Agent trace

Basic recommender gemini-3-8-flash-high-04

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

This recording includes 62 readable reasoning summaries, shown in full below.

Browse attempts on this task

Agent: gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation: 29:20 · 54 steps
Private reward: 0.009092 · Failed
Attempt cost: $0.60

Thinking
Model text
Tool call
Tool response

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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>

Task instructions

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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:

Family	Meaning	Columns
`listens`	Playback events	`uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds`
`likes`	Explicit positive actions	`uid`, `timestamp`, `item_id`, `is_organic`
`dislikes`	Explicit negative actions	`uid`, `timestamp`, `item_id`, `is_organic`
`unlikes`	Reversal of a prior like	`uid`, `timestamp`, `item_id`, `is_organic`
`undislikes`	Reversal of a prior dislike	`uid`, `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:

Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
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.

update_topic command

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Recorded reasoning summary 35 words

Reviewing Initial Requirements

I'm currently deep-diving into the core prompt, meticulously examining each mandate and instruction. My focus is on fully grasping the nuances of building this music-discovery ranker for the "Made for You" shelf.

Tool callupdate_topicTool call Returned

Tool call · Input +00:05

{
  "strategic_intent": "Explore the environment, dataset schemas, evaluation script, and baseline setup.",
  "summary": "Starting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).",
  "title": "Environment and Dataset Exploration"
}

Tool response +00:05

Current topic: "Environment and Dataset Exploration"
Topic summary: Starting exploration of the music discovery ranking task. We will inspect the task files, dataset shapes, evaluation scripts, and available compute resources (CPU/GPU, RAM, libraries).

Strategic Intent: Explore the environment, dataset schemas, evaluation script, and baseline setup.

Tool callrun_shell_commandCheck hardware resources (GPU, CPU, RAM) and inspect data files Returned

Tool call · Input +00:05

{
  "command": "nvidia-smi || lscpu; free -h; ls -lh /task/data",
  "description": "Check hardware resources (GPU, CPU, RAM) and inspect data files"
}

Tool response +00:05

<untrusted_context>
Output: /usr/bin/bash: line 4: nvidia-smi: command not found
Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                5299.99
Flags:                                   fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor:                       KVM
Virtualization type:                     full
L1d cache:                               128 KiB (4 instances)
L1i cache:                               128 KiB (4 instances)
L2 cache:                                2 MiB (4 instances)
L3 cache:                                16 MiB (1 instance)
NUMA node(s):                            1
NUMA node0 CPU(s):                       0-7
Vulnerability Gather data sampling:      Not affected
Vulnerability Ghostwrite:                Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit:             Not affected
Vulnerability L1tf:                      Not affected
Vulnerability Mds:                       Not affected
Vulnerability Meltdown:                  Not affected
Vulnerability Mmio stale data:           Not affected
Vulnerability Old microcode:             Not affected
Vulnerability Reg file data sampling:    Not affected
Vulnerability Retbleed:                  Not affected
Vulnerability Spec rstack overflow:      Mitigation; Safe RET
Vulnerability Spec store bypass:         Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1:                Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2:                Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds:                     Not affected
Vulnerability Tsa:                       Mitigation; Clear CPU buffers
Vulnerability Tsx async abort:           Not affected
Vulnerability Vmscape:                   Not affected
/usr/bin/bash: line 4: free: command not found
total 340M
-r--r--r-- 1 root root 342K Sep 29 04:14 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 29 04:14 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 04:14 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 04:14 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 04:14 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 04:14 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 04:14 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 04:14 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 29 04:14 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 04:14 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 04:14 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 04:14 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 29 04:14 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 04:14 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>

Tool callread_file/task/evaluate_public.py Returned

Tool call · Input +00:05

{
  "file_path": "/task/evaluate_public.py"
}

Tool response +00:05

#!/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 +00:05

{
  "file_path": "/task/ranking_evaluation.py"
}

Tool response +00:05

"""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),
    }

Check task requirements and installed packages

+00:16

Tool callrun_shell_commandCheck task requirements and installed packages Returned

Tool call · Input +00:16

{
  "command": "cat /task/requirements.txt; python -c \"import torch, lightgbm, implicit, polars, sklearn; print('Imports ok!')\"",
  "description": "Check task requirements and installed packages"
}

Tool response +00:16

<untrusted_context>
Output: # 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
Traceback (most recent call last):
  File "<string>", line 1, in <module>
ModuleNotFoundError: No module named 'torch'
Exit Code: 1
Process Group PGID: 1618
</untrusted_context>

Check system memory via /proc/meminfo

+00:22

Recorded reasoning summary 36 words

Reviewing Installed Packages

I've noted the specific versions of libraries like numpy, pandas, and scikit-learn that are currently available. It's also crucial that PyTorch and LightGBM are absent from this setup, as per the task guidelines.

Tool callrun_shell_commandCheck system memory via /proc/meminfo Returned

Tool call · Input +00:22

{
  "command": "python -c \"\nimport psutil, os\nmem = psutil.virtual_memory() if hasattr(psutil, 'virtual_memory') else None\nprint('RAM info:')\nwith open('/proc/meminfo') as f:\n    for line in f:\n        if 'MemTotal' in line or 'MemAvailable' in line or 'MemFree' in line:\n            print(line.strip())\n\"",
  "description": "Check system memory via /proc/meminfo"
}

Tool response +00:22

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 1622
</untrusted_context>

Check available memory from /proc/meminfo

+00:24

Tool callrun_shell_commandCheck available memory from /proc/meminfo Returned

Tool call · Input +00:24

{
  "description": "Check available memory from /proc/meminfo",
  "command": "grep -E 'MemTotal|MemAvailable|MemFree' /proc/meminfo"
}

Tool response +00:24

<untrusted_context>
Output: MemTotal:       15988968 kB
MemFree:         5513296 kB
MemAvailable:   14709032 kB
Process Group PGID: 1625
</untrusted_context>

Inspect dataset files and shapes

+00:31

Recorded reasoning summary 51 words

Inspecting Data Files

I'm currently examining the contents of the /task/data directory. My focus is on understanding the structure and characteristics of these files, specifically looking at row and column counts, unique user and item identifiers, and timestamp ranges. I'll be using Python for this analysis to quickly gather key metrics.

Tool callrun_shell_commandInspect dataset files and shapes Returned

Tool call · Input +00:31

{
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nprint('=== Candidate Catalog ===')\ncat = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\nprint('cat shape:', cat.shape)\nprint('cat cols:', cat.columns.tolist())\nprint(cat.head(3))\n\nprint('\\n=== Target Users ===')\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('users shape:', users.shape)\nprint('users cols:', users.columns.tolist())\nprint(users.head(3))\n\nprint('\\n=== Validation Candidates ===')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nprint('val_cand keys:', list(val_cand.keys()))\nprint('uids:', len(val_cand['uids']), 'item_ids:', len(val_cand['item_ids']), 'packed shape:', val_cand['packed_eligible'].shape)\n\nprint('\\n=== Test Candidates ===')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nprint('test_cand keys:', list(test_cand.keys()))\nprint('uids:', len(test_cand['uids']), 'item_ids:', len(test_cand['item_ids']), 'packed shape:', test_cand['packed_eligible'].shape)\n\nprint('\\n=== Preference states ===')\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nprint('val_pref shape:', val_pref.shape, val_pref.columns.tolist(), val_pref['state'].value_counts().to_dict() if 'state' in val_pref else '')\nprint(val_pref.head(3))\n\ntest_pref = pd.read_parquet(data_dir / 'test_preference_state.parquet')\nprint('test_pref shape:', test_pref.shape, test_pref.columns.tolist(), test_pref['state'].value_counts().to_dict() if 'state' in test_pref else '')\n\nfor prefix in ['train', 'val']:\n    for f in ['listens', 'likes', 'dislikes', 'unlikes', 'undislikes']:\n        p = data_dir / f'{prefix}_{f}.parquet'\n        if p.exists():\n            df = pd.read_parquet(p)\n            print(f'{prefix}_{f}: rows={len(df)}, min_ts={df.timestamp.min() if \\\"timestamp\\\" in df else None}, max_ts={df.timestamp.max() if \\\"timestamp\\\" in df else None}')\n\"",
  "description": "Inspect dataset files and shapes"
}

Tool response +00:33

<untrusted_context>
Output: === Candidate Catalog ===
cat shape: (30000, 4)
cat cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3

=== Target Users ===
users shape: (8456, 3)
users cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359

=== Validation Candidates ===
val_cand keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456 item_ids: 30000 packed shape: (8456, 3750)

=== Test Candidates ===
test_cand keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456 item_ids: 30000 packed shape: (8456, 3750)

=== Preference states ===
val_pref shape: (469124, 3) ['uid', 'item_id', 'state'] {'liked': 418435, 'disliked': 50689}
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
test_pref shape: (484776, 3) ['uid', 'item_id', 'state'] {'liked': 432659, 'disliked': 52117}
train_listens: rows=43484159, min_ts=0, max_ts=24786795
train_likes: rows=814831, min_ts=80, max_ts=24786790
train_dislikes: rows=101075, min_ts=75, max_ts=24786580
train_unlikes: rows=292665, min_ts=250, max_ts=24786570
train_undislikes: rows=19960, min_ts=15950, max_ts=24785395
val_listens: rows=1501697, min_ts=24788600, max_ts=25393395
val_likes: rows=37795, min_ts=24788815, max_ts=25393390
val_dislikes: rows=3563, min_ts=24789920, max_ts=25393280
val_unlikes: rows=9346, min_ts=24789005, max_ts=25393385
val_undislikes: rows=606, min_ts=24810715, max_ts=25393035
Process Group PGID: 1628
</untrusted_context>

Check eligible candidate counts per user

+00:48

Recorded reasoning summary 36 words

Analyzing Candidate Data

I've confirmed the candidate catalog contains exactly 30,000 items, aligning perfectly with the 30,000 candidates found in both the validation and test sets. I'm now moving on to examine the target users data.

Tool callrun_shell_commandCheck eligible candidate counts per user Returned

Tool call · Input +00:48

{
  "description": "Check eligible candidate counts per user",
  "command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\n# Count bits per user\n# np.unpackbits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\nnum_eligible = unpacked.sum(axis=1)\nprint(f'Eligible items per user in val: min={num_eligible.min()}, mean={num_eligible.mean():.1f}, max={num_eligible.max()}')\n\n# Check test eligible\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nunpacked_test = np.unpackbits(test_cand['packed_eligible'], axis=1, count=30000, bitorder='little')\nnum_eligible_test = unpacked_test.sum(axis=1)\nprint(f'Eligible items per user in test: min={num_eligible_test.min()}, mean={num_eligible_test.mean():.1f}, max={num_eligible_test.max()}')\n\""
}

Tool response +00:49

<untrusted_context>
Output: Eligible items per user in val: min=24657, mean=29067.3, max=30000
Eligible items per user in test: min=24552, mean=29041.3, max=30000
Process Group PGID: 1648
</untrusted_context>

Inspect validation ground truth statistics

+00:56

Recorded reasoning summary 49 words

Analyzing Eligibility

I've confirmed that the vast majority of items, around 30,000, are eligible for each user, with an average of 29,067. The few exclusions seem to be related to past user interactions, like listening history or explicit feedback. I'm now diving into the specific reasons for these ineligibilities.

Recorded reasoning summary 58 words

Refining Filtering Logic

I'm now focusing on how the system filters out items. It appears our eligibility service handles the broad exclusions, like previously consumed or disliked tracks. My current task is to understand the precise mechanics behind these exclusions, as shown in the validation_truth function, to ensure optimal ranking of the top 100 eligible tracks per user.

Tool callrun_shell_commandInspect validation ground truth statistics Returned

Tool call · Input +00:56

{
  "command": "python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nfrom evaluate_public import validation_truth\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\"",
  "description": "Inspect validation ground truth statistics"
}

Tool response +00:57

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1659
</untrusted_context>

Inspect validation ground truth with PYTHONPATH set

+01:04

Tool callrun_shell_commandInspect validation ground truth with PYTHONPATH set Returned

Tool call · Input +01:04

{
  "description": "Inspect validation ground truth with PYTHONPATH set",
  "command": "PYTHONPATH=/task python -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ntruth = validation_truth(eligibility, targets)\nprint(f'Users with truth: {len(truth)} / {len(targets)}')\nitems_per_user = [len(v) for v in truth.values()]\nimport numpy as np\nprint(f'Truth items per user: min={np.min(items_per_user)}, mean={np.mean(items_per_user):.2f}, median={np.median(items_per_user)}, max={np.max(items_per_user)}')\ngrades = [g for v in truth.values() for g in v.values()]\nprint(f'Total positive interactions: {len(grades)}, grades count: {pd.Series(grades).value_counts().to_dict()}')\n\""
}

Tool response +01:05

<untrusted_context>
Output: Users with truth: 4263 / 8456
Truth items per user: min=1, mean=8.86, median=4.0, max=442
Total positive interactions: 37769, grades count: {1: 33352, 3: 4417}
Process Group PGID: 1670
</untrusted_context>

Benchmark reading train_listens and check target users overlap

+01:11

Recorded reasoning summary 36 words

Checking Target Users

I'm now focusing on the training data, specifically train_listens. My next step is to determine how many of these listens originate from the identified target_users. This will provide a crucial measure of overlap.

