yambda-discovery-ranking-v01 / gemini-3-8-flash-high-basic-fixed-r05-3d6fca60
Recorded model: gemini-3.8-flash

## Step 1 · user · 2026-09-29T04:32:36.966Z

<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>

## Step 2 · user · 2026-09-29T04:32:37.013Z

# 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:

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

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

Additional files under `/task/data/`:

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

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

## Discovery objective

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

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

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

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

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

## Public tools

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

```sh
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.


## Step 3 · agent · 2026-09-29T04:32:40.973Z

### Recorded reasoning summary
Understanding the Problem Space

My current focus is on thoroughly grasping the intricacies of this music discovery ranking challenge. I'm dissecting the core requirements for the "Made for You" shelf, examining all associated constraints, and familiarizing myself with the project's directory structure, relevant files, and the given timeline. My priority is to establish a solid foundation before diving into model development.

### update_topic · 2026-09-29T04:32:40.973Z
{
  "strategic_intent": "Explore the environment, inspect the data files, evaluation scripts, and hardware resources.",
  "summary": "Starting initial exploration of the task environment, data schemas, public evaluation script, candidate sets, and hardware availability.",
  "title": "Environment and Data Exploration"
}
### Tool output · 2026-09-29T04:32:40.983Z
Current topic: "Environment and Data Exploration"
Topic summary: Starting initial exploration of the task environment, data schemas, public evaluation script, candidate sets, and hardware availability.

Strategic Intent: Explore the environment, inspect the data files, evaluation scripts, and hardware resources.

## Step 4 · agent · 2026-09-29T04:32:44.104Z

### Recorded reasoning summary
Investigating Environment and Scripts

I'm currently examining the `/task/` directory, specifically looking at `evaluate_public.py` and `ranking_evaluation.py`. Concurrently, I'm checking the available hardware resources like CPU, GPU, and RAM to understand the execution environment.

### run_shell_command · 2026-09-29T04:32:44.104Z
{
  "command": "lscpu || true; free -h; nvidia-smi || true; ls -la /task; ls -lh /task/data",
  "description": "Check hardware resources and inspect /task and /task/data directory"
}
### Tool output · 2026-09-29T04:32:44.147Z
<untrusted_context>
Output: Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                5300.00
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
/usr/bin/bash: line 4: nvidia-smi: command not found
total 36
dr-xr-xr-x 1 root root 4096 Sep 29 04:30 .
drwxr-xr-x 1 root root 4096 Sep 29 04:32 ..
dr-xr-xr-x 1 root root 4096 Sep 29 04:27 data
-r--r--r-- 1 root root 5774 Sep 29 04:26 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 04:26 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 29 04:26 requirements.txt
total 340M
-r--r--r-- 1 root root 342K Sep 29 04:26 candidate_catalog.parquet
-r--r--r-- 1 root root  83K Sep 29 04:26 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 04:26 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 04:27 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 04:26 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 04:26 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 04:26 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 04:26 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 04:26 train_unlikes.parquet
-r--r--r-- 1 root root  36K Sep 29 04:26 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 04:26 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 04:26 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 04:26 val_undislikes.parquet
-r--r--r-- 1 root root  71K Sep 29 04:26 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 04:26 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 04:27 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>

### read_file · 2026-09-29T04:32:44.104Z
{
  "file_path": "/task/evaluate_public.py"
}
### Tool output · 2026-09-29T04:32:44.147Z
#!/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()


### read_file · 2026-09-29T04:32:44.104Z
{
  "file_path": "/task/ranking_evaluation.py"
}
### Tool output · 2026-09-29T04:32:44.147Z
"""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),
    }


### read_file · 2026-09-29T04:32:44.104Z
{
  "file_path": "/task/requirements.txt"
}
### Tool output · 2026-09-29T04:32:44.147Z
# 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


## Step 5 · agent · 2026-09-29T04:32:47.681Z

### Recorded reasoning summary
Checking Installed Packages

I'm currently reviewing the installed Python packages in my environment, noting the versions of numpy, pandas, pyarrow, scikit-learn, and scipy. I've also observed that lightgbm and xgboost are not present. My next step is to confirm the active Python environment and assess available RAM.

### run_shell_command · 2026-09-29T04:32:47.681Z
{
  "description": "Check python version and system memory",
  "command": "python3 -V && cat /proc/meminfo | head -n 5"
}
### Tool output · 2026-09-29T04:32:47.698Z
<untrusted_context>
Output: Python 3.12.13
MemTotal:       15988968 kB
MemFree:         5429232 kB
MemAvailable:   14624200 kB
Buffers:          196328 kB
Cached:          8874312 kB
Process Group PGID: 1619
</untrusted_context>

## Step 6 · agent · 2026-09-29T04:32:49.796Z

### run_shell_command · 2026-09-29T04:32:49.796Z
{
  "description": "Check contents of /app/solution",
  "command": "ls -la /app/solution"
}
### Tool output · 2026-09-29T04:32:49.810Z
<untrusted_context>
Output: total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 04:30 .
drwxr-xr-x 3 agent agent 4096 Sep 29 04:30 ..
Process Group PGID: 1624
</untrusted_context>

## Step 7 · agent · 2026-09-29T04:32:55.164Z

### Recorded reasoning summary
Exploring Data Characteristics

I'm initiating an exploration of the dataset, focusing on its dimensions and content. My immediate plan is to develop a script to inspect shapes, column types, and timestamp details, as the `/app/solution` directory is currently empty.

### run_shell_command · 2026-09-29T04:32:55.164Z
{
  "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(f\"target_users count: {len(target_users)}, columns: {target_users.columns.tolist()}\")\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(f\"candidate_catalog count: {len(cat)}, columns: {cat.columns.tolist()}\")\n\nval_elig = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(f\"val_elig uids: {len(val_elig[\\\"uids\\\"])}, item_ids: {len(val_elig[\\\"item_ids\\\"])}, packed: {val_elig[\\\"packed_eligible\\\"].shape}\")\n\ntest_elig = np.load(\"/task/data/test_eligible_candidates.npz\")\nprint(f\"test_elig uids: {len(test_elig[\\\"uids\\\"])}, item_ids: {len(test_elig[\\\"item_ids\\\"])}, packed: {test_elig[\\\"packed_eligible\\\"].shape}\")\n\nval_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(f\"val_pref count: {len(val_pref)}, cols: {val_pref.columns.tolist()}, head:\\n{val_pref.head(2)}\")\n\ntest_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(f\"test_pref count: {len(test_pref)}, cols: {test_pref.columns.tolist()}\")\n\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(f\"val_listens count: {len(val_listens)}, min_ts: {val_listens.timestamp.min()}, max_ts: {val_listens.timestamp.max()}\")\n\nval_likes = pd.read_parquet(\"/task/data/val_likes.parquet\")\nprint(f\"val_likes count: {len(val_likes)}, min_ts: {val_likes.timestamp.min()}, max_ts: {val_likes.timestamp.max()}\")\n'",
  "description": "Inspect datasets metadata"
}
### Tool output · 2026-09-29T04:32:55.193Z
<untrusted_context>
Output: File "<string>", line 12
    print(f"val_elig uids: {len(val_elig[\"uids\"])}, item_ids: {len(val_elig[\"item_ids\"])}, packed: {val_elig[\"packed_eligible\"].shape}")
                                          ^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 1627
</untrusted_context>

## Step 8 · agent · 2026-09-29T04:33:09.015Z

### run_shell_command · 2026-09-29T04:33:09.015Z
{
  "description": "Inspect datasets metadata safely",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('target_users count:', len(target_users), 'cols:', target_users.columns.tolist())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('candidate_catalog count:', len(cat), 'cols:', cat.columns.tolist())\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_elig uids:', len(val_elig['uids']), 'item_ids:', len(val_elig['item_ids']), 'packed:', val_elig['packed_eligible'].shape)\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_elig uids:', len(test_elig['uids']), 'item_ids:', len(test_elig['item_ids']), 'packed:', test_elig['packed_eligible'].shape)\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val_pref count:', len(val_pref), 'cols:', val_pref.columns.tolist(), 'head:', val_pref.head(2).to_dict())\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_pref count:', len(test_pref), 'cols:', test_pref.columns.tolist())\n\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens count:', len(val_listens), 'min_ts:', val_listens.timestamp.min(), 'max_ts:', val_listens.timestamp.max())\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('val_likes count:', len(val_likes), 'min_ts:', val_likes.timestamp.min(), 'max_ts:', val_likes.timestamp.max())\n\""
}
### Tool output · 2026-09-29T04:33:10.067Z
<untrusted_context>
Output: target_users count: 8456 cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
candidate_catalog count: 30000 cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
val_elig uids: 8456 item_ids: 30000 packed: (8456, 3750)
test_elig uids: 8456 item_ids: 30000 packed: (8456, 3750)
val_pref count: 469124 cols: ['uid', 'item_id', 'state'] head: {'uid': {0: 100, 1: 100}, 'item_id': {0: 410868, 1: 732449}, 'state': {0: 'liked', 1: 'liked'}}
test_pref count: 484776 cols: ['uid', 'item_id', 'state']
val_listens count: 1501697 min_ts: 24788600 max_ts: 25393395
val_likes count: 37795 min_ts: 24788815 max_ts: 25393390
Process Group PGID: 1630
</untrusted_context>

## Step 9 · agent · 2026-09-29T04:33:15.169Z

### Recorded reasoning summary
Analyzing User Data

I'm currently processing the user data, noting 8456 distinct users with their recent and historical positive listening counts. This information, combined with the 30,000 items in the candidate catalog and validation eligibility data, forms the foundation for our next steps.

### run_shell_command · 2026-09-29T04:33:15.169Z
{
  "description": "Check train row counts and preference states",
  "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\np_meta = pq.read_metadata('/task/data/train_listens.parquet')\nprint('train_listens num rows:', p_meta.num_rows)\np_likes = pq.read_metadata('/task/data/train_likes.parquet')\nprint('train_likes num rows:', p_likes.num_rows)\np_dislikes = pq.read_metadata('/task/data/train_dislikes.parquet')\nprint('train_dislikes num rows:', p_dislikes.num_rows)\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val_pref states:', val_pref['state'].value_counts().to_dict())\n\""
}
### Tool output · 2026-09-29T04:33:15.980Z
<untrusted_context>
Output: train_listens num rows: 43484159
train_likes num rows: 814831
train_dislikes num rows: 101075
val_pref states: {'liked': 418435, 'disliked': 50689}
Process Group PGID: 1650
</untrusted_context>

## Step 10 · agent · 2026-09-29T04:33:21.705Z

### Recorded reasoning summary
Exploring Data Schemas

I'm currently examining the schemas of `train_listens` and `target_users`. Given the immense size of `train_listens` (over 43 million rows), I need to be mindful of resource consumption and efficiency. My focus is on understanding the structure and timestamp columns to inform how I'll proceed with data loading and initial analysis.

### run_shell_command · 2026-09-29T04:33:21.705Z
{
  "command": "python3 -c \"\nimport numpy as np\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\npacked = val_elig['packed_eligible']\n# Count bits set per row\n# np.bitwise_count is available in numpy 2.0+\nbits_per_row = np.bitwise_count(packed).sum(axis=1)\nprint('val eligible per user: min, median, mean, max:', bits_per_row.min(), np.median(bits_per_row), bits_per_row.mean(), bits_per_row.max())\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nt_packed = test_elig['packed_eligible']\nt_bits_per_row = np.bitwise_count(t_packed).sum(axis=1)\nprint('test eligible per user: min, median, mean, max:', t_bits_per_row.min(), np.median(t_bits_per_row), t_bits_per_row.mean(), t_bits_per_row.max())\n\"",
  "description": "Check eligible candidate count distribution"
}
### Tool output · 2026-09-29T04:33:22.252Z
<untrusted_context>
Output: val eligible per user: min, median, mean, max: 24657 29242.0 29067.306291390727 30000
test eligible per user: min, median, mean, max: 24552 29220.0 29041.3178807947 30000
Process Group PGID: 1668
</untrusted_context>

## Step 11 · agent · 2026-09-29T04:33:29.361Z

### Recorded reasoning summary
Investigating Eligibility Criteria

My current focus is on understanding the eligibility constraints for candidate items. It appears most items are eligible, with a small percentage marked ineligible, likely due to prior user interaction. I'm now digging into the `is_eligible` function to confirm these assumptions.

### Recorded reasoning summary
Confirming Eligibility Constraints

I'm now confirming my earlier hunch regarding item eligibility. It seems the `is_eligible` function accurately filters out items with prior user engagement, ensuring only suitable candidates are considered for recommendations.

### run_shell_command · 2026-09-29T04:33:29.361Z
{
  "command": "python3 -c \"\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint('Number of users with graded items in val:', len(graded), 'out of', len(targets))\nitem_counts = [len(items) for items in graded.values()]\nprint('Items per graded user: min, median, mean, max:', min(item_counts), pd.Series(item_counts).median(), pd.Series(item_counts).mean(), max(item_counts))\ngrade3_counts = [sum(1 for g in items.values() if g == 3) for items in graded.values()]\nprint('Grade 3 per user: mean:', pd.Series(grade3_counts).mean())\n\"",
  "description": "Analyze validation truth statistics"
}
### Tool output · 2026-09-29T04:33:30.104Z
<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1684
</untrusted_context>

## Step 12 · agent · 2026-09-29T04:33:36.447Z

### run_shell_command · 2026-09-29T04:33:36.447Z
{
  "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint('Number of users with graded items in val:', len(graded), 'out of', len(targets))\nitem_counts = [len(items) for items in graded.values()]\nprint('Items per graded user: min, median, mean, max:', min(item_counts), pd.Series(item_counts).median(), pd.Series(item_counts).mean(), max(item_counts))\ngrade3_counts = [sum(1 for g in items.values() if g == 3) for items in graded.values()]\nprint('Grade 3 per user: mean:', pd.Series(grade3_counts).mean())\n\"",
  "description": "Analyze validation truth statistics with path"
}
### Tool output · 2026-09-29T04:33:37.762Z
<untrusted_context>
Output: Number of users with graded items in val: 4263 out of 8456
Items per graded user: min, median, mean, max: 1 4.0 8.859723199624677 442
Grade 3 per user: mean: 1.0361247947454844
Process Group PGID: 1695
</untrusted_context>

## Step 13 · agent · 2026-09-29T04:33:44.291Z

### Recorded reasoning summary
Analyzing User Item Eligibility

I'm currently reviewing user data, focusing on those with eligible items in the validation window. It appears a significant portion, over 50%, meets this criterion, with an average of roughly 8.8 items per user. I'm also seeing about one grade 3 item per user on average.

### Recorded reasoning summary
Developing Baseline Strategy

I'm now focusing on establishing a baseline by ranking eligible candidates. The plan is to select the top 100 items per user, sorted by popularity metrics like `popularity_rank`, `organic_positive_listens`, or `positive_listens`, from the `candidate_catalog`. I'll then script an evaluation using `evaluate_public.py` and prepare a preliminary submission file.

### run_shell_command · 2026-09-29T04:33:44.291Z
{
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\npacked = val_elig['packed_eligible']\nprint(f'Load took {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Check if item_ids matches cat.item_id exactly\ncat_items = cat['item_id'].to_numpy()\nprint('item_ids match cat_items exactly?', np.array_equal(item_ids, cat_items))\n\"",
  "description": "Check candidate catalog vs item_ids in npz"
}
### Tool output · 2026-09-29T04:33:45.166Z
<untrusted_context>
Output: Load took 0.14s
item_ids match cat_items exactly? True
Process Group PGID: 1715
</untrusted_context>

## Step 14 · agent · 2026-09-29T04:33:48.490Z

### run_shell_command · 2026-09-29T04:33:48.490Z
{
  "description": "Verify catalog popularity ordering",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('cat popularity_rank head:', cat['popularity_rank'].head().tolist())\nprint('is monotonically increasing:', (cat['popularity_rank'].diff().dropna() > 0).all())\nprint('min, max rank:', cat['popularity_rank'].min(), cat['popularity_rank'].max())\n\""
}
### Tool output · 2026-09-29T04:33:49.270Z
<untrusted_context>
Output: cat popularity_rank head: [1, 2, 3, 4, 5]
is monotonically increasing: True
min, max rank: 1 30000
Process Group PGID: 1732
</untrusted_context>

## Step 15 · agent · 2026-09-29T04:33:52.740Z

### Recorded reasoning summary
Confirming Candidate Catalog Order

I'm currently verifying that the item IDs within `validation_eligible_candidates.npz` and `test_eligible_candidates.npz` precisely mirror the ordered `candidate_catalog` (1-30000 by popularity rank). This confirmation is a crucial next step.

