yambda-discovery-ranking-v01 / yambda-opus-high-2026-09-24-00-34-15-04330cf-3
Recorded model: claude-opus-5

## Step 1 · user · 2026-09-24T07:44:29.056Z

# 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.


### Harness context
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### Harness context
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    "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
    "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)",
    "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)",
    "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)"
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### Harness context
{
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  "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. 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Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch",
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### Harness context
{
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}

## Step 2 · agent · 2026-09-24T07:44:32.729Z

I'll start by exploring the data and tools.

### Bash · 2026-09-24T07:44:33.432Z
{
  "command": "ls -la /task/ /task/data/ && cat /task/requirements.txt && nproc && free -g",
  "description": "Inspect task directory"
}
### Tool output · 2026-09-24T07:44:33.501Z
/task/:
total 36
dr-xr-xr-x 1 root root 4096 Sep 24 07:41 .
drwxr-xr-x 1 root root 4096 Sep 24 07:43 ..
dr-xr-xr-x 1 root root 4096 Sep 24 07:37 data
-r--r--r-- 1 root root 5774 Sep 24 07:37 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 24 07:37 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 24 07:37 requirements.txt

/task/data/:
total 347392
dr-xr-xr-x 1 root root      4096 Sep 24 07:37 .
dr-xr-xr-x 1 root root      4096 Sep 24 07:41 ..
-r--r--r-- 1 root root    349352 Sep 24 07:37 candidate_catalog.parquet
-r--r--r-- 1 root root     84127 Sep 24 07:37 target_users.parquet
-r--r--r-- 1 root root   7736341 Sep 24 07:37 test_eligible_candidates.npz
-r--r--r-- 1 root root   1092329 Sep 24 07:37 test_preference_state.parquet
-r--r--r-- 1 root root    857213 Sep 24 07:37 train_dislikes.parquet
-r--r--r-- 1 root root   5374652 Sep 24 07:37 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 24 07:37 train_listens.parquet
-r--r--r-- 1 root root    155988 Sep 24 07:37 train_undislikes.parquet
-r--r--r-- 1 root root   1855885 Sep 24 07:37 train_unlikes.parquet
-r--r--r-- 1 root root     36571 Sep 24 07:37 val_dislikes.parquet
-r--r--r-- 1 root root    298484 Sep 24 07:37 val_likes.parquet
-r--r--r-- 1 root root   9947609 Sep 24 07:37 val_listens.parquet
-r--r--r-- 1 root root      7845 Sep 24 07:37 val_undislikes.parquet
-r--r--r-- 1 root root     72536 Sep 24 07:37 val_unlikes.parquet
-r--r--r-- 1 root root   7573821 Sep 24 07:37 validation_eligible_candidates.npz
-r--r--r-- 1 root root   1061761 Sep 24 07:37 validation_preference_state.parquet
# Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
8
               total        used        free      shared  buff/cache   available
Mem:              15           1           4           0           9          14
Swap:              0           0           0

### Bash · 2026-09-24T07:44:34.995Z
{
  "command": "cat /task/ranking_evaluation.py; echo ===; cat /task/evaluate_public.py",
  "description": "Read evaluation code"
}
### Tool output · 2026-09-24T07:44:35.027Z
"""Shared public primitives for Yambda discovery-ranking evaluation.

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

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

from pathlib import Path

import numpy as np
import pandas as pd


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

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


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


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

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

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

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

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

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

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

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


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

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

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


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

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

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

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

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

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

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


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

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

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


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

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

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

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


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

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

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    quality = float(
        np.mean(
            [
                ndcg_at_k(ranked[uid], gold, QUALITY_K)
                for uid, gold in graded.items()
            ]
        )
    )
    return {
        "quality_ndcg@10": round(quality, 6),
        "quality_users": len(graded),
    }
===
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.

``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.

Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.

The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.

Usage::

    python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

    Args:
        path: Ranking file to read.
        eligibility: Validation-cutoff candidate membership service.
        targets: Exact set of users the ranking must cover.

    Returns:
        A ``uid -> ranked item IDs`` mapping ordered by ascending rank.

    Raises:
        ValueError: If the ranking violates the required schema or invariants.
    """
    frame = validate_ranking(read_ranking(path), targets)
    if not all(
        eligibility.is_eligible(uid, item)
        for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
    ):
        raise RankingValidationError(
            "submission contains an item rejected by eligibility service"
        )
    return ranked_items(frame)


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

    Args:
        ranked: Ranked item IDs for every target user.
        graded: Eligible validation relevance keyed by user and item.

    Returns:
        The six-decimal ``quality_ndcg@10`` value and number of scored users.
    """
    return score_rankings(ranked, graded)


def main() -> None:
    """Run the public evaluator CLI and print its JSON score report."""
    targets_frame = pd.read_parquet(DATA / "target_users.parquet")
    targets = set(targets_frame.uid.astype(int))
    eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
    graded = validation_truth(eligibility, targets)

    if len(sys.argv) != 2:
        raise SystemExit(
            "usage: evaluate_public.py <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14982086 tokens left</total_tokens>"
}

## Step 3 · agent · 2026-09-24T07:44:40.943Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:44:43.387Z
{
  "command": "mkdir -p solution && python -c \"\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state']:\n    d=pd.read_parquet('/task/data/%s.parquet'%f); print(f,d.shape, list(d.columns)); print(d.head(3))\nprint(pq.ParquetFile('/task/data/train_listens.parquet').metadata)\nd=pd.read_parquet('/task/data/val_listens.parquet'); print(d.shape); print(d.head()); print(d.timestamp.min(),d.timestamp.max())\nz=np.load('/task/data/validation_eligible_candidates.npz'); print({k:z[k].shape for k in z})\n\"\n",
  "description": "Inspect data shapes"
}
### Tool output · 2026-09-24T07:44:44.404Z
candidate_catalog (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
   item_id  positive_listens  organic_positive_listens  popularity_rank
0  5862961             26615                     20620                1
1  6901374             25536                     17347                2
2  5635052             24691                     17194                3
target_users (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens']
   uid  recent_positive_listens  history_positive_listens
0  100                    844.0                      2604
1  200                     50.0                       286
2  300                    168.0                       359
validation_preference_state (469124, 3) ['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
test_preference_state (484776, 3) ['uid', 'item_id', 'state']
   uid  item_id  state
0  100   410868  liked
1  100   732449  liked
2  100   982903  liked
<pyarrow._parquet.FileMetaData object at 0x78979ca17830>
  created_by: parquet-cpp-arrow version 18.1.0
  num_columns: 6
  num_rows: 43484159
  num_row_groups: 42
  format_version: 2.6
  serialized_size: 25540
(1501697, 6)
   uid  timestamp  item_id  is_organic  played_ratio_pct  track_length_seconds
0  100   24943430  9321269           0               100                   195
1  100   24943605  5452713           0               100                   175
2  100   24943765  6448777           0               100                   160
3  100   24951960  6008056           0                 8                   175
4  100   25246895  7968587           0                45                   565
24788600 25393395
{'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)}

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14974428 tokens left</total_tokens>"
}

## Step 4 · agent · 2026-09-24T07:44:50.483Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:44:54.523Z
{
  "command": "cd /app/solution && python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\nd=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','item_id','played_ratio_pct','is_organic'])\nprint('load',time.time()-t, d.dtypes.to_dict())\nprint('users',d.uid.nunique(),'items',d.item_id.nunique())\ntg=set(pd.read_parquet('/task/data/target_users.parquet').uid)\nprint('target rows frac', d.uid.isin(tg).mean())\nz=np.load('/task/data/validation_eligible_candidates.npz')\nitems=z['item_ids']; uids=z['uids']; pk=z['packed_eligible']\nm=np.unpackbits(pk,axis=1,count=len(items),bitorder='little')\nprint('elig per user mean',m.sum(1).mean(), m.sum(1).min(), m.sum(1).max())\n# check overlap with history\nsub=d[(d.uid==uids[0])&(d.played_ratio_pct>=50)]\nhist=set(sub.item_id)\nelig=set(items[m[0]==1])\nprint('user0 hist',len(hist),'elig',len(elig),'overlap',len(hist&elig), 'hist in catalog', len(hist&set(items)))\n\" 2>&1 | tail -20",
  "description": "Check listens and eligibility structure"
}
### Tool output · 2026-09-24T07:44:59.212Z
load 0.3860151767730713 {'uid': dtype('uint32'), 'item_id': dtype('uint32'), 'played_ratio_pct': dtype('uint16'), 'is_organic': dtype('uint8')}
users 9238 items 845395
target rows frac 0.9939779909276847
elig per user mean 29067.306291390727 24657 30000
user0 hist 729 elig 29318 overlap 0 hist in catalog 600

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14972979 tokens left</total_tokens>"
}

## Step 5 · agent · 2026-09-24T07:46:01.550Z

### Thinking unavailable — no text recorded


Only numpy/scipy/sklearn available (no GBM libs beyond sklearn). Let me check preference-state/eligibility relationship, then build the model.

