic TruncatedSVD. This is over a 2.3x improvement! Next, I'm focusing on incorporating user preference data like likes to further enhance these results.

Recorded reasoning summary 48 words

Exploring Item-Item Interactions

I'm now considering the impact of explicit signals like likes, and weighting organic listens higher. I'm also exploring recency weighting and tuning the number of components for SVD. A key next step is comparing Item-Item Collaborative Filtering with cosine similarity against my current SVD approach.

Tool callrun_shell_commandBenchmark Item-Item CF via associative multiply Returned

Tool call · Input 

{
  "description": "Benchmark Item-Item CF via associative multiply",
  "command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nN, M = 8456, 30000\nnnz = 3000000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\n# Normalize items\ncol_norms = np.asarray(np.sqrt(R.power(2).sum(axis=0))).flatten()\ncol_norms[col_norms == 0] = 1.0\nP = R.multiply(1.0 / col_norms).tocsr()\n\nt0 = time.time()\n# Associative multiply: (R @ P.T) @ P\nRP = R @ P.T # 8456 x 8456 sparse\nprint(f\"R @ P.T nnz: {RP.nnz}, time: {time.time()-t0:.2f}s\")\nt1 = time.time()\nscores = RP @ P # 8456 x 30000\nprint(f\"RP @ P shape: {scores.shape}, nnz: {scores.nnz}, time: {time.time()-t1:.2f}s\")\n'"
}

Tool response 

<untrusted_context>
Output: R @ P.T nnz: 70371236, time: 2.04s
RP @ P shape: (8456, 30000), nnz: 253680000, time: 58.20s
Process Group PGID: 2263
</untrusted_context>
39

update_topic command

+09:19
Recorded reasoning summary 36 words

Exploring SVD Efficiency

I'm observing a significant performance disparity. RP@P is proving quite slow due to dense matrix expansion, whereas TruncatedSVD is remarkably fast for fitting and multiplication. I'm now investigating TruncatedSVD's behavior with normalized data.

Recorded reasoning summary 29 words

Testing Normalization Techniques

I'm now exploring how TF-IDF and BM25 transformations, alongside various normalization and feature engineering strategies like played ratio and recency, impact SVD performance and NDCG@ metrics.

Tool callupdate_topicTool call Returned

Tool call · Input 

{
  "summary": "Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.",
  "strategic_intent": "Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.",
  "title": "Feature Engineering and Matrix Weighting Optimization"
}

Tool response 

Current topic: "Feature Engineering and Matrix Weighting Optimization"
Topic summary: Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.

Strategic Intent: Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.
40

write_file tune_weights.py

+09:37
Recorded reasoning summary 41 words

Designing Evaluation Harness

I'm currently focused on developing the Python evaluation harness, specifically the /app/solution/tune_weights.py script. My primary goal is to efficiently load the data once and then construct the sparse user-item interaction matrix, taking into account the recency half-life parameter.

Tool callwrite_file/app/solution/tune_weights.py Returned

Tool call · Input 

{
  "file_path": "/app/solution/tune_weights.py",
  "instruction": "Write hyperparameter tuning script for weighting and SVD ranker",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n    t0 = time.time()\n    targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n    target_uids = targets_frame.uid.to_numpy()\n    uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n    catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n    cat_items = catalog.item_id.to_numpy()\n    item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n    val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n    packed = val_npz[\"packed_eligible\"]\n    eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n    graded = validation_truth(eligibility, set(target_uids))\n\n    cutoff = 24786800\n    days90 = cutoff - 90 * 86400\n\n    print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n    listens = pd.read_parquet(\n        \"/task/data/train_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n    )\n    # Filter to recent 90 days, target users, catalog items\n    mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n    listens = listens[mask].copy()\n    print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n    # Load preference state\n    pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n    pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n    print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n    # Convert uids and item_ids to row and col indices\n    listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n    listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n    pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n    pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n    likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n    # Helper function to evaluate given weights\n    def evaluate_model(\n        half_life_days=30.0,\n        organic_mult=2.0,\n        strong_mult=2.0,\n        like_weight=10.0,\n        n_components=128,\n        use_tfidf=False,\n        norm_rows=False,\n        pop_alpha=0.0\n    ):\n        t_start = time.time()\n        # Compute event weights\n        delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n        recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n        \n        # Base completion weight\n        completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n        completion = np.clip(completion, 0.0, 1.5)\n        # Strong listen boost\n        strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n        # Organic boost\n        organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n        \n        event_weight = recency_weight * completion * strong_boost * organic_boost\n        \n        # Aggregate weights per (row, col)\n        # Using coo matrix summing duplicates\n        rows = listens[\"row\"].to_numpy()\n        cols = listens[\"col\"].to_numpy()\n        \n        # Add likes\n        like_rows = likes_only[\"row\"].to_numpy()\n        like_cols = likes_only[\"col\"].to_numpy()\n        like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n        \n        all_rows = np.concatenate([rows, like_rows])\n        all_cols = np.concatenate([cols, like_cols])\n        all_weights = np.concatenate([event_weight, like_weights])\n        \n        R = sp.csr_matrix(\n            (all_weights, (all_rows, all_cols)),\n            shape=(len(target_uids), len(cat_items)),\n            dtype=np.float32\n        )\n        # Log1p transform\n        R.data = np.log1p(R.data)\n        \n        if use_tfidf:\n            tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n            R_transformed = tfidf.fit_transform(R)\n        elif norm_rows:\n            from sklearn.preprocessing import normalize\n            R_transformed = normalize(R, norm=\"l2\", axis=1)\n        else:\n            R_transformed = R\n\n        svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_transformed)\n        V = svd.components_\n        \n        scores = U @ V\n        \n        if pop_alpha > 0.0:\n            # Add popularity prior\n            pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n            pop_std = pop.std()\n            pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n            scores = scores + pop_alpha * pop_norm\n            \n        ranked = {}\n        for r in range(len(target_uids)):\n            uid = int(target_uids[r])\n            mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n            user_scores = scores[r].copy()\n            user_scores[~mask_eligible] = -1e9\n            top100_idx = np.argpartition(-user_scores, 100)[:100]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            ranked[uid] = list(cat_items[top100_idx])\n            \n        res = score_rankings(ranked, graded)\n        score = res[\"quality_ndcg@10\"]\n        print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n        return score\n\n    print(\"\\n--- Running parameter experiments ---\")\n    evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n    evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n    evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n    main()\n"
}