### run_shell_command · 2026-09-29T04:33:52.740Z
{
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('test item_ids match cat?', np.array_equal(test_elig['item_ids'], cat['item_id'].to_numpy()))\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('test uids match target_users?', np.array_equal(test_elig['uids'], target_users['uid'].to_numpy()))\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val uids match target_users?', np.array_equal(val_elig['uids'], target_users['uid'].to_numpy()))\n\"",
  "description": "Verify test and val uids match target_users"
}
### Tool output · 2026-09-29T04:33:53.505Z
<untrusted_context>
Output: test item_ids match cat? True
test uids match target_users? True
val uids match target_users? True
Process Group PGID: 1749
</untrusted_context>

## Step 16 · agent · 2026-09-29T04:33:59.590Z

### Recorded reasoning summary
Verifying Indexing Strategy

My current focus is on confirming the perfect alignment between the uids, item_ids, target_users, and candidate_catalog sequences. I'm now investigating the performance implications of bit unpacking and efficient top-K eligible item retrieval given the scale of 30,000 items per user, aiming to optimize for speed.

### run_shell_command · 2026-09-29T04:33:59.590Z
{
  "description": "Benchmark unpacking entire eligible candidate matrix",
  "command": "python3 -c \"\nimport time\nimport numpy as np\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\npacked = val_elig['packed_eligible']\nt0 = time.time()\nmask = np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool)\nprint(f'unpackbits took {time.time()-t0:.2f}s, shape: {mask.shape}, memory: {mask.nbytes / 1e6:.1f} MB')\n\""
}
### Tool output · 2026-09-29T04:34:00.055Z
<untrusted_context>
Output: unpackbits took 0.06s, shape: (8456, 30000), memory: 253.7 MB
Process Group PGID: 1766
</untrusted_context>

## Step 17 · agent · 2026-09-29T04:34:10.887Z

### Recorded reasoning summary
Achieving Instantaneous Masking

I've confirmed candidate masking operates in effectively zero time for our 8456x30000 matrix. The next step is assessing the speed of retrieving top 100 items from user scores.

### run_shell_command · 2026-09-29T04:34:10.887Z
{
  "description": "Benchmark top100 extraction for all users",
  "command": "python3 -c \"\nimport time\nimport numpy as np\n\nS = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\ntop100_idx = np.argpartition(-S, 100, axis=1)[:, :100]\nrow_idx = np.arange(8456)[:, None]\ntop100_scores = S[row_idx, top100_idx]\norder = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\nprint(f'top100 extraction took {time.time()-t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:34:17.018Z
<untrusted_context>
Output: top100 extraction took 0.96s
Process Group PGID: 1776
</untrusted_context>

## Step 18 · agent · 2026-09-29T04:34:25.460Z

### Recorded reasoning summary
Optimizing Data Handling

I've achieved impressive speed, retrieving and sorting the top 100 items for all 8456 users in under a second. My current focus is on verifying a new strategy: assigning ineligibles to negative infinity should naturally position eligible items at the top using `np.argpartition`.

### run_shell_command · 2026-09-29T04:34:25.460Z
{
  "description": "Evaluate catalog popularity baselines",
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings, ranked_items\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nuids = eligibility.uids\nitem_ids = eligibility.item_ids\npacked = eligibility.packed\nmask = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little').astype(bool)\n\nfor pop_col in ['positive_listens', 'organic_positive_listens']:\n    scores = np.broadcast_to(cat[pop_col].to_numpy(dtype=np.float32), (len(uids), len(item_ids))).copy()\n    scores[~mask] = -np.inf\n    top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n    row_idx = np.arange(len(uids))[:, None]\n    top100_scores = scores[row_idx, top100_idx]\n    order = np.argsort(-top100_scores, axis=1)\n    sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n    \n    # Check score\n    ranked = {int(uids[i]): [int(item_ids[idx]) for idx in sorted_top100_idx[i]] for i in range(len(uids))}\n    res = score_rankings(ranked, graded)\n    print(f'Baseline {pop_col}: {res}')\n\""
}
### Tool output · 2026-09-29T04:34:30.528Z
<untrusted_context>
Output: Baseline positive_listens: {'quality_ndcg@10': 0.0107, 'quality_users': 4263}
Baseline organic_positive_listens: {'quality_ndcg@10': 0.010577, 'quality_users': 4263}
Process Group PGID: 1786
</untrusted_context>

## Step 19 · agent · 2026-09-29T04:34:38.437Z

### Recorded reasoning summary
Refining Submission Logic

My focus is on ensuring the best valid submission is consistently saved, adhering strictly to the 100-item limit for `/app/solution/submission.parquet` or `/app/solution/submission.csv`. This ensures compliance with operational requirements.

### run_shell_command · 2026-09-29T04:34:38.437Z
{
  "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nuids = test_elig['uids']\nitem_ids = test_elig['item_ids']\npacked = test_elig['packed_eligible']\nmask = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little').astype(bool)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nscores = np.broadcast_to(cat['positive_listens'].to_numpy(dtype=np.float32), (len(uids), len(item_ids))).copy()\nscores[~mask] = -np.inf\n\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nrow_idx = np.arange(len(uids))[:, None]\ntop100_scores = scores[row_idx, top100_idx]\norder = np.argsort(-top100_scores, axis=1)\nsorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n# Construct flat arrays for DataFrame\npred_uids = np.repeat(uids, 100)\npred_items = item_ids[sorted_top100_idx].reshape(-1)\npred_ranks = np.tile(np.arange(1, 101, dtype=np.int64), len(uids))\n\nsub = pd.DataFrame({'uid': pred_uids, 'item_id': pred_items, 'rank': pred_ranks})\nsub.to_parquet('/app/solution/submission.parquet', index=False)\nprint(f'Done in {time.time()-t0:.2f}s, rows: {len(sub)}')\n\"",
  "description": "Generate initial baseline submission.parquet"
}
### Tool output · 2026-09-29T04:34:40.574Z
<untrusted_context>
Output: Done in 1.40s, rows: 845600
Process Group PGID: 1812
</untrusted_context>

## Step 20 · agent · 2026-09-29T04:34:44.282Z

### run_shell_command · 2026-09-29T04:34:44.282Z
{
  "description": "Validate baseline submission against test requirements",
  "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import validate_ranking, read_ranking, CandidateSet\nfrom pathlib import Path\nimport pandas as pd\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\ntest_eligibility = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\n\nsub_df = read_ranking(Path('/app/solution/submission.parquet'))\nnorm_df = validate_ranking(sub_df, targets)\nprint('Structural validation passed! Checking candidate eligibility...')\n\n# Vectorized check\nall_eligible = all(\n    test_eligibility.is_eligible(uid, item)\n    for uid, item in norm_df[['uid', 'item_id']].itertuples(index=False, name=None)\n)\nprint('All candidates eligible?', all_eligible)\n\""
}
### Tool output · 2026-09-29T04:34:46.102Z
<untrusted_context>
Output: Structural validation passed! Checking candidate eligibility...
All candidates eligible? True
Process Group PGID: 1829
</untrusted_context>

## Step 21 · agent · 2026-09-29T04:34:49.218Z

### Recorded reasoning summary
Investigating Further Details

My current focus is on thoroughly understanding the core problem and the nuances of the provided data. Simultaneously, I'm actively working on designing potential solutions, building upon the established structural integrity of the submission.

### update_topic · 2026-09-29T04:34:49.218Z
{
  "strategic_intent": "Investigate interactions, user preferences, item features, and modeling options for discovery ranking.",
  "summary": "Generated an initial verified baseline submission to ensure deadline safety. Now conducting deep exploratory data analysis on the user interaction logs, preference states, timestamps, and candidate features to design an effective discovery recommendation model.",
  "title": "Data Analysis and Modeling Strategy"
}
### Tool output · 2026-09-29T04:34:49.221Z
Current topic: "Data Analysis and Modeling Strategy"
Topic summary: Generated an initial verified baseline submission to ensure deadline safety. Now conducting deep exploratory data analysis on the user interaction logs, preference states, timestamps, and candidate features to design an effective discovery recommendation model.

Strategic Intent: Investigate interactions, user preferences, item features, and modeling options for discovery ranking.

## Step 22 · agent · 2026-09-29T04:34:55.762Z

### Recorded reasoning summary
Analyzing Ranking Inputs

I'm currently dissecting the available data and the nuances of discovery ranking. My focus is on understanding how per-user candidate eligibility and cutoff-specific explicit preference states influence the "Made for You" shelf.

### run_shell_command · 2026-09-29T04:34:55.762Z
{
  "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(target_users['uid'].to_numpy())\nprint(f'target_uids: {len(target_uids)}')\n\n# Read train_listens filtering or scanning\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read table in {time.time()-t0:.2f}s, total rows: {len(table)}')\n\n# Filter to target_uids\nt1 = time.time()\ntarget_uid_array = target_users['uid'].to_numpy()\nmask = pc.is_in(table['uid'], value_set=pc.scalar(target_uid_array))\nsub_table = table.filter(pc.is_in(table['uid'], value_set=target_uid_array))\nprint(f'Filtered table in {time.time()-t1:.2f}s, target user rows: {len(sub_table)}')\n\ndf_target = sub_table.to_pandas()\nprint('Target listens summary:')\nprint(df_target.head())\nprint('Min timestamp:', df_target['timestamp'].min(), 'Max timestamp:', df_target['timestamp'].max())\n\"",
  "description": "Analyze target user listens in train_listens"
}
### Tool output · 2026-09-29T04:34:57.001Z
Output too large. Showing first 8,000 and last 32,000 characters. For full output see: /home/agent/.gemini/tmp/app/tool-outputs/session-66408c9f-5bb8-4126-8cbb-737dee2707e4/run_shell_command__call_239296.txt
<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 19, in <module>
  File "/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py", line 260, in wrapper
    options = _handle_options(func_name, options_class, options,
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/pyarrow/compute.py", line 223, in _handle_options
    return options_class(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "pyarrow/_compute.pyx", line 1668, in pyarrow._compute.SetLookupOptions.__init__
  File "pyarrow/_compute.pyx", line 1647, in pyarrow._compute._SetLookupOptions._set_options
  File "pyarrow/_compute.pyx", line 657, in pyarrow._compute._raise_invalid_function_option
TypeError: "list<item: uint32>[100, 200, 300, 600, 700, 800, 900, 1000, 1100, 1400, 1500, 1600, 1700, 1900, 2000, 2100, 2200, 2300, 2400, 2500, 2600, 2700, 2800, 2900, 3000, 3200, 3300, 3400, 3600, 3700, 3800, 3900, 4000, 4100, 4200, 4300, 4400, 4600, 4800, 4900, 5000, 5100, 5200, 5400, 5500, 5600, 5700, 5900, 6100, 6300, 6500, 6600, 6700, 6800, 6900, 7000, 7100, 7200, 7300, 7400, 7500, 7600, 7700, 7800, 7900, 8000, 8200, 8300, 8700, 8900, 9000, 9100, 9200, 9300, 9500, 9600, 9700, 9800, 9900, 10000, 10200, 10300, 10400, 10500, 10600, 10700, 10800, 10900, 11100, 11200, 11300, 11400, 11500, 11600, 11700, 11800, 11900, 12000, 12100, 12300, 12400, 12500, 12600, 12800, 12900, 13000, 13100, 13200, 13300, 13400, 13500, 13600, 13700, 14000, 14100, 14200, 14300, 14400, 14500, 14600, 14700, 14800, 14900, 15000, 15100, 15200, 15300, 15400, 15500, 15600, 15700, 15800, 15900, 16000, 16100, 16200, 16300, 16500, 16600, 16700, 16800, 16900, 17000, 17100, 17200, 17300, 17400, 17600, 17700, 17800, 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... [27,693 characters omitted] ...

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987500, 987600, 987800, 987900, 988000, 988100, 988200, 988300, 988400, 988500, 988600, 988700, 988800, 988900, 989000, 989100, 989200, 989300, 989400, 989500, 989600, 989700, 989800, 989900, 990100, 990200, 990300, 990400, 990500, 990600, 990700, 990800, 990900, 991000, 991100, 991200, 991300, 991500, 991700, 991800, 991900, 992000, 992100, 992200, 992300, 992400, 992500, 992600, 992700, 992800, 993100, 993200, 993300, 993400, 993600, 993700, 993800, 994000, 994100, 994200, 994400, 994700, 994800, 995000, 995100, 995200, 995300, 995400, 995500, 995600, 995700, 995800, 995900, 996100, 996200, 996300, 996400, 996500, 996700, 996900, 997000, 997200, 997300, 997500, 997600, 997800, 997900, 998000, 998100, 998200, 998300, 998400, 998500, 998600, 998700, 998800, 998900, 999000, 999100, 999200, 999400, 999500, 999600, 999700, 999800, 999900, 1000000]" is not a valid value set
target_uids: 8456
Read table in 0.39s, total rows: 43484159
Exit Code: 1
Process Group PGID: 1849
</untrusted_context>

## Step 23 · agent · 2026-09-29T04:35:02.532Z

### Recorded reasoning summary
Filtering Data Efficiently

I've realized that `pc.is_in` in pyarrow requires a `pa.array` for `value_set`, not a `pc.scalar`. I'm adapting my filtering logic to use `pa.array(target_users['uid'].to_numpy())` for proper data selection. It's also impressive how quickly `train_listens.parquet` loads – just 0.39s.

### run_shell_command · 2026-09-29T04:35:02.532Z
{
  "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport time\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uid_array = pa.array(target_users['uid'].to_numpy())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read table in {time.time()-t0:.2f}s, total rows: {len(table)}')\n\nt1 = time.time()\nmask = pc.is_in(table['uid'], value_set=target_uid_array)\nsub_table = table.filter(mask)\nprint(f'Filtered table in {time.time()-t1:.2f}s, target user rows: {len(sub_table)}')\n\nt2 = time.time()\ndf_target = sub_table.to_pandas()\nprint(f'to_pandas in {time.time()-t2:.2f}s, target user rows: {len(df_target)}')\nprint('Min ts:', df_target['timestamp'].min(), 'Max ts:', df_target['timestamp'].max())\nprint('Organic ratio in target listens:', (df_target['is_organic'] == 1).mean())\nprint('>=80% ratio in target listens:', (df_target['played_ratio_pct'] >= 80).mean())\nprint('Number of unique items played by target users:', df_target['item_id'].nunique())\n\"",
  "description": "Proper pyarrow filter for target user listens"
}
### Tool output · 2026-09-29T04:35:04.900Z
<untrusted_context>
Output: Read table in 0.40s, total rows: 43484159
Filtered table in 0.65s, target user rows: 43222297
to_pandas in 0.10s, target user rows: 43222297
Min ts: 5 Max ts: 24786795
Organic ratio in target listens: 0.5171260564888535
>=80% ratio in target listens: 0.5930057812522088
Number of unique items played by target users: 839582
Process Group PGID: 1871
</untrusted_context>

## Step 24 · agent · 2026-09-29T04:35:09.047Z

### Recorded reasoning summary
Analyzing Listen Data

My focus has shifted to understanding the composition of listens. It appears nearly all listens come from target users, which is a strong indicator. I'm now investigating the candidate catalog size, which seems remarkably small at 30,000 items, and will compare this to the number of listens within the training data.

### run_shell_command · 2026-09-29T04:35:09.047Z
{
  "command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport time\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = pa.array(cat['item_id'].to_numpy())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask_cat = pc.is_in(table['item_id'], value_set=cat_items)\nsub_table = table.filter(mask_cat)\nprint(f'Filtered to catalog items in {time.time()-t0:.2f}s, rows: {len(sub_table)}')\n\"",
  "description": "Check how many listens are for candidate catalog items"
}
### Tool output · 2026-09-29T04:35:11.259Z
<untrusted_context>
Output: Filtered to catalog items in 1.37s, rows: 31249613
Process Group PGID: 1893
</untrusted_context>

## Step 25 · agent · 2026-09-29T04:35:31.530Z

### Recorded reasoning summary
Analyzing Discovery Shelves

I'm currently focused on understanding the "Made for You" discovery shelf. My current thought process is to analyze the relationship between user listening history and an item's eligibility for this shelf, specifically investigating if previously listened-to items can appear.

### run_shell_command · 2026-09-29T04:35:31.530Z
{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nval_elig = CandidateSet('/task/data/validation_eligible_candidates.npz')\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val_pref head:\\n', val_pref.head())\n\n# Check how many (uid, item_id) in val_pref are eligible in val_elig\npref_sample = val_pref.head(1000)\npref_elig = [val_elig.is_eligible(r.uid, r.item_id) for r in pref_sample.itertuples()]\nprint('Ratio of preference items that are eligible:', np.mean(pref_elig))\n\n# Check disliked vs liked in preference state\ndisliked = val_pref[val_pref.state == 'disliked'].head(1000)\ndisliked_elig = [val_elig.is_eligible(r.uid, r.item_id) for r in disliked.itertuples()]\nprint('Ratio of disliked items that are eligible:', np.mean(disliked_elig))\n\"",
  "description": "Check if previously interacted items are eligible"
}
### Tool output · 2026-09-29T04:35:32.450Z
<untrusted_context>
Output: val_pref head:
    uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
3  100  1730273  liked
4  100  2621343  liked
Ratio of preference items that are eligible: 0.0
Ratio of disliked items that are eligible: 0.0
Process Group PGID: 1915
</untrusted_context>

## Step 26 · agent · 2026-09-29T04:36:02.904Z

### run_shell_command · 2026-09-29T04:36:02.904Z
{
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nval_elig = CandidateSet('/task/data/validation_eligible_candidates.npz')\n# Take a user with some listens\nsample_listens = pd.read_parquet('/task/data/val_listens.parquet').head(2000)\n# val_listens is validation period, let's check train_listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ndf_sample = table.slice(0, 10000).to_pandas()\nis_el = [val_elig.is_eligible(r.uid, r.item_id) for r in df_sample.itertuples()]\nprint('Ratio of train_listens items eligible in val_elig:', np.mean(is_el))\n\"",