### Bash · 2026-09-24T07:46:07.104Z
{
  "command": "python -c \"\nimport pandas as pd, numpy as np\nz=np.load('/task/data/validation_eligible_candidates.npz')\nitems=z['item_ids']; uids=z['uids']\nm=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nps=pd.read_parquet('/task/data/validation_preference_state.parquet')\nicol={int(v):i for i,v in enumerate(items)}\nurow={int(v):i for i,v in enumerate(uids)}\nps['c']=ps.item_id.map(icol); ps['r']=ps.uid.map(urow)\np=ps.dropna(subset=['c','r']); p=p.astype({'c':int,'r':int})\nfor st in ['liked','disliked']:\n    q=p[p.state==st]; print(st, len(q), 'eligible frac', m[q.r.values,q.c.values].mean())\nd=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','timestamp'])\nprint('train ts', d.timestamp.min(), d.timestamp.max())\nprint('A nnz est', pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','item_id','played_ratio_pct']).query('played_ratio_pct>=50')[['uid','item_id']].drop_duplicates().shape)\n\"",
  "description": "Check pref state vs eligibility"
}
### Tool output · 2026-09-24T07:46:11.413Z
liked 418435 eligible frac 0.0
disliked 50689 eligible frac 0.0
train ts 0 24786795
A nnz est (8471966, 2)

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14968033 tokens left</total_tokens>"
}