Tool response 

Successfully created and wrote to new file: /app/solution/tune_weights.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfTransformer
import sys
sys.path.append("/task")
from ranking_evaluation import score_rankings
from evaluate_public import validation_truth, CandidateSet
from pathlib import Path

def main():
    t0 = time.time()
    targets_frame = pd.read_parquet("/task/data/target_users.parquet")
    target_uids = targets_frame.uid.to_numpy()
    uid_to_row = {uid: i for i, uid in enumerate(target_uids)}

    catalog = pd.read_parquet("/task/data/candidate_catalog.parquet")
    cat_items = catalog.item_id.to_numpy()
    item_to_col = {item: i for i, item in enumerate(cat_items)}

    val_npz = np.load("/task/data/validation_eligible_candidates.npz")
    packed = val_npz["packed_eligible"]
    eligibility = CandidateSet(Path("/task/data/validation_eligible_candidates.npz"))
    graded = validation_truth(eligibility, set(target_uids))

    cutoff = 24786800
    days90 = cutoff - 90 * 86400

    print(f"Loading data... [{time.time()-t0:.1f}s]")
    listens = pd.read_parquet(
        "/task/data/train_listens.parquet",
        columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"]
    )
    # Filter to recent 90 days, target users, catalog items
    mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)
    listens = listens[mask].copy()
    print(f"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]")

    # Load preference state
    pref = pd.read_parquet("/task/data/validation_preference_state.parquet")
    pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()
    print(f"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]")

    # Convert uids and item_ids to row and col indices
    listens["row"] = listens.uid.map(uid_to_row).astype(np.int32)
    listens["col"] = listens.item_id.map(item_to_col).astype(np.int32)

    pref_targets["row"] = pref_targets.uid.map(uid_to_row).astype(np.int32)
    pref_targets["col"] = pref_targets.item_id.map(item_to_col).astype(np.int32)
    likes_only = pref_targets[pref_targets.state == "liked"]

    # Helper function to evaluate given weights
    def evaluate_model(
        half_life_days=30.0,
        organic_mult=2.0,
        strong_mult=2.0,
        like_weight=10.0,
        n_components=128,
        use_tfidf=False,
        norm_rows=False,
        pop_alpha=0.0
    ):
        t_start = time.time()
        # Compute event weights
        delta_days = (cutoff - listens["timestamp"].to_numpy()) / 86400.0
        recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)
        
        # Base completion weight
        completion = listens["played_ratio_pct"].to_numpy(dtype=np.float32) / 100.0
        completion = np.clip(completion, 0.0, 1.5)
        # Strong listen boost
        strong_boost = np.where(listens["played_ratio_pct"].to_numpy() >= 80, strong_mult, 1.0)
        # Organic boost
        organic_boost = np.where(listens["is_organic"].to_numpy() == 1, organic_mult, 1.0)
        
        event_weight = recency_weight * completion * strong_boost * organic_boost
        
        # Aggregate weights per (row, col)
        # Using coo matrix summing duplicates
        rows = listens["row"].to_numpy()
        cols = listens["col"].to_numpy()
        
        # Add likes
        like_rows = likes_only["row"].to_numpy()
        like_cols = likes_only["col"].to_numpy()
        like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)
        
        all_rows = np.concatenate([rows, like_rows])
        all_cols = np.concatenate([cols, like_cols])
        all_weights = np.concatenate([event_weight, like_weights])
        
        R = sp.csr_matrix(
            (all_weights, (all_rows, all_cols)),
            shape=(len(target_uids), len(cat_items)),
            dtype=np.float32
        )
        # Log1p transform
        R.data = np.log1p(R.data)
        
        if use_tfidf:
            tfidf = TfidfTransformer(norm="l2", sublinear_tf=True)
            R_transformed = tfidf.fit_transform(R)
        elif norm_rows:
            from sklearn.preprocessing import normalize
            R_transformed = normalize(R, norm="l2", axis=1)
        else:
            R_transformed = R

        svd = TruncatedSVD(n_components=n_components, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_transformed)
        V = svd.components_
        
        scores = U @ V
        
        if pop_alpha > 0.0:
            # Add popularity prior
            pop = np.log1p(catalog["organic_positive_listens"].to_numpy(dtype=np.float32))
            pop_std = pop.std()
            pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)
            scores = scores + pop_alpha * pop_norm
            