  "description": "Check if train listened items are eligible in validation"
}
### Tool output · 2026-09-29T04:36:04.038Z
<untrusted_context>
Output: Ratio of train_listens items eligible in val_elig: 0.0
Process Group PGID: 1937
</untrusted_context>

## Step 27 · agent · 2026-09-29T04:36:13.630Z

### Recorded reasoning summary
Discovering New Insights

I've stumbled upon a crucial finding: the ratio of eligible items for training within the validation set is zero. This points to a potential issue in how we're identifying candidates for the "Made for You" discovery shelf, where we aim to surface entirely new music for users.

### Recorded reasoning summary
Refining Candidate Generation

I've realized the candidate mask already handles exclusions, so my initial zero-eligibility finding stems from that. Now I'm exploring how collaborative filtering, particularly Item-Item and Matrix Factorization methods like EASE, can effectively recommend genuinely novel items.

### Recorded reasoning summary
Exploring Computational Constraints

My focus has shifted to the sheer scale of computations involved. The zero-eligibility issue might be intertwined with the impracticality of inverting large matrices. I'm now investigating more efficient methods like sparse item-item similarity or SVD to bypass these memory and time constraints.

### run_shell_command · 2026-09-29T04:36:13.630Z
{
  "description": "Verify number of users in train_listens",
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable_users = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = table_users['uid'].to_numpy()\nimport numpy as np\nunique_uids = np.unique(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_users = pq.read_table('/task/data/target_users.parquet')['uid'].to_numpy()\nprint('Target users count:', len(target_users))\nprint('Intersection:', len(set(unique_uids).intersection(set(target_users))))\n\""
}
### Tool output · 2026-09-29T04:36:14.862Z
<untrusted_context>
Output: Total unique uids in train_listens: 9238
Target users count: 8456
Intersection: 8456
Process Group PGID: 1961
</untrusted_context>

## Step 28 · agent · 2026-09-29T04:36:34.771Z

### Recorded reasoning summary
Analyzing User Interaction

My focus has shifted to understanding the user interaction landscape. It appears all 8,456 target users are present within the `train_listens` dataset, which comprises a surprisingly small 9,238 unique users in total. I'm now exploring the interaction matrix between these 8,456 target users and the 30,000 catalog items.

### Recorded reasoning summary
Calculating SVD Performance

I've confirmed the feasibility of performing Truncated SVD on the user-item matrix. The dimensions are manageable, allowing for rapid computation of latent representations and subsequent prediction score generation. This opens avenues for exploring various weighting and dimensionality schemes quickly.

### Recorded reasoning summary
Computing Item-Item Similarity

I've realized that calculating the full $30,000 \times 30,000$ item-item similarity matrix is unnecessary. Through careful matrix manipulation and associativity, I can achieve the desired item-item computations by first performing operations that result in an $8456 \times 8456$ matrix, avoiding the materialization of the larger, prohibitive matrix.

### Recorded reasoning summary
Deriving Item Similarities

I've confirmed that the formula $(X \tilde{X}^T) \tilde{X}$ elegantly yields linear item-item similarity, aligning perfectly with user-user similarity applied to the item matrix. My next step is to meticulously examine the `train_listens.parquet` dataset for temporal features like `timestamp` and engagement metrics such as `played_ratio_pct` to inform non-linear similarity calculations.

### Recorded reasoning summary
Refining Engagement Signals

I'm now focusing on refining the engagement signals. The discovery objective specifically rewards completions (`played_ratio_pct >= 80`), and I'm considering the distinction between organic listens versus radio/recommendations. Explicit actions like likes and dislikes from `train_likes.parquet` and `train_dislikes.parquet` are also crucial for deriving non-linear similarity.

### run_shell_command · 2026-09-29T04:36:34.771Z
{
  "description": "Analyze train_listens timestamp and features distribution",
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'played_ratio_pct', 'is_organic'])\nts = table['timestamp'].to_numpy()\npct = table['played_ratio_pct'].to_numpy()\norg = table['is_organic'].to_numpy()\n\nprint(f'Train listens: min_ts={ts.min()}, max_ts={ts.max()}, span={(ts.max()-ts.min())/86400:.1f} days')\n# Quantiles of timestamp\nprint('Timestamp quantiles (days from end):')\nmax_t = ts.max()\nfor q in [0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99]:\n    val = np.quantile(ts, q)\n    print(f'quantile {q:.2f}: {(max_t - val)/86400:.1f} days before cutoff')\n\nprint('Played ratio pct quantiles:', np.quantile(pct, [0.1, 0.25, 0.5, 0.75, 0.9]))\nprint('Organic fraction:', np.mean(org))\n\""
}
### Tool output · 2026-09-29T04:36:37.527Z
<untrusted_context>
Output: Train listens: min_ts=0, max_ts=24786795, span=286.9 days
Timestamp quantiles (days from end):
quantile 0.01: 282.6 days before cutoff
quantile 0.05: 267.2 days before cutoff
quantile 0.10: 248.4 days before cutoff
quantile 0.25: 197.3 days before cutoff
quantile 0.50: 122.0 days before cutoff
quantile 0.75: 55.3 days before cutoff
quantile 0.90: 21.1 days before cutoff
quantile 0.99: 1.9 days before cutoff
Played ratio pct quantiles: [  1.   7. 100. 100. 100.]
Organic fraction: 0.5169151828370419
Process Group PGID: 1981
</untrusted_context>

## Step 29 · agent · 2026-09-29T04:36:54.783Z

### Recorded reasoning summary
Analyzing Listening Patterns

I've been examining the data, noticing how concentrated the listening activity is in recent periods. It's quite striking that half of all listens occurred within the last 122 days, with a quarter of them happening in the last 55. Additionally, I'm seeing that the 10% quantile for `played_ratio_pct` is 1%.

### Recorded reasoning summary
Observing Engagement Dichotomy

My analysis reveals a striking pattern: listeners either abandon tracks very early, around 1-10% completion, or they engage fully, listening to 100%. This suggests a strong dichotomy in user interaction with the content.

### run_shell_command · 2026-09-29T04:36:54.783Z
{
  "description": "Check preference state items in candidate catalog",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat['item_id'])\n\nprint('val_pref total rows:', len(val_pref))\nprint('val_pref items in candidate_catalog:', val_pref['item_id'].isin(cat_set).sum())\nprint('val_pref unique items:', val_pref['item_id'].nunique())\nprint('unique items in cat_set:', len(set(val_pref['item_id']).intersection(cat_set)))\n\""
}
### Tool output · 2026-09-29T04:36:55.702Z
<untrusted_context>
Output: val_pref total rows: 469124
val_pref items in candidate_catalog: 469124
val_pref unique items: 28814
unique items in cat_set: 28814
Process Group PGID: 2005
</untrusted_context>

## Step 30 · agent · 2026-09-29T04:36:59.131Z

### Recorded reasoning summary
Confirming Catalog Size

I've confirmed that every single entry within `validation_preference_state` corresponds to an item in our catalog. This means all 469,124 rows are indeed part of the candidate catalog. Now I'm shifting focus to `train_likes` and `train_dislikes` to see how they align.

### run_shell_command · 2026-09-29T04:36:59.131Z
{
  "description": "Check train likes in candidate catalog",
  "command": "python3 -c \"\nimport pandas as pd\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat['item_id'])\nprint('train_likes in catalog:', train_likes['item_id'].isin(cat_set).mean())\nprint('train_likes rows:', len(train_likes))\n\""
}
### Tool output · 2026-09-29T04:37:00.018Z
<untrusted_context>
Output: train_likes in catalog: 0.6809068383505291
train_likes rows: 814831
Process Group PGID: 2022
</untrusted_context>

## Step 31 · agent · 2026-09-29T04:37:17.823Z

### Recorded reasoning summary
Exploring Candidate Catalog

I'm now examining the `candidate_catalog.parquet` file. Initial checks reveal it contains `item_id`, `positive_listens`, `organic_positive_listens`, and `popularity_rank`. I'm currently investigating if any other related files or metadata exist.

### Recorded reasoning summary
Refining Model Scope

I've confirmed the available data is solely interaction logs and preference states, excluding item features. This points to a classic collaborative filtering and sequential recommendation approach. My focus now is on identifying the most efficient and effective models for this type of problem, considering the dataset size and evaluation metric.

### Recorded reasoning summary
Validating Recommendation Approach

I'm currently validating the setup for recommendation evaluation. The history includes training events and validation preference states, with outcomes being validation events scored by `/task/evaluate_public.py`. I need to ensure efficient testing of collaborative filtering architectures within the time constraints.

### Recorded reasoning summary
Testing Matrix Factorization

I'm currently exploring Implicit Matrix Factorization, specifically TruncatedSVD. My focus is on defining the entries of the user-item interaction matrix, considering how to represent positive listens and likes, and then implementing SVD decomposition in Python.

### Recorded reasoning summary
Evaluating Similarity Metrics

I'm now considering alternative similarity measures to TruncatedSVD. Specifically, I'm investigating the feasibility and performance of computing dense cosine similarity and top-k item similarities on the user-item interaction matrix, and comparing these against the speed of TruncatedSVD. I'm also exploring user-user collaborative filtering methods.

### Recorded reasoning summary
Comparing User-User Baselines

I'm now comparing user-user KNN with power/top-k thresholding against EASE. User-user KNN offers impressive speed for predictions. I'm benchmarking matrix inversions for EASE and considering their practical implications. This comparison should highlight strong collaborative filtering baselines.

### run_shell_command · 2026-09-29T04:37:17.823Z
{
  "description": "Benchmark 8456x8456 matrix operations",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.linalg\n\nn = 8456\nA = np.random.randn(n, 100).astype(np.float32)\nt0 = time.time()\nG = A @ A.T + 100.0 * np.eye(n, dtype=np.float32)\nprint(f'Gram matrix computation took {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Cholesky inversion\nc, lower = scipy.linalg.cho_factor(G)\nP = scipy.linalg.cho_solve((c, lower), np.eye(n, dtype=np.float32))\nprint(f'Cholesky inverse took {time.time()-t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:37:29.730Z
<untrusted_context>
Output: Gram matrix computation took 0.23s
Cholesky inverse took 6.94s
Process Group PGID: 2040
</untrusted_context>

## Step 32 · agent · 2026-09-29T04:37:34.513Z

### Recorded reasoning summary
Benchmarking Speed Gains

I've been focusing on performance optimizations. The Cholesky inverse now clocks in at a mere 6.94 seconds, and Gram matrix computations are a swift 0.23 seconds. User-User EASE is also efficiently solved in under 8 seconds. Next, I'm benchmarking TruncatedSVD on sparse matrices from scikit-learn.

### run_shell_command · 2026-09-29T04:37:34.513Z
{
  "description": "Benchmark TruncatedSVD on sparse matrix",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# Simulate sparse matrix (8456, 30000) with ~3 million non-zeros (density ~1.2%)\nM = sp.random(8456, 30000, density=0.012, format='csr', dtype=np.float32)\nprint(f'Matrix nnz: {M.nnz}')\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', random_state=42)\nU = svd.fit_transform(M)\nV = svd.components_\nprint(f'TruncatedSVD k=128 took {time.time()-t0:.2f}s, explained variance: {svd.explained_variance_ratio_.sum():.4f}')\n\""
}
### Tool output · 2026-09-29T04:37:49.202Z
<untrusted_context>
Output: Matrix nnz: 3044160
TruncatedSVD k=128 took 2.48s, explained variance: 0.0312
Process Group PGID: 2057
</untrusted_context>

## Step 33 · agent · 2026-09-29T04:37:52.930Z

### Recorded reasoning summary
Exploring Interaction Matrix Construction

I'm focusing on building the user-item interaction matrix now, after a swift 2.48-second truncated SVD. Next steps involve examining available events and determining appropriate weighting for them. This work is a precursor to the experimental validation phase.

### update_topic · 2026-09-29T04:37:52.930Z
{
  "strategic_intent": "Build data pipeline for training interaction matrix and test collaborative filtering models on validation set.",
  "title": "Collaborative Filtering Model Exploration",
  "summary": "Completed initial benchmarks and mathematical formulations for User-User EASE, Item-Item CF, and TruncatedSVD. Now building data extraction pipelines to create weighted user-item interaction matrices from listens, preference states, and likes, and evaluating models on the public validation window."
}
### Tool output · 2026-09-29T04:37:52.934Z
Current topic: "Collaborative Filtering Model Exploration"
Topic summary: Completed initial benchmarks and mathematical formulations for User-User EASE, Item-Item CF, and TruncatedSVD. Now building data extraction pipelines to create weighted user-item interaction matrices from listens, preference states, and likes, and evaluating models on the public validation window.

Strategic Intent: Build data pipeline for training interaction matrix and test collaborative filtering models on validation set.

## Step 34 · agent · 2026-09-29T04:37:57.771Z

### Recorded reasoning summary
Exploring User Data

I'm currently examining the `target_users.parquet` file, which contains 8,456 users. My immediate task is to establish a mapping of each `uid` to its corresponding row index, from 0 to 8455. Subsequently, I will turn my attention to the `candidate_catalog.parquet` file, which holds 30,000 items.

### run_shell_command · 2026-09-29T04:37:57.771Z
{
  "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nu_items, counts = np.unique(items, return_counts=True)\nprint(f'Total items: {len(u_items)}')\nfor min_c in [1, 5, 10, 50, 100]:\n    print(f'Items with >= {min_c} listens: {(counts >= min_c).sum()}')\n\"",
  "description": "Check item frequency distribution in train_listens"
}
### Tool output · 2026-09-29T04:37:59.227Z
<untrusted_context>
Output: Total items: 845395
Items with >= 1 listens: 845395
Items with >= 5 listens: 339975
Items with >= 10 listens: 240543
Items with >= 50 listens: 96719
Items with >= 100 listens: 61133
Process Group PGID: 2081
</untrusted_context>

## Step 35 · agent · 2026-09-29T04:38:56.948Z

### Recorded reasoning summary
Analyzing Listen Distributions

My focus has shifted to understanding listen distributions, specifically comparing catalog items against non-catalog items for target users. I'm currently examining a dataset of 845,395 listens against a catalog of 30,000 items.

### Recorded reasoning summary
Examining Listen Patterns

I've discovered that a significant majority of listens, approximately 72.3%, are for catalog items. This concentration is important as my recommendation system only scores these catalog items. I am now proceeding to analyze validation event data.

### Recorded reasoning summary
Investigating User Engagement

I'm now scrutinizing how user engagement, specifically `is_organic`, differentiates between direct listens and dislikes/undislikes. I'm also verifying that validation preference states accurately reflect prior explicit feedback up to the training cutoff.

### Recorded reasoning summary
Testing Collaborative Filtering Baseline

I'm now focused on implementing a basic collaborative filtering model. My goal is to construct a sparse matrix representing user-item interactions, incorporating recent positive listens and preference states for a robust baseline test.

### run_shell_command · 2026-09-29T04:38:56.948Z
{
  "description": "Benchmark filtering train_listens to catalog items",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users['uid'].to_numpy()\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat['item_id'].to_numpy()\nitem_to_idx = {item: j for j, item in enumerate(items)}\n\nprint(f'Index mapping done in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Read train_listens with only relevant columns\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time()-t0:.2f}s')\n\nt0 = time.time()\nu_arr = table['uid'].to_numpy()\ni_arr = table['item_id'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\npct_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\n\n# Filter to catalog items and target users\n# Using fast lookup:\ncat_set = set(items)\n# We can do boolean indexing in numpy\nmask = np.isin(i_arr, items)\nprint(f'np.isin mask done in {time.time()-t0:.2f}s, matching listens: {mask.sum()}')\n\""
}
### Tool output · 2026-09-29T04:38:59.054Z
<untrusted_context>
Output: Index mapping done in 0.02s
Read table in 0.41s
np.isin mask done in 0.52s, matching listens: 31249613
Process Group PGID: 2103
</untrusted_context>

## Step 36 · agent · 2026-09-29T04:39:51.859Z

### Recorded reasoning summary
Analyzing Interaction Matrix Build

The recent focus has been on precisely quantifying the computational cost of constructing the sparse interaction matrix $R$. Initial benchmarks for indexing, reading tables, and masking are promisingly rapid, paving the way to examine the per-pair computation of $R$.

### Recorded reasoning summary
Developing Event Weighting Logic

I'm currently refining the weighting system for individual listen events, incorporating factors like play duration, organic listening, and time decay. My next step is to test these parameters, especially the preference state and log-count compression, through a dedicated Python script to evaluate recommender algorithm performance.

### write_file · 2026-09-29T04:39:51.859Z
{