Tool callrun_shell_commandBenchmark reading train_listens and check target users overlap Returned

Tool call · Input +01:11

{
  "command": "python -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nprint(f'Read pq table in {time.time()-t0:.2f}s, num rows = {len(table)}')\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid)\nuids = table['uid'].to_numpy()\nmask = np.isin(uids, list(targets)) if 'np' in dir() else None\nimport numpy as np\nmask = np.isin(uids, list(targets))\nprint(f'Rows belonging to target users: {mask.sum()} / {len(uids)} ({mask.sum()/len(uids):.1%})')\n\"",
  "description": "Benchmark reading train_listens and check target users overlap"
}

Tool response +01:12

<untrusted_context>
Output: Read pq table in 0.39s, num rows = 43484159
Rows belonging to target users: 43222297 / 43484159 (99.4%)
Process Group PGID: 1690
</untrusted_context>

Verify catalog item IDs match candidate arrays exactly

+01:16

Recorded reasoning summary 43 words

Analyzing Data Speed

I've been impressed by PyArrow's speed, reading millions of rows in fractions of a second. Now I'm examining memory usage and the relationship between unique items in train_listens and candidate_catalog.parquet. My next step is a deeper dive into candidate_catalog.parquet itself.

Tool callrun_shell_commandVerify catalog item IDs match candidate arrays exactly Returned

Tool call · Input +01:16

{
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog item_ids:', len(catalog['item_id']), 'min:', catalog['item_id'].min(), 'max:', catalog['item_id'].max())\nprint(catalog.head())\n\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test candidate item_ids equals catalog item_ids?', np.array_equal(test_cand['item_ids'], catalog['item_id'].values))\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val candidate item_ids equals catalog item_ids?', np.array_equal(val_cand['item_ids'], catalog['item_id'].values))\n\"",
  "description": "Verify catalog item IDs match candidate arrays exactly"
}

Tool response +01:17

<untrusted_context>
Output: Catalog item_ids: 30000 min: 206 max: 9390294
   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
Test candidate item_ids equals catalog item_ids? True
Val candidate item_ids equals catalog item_ids? True
Process Group PGID: 1712
</untrusted_context>

Verify target users match candidate archive uids exactly

+01:29

Recorded reasoning summary 34 words

Confirming Catalog Consistency

I've confirmed that the candidate item IDs in both validation_eligible_candidates.npz and test_eligible_candidates.npz perfectly align with the candidate_catalog.parquet entries, in identical order. My next step is to verify the target user data.

Tool callrun_shell_commandVerify target users match candidate archive uids exactly Returned

Tool call · Input +01:29

{
  "description": "Verify target users match candidate archive uids exactly",
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\n\nusers = pd.read_parquet('/task/data/target_users.parquet')\nval_cand = np.load('/task/data/validation_eligible_candidates.npz')\ntest_cand = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('Val uids equals users uid?', np.array_equal(val_cand['uids'], users['uid'].values))\nprint('Test uids equals users uid?', np.array_equal(test_cand['uids'], users['uid'].values))\n\""
}

Tool response +01:30

<untrusted_context>
Output: Val uids equals users uid? True
Test uids equals users uid? True
Process Group PGID: 1735
</untrusted_context>

Generate baseline popularity ranking for validation

+01:37

Recorded reasoning summary 29 words

Analyzing Data Structure

I've confirmed the precise alignment of 8,456 target users across target_users.parquet, validation_eligible_candidates.npz, and test_eligible_candidates.npz. The candidate item count also matches at 30,000, maintaining the established ordering.

Tool callrun_shell_commandGenerate baseline popularity ranking for validation Returned

Tool call · Input +01:37

{
  "description": "Generate baseline popularity ranking for validation",
  "command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\ncatalog = pd.read_parquet(data_dir / 'candidate_catalog.parquet')\n\n# catalog is already sorted by popularity rank 1..30000\n# Unpack bits\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little') # (8456, 30000)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n    eligible_mask = unpacked[r] # bool array of len 30000\n    # The item_ids are aligned with catalog\n    eligible_items = item_ids[eligible_mask]\n    top_100 = eligible_items[:100]\n    rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n    rows_item.append(top_100)\n    rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n    'uid': np.concatenate(rows_uid),\n    'item_id': np.concatenate(rows_item),\n    'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\""
}

Tool response +01:39

<untrusted_context>
Output: Generated baseline in 1.09s, saved to /app/solution/val_baseline_pop.parquet
Process Group PGID: 1751
</untrusted_context>

Evaluate baseline popularity ranking with public evaluator

+01:41

Tool callrun_shell_commandEvaluate baseline popularity ranking with public evaluator Returned

Tool call · Input +01:41

{
  "command": "PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet",
  "description": "Evaluate baseline popularity ranking with public evaluator"
}

Tool response +01:43

<untrusted_context>
Output: Traceback (most recent call last):
  File "/task/evaluate_public.py", line 157, in <module>
    main()
  File "/task/evaluate_public.py", line 152, in main
    ranked = load_submission(path, eligibility, targets)
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/task/evaluate_public.py", line 113, in load_submission
    frame = validate_ranking(read_ranking(path), targets)
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/task/ranking_evaluation.py", line 153, in validate_ranking
    raise RankingValidationError(
ranking_evaluation.RankingValidationError: submission contains duplicate (uid, item_id) pairs
Exit Code: 1
Process Group PGID: 1768
</untrusted_context>

Inspect duplicates in baseline dataframe

+01:45

Recorded reasoning summary 40 words

Investigating Duplicate Items

I'm currently digging into the submission to understand why it flagged "submission contains duplicate (uid, item_id) pairs." I'm specifically examining the first 100 eligible items and the unpacking logic to pinpoint the source of these unexpected duplicates.

Tool callrun_shell_commandInspect duplicates in baseline dataframe Returned

Tool call · Input +01:45

{
  "description": "Inspect duplicates in baseline dataframe",
  "command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ndf = pd.read_parquet('/app/solution/val_baseline_pop.parquet')\nprint('df shape:', df.shape)\nprint('duplicated:', df.duplicated(['uid', 'item_id']).sum())\ndups = df[df.duplicated(['uid', 'item_id'], keep=False)]\nprint(dups.head(10))\n\""
}

Tool response +01:46

<untrusted_context>
Output: df shape: (845600, 3)
duplicated: 828967
   uid  item_id  rank
0  100  6901374     1
1  100  6901374     2
2  100  6901374     3
3  100  6901374     4
4  100  5862961     5
5  100  5862961     6
6  100  6901374     7
7  100  6901374     8
8  100  6901374     9
9  100  6901374    10
Process Group PGID: 1788
</untrusted_context>

Verify numpy indexing behavior between uint8 and bool

+01:50

Recorded reasoning summary 39 words

Clarifying Data Types

I'm currently focusing on understanding the precise data type of unpacked. My initial assumption was boolean, but I've now confirmed that np.unpackbits yields uint8 values (0s and 1s). This clarification is crucial for correctly indexing item_ids.

Tool callrun_shell_commandVerify numpy indexing behavior between uint8 and bool Returned

Tool call · Input +01:50

{
  "description": "Verify numpy indexing behavior between uint8 and bool",
  "command": "python -c \"\nimport numpy as np\n\narr = np.array([10, 20, 30])\nmask = np.array([1, 0, 1], dtype=np.uint8)\nprint('With uint8:', arr[mask])\nprint('With bool:', arr[mask.astype(bool)])\n\""
}

Tool response +01:50

<untrusted_context>
Output: With uint8: [20 10 20]
With bool: [10 30]
Process Group PGID: 1805
</untrusted_context>

Regenerate popularity baseline with bool mask and evaluate

+01:55

Tool callrun_shell_commandRegenerate popularity baseline with bool mask and evaluate Returned

Tool call · Input +01:55

{
  "description": "Regenerate popularity baseline with bool mask and evaluate",
  "command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n    eligible_mask = unpacked[r] # bool array of len 30000\n    eligible_items = item_ids[eligible_mask]\n    top_100 = eligible_items[:100]\n    rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n    rows_item.append(top_100)\n    rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n    'uid': np.concatenate(rows_uid),\n    'item_id': np.concatenate(rows_item),\n    'rank': np.concatenate(rows_rank)\n})\n\nout_dir = Path('/app/solution')\nout_dir.mkdir(parents=True, exist_ok=True)\nval_path = out_dir / 'val_baseline_pop.parquet'\ndf.to_parquet(val_path, index=False)\nprint(f'Generated baseline in {time.time()-t0:.2f}s, saved to {val_path}')\n\" && PYTHONPATH=/task python /task/evaluate_public.py /app/solution/val_baseline_pop.parquet"
}

Tool response +01:59

<untrusted_context>
Output: Generated baseline in 0.96s, saved to /app/solution/val_baseline_pop.parquet
{
  "model": "/app/solution/val_baseline_pop.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Process Group PGID: 1815
</untrusted_context>

Generate initial valid submission.parquet and verify invariants

+02:05

Recorded reasoning summary 57 words

Achieving Baseline Performance

My recent thoughts have focused on establishing a foundational understanding. I've successfully ran an end-to-end evaluation, confirming that my initial setup achieves a global popularity baseline of NDCG@10 = 0.010710. This is a crucial first step, and all validation checks have passed, ensuring a stable starting point. I'm keeping this best valid submission safe.

Tool callrun_shell_commandGenerate initial valid submission.parquet and verify invariants Returned

Tool call · Input +02:05

{
  "description": "Generate initial valid submission.parquet and verify invariants",
  "command": "python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n    eligible_mask = unpacked[r]\n    eligible_items = item_ids[eligible_mask]\n    top_100 = eligible_items[:100]\n    rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n    rows_item.append(top_100)\n    rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n    'uid': np.concatenate(rows_uid),\n    'item_id': np.concatenate(rows_item),\n    'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
}

Tool response +02:06

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 5, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1844
</untrusted_context>

Generate and verify fallback submission.parquet

+02:11

Tool callrun_shell_commandGenerate and verify fallback submission.parquet Returned

Tool call · Input +02:11

{
  "description": "Generate and verify fallback submission.parquet",
  "command": "PYTHONPATH=/task python -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ndata_dir = Path('/task/data')\ntest_cand = np.load(data_dir / 'test_eligible_candidates.npz')\nuids = test_cand['uids']\nitem_ids = test_cand['item_ids']\npacked = test_cand['packed_eligible']\n\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nrows_uid = []\nrows_item = []\nrows_rank = []\n\nfor r in range(len(uids)):\n    eligible_mask = unpacked[r]\n    eligible_items = item_ids[eligible_mask]\n    top_100 = eligible_items[:100]\n    rows_uid.append(np.full(100, uids[r], dtype=np.int64))\n    rows_item.append(top_100)\n    rows_rank.append(np.arange(1, 101, dtype=np.int64))\n\ndf = pd.DataFrame({\n    'uid': np.concatenate(rows_uid),\n    'item_id': np.concatenate(rows_item),\n    'rank': np.concatenate(rows_rank)\n})\n\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\nprint('Saved baseline submission to', out_path)\n\n# Verify against test invariants\ntargets_frame = pd.read_parquet(data_dir / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(data_dir / 'test_eligible_candidates.npz')\n\nvalidated = validate_ranking(df, targets)\nall_eligible = all(eligibility.is_eligible(uid, item) for uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None))\nprint('Test submission validation passed:', all_eligible, 'shape:', df.shape)\n\""
}

Tool response +02:14

<untrusted_context>
Output: Saved baseline submission to /app/solution/submission.parquet
Test submission validation passed: True shape: (845600, 3)
Process Group PGID: 1861
</untrusted_context>

update_topic command

+02:19

Recorded reasoning summary 33 words

Exploring Data

I've confirmed a solid fallback is ready. My current focus is a deep dive into the data, understanding user signals, and the domain to design effective collaborative filtering and recommendation strategies.

Tool callupdate_topicTool call Returned

Tool call · Input +02:19

{
  "summary": "We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.",
  "strategic_intent": "Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.",
  "title": "Interaction Analysis and Model Architecture Research"
}

Tool response +02:19

Current topic: "Interaction Analysis and Model Architecture Research"
Topic summary: We have confirmed the environment, validated the baseline pipeline, and created an initial valid fallback submission. We are now entering the deep analysis and modeling phase. We will examine user and item interaction patterns, explicit feedback (likes/dislikes), completion rates, temporal decay, and evaluate collaborative filtering techniques (item-item CF, BM25/TF-IDF, SVD/ALS, graph walks) on the validation split.

Strategic Intent: Analyze data distributions, interaction types, temporal characteristics, and design recommendation algorithms.

Inspect preference state relationship to candidate eligibility

+02:28

Recorded reasoning summary 43 words

Defining Relevance Criteria

My focus has been on dissecting the core of relevance. I'm meticulously examining the provided grades, particularly the nuances between organic liking (grade 3) and sustained listening (grade 1, 80% threshold), and contrasting them with the 'otherwise' condition (grade 0).

Recorded reasoning summary 39 words

Refining Evaluation Metrics

I'm now examining the nuances of graded NDCG@10, specifically how the DCG formula incorporates gains and discounts. I'm also solidifying the validation and test set split strategy, confirming the timestamps and ensuring a proper 30-minute gap.