        ranked = {}
        for r in range(len(target_uids)):
            uid = int(target_uids[r])
            mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder="little") == 1
            user_scores = scores[r].copy()
            user_scores[~mask_eligible] = -1e9
            top100_idx = np.argpartition(-user_scores, 100)[:100]
            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]
            ranked[uid] = list(cat_items[top100_idx])
            
        res = score_rankings(ranked, graded)
        score = res["quality_ndcg@10"]
        print(f"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)")
        return score

    print("\n--- Running parameter experiments ---")
    evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)
    evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)
    evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)

if __name__ == "__main__":
    main()
41

Run tuning experiments on validation set

+09:45
Tool callrun_shell_commandRun tuning experiments on validation set Returned

Tool call · Input 

{
  "description": "Run tuning experiments on validation set",
  "command": "python3 /app/solution/tune_weights.py"
}

Tool response 

<untrusted_context>
Output: /opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py:1713: RuntimeWarning: divide by zero encountered in log
  np.log(X.data, X.data)
Loading data... [0.7s]
Filtered listens: 12099707 rows [10.7s]
Pref state rows: 469124 [10.8s]

--- Running parameter experiments ---
HL=30.0d org=1.0 str=1.0 like=0.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03080 (4.9s)
HL=30.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03359 (4.5s)
HL=14.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03491 (4.5s)
HL=7.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03527 (4.4s)
HL=14.0d org=2.0 str=2.0 like=10.0 k=128 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03459 (5.2s)
HL=14.0d org=2.0 str=2.0 like=10.0 k=256 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03370 (7.5s)
Traceback (most recent call last):
  File "/app/solution/tune_weights.py", line 150, in <module>
    main()
  File "/app/solution/tune_weights.py", line 146, in main
    evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)
  File "/app/solution/tune_weights.py", line 104, in evaluate_model
    R_transformed = tfidf.fit_transform(R)
                    ^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped
    data_to_wrap = f(self, X, *args, **kwargs)
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py", line 894, in fit_transform
    return self.fit(X, **fit_params).transform(X)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py", line 1722, in transform
    X = normalize(X, norm=self.norm, copy=False)
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py", line 218, in wrapper
    return func(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py", line 1979, in normalize
    X = check_array(
        ^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 1012, in check_array
    array = _ensure_sparse_format(
            ^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 647, in _ensure_sparse_format
    _assert_all_finite(
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 120, in _assert_all_finite
    _assert_all_finite_element_wise(
  File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise
    raise ValueError(msg_err)
ValueError: Input contains infinity or a value too large for dtype('float32').
Exit Code: 1
Process Group PGID: 2286
</untrusted_context>
42

write_file tune_weights.py

+10:51
Recorded reasoning summary 41 words

Analyzing Pre-TFIDF Data

I'm examining performance metrics prior to addressing the TF-IDF issue. Initial runs with varying historical lengths and organization/stringency parameters show promising, though slightly lower, accuracy scores. I'm particularly noting the slight improvements seen with the 14-day historical window.

Recorded reasoning summary 57 words

Improving Scoring Model

The model's score has significantly improved, exceeding a 3.3x baseline increase through cumulative adjustments like adding likes and weighting organic completion. I'm now investigating TF-IDF transformer errors, specifically the log(0) issue, and exploring alternative IDF calculation methods and normalization techniques. Further tuning of half-life, multipliers, like weights, and the inclusion of dislikes is planned.