  "file_path": "/app/solution/exp1.py",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    uid_to_idx = {uid: i for i, uid in enumerate(uids)}\n    num_users = len(uids)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    item_to_idx = {item: j for j, item in enumerate(items)}\n    num_items = len(items)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\")\n\n    # 1. Load train listens\n    t0 = time.time()\n    table = pq.read_table(\n        DATA / \"train_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n    )\n    print(f\"Read train_listens in {time.time()-t0:.2f}s\")\n\n    t0 = time.time()\n    u_raw = table[\"uid\"].to_numpy()\n    i_raw = table[\"item_id\"].to_numpy()\n    ts_raw = table[\"timestamp\"].to_numpy()\n    pct_raw = table[\"played_ratio_pct\"].to_numpy()\n    org_raw = table[\"is_organic\"].to_numpy()\n\n    # Map to indices\n    # Filter only catalog items\n    in_cat = np.isin(i_raw, items)\n    u_filt = u_raw[in_cat]\n    i_filt = i_raw[in_cat]\n    ts_filt = ts_raw[in_cat]\n    pct_filt = pct_raw[in_cat]\n    org_filt = org_raw[in_cat]\n\n    # Map uids to idx\n    u_idx = np.array([uid_to_idx.get(u, -1) for u in u_filt], dtype=np.int32)\n    # Map item_ids to idx\n    i_idx = np.array([item_to_idx.get(it, -1) for it in i_filt], dtype=np.int32)\n\n    valid_mask = (u_idx >= 0) & (i_idx >= 0)\n    u_idx = u_idx[valid_mask]\n    i_idx = i_idx[valid_mask]\n    ts_filt = ts_filt[valid_mask]\n    pct_filt = pct_filt[valid_mask]\n    org_filt = org_filt[valid_mask]\n    print(f\"Mapped {len(u_idx)} listens in {time.time()-t0:.2f}s\")\n\n    # Experiment with weights:\n    # Strong listens: pct >= 80 get 1.0, 50 <= pct < 80 get 0.5, skips get 0\n    cutoff_ts = 24786800\n    days_before = (cutoff_ts - ts_filt) / 86400.0\n    # Half life of 60 days\n    time_decay = np.exp(-days_before / 60.0)\n\n    listen_quality = np.where(pct_filt >= 80, 1.0, np.where(pct_filt >= 50, 0.5, 0.1))\n    organic_boost = np.where(org_filt == 1, 1.5, 1.0)\n    w_listens = listen_quality * organic_boost * time_decay\n\n    # Build sparse matrix of listens\n    R_listens = sp.csr_matrix(\n        (w_listens, (u_idx, i_idx)), shape=(num_users, num_items), dtype=np.float32\n    )\n\n    # 2. Add validation preference state (likes/dislikes)\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = np.array([uid_to_idx.get(u, -1) for u in val_pref.uid], dtype=np.int32)\n    pref_i = np.array([item_to_idx.get(it, -1) for it in val_pref.item_id], dtype=np.int32)\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 3.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix(\n        (pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32\n    )\n\n    # Combine listens and preference state\n    # Apply log1p to listens to dampen extreme frequencies\n    R_listens.data = np.log1p(R_listens.data)\n    R = (R_listens + R_pref).tocsr()\n\n    print(f\"Interaction matrix R built. Shape: {R.shape}, nnz: {R.nnz}\")\n\n    def evaluate_scores(scores, name):\n        # Mask ineligible items\n        scores = scores.copy()\n        scores[~mask] = -np.inf\n        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = scores[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\")\n        return res[\"quality_ndcg@10\"]\n\n    # Test 1: User-User EASE\n    print(\"Testing User-User EASE...\")\n    t0 = time.time()\n    # Normalize rows of R for Gram matrix\n    # Row L2 normalization\n    row_norms = sp.linalg.norm(R, axis=1)\n    row_norms[row_norms == 0] = 1.0\n    R_norm = sp.diags(1.0 / row_norms) @ R\n\n    # Gram matrix G = R_norm @ R_norm.T\n    G = (R_norm @ R_norm.T).toarray()\n    print(f\"Gram matrix computed in {time.time()-t0:.2f}s\")\n\n    for reg in [50.0, 100.0, 200.0, 500.0, 1000.0]:\n        t_inv = time.time()\n        G_reg = G.copy()\n        np.fill_diagonal(G_reg, G_reg.diagonal() + reg)\n        c, lower = scipy.linalg.cho_factor(G_reg)\n        P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))\n        B = -P / np.diag(P)[:, None]\n        np.fill_diagonal(B, 0.0)\n        # Compute scores: B @ R\n        scores = B @ R.toarray()\n        print(f\"Solved EASE reg={reg} in {time.time()-t_inv:.2f}s\")\n        evaluate_scores(scores, f\"User-EASE reg={reg}\")\n\nif __name__ == \"__main__\":\n    main()\n",
  "instruction": "Create initial collaborative filtering exploration script"
}
### Tool output · 2026-09-29T04:39:51.871Z
Successfully created and wrote to new file: /app/solution/exp1.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import scipy.linalg
import pyarrow.parquet as pq
import sys
from pathlib import Path

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    uid_to_idx = {uid: i for i, uid in enumerate(uids)}
    num_users = len(uids)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    item_to_idx = {item: j for j, item in enumerate(items)}
    num_items = len(items)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}")

    # 1. Load train listens
    t0 = time.time()
    table = pq.read_table(
        DATA / "train_listens.parquet",
        columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"],
    )
    print(f"Read train_listens in {time.time()-t0:.2f}s")

    t0 = time.time()
    u_raw = table["uid"].to_numpy()
    i_raw = table["item_id"].to_numpy()
    ts_raw = table["timestamp"].to_numpy()
    pct_raw = table["played_ratio_pct"].to_numpy()
    org_raw = table["is_organic"].to_numpy()

    # Map to indices
    # Filter only catalog items
    in_cat = np.isin(i_raw, items)
    u_filt = u_raw[in_cat]
    i_filt = i_raw[in_cat]
    ts_filt = ts_raw[in_cat]
    pct_filt = pct_raw[in_cat]
    org_filt = org_raw[in_cat]

    # Map uids to idx
    u_idx = np.array([uid_to_idx.get(u, -1) for u in u_filt], dtype=np.int32)
    # Map item_ids to idx
    i_idx = np.array([item_to_idx.get(it, -1) for it in i_filt], dtype=np.int32)

    valid_mask = (u_idx >= 0) & (i_idx >= 0)
    u_idx = u_idx[valid_mask]
    i_idx = i_idx[valid_mask]
    ts_filt = ts_filt[valid_mask]
    pct_filt = pct_filt[valid_mask]
    org_filt = org_filt[valid_mask]
    print(f"Mapped {len(u_idx)} listens in {time.time()-t0:.2f}s")

    # Experiment with weights:
    # Strong listens: pct >= 80 get 1.0, 50 <= pct < 80 get 0.5, skips get 0
    cutoff_ts = 24786800
    days_before = (cutoff_ts - ts_filt) / 86400.0
    # Half life of 60 days
    time_decay = np.exp(-days_before / 60.0)

    listen_quality = np.where(pct_filt >= 80, 1.0, np.where(pct_filt >= 50, 0.5, 0.1))
    organic_boost = np.where(org_filt == 1, 1.5, 1.0)
    w_listens = listen_quality * organic_boost * time_decay

    # Build sparse matrix of listens
    R_listens = sp.csr_matrix(
        (w_listens, (u_idx, i_idx)), shape=(num_users, num_items), dtype=np.float32
    )

    # 2. Add validation preference state (likes/dislikes)
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = np.array([uid_to_idx.get(u, -1) for u in val_pref.uid], dtype=np.int32)
    pref_i = np.array([item_to_idx.get(it, -1) for it in val_pref.item_id], dtype=np.int32)
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 3.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix(
        (pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32
    )

    # Combine listens and preference state
    # Apply log1p to listens to dampen extreme frequencies
    R_listens.data = np.log1p(R_listens.data)
    R = (R_listens + R_pref).tocsr()

    print(f"Interaction matrix R built. Shape: {R.shape}, nnz: {R.nnz}")

    def evaluate_scores(scores, name):
        # Mask ineligible items
        scores = scores.copy()
        scores[~mask] = -np.inf
        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = scores[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)")
        return res["quality_ndcg@10"]

    # Test 1: User-User EASE
    print("Testing User-User EASE...")
    t0 = time.time()
    # Normalize rows of R for Gram matrix
    # Row L2 normalization
    row_norms = sp.linalg.norm(R, axis=1)
    row_norms[row_norms == 0] = 1.0
    R_norm = sp.diags(1.0 / row_norms) @ R

    # Gram matrix G = R_norm @ R_norm.T
    G = (R_norm @ R_norm.T).toarray()
    print(f"Gram matrix computed in {time.time()-t0:.2f}s")

    for reg in [50.0, 100.0, 200.0, 500.0, 1000.0]:
        t_inv = time.time()
        G_reg = G.copy()
        np.fill_diagonal(G_reg, G_reg.diagonal() + reg)
        c, lower = scipy.linalg.cho_factor(G_reg)
        P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))
        B = -P / np.diag(P)[:, None]
        np.fill_diagonal(B, 0.0)
        # Compute scores: B @ R
        scores = B @ R.toarray()
        print(f"Solved EASE reg={reg} in {time.time()-t_inv:.2f}s")
        evaluate_scores(scores, f"User-EASE reg={reg}")

if __name__ == "__main__":
    main()


## Step 37 · agent · 2026-09-29T04:39:54.322Z

### run_shell_command · 2026-09-29T04:39:54.322Z
{
  "command": "python3 /app/solution/exp1.py",
  "description": "Run exp1.py to test User-User EASE"
}
### Tool output · 2026-09-29T04:40:48.821Z
<untrusted_context>
Output: /usr/bin/bash: line 5:  2140 Killed                  python3 /app/solution/exp1.py
Exit Code: 137
Process Group PGID: 2138
</untrusted_context>

## Step 38 · agent · 2026-09-29T04:42:18.841Z

### Recorded reasoning summary
Investigating Memory Usage

I'm currently digging into the out-of-memory error, specifically analyzing the memory footprint of reading the `train_listens.parquet` table and its conversion to a NumPy array for `uid`.

### Recorded reasoning summary
Analyzing Memory Leaks

I'm pinpointing memory usage during data loading and processing. The creation of large NumPy arrays and Python lists for `uid`, `items`, and indices appears to be a significant contributor to the memory spike, alongside intermediate matrix calculations.

### run_shell_command · 2026-09-29T04:42:18.841Z
{
  "description": "Check min and max of uid and item_id",
  "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('uid min, max:', target_users['uid'].min(), target_users['uid'].max())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('item_id min, max:', cat['item_id'].min(), cat['item_id'].max())\n\""
}
### Tool output · 2026-09-29T04:42:19.647Z
<untrusted_context>
Output: uid min, max: 100 1000000
item_id min, max: 206 9390294
Process Group PGID: 2179
</untrusted_context>

## Step 39 · agent · 2026-09-29T04:42:26.212Z

### Recorded reasoning summary
Allocating Memory Efficiently

I'm currently optimizing memory allocation. I've determined the necessary size for `uid_map` and `item_map` arrays, realizing they'll only consume a small portion of memory, around 4 MB and 37 MB respectively. This is a promising step towards efficient resource utilization.

### run_shell_command · 2026-09-29T04:42:26.212Z
{
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users['uid'].to_numpy(dtype=np.int64)\nuid_map = np.full(1000001, -1, dtype=np.int32)\nuid_map[uids] = np.arange(len(uids), dtype=np.int32)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat['item_id'].to_numpy(dtype=np.int64)\nitem_map = np.full(9390295, -1, dtype=np.int32)\nitem_map[items] = np.arange(len(items), dtype=np.int32)\nprint(f'Maps built in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Read parquet file in batches\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint(f'Num row groups: {pf.num_row_groups}')\n\"",
  "description": "Test lookup maps and inspect row groups"
}
### Tool output · 2026-09-29T04:42:26.994Z
<untrusted_context>
Output: Maps built in 0.03s
Num row groups: 42
Process Group PGID: 2202
</untrusted_context>

## Step 40 · agent · 2026-09-29T04:42:35.746Z

### run_shell_command · 2026-09-29T04:42:35.746Z
{
  "description": "Benchmark batched processing of train_listens",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport scipy.sparse as sp\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users['uid'].to_numpy(dtype=np.int64)\nuid_map = np.full(1000001, -1, dtype=np.int32)\nuid_map[uids] = np.arange(len(uids), dtype=np.int32)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat['item_id'].to_numpy(dtype=np.int64)\nitem_map = np.full(9390295, -1, dtype=np.int32)\nitem_map[items] = np.arange(len(items), dtype=np.int32)\n\ncutoff_ts = 24786800\n# Filter listens to last 120 days: 120 * 86400 = 10,368,000\nmin_ts = cutoff_ts - 120 * 86400\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nrow_list, col_list, data_list = [], [], []\n\ntot_listens = 0\nfor batch in pf.iter_batches(columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic']):\n    u_raw = batch['uid'].to_numpy()\n    i_raw = batch['item_id'].to_numpy()\n    ts = batch['timestamp'].to_numpy()\n    \n    # Filter by timestamp first\n    m_ts = ts >= min_ts\n    if not np.any(m_ts):\n        continue\n    u_raw = u_raw[m_ts]\n    i_raw = i_raw[m_ts]\n    ts = ts[m_ts]\n    pct = batch['played_ratio_pct'].to_numpy()[m_ts]\n    org = batch['is_organic'].to_numpy()[m_ts]\n\n    # Map to catalog items and target users\n    m_item = (i_raw < len(item_map))\n    i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n    i_idx[m_item] = item_map[i_raw[m_item]]\n\n    m_user = (u_raw < len(uid_map))\n    u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n    u_idx[m_user] = uid_map[u_raw[m_user]]\n\n    valid = (u_idx >= 0) & (i_idx >= 0)\n    if not np.any(valid):\n        continue\n\n    u_idx = u_idx[valid]\n    i_idx = i_idx[valid]\n    ts = ts[valid]\n    pct = pct[valid]\n    org = org[valid]\n\n    # Weighting\n    days = (cutoff_ts - ts) / 86400.0\n    decay = np.exp(-days / 45.0)\n    quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n    boost = np.where(org == 1, 1.5, 1.0)\n    w = (quality * boost * decay).astype(np.float32)\n\n    row_list.append(u_idx)\n    col_list.append(i_idx)\n    data_list.append(w)\n    tot_listens += len(u_idx)\n\nprint(f'Processed {tot_listens} listens in {time.time()-t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:42:38.338Z
<untrusted_context>
Output: Processed 15487518 listens in 1.58s
Process Group PGID: 2219
</untrusted_context>

## Step 41 · agent · 2026-09-29T04:42:55.334Z

### Recorded reasoning summary
Achieving incredible speed

I've just processed an astonishing 15.5 million listens in a mere 1.58 seconds, with impressively low memory usage. My current focus is on efficiently combining `row_list`, `col_list`, and `data_list` for matrix construction, exploring `np.concatenate` and the creation of `coo_matrix` or `csr_matrix`.

### write_file · 2026-09-29T04:42:55.334Z
{
  "instruction": "Memory-efficient collaborative filtering pipeline",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\")\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    # Include up to 150 days of history\n    min_ts = cutoff_ts - 150 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 60.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n    print(f\"R_listens built in {time.time()-t0:.2f}s, nnz={R_listens.nnz}\")\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    R = (R_listens + R_pref).tocsr()\n    del R_listens, R_pref\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\")\n\n    def evaluate_scores(scores, name):\n        scores = scores.copy()\n        scores[~mask] = -np.inf\n        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = scores[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\")\n        return res[\"quality_ndcg@10\"]\n\n    # 1. User-User Cosine KNN\n    print(\"Testing User-User Cosine Similarity...\")\n    t0 = time.time()\n    # Normalize rows\n    row_norms = sp.linalg.norm(R, axis=1)\n    row_norms[row_norms == 0] = 1.0\n    R_norm = sp.diags(1.0 / row_norms) @ R\n\n    # Dense Gram matrix G = R_norm @ R_norm.T\n    G = (R_norm @ R_norm.T).toarray()\n    np.fill_diagonal(G, 0.0)\n    print(f\"User Gram matrix computed in {time.time()-t0:.2f}s\")\n\n    for power in [1.0, 2.0, 3.0]:\n        t_p = time.time()\n        # Non-negative weights with power\n        G_p = np.maximum(G, 0.0) ** power\n        scores = G_p @ R.toarray()\n        print(f\"User-KNN power={power} took {time.time()-t_p:.2f}s\")\n        evaluate_scores(scores, f\"User-KNN power={power}\")\n\n    # 2. User-User EASE\n    print(\"Testing User-User EASE...\")\n    # Recompute G with diagonal\n    G_ease = (R_norm @ R_norm.T).toarray()\n    for reg in [200.0, 500.0, 1000.0, 2000.0]:\n        t_inv = time.time()\n        G_reg = G_ease.copy()\n        np.fill_diagonal(G_reg, G_reg.diagonal() + reg)\n        c, lower = scipy.linalg.cho_factor(G_reg)\n        P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))\n        B = -P / np.diag(P)[:, None]\n        np.fill_diagonal(B, 0.0)\n        scores = B @ R.toarray()\n        print(f\"EASE reg={reg} took {time.time()-t_inv:.2f}s\")\n        evaluate_scores(scores, f\"User-EASE reg={reg}\")\n\nif __name__ == \"__main__\":\n    main()\n",
  "file_path": "/app/solution/exp1.py"
}
### Tool output · 2026-09-29T04:42:55.361Z
Successfully overwrote file: /app/solution/exp1.py. Here is the updated code:
...
def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}")

    t0 = time.time()
    cutoff_ts = 24786800
    # Include up to 150 days of history
    min_ts = cutoff_ts - 150 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 60.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)
    print(f"R_listens built in {time.time()-t0:.2f}s, nnz={R_listens.nnz}")

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    R = (R_listens + R_pref).tocsr()
    del R_listens, R_pref
    print(f"R combined: shape={R.shape}, nnz={R.nnz}")

    def evaluate_scores(scores, name):
        scores = scores.copy()
        scores[~mask] = -np.inf
        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = scores[row_idx, top100_idx]
...
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)")
        return res["quality_ndcg@10"]

    # 1. User-User Cosine KNN
    print("Testing User-User Cosine Similarity...")
    t0 = time.time()
    # Normalize rows
    row_norms = sp.linalg.norm(R, axis=1)
    row_norms[row_norms == 0] = 1.0
    R_norm = sp.diags(1.0 / row_norms) @ R

    # Dense Gram matrix G = R_norm @ R_norm.T
    G = (R_norm @ R_norm.T).toarray()
    np.fill_diagonal(G, 0.0)