Tool callrun_shell_commandInspect preference state relationship to candidate eligibility Returned

Tool call · Input +02:28

{
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\n\n# Check preference state overlap with eligibility\nval_pref = pd.read_parquet(data_dir / 'validation_preference_state.parquet')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check if any liked/disliked items in val_pref are eligible\neligible_pref_count = 0\nin_catalog_count = 0\nfor uid, item, state in val_pref.itertuples(index=False):\n    r = uid_to_row.get(uid)\n    c = item_to_col.get(item)\n    if c is not None:\n        in_catalog_count += 1\n        if r is not None and unpacked[r, c]:\n            eligible_pref_count += 1\n\nprint(f'Total val_pref rows: {len(val_pref)}')\nprint(f'val_pref items in catalog: {in_catalog_count}')\nprint(f'val_pref items that are eligible: {eligible_pref_count}')\n\"",
  "description": "Inspect preference state relationship to candidate eligibility"
}

Tool response +02:29

<untrusted_context>
Output: Total val_pref rows: 469124
val_pref items in catalog: 469124
val_pref items that are eligible: 0
Process Group PGID: 1877
</untrusted_context>

Check if previously listened items are eligible

+02:35

Recorded reasoning summary 37 words

Investigating Eligibility Criteria

I'm digging into why all validation tracks are marked ineligible. It appears every single one, regardless of preference, has been flagged. I'm now focusing on identifying the specific criteria that determine eligibility or ineligibility.

Tool callrun_shell_commandCheck if previously listened items are eligible Returned

Tool call · Input +02:35

{
  "description": "Check if previously listened items are eligible",
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Sample 100k train_listens rows\nlistens = pq.read_table(data_dir / 'train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct']).to_pandas()\nprint('Total train_listens:', len(listens))\n\n# Check eligibility of items that appeared in train_listens\nsample = listens.iloc[:100000]\neligible_in_listens = 0\ntotal_checked = 0\nfor uid, item, org, ratio in sample.itertuples(index=False):\n    r = uid_to_row.get(uid)\n    c = item_to_col.get(item)\n    if r is not None and c is not None:\n        total_checked += 1\n        if unpacked[r, c]:\n            eligible_in_listens += 1\n\nprint(f'Checked {total_checked} train_listens pairs: {eligible_in_listens} are eligible ({eligible_in_listens/total_checked:.4f})')\n\""
}

Tool response +02:36

<untrusted_context>
Output: Total train_listens: 43484159
Checked 70840 train_listens pairs: 0 are eligible (0.0000)
Process Group PGID: 1894
</untrusted_context>

Inspect mean ineligible tracks per user

+02:41

Recorded reasoning summary 39 words

Discovering New Music

I've analyzed 70,840 train-listen pairs, and it seems none are eligible for this "Made for You" shelf. This indicates a strong constraint where any previously interacted-with track is excluded, pushing the focus purely towards true discovery.

Tool callrun_shell_commandInspect mean ineligible tracks per user Returned

Tool call · Input +02:41

{
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nval_cand = np.load(data_dir / 'validation_eligible_candidates.npz')\nuids = val_cand['uids']\nitem_ids = val_cand['item_ids']\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Let's check target_users\n# Target user history length:\n# In target_users.parquet: 'recent_positive_listens', 'history_positive_listens'\nusers = pd.read_parquet(data_dir / 'target_users.parquet')\nprint('Target users count:', len(users))\nprint('Eligible per user mean:', unpacked.sum(axis=1).mean())\nprint('Ineligible per user mean:', (~unpacked).sum(axis=1).mean())\n\"",
  "description": "Inspect mean ineligible tracks per user"
}

Tool response +02:42

<untrusted_context>
Output: Target users count: 8456
Eligible per user mean: 29067.306291390727
Ineligible per user mean: 932.6937086092715
Process Group PGID: 1918
</untrusted_context>

Check unique users and timestamp range in train_listens

+02:49

Recorded reasoning summary 57 words

Refining Track Eligibility

I'm currently wrestling with the high volume of ineligible tracks per user, approximately 933, which seem to be directly linked to their past interactions and preferences. This significantly impacts the "Made for You" shelf's candidate pool. My focus is on optimizing how we define and manage this eligibility to ensure stronger, more relevant recommendations.

Recorded reasoning summary 52 words

Analyzing Discovery Drivers

My current focus is on understanding what drives organic track discovery. I'm investigating how user interaction history, particularly listen completion rates and organic engagement with candidate tracks, correlates with their preference for similar content. The goal is to refine how we predict future organic discovery by leveraging these insights.

Tool callrun_shell_commandCheck unique users and timestamp range in train_listens Returned

Tool call · Input +02:49

{
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Check train_listens\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp']).to_pandas()\nuids_in_listens = t_listens['uid'].unique()\ntargets = pd.read_parquet('/task/data/target_users.parquet')['uid'].values\n\nprint('Total unique uids in train_listens:', len(uids_in_listens))\nprint('Target uids in train_listens:', np.isin(targets, uids_in_listens).sum(), 'out of', len(targets))\nprint('Non-target uids in train_listens:', len(set(uids_in_listens) - set(targets)))\n\nmin_ts = t_listens['timestamp'].min()\nmax_ts = t_listens['timestamp'].max()\nprint(f'Timestamp range: {min_ts} to {max_ts} ({(max_ts-min_ts)/86400:.1f} days)')\n\"",
  "description": "Check unique users and timestamp range in train_listens"
}

Tool response +02:50

<untrusted_context>
Output: Total unique uids in train_listens: 9238
Target uids in train_listens: 8456 out of 8456
Non-target uids in train_listens: 782
Timestamp range: 0 to 24786795 (286.9 days)
Process Group PGID: 1934
</untrusted_context>

Check item overlap with candidate catalog in train and val

+02:55

Recorded reasoning summary 46 words

Analyzing Item Uniqueness

I'm currently investigating the uniqueness of items within train_listens, specifically looking for any data points that fall outside our 30,000 candidate catalog. Concurrently, I'm examining the distribution of items in val_listens and val_likes to ensure all ground-truth validation items are represented as expected.

Tool callrun_shell_commandCheck item overlap with candidate catalog in train and val Returned

Tool call · Input +02:55

{
  "description": "Check item overlap with candidate catalog in train and val",
  "command": "python -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = set(cat['item_id'])\n\n# Check items in train_listens\nitems_in_train = set(pq.read_table('/task/data/train_listens.parquet', columns=['item_id']).to_pandas()['item_id'].unique())\nprint(f'Unique items in train_listens: {len(items_in_train)}')\nprint(f'Items in train_listens that are in catalog: {len(items_in_train & cat_items)} / {len(cat_items)}')\n\n# Check items in val ground truth\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nval_organic_listens_80 = set(val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]['item_id'].unique())\nval_organic_likes = set(val_likes[val_likes.is_organic == 1]['item_id'].unique())\nval_pos = val_organic_listens_80 | val_organic_likes\nprint(f'Unique items with positive feedback in val: {len(val_pos)}')\nprint(f'Positive val items in catalog: {len(val_pos & cat_items)} / {len(val_pos)}')\n\""
}

Tool response +02:57

<untrusted_context>
Output: Unique items in train_listens: 845395
Items in train_listens that are in catalog: 30000 / 30000
Unique items with positive feedback in val: 93190
Positive val items in catalog: 25709 / 93190
Process Group PGID: 1956
</untrusted_context>

Benchmark matrix product and top-100 extraction

+03:07

Recorded reasoning summary 43 words

Analyzing Interaction Attributes

My focus has shifted to dissecting the train_listens data. Specifically, I'm examining the attributes of interactions, and I've noted that the is_organic attribute is consistently marked as 0. This is a key observation in understanding the nature of these interactions.

Recorded reasoning summary 50 words

Exploring Collaborative Filtering

I'm now diving into collaborative filtering techniques suitable for music discovery item ranking, considering the dataset's scale of 8,456 users and 30,000 items. My next step is to evaluate the computational cost of various approaches to score eligible items for each user before selecting the top 100.

Recorded reasoning summary 47 words

Evaluating Matrix Factorization

I'm now considering matrix factorization, specifically the computational feasibility of generating an $8456 \times 30000$ prediction matrix. The $1.01$ GB RAM requirement for this dense representation seems manageable, and I'm exploring the efficiency of computing the $U \times V^T$ product with $K=10$ latent dimensions.

Tool callrun_shell_commandBenchmark matrix product and top-100 extraction Returned

Tool call · Input +03:07

{
  "description": "Benchmark matrix product and top-100 extraction",
  "command": "python -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(30000, 128).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V.T\nt1 = time.time()\nprint(f'Matrix multiplication shape {scores.shape} took {t1-t0:.4f}s')\n\n# And top-100 argpartition\nt0 = time.time()\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nt1 = time.time()\nprint(f'Top 100 argpartition took {t1-t0:.4f}s')\n\""
}

Tool response +03:09

<untrusted_context>
Output: Matrix multiplication shape (8456, 30000) took 0.3894s
Top 100 argpartition took 0.9599s
Process Group PGID: 1978
</untrusted_context>

Summary statistics of train_listens on target users and catalog items

+03:15

Recorded reasoning summary 58 words

Accelerating Model Evaluation

My latest simulations reveal significant performance gains. Matrix multiplications now take a mere 0.39 seconds, and top-100 argpartitions are completed in 0.96 seconds. This efficiency allows for rapid iteration and evaluation of complete models, potentially within seconds. I am now delving into the nuances of the train_listens, train_likes, and train_dis datasets to understand their interrelationships.

Tool callrun_shell_commandSummary statistics of train_listens on target users and catalog items Returned

Tool call · Input +03:15

{
  "description": "Summary statistics of train_listens on target users and catalog items",
  "command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\nprint('Reading sample of train_listens...')\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds', 'timestamp'])\ndf = tbl.to_pandas()\nprint('Number of rows:', len(df))\n\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nin_cat = df['item_id'].isin(cat)\nin_targets = df['uid'].isin(targets)\n\nprint(f'Rows with catalog item: {in_cat.sum()} / {len(df)} ({in_cat.sum()/len(df):.1%})')\nprint(f'Rows with target user: {in_targets.sum()} / {len(df)} ({in_targets.sum()/len(df):.1%})')\nboth = in_cat & in_targets\nprint(f'Rows with both: {both.sum()} / {len(df)} ({both.sum()/len(df):.1%})')\n\nsub = df[both]\nprint('Summary of both:')\nprint('is_organic mean:', sub['is_organic'].mean())\nprint('played_ratio_pct percentiles:', np.percentile(sub['played_ratio_pct'], [10, 25, 50, 75, 80, 90]))\nprint('played_ratio_pct >= 80%:', (sub['played_ratio_pct'] >= 80).mean())\nprint('played_ratio_pct >= 50%:', (sub['played_ratio_pct'] >= 50).mean())\n\""
}

Tool response +03:23

<untrusted_context>
Output: Reading sample of train_listens...
Number of rows: 43484159
Rows with catalog item: 31249613 / 43484159 (71.9%)
Rows with target user: 43222297 / 43484159 (99.4%)
Rows with both: 31062540 / 43484159 (71.4%)
Summary of both:
is_organic mean: 0.5001908408005269
played_ratio_pct percentiles: [  1.   8. 100. 100. 100. 100.]
played_ratio_pct >= 80%: 0.6023763349680998
played_ratio_pct >= 50%: 0.6425698928677436
Process Group PGID: 1988
</untrusted_context>

Check listens distribution over recency windows

+03:30

Tool callrun_shell_commandCheck listens distribution over recency windows Returned

Tool call · Input +03:30

{
  "command": "python -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\nts = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'])['timestamp'].to_numpy()\nmax_ts = ts.max()\ndiff_days = (max_ts - ts) / 86400.0\n\nfor days in [7, 14, 30, 60, 90, 180]:\n    print(f'Listens in last {days:3d} days: {(diff_days <= days).sum()} ({(diff_days <= days).mean():.1%})')\n\"",
  "description": "Check listens distribution over recency windows"
}

Tool response +03:32

<untrusted_context>
Output: Listens in last   7 days: 1491707 (3.4%)
Listens in last  14 days: 2920236 (6.7%)
Listens in last  30 days: 6126106 (14.1%)
Listens in last  60 days: 11718765 (26.9%)
Listens in last  90 days: 16769280 (38.6%)
Listens in last 180 days: 30187041 (69.4%)
Process Group PGID: 2015
</untrusted_context>

Benchmark filtering train_listens to target users and catalog items

+03:53

Recorded reasoning summary 37 words

Analyzing Recent Listen Counts

My analysis shows that the ~1.5 million listens observed in the last 7 days of training precisely align with the val_listens metric. I'm now examining the underlying data structure and the available signals.

Recorded reasoning summary 34 words

Exploring ItemCF's Potential

I'm investigating the efficacy of Item-Item Collaborative Filtering, a robust method for implicit feedback. Given its strong performance in similar music streaming contexts, I'm particularly interested in its potential application here.

Recorded reasoning summary 54 words

Refining Collaborative Filtering

I'm now focusing on fine-tuning collaborative filtering strategies. Specifically, I'm exploring how to leverage item co-occurrence, emphasizing popularity-normalized similarity metrics like BM25, and developing a mechanism to generate candidate recommendations based on user interaction history. I am also investigating the feasibility of implementing EASE, evaluating memory constraints for large matrix inversions.

Recorded reasoning summary 41 words

Evaluating SVD Performance

I'm assessing the computational cost of matrix inversions for methods like EASE, realizing $O(M^3)$ operations might be too slow. However, I've discovered that using TruncatedSVD on the user-item matrix is surprisingly fast, taking only seconds for rank-k approximations.