Tool callwrite_file/app/solution/tune_weights.py Returned

Tool call · Input 

{
  "instruction": "Update tuning script to sweep half-life, organic/strong multipliers, like weight, components, idf, and normalization",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n    t0 = time.time()\n    targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n    target_uids = targets_frame.uid.to_numpy()\n    uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n    catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n    cat_items = catalog.item_id.to_numpy()\n    item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n    val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n    packed = val_npz[\"packed_eligible\"]\n    eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n    graded = validation_truth(eligibility, set(target_uids))\n\n    cutoff = 24786800\n    days90 = cutoff - 90 * 86400\n\n    print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n    listens = pd.read_parquet(\n        \"/task/data/train_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n    )\n    mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n    listens = listens[mask].copy()\n    print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n    # Load preference state\n    pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n    pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n    listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n    listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n    pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n    pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n    likes_only = pref_targets[pref_targets.state == \"liked\"]\n    dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n    def build_matrix(\n        half_life_days=7.0,\n        organic_mult=2.0,\n        strong_mult=2.0,\n        like_weight=10.0,\n        dislike_weight=0.0,\n        use_idf=False,\n        norm_type=\"none\"\n    ):\n        delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n        recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n        completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n        strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n        organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n        event_weight = recency_weight * completion * strong_boost * organic_boost\n\n        rows = [listens[\"row\"].to_numpy()]\n        cols = [listens[\"col\"].to_numpy()]\n        weights = [event_weight]\n\n        if like_weight > 0 and len(likes_only) > 0:\n            rows.append(likes_only[\"row\"].to_numpy())\n            cols.append(likes_only[\"col\"].to_numpy())\n            weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n        if dislike_weight > 0 and len(dislikes_only) > 0:\n            rows.append(dislikes_only[\"row\"].to_numpy())\n            cols.append(dislikes_only[\"col\"].to_numpy())\n            weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n        all_rows = np.concatenate(rows)\n        all_cols = np.concatenate(cols)\n        all_weights = np.concatenate(weights)\n\n        R = sp.csr_matrix(\n            (all_weights, (all_rows, all_cols)),\n            shape=(len(target_uids), len(cat_items)),\n            dtype=np.float32\n        )\n        # Apply signed log1p: sign(x) * log1p(|x|)\n        pos_mask = R.data > 0\n        neg_mask = R.data < 0\n        R.data[pos_mask] = np.log1p(R.data[pos_mask])\n        R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n        if use_idf:\n            df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n            idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n            # Multiply columns by idf\n            R = R.dot(sp.diags(idf.astype(np.float32)))\n\n        if norm_type == \"l2_row\":\n            R = normalize(R, norm=\"l2\", axis=1)\n        elif norm_type == \"l2_col\":\n            R = normalize(R, norm=\"l2\", axis=0)\n\n        return R\n\n    def evaluate_scores(scores, label=\"\"):\n        ranked = {}\n        for r in range(len(target_uids)):\n            uid = int(target_uids[r])\n            mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n            user_scores = scores[r].copy()\n            user_scores[~mask_eligible] = -1e9\n            top100_idx = np.argpartition(-user_scores, 100)[:100]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            ranked[uid] = list(cat_items[top100_idx])\n            \n        res = score_rankings(ranked, graded)\n        score = res[\"quality_ndcg@10\"]\n        print(f\"[{label}] NDCG@10: {score:.5f}\")\n        return score\n\n    print(\"\\n--- Tuning Half-Life ---\")\n    for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n        R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n        svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n        scores = svd.fit_transform(R) @ svd.components_\n        evaluate_scores(scores, f\"HL={hl}d\")\n\n    print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n    for org in [1.5, 2.0, 3.0, 4.0]:\n        for str_m in [1.5, 2.0, 3.0]:\n            R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n            svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n            scores = svd.fit_transform(R) @ svd.components_\n            evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n    print(\"\\n--- Tuning Like Weight ---\")\n    for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n        R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n        svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n        scores = svd.fit_transform(R) @ svd.components_\n        evaluate_scores(scores, f\"like_weight={lw}\")\n\n    print(\"\\n--- Tuning SVD Components ---\")\n    R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n    for k in [32, 48, 64, 80, 96, 128]:\n        svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n        scores = svd.fit_transform(R) @ svd.components_\n        evaluate_scores(scores, f\"k={k}\")\n\n    print(\"\\n--- Testing IDF and Normalization ---\")\n    for idf_val in [False, True]:\n        for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n            R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n            svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n            scores = svd.fit_transform(R) @ svd.components_\n            evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n    main()\n",
  "file_path": "/app/solution/tune_weights.py"
}

Tool response 

Successfully overwrote file: /app/solution/tune_weights.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from sklearn.preprocessing import normalize
import sys
sys.path.append("/task")
from ranking_evaluation import score_rankings
from evaluate_public import validation_truth, CandidateSet
from pathlib import Path
...
    print(f"Loading data... [{time.time()-t0:.1f}s]")
    listens = pd.read_parquet(
        "/task/data/train_listens.parquet",
        columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"]
    )
    mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)
    listens = listens[mask].copy()
    print(f"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]")

    # Load preference state
    pref = pd.read_parquet("/task/data/validation_preference_state.parquet")
    pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()

    listens["row"] = listens.uid.map(uid_to_row).astype(np.int32)
    listens["col"] = listens.item_id.map(item_to_col).astype(np.int32)

    pref_targets["row"] = pref_targets.uid.map(uid_to_row).astype(np.int32)
    pref_targets["col"] = pref_targets.item_id.map(item_to_col).astype(np.int32)
    likes_only = pref_targets[pref_targets.state == "liked"]
    dislikes_only = pref_targets[pref_targets.state == "disliked"]

    def build_matrix(
        half_life_days=7.0,
        organic_mult=2.0,
        strong_mult=2.0,
        like_weight=10.0,
        dislike_weight=0.0,
        use_idf=False,
        norm_type="none"
    ):
        delta_days = (cutoff - listens["timestamp"].to_numpy()) / 86400.0
        recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)
        completion = np.clip(listens["played_ratio_pct"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)
        strong_boost = np.where(listens["played_ratio_pct"].to_numpy() >= 80, strong_mult, 1.0)
        organic_boost = np.where(listens["is_organic"].to_numpy() == 1, organic_mult, 1.0)
        event_weight = recency_weight * completion * strong_boost * organic_boost

        rows = [listens["row"].to_numpy()]
        cols = [listens["col"].to_numpy()]
        weights = [event_weight]

        if like_weight > 0 and len(likes_only) > 0:
            rows.append(likes_only["row"].to_numpy())
            cols.append(likes_only["col"].to_numpy())
            weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))

        if dislike_weight > 0 and len(dislikes_only) > 0:
            rows.append(dislikes_only["row"].to_numpy())
            cols.append(dislikes_only["col"].to_numpy())
            weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))

        all_rows = np.concatenate(rows)
        all_cols = np.concatenate(cols)
        all_weights = np.concatenate(weights)