    print(f"User Gram matrix computed in {time.time()-t0:.2f}s")

    for power in [1.0, 2.0, 3.0]:
        t_p = time.time()
        # Non-negative weights with power
        G_p = np.maximum(G, 0.0) ** power
        scores = G_p @ R.toarray()
        print(f"User-KNN power={power} took {time.time()-t_p:.2f}s")
        evaluate_scores(scores, f"User-KNN power={power}")

    # 2. User-User EASE
    print("Testing User-User EASE...")
    # Recompute G with diagonal
    G_ease = (R_norm @ R_norm.T).toarray()
    for reg in [200.0, 500.0, 1000.0, 2000.0]:
        t_inv = time.time()
        G_reg = G_ease.copy()
        np.fill_diagonal(G_reg, G_reg.diagonal() + reg)
        c, lower = scipy.linalg.cho_factor(G_reg)
        P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))
        B = -P / np.diag(P)[:, None]
        np.fill_diagonal(B, 0.0)
        scores = B @ R.toarray()
        print(f"EASE reg={reg} took {time.time()-t_inv:.2f}s")
        evaluate_scores(scores, f"User-EASE reg={reg}")

if __name__ == "__main__":
    main()


## Step 42 · agent · 2026-09-29T04:42:57.455Z

### run_shell_command · 2026-09-29T04:42:57.455Z
{
  "description": "Run exp1.py to test models on validation set",
  "command": "python3 /app/solution/exp1.py"
}
### Tool output · 2026-09-29T04:46:18.232Z
<untrusted_context>
Output: Setup loaded in 0.61s. Scored users: 4263
R_listens built in 2.51s, nnz=5675716
R combined: shape=(8456, 30000), nnz=5734362
Testing User-User Cosine Similarity...
User Gram matrix computed in 8.12s
User-KNN power=1.0 took 20.04s
[User-KNN power=1.0] NDCG@10: 0.018398 (4263 users)
User-KNN power=2.0 took 20.00s
[User-KNN power=2.0] NDCG@10: 0.021747 (4263 users)
User-KNN power=3.0 took 20.54s
[User-KNN power=3.0] NDCG@10: 0.023824 (4263 users)
Testing User-User EASE...
EASE reg=200.0 took 27.02s
[User-EASE reg=200.0] NDCG@10: 0.021402 (4263 users)
EASE reg=500.0 took 27.01s
[User-EASE reg=500.0] NDCG@10: 0.019659 (4263 users)
EASE reg=1000.0 took 27.09s
[User-EASE reg=1000.0] NDCG@10: 0.019020 (4263 users)
EASE reg=2000.0 took 27.09s
[User-EASE reg=2000.0] NDCG@10: 0.018676 (4263 users)
Process Group PGID: 2243
</untrusted_context>

## Step 43 · agent · 2026-09-29T04:46:33.457Z

### Recorded reasoning summary
Analyzing User-KNN Performance

I'm thrilled to report that the User-KNN model with a power of 3.0 has significantly surpassed the baseline popularity metric, achieving more than double the original NDCG@10 score. My current focus is on understanding the underlying reasons for this impressive improvement.

### Recorded reasoning summary
Exploring Model Strengths

I'm now delving into the theoretical underpinnings of why User-User KNN with elevated powers, like 3.0, excels. The current hypothesis suggests that this amplifies genuine user similarities while suppressing spurious ones in sparse, high-dimensional data. I am also considering the impact of top-K filtering on this phenomenon.

### Recorded reasoning summary
Investigating Item Similarity Computation

I'm now exploring the computational feasibility of item-item similarity calculations, specifically how quickly we can derive top-k similarities. I'm considering matrix multiplications like $R C^T$ and then $(R C^T) C$, and also assessing the performance of sparse matrix multiplication for $C^T C$ given $C$'s sparsity.

### run_shell_command · 2026-09-29T04:46:33.457Z
{
  "description": "Benchmark chunked item-item similarity",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# Test sparse matrix multiplication C.T @ C\n# Create a sparse matrix with 8456 rows, 30000 cols, 5.7M nnz\nnp.random.seed(42)\nrows = np.random.randint(0, 8456, size=5700000)\ncols = np.random.randint(0, 30000, size=5700000)\ndata = np.ones(5700000, dtype=np.float32)\nC = sp.csr_matrix((data, (rows, cols)), shape=(8456, 30000))\nC = C.tocsc()\nprint('C ready')\nt0 = time.time()\n# Let us test multiplying a chunk of 1000 items\nchunk = C[:, :1000]\nsim = (C.T @ chunk)\nprint(f'1000 items similarity took {time.time()-t0:.2f}s, nnz={sim.nnz}')\n\""
}
### Tool output · 2026-09-29T04:46:35.490Z
<untrusted_context>
Output: C ready
1000 items similarity took 0.88s, nnz=29547668
Process Group PGID: 2288
</untrusted_context>

## Step 44 · agent · 2026-09-29T04:46:45.841Z

### Recorded reasoning summary
Analyzing Matrix Density

My recent focus has been on observing the sparsity of matrices. The current analysis highlights an unexpectedly high number of non-zero elements (nnz=29547668) for just 1,000 items, suggesting a near-dense structure due to overlapping columns in the random matrix. This starkly contrasts with the expected sparsity of the User-User matrix.

### Recorded reasoning summary
Exploring Matrix Efficiency

I've realized the $8456 \times 8456$ matrix, while large, is actually quite manageable in dense format, fitting into about 286 MB. This means dense operations are fast and exact, and I've already computed $G$ in just over 8 seconds. I'm now considering further analysis on $G$, including power scaling and exploring Top-K nearest neighbor approaches, as well as incorporating TF-IDF and BM25 item weighting to enhance similarity metrics.

### Recorded reasoning summary
Refining Similarity Calculations

I'm now considering how to better refine similarity calculations. Specifically, I'm investigating the impact of weighting columns by IDF before computing User-User cosine similarity, hypothesizing that this will significantly boost the influence of shared obscure tracks over popular ones. I'm also exploring how item popularity might influence candidate scoring and potential damping or prior combination techniques for the final scores.

### update_topic · 2026-09-29T04:46:45.841Z
{
  "strategic_intent": "Experiment with IDF weighting, Top-K neighbors, Power tuning, and TruncatedSVD on validation set.",
  "title": "Advanced CF and Feature Engineering",
  "summary": "User-User KNN with power=3.0 achieved 0.023824 NDCG@10, more than double the popularity baseline. Now testing IDF column weighting, neighborhood top-K sparsification, power tuning, TruncatedSVD, and hybrid scoring on the public validation window."
}
### Tool output · 2026-09-29T04:46:45.846Z
Current topic: "Advanced CF and Feature Engineering"
Topic summary: User-User KNN with power=3.0 achieved 0.023824 NDCG@10, more than double the popularity baseline. Now testing IDF column weighting, neighborhood top-K sparsification, power tuning, TruncatedSVD, and hybrid scoring on the public validation window.

Strategic Intent: Experiment with IDF weighting, Top-K neighbors, Power tuning, and TruncatedSVD on validation set.

## Step 45 · agent · 2026-09-29T04:47:05.926Z

### Recorded reasoning summary
Developing Experimentation Script

I'm currently focused on creating `exp2.py` to evaluate IDF weighting for user similarity. The formula $IDF_j = \log((N + 1) / (n_j + 1)) + 1$ is being integrated for systematic testing.

### write_file · 2026-09-29T04:47:05.926Z
{
  "instruction": "Systematic experimentation with IDF, powers, top-k, and SVD",
  "file_path": "/app/solution/exp2.py",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\")\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    # Include up to 180 days of history\n    min_ts = cutoff_ts - 180 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        # Time decay with half-life ~60 days\n        decay = np.exp(-days / 60.0)\n        # Quality: >= 80% is high, 50-80% is medium, < 50% is low\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    R = (R_listens + R_pref).tocsr()\n    del R_listens, R_pref\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\")\n\n    # Compute IDF per item\n    # Item user frequency (number of users who have > 0 interaction)\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n    print(\"Computed item IDF weights.\")\n\n    def evaluate_scores(scores, name):\n        scores = scores.copy()\n        scores[~mask] = -np.inf\n        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = scores[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\")\n        return res[\"quality_ndcg@10\"], scores\n\n    # 1. Cosine similarity on R vs R_idf\n    for use_idf in [False, True]:\n        label = \"IDF\" if use_idf else \"NoIDF\"\n        mat = R_idf if use_idf else R\n        row_norms = sp.linalg.norm(mat, axis=1)\n        row_norms[row_norms == 0] = 1.0\n        mat_norm = sp.diags(1.0 / row_norms) @ mat\n\n        t0 = time.time()\n        G = (mat_norm @ mat_norm.T).toarray()\n        np.fill_diagonal(G, 0.0)\n        print(f\"Gram matrix ({label}) computed in {time.time()-t0:.2f}s\")\n\n        for power in [2.0, 3.0, 4.0, 5.0]:\n            G_p = np.maximum(G, 0.0) ** power\n            # Try predicting with R.toarray()\n            scores = G_p @ R.toarray()\n            evaluate_scores(scores, f\"User-KNN ({label}) power={power}\")\n\n    # 2. Test Top-K neighbors filtering\n    print(\"\\nTesting Top-K neighbors filtering on best Gram matrix (IDF)...\")\n    row_norms = sp.linalg.norm(R_idf, axis=1)\n    row_norms[row_norms == 0] = 1.0\n    R_norm = sp.diags(1.0 / row_norms) @ R_idf\n    G = (R_norm @ R_norm.T).toarray()\n    np.fill_diagonal(G, 0.0)\n\n    for top_k in [100, 200, 500]:\n        for power in [2.0, 3.0, 4.0]:\n            # Keep top-k per row\n            G_topk = np.zeros_like(G)\n            # Find threshold\n            idx = np.argpartition(-G, top_k, axis=1)[:, :top_k]\n            row_idx = np.arange(num_users)[:, None]\n            val = G[row_idx, idx]\n            val_p = np.maximum(val, 0.0) ** power\n            np.put_along_axis(G_topk, idx, val_p, axis=1)\n\n            scores = G_topk @ R.toarray()\n            evaluate_scores(scores, f\"User-KNN (IDF) topK={top_k} power={power}\")\n\n    # 3. Test TruncatedSVD\n    print(\"\\nTesting TruncatedSVD...\")\n    for n_comp in [128, 256, 512]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        scores_svd = U @ V\n        print(f\"SVD n_comp={n_comp} took {time.time()-t0:.2f}s\")\n        evaluate_scores(scores_svd, f\"TruncatedSVD n_comp={n_comp}\")\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-29T04:47:05.936Z
Successfully created and wrote to new file: /app/solution/exp2.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}")

    t0 = time.time()
    cutoff_ts = 24786800
    # Include up to 180 days of history
    min_ts = cutoff_ts - 180 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        # Time decay with half-life ~60 days
        decay = np.exp(-days / 60.0)
        # Quality: >= 80% is high, 50-80% is medium, < 50% is low
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    R = (R_listens + R_pref).tocsr()
    del R_listens, R_pref
    print(f"R combined: shape={R.shape}, nnz={R.nnz}")

    # Compute IDF per item
    # Item user frequency (number of users who have > 0 interaction)
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()
    print("Computed item IDF weights.")

    def evaluate_scores(scores, name):
        scores = scores.copy()
        scores[~mask] = -np.inf
        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = scores[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)")
        return res["quality_ndcg@10"], scores

    # 1. Cosine similarity on R vs R_idf
    for use_idf in [False, True]:
        label = "IDF" if use_idf else "NoIDF"
        mat = R_idf if use_idf else R
        row_norms = sp.linalg.norm(mat, axis=1)
        row_norms[row_norms == 0] = 1.0
        mat_norm = sp.diags(1.0 / row_norms) @ mat

        t0 = time.time()
        G = (mat_norm @ mat_norm.T).toarray()
        np.fill_diagonal(G, 0.0)
        print(f"Gram matrix ({label}) computed in {time.time()-t0:.2f}s")

        for power in [2.0, 3.0, 4.0, 5.0]:
            G_p = np.maximum(G, 0.0) ** power
            # Try predicting with R.toarray()
            scores = G_p @ R.toarray()
            evaluate_scores(scores, f"User-KNN ({label}) power={power}")

    # 2. Test Top-K neighbors filtering
    print("\nTesting Top-K neighbors filtering on best Gram matrix (IDF)...")
    row_norms = sp.linalg.norm(R_idf, axis=1)
    row_norms[row_norms == 0] = 1.0
    R_norm = sp.diags(1.0 / row_norms) @ R_idf
    G = (R_norm @ R_norm.T).toarray()
    np.fill_diagonal(G, 0.0)

    for top_k in [100, 200, 500]:
        for power in [2.0, 3.0, 4.0]:
            # Keep top-k per row
            G_topk = np.zeros_like(G)
            # Find threshold
            idx = np.argpartition(-G, top_k, axis=1)[:, :top_k]
            row_idx = np.arange(num_users)[:, None]
            val = G[row_idx, idx]
            val_p = np.maximum(val, 0.0) ** power
            np.put_along_axis(G_topk, idx, val_p, axis=1)

            scores = G_topk @ R.toarray()
            evaluate_scores(scores, f"User-KNN (IDF) topK={top_k} power={power}")

    # 3. Test TruncatedSVD
    print("\nTesting TruncatedSVD...")
    for n_comp in [128, 256, 512]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        scores_svd = U @ V
        print(f"SVD n_comp={n_comp} took {time.time()-t0:.2f}s")
        evaluate_scores(scores_svd, f"TruncatedSVD n_comp={n_comp}")

if __name__ == "__main__":
    main()


## Step 46 · agent · 2026-09-29T04:47:09.575Z

### run_shell_command · 2026-09-29T04:47:09.575Z
{
  "description": "Run exp2.py to compare IDF, power, top-k, and SVD",
  "command": "python3 /app/solution/exp2.py"
}
### Tool output · 2026-09-29T04:52:12.347Z
<untrusted_context>
Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.
</untrusted_context>

## Step 47 · agent · 2026-09-29T04:52:25.287Z

### Recorded reasoning summary
Investigating Execution Delays

I'm currently pinpointing the cause of the timeout. My attention is focused on why no output was generated prior to cancellation, suspecting Python's output buffering behavior might be at play. I'm also examining the `R.toarray()` computation within the loop for potential performance bottlenecks.

### Recorded reasoning summary
Optimizing Matrix Multiplication

I've identified the core issue: repeated, costly matrix multiplications are exceeding the timeout. Specifically, the conversion of the sparse matrix R to a dense array before multiplication is the bottleneck. I’m now exploring direct sparse-dense multiplication using `(R.T @ G_p.T).T` or other optimized scipy methods.

### run_shell_command · 2026-09-29T04:52:25.287Z
{
  "description": "Benchmark A @ B matmul with pre-allocated dense array",
  "command": "python3 -c \"\nimport time\nimport numpy as np\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nC = A @ B\nprint(f'Dense matmul took {time.time()-t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T04:52:51.591Z
<untrusted_context>
Output: Dense matmul took 19.84s
Process Group PGID: 2357
</untrusted_context>

## Step 48 · agent · 2026-09-29T04:53:01.299Z

### Recorded reasoning summary
Refining Matrix Multiplication Timings

I'm analyzing the recent matrix multiplication performance. It appears a single operation is taking approximately 20 seconds, largely due to the immense number of floating-point operations involved ($4.3 \times 10^{12}$). This means executing the planned 17 matrix multiplications would consume roughly 340 seconds. I am now re-evaluating the necessity of running all 17 operations.

### run_shell_command · 2026-09-29T04:53:01.299Z
{
  "description": "Benchmark sparse G @ sparse R matmul",
  "command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# G_sparse has 100 neighbors per row\nn = 8456\nrows = np.repeat(np.arange(n), 100)\ncols = np.random.randint(0, n, size=n*100)\ndata = np.random.rand(n*100).astype(np.float32)\nG_sp = sp.csr_matrix((data, (rows, cols)), shape=(n, n))\n\n# R_csr has ~5.7M nonzeros\nR_rows = np.random.randint(0, n, size=5700000)\nR_cols = np.random.randint(0, 30000, size=5700000)\nR_data = np.random.rand(5700000).astype(np.float32)\nR_sp = sp.csr_matrix((R_data, (R_rows, R_cols)), shape=(n, 30000))\n\nt0 = time.time()\nscores = G_sp @ R_sp\nprint(f'Sparse matmul took {time.time()-t0:.2f}s, nnz={scores.nnz}')\n\""
}
### Tool output · 2026-09-29T04:53:08.014Z
<untrusted_context>
Output: Sparse matmul took 5.55s, nnz=226502931
Process Group PGID: 2367
</untrusted_context>

## Step 49 · agent · 2026-09-29T04:53:11.416Z

### Recorded reasoning summary
Optimizing SVD Performance

I'm excited to report that sparse matrix multiplication is now incredibly fast, clocking in at just 5.55 seconds. My current focus is on TruncatedSVD. I've identified the dimensions of U and V as (8456, k) and (k, 30000) respectively, and I'm now benchmarking the speed of their matrix product U @ V.

### run_shell_command · 2026-09-29T04:53:11.416Z
{
  "command": "python3 -c \"\nimport time\nimport numpy as np\n\nU = np.random.randn(8456, 256).astype(np.float32)\nV = np.random.randn(256, 30000).astype(np.float32)\nt0 = time.time()\nscores = U @ V\nprint(f'SVD matmul took {time.time()-t0:.2f}s')\n\"",
  "description": "Benchmark SVD matmul"
}
### Tool output · 2026-09-29T04:53:12.601Z
<untrusted_context>
Output: SVD matmul took 0.71s
Process Group PGID: 2384
</untrusted_context>

## Step 50 · agent · 2026-09-29T04:53:28.804Z

### Recorded reasoning summary
Testing Model Performance

I'm focusing on evaluating TruncatedSVD against User-User KNN. A dedicated script, `test_models.py`, is being prepared with immediate print output for clear validation score tracking.

### write_file · 2026-09-29T04:53:28.804Z
{
  "file_path": "/app/solution/test_models.py",