Recorded reasoning summary 43 words

Considering User-User CF

I'm now exploring the potential of user-user collaborative filtering, particularly given the smaller number of users compared to items. This approach, with an $8456 \times 8456$ similarity matrix, seems computationally feasible and avoids the $O(M^3)$ issues of some other methods.

Recorded reasoning summary 64 words

Testing User-User CF Efficiency

I'm now focusing on the computational feasibility of user-user collaborative filtering. It appears that generating predictions with a precomputed similarity matrix is a single matrix multiplication, which is highly efficient, especially with sparse similarity. User-User EASE is also surprisingly quick, with matrix inversion taking only seconds. I'll be testing these ideas and optimizing the construction of the user interaction vector.

Tool callrun_shell_commandBenchmark filtering train_listens to target users and catalog items Returned

Tool call · Input +03:53

{
  "command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nt0 = time.time()\nprint('Testing data loading...')\n\n# Load catalog and users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nusers = pd.read_parquet('/task/data/target_users.parquet')\n\nuid_map = {uid: i for i, uid in enumerate(users['uid'])}\nitem_map = {item: j for j, item in enumerate(cat['item_id'])}\n\n# Load preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state rows:', len(val_pref))\n\n# Load train_listens\nlistens = pq.read_table('/task/data/train_listens.parquet', \n                        columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp']).to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to target users and catalog items\nmask = listens['uid'].isin(uid_map) & listens['item_id'].isin(item_map)\ndf = listens[mask].copy()\nprint(f'Filtered rows: {len(df)} in {time.time()-t0:.2f}s')\n\"",
  "description": "Benchmark filtering train_listens to target users and catalog items"
}

Tool response +04:01

<untrusted_context>
Output: Testing data loading...
Preference state rows: 469124
Loaded train_listens in 0.56s
Filtered rows: 31062540 in 6.92s
Process Group PGID: 2037
</untrusted_context>

Count unique user-item interaction pairs in train_listens

+04:29

Recorded reasoning summary 43 words

Analyzing User Behavior

I'm currently analyzing the filtered data, which contains 31 million rows. My next step is to determine the number of unique user-item interactions. I'm also considering the implications of repeated listens, wondering how to best represent that in the data.

Tool callrun_shell_commandCount unique user-item interaction pairs in train_listens Returned

Tool call · Input +04:29

{
  "description": "Count unique user-item interaction pairs in train_listens",
  "command": "python -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntbl = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\ndf = df[df['uid'].isin(targets) & df['item_id'].isin(cat)]\n\npairs = len(df[['uid', 'item_id']].drop_duplicates())\nprint('Unique (uid, item_id) pairs:', pairs)\n\""
}

Tool response +04:41

<untrusted_context>
Output: Unique (uid, item_id) pairs: 7868664
Process Group PGID: 2066
</untrusted_context>

Check explicit feedback overlap with catalog

+04:48

Recorded reasoning summary 40 words

Analyzing Data Density

My recent analysis reveals a significant density in the dataset. With 7.87 million unique user-item pairs against 253.68 million potential combinations, the current density stands around 3.1%. This figure is notably high for typical recommendation system benchmarks.

Recorded reasoning summary 47 words

Considering Catalog Scope

I'm re-evaluating the scope of items. While the overall density is good, I'm now focused on whether using only catalog items for user representation impacts performance, especially since validation targets are exclusively within the catalog. It seems the majority of listens are also catalog-related.

Tool callrun_shell_commandCheck explicit feedback overlap with catalog Returned

Tool call · Input +04:48

{
  "description": "Check explicit feedback overlap with catalog",
  "command": "python -c \"\nimport pandas as pd\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet')['item_id'])\nfor f in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n    df = pd.read_parquet(f'/task/data/train_{f}.parquet')\n    in_cat = df['item_id'].isin(cat).mean()\n    print(f'train_{f}: {len(df)} rows, {in_cat:.1%} in catalog')\n\""
}

Tool response +04:49

<untrusted_context>
Output: train_likes: 814831 rows, 68.1% in catalog
train_dislikes: 101075 rows, 64.6% in catalog
train_unlikes: 292665 rows, 31.9% in catalog
train_undislikes: 19960 rows, 34.0% in catalog
Process Group PGID: 2094
</untrusted_context>

Verify in-memory validation evaluation speed

+05:02

Recorded reasoning summary 47 words

Clarifying Validation Targets

My current focus is on precisely defining the target labels within the validation set. I've identified that grade 3 specifically refers to "organic like," while grade 1 signifies "organic listen >= 80% completion." I'm now cross-referencing these with the training data to ensure consistency.

Recorded reasoning summary 59 words

Refining Organic Engagement Logic

I'm exploring how users will encounter and engage with novel items. Specifically, I'm diving into the underlying collaborative filtering mechanisms, both item-item and user-user, to model how a user's existing preferences will lead them to organically listen to or like items they haven't experienced before. This involves examining the mathematical formulations for similarity and prediction.

Recorded reasoning summary 46 words

Modeling User Discoveries

I'm delving into collaborative filtering models. Specifically, I'm analyzing how user-item affinities and user-user similarities, derived from training data, can predict new item discoveries. This involves exploring the mathematical underpinnings of item-item and user-user co-occurrence, as well as matrix factorization techniques like SVD.

Tool callrun_shell_commandVerify in-memory validation evaluation speed Returned

Tool call · Input +05:02

{
  "description": "Verify in-memory validation evaluation speed",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings, ranked_items, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nt0 = time.time()\ngraded = validation_truth(eligibility, targets)\nprint(f'Computed validation_truth in {time.time()-t0:.2f}s')\n\n# Test scoring dummy ranked items\nt0 = time.time()\ndummy_ranked = {uid: list(range(100)) for uid in targets}\nres = score_rankings(dummy_ranked, graded)\nprint(f'Computed score_rankings in {time.time()-t0:.2f}s: {res}')\n\""
}

Tool response +05:03

<untrusted_context>
Output: Computed validation_truth in 0.39s
Computed score_rankings in 0.09s: {'quality_ndcg@10': 0.0, 'quality_users': 4263}
Process Group PGID: 2113
</untrusted_context>

Test fast SVD model on validation

+05:17

Recorded reasoning summary 59 words

Achieving Faster Evaluations

My recent focus has been on optimizing the evaluation pipeline. I'm thrilled to report that by analyzing the timings, I've confirmed we can now complete full validation evaluations in under half a second, a significant speedup. Next, I'll be delving into the construction of the interaction matrix, $R$, and exploring the optimal nature of its elements.

Recorded reasoning summary 56 words

Analyzing Listen Data

I've been examining the train_listens data. I see that a listen with played_ratio_pct >= 80% is a definitive positive, but I'm also considering the implications of listens between 50% and 80%, and distinguishing skips below 20%. The recency of listens and organic intent are also key factors I'm exploring for their predictive power.

Tool callrun_shell_commandTest fast SVD model on validation Returned

Tool call · Input +05:17

{
  "command": "python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items)    # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\n# User and item indices\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\n# Interaction count or binary\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary: 1 if user listened to item >= 50%\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\n# Let us test SVD\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_     # (64, 30000)\npred_svd = U_svd @ V_svd    # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\n# Get top 100 for each user\nt0 = time.time()\n# argpartition for top 100\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\n# sort within top 100\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n    ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\"",
  "description": "Test fast SVD model on validation"
}

Tool response +05:18

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 8, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 2133
</untrusted_context>

Run SVD-64 experiment with correct PYTHONPATH

+05:28

Tool callrun_shell_commandRun SVD-64 experiment with correct PYTHONPATH Returned

Tool call · Input +05:28

{
  "description": "Run SVD-64 experiment with correct PYTHONPATH",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\n\nN = len(targets_list) # 8456\nM = len(cat_items)    # 30000\n\nprint(f'N={N}, M={M}')\n\n# Load eligibility\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\npacked = val_cand['packed_eligible']\nunpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Let us load train_listens with a fast filter\nt0 = time.time()\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\ndf = tbl.to_pandas()\nprint(f'Loaded train_listens in {time.time()-t0:.2f}s')\n\n# Filter to targets and catalog\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)].copy()\nprint(f'Filtered {len(df)} rows in {time.time()-t0:.2f}s')\n\n# Simple interaction weight: played_ratio_pct >= 50\nstrong = df[df.played_ratio_pct >= 50].copy()\nu_idx = strong.uid.map(uid_to_idx).to_numpy()\ni_idx = strong.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\nprint(f'R matrix shape: {R.shape}, nnz: {R.nnz}')\n\nfrom sklearn.decomposition import TruncatedSVD\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(R) # (8456, 64)\nV_svd = svd.components_     # (64, 30000)\npred_svd = U_svd @ V_svd    # (8456, 30000)\nprint(f'SVD-64 fit & predict in {time.time()-t0:.2f}s')\n\n# Mask ineligible items: set to -1e9\npred_svd[~unpacked] = -1e9\n\nt0 = time.time()\ntop100_idx = np.argpartition(-pred_svd, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred_svd[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {}\nfor r, uid in enumerate(targets_list):\n    ranked[uid] = [cat_items[c] for c in sorted_top100_idx[r]]\n\nres = score_rankings(ranked, graded)\nprint(f'SVD-64 NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (scored {res[\\\"quality_users\\\"]} users)')\n\""
}

Tool response +05:43

<untrusted_context>
Output: N=8456, M=30000
Loaded train_listens in 0.47s
Filtered 31062540 rows in 6.91s
R matrix shape: (8456, 30000), nnz: 5514505
SVD-64 fit & predict in 2.52s
SVD-64 NDCG@10: 0.017121 (scored 4263 users)
Process Group PGID: 2151
</untrusted_context>

Evaluate Item-Item Cosine via associative multiplication

+05:59

Recorded reasoning summary 53 words

Improving Popularity Models

I'm thrilled by the immediate 60% jump to 0.017121 with SVD-64, achieved in just 2.5 seconds. This initial success is exciting, and my current focus is on understanding the underlying reasons for this rapid improvement. I'm eager to explore other collaborative filtering models and hybrid approaches to further boost performance.

Recorded reasoning summary 62 words

Refining Item Similarity

I've discovered a more efficient approach for item-item collaborative filtering. Instead of computing the large item-item similarity matrix directly, I can leverage matrix multiplication properties. By calculating $R R^T$ first, which results in a manageable $N \times N$ matrix, I can then multiply it by $R$ to get the predictions. This avoids the massive $M \times M$ matrix computation.

Tool callrun_shell_commandEvaluate Item-Item Cosine via associative multiplication Returned

Tool call · Input +05:59

{
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\n# Normalize items (column L2 normalization: equivalent to cosine similarity between items)\nt0 = time.time()\nR_item_norm = normalize(R, norm='l2', axis=0) # column-normalized\n# user predictions = R_user * (R_item_norm.T * R_item_norm)\n# Associative: = (R_user * R_item_norm.T) * R_item_norm\n# Here R_user can be normalized as well\nR_user_norm = normalize(R, norm='l2', axis=1)\n\n# Step 1: User-Item_norm product: R_user_norm @ R_item_norm.T is (8456, 8456)\nC = R_user_norm @ R_item_norm.T # sparse (8456, 8456)\nprint(f'Computed C = R_u @ R_i.T in {time.time()-t0:.2f}s, nnz={C.nnz}')\n\n# Step 2: C @ R_item_norm is (8456, 30000)\n# But wait, self-recommendations (items the user already listened to) are already ineligible in unpacked!\nt1 = time.time()\npred = (C @ R_item_norm).toarray()\nprint(f'Computed pred in {time.time()-t1:.2f}s')\n\npred[~unpacked] = -1e9\n\ntop100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\nrow_indices = np.arange(N)[:, None]\ntop100_scores = pred[row_indices, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n\nranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\nres = score_rankings(ranked, graded)\nprint(f'Item-Item Cosine NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n\"",
  "description": "Evaluate Item-Item Cosine via associative multiplication"
}

Tool response +07:48

<untrusted_context>
Output: Computed C = R_u @ R_i.T in 5.86s, nnz=63275517
Computed pred in 92.31s
Item-Item Cosine NDCG@10: 0.01249
Process Group PGID: 2186
</untrusted_context>

Test SVD rank k and singular value power alpha

+08:01

Recorded reasoning summary 30 words

Calculating Prediction

I've just finished computing the prediction. The sheer density of matrix $C$, at nearly 90%, made this step quite time-consuming, taking 92 seconds. The resulting score is 0.01249.

Recorded reasoning summary 65 words

Refining SVD Approach

My recent exploration has focused on improving the SVD prediction. I'm investigating the impact of tuning components and weighting schemes, considering options like TF-IDF or log frequency. I'm also experimenting with different numbers of SVD components and normalizing user/item vectors. Additionally, I'm delving into powering singular values, specifically exploring the PureSVD approach ($\alpha=0$) as it appears to mitigate overemphasis on popular items.