        R = sp.csr_matrix(
            (all_weights, (all_rows, all_cols)),
            shape=(len(target_uids), len(cat_items)),
            dtype=np.float32
        )
        # Apply signed log1p: sign(x) * log1p(|x|)
        pos_mask = R.data > 0
        neg_mask = R.data < 0
        R.data[pos_mask] = np.log1p(R.data[pos_mask])
        R.data[neg_mask] = -np.log1p(-R.data[neg_mask])

        if use_idf:
            df = np.bincount(R.nonzero()[1], minlength=len(cat_items))
            idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0
            # Multiply columns by idf
            R = R.dot(sp.diags(idf.astype(np.float32)))

        if norm_type == "l2_row":
            R = normalize(R, norm="l2", axis=1)
        elif norm_type == "l2_col":
            R = normalize(R, norm="l2", axis=0)

        return R

    def evaluate_scores(scores, label=""):
        ranked = {}
        for r in range(len(target_uids)):
            uid = int(target_uids[r])
            mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder="little") == 1
            user_scores = scores[r].copy()
...
            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]
            ranked[uid] = list(cat_items[top100_idx])
            
        res = score_rankings(ranked, graded)
        score = res["quality_ndcg@10"]
        print(f"[{label}] NDCG@10: {score:.5f}")
        return score

    print("\n--- Tuning Half-Life ---")
    for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:
        R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)
        svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42)
        scores = svd.fit_transform(R) @ svd.components_
        evaluate_scores(scores, f"HL={hl}d")

    print("\n--- Tuning Organic and Strong Multipliers (HL=7d) ---")
    for org in [1.5, 2.0, 3.0, 4.0]:
        for str_m in [1.5, 2.0, 3.0]:
            R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)
            svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42)
            scores = svd.fit_transform(R) @ svd.components_
            evaluate_scores(scores, f"org={org} str={str_m}")

    print("\n--- Tuning Like Weight ---")
    for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:
        R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)
        svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42)
        scores = svd.fit_transform(R) @ svd.components_
        evaluate_scores(scores, f"like_weight={lw}")

    print("\n--- Tuning SVD Components ---")
    R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)
    for k in [32, 48, 64, 80, 96, 128]:
        svd = TruncatedSVD(n_components=k, algorithm="randomized", random_state=42)
        scores = svd.fit_transform(R) @ svd.components_
        evaluate_scores(scores, f"k={k}")

    print("\n--- Testing IDF and Normalization ---")
    for idf_val in [False, True]:
        for norm_val in ["none", "l2_row", "l2_col"]:
            R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)
            svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42)
            scores = svd.fit_transform(R) @ svd.components_
            evaluate_scores(scores, f"idf={idf_val} norm={norm_val}")

if __name__ == "__main__":
    main()
43

Run parameter tuning experiments

+10:54
Tool callrun_shell_commandRun parameter tuning experiments Returned

Tool call · Input 

{
  "description": "Run parameter tuning experiments",
  "command": "python3 /app/solution/tune_weights.py"
}

Tool response 

<untrusted_context>
Output: Loading data... [0.7s]
Filtered listens: 12099707 rows [10.7s]

--- Tuning Half-Life ---
[HL=3.0d] NDCG@10: 0.03181
[HL=5.0d] NDCG@10: 0.03448
[HL=7.0d] NDCG@10: 0.03527
[HL=10.0d] NDCG@10: 0.03608
[HL=14.0d] NDCG@10: 0.03491
[HL=21.0d] NDCG@10: 0.03424

--- Tuning Organic and Strong Multipliers (HL=7d) ---
[org=1.5 str=1.5] NDCG@10: 0.03347
[org=1.5 str=2.0] NDCG@10: 0.03446
[org=1.5 str=3.0] NDCG@10: 0.03580
[org=2.0 str=1.5] NDCG@10: 0.03454
[org=2.0 str=2.0] NDCG@10: 0.03527
[org=2.0 str=3.0] NDCG@10: 0.03642
[org=3.0 str=1.5] NDCG@10: 0.03570
[org=3.0 str=2.0] NDCG@10: 0.03726
[org=3.0 str=3.0] NDCG@10: 0.03742
[org=4.0 str=1.5] NDCG@10: 0.03680
[org=4.0 str=2.0] NDCG@10: 0.03756
[org=4.0 str=3.0] NDCG@10: 0.03817

--- Tuning Like Weight ---
[like_weight=5.0] NDCG@10: 0.03714
[like_weight=10.0] NDCG@10: 0.03527
[like_weight=15.0] NDCG@10: 0.03438
[like_weight=25.0] NDCG@10: 0.03322
[like_weight=40.0] NDCG@10: 0.03201

--- Tuning SVD Components ---
[k=32] NDCG@10: 0.03309
[k=48] NDCG@10: 0.03291
[k=64] NDCG@10: 0.03438
[k=80] NDCG@10: 0.03337
[k=96] NDCG@10: 0.03426
[k=128] NDCG@10: 0.03362

--- Testing IDF and Normalization ---
[idf=False norm=none] NDCG@10: 0.03438
[idf=False norm=l2_row] NDCG@10: 0.03260
[idf=False norm=l2_col] NDCG@10: 0.01218
[idf=True norm=none] NDCG@10: 0.03309
[idf=True norm=l2_row] NDCG@10: 0.03729
[idf=True norm=l2_col] NDCG@10: 0.01218
Process Group PGID: 2320
</untrusted_context>
44

Test combinations of weighting and normalization

+14:00
Recorded reasoning summary 38 words

Analyzing Engagement Patterns

I'm noticing a clear trend where a recency half-life of 10 days and higher organic/strong multipliers are driving better engagement metrics. It appears lower "like weight" values also contribute positively, suggesting overemphasis can dilute impact.