## Step 6 · agent · 2026-09-24T07:46:15.554Z

### Thinking unavailable — no text recorded


### Write · 2026-09-24T07:47:05.661Z
{
  "file_path": "/app/solution/rank.py",
  "content": "#!/usr/bin/env python3\n\"\"\"Discovery ranker for the \"Made for You\" shelf (Yambda organic NDCG@10).\n\nSignals are computed per stage (validation history = train_*, test history =\ntrain_* + val_*) and blended with weights hill-climbed on the public\nvalidation window.\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.utils.extmath import randomized_svd\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nT0 = time.time()\nDAY = 86400\n\n\ndef log(*a):\n    print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n\n\n# ---------------------------------------------------------------- data loading\ndef load_events(split: str):\n    ls = pd.read_parquet(\n        DATA / f\"{split}_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n    )\n    lk = pd.read_parquet(\n        DATA / f\"{split}_likes.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\"]\n    )\n    return ls, lk\n\n\nLS_TR, LK_TR = load_events(\"train\")\nLS_VA, LK_VA = load_events(\"val\")\nCATALOG = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nTARGETS = pd.read_parquet(DATA / \"target_users.parquet\")\n\n# global user index over every user seen anywhere (CF neighbours need all users)\nALL_UIDS = np.union1d(LS_TR.uid.unique(), LS_VA.uid.unique()).astype(np.int64)\nUROW = pd.Series(np.arange(len(ALL_UIDS)), index=ALL_UIDS)\n# global item index: catalog items first, then the rest of the long tail\ntail = np.setdiff1d(\n    np.union1d(LS_TR.item_id.unique(), LS_VA.item_id.unique()).astype(np.int64),\n    CATALOG.item_id.values.astype(np.int64),\n)\nITEMS = np.concatenate([CATALOG.item_id.values.astype(np.int64), tail])\nICOL = pd.Series(np.arange(len(ITEMS)), index=ITEMS)\nNCAT = len(CATALOG)\nlog(f\"users={len(ALL_UIDS)} items={len(ITEMS)} catalog={NCAT}\")\n\n\ndef build_history(stage: str):\n    \"\"\"Return (listens, likes) frames usable as observed history for a stage.\"\"\"\n    if stage == \"val\":\n        return LS_TR, LK_TR\n    return (\n        pd.concat([LS_TR, LS_VA], ignore_index=True),\n        pd.concat([LK_TR, LK_VA], ignore_index=True),\n    )\n\n\ndef coo(rows, cols, vals, shape):\n    m = sp.coo_matrix((vals, (rows, cols)), shape=shape, dtype=np.float32).tocsr()\n    m.sum_duplicates()\n    return m\n\n\n# ---------------------------------------------------------------- signals\ndef build_signals(stage: str, target_uids: np.ndarray, elig_mask: np.ndarray):\n    ls, lk = build_history(stage)\n    cutoff = int(ls.timestamp.max())\n    nU, nI = len(ALL_UIDS), len(ITEMS)\n\n    pos = ls.played_ratio_pct.values >= 50\n    strong = ls.played_ratio_pct.values >= 80\n    lr = UROW.reindex(ls.uid.values).values\n    lc = ICOL.reindex(ls.item_id.values).values\n    kr = UROW.reindex(lk.uid.values).values\n    kc = ICOL.reindex(lk.item_id.values).values\n    lorg = ls.is_organic.values == 1\n\n    # ---- user x item affinity matrices\n    A_pos = coo(lr[pos], lc[pos], np.ones(pos.sum(), np.float32), (nU, nI))\n    A_pos.data = np.log1p(A_pos.data).astype(np.float32)\n    A_str = coo(lr[strong & lorg], lc[strong & lorg],\n                np.ones(int((strong & lorg).sum()), np.float32), (nU, nI))\n    A_str.data = np.log1p(A_str.data).astype(np.float32)\n    A_like = coo(kr, kc, np.ones(len(kr), np.float32), (nU, nI))\n    A_like.data = np.minimum(A_like.data, 1.0)\n    A = (A_pos + 0.7 * A_str + 2.0 * A_like).tocsr()\n    A.data = A.data.astype(np.float32)\n    log(f\"{stage}: A nnz={A.nnz}\")\n\n    # column (item) popularity normalisation -> cosine-ish item space\n    cn = np.sqrt(np.asarray(A.multiply(A).sum(axis=0)).ravel()) + 1e-6\n    C = A @ sp.diags((1.0 / cn).astype(np.float32))\n    C = C.tocsr()\n    rn = np.sqrt(np.asarray(C.multiply(C).sum(axis=1)).ravel()) + 1e-6\n    Cn = sp.diags((1.0 / rn).astype(np.float32)) @ C  # row-normalised\n\n    trow = UROW.reindex(target_uids).values\n    nT = len(trow)\n    Cn_t = Cn[trow]\n    C_cat = C[:, :NCAT].tocsr()\n\n    signals: dict[str, np.ndarray] = {}\n\n    # ---- popularity signals (broadcast over users)\n    def pop(mask, weight_rows, since=None):\n        sel = mask\n        if since is not None:\n            sel = sel & (weight_rows >= since)\n        cols = lc[sel]\n        cols = cols[cols < NCAT]\n        v = np.bincount(cols, minlength=NCAT).astype(np.float32)\n        return np.log1p(v)\n\n    ts = ls.timestamp.values\n    signals[\"pop\"] = np.tile(pop(pos & lorg, ts), (nT, 1))\n    signals[\"pop7\"] = np.tile(pop(strong & lorg, ts, cutoff - 7 * DAY), (nT, 1))\n    signals[\"pop2\"] = np.tile(pop(strong & lorg, ts, cutoff - 2 * DAY), (nT, 1))\n    kts = lk.timestamp.values\n    kcols = kc[(lk.is_organic.values == 1) & (kts >= cutoff - 14 * DAY)]\n    kcols = kcols[kcols < NCAT]\n    signals[\"poplike\"] = np.tile(\n        np.log1p(np.bincount(kcols, minlength=NCAT).astype(np.float32)), (nT, 1)\n    )\n    log(f\"{stage}: popularity done\")\n\n    # ---- user-kNN CF\n    K = 250\n    G = np.empty((nT, nU), dtype=np.float32)\n    step = 1500\n    CnT = Cn.T.tocsr()\n    for s in range(0, nT, step):\n        G[s:s + step] = (Cn_t[s:s + step] @ CnT).toarray()\n    G[np.arange(nT), trow] = 0.0\n    thr = np.partition(G, nU - K, axis=1)[:, nU - K][:, None]\n    G[G < thr] = 0.0\n    Gs = sp.csr_matrix(G ** 3)\n    del G\n    knn = np.empty((nT, NCAT), dtype=np.float32)\n    for s in range(0, nT, step):\n        knn[s:s + step] = (Gs[s:s + step] @ C_cat).toarray()\n    signals[\"knn\"] = knn\n    log(f\"{stage}: knn done\")\n\n    # ---- latent factor signal (truncated SVD of the affinity matrix)\n    U, S, Vt = randomized_svd(Cn, n_components=192, n_iter=4, random_state=0)\n    svd = (U[trow] * S) @ Vt[:, :NCAT]\n    signals[\"svd\"] = svd.astype(np.float32)\n    log(f\"{stage}: svd done\")\n\n    # ---- item-item on catalog: pure co-occurrence from the user's own history\n    #      (asymmetric, popularity-damped target side)\n    Cpow = C.copy()\n    Cpow.data = Cpow.data ** 0.5\n    Ct = Cpow[trow]\n    ii = np.empty((nT, NCAT), dtype=np.float32)\n    CatT = Cpow[:, :NCAT].tocsr()\n    for s in range(0, nT, step):\n        Gm = (Ct[s:s + step] @ Cpow.T).toarray()\n        np.power(Gm, 2.0, out=Gm)\n        ii[s:s + step] = Gm @ CatT.toarray() if False else (sp.csr_matrix(Gm) @ CatT).toarray()\n    signals[\"ii\"] = ii\n    log(f\"{stage}: ii done\")\n\n    # per-user standardise each signal over eligible items, mask ineligible\n    for k, v in signals.items():\n        v = v.astype(np.float32)\n        v[~elig_mask] = 0.0\n        mu = v.sum(1, keepdims=True) / np.maximum(elig_mask.sum(1, keepdims=True), 1)\n        sd = np.sqrt(((v - mu) ** 2 * elig_mask).sum(1, keepdims=True)\n                     / np.maximum(elig_mask.sum(1, keepdims=True), 1)) + 1e-6\n        v = (v - mu) / sd\n        v[~elig_mask] = -1e9\n        signals[k] = v\n    return signals\n\n\n# ---------------------------------------------------------------- eligibility\ndef load_elig(name: str, target_uids: np.ndarray):\n    z = np.load(DATA / f\"{name}_eligible_candidates.npz\", allow_pickle=False)\n    uids = z[\"uids\"].astype(np.int64)\n    items = z[\"item_ids\"].astype(np.int64)\n    packed = z[\"packed_eligible\"]\n    m = np.unpackbits(packed, axis=1, count=len(items), bitorder=\"little\").astype(bool)\n    # reorder columns to the global catalog-first ordering\n    order = ICOL.reindex(items).values\n    assert (order < NCAT).all()\n    M = np.zeros((len(uids), NCAT), dtype=bool)\n    M[:, order] = m\n    row = pd.Series(np.arange(len(uids)), index=uids).reindex(target_uids).values\n    return M[row]\n\n\n# ---------------------------------------------------------------- evaluation\ndef val_gold(target_uids, elig_mask):\n    tg = set(target_uids.tolist())\n    st = LS_VA[(LS_VA.is_organic == 1) & (LS_VA.played_ratio_pct >= 80)\n               & LS_VA.uid.isin(tg)][[\"uid\", \"item_id\"]].drop_duplicates()\n    lk = LK_VA[(LK_VA.is_organic == 1) & LK_VA.uid.isin(tg)][\n        [\"uid\", \"item_id\"]].drop_duplicates()\n    urow = pd.Series(np.arange(len(target_uids)), index=target_uids)\n    G = np.zeros((len(target_uids), NCAT), dtype=np.int8)\n    for frame, grade in ((st, 1), (lk, 3)):\n        r = urow.reindex(frame.uid.values).values\n        c = ICOL.reindex(frame.item_id.values).values\n        ok = (c < NCAT) & ~pd.isna(r)\n        r, c = r[ok].astype(int), c[ok].astype(int)\n        keep = elig_mask[r, c]\n        G[r[keep], c[keep]] = grade\n    return G\n\n\nDISC = 1.0 / np.log2(np.arange(2, 12))\n\n\ndef ndcg10(scores: np.ndarray, gold: np.ndarray) -> float:\n    top = np.argpartition(-scores, 10, axis=1)[:, :10]\n    ord_ = np.take_along_axis(scores, top, 1).argsort(axis=1)[:, ::-1]\n    top = np.take_along_axis(top, ord_, 1)\n    g = np.take_along_axis(gold, top, 1).astype(np.float64)\n    dcg = ((2.0 ** g - 1.0) * DISC).sum(1)\n    idcg = np.empty(len(gold))\n    for i in range(len(gold)):\n        gv = gold[i][gold[i] > 0]\n        gv = -np.sort(-gv)[:10].astype(np.float64)\n        idcg[i] = ((2.0 ** gv - 1.0) * DISC[:len(gv)]).sum()\n    sel = idcg > 0\n    return float((dcg[sel] / idcg[sel]).mean()), int(sel.sum())\n\n\n# ---------------------------------------------------------------- write out\ndef write_ranking(scores, target_uids, path):\n    top = np.argpartition(-scores, 100, axis=1)[:, :100]\n    ord_ = np.take_along_axis(scores, top, 1).argsort(axis=1)[:, ::-1]\n    top = np.take_along_axis(top, ord_, 1)\n    items = ITEMS[top]\n    n = len(target_uids)\n    df = pd.DataFrame({\n        \"uid\": np.repeat(target_uids, 100).astype(np.int64),\n        \"item_id\": items.ravel().astype(np.int64),\n        \"rank\": np.tile(np.arange(1, 101), n).astype(np.int64),\n    })\n    df.to_parquet(path, index=False)\n    log(f\"wrote {path}\")\n\n\ndef main():\n    target_uids = TARGETS.uid.values.astype(np.int64)\n    em_val = load_elig(\"validation\", target_uids)\n    sig_val = build_signals(\"val\", target_uids, em_val)\n    gold = val_gold(target_uids, em_val)\n    log(f\"gold users={int((gold>0).any(1).sum())}\")\n\n    keys = list(sig_val.keys())\n    base = {\"pop\": 0.15, \"pop7\": 0.25, \"pop2\": 0.1, \"poplike\": 0.15,\n            \"knn\": 1.0, \"svd\": 0.6, \"ii\": 0.6}\n    w = {k: base.get(k, 0.0) for k in keys}\n\n    def blend(sig, w):\n        out = np.zeros_like(sig[keys[0]])\n        for k in keys:\n            if w[k]:\n                out += w[k] * sig[k]\n        return out\n\n    for k in keys:\n        s, n = ndcg10(sig_val[k], gold)\n        log(f\"single {k:8s} ndcg@10={s:.5f} users={n}\")\n\n    best, nu = ndcg10(blend(sig_val, w), gold)\n    log(f\"init blend {best:.5f} users={nu}\")\n    grid = [0.0, 0.05, 0.1, 0.2, 0.35, 0.5, 0.75, 1.0, 1.5, 2.0]\n    for rnd in range(3):\n        for k in keys:\n            cur = w[k]\n            for cand in grid:\n                if cand == cur:\n                    continue\n                w[k] = cand\n                s, _ = ndcg10(blend(sig_val, w), gold)\n                if s > best + 1e-6:\n                    best, cur = s, cand\n                w[k] = cur\n        log(f\"round {rnd} best={best:.5f} w={w}\")\n        if time.time() - T0 > 900:\n            break\n    log(f\"FINAL val ndcg@10={best:.5f} weights={w}\")\n    (OUT / \"weights.json\").write_text(json.dumps({\"weights\": w, \"val_ndcg\": best}, indent=2))\n\n    write_ranking(blend(sig_val, w), target_uids, OUT / \"validation.parquet\")\n    del sig_val\n\n    em_test = load_elig(\"test\", target_uids)\n    sig_test = build_signals(\"test\", target_uids, em_test)\n    write_ranking(blend(sig_test, w), target_uids, OUT / \"submission.parquet\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-24T07:47:05.680Z
File created successfully at: /app/solution/rank.py (file state is current in your context — no need to Read it back)

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14962251 tokens left</total_tokens>"
}

## Step 7 · agent · 2026-09-24T07:47:42.388Z

### Thinking unavailable — no text recorded


Let me probe the cost of the neighbor computation before finalizing.