Tool callrun_shell_commandTest SVD rank k and singular value power alpha Returned

Tool call · Input +08:01

{
  "description": "Test SVD rank k and singular value power alpha",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nprint(f'R constructed: shape={R.shape}, nnz={R.nnz}')\n\n# Compute SVD with k=256\nt0 = time.time()\nU, S, Vt = svds(R, k=256)\n# svds returns ascending singular values, sort descending\nidx = np.argsort(-S)\nU = U[:, idx]\nS = S[idx]\nVt = Vt[idx, :]\nprint(f'Computed svds(k=256) in {time.time()-t0:.2f}s')\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\nfor k in [64, 128, 192, 256]:\n    for alpha in [0.0, 0.25, 0.5, 0.75, 1.0]:\n        t0 = time.time()\n        # pred = (U[:, :k] * (S[:k]**alpha)) @ Vt[:k, :]\n        U_k = U[:, :k] * (S[:k]**alpha)\n        pred = U_k @ Vt[:k, :]\n        score = eval_pred(pred)\n        print(f'k={k:3d}, alpha={alpha:.2f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
}

Tool response +09:10

<untrusted_context>
Output: R constructed: shape=(8456, 30000), nnz=5514505
Computed svds(k=256) in 21.37s
k= 64, alpha=0.00 -> NDCG@10 = 0.016787 (1.83s)
k= 64, alpha=0.25 -> NDCG@10 = 0.016857 (1.73s)
k= 64, alpha=0.50 -> NDCG@10 = 0.017075 (1.68s)
k= 64, alpha=0.75 -> NDCG@10 = 0.017572 (1.72s)
k= 64, alpha=1.00 -> NDCG@10 = 0.017077 (1.70s)
k=128, alpha=0.00 -> NDCG@10 = 0.019034 (1.85s)
k=128, alpha=0.25 -> NDCG@10 = 0.019406 (1.89s)
k=128, alpha=0.50 -> NDCG@10 = 0.019274 (1.84s)
k=128, alpha=0.75 -> NDCG@10 = 0.019152 (1.90s)
k=128, alpha=1.00 -> NDCG@10 = 0.018916 (1.89s)
k=192, alpha=0.00 -> NDCG@10 = 0.018039 (2.01s)
k=192, alpha=0.25 -> NDCG@10 = 0.018478 (1.99s)
k=192, alpha=0.50 -> NDCG@10 = 0.018716 (2.00s)
k=192, alpha=0.75 -> NDCG@10 = 0.019405 (2.01s)
k=192, alpha=1.00 -> NDCG@10 = 0.019673 (2.01s)
k=256, alpha=0.00 -> NDCG@10 = 0.018581 (2.15s)
k=256, alpha=0.25 -> NDCG@10 = 0.018655 (2.14s)
k=256, alpha=0.50 -> NDCG@10 = 0.018902 (2.17s)
k=256, alpha=0.75 -> NDCG@10 = 0.018778 (2.14s)
k=256, alpha=1.00 -> NDCG@10 = 0.019073 (2.18s)
Process Group PGID: 2225
</untrusted_context>

Test User-User k-NN CF with various neighborhood sizes

+09:23

Recorded reasoning summary 31 words

Analyzing SVD Performance

I'm seeing promising initial SVD results, with $k=128, \alpha=0.25$ slightly outperforming $k=192, \alpha=1.00$ on the simple binary played ratio matrix. The next step is integrating interaction rec data.

Tool callrun_shell_commandTest User-User k-NN CF with various neighborhood sizes Returned

Tool call · Input +09:23

{
  "description": "Test User-User k-NN CF with various neighborhood sizes",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test User-User Cosine with Top-K neighbors\nt0 = time.time()\nR_norm = normalize(R, norm='l2', axis=1) # (8456, 30000)\n# Compute user-user similarity: W = R_norm @ R_norm.T\n# Since N=8456, W is (8456, 8456)\nW = (R_norm @ R_norm.T).toarray()\nprint(f'Computed user similarity W (8456x8456) in {time.time()-t0:.2f}s')\n\n# Set diagonal of W to 0\nnp.fill_diagonal(W, 0)\n\nfor top_k_users in [50, 100, 200, 500, 1000]:\n    t0 = time.time()\n    # Zero out all except top_k_users per row\n    # Use partition to find threshold\n    kth = W.shape[1] - top_k_users\n    # partition along columns\n    thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n    W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n    # Convert W_k to csr_matrix for fast multiplication with sparse R\n    W_k_sparse = sp.csr_matrix(W_k)\n    pred_user = (W_k_sparse @ R).toarray()\n    score = eval_pred(pred_user)\n    print(f'User-User k-NN k={top_k_users:4d} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
}

Tool response +10:38

<untrusted_context>
Output: Computed user similarity W (8456x8456) in 5.89s
User-User k-NN k=  50 -> NDCG@10 = 0.016141 (5.22s)
User-User k-NN k= 100 -> NDCG@10 = 0.015614 (6.71s)
User-User k-NN k= 200 -> NDCG@10 = 0.015026 (9.26s)
User-User k-NN k= 500 -> NDCG@10 = 0.014031 (15.11s)
User-User k-NN k=1000 -> NDCG@10 = 0.013844 (23.06s)
Process Group PGID: 2264
</untrusted_context>

Test IDF weighting with SVD

+11:22

Recorded reasoning summary 47 words

Exploring User-User kNN

My current focus is on refining User-User kNN. I've observed a promising result with k=50 yielding an accuracy of 0.016141. I'm now investigating how BM25/TF-IDF item weighting might further enhance these User-User calculations, particularly in how to appropriately weigh co-occurrences of shared listening habits.

Tool callrun_shell_commandTest IDF weighting with SVD Returned

Tool call · Input +11:22

{
  "description": "Test IDF weighting with SVD",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Item popularity n_j\nn_j = np.array(R.sum(axis=0)).flatten()\n# IDF\nidf = np.log((N + 1.0) / (n_j + 1.0))\n# Let us test SVD on IDF-weighted R\nfor p in [0.25, 0.5, 0.75, 1.0]:\n    R_idf = R.multiply(idf**p).tocsr()\n    U, S, Vt = svds(R_idf, k=192)\n    idx = np.argsort(-S)\n    U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n    for alpha in [0.5, 0.75, 1.0]:\n        pred = (U * (S**alpha)) @ Vt\n        print(f'IDF p={p:.2f}, alpha={alpha:.2f} -> SVD NDCG@10 = {eval_pred(pred):.6f}')\n\""
}

Tool response +12:56

<untrusted_context>
Output: IDF p=0.25, alpha=0.50 -> SVD NDCG@10 = 0.019336
IDF p=0.25, alpha=0.75 -> SVD NDCG@10 = 0.019684
IDF p=0.25, alpha=1.00 -> SVD NDCG@10 = 0.019117
IDF p=0.50, alpha=0.50 -> SVD NDCG@10 = 0.019292
IDF p=0.50, alpha=0.75 -> SVD NDCG@10 = 0.019479
IDF p=0.50, alpha=1.00 -> SVD NDCG@10 = 0.019935
IDF p=0.75, alpha=0.50 -> SVD NDCG@10 = 0.018273
IDF p=0.75, alpha=0.75 -> SVD NDCG@10 = 0.018423
IDF p=0.75, alpha=1.00 -> SVD NDCG@10 = 0.018897
IDF p=1.00, alpha=0.50 -> SVD NDCG@10 = 0.016926
IDF p=1.00, alpha=0.75 -> SVD NDCG@10 = 0.017248
IDF p=1.00, alpha=1.00 -> SVD NDCG@10 = 0.017685
Process Group PGID: 2309
</untrusted_context>

Test User-User k-NN with IDF weighting and power

+13:06

Recorded reasoning summary 38 words

Exploring User-User CF

I'm now investigating the impact of IDF weighting on our User-User collaborative filtering approach. My current focus is on how applying IDF to user vectors, specifically in the similarity calculation, influences the overall performance metrics.

Tool callrun_shell_commandTest User-User k-NN with IDF weighting and power Returned

Tool call · Input +13:06

{
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\nn_j = np.array(R.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\n\nfor p in [0.5, 1.0]:\n    R_idf = R.multiply(idf**p).tocsr()\n    R_norm = normalize(R_idf, norm='l2', axis=1)\n    W = (R_norm @ R_norm.T).toarray()\n    np.fill_diagonal(W, 0)\n    for top_k in [30, 50, 100]:\n        kth = W.shape[1] - top_k\n        thresh = np.partition(W, kth, axis=1)[:, kth:kth+1]\n        W_k = np.where(W >= thresh, W, 0.0).astype(np.float32)\n        # Power weights\n        for power in [1.0, 2.0]:\n            W_kp = sp.csr_matrix(W_k**power)\n            pred = (W_kp @ R).toarray()\n            print(f'User-User IDF p={p}, k={top_k}, power={power} -> NDCG@10 = {eval_pred(pred):.6f}')\n\"",
  "description": "Test User-User k-NN with IDF weighting and power"
}

Tool response +14:29

<untrusted_context>
Output: User-User IDF p=0.5, k=30, power=1.0 -> NDCG@10 = 0.017023
User-User IDF p=0.5, k=30, power=2.0 -> NDCG@10 = 0.017023
User-User IDF p=0.5, k=50, power=1.0 -> NDCG@10 = 0.016660
User-User IDF p=0.5, k=50, power=2.0 -> NDCG@10 = 0.016718
User-User IDF p=0.5, k=100, power=1.0 -> NDCG@10 = 0.016198
User-User IDF p=0.5, k=100, power=2.0 -> NDCG@10 = 0.016450
User-User IDF p=1.0, k=30, power=1.0 -> NDCG@10 = 0.016281
User-User IDF p=1.0, k=30, power=2.0 -> NDCG@10 = 0.016289
User-User IDF p=1.0, k=50, power=1.0 -> NDCG@10 = 0.017501
User-User IDF p=1.0, k=50, power=2.0 -> NDCG@10 = 0.017175
User-User IDF p=1.0, k=100, power=1.0 -> NDCG@10 = 0.016475
User-User IDF p=1.0, k=100, power=2.0 -> NDCG@10 = 0.016862
Process Group PGID: 2348
</untrusted_context>

Test User-EASE model on validation

+14:36

Recorded reasoning summary 44 words

Exploring User-EASE

My current focus is on implementing the User-EASE model, following its definition from Steck's 2019 WWW paper. I've successfully integrated SVD and User-User k-NN, and now I'm diving into the mathematical formulation of EASE, specifically the matrix operations involving G and P.

Tool callrun_shell_commandTest User-EASE model on validation Returned

Tool call · Input +14:36

{
  "description": "Test User-EASE model on validation",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom sklearn.preprocessing import normalize\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = tbl.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set) & (df.played_ratio_pct >= 50)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Compute Gram matrix G = R @ R.T\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float32)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [50.0, 100.0, 200.0, 500.0, 1000.0, 2000.0]:\n    t0 = time.time()\n    G_reg = G.copy()\n    diag_indices = np.diag_indices_from(G_reg)\n    G_reg[diag_indices] += reg\n    P = np.linalg.inv(G_reg)\n    P = -P / np.diag(P)[:, None]\n    P[diag_indices] = 0.0\n    \n    # Compute pred = P @ R\n    pred = P @ R\n    score = eval_pred(pred)\n    print(f'User-EASE reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
}

Tool response +17:49

<untrusted_context>
Output: G computed in 5.96s
User-EASE reg=  50.0 -> NDCG@10 = 0.016856 (29.47s)
User-EASE reg= 100.0 -> NDCG@10 = 0.018053 (29.40s)
User-EASE reg= 200.0 -> NDCG@10 = 0.019040 (29.48s)
User-EASE reg= 500.0 -> NDCG@10 = 0.019749 (29.44s)
User-EASE reg=1000.0 -> NDCG@10 = 0.019765 (29.79s)
User-EASE reg=2000.0 -> NDCG@10 = 0.020333 (29.42s)
Process Group PGID: 2393
</untrusted_context>

Benchmark Cholesky inversion vs np.linalg.inv

+18:38

Recorded reasoning summary 41 words

Exploring Higher Regularization

I'm excited by the recent jump in NDCG@10 to 0.020333 with reg=2000. I'm now investigating even greater regularization values (3000, 5000, 10000) and exploring the impact of incorporating recency decay, both exponential and time-weighted, to further refine performance.

Tool callrun_shell_commandBenchmark Cholesky inversion vs np.linalg.inv Returned

Tool call · Input +18:38

{
  "command": "python -c \"\nimport numpy as np\nimport scipy.linalg.lapack as lapack\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float64)\nG = A.T @ A + 1000.0 * np.eye(8456)\n\nt0 = time.time()\nc, info = lapack.dpotrf(G, lower=False)\ninv, info = lapack.dpotri(c, lower=False)\n# dpotri only fills upper triangle, fill lower\ninv = np.triu(inv) + np.triu(inv, 1).T\nprint(f'Cholesky inversion (dpotri) took: {time.time()-t0:.2f}s')\n\nt0 = time.time()\ninv2 = np.linalg.inv(G)\nprint(f'np.linalg.inv took: {time.time()-t0:.2f}s')\n\"",
  "description": "Benchmark Cholesky inversion vs np.linalg.inv"
}

Tool response +19:10

<untrusted_context>
Output: Cholesky inversion (dpotri) took: 7.74s
np.linalg.inv took: 16.32s
Process Group PGID: 2438
</untrusted_context>

Test temporal decay half-life on SVD performance

+19:30

Recorded reasoning summary 50 words

Optimizing Cholesky Inversion

I've significantly reduced Cholesky inversion time from 16.32s to 7.74s by leveraging dpotrf and dpotri. My next focus is to dissect the feature set and weights in R that contribute most to accuracy, starting with an in-depth analysis of played_ratio_pct and its impact when played_ratio_pct >= 80.

Recorded reasoning summary 45 words

Investigating Feature Weighting

I'm now exploring the influence of played_ratio_pct categories on model performance, assigning differential weights to segments like >= 80% and 50-80%. I'm also considering boosts for organic listens and investigating time-based decay mechanisms. Explicit likes and dislikes are also on my radar.