Recorded reasoning summary 50 words

Refining Interaction Weighting

I'm now exploring the nuanced interaction of listen weights, specifically hypothesizing that over-weighting likes might be diluting their impact. I'm considering a low 'like_weight' and a significant disparity between organic and non-organic listen weights, potentially setting non-organic to 0.2 and organic to 1.0 for a 5.0x ratio.

Tool callrun_shell_commandTest combinations of weighting and normalization Returned

Tool call · Input 

{
  "description": "Test combinations of weighting and normalization",
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n    \"/task/data/train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\ndislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\ndef test_config(hl, org_mult, str_mult, non_org_mult, lw, dw, use_idf, norm_l2, k=64):\n    delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n    rec = np.exp(-np.log(2) * delta_days / hl)\n    comp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n    s_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, str_mult, 1.0)\n    o_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, org_mult, non_org_mult)\n    weights = rec * comp * s_boost * o_boost\n\n    rows = [listens[\"row\"].to_numpy()]\n    cols = [listens[\"col\"].to_numpy()]\n    vals = [weights]\n\n    if lw > 0:\n        rows.append(likes_only[\"row\"].to_numpy())\n        cols.append(likes_only[\"col\"].to_numpy())\n        vals.append(np.full(len(likes_only), fill_value=lw, dtype=np.float32))\n\n    if dw > 0:\n        rows.append(dislikes_only[\"row\"].to_numpy())\n        cols.append(dislikes_only[\"col\"].to_numpy())\n        vals.append(np.full(len(dislikes_only), fill_value=-dw, dtype=np.float32))\n\n    R = sp.csr_matrix(\n        (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n        shape=(len(target_uids), len(cat_items)),\n        dtype=np.float32\n    )\n    pos_mask = R.data > 0\n    neg_mask = R.data < 0\n    R.data[pos_mask] = np.log1p(R.data[pos_mask])\n    R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n    if use_idf:\n        df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n        idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n        R = R.dot(sp.diags(idf.astype(np.float32)))\n\n    if norm_l2:\n        R = normalize(R, norm=\"l2\", axis=1)\n\n    svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n    scores = svd.fit_transform(R) @ svd.components_\n\n    ranked = {}\n    for r in range(len(target_uids)):\n        uid = int(target_uids[r])\n        mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n        user_scores = scores[r].copy()\n        user_scores[~mask_eligible] = -1e9\n        top100_idx = np.argpartition(-user_scores, 100)[:100]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[uid] = list(cat_items[top100_idx])\n        \n    res = score_rankings(ranked, graded)\n    score = res[\"quality_ndcg@10\"]\n    print(f\"hl={hl} org={org_mult} non_org={non_org_mult} str={str_mult} lw={lw} dw={dw} idf={use_idf} norm={norm_l2} k={k} -> {score:.5f}\")\n    return scores, score\n\nprint(\"--- Testing configurations ---\")\ntest_config(hl=10.0, org_mult=4.0, str_mult=3.0, non_org_mult=1.0, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.5, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=2.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True, k=96)\n'"
}

Tool response 

<untrusted_context>
Output: --- Testing configurations ---
hl=10.0 org=4.0 non_org=1.0 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03817
hl=10.0 org=5.0 non_org=0.5 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03862
hl=10.0 org=5.0 non_org=0.2 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03698
hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03689
hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=False norm=False k=64 -> 0.03758
hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=2.0 idf=False norm=False k=64 -> 0.03701
hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=64 -> 0.03714
hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=96 -> 0.03660
Process Group PGID: 2366
</untrusted_context>
45

Benchmark Cholesky factorization for 5000x5000

+15:00
Recorded reasoning summary 49 words

Testing New Parameters

I've achieved a score of 0.03862 with the current configuration. My next steps involve exploring variations around non_org at 0.5 and 0.7, and org at 6.0 and 8.0. I'll also be adjusting the half-life to 8, 10, and 12 days to see how these impact performance.

Recorded reasoning summary 43 words

Incorporating Recency Decay

I'm now considering incorporating recency decay for train_likes, as their timestamps offer valuable temporal information. I'm also exploring item frequency calibration to address potential biases in SVD scores and investigating ensembling various SVD models with different half-lives, alongside item-item similarity.

Tool callrun_shell_commandBenchmark Cholesky factorization for 5000x5000 Returned

Tool call · Input 

{
  "description": "Benchmark Cholesky factorization for 5000x5000",
  "command": "python3 -c '\nimport time\nimport numpy as np\n\n# test 5000 x 5000\nA = np.random.randn(5000, 5000).astype(np.float32)\nG = A.T @ A + 10.0 * np.eye(5000, dtype=np.float32)\nt0 = time.time()\nnp.linalg.cholesky(G)\nprint(f\"5000x5000 Cholesky time: {time.time()-t0:.2f}s\")\n'"
}

Tool response 

<untrusted_context>
Output: 5000x5000 Cholesky time: 0.72s
Process Group PGID: 2400
</untrusted_context>
46

Benchmark 8456x8456 Cholesky time

+15:08
Recorded reasoning summary 51 words

Accelerating Cholesky Factorization

I'm thrilled by the speed of the 5000x5000 Cholesky, clocking in at just 0.72s. I'm now contemplating scaling this up to 15,000x15,000 and even 30,000x30,000. Based on the $O(N^3)$ complexity, I estimate the larger matrices will take around 2.5 minutes. However, a question arises: is full EASE necessary?