  "instruction": "Targeted model evaluation script",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    # Include up to 180 days of history\n    min_ts = cutoff_ts - 180 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 60.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    R = (R_listens + R_pref).tocsr()\n    del R_listens, R_pref\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores(scores, name):\n        scores = scores.copy()\n        scores[~mask] = -np.inf\n        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = scores[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"], scores\n\n    # Test TruncatedSVD on R_idf\n    print(\"\\n--- Testing TruncatedSVD on R_idf ---\", flush=True)\n    for n_comp in [64, 128, 256, 384]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        scores_svd = (U @ V).astype(np.float32)\n        print(f\"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s\", flush=True)\n        evaluate_scores(scores_svd, f\"TruncatedSVD n_comp={n_comp}\")\n\n    # Test SVD on R (without IDF)\n    print(\"\\n--- Testing TruncatedSVD on R (No IDF) ---\", flush=True)\n    for n_comp in [128, 256]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R)\n        V = svd.components_\n        scores_svd = (U @ V).astype(np.float32)\n        print(f\"SVD (No IDF) n_comp={n_comp} fitted in {time.time()-t0:.2f}s\", flush=True)\n        evaluate_scores(scores_svd, f\"TruncatedSVD (No IDF) n_comp={n_comp}\")\n\n    # Test User-User Cosine with IDF and power 3, 4, 5\n    print(\"\\n--- Testing User-KNN with IDF ---\", flush=True)\n    row_norms = sp.linalg.norm(R_idf, axis=1)\n    row_norms[row_norms == 0] = 1.0\n    mat_norm = sp.diags(1.0 / row_norms) @ R_idf\n\n    t0 = time.time()\n    G_idf = (mat_norm @ mat_norm.T).toarray()\n    np.fill_diagonal(G_idf, 0.0)\n    print(f\"G_idf computed in {time.time()-t0:.2f}s\", flush=True)\n\n    R_dense = R.toarray()\n\n    for p in [3.0, 4.0, 5.0]:\n        t0 = time.time()\n        G_p = np.maximum(G_idf, 0.0) ** p\n        scores_knn = G_p @ R_dense\n        print(f\"User-KNN (IDF) power={p} matmul took {time.time()-t0:.2f}s\", flush=True)\n        evaluate_scores(scores_knn, f\"User-KNN (IDF) power={p}\")\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-29T04:53:28.815Z
Successfully created and wrote to new file: /app/solution/test_models.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    # Include up to 180 days of history
    min_ts = cutoff_ts - 180 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 60.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    R = (R_listens + R_pref).tocsr()
    del R_listens, R_pref
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores(scores, name):
        scores = scores.copy()
        scores[~mask] = -np.inf
        top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = scores[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"], scores

    # Test TruncatedSVD on R_idf
    print("\n--- Testing TruncatedSVD on R_idf ---", flush=True)
    for n_comp in [64, 128, 256, 384]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        scores_svd = (U @ V).astype(np.float32)
        print(f"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s", flush=True)
        evaluate_scores(scores_svd, f"TruncatedSVD n_comp={n_comp}")

    # Test SVD on R (without IDF)
    print("\n--- Testing TruncatedSVD on R (No IDF) ---", flush=True)
    for n_comp in [128, 256]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R)
        V = svd.components_
        scores_svd = (U @ V).astype(np.float32)
        print(f"SVD (No IDF) n_comp={n_comp} fitted in {time.time()-t0:.2f}s", flush=True)
        evaluate_scores(scores_svd, f"TruncatedSVD (No IDF) n_comp={n_comp}")

    # Test User-User Cosine with IDF and power 3, 4, 5
    print("\n--- Testing User-KNN with IDF ---", flush=True)
    row_norms = sp.linalg.norm(R_idf, axis=1)
    row_norms[row_norms == 0] = 1.0
    mat_norm = sp.diags(1.0 / row_norms) @ R_idf

    t0 = time.time()
    G_idf = (mat_norm @ mat_norm.T).toarray()
    np.fill_diagonal(G_idf, 0.0)
    print(f"G_idf computed in {time.time()-t0:.2f}s", flush=True)

    R_dense = R.toarray()

    for p in [3.0, 4.0, 5.0]:
        t0 = time.time()
        G_p = np.maximum(G_idf, 0.0) ** p
        scores_knn = G_p @ R_dense
        print(f"User-KNN (IDF) power={p} matmul took {time.time()-t0:.2f}s", flush=True)
        evaluate_scores(scores_knn, f"User-KNN (IDF) power={p}")

if __name__ == "__main__":
    main()


## Step 51 · agent · 2026-09-29T04:53:36.115Z

### run_shell_command · 2026-09-29T04:53:36.115Z
{
  "command": "python3 -u /app/solution/test_models.py",
  "description": "Run test_models.py to evaluate TruncatedSVD and User-KNN with IDF"
}
### Tool output · 2026-09-29T04:55:01.981Z
<untrusted_context>
Output: Setup loaded in 0.60s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=6266853

--- Testing TruncatedSVD on R_idf ---
SVD n_comp=64 fitted in 3.01s
[TruncatedSVD n_comp=64] NDCG@10: 0.024307 (4263 users)
SVD n_comp=128 fitted in 5.04s
[TruncatedSVD n_comp=128] NDCG@10: 0.025427 (4263 users)
SVD n_comp=256 fitted in 7.93s
[TruncatedSVD n_comp=256] NDCG@10: 0.025775 (4263 users)
SVD n_comp=384 fitted in 12.34s
[TruncatedSVD n_comp=384] NDCG@10: 0.025496 (4263 users)

--- Testing TruncatedSVD on R (No IDF) ---
SVD (No IDF) n_comp=128 fitted in 4.65s
[TruncatedSVD (No IDF) n_comp=128] NDCG@10: 0.025156 (4263 users)
SVD (No IDF) n_comp=256 fitted in 7.44s
[TruncatedSVD (No IDF) n_comp=256] NDCG@10: 0.023592 (4263 users)

--- Testing User-KNN with IDF ---
G_idf computed in 8.66s
User-KNN (IDF) power=3.0 matmul took 20.48s
/usr/bin/bash: line 5:  2402 Killed                  python3 -u /app/solution/test_models.py
Exit Code: 137
Process Group PGID: 2400
</untrusted_context>

## Step 52 · agent · 2026-09-29T04:55:13.670Z

### Recorded reasoning summary
Evaluating TruncatedSVD Performance

My recent runs with TruncatedSVD on R_idf are showing promising speed, completing in just under 8 seconds. The NDCG@10 score of 0.025775 indicates a baseline level of effectiveness. This approach is notably efficient in terms of memory and prediction time.

### Recorded reasoning summary
Adding Explicit Likes

I'm now considering incorporating explicit likes from `train_likes.parquet` into the interaction matrix. This explicit user feedback, including timestamps, could significantly enhance the model's ability to capture user preferences and push NDCG@10 scores higher, complementing the existing listening behavior features.

### Recorded reasoning summary
Incorporating Organic Likes

I'm realizing that not all likes are equal; organic likes, as noted in `train_likes.parquet`, represent a strong positive signal. I need to ensure this "grade 3" event is properly handled to better reflect user preference, especially considering multiple or recent organic likes.

**Testing BM25 Weighting**

Furthermore, I'm exploring BM25 weighting as a transformation for my interaction matrix. This approach, standard in information retrieval, should help by saturating high-frequency counts and normalizing for varying user activity lengths before applying SVD.

### run_shell_command · 2026-09-29T04:55:13.670Z
{
  "description": "Analyze ground truth relevance vs item popularity rank",
  "command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\nall_graded_items = []\ngrades = []\nfor uid, items in graded.items():\n    for item, grade in items.items():\n        all_graded_items.append(item)\n        grades.append(grade)\n\npop_ranks = [item_pop.get(it, 30000) for it in all_graded_items]\ngrades = np.array(grades)\npop_ranks = np.array(pop_ranks)\n\nprint('Total relevant events in validation:', len(pop_ranks))\nprint('Popularity rank percentiles for all relevant items:')\nfor q in [0.1, 0.25, 0.5, 0.75, 0.9]:\n    print(f'quantile {q:.2f}: rank {np.quantile(pop_ranks, q):.0f}')\n\nprint('\\nPopularity rank percentiles for Grade 3 (likes):')\np3 = pop_ranks[grades == 3]\nfor q in [0.1, 0.25, 0.5, 0.75, 0.9]:\n    print(f'quantile {q:.2f}: rank {np.quantile(p3, q):.0f}')\n\""
}
### Tool output · 2026-09-29T04:55:15.105Z
<untrusted_context>
Output: Total relevant events in validation: 37769
Popularity rank percentiles for all relevant items:
quantile 0.10: rank 428
quantile 0.25: rank 1733
quantile 0.50: rank 5622
quantile 0.75: rank 13374
quantile 0.90: rank 21680

Popularity rank percentiles for Grade 3 (likes):
quantile 0.10: rank 618
quantile 0.25: rank 2107
quantile 0.50: rank 6371
quantile 0.75: rank 14181
quantile 0.90: rank 21802
Process Group PGID: 2427
</untrusted_context>

## Step 53 · agent · 2026-09-29T04:55:36.469Z

### Recorded reasoning summary
Analyzing Popularity Trends

My current focus is on dissecting the popularity distribution of relevant discovery items. I've noted a significant skew towards the upper half, with a median rank of 5,622 and a substantial portion residing within the top percentiles, suggesting a power-law characteristic.

### Recorded reasoning summary
Testing Blended Recommendations

I'm now exploring how to blend SVD predictions with catalog popularity. My current experiments involve a script to test different weighting factors for this blend, alongside variations in feature engineering for the SVD model itself, including different data window sizes and weighting schemes.

### write_file · 2026-09-29T04:55:36.469Z
{
  "file_path": "/app/solution/exp3.py",
  "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    # Include up to 240 days of history\n    min_ts = cutoff_ts - 240 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 75.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    # Also add organic likes from train_likes.parquet\n    train_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n    m_tl_item = (train_likes.item_id < len(item_map))\n    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]\n    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]\n    tl_org = train_likes.is_organic[m_tl_item].to_numpy()\n    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()\n    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)\n    tl_u = tl_u[tl_valid]\n    tl_i = tl_i[tl_valid]\n    tl_org = tl_org[tl_valid]\n    tl_ts = tl_ts[tl_valid]\n    tl_days = (cutoff_ts - tl_ts) / 86400.0\n    tl_decay = np.exp(-tl_days / 75.0)\n    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay\n    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n\n    R = (R_listens + R_pref + R_likes).tocsr()\n    del R_listens, R_pref, R_likes\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores(scores, name):\n        s = scores.copy()\n        s[~mask] = -np.inf\n        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = s[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"]\n\n    # Test TruncatedSVD components\n    for n_comp in [200, 256, 320]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        print(f\"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s\", flush=True)\n\n        # Baseline SVD\n        scores_base = U @ V\n        evaluate_scores(scores_base, f\"SVD n_comp={n_comp} base\")\n\n        # Test Normalized User vector\n        u_norm = np.linalg.norm(U, axis=1, keepdims=True)\n        u_norm[u_norm == 0] = 1.0\n        scores_unorm = (U / u_norm) @ V\n        evaluate_scores(scores_unorm, f\"SVD n_comp={n_comp} user_normalized\")\n\n        # Test popularity blending on scores_base\n        # log_pop normalized to [0, 1]\n        log_pop = np.log1p(pop_listens)\n        log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n        # Standard deviation of scores_base\n        std_scores = np.std(scores_base)\n\n        for gamma in [0.01, 0.03, 0.05, 0.1, 0.2]:\n            scores_blend = scores_base + (gamma * std_scores) * log_pop[None, :]\n            evaluate_scores(scores_blend, f\"SVD n_comp={n_comp} + pop gamma={gamma}\")\n\nif __name__ == \"__main__\":\n    main()\n",
  "instruction": "Test feature enhancements, normalization, and popularity blending"
}
### Tool output · 2026-09-29T04:55:36.480Z
Successfully created and wrote to new file: /app/solution/exp3.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    # Include up to 240 days of history
    min_ts = cutoff_ts - 240 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 75.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    # Also add organic likes from train_likes.parquet
    train_likes = pd.read_parquet(DATA / "train_likes.parquet")
    m_tl_item = (train_likes.item_id < len(item_map))
    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]
    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]
    tl_org = train_likes.is_organic[m_tl_item].to_numpy()
    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()
    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)
    tl_u = tl_u[tl_valid]
    tl_i = tl_i[tl_valid]
    tl_org = tl_org[tl_valid]
    tl_ts = tl_ts[tl_valid]
    tl_days = (cutoff_ts - tl_ts) / 86400.0
    tl_decay = np.exp(-tl_days / 75.0)
    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay
    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()

    R = (R_listens + R_pref + R_likes).tocsr()
    del R_listens, R_pref, R_likes
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores(scores, name):
        s = scores.copy()
        s[~mask] = -np.inf
        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = s[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"]

    # Test TruncatedSVD components
    for n_comp in [200, 256, 320]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        print(f"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s", flush=True)

        # Baseline SVD
        scores_base = U @ V
        evaluate_scores(scores_base, f"SVD n_comp={n_comp} base")

        # Test Normalized User vector
        u_norm = np.linalg.norm(U, axis=1, keepdims=True)
        u_norm[u_norm == 0] = 1.0
        scores_unorm = (U / u_norm) @ V
        evaluate_scores(scores_unorm, f"SVD n_comp={n_comp} user_normalized")

        # Test popularity blending on scores_base
        # log_pop normalized to [0, 1]
        log_pop = np.log1p(pop_listens)
        log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())
        # Standard deviation of scores_base
        std_scores = np.std(scores_base)

        for gamma in [0.01, 0.03, 0.05, 0.1, 0.2]:
            scores_blend = scores_base + (gamma * std_scores) * log_pop[None, :]
            evaluate_scores(scores_blend, f"SVD n_comp={n_comp} + pop gamma={gamma}")

if __name__ == "__main__":
    main()


## Step 54 · agent · 2026-09-29T04:55:42.760Z

### run_shell_command · 2026-09-29T04:55:42.760Z
{
  "command": "python3 -u /app/solution/exp3.py",
  "description": "Run exp3.py to test enhancements and popularity blending"
}
### Tool output · 2026-09-29T04:56:19.116Z
<untrusted_context>
Output: Setup loaded in 0.59s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=7240426
SVD n_comp=200 fitted in 7.52s
[SVD n_comp=200 base] NDCG@10: 0.027756 (4263 users)
[SVD n_comp=200 user_normalized] NDCG@10: 0.027756 (4263 users)
[SVD n_comp=200 + pop gamma=0.01] NDCG@10: 0.027762 (4263 users)
[SVD n_comp=200 + pop gamma=0.03] NDCG@10: 0.027790 (4263 users)
[SVD n_comp=200 + pop gamma=0.05] NDCG@10: 0.027738 (4263 users)
[SVD n_comp=200 + pop gamma=0.1] NDCG@10: 0.027940 (4263 users)
[SVD n_comp=200 + pop gamma=0.2] NDCG@10: 0.028123 (4263 users)
SVD n_comp=256 fitted in 7.62s
/usr/bin/bash: line 5:  2455 Killed                  python3 -u /app/solution/exp3.py
Exit Code: 137
Process Group PGID: 2453
</untrusted_context>

## Step 55 · agent · 2026-09-29T04:56:43.109Z

### Recorded reasoning summary
Improving SVD Performance

My recent focus has been on optimizing the Singular Value Decomposition (SVD) model. I've successfully boosted NDCG@10 to 0.028123 with `n_comp=200` and `pop gamma=0.2`, a significant leap from the baseline. I also identified a critical memory leak in `evaluate_scores` related to unmanaged arrays, which is now being addressed.

### write_file · 2026-09-29T04:56:43.109Z
{
  "content": "import time\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    min_ts = cutoff_ts - 240 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 75.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    # Organic likes from train_likes.parquet\n    train_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n    m_tl_item = (train_likes.item_id < len(item_map))\n    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]\n    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]\n    tl_org = train_likes.is_organic[m_tl_item].to_numpy()\n    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()\n    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)\n    tl_u = tl_u[tl_valid]\n    tl_i = tl_i[tl_valid]\n    tl_org = tl_org[tl_valid]\n    tl_ts = tl_ts[tl_valid]\n    tl_days = (cutoff_ts - tl_ts) / 86400.0\n    tl_decay = np.exp(-tl_days / 75.0)\n    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay\n    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n\n    R = (R_listens + R_pref + R_likes).tocsr()\n    del R_listens, R_pref, R_likes\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores_inplace(scores, name):\n        # We can evaluate without copying the whole scores array\n        # Create a working copy for masked argpartition\n        s = np.where(mask, scores, -np.inf)\n        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = s[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n        del s\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"]\n\n    log_org = np.log1p(org_listens)\n    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())\n\n    log_pop = np.log1p(pop_listens)\n    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n    best_score = 0.0\n    best_config = None\n\n    for n_comp in [200, 256, 320]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        print(f\"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s\", flush=True)\n\n        scores_base = (U @ V).astype(np.float32)\n        std_scores = np.std(scores_base)\n\n        for pop_type, pop_feature in [(\"org\", log_org), (\"all\", log_pop)]:\n            for gamma in [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0]:\n                scores = scores_base + (gamma * std_scores) * pop_feature[None, :]\n                sc = evaluate_scores_inplace(scores, f\"SVD n_comp={n_comp} + {pop_type} gamma={gamma}\")\n                if sc > best_score:\n                    best_score = sc\n                    best_config = (n_comp, pop_type, gamma)\n                del scores\n\n        del scores_base, U, V\n        gc.collect()\n\n    print(f\"\\nBEST VALIDATION SCORE: {best_score:.6f} with config {best_config}\", flush=True)\n\nif __name__ == \"__main__\":\n    main()\n",
  "instruction": "Memory-safe grid search for n_comp, popularity feature, and gamma",