Tool callrun_shell_commandTest temporal decay half-life on SVD performance Returned

Tool call · Input +19:30

{
  "description": "Test temporal decay half-life on SVD performance",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\n# Load preference state\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\nprint(f'Listens: {len(listens)}, val_pref: {len(val_pref)}')\n\ncutoff_ts = 24786800.0\n\ndef build_R(half_life_days=60.0, organic_boost=1.5, like_weight=3.0, dislike_weight=-2.0, min_played=50.0):\n    # Filter listens\n    sub = listens[listens.played_ratio_pct >= min_played].copy()\n    \n    # Recency weight\n    if half_life_days is not None and half_life_days > 0:\n        age_days = (cutoff_ts - sub.timestamp) / 86400.0\n        time_w = 0.5 ** (age_days / half_life_days)\n    else:\n        time_w = 1.0\n        \n    # Played ratio weight: >= 80 gets 1.0, 50-80 gets 0.5\n    ratio_w = np.where(sub.played_ratio_pct >= 80, 1.0, 0.5)\n    \n    # Organic boost\n    org_w = np.where(sub.is_organic == 1, organic_boost, 1.0)\n    \n    w = time_w * ratio_w * org_w\n    sub['weight'] = w\n    \n    # Group by (uid, item_id) summing weights\n    agg = sub.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n    \n    # Combine with preferences\n    pref_w = np.where(val_pref.state == 'liked', like_weight, dislike_weight)\n    pref_df = pd.DataFrame({'uid': val_pref.uid, 'item_id': val_pref.item_id, 'weight': pref_w})\n    \n    comb = pd.concat([agg, pref_df], ignore_index=True)\n    comb_agg = comb.groupby(['uid', 'item_id'])['weight'].sum().reset_index()\n    \n    u_idx = comb_agg.uid.map(uid_to_idx).to_numpy()\n    i_idx = comb_agg.item_id.map(item_to_idx).to_numpy()\n    vals = comb_agg.weight.to_numpy(dtype=np.float32)\n    \n    R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n    return R\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test SVD with half_life = 60, 90, None\nfor hl in [30.0, 60.0, 90.0, None]:\n    t0 = time.time()\n    R = build_R(half_life_days=hl)\n    U, S, Vt = svds(R, k=192)\n    idx = np.argsort(-S)\n    U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n    pred = (U * (S**0.75)) @ Vt\n    score = eval_pred(pred)\n    print(f'Half-life {hl} -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\""
}

Tool response +20:54

<untrusted_context>
Output: Listens: 31062540, val_pref: 469124
Half-life 30.0 -> SVD-192 NDCG@10 = 0.017831 (19.27s)
Half-life 60.0 -> SVD-192 NDCG@10 = 0.016437 (18.61s)
Half-life 90.0 -> SVD-192 NDCG@10 = 0.014718 (18.60s)
Half-life None -> SVD-192 NDCG@10 = 0.011525 (18.16s)
Process Group PGID: 2461
</untrusted_context>

Test log1p and recency transforms vs binary on SVD

+22:13

Recorded reasoning summary 37 words

Analyzing Weighting Schemes

I'm currently investigating why summing weights yielded a lower NDCG@10 (0.0199) than anticipated, especially when compared to simpler binary or log-frequency approaches. My focus is on understanding the nuances of this observed performance drop.

Tool callrun_shell_commandTest log1p and recency transforms vs binary on SVD Returned

Tool call · Input +22:13

{
  "description": "Test log1p and recency transforms vs binary on SVD",
  "command": "PYTHONPATH=/task python -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\n# Load listens\ntbl = pq.read_table(DATA / 'train_listens.parquet', \n                    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])\nlistens = tbl.to_pandas()\nlistens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()\n\nval_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ncutoff_ts = 24786800.0\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Let us test log1p and capping\nfor tf in ['binary', 'log1p', 'log1p_recency']:\n    if tf == 'binary':\n        sub = listens[listens.played_ratio_pct >= 50]\n        u_idx = sub.uid.map(uid_to_idx).to_numpy()\n        i_idx = sub.item_id.map(item_to_idx).to_numpy()\n        R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n        R.data = np.ones_like(R.data)\n    elif tf == 'log1p':\n        sub = listens[listens.played_ratio_pct >= 50]\n        u_idx = sub.uid.map(uid_to_idx).to_numpy()\n        i_idx = sub.item_id.map(item_to_idx).to_numpy()\n        R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n        R.data = np.log1p(R.data)\n    elif tf == 'log1p_recency':\n        sub = listens[listens.played_ratio_pct >= 50].copy()\n        age_days = (cutoff_ts - sub.timestamp) / 86400.0\n        time_w = 0.5 ** (age_days / 60.0)\n        sub['w'] = time_w\n        agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()\n        u_idx = agg.uid.map(uid_to_idx).to_numpy()\n        i_idx = agg.item_id.map(item_to_idx).to_numpy()\n        vals = np.log1p(agg.w.to_numpy(dtype=np.float32))\n        R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))\n        \n    n_j = np.array(R.sum(axis=0)).flatten()\n    idf = np.log((N + 1.0) / (n_j + 1.0))\n    R_idf = R.multiply(np.sqrt(idf)).tocsr()\n    \n    U, S, Vt = svds(R_idf, k=192)\n    idx = np.argsort(-S)\n    U, S, Vt = U[:, idx], S[idx], Vt[idx, :]\n    pred = (U * S) @ Vt\n    score = eval_pred(pred)\n    print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')\n\""
}

Tool response +22:59

<untrusted_context>
Output: /usr/bin/bash: line 91:  2510 Killed                  PYTHONPATH=/task python -c "
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
from pathlib import Path
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
from scipy.sparse.linalg import svds

DATA = Path('/task/data')
targets_frame = pd.read_parquet(DATA / 'target_users.parquet')
targets_list = targets_frame.uid.astype(int).tolist()
targets_set = set(targets_list)
cat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')
cat_items = cat_frame.item_id.astype(int).tolist()
cat_set = set(cat_items)

uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}
item_to_idx = {item: j for j, item in enumerate(cat_items)}
N, M = len(targets_list), len(cat_items)

val_cand = np.load(DATA / 'validation_eligible_candidates.npz')
unpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)

eligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets_set)

# Load listens
tbl = pq.read_table(DATA / 'train_listens.parquet', 
                    columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'])
listens = tbl.to_pandas()
listens = listens[listens.uid.isin(targets_set) & listens.item_id.isin(cat_set)].copy()

val_pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')
val_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()

cutoff_ts = 24786800.0

def eval_pred(pred):
    pred_c = pred.copy()
    pred_c[~unpacked] = -1e9
    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]
    row_indices = np.arange(N)[:, None]
    top100_scores = pred_c[row_indices, top100_idx]
    sort_order = np.argsort(-top100_scores, axis=1)
    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)
    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}
    return score_rankings(ranked, graded)['quality_ndcg@10']

# Let us test log1p and capping
for tf in ['binary', 'log1p', 'log1p_recency']:
    if tf == 'binary':
        sub = listens[listens.played_ratio_pct >= 50]
        u_idx = sub.uid.map(uid_to_idx).to_numpy()
        i_idx = sub.item_id.map(item_to_idx).to_numpy()
        R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))
        R.data = np.ones_like(R.data)
    elif tf == 'log1p':
        sub = listens[listens.played_ratio_pct >= 50]
        u_idx = sub.uid.map(uid_to_idx).to_numpy()
        i_idx = sub.item_id.map(item_to_idx).to_numpy()
        R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))
        R.data = np.log1p(R.data)
    elif tf == 'log1p_recency':
        sub = listens[listens.played_ratio_pct >= 50].copy()
        age_days = (cutoff_ts - sub.timestamp) / 86400.0
        time_w = 0.5 ** (age_days / 60.0)
        sub['w'] = time_w
        agg = sub.groupby(['uid', 'item_id'])['w'].sum().reset_index()
        u_idx = agg.uid.map(uid_to_idx).to_numpy()
        i_idx = agg.item_id.map(item_to_idx).to_numpy()
        vals = np.log1p(agg.w.to_numpy(dtype=np.float32))
        R = sp.csr_matrix((vals, (u_idx, i_idx)), shape=(N, M))
        
    n_j = np.array(R.sum(axis=0)).flatten()
    idf = np.log((N + 1.0) / (n_j + 1.0))
    R_idf = R.multiply(np.sqrt(idf)).tocsr()
    
    U, S, Vt = svds(R_idf, k=192)
    idx = np.argsort(-S)
    U, S, Vt = U[:, idx], S[idx], Vt[idx, :]
    pred = (U * S) @ Vt
    score = eval_pred(pred)
    print(f'Transform {tf:15s} -> SVD NDCG@10 = {score:.6f}')
"
Exit Code: 137
Process Group PGID: 2508
</untrusted_context>

Check pyarrow filtered read memory efficiency

+23:06

Recorded reasoning summary 47 words

Analyzing Memory Usage

I've identified a significant memory bottleneck within the loop where (U * S) @ Vt is computed. The resulting float64 array of shape $(8456, 30000)$ consumes approximately 2 GB. This, coupled with the prior loading of tbl.to_pandas(), likely triggered the Out Of Memory error.

Tool callrun_shell_commandCheck pyarrow filtered read memory efficiency Returned

Tool call · Input +23:06

{
  "command": "python -c \"\nimport gc\nimport pyarrow.compute as pc\nimport pyarrow.parquet as pq\nimport numpy as np\n\n# PyArrow filter at read time!\ncat_arr = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])['item_id']\n# Filter: played_ratio_pct >= 50\ntable = pq.read_table('/task/data/train_listens.parquet', \n                      columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n                      filters=[('played_ratio_pct', '>=', 50)])\nprint('Filtered table rows:', len(table))\n\"",
  "description": "Check pyarrow filtered read memory efficiency"
}

Tool response +23:07

<untrusted_context>
Output: Filtered table rows: 27500755
Process Group PGID: 2537
</untrusted_context>

Check rows count with played_ratio_pct >= 80

+23:11

Recorded reasoning summary 39 words

Exploring Data Filtration

I'm currently investigating the impact of applying more stringent filtration criteria. Specifically, I'm evaluating how a played_ratio_pct threshold of 80% affects the dataset size, building upon the observation that a 50% threshold significantly reduced the data.

Tool callrun_shell_commandCheck rows count with played_ratio_pct >= 80 Returned

Tool call · Input +23:11

{
  "command": "python -c \"\nimport pyarrow.parquet as pq\n\ntable = pq.read_table('/task/data/train_listens.parquet', \n                      columns=['uid', 'item_id', 'is_organic', 'played_ratio_pct', 'timestamp'],\n                      filters=[('played_ratio_pct', '>=', 80)])\nprint('Filtered played_ratio_pct >= 80 rows:', len(table))\n\"",
  "description": "Check rows count with played_ratio_pct >= 80"
}

Tool response +23:18

<untrusted_context>
Output: Filtered played_ratio_pct >= 80 rows: 25755197
Process Group PGID: 2559
</untrusted_context>

Test history window length on SVD performance

+23:46

Recorded reasoning summary 43 words

Analyzing Time Windows

I'm currently focused on defining and analyzing specific time windows within the training data. My immediate task is to determine the number of data rows corresponding to the last 90, 60, and 30 days, leveraging the maximum timestamp identified earlier.

Tool callrun_shell_commandTest history window length on SVD performance Returned

Tool call · Input +23:46

{
  "command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\n# Test different history windows: 30 days, 60 days, 90 days, 180 days, all\nmax_ts = 24786795\nfor days in [30, 60, 90, 180, None]:\n    t0 = time.time()\n    min_ts = (max_ts - days * 86400) if days is not None else 0\n    filters = [('played_ratio_pct', '>=', 50)]\n    if days is not None:\n        filters.append(('timestamp', '>=', min_ts))\n        \n    table = pq.read_table(DATA / 'train_listens.parquet', \n                          columns=['uid', 'item_id'],\n                          filters=filters)\n    df = table.to_pandas()\n    df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n    u_idx = df.uid.map(uid_to_idx).to_numpy()\n    i_idx = df.item_id.map(item_to_idx).to_numpy()\n    del table, df\n    gc.collect()\n    \n    R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n    R.data = np.ones_like(R.data) # binary\n    \n    # SVD\n    n_j = np.array(R.sum(axis=0)).flatten()\n    idf = np.log((N + 1.0) / (n_j + 1.0))\n    R_idf = R.multiply(np.sqrt(idf)).tocsr()\n    \n    U, S, Vt = svds(R_idf, k=192)\n    idx = np.argsort(-S)\n    U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n    pred = (U * S) @ Vt\n    score = eval_pred(pred)\n    del R, R_idf, U, S, Vt, pred\n    gc.collect()\n    print(f'Window {str(days):>4s} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
  "description": "Test history window length on SVD performance"
}

Tool response +25:03

<untrusted_context>
Output: Window   30 days -> SVD-192 NDCG@10 = 0.032118 (8.79s)
Window   60 days -> SVD-192 NDCG@10 = 0.029739 (11.43s)
Window   90 days -> SVD-192 NDCG@10 = 0.026048 (13.55s)
Window  180 days -> SVD-192 NDCG@10 = 0.020980 (18.60s)
Window None days -> SVD-192 NDCG@10 = 0.019935 (22.80s)
Process Group PGID: 2587
</untrusted_context>

Fine-grained test of recent history window on SVD

+25:13

Recorded reasoning summary 41 words

Observing Score Progression

I'm noticing a distinct upward trend in the score as I look at shorter, more recent time windows. The gains are quite substantial, especially when comparing the last 30 days to the overall period and even a baseline.