Tool callrun_shell_commandBenchmark 8456x8456 Cholesky time Returned

Tool call · Input 

{
  "description": "Benchmark 8456x8456 Cholesky time",
  "command": "python3 -c '\nimport time\nimport numpy as np\n\nN = 8456\nA = np.random.randn(N, 64).astype(np.float32)\nG = A @ A.T + 10.0 * np.eye(N, dtype=np.float32)\n\nt0 = time.time()\nL = np.linalg.cholesky(G)\nprint(f\"8456x8456 Cholesky time: {time.time()-t0:.2f}s\")\n'"
}

Tool response 

<untrusted_context>
Output: 8456x8456 Cholesky time: 2.97s
Process Group PGID: 2410
</untrusted_context>
47

Test User-EASE collaborative filtering

+15:26
Recorded reasoning summary 49 words

Calculating Cholesky Efficiency

My recent computations show an $8456 \times 8456$ Cholesky decomposition completing in just 2.97 seconds, which is quite promising. My next step is to delve into the specifics of user-user EASE, particularly how to predict a user's interactions by modeling them based on other users' behaviors.

Tool callrun_shell_commandTest User-EASE collaborative filtering Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n    \"/task/data/train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n    rows.append(likes_only[\"row\"].to_numpy())\n    cols.append(likes_only[\"col\"].to_numpy())\n    vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n    (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n    shape=(len(target_uids), len(cat_items)),\n    dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nt0 = time.time()\nprint(\"Computing G = R @ R.T...\")\nG = (R @ R.T).toarray()\nprint(f\"G computed in {time.time()-t0:.2f}s\")\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0, 5000.0]:\n    t1 = time.time()\n    G_reg = G.copy()\n    np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n    P = np.linalg.inv(G_reg)\n    B = -P / np.diag(P)[:, None]\n    np.fill_diagonal(B, 0.0)\n    scores = B @ R # dense x sparse\n    \n    ranked = {}\n    for r in range(len(target_uids)):\n        uid = int(target_uids[r])\n        mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n        user_scores = scores[r].copy()\n        user_scores[~mask_eligible] = -1e9\n        top100_idx = np.argpartition(-user_scores, 100)[:100]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[uid] = list(cat_items[top100_idx])\n        \n    res = score_rankings(ranked, graded)\n    score = res[\"quality_ndcg@10\"]\n    print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
  "description": "Test User-EASE collaborative filtering"
}

Tool response 

<untrusted_context>
Output: Computing G = R @ R.T...
G computed in 4.57s
User-EASE lam=100.0 -> NDCG@10: 0.02989 (27.08s)
User-EASE lam=500.0 -> NDCG@10: 0.03617 (26.94s)
User-EASE lam=1000.0 -> NDCG@10: 0.03871 (27.14s)
User-EASE lam=2000.0 -> NDCG@10: 0.04170 (27.07s)
User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.89s)
Process Group PGID: 2420
</untrusted_context>
48

Test higher lambda values for User-EASE

+18:13
Recorded reasoning summary 50 words

Refining User-EASE

I've successfully boosted the User-EASE metric to 0.04417 with a lambda of 5000, a significant leap from previous methods like Naive and Tuned SVD. The convergence was remarkably swift. Now, I'm exploring the impact of even higher lambda values, such as 10,000 and 20,000, to further optimize performance.

Tool callrun_shell_commandTest higher lambda values for User-EASE Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n    \"/task/data/train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n    rows.append(likes_only[\"row\"].to_numpy())\n    cols.append(likes_only[\"col\"].to_numpy())\n    vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n    (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n    shape=(len(target_uids), len(cat_items)),\n    dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nG = (R @ R.T).toarray()\n\nfor lam in [5000.0, 10000.0, 20000.0, 40000.0, 100000.0]:\n    t1 = time.time()\n    G_reg = G.copy()\n    np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n    P = np.linalg.inv(G_reg)\n    B = -P / np.diag(P)[:, None]\n    np.fill_diagonal(B, 0.0)\n    scores = B @ R\n    \n    ranked = {}\n    for r in range(len(target_uids)):\n        uid = int(target_uids[r])\n        mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n        user_scores = scores[r].copy()\n        user_scores[~mask_eligible] = -1e9\n        top100_idx = np.argpartition(-user_scores, 100)[:100]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[uid] = list(cat_items[top100_idx])\n        \n    res = score_rankings(ranked, graded)\n    score = res[\"quality_ndcg@10\"]\n    print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
  "description": "Test higher lambda values for User-EASE"
}

Tool response 

<untrusted_context>
Output: User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.98s)
User-EASE lam=10000.0 -> NDCG@10: 0.04491 (26.89s)
User-EASE lam=20000.0 -> NDCG@10: 0.04296 (27.27s)
User-EASE lam=40000.0 -> NDCG@10: 0.04076 (27.10s)
User-EASE lam=100000.0 -> NDCG@10: 0.03593 (27.20s)
Process Group PGID: 2460
</untrusted_context>
49

Test Cosine User-EASE

+22:37
Recorded reasoning summary 31 words

Testing Lambda Values

I'm exploring lambda values around 10000, specifically testing 7500, 10000, 12500, and 15000. I've also started investigating the impact of row-wise normalization of users prior to calculating G.