### Bash · 2026-09-24T07:47:49.856Z
{
  "command": "python -c \"\nimport numpy as np,pandas as pd,scipy.sparse as sp,time\nls=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','item_id','played_ratio_pct'])\nls=ls[ls.played_ratio_pct>=50]\nu,ui=np.unique(ls.uid.values,return_inverse=True); it,ii=np.unique(ls.item_id.values,return_inverse=True)\nA=sp.coo_matrix((np.ones(len(ui),np.float32),(ui,ii)),shape=(len(u),len(it))).tocsr(); A.sum_duplicates()\nA.data=np.log1p(A.data).astype(np.float32)\ncnt=np.asarray((A>0).sum(0)).ravel()\nprint('items',len(it),'nnz',A.nnz, 'itemcount>3000:',(cnt>3000).sum(),'sumsq',(np.minimum(cnt,10**9).astype(float)**2).sum())\ncn=np.sqrt(np.asarray(A.multiply(A).sum(0)).ravel())+1e-6\nC=(A@sp.diags((1/cn).astype(np.float32))).tocsr()\nrn=np.sqrt(np.asarray(C.multiply(C).sum(1)).ravel())+1e-6\nCn=(sp.diags((1/rn).astype(np.float32))@C).tocsr()\nt=time.time(); G=(Cn[:500]@Cn.T).toarray(); print('chunk500 exact',time.time()-t, G.shape)\nkeep=cnt<=3000; Cp=Cn[:,keep].tocsr(); print('pruned nnz',Cp.nnz)\nt=time.time(); G2=(Cp[:500]@Cp.T.tocsr()).toarray(); print('chunk500 pruned',time.time()-t)\n\" 2>&1|tail",
  "description": "Time neighbor matmul"
}
### Tool output · 2026-09-24T07:47:58.311Z
items 607664 nnz 8471966 itemcount>3000: 8 sumsq 2808935326.0
chunk500 exact 0.5366322994232178 (500, 9192)
pruned nnz 8445236
chunk500 pruned 0.5229349136352539

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14959449 tokens left</total_tokens>"
}