Tool callrun_shell_commandFine-grained test of recent history window on SVD Returned

Tool call · Input +25:13

{
  "command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nfrom scipy.sparse.linalg import svds\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\nfor days in [7, 10, 14, 21, 28, 35, 45]:\n    t0 = time.time()\n    min_ts = max_ts - days * 86400\n    filters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\n    table = pq.read_table(DATA / 'train_listens.parquet', \n                          columns=['uid', 'item_id'],\n                          filters=filters)\n    df = table.to_pandas()\n    df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\n    u_idx = df.uid.map(uid_to_idx).to_numpy()\n    i_idx = df.item_id.map(item_to_idx).to_numpy()\n    del table, df\n    gc.collect()\n    \n    R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n    R.data = np.ones_like(R.data) # binary\n    \n    n_j = np.array(R.sum(axis=0)).flatten()\n    idf = np.log((N + 1.0) / (n_j + 1.0))\n    R_idf = R.multiply(np.sqrt(idf)).tocsr()\n    \n    U, S, Vt = svds(R_idf, k=192)\n    idx = np.argsort(-S)\n    U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n    pred = (U * S) @ Vt\n    score = eval_pred(pred)\n    del R, R_idf, U, S, Vt, pred\n    gc.collect()\n    print(f'Window {days:2d} days -> SVD-192 NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
  "description": "Fine-grained test of recent history window on SVD"
}

Tool response +26:11

<untrusted_context>
Output: Window  7 days -> SVD-192 NDCG@10 = 0.030192 (6.43s)
Window 10 days -> SVD-192 NDCG@10 = 0.032551 (6.78s)
Window 14 days -> SVD-192 NDCG@10 = 0.032901 (7.28s)
Window 21 days -> SVD-192 NDCG@10 = 0.032399 (8.30s)
Window 28 days -> SVD-192 NDCG@10 = 0.032255 (8.41s)
Window 35 days -> SVD-192 NDCG@10 = 0.032803 (9.04s)
Window 45 days -> SVD-192 NDCG@10 = 0.031721 (9.95s)
Process Group PGID: 2626
</untrusted_context>

Test User-EASE on 14-day history window

+26:20

Recorded reasoning summary 33 words

Analyzing User-EASE Impact

I'm now investigating how User-EASE performs across 14, 21, and 30-day windows. This follows a promising 0.032901 score at 14 days, building on previous work with binary R yielding 0.02033.

Tool callrun_shell_commandTest User-EASE on 14-day history window Returned

Tool call · Input +26:20

{
  "command": "PYTHONPATH=/task python -c \"\nimport gc, time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\nimport scipy.linalg.lapack as lapack\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets_set)\n\ndef eval_pred(pred):\n    pred_c = pred.copy()\n    pred_c[~unpacked] = -1e9\n    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred_c[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    return score_rankings(ranked, graded)['quality_ndcg@10']\n\nmax_ts = 24786795\ndays = 14\nmin_ts = max_ts - days * 86400\nfilters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]\ntable = pq.read_table(DATA / 'train_listens.parquet', \n                      columns=['uid', 'item_id'],\n                      filters=filters)\ndf = table.to_pandas()\ndf = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]\nu_idx = df.uid.map(uid_to_idx).to_numpy()\ni_idx = df.item_id.map(item_to_idx).to_numpy()\ndel table, df\ngc.collect()\n\nR = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR.data = np.ones_like(R.data) # binary\n\nt0 = time.time()\nG = (R @ R.T).toarray().astype(np.float64)\nprint(f'G computed in {time.time()-t0:.2f}s')\n\nfor reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:\n    t0 = time.time()\n    G_reg = G.copy()\n    diag_indices = np.diag_indices_from(G_reg)\n    G_reg[diag_indices] += reg\n    c, info = lapack.dpotrf(G_reg, lower=False)\n    inv, info = lapack.dpotri(c, lower=False)\n    inv = np.triu(inv) + np.triu(inv, 1).T\n    \n    P = -inv / np.diag(inv)[:, None]\n    P[diag_indices] = 0.0\n    \n    pred = (P.astype(np.float32) @ R).astype(np.float32)\n    score = eval_pred(pred)\n    print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')\n\"",
  "description": "Test User-EASE on 14-day history window"
}

Tool response +26:35

<untrusted_context>
Output: /usr/bin/bash: line 81:  2665 Killed                  PYTHONPATH=/task python -c "
import gc, time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
from pathlib import Path
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
import scipy.linalg.lapack as lapack

DATA = Path('/task/data')
targets_frame = pd.read_parquet(DATA / 'target_users.parquet')
targets_list = targets_frame.uid.astype(int).tolist()
targets_set = set(targets_list)
cat_frame = pd.read_parquet(DATA / 'candidate_catalog.parquet')
cat_items = cat_frame.item_id.astype(int).tolist()
cat_set = set(cat_items)

uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}
item_to_idx = {item: j for j, item in enumerate(cat_items)}
N, M = len(targets_list), len(cat_items)

val_cand = np.load(DATA / 'validation_eligible_candidates.npz')
unpacked = np.unpackbits(val_cand['packed_eligible'], axis=1, count=30000, bitorder='little').astype(bool)

eligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets_set)

def eval_pred(pred):
    pred_c = pred.copy()
    pred_c[~unpacked] = -1e9
    top100_idx = np.argpartition(-pred_c, 100, axis=1)[:, :100]
    row_indices = np.arange(N)[:, None]
    top100_scores = pred_c[row_indices, top100_idx]
    sort_order = np.argsort(-top100_scores, axis=1)
    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)
    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}
    return score_rankings(ranked, graded)['quality_ndcg@10']

max_ts = 24786795
days = 14
min_ts = max_ts - days * 86400
filters = [('played_ratio_pct', '>=', 50), ('timestamp', '>=', min_ts)]
table = pq.read_table(DATA / 'train_listens.parquet', 
                      columns=['uid', 'item_id'],
                      filters=filters)
df = table.to_pandas()
df = df[df.uid.isin(targets_set) & df.item_id.isin(cat_set)]
u_idx = df.uid.map(uid_to_idx).to_numpy()
i_idx = df.item_id.map(item_to_idx).to_numpy()
del table, df
gc.collect()

R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))
R.data = np.ones_like(R.data) # binary

t0 = time.time()
G = (R @ R.T).toarray().astype(np.float64)
print(f'G computed in {time.time()-t0:.2f}s')

for reg in [100.0, 300.0, 500.0, 1000.0, 2000.0, 5000.0]:
    t0 = time.time()
    G_reg = G.copy()
    diag_indices = np.diag_indices_from(G_reg)
    G_reg[diag_indices] += reg
    c, info = lapack.dpotrf(G_reg, lower=False)
    inv, info = lapack.dpotri(c, lower=False)
    inv = np.triu(inv) + np.triu(inv, 1).T
    
    P = -inv / np.diag(inv)[:, None]
    P[diag_indices] = 0.0
    
    pred = (P.astype(np.float32) @ R).astype(np.float32)
    score = eval_pred(pred)
    print(f'User-EASE 14d reg={reg:6.1f} -> NDCG@10 = {score:.6f} ({time.time()-t0:.2f}s)')
"
Exit Code: 137
Process Group PGID: 2663
</untrusted_context>

update_topic command

+26:48

Recorded reasoning summary 38 words

Analyzing Matrix Multiplication

I'm currently dissecting the memory consumption and computational cost of matrix multiplication, specifically focusing on the P @ R operation. My initial analysis indicates significant memory overhead associated with creating the intermediate dense matrix P.

Recorded reasoning summary 46 words

Optimizing SVD Usage

I've identified that the dense @ sparse multiplication in SciPy creates significant temporary memory structures, whereas SVD is far more efficient. My current focus is on refining the SVD approach and exploring further modeling innovations, specifically considering how to incorporate explicit preference states.

Recorded reasoning summary 58 words

Incorporating Engagement Metrics

I'm now exploring how to integrate explicit user feedback, like likes and dislikes, into the model. I'm also investigating the impact of weighting organic listens higher and considering different approaches to completion rates, including negative weights for skips. The current focus is on refining time decay models, specifically exploring exponential decay with a 14-day half-life.

Recorded reasoning summary 44 words

Refining Interaction Weighting

I'm now experimenting with combining recency, organic listens, and completion rates into a single weight. I'm also exploring sublinear transformations like log or square root to prevent any single interaction from dominating, and investigating IDF powers and user normalization for SVD.

Tool callupdate_topicTool call Returned

Tool call · Input +26:48

{
  "title": "Systematic Optimization of the Latent Factor Ranker",
  "summary": "We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.",
  "strategic_intent": "Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending."
}

Tool response +26:48

Current topic: "Systematic Optimization of the Latent Factor Ranker"
Topic summary: We discovered that recency gating dramatically improved validation NDCG@10 from 0.0107 to over 0.0329. We are now running systematic ablation experiments to optimize: 1) explicit preferences (likes/dislikes), 2) completion and organic weighting with saturation, 3) row and column normalization, 4) SVD dimensionality and singular value powers, and 5) catalog popularity blending.

Strategic Intent: Run systematic experiments optimizing interaction weighting, explicit preferences, IDF scaling, rank K, and popularity blending.

write_file experiment_svd.py

+27:00

Recorded reasoning summary 42 words

Designing Modular Experiment Script

I'm focusing on architecting a robust, modular Python script for experiments. Key features include secure data loading, efficient ablation studies, precise NDCG@10 metric recording, and transparent result reporting. I'm also preparing to implement and execute the /app/solution/experiment_svd.py script.

Tool callwrite_file/app/solution/experiment_svd.py Returned

Tool call · Input +27:00

{
  "file_path": "/app/solution/experiment_svd.py",
  "content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\nfrom sklearn.preprocessing import normalize\n\n# Make sure /task is in sys.path\nif \"/task\" not in sys.path:\n    sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\nprint(\"Loading metadata...\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\nprint(f\"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}\")\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n    \"\"\"Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix.\"\"\"\n    pred = pred_matrix.copy()\n    pred[~unpacked] = -1e9\n    \n    top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    \n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    res = score_rankings(ranked, graded)\n    return res[\"quality_ndcg@10\"]\n\n# Load listens in the last 45 days\ncutoff_ts = 24786795\nmin_ts_45d = cutoff_ts - 45 * 86400\n\nprint(\"Reading recent listens (last 45 days)...\")\ntbl = pq.read_table(\n    DATA / \"train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n    filters=[(\"timestamp\", \">=\", min_ts_45d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\nprint(f\"Loaded {len(listens_df)} listens.\")\n\n# Load validation preference state\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\nprint(f\"Loaded {len(val_pref)} preferences.\")\n\n# Experiment 1: Window size and IDF scaling on binary listens\nprint(\"\\n--- Experiment 1: Window size vs IDF scaling (Binary) ---\")\nfor days in [10, 14, 18, 24, 30]:\n    cur_min_ts = cutoff_ts - days * 86400\n    sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\n    u_idx = sub.uid.map(uid_to_idx).to_numpy()\n    i_idx = sub.item_id.map(item_to_idx).to_numpy()\n    \n    R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\n    R.data = np.ones_like(R.data) # binary\n    \n    n_j = np.array(R.sum(axis=0)).flatten()\n    idf = np.log((N + 1.0) / (n_j + 1.0))\n    \n    for idf_pow in [0.3, 0.5, 0.7]:\n        R_idf = R.multiply(idf**idf_pow).tocsr()\n        U, S, Vt = svds(R_idf, k=192)\n        idx = np.argsort(-S)\n        U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\n        pred = (U * S) @ Vt\n        score = evaluate_predictions(pred)\n        print(f\"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}\")\n        del R_idf, U, S, Vt, pred\n        gc.collect()\n",
  "instruction": "Create experiment_svd.py to test window sizes and IDF scaling"
}

Tool response +27:00

Successfully created and wrote to new file: /app/solution/experiment_svd.py. Here is the updated code:
import gc
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import scipy.sparse as sp
from scipy.sparse.linalg import svds
from sklearn.preprocessing import normalize

# Make sure /task is in sys.path
if "/task" not in sys.path:
    sys.path.insert(0, "/task")

from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

DATA = Path("/task/data")

print("Loading metadata...")
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets_list = targets_frame.uid.astype(int).tolist()
targets_set = set(targets_list)
cat_frame = pd.read_parquet(DATA / "candidate_catalog.parquet")
cat_items = cat_frame.item_id.astype(int).tolist()
cat_set = set(cat_items)

uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}
item_to_idx = {item: j for j, item in enumerate(cat_items)}
N, M = len(targets_list), len(cat_items)

val_cand = np.load(DATA / "validation_eligible_candidates.npz")
unpacked = np.unpackbits(val_cand["packed_eligible"], axis=1, count=30000, bitorder="little").astype(bool)

eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets_set)

print(f"Loaded: N={N} users, M={M} items. Ground truth users: {len(graded)}")

def evaluate_predictions(pred_matrix: np.ndarray) -> float:
    """Helper to evaluate NDCG@10 from a dense (N, M) prediction matrix."""
    pred = pred_matrix.copy()
    pred[~unpacked] = -1e9
    
    top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]
    row_indices = np.arange(N)[:, None]
    top100_scores = pred[row_indices, top100_idx]
    sort_order = np.argsort(-top100_scores, axis=1)
    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)
    
    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}
    res = score_rankings(ranked, graded)
    return res["quality_ndcg@10"]

# Load listens in the last 45 days
cutoff_ts = 24786795
min_ts_45d = cutoff_ts - 45 * 86400

print("Reading recent listens (last 45 days)...")
tbl = pq.read_table(
    DATA / "train_listens.parquet",
    columns=["uid", "item_id", "is_organic", "played_ratio_pct", "timestamp"],
    filters=[("timestamp", ">=", min_ts_45d)]
)
listens_df = tbl.to_pandas()
listens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()
del tbl
gc.collect()
print(f"Loaded {len(listens_df)} listens.")