Tool callrun_shell_commandTest Cosine User-EASE Returned

Tool call · Input 

{
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n    \"/task/data/train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n    rows.append(likes_only[\"row\"].to_numpy())\n    cols.append(likes_only[\"col\"].to_numpy())\n    vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n    (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n    shape=(len(target_uids), len(cat_items)),\n    dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [10.0, 50.0, 100.0, 200.0, 500.0, 1000.0]:\n    t1 = time.time()\n    G_reg = G_cos.copy()\n    np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n    P = np.linalg.inv(G_reg)\n    B = -P / np.diag(P)[:, None]\n    np.fill_diagonal(B, 0.0)\n    scores = B @ R # user weights applied to original R\n    \n    ranked = {}\n    for r in range(len(target_uids)):\n        uid = int(target_uids[r])\n        mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n        user_scores = scores[r].copy()\n        user_scores[~mask_eligible] = -1e9\n        top100_idx = np.argpartition(-user_scores, 100)[:100]\n        top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n        ranked[uid] = list(cat_items[top100_idx])\n        \n    res = score_rankings(ranked, graded)\n    score = res[\"quality_ndcg@10\"]\n    print(f\"Cosine User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'",
  "description": "Test Cosine User-EASE"
}

Tool response 

<untrusted_context>
Output: Cosine User-EASE lam=10.0 -> NDCG@10: 0.04518 (27.08s)
Cosine User-EASE lam=50.0 -> NDCG@10: 0.03998 (27.08s)
Cosine User-EASE lam=100.0 -> NDCG@10: 0.03626 (27.21s)
Cosine User-EASE lam=200.0 -> NDCG@10: 0.03315 (27.14s)
Cosine User-EASE lam=500.0 -> NDCG@10: 0.02964 (27.01s)
Cosine User-EASE lam=1000.0 -> NDCG@10: 0.02779 (27.10s)
Process Group PGID: 2506
</untrusted_context>
50

Test fine lambda grid and R vs R_norm for Cosine User-EASE

+25:48
Tool callrun_shell_commandTest fine lambda grid and R vs R_norm for Cosine User-EASE Returned

Tool call · Input 

{
  "description": "Test fine lambda grid and R vs R_norm for Cosine User-EASE",
  "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n    \"/task/data/train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n    rows.append(likes_only[\"row\"].to_numpy())\n    cols.append(likes_only[\"col\"].to_numpy())\n    vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n    (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n    shape=(len(target_uids), len(cat_items)),\n    dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [1.0, 2.0, 5.0, 10.0, 15.0]:\n    G_reg = G_cos.copy()\n    np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n    P = np.linalg.inv(G_reg)\n    B = -P / np.diag(P)[:, None]\n    np.fill_diagonal(B, 0.0)\n    \n    # Try B @ R vs B @ R_norm\n    for use_r_norm in [False, True]:\n        scores = B @ (R_norm if use_r_norm else R)\n        ranked = {}\n        for r in range(len(target_uids)):\n            uid = int(target_uids[r])\n            mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n            user_scores = scores[r].copy()\n            user_scores[~mask_eligible] = -1e9\n            top100_idx = np.argpartition(-user_scores, 100)[:100]\n            top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n            ranked[uid] = list(cat_items[top100_idx])\n            \n        res = score_rankings(ranked, graded)\n        score = res[\"quality_ndcg@10\"]\n        print(f\"lam={lam} use_r_norm={use_r_norm} -> NDCG@10: {score:.5f}\")\n'"
}

Tool response 

<untrusted_context>
Output: lam=1.0 use_r_norm=False -> NDCG@10: 0.03891
lam=1.0 use_r_norm=True -> NDCG@10: 0.03356
lam=2.0 use_r_norm=False -> NDCG@10: 0.04149
lam=2.0 use_r_norm=True -> NDCG@10: 0.03741
lam=5.0 use_r_norm=False -> NDCG@10: 0.04439
lam=5.0 use_r_norm=True -> NDCG@10: 0.04081
lam=10.0 use_r_norm=False -> NDCG@10: 0.04518
lam=10.0 use_r_norm=True -> NDCG@10: 0.04312
lam=15.0 use_r_norm=False -> NDCG@10: 0.04467
lam=15.0 use_r_norm=True -> NDCG@10: 0.04337
Process Group PGID: 2552
</untrusted_context>
51

Final response

+29:47
Recorded reasoning summary 33 words

Exploring Ensemble Methods

I'm exploring how ensembling Cosine User-EASE with TruncatedSVD might improve performance. My current focus is understanding how these distinct structural representations, User-EASE and SVD, might complement each other when combined.

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About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt
gemini-3-8-flash-high-basic-fixed-r03-d237f45e
Recorded model
gemini-3.8-flash
Harness version
0.61.0
Session
6212091c-7fda-46d9-87f3-27fb24d0692b
Source SHA-256
abd42d18501d39718117dd89e334d7e64ddcc09616113cf1b538d5a5c3cce925

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.

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