## Step 8 · agent · 2026-09-24T07:48:15.732Z

### Thinking unavailable — no text recorded


### Write · 2026-09-24T07:49:11.609Z
{
  "file_path": "/app/solution/rank.py",
  "content": "#!/usr/bin/env python3\n\"\"\"Discovery ranker for the \"Made for You\" shelf (Yambda organic NDCG@10).\n\nPipeline\n--------\n1. Build a user x item affinity matrix from observed history (positive listens,\n   strong organic listens, likes; explicit dislikes subtracted).\n2. Derive several ranking signals over the 30k retrieval catalog:\n   popularity / trending / like-velocity (global), two user-kNN collaborative\n   variants (exact cosine neighbours, top-K truncated, different weightings and\n   similarity sharpening) and a truncated-SVD latent-factor score.\n3. Per-user standardise each signal over that user's eligible candidates and\n   blend them with weights hill-climbed on the public validation window.\n4. Refit every signal on train + val history and emit the hidden-test ranking\n   with the selected weights.\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.utils.extmath import randomized_svd\n\nDATA = Path(\"/task/data\")\nOUT = Path(\"/app/solution\")\nT0 = time.time()\nDAY = 86400\nNEG_INF = -1e9\n\n\ndef log(*a):\n    print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n\n\ndef load_events(split: str):\n    ls = pd.read_parquet(\n        DATA / f\"{split}_listens.parquet\",\n        columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n    )\n    lk = pd.read_parquet(DATA / f\"{split}_likes.parquet\",\n                         columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\"])\n    dk = pd.read_parquet(DATA / f\"{split}_dislikes.parquet\",\n                         columns=[\"uid\", \"item_id\", \"timestamp\"])\n    return ls, lk, dk\n\n\nLS_TR, LK_TR, DK_TR = load_events(\"train\")\nLS_VA, LK_VA, DK_VA = load_events(\"val\")\nCATALOG = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nTARGETS = pd.read_parquet(DATA / \"target_users.parquet\")\n\nALL_UIDS = np.union1d(LS_TR.uid.unique(), LS_VA.uid.unique()).astype(np.int64)\nUROW = pd.Series(np.arange(len(ALL_UIDS)), index=ALL_UIDS)\ntail = np.setdiff1d(np.union1d(LS_TR.item_id.unique(), LS_VA.item_id.unique()).astype(np.int64),\n                    CATALOG.item_id.values.astype(np.int64))\nITEMS = np.concatenate([CATALOG.item_id.values.astype(np.int64), tail])\nICOL = pd.Series(np.arange(len(ITEMS)), index=ITEMS)\nNCAT = len(CATALOG)\nNU, NI = len(ALL_UIDS), len(ITEMS)\nlog(f\"users={NU} items={NI} catalog={NCAT}\")\n\n\ndef history(stage: str):\n    if stage == \"val\":\n        return LS_TR, LK_TR, DK_TR\n    return (pd.concat([LS_TR, LS_VA], ignore_index=True),\n            pd.concat([LK_TR, LK_VA], ignore_index=True),\n            pd.concat([DK_TR, DK_VA], ignore_index=True))\n\n\ndef mat(rows, cols, vals, log1p=False, clip=None):\n    m = sp.coo_matrix((vals.astype(np.float32), (rows, cols)), shape=(NU, NI)).tocsr()\n    m.sum_duplicates()\n    if log1p:\n        m.data = np.log1p(m.data).astype(np.float32)\n    if clip is not None:\n        m.data = np.minimum(m.data, clip).astype(np.float32)\n    return m\n\n\ndef normalise(A, col_pow=1.0, row_norm=True):\n    cn = np.sqrt(np.asarray(A.multiply(A).sum(0)).ravel()) + 1e-6\n    X = (A @ sp.diags((cn ** -col_pow).astype(np.float32))).tocsr()\n    if row_norm:\n        rn = np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel()) + 1e-6\n        X = (sp.diags((1.0 / rn).astype(np.float32)) @ X).tocsr()\n    return X\n\n\ndef neighbour_score(X, trow, Ycat, K, power, chunk=2000):\n    \"\"\"Top-K cosine-neighbour aggregation of Ycat over the catalog.\"\"\"\n    nT = len(trow)\n    out = np.empty((nT, NCAT), dtype=np.float32)\n    XT = X.T.tocsr()\n    for s in range(0, nT, chunk):\n        idx = trow[s:s + chunk]\n        G = (X[idx] @ XT).toarray()\n        G[np.arange(len(idx)), idx] = 0.0\n        k = min(K, G.shape[1] - 1)\n        thr = np.partition(G, G.shape[1] - k, axis=1)[:, G.shape[1] - k][:, None]\n        G[G < thr] = 0.0\n        if power != 1.0:\n            np.power(G, power, out=G)\n        out[s:s + chunk] = (sp.csr_matrix(G) @ Ycat).toarray()\n        del G\n    return out\n\n\ndef build_signals(stage: str, target_uids: np.ndarray, elig: np.ndarray):\n    ls, lk, dk = history(stage)\n    cutoff = int(ls.timestamp.max())\n    pr = ls.played_ratio_pct.values\n    org = ls.is_organic.values == 1\n    pos, strong = pr >= 50, pr >= 80\n    lr = UROW.reindex(ls.uid.values).values\n    lc = ICOL.reindex(ls.item_id.values).values\n    ts = ls.timestamp.values\n\n    A_pos = mat(lr[pos], lc[pos], np.ones(pos.sum()), log1p=True)\n    sel = strong & org\n    A_str = mat(lr[sel], lc[sel], np.ones(sel.sum()), log1p=True)\n    A_like = mat(UROW.reindex(lk.uid.values).values, ICOL.reindex(lk.item_id.values).values,\n                 np.ones(len(lk)), clip=1.0)\n    A_dis = mat(UROW.reindex(dk.uid.values).values, ICOL.reindex(dk.item_id.values).values,\n                np.ones(len(dk)), clip=1.0)\n    A = (A_pos + 0.7 * A_str + 2.0 * A_like - 1.0 * A_dis).tocsr()\n    A.data = np.maximum(A.data, 0.0).astype(np.float32)\n    A.eliminate_zeros()\n    log(f\"{stage}: A nnz={A.nnz}\")\n\n    trow = UROW.reindex(target_uids).values\n    nT = len(trow)\n    sig: dict[str, np.ndarray] = {}\n\n    def popvec(mask, tmin=None, frame_ts=None, cols=None):\n        m = mask if tmin is None else (mask & (frame_ts >= tmin))\n        c = cols[m]\n        c = c[c < NCAT]\n        return np.log1p(np.bincount(c, minlength=NCAT).astype(np.float32))\n\n    sig[\"pop\"] = popvec(pos & org, cols=lc)\n    sig[\"pop7\"] = popvec(strong & org, cutoff - 7 * DAY, ts, lc)\n    sig[\"pop2\"] = popvec(strong & org, cutoff - 2 * DAY, ts, lc)\n    kc = ICOL.reindex(lk.item_id.values).values\n    korg = lk.is_organic.values == 1\n    sig[\"poplike\"] = popvec(korg, cutoff - 14 * DAY, lk.timestamp.values, kc)\n    log(f\"{stage}: popularity done\")\n\n    Cn = normalise(A, col_pow=1.0, row_norm=True)\n    C_cat = normalise(A, col_pow=1.0, row_norm=False)[:, :NCAT].tocsr()\n    sig[\"knn\"] = neighbour_score(Cn, trow, C_cat, K=250, power=3.0)\n    log(f\"{stage}: knn done\")\n\n    Ch = normalise(A, col_pow=0.4, row_norm=True)\n    Ch_cat = Ch[:, :NCAT].tocsr()\n    sig[\"knn2\"] = neighbour_score(Ch, trow, Ch_cat, K=800, power=1.0)\n    log(f\"{stage}: knn2 done\")\n\n    U, S, Vt = randomized_svd(Cn, n_components=192, n_iter=4, random_state=0)\n    sig[\"svd\"] = ((U[trow] * S) @ Vt[:, :NCAT]).astype(np.float32)\n    log(f\"{stage}: svd done\")\n\n    ncand = np.maximum(elig.sum(1, keepdims=True), 1).astype(np.float32)\n    for k, v in list(sig.items()):\n        if v.ndim == 1:\n            v = np.tile(v, (1, 1))\n            mu, sd = v.mean(), v.std() + 1e-6\n            sig[k] = ((v - mu) / sd).astype(np.float32)  # shape (1, NCAT)\n        else:\n            v = np.where(elig, v, 0.0).astype(np.float32)\n            mu = v.sum(1, keepdims=True) / ncand\n            sd = np.sqrt(np.maximum((v * v).sum(1, keepdims=True) / ncand - mu * mu, 0)) + 1e-6\n            sig[k] = ((v - mu) / sd).astype(np.float32)\n    return sig\n\n\ndef load_elig(name: str, target_uids: np.ndarray):\n    with np.load(DATA / f\"{name}_eligible_candidates.npz\", allow_pickle=False) as z:\n        uids = z[\"uids\"].astype(np.int64)\n        items = z[\"item_ids\"].astype(np.int64)\n        m = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(items),\n                          bitorder=\"little\").astype(bool)\n    order = ICOL.reindex(items).values\n    assert (order < NCAT).all()\n    M = np.zeros((len(uids), NCAT), dtype=bool)\n    M[:, order] = m\n    row = pd.Series(np.arange(len(uids)), index=uids).reindex(target_uids).values\n    return M[row]\n\n\ndef val_gold(target_uids, elig):\n    tg = set(target_uids.tolist())\n    st = LS_VA[(LS_VA.is_organic == 1) & (LS_VA.played_ratio_pct >= 80)\n               & LS_VA.uid.isin(tg)][[\"uid\", \"item_id\"]].drop_duplicates()\n    lk = LK_VA[(LK_VA.is_organic == 1) & LK_VA.uid.isin(tg)][\n        [\"uid\", \"item_id\"]].drop_duplicates()\n    urow = pd.Series(np.arange(len(target_uids)), index=target_uids)\n    G = np.zeros((len(target_uids), NCAT), dtype=np.int8)\n    for frame, grade in ((st, 1), (lk, 3)):\n        r = urow.reindex(frame.uid.values).values.astype(np.float64)\n        c = ICOL.reindex(frame.item_id.values).values.astype(np.float64)\n        ok = np.isfinite(r) & np.isfinite(c) & (np.nan_to_num(c, nan=1e18) < NCAT)\n        r, c = r[ok].astype(int), c[ok].astype(int)\n        keep = elig[r, c]\n        G[r[keep], c[keep]] = grade\n    return G\n\n\nDISC = 1.0 / np.log2(np.arange(2, 12))\n\n\ndef ideal(gold):\n    idcg = np.zeros(len(gold))\n    r, c = np.nonzero(gold)\n    df = pd.DataFrame({\"r\": r, \"g\": gold[r, c].astype(np.float64)})\n    for rr, grp in df.groupby(\"r\").g:\n        gv = -np.sort(-grp.to_numpy())[:10]\n        idcg[rr] = ((2.0 ** gv - 1.0) * DISC[:len(gv)]).sum()\n    return idcg\n\n\ndef blend_full(sig, w, keys):\n    out = None\n    for k in keys:\n        if not w[k]:\n            continue\n        v = sig[k] * w[k]\n        out = v if out is None else out + v\n    return out\n\n\ndef main():\n    target_uids = TARGETS.uid.values.astype(np.int64)\n    em = load_elig(\"validation\", target_uids)\n    sig = build_signals(\"val\", target_uids, em)\n    gold = val_gold(target_uids, em)\n    idcg = ideal(gold)\n    scored = idcg > 0\n    log(f\"gold users={int(scored.sum())}\")\n\n    keys = [\"pop\", \"pop7\", \"pop2\", \"poplike\", \"knn\", \"knn2\", \"svd\"]\n    w = {\"pop\": 0.1, \"pop7\": 0.3, \"pop2\": 0.1, \"poplike\": 0.2,\n         \"knn\": 1.0, \"knn2\": 0.5, \"svd\": 0.5}\n\n    # Reduced tuning arena: union of each signal's per-user top items.\n    nT = len(target_uids)\n    tops = []\n    for k in keys:\n        v = sig[k]\n        if v.shape[0] == 1:\n            t = np.argpartition(-v[0], 60)[:60]\n            tops.append(np.tile(t, (nT, 1)))\n        else:\n            vm = np.where(em, v, NEG_INF)\n            tops.append(np.argpartition(-vm, 120, axis=1)[:, :120])\n    CAND = np.concatenate(tops, axis=1)\n    CANDW = CAND.shape[1]\n    small = {}\n    for k in keys:\n        v = sig[k]\n        small[k] = (np.take_along_axis(np.broadcast_to(v, (nT, NCAT)), CAND, 1)\n                    .astype(np.float32).copy())\n    elig_s = np.take_along_axis(em, CAND, 1)\n    gold_s = np.take_along_axis(gold, CAND, 1).astype(np.float64)\n    # duplicate columns across signals: keep first occurrence only\n    order = np.argsort(CAND, axis=1, kind=\"stable\")\n    sorted_c = np.take_along_axis(CAND, order, 1)\n    dup = np.zeros_like(elig_s)\n    dup[:, 1:] = sorted_c[:, 1:] == sorted_c[:, :-1]\n    dupmask = np.zeros_like(elig_s)\n    np.put_along_axis(dupmask, order, dup, 1)\n    valid = elig_s & ~dupmask\n    log(f\"arena width={CANDW}, gold recall=\"\n        f\"{float((gold_s * valid).sum() / max(gold.sum(), 1)):.3f}\")\n\n    def score_small(w):\n        out = np.zeros((nT, CANDW), dtype=np.float32)\n        for k in keys:\n            if w[k]:\n                out += w[k] * small[k]\n        out[~valid] = NEG_INF\n        top = np.argpartition(-out, 10, axis=1)[:, :10]\n        o = np.take_along_axis(out, top, 1).argsort(axis=1)[:, ::-1]\n        top = np.take_along_axis(top, o, 1)\n        g = np.take_along_axis(gold_s, top, 1)\n        dcg = ((2.0 ** g - 1.0) * DISC).sum(1)\n        return float((dcg[scored] / idcg[scored]).mean())\n\n    best = score_small(w)\n    log(f\"init blend ndcg@10={best:.5f}\")\n    for k in keys:\n        w0 = dict.fromkeys(keys, 0.0)\n        w0[k] = 1.0\n        log(f\"single {k:8s} {score_small(w0):.5f}\")\n\n    grid = [0.0, 0.05, 0.1, 0.2, 0.3, 0.45, 0.6, 0.8, 1.0, 1.3, 1.7, 2.2]\n    for rnd in range(4):\n        improved = False\n        for k in keys:\n            cur = w[k]\n            for c in grid:\n                if c == cur:\n                    continue\n                w[k] = c\n                s = score_small(w)\n                if s > best + 1e-6:\n                    best, cur, improved = s, c, True\n                w[k] = cur\n        log(f\"round {rnd}: best={best:.5f} w={w}\")\n        if not improved:\n            break\n    log(f\"FINAL val ndcg@10={best:.5f} weights={w}\")\n    (OUT / \"weights.json\").write_text(json.dumps({\"weights\": w, \"val_ndcg_arena\": best},\n                                                 indent=2))\n\n    def write(sig, w, path):\n        s = blend_full(sig, w, keys)\n        s = np.where(em_cur, s, NEG_INF)\n        top = np.argpartition(-s, 100, axis=1)[:, :100]\n        o = np.take_along_axis(s, top, 1).argsort(axis=1)[:, ::-1]\n        top = np.take_along_axis(top, o, 1)\n        pd.DataFrame({\n            \"uid\": np.repeat(target_uids, 100),\n            \"item_id\": ITEMS[top].ravel().astype(np.int64),\n            \"rank\": np.tile(np.arange(1, 101), nT).astype(np.int64),\n        }).to_parquet(path, index=False)\n        log(f\"wrote {path}\")\n\n    em_cur = em\n    write(sig, w, OUT / \"validation.parquet\")\n    del sig, small\n    em_cur = load_elig(\"test\", target_uids)\n    sig_t = build_signals(\"test\", target_uids, em_cur)\n    write(sig_t, w, OUT / \"submission.parquet\")\n\n\nif __name__ == \"__main__\":\n    main()\n"
}
### Tool output · 2026-09-24T07:49:11.644Z
The file /app/solution/rank.py has been updated successfully. (file state is current in your context — no need to Read it back)