# Load validation preference state
val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
val_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()
print(f"Loaded {len(val_pref)} preferences.")

# Experiment 1: Window size and IDF scaling on binary listens
print("\n--- Experiment 1: Window size vs IDF scaling (Binary) ---")
for days in [10, 14, 18, 24, 30]:
    cur_min_ts = cutoff_ts - days * 86400
    sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]
    u_idx = sub.uid.map(uid_to_idx).to_numpy()
    i_idx = sub.item_id.map(item_to_idx).to_numpy()
    
    R = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))
    R.data = np.ones_like(R.data) # binary
    
    n_j = np.array(R.sum(axis=0)).flatten()
    idf = np.log((N + 1.0) / (n_j + 1.0))
    
    for idf_pow in [0.3, 0.5, 0.7]:
        R_idf = R.multiply(idf**idf_pow).tocsr()
        U, S, Vt = svds(R_idf, k=192)
        idx = np.argsort(-S)
        U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)
        pred = (U * S) @ Vt
        score = evaluate_predictions(pred)
        print(f"Days={days:2d}, idf_pow={idf_pow:.1f} -> NDCG@10 = {score:.6f}")
        del R_idf, U, S, Vt, pred
        gc.collect()

Run experiment 1 to compare days and idf_pow

+27:02

Tool callrun_shell_commandRun experiment 1 to compare days and idf_pow Returned

Tool call · Input +27:02

{
  "command": "PYTHONPATH=/task python /app/solution/experiment_svd.py",
  "description": "Run experiment 1 to compare days and idf_pow"
}

Tool response +28:53

<untrusted_context>
Output: Loading metadata...
Loaded: N=8456 users, M=30000 items. Ground truth users: 4263
Reading recent listens (last 45 days)...
Loaded 6409037 listens.
Loaded 469124 preferences.

--- Experiment 1: Window size vs IDF scaling (Binary) ---
Days=10, idf_pow=0.3 -> NDCG@10 = 0.032113
Days=10, idf_pow=0.5 -> NDCG@10 = 0.032619
Days=10, idf_pow=0.7 -> NDCG@10 = 0.032033
Days=14, idf_pow=0.3 -> NDCG@10 = 0.032767
Days=14, idf_pow=0.5 -> NDCG@10 = 0.032901
Days=14, idf_pow=0.7 -> NDCG@10 = 0.032594
Days=18, idf_pow=0.3 -> NDCG@10 = 0.031356
Days=18, idf_pow=0.5 -> NDCG@10 = 0.032168
Days=18, idf_pow=0.7 -> NDCG@10 = 0.031984
Days=24, idf_pow=0.3 -> NDCG@10 = 0.032113
Days=24, idf_pow=0.5 -> NDCG@10 = 0.032704
Days=24, idf_pow=0.7 -> NDCG@10 = 0.032900
Days=30, idf_pow=0.3 -> NDCG@10 = 0.031707
Days=30, idf_pow=0.5 -> NDCG@10 = 0.032118
Days=30, idf_pow=0.7 -> NDCG@10 = 0.032051
Process Group PGID: 2694
</untrusted_context>

write_file experiment_blend.py

+29:18

Recorded reasoning summary 46 words

Exploring Preference Integration

I'm now investigating how incorporating explicit user preferences, specifically likes and dislikes captured in val_pref, might influence NDCG@10, building on the recent NDCG@10 score of 0.032901 derived from idf_pow=0.5 over 14 days. My current focus is on the impact of these accumulated preferences.

Tool callwrite_file/app/solution/experiment_blend.py Returned

Tool call · Input +29:18

{
  "content": "import gc\nimport sys\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\nfrom scipy.sparse.linalg import svds\n\nif \"/task\" not in sys.path:\n    sys.path.insert(0, \"/task\")\n\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\n\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets_list = targets_frame.uid.astype(int).tolist()\ntargets_set = set(targets_list)\ncat_frame = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_items = cat_frame.item_id.astype(int).tolist()\ncat_set = set(cat_items)\n\nuid_to_idx = {uid: i for i, uid in enumerate(targets_list)}\nitem_to_idx = {item: j for j, item in enumerate(cat_items)}\nN, M = len(targets_list), len(cat_items)\n\nval_cand = np.load(DATA / \"validation_eligible_candidates.npz\")\nunpacked = np.unpackbits(val_cand[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\").astype(bool)\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = validation_truth(eligibility, targets_set)\n\ncutoff_ts = 24786795\nmin_ts_21d = cutoff_ts - 21 * 86400\n\ntbl = pq.read_table(\n    DATA / \"train_listens.parquet\",\n    columns=[\"uid\", \"item_id\", \"is_organic\", \"played_ratio_pct\", \"timestamp\"],\n    filters=[(\"timestamp\", \">=\", min_ts_21d)]\n)\nlistens_df = tbl.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()\ndel tbl\ngc.collect()\n\nval_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()\n\ndef evaluate_predictions(pred_matrix: np.ndarray) -> float:\n    pred = pred_matrix.copy()\n    pred[~unpacked] = -1e9\n    \n    top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]\n    row_indices = np.arange(N)[:, None]\n    top100_scores = pred[row_indices, top100_idx]\n    sort_order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)\n    \n    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}\n    res = score_rankings(ranked, graded)\n    return res[\"quality_ndcg@10\"]\n\n# Baseline matrix (14 days, binary)\ncur_min_ts = cutoff_ts - 14 * 86400\nsub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]\nu_idx = sub.uid.map(uid_to_idx).to_numpy()\ni_idx = sub.item_id.map(item_to_idx).to_numpy()\n\nR_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))\nR_base.data = np.ones_like(R_base.data)\n\nn_j = np.array(R_base.sum(axis=0)).flatten()\nidf = np.log((N + 1.0) / (n_j + 1.0))\nR_idf = R_base.multiply(np.sqrt(idf)).tocsr()\n\nU, S, Vt = svds(R_idf, k=192)\nidx = np.argsort(-S)\nU, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)\npred_base = (U * S) @ Vt\nprint(f\"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}\")\n\n# Test Popularity Blend\nprint(\"\\n--- Popularity Blending ---\")\n# Organic positive listens from catalog\norg_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))\n# Normalize org_pop to same standard deviation or range\norg_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()\npred_std = pred_base.std()\n\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:\n    blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]\n    print(f\"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n\n# Also test positive_listens popularity\npos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))\npos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()\nfor beta in [0.01, 0.03, 0.05, 0.1, 0.2]:\n    blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]\n    print(f\"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}\")\n",
  "instruction": "Create experiment_blend.py to test popularity blending",
  "file_path": "/app/solution/experiment_blend.py"
}

Tool response +29:18

Successfully created and wrote to new file: /app/solution/experiment_blend.py. Here is the updated code:
import gc
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import scipy.sparse as sp
from scipy.sparse.linalg import svds

if "/task" not in sys.path:
    sys.path.insert(0, "/task")

from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth

DATA = Path("/task/data")

targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets_list = targets_frame.uid.astype(int).tolist()
targets_set = set(targets_list)
cat_frame = pd.read_parquet(DATA / "candidate_catalog.parquet")
cat_items = cat_frame.item_id.astype(int).tolist()
cat_set = set(cat_items)

uid_to_idx = {uid: i for i, uid in enumerate(targets_list)}
item_to_idx = {item: j for j, item in enumerate(cat_items)}
N, M = len(targets_list), len(cat_items)

val_cand = np.load(DATA / "validation_eligible_candidates.npz")
unpacked = np.unpackbits(val_cand["packed_eligible"], axis=1, count=30000, bitorder="little").astype(bool)

eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets_set)

cutoff_ts = 24786795
min_ts_21d = cutoff_ts - 21 * 86400

tbl = pq.read_table(
    DATA / "train_listens.parquet",
    columns=["uid", "item_id", "is_organic", "played_ratio_pct", "timestamp"],
    filters=[("timestamp", ">=", min_ts_21d)]
)
listens_df = tbl.to_pandas()
listens_df = listens_df[listens_df.uid.isin(targets_set) & listens_df.item_id.isin(cat_set)].copy()
del tbl
gc.collect()

val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
val_pref = val_pref[val_pref.uid.isin(targets_set) & val_pref.item_id.isin(cat_set)].copy()

def evaluate_predictions(pred_matrix: np.ndarray) -> float:
    pred = pred_matrix.copy()
    pred[~unpacked] = -1e9
    
    top100_idx = np.argpartition(-pred, 100, axis=1)[:, :100]
    row_indices = np.arange(N)[:, None]
    top100_scores = pred[row_indices, top100_idx]
    sort_order = np.argsort(-top100_scores, axis=1)
    sorted_top100_idx = np.take_along_axis(top100_idx, sort_order, axis=1)
    
    ranked = {uid: [cat_items[c] for c in sorted_top100_idx[r]] for r, uid in enumerate(targets_list)}
    res = score_rankings(ranked, graded)
    return res["quality_ndcg@10"]

# Baseline matrix (14 days, binary)
cur_min_ts = cutoff_ts - 14 * 86400
sub = listens_df[(listens_df.timestamp >= cur_min_ts) & (listens_df.played_ratio_pct >= 50)]
u_idx = sub.uid.map(uid_to_idx).to_numpy()
i_idx = sub.item_id.map(item_to_idx).to_numpy()

R_base = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(N, M))
R_base.data = np.ones_like(R_base.data)

n_j = np.array(R_base.sum(axis=0)).flatten()
idf = np.log((N + 1.0) / (n_j + 1.0))
R_idf = R_base.multiply(np.sqrt(idf)).tocsr()

U, S, Vt = svds(R_idf, k=192)
idx = np.argsort(-S)
U, S, Vt = U[:, idx].astype(np.float32), S[idx].astype(np.float32), Vt[idx, :].astype(np.float32)
pred_base = (U * S) @ Vt
print(f"Base SVD-192 (14d binary, sqrt idf): NDCG@10 = {evaluate_predictions(pred_base):.6f}")

# Test Popularity Blend
print("\n--- Popularity Blending ---")
# Organic positive listens from catalog
org_pop = np.log1p(cat_frame.organic_positive_listens.to_numpy(dtype=np.float32))
# Normalize org_pop to same standard deviation or range
org_pop_norm = (org_pop - org_pop.mean()) / org_pop.std()
pred_std = pred_base.std()

for beta in [0.01, 0.03, 0.05, 0.1, 0.2, 0.5]:
    blended = pred_base + (beta * pred_std) * org_pop_norm[None, :]
    print(f"Beta {beta:.2f} (organic pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}")

# Also test positive_listens popularity
pos_pop = np.log1p(cat_frame.positive_listens.to_numpy(dtype=np.float32))
pos_pop_norm = (pos_pop - pos_pop.mean()) / pos_pop.std()
for beta in [0.01, 0.03, 0.05, 0.1, 0.2]:
    blended = pred_base + (beta * pred_std) * pos_pop_norm[None, :]
    print(f"Beta {beta:.2f} (total pop) -> NDCG@10 = {evaluate_predictions(blended):.6f}")

Run experiment_blend.py

+29:20

Tool callrun_shell_commandRun experiment_blend.py Returned

Tool call · Input +29:20

{
  "command": "PYTHONPATH=/task python /app/solution/experiment_blend.py",
  "description": "Run experiment_blend.py"
}

Tool response +29:48

<untrusted_context>
Output: Base SVD-192 (14d binary, sqrt idf): NDCG@10 = 0.032901

--- Popularity Blending ---
Beta 0.01 (organic pop) -> NDCG@10 = 0.033837
Beta 0.03 (organic pop) -> NDCG@10 = 0.033782
Beta 0.05 (organic pop) -> NDCG@10 = 0.033740
Beta 0.10 (organic pop) -> NDCG@10 = 0.033658
Beta 0.20 (organic pop) -> NDCG@10 = 0.034038
Beta 0.50 (organic pop) -> NDCG@10 = 0.034051
Beta 0.01 (total pop) -> NDCG@10 = 0.033729
Beta 0.03 (total pop) -> NDCG@10 = 0.033665
Beta 0.05 (total pop) -> NDCG@10 = 0.033651
Beta 0.10 (total pop) -> NDCG@10 = 0.033480
Beta 0.20 (total pop) -> NDCG@10 = 0.033505
Process Group PGID: 2735
</untrusted_context>

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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-r04-d237f45e
Recorded model: gemini-3.8-flash
Harness version: 0.61.0
Session: 8e1766b7-11bd-455d-94c6-e1a1404b2157
Source SHA-256: 6c52341dc742c3096af97a701ba2b00deb5446dd85f39dc4e873311cb707c3c8

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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