  "file_path": "/app/solution/exp4.py"
}
### Tool output · 2026-09-29T04:56:43.123Z
Successfully created and wrote to new file: /app/solution/exp4.py. Here is the updated code:
import time
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    min_ts = cutoff_ts - 240 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 75.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    # Organic likes from train_likes.parquet
    train_likes = pd.read_parquet(DATA / "train_likes.parquet")
    m_tl_item = (train_likes.item_id < len(item_map))
    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]
    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]
    tl_org = train_likes.is_organic[m_tl_item].to_numpy()
    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()
    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)
    tl_u = tl_u[tl_valid]
    tl_i = tl_i[tl_valid]
    tl_org = tl_org[tl_valid]
    tl_ts = tl_ts[tl_valid]
    tl_days = (cutoff_ts - tl_ts) / 86400.0
    tl_decay = np.exp(-tl_days / 75.0)
    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay
    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()

    R = (R_listens + R_pref + R_likes).tocsr()
    del R_listens, R_pref, R_likes
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores_inplace(scores, name):
        # We can evaluate without copying the whole scores array
        # Create a working copy for masked argpartition
        s = np.where(mask, scores, -np.inf)
        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = s[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
        del s

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"]

    log_org = np.log1p(org_listens)
    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())

    log_pop = np.log1p(pop_listens)
    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

    best_score = 0.0
    best_config = None

    for n_comp in [200, 256, 320]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        print(f"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s", flush=True)

        scores_base = (U @ V).astype(np.float32)
        std_scores = np.std(scores_base)

        for pop_type, pop_feature in [("org", log_org), ("all", log_pop)]:
            for gamma in [0.1, 0.2, 0.3, 0.4, 0.5, 0.7, 1.0]:
                scores = scores_base + (gamma * std_scores) * pop_feature[None, :]
                sc = evaluate_scores_inplace(scores, f"SVD n_comp={n_comp} + {pop_type} gamma={gamma}")
                if sc > best_score:
                    best_score = sc
                    best_config = (n_comp, pop_type, gamma)
                del scores

        del scores_base, U, V
        gc.collect()

    print(f"\nBEST VALIDATION SCORE: {best_score:.6f} with config {best_config}", flush=True)

if __name__ == "__main__":
    main()


## Step 56 · agent · 2026-09-29T04:56:45.919Z

### run_shell_command · 2026-09-29T04:56:45.919Z
{
  "description": "Run exp4.py for grid search on validation set",
  "command": "python3 -u /app/solution/exp4.py"
}
### Tool output · 2026-09-29T04:58:30.254Z
<untrusted_context>
Output: Setup loaded in 0.58s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=7240426
SVD n_comp=200 fitted in 6.32s
[SVD n_comp=200 + org gamma=0.1] NDCG@10: 0.027776 (4263 users)
[SVD n_comp=200 + org gamma=0.2] NDCG@10: 0.028188 (4263 users)
[SVD n_comp=200 + org gamma=0.3] NDCG@10: 0.028186 (4263 users)
[SVD n_comp=200 + org gamma=0.4] NDCG@10: 0.028266 (4263 users)
[SVD n_comp=200 + org gamma=0.5] NDCG@10: 0.028312 (4263 users)
[SVD n_comp=200 + org gamma=0.7] NDCG@10: 0.028133 (4263 users)
[SVD n_comp=200 + org gamma=1.0] NDCG@10: 0.028583 (4263 users)
[SVD n_comp=200 + all gamma=0.1] NDCG@10: 0.027940 (4263 users)
[SVD n_comp=200 + all gamma=0.2] NDCG@10: 0.028123 (4263 users)
[SVD n_comp=200 + all gamma=0.3] NDCG@10: 0.028033 (4263 users)
[SVD n_comp=200 + all gamma=0.4] NDCG@10: 0.028279 (4263 users)
[SVD n_comp=200 + all gamma=0.5] NDCG@10: 0.028344 (4263 users)
[SVD n_comp=200 + all gamma=0.7] NDCG@10: 0.028513 (4263 users)
[SVD n_comp=200 + all gamma=1.0] NDCG@10: 0.028500 (4263 users)
SVD n_comp=256 fitted in 7.76s
[SVD n_comp=256 + org gamma=0.1] NDCG@10: 0.026434 (4263 users)
[SVD n_comp=256 + org gamma=0.2] NDCG@10: 0.026295 (4263 users)
[SVD n_comp=256 + org gamma=0.3] NDCG@10: 0.026529 (4263 users)
[SVD n_comp=256 + org gamma=0.4] NDCG@10: 0.026549 (4263 users)
[SVD n_comp=256 + org gamma=0.5] NDCG@10: 0.026598 (4263 users)
[SVD n_comp=256 + org gamma=0.7] NDCG@10: 0.026886 (4263 users)
[SVD n_comp=256 + org gamma=1.0] NDCG@10: 0.027173 (4263 users)
[SVD n_comp=256 + all gamma=0.1] NDCG@10: 0.026280 (4263 users)
[SVD n_comp=256 + all gamma=0.2] NDCG@10: 0.026283 (4263 users)
[SVD n_comp=256 + all gamma=0.3] NDCG@10: 0.026482 (4263 users)
[SVD n_comp=256 + all gamma=0.4] NDCG@10: 0.026649 (4263 users)
[SVD n_comp=256 + all gamma=0.5] NDCG@10: 0.027050 (4263 users)
[SVD n_comp=256 + all gamma=0.7] NDCG@10: 0.027361 (4263 users)
[SVD n_comp=256 + all gamma=1.0] NDCG@10: 0.027516 (4263 users)
SVD n_comp=320 fitted in 10.06s
[SVD n_comp=320 + org gamma=0.1] NDCG@10: 0.026759 (4263 users)
[SVD n_comp=320 + org gamma=0.2] NDCG@10: 0.026972 (4263 users)
[SVD n_comp=320 + org gamma=0.3] NDCG@10: 0.027053 (4263 users)
[SVD n_comp=320 + org gamma=0.4] NDCG@10: 0.027219 (4263 users)
[SVD n_comp=320 + org gamma=0.5] NDCG@10: 0.027338 (4263 users)
[SVD n_comp=320 + org gamma=0.7] NDCG@10: 0.027515 (4263 users)
[SVD n_comp=320 + org gamma=1.0] NDCG@10: 0.027782 (4263 users)
[SVD n_comp=320 + all gamma=0.1] NDCG@10: 0.026839 (4263 users)
[SVD n_comp=320 + all gamma=0.2] NDCG@10: 0.027189 (4263 users)
[SVD n_comp=320 + all gamma=0.3] NDCG@10: 0.027333 (4263 users)
[SVD n_comp=320 + all gamma=0.4] NDCG@10: 0.027527 (4263 users)
[SVD n_comp=320 + all gamma=0.5] NDCG@10: 0.027753 (4263 users)
[SVD n_comp=320 + all gamma=0.7] NDCG@10: 0.028020 (4263 users)
[SVD n_comp=320 + all gamma=1.0] NDCG@10: 0.028176 (4263 users)

BEST VALIDATION SCORE: 0.028583 with config (200, 'org', 1.0)
Process Group PGID: 2486
</untrusted_context>

## Step 57 · agent · 2026-09-29T04:58:51.042Z

### Recorded reasoning summary
Testing Hyperparameters Further

I've found that `n_comp=200` with gamma=1.0 yielded a score of 0.028583. Intriguingly, gamma=1.0 outperformed gamma=0.7. I'm now exploring higher gamma values (1.2 to 2.5) and different component counts (128, 160, 200) to see if further improvements are possible, and I'm also considering rank-normalization and combining multiple SVDs with varying random seeds.

### write_file · 2026-09-29T04:58:51.042Z
{
  "file_path": "/app/solution/exp5.py",
  "instruction": "Test ensemble of SVD + User-KNN + popularity tuning",
  "content": "import time\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    min_ts = cutoff_ts - 240 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 75.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    # Organic likes from train_likes.parquet\n    train_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n    m_tl_item = (train_likes.item_id < len(item_map))\n    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]\n    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]\n    tl_org = train_likes.is_organic[m_tl_item].to_numpy()\n    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()\n    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)\n    tl_u = tl_u[tl_valid]\n    tl_i = tl_i[tl_valid]\n    tl_org = tl_org[tl_valid]\n    tl_ts = tl_ts[tl_valid]\n    tl_days = (cutoff_ts - tl_ts) / 86400.0\n    tl_decay = np.exp(-tl_days / 75.0)\n    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay\n    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n\n    R = (R_listens + R_pref + R_likes).tocsr()\n    del R_listens, R_pref, R_likes\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores_inplace(scores, name):\n        s = np.where(mask, scores, -np.inf)\n        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = s[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n        del s\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"]\n\n    log_org = np.log1p(org_listens)\n    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())\n\n    log_pop = np.log1p(pop_listens)\n    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n    # 1. Compute User-KNN scores\n    print(\"Computing User-KNN scores...\", flush=True)\n    row_norms = sp.linalg.norm(R_idf, axis=1)\n    row_norms[row_norms == 0] = 1.0\n    mat_norm = sp.diags(1.0 / row_norms) @ R_idf\n    G_idf = (mat_norm @ mat_norm.T).toarray()\n    np.fill_diagonal(G_idf, 0.0)\n    G_3 = np.maximum(G_idf, 0.0) ** 3.0\n    del G_idf, mat_norm\n    gc.collect()\n\n    scores_knn = (G_3 @ R.toarray()).astype(np.float32)\n    del G_3\n    gc.collect()\n\n    # Standardize KNN scores per user\n    knn_mean = np.mean(scores_knn, axis=1, keepdims=True)\n    knn_std = np.std(scores_knn, axis=1, keepdims=True)\n    knn_std[knn_std == 0] = 1.0\n    scores_knn_norm = (scores_knn - knn_mean) / knn_std\n    del scores_knn\n    gc.collect()\n\n    # 2. Compute SVD scores with n_comp=180\n    print(\"Fitting SVD...\", flush=True)\n    svd = TruncatedSVD(n_components=180, algorithm=\"randomized\", random_state=42)\n    U = svd.fit_transform(R_idf)\n    V = svd.components_\n    scores_svd = (U @ V).astype(np.float32)\n    del U, V\n    gc.collect()\n\n    svd_mean = np.mean(scores_svd, axis=1, keepdims=True)\n    svd_std = np.std(scores_svd, axis=1, keepdims=True)\n    svd_std[svd_std == 0] = 1.0\n    scores_svd_norm = (scores_svd - svd_mean) / svd_std\n    del scores_svd\n    gc.collect()\n\n    # Test blending weights\n    for w_knn in [0.0, 0.15, 0.3, 0.5]:\n        base = scores_svd_norm + w_knn * scores_knn_norm\n        for gamma in [0.8, 1.0, 1.2, 1.5, 1.8, 2.2]:\n            scores = base + gamma * log_org[None, :]\n            evaluate_scores_inplace(scores, f\"Blend w_knn={w_knn} + org gamma={gamma}\")\n            del scores\n            gc.collect()\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-29T04:58:51.052Z
Successfully created and wrote to new file: /app/solution/exp5.py. Here is the updated code:
import time
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    min_ts = cutoff_ts - 240 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 75.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    # Organic likes from train_likes.parquet
    train_likes = pd.read_parquet(DATA / "train_likes.parquet")
    m_tl_item = (train_likes.item_id < len(item_map))
    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]
    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]
    tl_org = train_likes.is_organic[m_tl_item].to_numpy()
    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()
    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)
    tl_u = tl_u[tl_valid]
    tl_i = tl_i[tl_valid]
    tl_org = tl_org[tl_valid]
    tl_ts = tl_ts[tl_valid]
    tl_days = (cutoff_ts - tl_ts) / 86400.0
    tl_decay = np.exp(-tl_days / 75.0)
    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay
    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()

    R = (R_listens + R_pref + R_likes).tocsr()
    del R_listens, R_pref, R_likes
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores_inplace(scores, name):
        s = np.where(mask, scores, -np.inf)
        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = s[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
        del s

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"]

    log_org = np.log1p(org_listens)
    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())

    log_pop = np.log1p(pop_listens)
    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

    # 1. Compute User-KNN scores
    print("Computing User-KNN scores...", flush=True)
    row_norms = sp.linalg.norm(R_idf, axis=1)
    row_norms[row_norms == 0] = 1.0
    mat_norm = sp.diags(1.0 / row_norms) @ R_idf
    G_idf = (mat_norm @ mat_norm.T).toarray()
    np.fill_diagonal(G_idf, 0.0)
    G_3 = np.maximum(G_idf, 0.0) ** 3.0
    del G_idf, mat_norm
    gc.collect()

    scores_knn = (G_3 @ R.toarray()).astype(np.float32)
    del G_3
    gc.collect()

    # Standardize KNN scores per user
    knn_mean = np.mean(scores_knn, axis=1, keepdims=True)
    knn_std = np.std(scores_knn, axis=1, keepdims=True)
    knn_std[knn_std == 0] = 1.0
    scores_knn_norm = (scores_knn - knn_mean) / knn_std
    del scores_knn
    gc.collect()

    # 2. Compute SVD scores with n_comp=180
    print("Fitting SVD...", flush=True)
    svd = TruncatedSVD(n_components=180, algorithm="randomized", random_state=42)
    U = svd.fit_transform(R_idf)
    V = svd.components_
    scores_svd = (U @ V).astype(np.float32)
    del U, V
    gc.collect()

    svd_mean = np.mean(scores_svd, axis=1, keepdims=True)
    svd_std = np.std(scores_svd, axis=1, keepdims=True)
    svd_std[svd_std == 0] = 1.0
    scores_svd_norm = (scores_svd - svd_mean) / svd_std
    del scores_svd
    gc.collect()

    # Test blending weights
    for w_knn in [0.0, 0.15, 0.3, 0.5]:
        base = scores_svd_norm + w_knn * scores_knn_norm
        for gamma in [0.8, 1.0, 1.2, 1.5, 1.8, 2.2]:
            scores = base + gamma * log_org[None, :]
            evaluate_scores_inplace(scores, f"Blend w_knn={w_knn} + org gamma={gamma}")
            del scores
            gc.collect()

if __name__ == "__main__":
    main()


## Step 58 · agent · 2026-09-29T04:58:53.519Z

### run_shell_command · 2026-09-29T04:58:53.519Z
{
  "command": "python3 -u /app/solution/exp5.py",
  "description": "Run exp5.py to evaluate ensemble model"
}
### Tool output · 2026-09-29T04:59:40.532Z
<untrusted_context>
Output: Setup loaded in 0.58s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=7240426
Computing User-KNN scores...
Fitting SVD...
/usr/bin/bash: line 5:  2521 Killed                  python3 -u /app/solution/exp5.py
Exit Code: 137
Process Group PGID: 2519
</untrusted_context>

## Step 59 · agent · 2026-09-29T04:59:57.720Z

### Recorded reasoning summary
Analyzing Memory Usage

I'm currently investigating the memory footprint of our SVD implementation. Specifically, I'm pinpointing the impact of the `scores_knn_norm` and `scores_svd_norm` arrays, which together consume 2 GB. My focus is on understanding the internal memory allocations made by scikit-learn's randomized SVD during the `fit_transform` process, especially when handling large input matrices like `R_idf`.

### write_file · 2026-09-29T04:59:57.720Z
{
  "instruction": "Tuning SVD components and gamma",
  "file_path": "/app/solution/test_svd_tuning.py",
  "content": "import time\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    min_ts = cutoff_ts - 240 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 75.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    # Organic likes from train_likes.parquet\n    train_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n    m_tl_item = (train_likes.item_id < len(item_map))\n    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]\n    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]\n    tl_org = train_likes.is_organic[m_tl_item].to_numpy()\n    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()\n    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)\n    tl_u = tl_u[tl_valid]\n    tl_i = tl_i[tl_valid]\n    tl_org = tl_org[tl_valid]\n    tl_ts = tl_ts[tl_valid]\n    tl_days = (cutoff_ts - tl_ts) / 86400.0\n    tl_decay = np.exp(-tl_days / 75.0)\n    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay\n    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n\n    R = (R_listens + R_pref + R_likes).tocsr()\n    del R_listens, R_pref, R_likes\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores_inplace(scores, name):\n        s = np.where(mask, scores, -np.inf)\n        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = s[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n        del s\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"]\n\n    log_org = np.log1p(org_listens)\n    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())\n\n    log_pop = np.log1p(pop_listens)\n    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n    for n_comp in [160, 200, 240]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        print(f\"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s\", flush=True)\n\n        scores_base = (U @ V).astype(np.float32)\n        std_scores = np.std(scores_base)\n\n        for gamma in [0.8, 1.0, 1.2, 1.4, 1.7, 2.0, 2.5]:\n            scores = scores_base + (gamma * std_scores) * log_org[None, :]\n            evaluate_scores_inplace(scores, f\"SVD n_comp={n_comp} + org gamma={gamma}\")\n            del scores\n\n        del scores_base, U, V\n        gc.collect()\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-29T04:59:57.740Z
Successfully created and wrote to new file: /app/solution/test_svd_tuning.py. Here is the updated code:
import time
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    min_ts = cutoff_ts - 240 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 75.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    # Organic likes from train_likes.parquet
    train_likes = pd.read_parquet(DATA / "train_likes.parquet")
    m_tl_item = (train_likes.item_id < len(item_map))
    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]
    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]
    tl_org = train_likes.is_organic[m_tl_item].to_numpy()
    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()
    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)
    tl_u = tl_u[tl_valid]
    tl_i = tl_i[tl_valid]
    tl_org = tl_org[tl_valid]
    tl_ts = tl_ts[tl_valid]
    tl_days = (cutoff_ts - tl_ts) / 86400.0
    tl_decay = np.exp(-tl_days / 75.0)
    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay
    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()

    R = (R_listens + R_pref + R_likes).tocsr()
    del R_listens, R_pref, R_likes
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores_inplace(scores, name):
        s = np.where(mask, scores, -np.inf)
        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = s[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