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14952099 tokens left</total_tokens>"
}

## Step 9 · agent · 2026-09-24T07:49:16.432Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:49:16.883Z
{
  "command": "nohup python -u rank.py > run.log 2>&1 &\necho started; sleep 60; tail -30 run.log",
  "description": "Run ranker"
}
### Tool output · 2026-09-24T07:50:16.903Z
started
[1]+  Exit 1                  nohup python -u rank.py > run.log 2>&1
[    1.0s] users=9238 items=862029 catalog=30000
/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py:60: RuntimeWarning: invalid value encountered in cast
  self.coords = tuple(np.array(idx, copy=copy, dtype=idx_dtype)
Traceback (most recent call last):
  File "/app/solution/rank.py", line 342, in <module>
    main()
  File "/app/solution/rank.py", line 239, in main
    sig = build_signals("val", target_uids, em)
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/rank.py", line 126, in build_signals
    A_like = mat(UROW.reindex(lk.uid.values).values, ICOL.reindex(lk.item_id.values).values,
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/app/solution/rank.py", line 76, in mat
    m = sp.coo_matrix((vals.astype(np.float32), (rows, cols)), shape=(NU, NI)).tocsr()
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 101, in __init__
    self._check()
  File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 225, in _check
    raise ValueError(f'negative axis {i} index: {idx.min()}')
ValueError: negative axis 0 index: -9223372036854775808

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14951786 tokens left</total_tokens>"
}

## Step 10 · agent · 2026-09-24T07:50:23.907Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:50:29.365Z
{
  "command": "python - <<'EOF'\nimport re\ns=open('rank.py').read()\ns=s.replace('''ALL_UIDS = np.union1d(LS_TR.uid.unique(), LS_VA.uid.unique()).astype(np.int64)''',\n'''ALL_UIDS = np.unique(np.concatenate([\n    f.uid.unique().astype(np.int64)\n    for f in (LS_TR, LS_VA, LK_TR, LK_VA, DK_TR, DK_VA)]))''')\ns=s.replace('''tail = np.setdiff1d(np.union1d(LS_TR.item_id.unique(), LS_VA.item_id.unique()).astype(np.int64),\n                    CATALOG.item_id.values.astype(np.int64))''',\n'''_all_items = np.unique(np.concatenate([\n    f.item_id.unique().astype(np.int64)\n    for f in (LS_TR, LS_VA, LK_TR, LK_VA, DK_TR, DK_VA)]))\ntail = np.setdiff1d(_all_items, CATALOG.item_id.values.astype(np.int64))''')\ns=s.replace('''def mat(rows, cols, vals, log1p=False, clip=None):\n    m = sp.coo_matrix''','''def mat(rows, cols, vals, log1p=False, clip=None):\n    rows = np.asarray(rows, dtype=np.float64)\n    cols = np.asarray(cols, dtype=np.float64)\n    ok = np.isfinite(rows) & np.isfinite(cols)\n    rows, cols, vals = rows[ok].astype(np.int64), cols[ok].astype(np.int64), np.asarray(vals)[ok]\n    m = sp.coo_matrix''')\nopen('rank.py','w').write(s)\nEOF\nnohup python -u rank.py > run.log 2>&1 & echo started; sleep 75; tail -25 run.log",
  "description": "Fix index coverage and rerun"
}
### Tool output · 2026-09-24T07:51:44.465Z
started
[    1.0s] users=10000 items=871816 catalog=30000
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.3s] val: knn2 done

### Harness context
{
  "type": "edited_text_file",
  "filename": "/app/solution/rank.py",
  "snippet": "1\t#!/usr/bin/env python3\n2\t\"\"\"Discovery ranker for the \"Made for You\" shelf (Yambda organic NDCG@10).\n3\t\n4\tPipeline\n5\t--------\n6\t1. Build a user x item affinity matrix from observed history (positive listens,\n7\t   strong organic listens, likes; explicit dislikes subtracted).\n8\t2. Derive several ranking signals over the 30k retrieval catalog:\n9\t   popularity / trending / like-velocity (global), two user-kNN collaborative\n10\t   variants (exact cosine neighbours, top-K truncated, different weightings and\n11\t   similarity sharpening) and a truncated-SVD latent-factor score.\n12\t3. Per-user standardise each signal over that user's eligible candidates and\n13\t   blend them with weights hill-climbed on the public validation window.\n14\t4. Refit every signal on train + val history and emit the hidden-test ranking\n15\t   with the selected weights.\n16\t\"\"\"\n17\tfrom __future__ import annotations\n18\t\n19\timport json\n20\timport time\n21\tfrom pathlib import Path\n22\t\n23\timport numpy as np\n24\timport pandas as pd\n25\timport scipy.sparse as sp\n26\tfrom sklearn.utils.extmath import randomized_svd\n27\t\n28\tDATA = Path(\"/task/data\")\n29\tOUT = Path(\"/app/solution\")\n30\tT0 = time.time()\n31\tDAY = 86400\n32\tNEG_INF = -1e9\n33\t\n34\t\n35\tdef log(*a):\n36\t    print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n37\t\n38\t\n39\tdef load_events(split: str):\n40\t    ls = pd.read_parquet(\n41\t        DATA / f\"{split}_listens.parquet\",\n42\t        columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n43\t    )\n44\t    lk = pd.read_parquet(DATA / f\"{split}_likes.parquet\",\n45\t                         columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\"])\n46\t    dk = pd.read_parquet(DATA / f\"{split}_dislikes.parquet\",\n47\t                         columns=[\"uid\", \"item_id\", \"timestamp\"])\n48\t    return ls, lk, dk\n49\t\n50\t\n51\tLS_TR, LK_TR, DK_TR = load_events(\"train\")\n52\tLS_VA, LK_VA, DK_VA = load_events(\"val\")\n53\tCATALOG = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n54\tTARGETS = pd.read_parquet(DATA / \"target_users.parquet\")\n55\t\n56\tALL_UIDS = np.unique(np.concatenate([\n57\t    f.uid.unique().astype(np.int64)\n58\t    for f in (LS_TR, LS_VA, LK_TR, LK_VA, DK_TR, DK_VA)]))\n59\tUROW = pd.Series(np.arange(len(ALL_UIDS)), index=ALL_UIDS)\n60\t_all_items = np.unique(np.concatenate([\n61\t    f.item_id.unique().astype(np.int64)\n62\t    for f in (LS_TR, LS_VA, LK_TR, LK_VA, DK_TR, DK_VA)]))\n63\ttail = np.setdiff1d(_all_items, CATALOG.item_id.values.astype(np.int64))\n64\tITEMS = np.concatenate([CATALOG.item_id.values.astype(np.int64), tail])\n65\tICOL = pd.Series(np.arange(len(ITEMS)), index=ITEMS)\n66\tNCAT = len(CATALOG)\n67\tNU, NI = len(ALL_UIDS), len(ITEMS)\n68\tlog(f\"users={NU} items={NI} catalog={NCAT}\")\n69\t\n70\t\n71\tdef history(stage: str):\n72\t    if stage == \"val\":\n73\t        return LS_TR, LK_TR, DK_TR\n74\t    return (pd.concat([LS_TR, LS_VA], ignore_index=True),\n75\t            pd.concat([LK_TR, LK_VA], ignore_index=True),\n76\t            pd.concat([DK_TR, DK_VA], ignore_index=True))\n77\t\n78\t\n79\tdef mat(rows, cols, vals, log1p=False, clip=None):\n80\t    rows = np.asarray(rows, dtype=np.float64)\n81\t    cols = np.asarray(cols, dtype=np.float64)\n82\t    ok = np.isfinite(rows) & np.isfinite(cols)\n83\t    rows, cols, vals = rows[ok].astype(np.int64), cols[ok].astype(np.int64), np.asarray(vals)[ok]\n84\t    m = sp.coo_matrix((vals.astype(np.float32), (rows, cols)), shape=(NU, NI)).tocsr()\n85\t    m.sum_duplicates()\n86\t    if log1p:\n87\t        m.data = np.log1p(m.data).astype(np.float32)\n88\t    if clip is not None:\n89\t        m.data = np.minimum(m.data, clip).astype(np.float32)\n90\t    return m\n91\t\n92\t\n93\tdef normalise(A, col_pow=1.0, row_norm=True):\n94\t    cn = np.sqrt(np.asarray(A.multiply(A).sum(0)).ravel()) + 1e-6\n95\t    X = (A @ sp.diags((cn ** -col_pow).astype(np.float32))).tocsr()\n96\t    if row_norm:\n97\t        rn = np.sqrt(np.asarray(X.multiply(X).sum(1)).ravel()) + 1e-6\n98\t        X = (sp.diags((1.0 / rn).astype(np.float32)) @ X).tocsr()\n99\t    return X\n100\t\n101\t\n102\tdef neighbour_score(X, trow, Ycat, K, power, chunk=2000):\n103\t    \"\"\"Top-K cosine-neighbour aggregation of Ycat over the catalog.\"\"\"\n104\t    nT = len(trow)\n105\t    out = np.empty((nT, NCAT), dtype=np.float32)\n106\t    XT = X.T.tocsr()\n107\t    for s in range(0, nT, chunk):\n108\t        idx = trow[s:s + chunk]\n109\t        G = (X[idx] @ XT).toarray()\n110\t        G[np.arange(len(idx)), idx] = 0.0\n111\t        k = min(K, G.shape[1] - 1)\n112\t        thr = np.partition(G, G.shape[1] - k, axis=1)[:, G.shape[1] - k][:, None]\n113\t        G[G < thr] = 0.0\n114\t        if power != 1.0:\n115\t            np.power(G, power, out=G)\n116\t        out[s:s + chunk] = (sp.csr_matrix(G) @ Ycat).toarray()\n117\t        del G\n118\t    return out\n119\t\n120\t\n121\tdef build_signals(stage: str, target_uids: np.ndarray, elig: np.ndarray):\n122\t    ls, lk, dk = history(stage)\n123\t    cutoff = int(ls.timestamp.max())\n124\t    pr = ls.played_ratio_pct.values\n125\t    org = ls.is_organic.values == 1\n126\t    pos, strong = pr >= 50, pr >= 80\n127\t    lr = UROW.reindex(ls.uid.values).values\n128\t    lc = ICOL.reindex(ls.item_id.values).values\n129\t    ts = ls.timestamp.values\n130\t\n131\t    A_pos = mat(lr[pos], lc[pos], np.ones(pos.sum()), log1p=True)\n132\t    sel = strong & org\n133\t    A_str = mat(lr[sel], lc[sel], np.ones(sel.sum()), log1p=True)\n134\t    A_like = mat(UROW.reindex(lk.uid.values).values, ICOL.reindex(lk.item_id.values).values,\n135\t                 np.ones(len(lk)), clip=1.0)\n136\t    A_dis = mat(UROW.reindex(dk.uid.values).values, ICOL.reindex(dk.item_id.values).values,\n137\t                np.ones(len(dk)), clip=1.0)\n138\t    A = (A_pos + 0.7 * A_str + 2.0 * A_like - 1.0 * A_dis).tocsr()\n139\t    A.data = np.maximum(A.data, 0.0).astype(np.float32)\n140\t    A.eliminate_zeros()\n141\t    log(f\"{stage}: A nnz={A.nnz}\")\n142\t\n143\t    trow = UROW.reindex(target_uids).values\n144\t    nT = len(trow)\n145\t    sig: dict[str, np.ndarray] = {}\n146\t\n147\t    def popvec(mask, tmin=None, frame_ts=None, cols=None):\n148\t        m = mask if tmin is None else (mask & (frame_ts >= tmin))\n149\t        c = cols[m]\n150\t        c = c[c < NCAT]\n151\t        return np.log1p(np.bincount(c, minlength=NCAT).astype(np.float32))\n152\t\n153\t    sig[\"pop\"] = popvec(pos & org, cols=lc)\n154\t    sig[\"pop7\"] = popvec(strong & org, cutoff - 7 * DAY, ts, lc)\n155\t    sig[\"pop2\"] = popvec(strong & org, cutoff - 2 * DAY, ts, lc)\n156\t    kc = ICOL.reindex(lk.item_id.values).values\n157\t    korg = lk.is_organic.values == 1\n158\t    sig[\"poplike\"] = popvec(korg, cutoff - 14 * DAY, lk.timestamp.values, kc)\n159\t    log(f\"{stage}: popularity done\")\n160\t\n161\t    Cn = normalise(A, col_pow=1.0, row_norm=True)\n162\t    C_cat = normalise(A, col_pow=1.0, row_norm=False)[:, :NCAT].tocsr()\n163\t    sig[\"knn\"] = neighbour_score(Cn, trow, C_cat, K=250, power=3.0)\n164\t    log(f\"{stage}: knn done\")\n165\t\n166\t    Ch = normalise(A, col_pow=0.4, row_norm=True)\n167\t    Ch_cat = Ch[:, :NCAT].tocsr()\n168\t    sig[\"knn2\"] = neighbour_score(Ch, trow, Ch_cat, K=800, power=1.0)\n169\t    log(f\"{stage}: knn2 done\")\n170\t\n171\t    U, S, Vt = randomized_svd(Cn, n_components=192, n_iter=4, random_state=0)\n172\t    sig[\"svd\"] = ((U[trow] * S) @ Vt[:, :NCAT]).astype(np.float32)\n173\t    log(f\"{stage}: svd done\")\n174\t\n175\t    ncand = np.maximum(elig.sum(1, keepdims=True), 1).astype(np.float32)\n176\t    for k, v in list(sig.items()):\n177\t        if v.ndim == 1:\n178\t            v = np.tile(v, (1, 1))\n179\t            mu, sd = v.mean(), v.std() + 1e-6\n180\t            sig[k] = ((v - mu) / sd).astype(np.float32)  # shape (1, NCAT)\n181\t        else:\n182\t            v = np.where(elig, v, 0.0).astype(np.float32)\n183\t            mu = v.sum(1, keepdims=True) / ncand\n184\t            sd = np.sqrt(np.maximum((v * v).sum(1, keepdims=True) / ncand - mu * mu, 0)) + 1e-6\n185\t            sig[k] = ((v - mu) / sd).astype(np.float32)\n186\t    return sig\n187\t\n188\t\n189\tdef load_elig(name: str, target_uids: np.ndarray):\n190\t    with np.load(DATA / f\"{name}_eligible_candidates.npz\", allow_pickle=False) as z:\n191\t        uids = z[\"uids\"].astype(np.int64)\n192\t        items = z[\"item_ids\"].astype(np.int64)\n193\t        m = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(items),\n\n... [157 lines truncated] ..."
}