        del s

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"]

    log_org = np.log1p(org_listens)
    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())

    log_pop = np.log1p(pop_listens)
    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

    for n_comp in [160, 200, 240]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        print(f"SVD n_comp={n_comp} fitted in {time.time()-t0:.2f}s", flush=True)

        scores_base = (U @ V).astype(np.float32)
        std_scores = np.std(scores_base)

        for gamma in [0.8, 1.0, 1.2, 1.4, 1.7, 2.0, 2.5]:
            scores = scores_base + (gamma * std_scores) * log_org[None, :]
            evaluate_scores_inplace(scores, f"SVD n_comp={n_comp} + org gamma={gamma}")
            del scores

        del scores_base, U, V
        gc.collect()

if __name__ == "__main__":
    main()


## Step 60 · agent · 2026-09-29T05:00:01.178Z

### run_shell_command · 2026-09-29T05:00:01.178Z
{
  "description": "Run test_svd_tuning.py to find optimal n_comp and gamma",
  "command": "python3 -u /app/solution/test_svd_tuning.py"
}
### Tool output · 2026-09-29T05:01:06.559Z
<untrusted_context>
Output: Setup loaded in 0.58s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=7240426
SVD n_comp=160 fitted in 6.21s
[SVD n_comp=160 + org gamma=0.8] NDCG@10: 0.028038 (4263 users)
[SVD n_comp=160 + org gamma=1.0] NDCG@10: 0.028180 (4263 users)
[SVD n_comp=160 + org gamma=1.2] NDCG@10: 0.028228 (4263 users)
[SVD n_comp=160 + org gamma=1.4] NDCG@10: 0.028232 (4263 users)
[SVD n_comp=160 + org gamma=1.7] NDCG@10: 0.028199 (4263 users)
[SVD n_comp=160 + org gamma=2.0] NDCG@10: 0.028502 (4263 users)
[SVD n_comp=160 + org gamma=2.5] NDCG@10: 0.028449 (4263 users)
SVD n_comp=200 fitted in 6.70s
[SVD n_comp=200 + org gamma=0.8] NDCG@10: 0.028413 (4263 users)
[SVD n_comp=200 + org gamma=1.0] NDCG@10: 0.028583 (4263 users)
[SVD n_comp=200 + org gamma=1.2] NDCG@10: 0.028772 (4263 users)
[SVD n_comp=200 + org gamma=1.4] NDCG@10: 0.028681 (4263 users)
[SVD n_comp=200 + org gamma=1.7] NDCG@10: 0.028644 (4263 users)
[SVD n_comp=200 + org gamma=2.0] NDCG@10: 0.028758 (4263 users)
[SVD n_comp=200 + org gamma=2.5] NDCG@10: 0.028601 (4263 users)
SVD n_comp=240 fitted in 7.16s
[SVD n_comp=240 + org gamma=0.8] NDCG@10: 0.027948 (4263 users)
[SVD n_comp=240 + org gamma=1.0] NDCG@10: 0.028137 (4263 users)
[SVD n_comp=240 + org gamma=1.2] NDCG@10: 0.028282 (4263 users)
[SVD n_comp=240 + org gamma=1.4] NDCG@10: 0.028291 (4263 users)
[SVD n_comp=240 + org gamma=1.7] NDCG@10: 0.028473 (4263 users)
[SVD n_comp=240 + org gamma=2.0] NDCG@10: 0.028406 (4263 users)
[SVD n_comp=240 + org gamma=2.5] NDCG@10: 0.028397 (4263 users)
Process Group PGID: 2552
</untrusted_context>

## Step 61 · agent · 2026-09-29T05:01:26.310Z

### Recorded reasoning summary
Improving SVD performance

I've made significant progress, achieving a nearly threefold improvement with `SVD n_comp=200 + org gamma=1.2`, reaching **0.028772** compared to the baseline of **0.0107**. I'm now investigating further avenues to enhance performance.

### write_file · 2026-09-29T05:01:26.310Z
{
  "instruction": "Test multi-seed SVD ensemble with joint popularity prior",
  "file_path": "/app/solution/exp6.py",
  "content": "import time\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n    t_start = time.time()\n    targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n    targets = set(targets_frame.uid.astype(int))\n    uids = targets_frame.uid.to_numpy(dtype=np.int64)\n    num_users = len(uids)\n\n    uid_map = np.full(1000001, -1, dtype=np.int32)\n    uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n    cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n    items = cat.item_id.to_numpy(dtype=np.int64)\n    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)\n    num_items = len(items)\n\n    item_map = np.full(9390295, -1, dtype=np.int32)\n    item_map[items] = np.arange(num_items, dtype=np.int32)\n\n    eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n    graded = validation_truth(eligibility, targets)\n    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n    print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\", flush=True)\n\n    t0 = time.time()\n    cutoff_ts = 24786800\n    min_ts = cutoff_ts - 240 * 86400\n\n    pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n    row_list, col_list, data_list = [], [], []\n\n    for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n        u_raw = batch[\"uid\"].to_numpy()\n        i_raw = batch[\"item_id\"].to_numpy()\n        ts = batch[\"timestamp\"].to_numpy()\n\n        m_ts = ts >= min_ts\n        if not np.any(m_ts):\n            continue\n        u_raw = u_raw[m_ts]\n        i_raw = i_raw[m_ts]\n        ts = ts[m_ts]\n        pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n        org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n        m_item = (i_raw < len(item_map))\n        i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n        i_idx[m_item] = item_map[i_raw[m_item]]\n\n        m_user = (u_raw < len(uid_map))\n        u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n        u_idx[m_user] = uid_map[u_raw[m_user]]\n\n        valid = (u_idx >= 0) & (i_idx >= 0)\n        if not np.any(valid):\n            continue\n\n        u_idx = u_idx[valid]\n        i_idx = i_idx[valid]\n        ts = ts[valid]\n        pct = pct[valid]\n        org = org[valid]\n\n        days = (cutoff_ts - ts) / 86400.0\n        decay = np.exp(-days / 75.0)\n        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n        boost = np.where(org == 1, 1.5, 1.0)\n        w = (quality * boost * decay).astype(np.float32)\n\n        row_list.append(u_idx)\n        col_list.append(i_idx)\n        data_list.append(w)\n\n    row_arr = np.concatenate(row_list)\n    col_arr = np.concatenate(col_list)\n    data_arr = np.concatenate(data_list)\n    del row_list, col_list, data_list\n\n    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n    del row_arr, col_arr, data_arr\n    R_listens.data = np.log1p(R_listens.data)\n\n    # Add validation preference state\n    val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n    pref_u = uid_map[val_pref.uid.to_numpy()]\n    pref_i = item_map[val_pref.item_id.to_numpy()]\n    pref_valid = (pref_u >= 0) & (pref_i >= 0)\n    pref_u = pref_u[pref_valid]\n    pref_i = pref_i[pref_valid]\n    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n    # Organic likes from train_likes.parquet\n    train_likes = pd.read_parquet(DATA / \"train_likes.parquet\")\n    m_tl_item = (train_likes.item_id < len(item_map))\n    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]\n    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]\n    tl_org = train_likes.is_organic[m_tl_item].to_numpy()\n    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()\n    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)\n    tl_u = tl_u[tl_valid]\n    tl_i = tl_i[tl_valid]\n    tl_org = tl_org[tl_valid]\n    tl_ts = tl_ts[tl_valid]\n    tl_days = (cutoff_ts - tl_ts) / 86400.0\n    tl_decay = np.exp(-tl_days / 75.0)\n    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay\n    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n\n    R = (R_listens + R_pref + R_likes).tocsr()\n    del R_listens, R_pref, R_likes\n    print(f\"R combined: shape={R.shape}, nnz={R.nnz}\", flush=True)\n\n    # Compute IDF per item\n    R_bin = (R > 0).astype(np.float32)\n    user_counts = np.array(R_bin.sum(axis=0)).ravel()\n    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n    idf_diag = sp.diags(idf.astype(np.float32))\n\n    # IDF-weighted matrix for similarity\n    R_idf = (R @ idf_diag).tocsr()\n\n    def evaluate_scores_inplace(scores, name):\n        s = np.where(mask, scores, -np.inf)\n        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]\n        row_idx = np.arange(num_users)[:, None]\n        top100_scores = s[row_idx, top100_idx]\n        order = np.argsort(-top100_scores, axis=1)\n        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n        del s\n\n        ranked = {\n            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n            for i in range(num_users)\n        }\n        res = score_rankings(ranked, graded)\n        print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\", flush=True)\n        return res[\"quality_ndcg@10\"]\n\n    log_org = np.log1p(org_listens)\n    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())\n\n    log_pop = np.log1p(pop_listens)\n    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n    # Multi-seed SVD ensemble\n    print(\"\\n--- Fitting 3-seed SVD ensemble (seeds 42, 101, 2026) ---\", flush=True)\n    scores_ens = np.zeros((num_users, num_items), dtype=np.float32)\n\n    for seed in [42, 101, 2026]:\n        t0 = time.time()\n        svd = TruncatedSVD(n_components=200, algorithm=\"randomized\", random_state=seed)\n        U = svd.fit_transform(R_idf)\n        V = svd.components_\n        sc = (U @ V).astype(np.float32)\n        scores_ens += sc / 3.0\n        print(f\"Seed {seed} fitted in {time.time()-t0:.2f}s\", flush=True)\n        del U, V, sc\n        gc.collect()\n\n    std_ens = np.std(scores_ens)\n\n    for gamma_org in [1.0, 1.2, 1.4]:\n        for gamma_all in [0.0, 0.2, 0.4]:\n            scores = scores_ens + (gamma_org * std_ens) * log_org[None, :] + (gamma_all * std_ens) * log_pop[None, :]\n            evaluate_scores_inplace(scores, f\"3-SVD Ensemble org={gamma_org} all={gamma_all}\")\n            del scores\n            gc.collect()\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-29T05:01:28.089Z
Successfully created and wrote to new file: /app/solution/exp6.py. Here is the updated code:
import time
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD

sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings

def main():
    t_start = time.time()
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    uids = targets_frame.uid.to_numpy(dtype=np.int64)
    num_users = len(uids)

    uid_map = np.full(1000001, -1, dtype=np.int32)
    uid_map[uids] = np.arange(num_users, dtype=np.int32)

    cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
    items = cat.item_id.to_numpy(dtype=np.int64)
    pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
    org_listens = cat.organic_positive_listens.to_numpy(dtype=np.float32)
    num_items = len(items)

    item_map = np.full(9390295, -1, dtype=np.int32)
    item_map[items] = np.arange(num_items, dtype=np.int32)

    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)
    mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)

    print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}", flush=True)

    t0 = time.time()
    cutoff_ts = 24786800
    min_ts = cutoff_ts - 240 * 86400

    pf = pq.ParquetFile(DATA / "train_listens.parquet")
    row_list, col_list, data_list = [], [], []

    for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
        u_raw = batch["uid"].to_numpy()
        i_raw = batch["item_id"].to_numpy()
        ts = batch["timestamp"].to_numpy()

        m_ts = ts >= min_ts
        if not np.any(m_ts):
            continue
        u_raw = u_raw[m_ts]
        i_raw = i_raw[m_ts]
        ts = ts[m_ts]
        pct = batch["played_ratio_pct"].to_numpy()[m_ts]
        org = batch["is_organic"].to_numpy()[m_ts]

        m_item = (i_raw < len(item_map))
        i_idx = np.full(len(i_raw), -1, dtype=np.int32)
        i_idx[m_item] = item_map[i_raw[m_item]]

        m_user = (u_raw < len(uid_map))
        u_idx = np.full(len(u_raw), -1, dtype=np.int32)
        u_idx[m_user] = uid_map[u_raw[m_user]]

        valid = (u_idx >= 0) & (i_idx >= 0)
        if not np.any(valid):
            continue

        u_idx = u_idx[valid]
        i_idx = i_idx[valid]
        ts = ts[valid]
        pct = pct[valid]
        org = org[valid]

        days = (cutoff_ts - ts) / 86400.0
        decay = np.exp(-days / 75.0)
        quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
        boost = np.where(org == 1, 1.5, 1.0)
        w = (quality * boost * decay).astype(np.float32)

        row_list.append(u_idx)
        col_list.append(i_idx)
        data_list.append(w)

    row_arr = np.concatenate(row_list)
    col_arr = np.concatenate(col_list)
    data_arr = np.concatenate(data_list)
    del row_list, col_list, data_list

    R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
    del row_arr, col_arr, data_arr
    R_listens.data = np.log1p(R_listens.data)

    # Add validation preference state
    val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
    pref_u = uid_map[val_pref.uid.to_numpy()]
    pref_i = item_map[val_pref.item_id.to_numpy()]
    pref_valid = (pref_u >= 0) & (pref_i >= 0)
    pref_u = pref_u[pref_valid]
    pref_i = pref_i[pref_valid]
    pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)

    R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)

    # Organic likes from train_likes.parquet
    train_likes = pd.read_parquet(DATA / "train_likes.parquet")
    m_tl_item = (train_likes.item_id < len(item_map))
    tl_i = item_map[train_likes.item_id[m_tl_item].to_numpy()]
    tl_u = uid_map[train_likes.uid[m_tl_item].to_numpy()]
    tl_org = train_likes.is_organic[m_tl_item].to_numpy()
    tl_ts = train_likes.timestamp[m_tl_item].to_numpy()
    tl_valid = (tl_u >= 0) & (tl_i >= 0) & (tl_ts >= min_ts)
    tl_u = tl_u[tl_valid]
    tl_i = tl_i[tl_valid]
    tl_org = tl_org[tl_valid]
    tl_ts = tl_ts[tl_valid]
    tl_days = (cutoff_ts - tl_ts) / 86400.0
    tl_decay = np.exp(-tl_days / 75.0)
    tl_w = np.where(tl_org == 1, 3.0, 1.5) * tl_decay
    R_likes = sp.coo_matrix((tl_w, (tl_u, tl_i)), shape=(num_users, num_items), dtype=np.float32).tocsr()

    R = (R_listens + R_pref + R_likes).tocsr()
    del R_listens, R_pref, R_likes
    print(f"R combined: shape={R.shape}, nnz={R.nnz}", flush=True)

    # Compute IDF per item
    R_bin = (R > 0).astype(np.float32)
    user_counts = np.array(R_bin.sum(axis=0)).ravel()
    idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
    idf_diag = sp.diags(idf.astype(np.float32))

    # IDF-weighted matrix for similarity
    R_idf = (R @ idf_diag).tocsr()

    def evaluate_scores_inplace(scores, name):
        s = np.where(mask, scores, -np.inf)
        top100_idx = np.argpartition(-s, 100, axis=1)[:, :100]
        row_idx = np.arange(num_users)[:, None]
        top100_scores = s[row_idx, top100_idx]
        order = np.argsort(-top100_scores, axis=1)
        sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
        del s

        ranked = {
            int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
            for i in range(num_users)
        }
        res = score_rankings(ranked, graded)
        print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)", flush=True)
        return res["quality_ndcg@10"]

    log_org = np.log1p(org_listens)
    log_org = (log_org - log_org.min()) / (log_org.max() - log_org.min())

    log_pop = np.log1p(pop_listens)
    log_pop = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())

    # Multi-seed SVD ensemble
    print("\n--- Fitting 3-seed SVD ensemble (seeds 42, 101, 2026) ---", flush=True)
    scores_ens = np.zeros((num_users, num_items), dtype=np.float32)

    for seed in [42, 101, 2026]:
        t0 = time.time()
        svd = TruncatedSVD(n_components=200, algorithm="randomized", random_state=seed)
        U = svd.fit_transform(R_idf)
        V = svd.components_
        sc = (U @ V).astype(np.float32)
        scores_ens += sc / 3.0
        print(f"Seed {seed} fitted in {time.time()-t0:.2f}s", flush=True)
        del U, V, sc
        gc.collect()

    std_ens = np.std(scores_ens)

    for gamma_org in [1.0, 1.2, 1.4]:
        for gamma_all in [0.0, 0.2, 0.4]:
            scores = scores_ens + (gamma_org * std_ens) * log_org[None, :] + (gamma_all * std_ens) * log_pop[None, :]
            evaluate_scores_inplace(scores, f"3-SVD Ensemble org={gamma_org} all={gamma_all}")
            del scores
            gc.collect()

if __name__ == "__main__":
    main()


## Step 62 · agent · 2026-09-29T05:01:31.927Z

### run_shell_command · 2026-09-29T05:01:31.927Z
{
  "command": "python3 -u /app/solution/exp6.py",
  "description": "Run exp6.py for multi-seed SVD ensemble"
}
### Tool output · 2026-09-29T05:02:15.163Z
<untrusted_context>
Output: Setup loaded in 0.59s. Scored users: 4263
R combined: shape=(8456, 30000), nnz=7240426

--- Fitting 3-seed SVD ensemble (seeds 42, 101, 2026) ---
Seed 42 fitted in 6.59s
Seed 101 fitted in 6.56s
Seed 2026 fitted in 6.94s
[3-SVD Ensemble org=1.0 all=0.0] NDCG@10: 0.028538 (4263 users)
[3-SVD Ensemble org=1.0 all=0.2] NDCG@10: 0.028754 (4263 users)
[3-SVD Ensemble org=1.0 all=0.4] NDCG@10: 0.028712 (4263 users)
[3-SVD Ensemble org=1.2 all=0.0] NDCG@10: 0.028746 (4263 users)
[3-SVD Ensemble org=1.2 all=0.2] NDCG@10: 0.028841 (4263 users)
[3-SVD Ensemble org=1.2 all=0.4] NDCG@10: 0.028872 (4263 users)
[3-SVD Ensemble org=1.4 all=0.0] NDCG@10: 0.028893 (4263 users)
[3-SVD Ensemble org=1.4 all=0.2] NDCG@10: 0.028841 (4263 users)
[3-SVD Ensemble org=1.4 all=0.4] NDCG@10: 0.028994 (4263 users)
Process Group PGID: 2585
</untrusted_context>

## Step 63 · agent · 2026-09-29T05:02:20.718Z

### Recorded reasoning summary
Testing Ensemble Parameters

My latest runs are showing promising NDCG@10 scores, nearing 0.0290. I'm now exploring variations like `org=1.5 all=0.5` and `org=1.6 all=0.4`, as well as `org=1.8`, to push this further. The speed is also impressive, with each seed completing in about 6.6 seconds.

### run_shell_command · 2026-09-29T05:02:20.718Z
{
  "description": "Check fast eval setup",
  "command": "python3 -c \"\nimport numpy as np\nimport pyarrow.parquet as pq\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom sklearn.decomposition import TruncatedSVD\nimport scipy.sparse as sp\nimport pandas as pd\n\n# Let's import the fitted components or run a fast eval\n\""
}
### Tool output · 2026-09-29T05:02:22.264Z
<untrusted_context>
Output: (empty)
Process Group PGID: 2618
</untrusted_context>

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