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14950010 tokens left</total_tokens>"
}

## Step 11 · agent · 2026-09-24T07:51:47.069Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:51:47.269Z
{
  "command": "sleep 75; tail -25 run.log",
  "description": "Check progress"
}
### Tool output · 2026-09-24T07:53:02.425Z
[    1.0s] users=10000 items=871816 catalog=30000
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.3s] val: knn2 done
[   77.7s] val: svd done

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14945508 tokens left</total_tokens>"
}

## Step 12 · agent · 2026-09-24T07:53:05.557Z

### Bash · 2026-09-24T07:53:05.557Z
{
  "command": "sleep 70; tail -25 run.log",
  "description": "Check progress"
}
### Tool output · 2026-09-24T07:54:15.604Z
[    1.0s] users=10000 items=871816 catalog=30000
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.3s] val: knn2 done
[   77.7s] val: svd done

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14945299 tokens left</total_tokens>"
}

## Step 13 · agent · 2026-09-24T07:54:19.901Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:54:20.541Z
{
  "command": "sleep 45; tail -5 run.log; ps -o pid,pcpu,rss,etime,cmd -C python | head",
  "description": "Check process state"
}
### Tool output · 2026-09-24T07:55:05.582Z
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.3s] val: knn2 done
[   77.7s] val: svd done
    PID %CPU   RSS     ELAPSED CMD

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14944955 tokens left</total_tokens>"
}

## Step 14 · agent · 2026-09-24T07:55:08.388Z

### Bash · 2026-09-24T07:55:08.388Z
{
  "command": "tail -30 run.log; echo \"---\"; ps aux | grep -c rank.py; dmesg 2>/dev/null | tail -3",
  "description": "Check for OOM"
}
### Tool output · 2026-09-24T07:55:08.451Z
[    1.0s] users=10000 items=871816 catalog=30000
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.3s] val: knn2 done
[   77.7s] val: svd done
---
3

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14944714 tokens left</total_tokens>"
}

## Step 15 · agent · 2026-09-24T07:55:16.552Z

### Thinking unavailable — no text recorded


Process was OOM-killed. Patching memory usage to in-place ops:

### Bash · 2026-09-24T07:55:29.142Z
{
  "command": "python - <<'EOF'\ns=open('rank.py').read()\ns=s.replace('''    U, S, Vt = randomized_svd(Cn, n_components=192, n_iter=4, random_state=0)\n    sig[\"svd\"] = ((U[trow] * S) @ Vt[:, :NCAT]).astype(np.float32)\n    log(f\"{stage}: svd done\")\n''','''    U, S, Vt = randomized_svd(Cn, n_components=160, n_iter=4, random_state=0)\n    sig[\"svd\"] = np.ascontiguousarray(((U[trow] * S) @ Vt[:, :NCAT]), dtype=np.float32)\n    del U, S, Vt, Cn, Ch, C_cat, Ch_cat, A, A_pos, A_str, A_like, A_dis\n    log(f\"{stage}: svd done\")\n''')\ns=s.replace('''        else:\n            v = np.where(elig, v, 0.0).astype(np.float32)\n            mu = v.sum(1, keepdims=True) / ncand\n            sd = np.sqrt(np.maximum((v * v).sum(1, keepdims=True) / ncand - mu * mu, 0)) + 1e-6\n            sig[k] = ((v - mu) / sd).astype(np.float32)''','''        else:\n            v *= elig\n            mu = v.sum(1, dtype=np.float64, keepdims=True) / ncand\n            sq = np.einsum(\"ij,ij->i\", v, v).reshape(-1, 1).astype(np.float64) / ncand\n            sd = (np.sqrt(np.maximum(sq - mu * mu, 0)) + 1e-6).astype(np.float32)\n            v -= mu.astype(np.float32)\n            v /= sd\n            sig[k] = v''')\ns=s.replace('''def blend_full(sig, w, keys):\n    out = None\n    for k in keys:\n        if not w[k]:\n            continue\n        v = sig[k] * w[k]\n        out = v if out is None else out + v\n    return out''','''def blend_full(sig, w, keys, nT):\n    out = np.zeros((nT, NCAT), dtype=np.float32)\n    for k in keys:\n        if not w[k]:\n            continue\n        v = sig[k]\n        if v.shape[0] == 1:\n            out += w[k] * v[0]\n        else:\n            out += w[k] * v\n    return out''')\ns=s.replace('''        s = blend_full(sig, w, keys)\n        s = np.where(em_cur, s, NEG_INF)''','''        s = blend_full(sig, w, keys, nT)\n        s[~em_cur] = NEG_INF''')\ns=s.replace('''    small = {}\n    for k in keys:\n        v = sig[k]\n        small[k] = (np.take_along_axis(np.broadcast_to(v, (nT, NCAT)), CAND, 1)\n                    .astype(np.float32).copy())''','''    small = {}\n    for k in keys:\n        v = sig[k]\n        if v.shape[0] == 1:\n            small[k] = np.ascontiguousarray(v[0][CAND], dtype=np.float32)\n        else:\n            small[k] = np.ascontiguousarray(np.take_along_axis(v, CAND, 1), dtype=np.float32)''')\ns=s.replace('''            vm = np.where(em, v, NEG_INF)\n            tops.append(np.argpartition(-vm, 120, axis=1)[:, :120])''','''            tops.append(np.argpartition(-v, 120, axis=1)[:, :120])''')\ns=s.replace('for rnd in range(4):','for rnd in range(3):')\ns=s.replace('''    em_cur = em\n    write(sig, w, OUT / \"validation.parquet\")\n    del sig, small''','''    em_cur = em\n    write(sig, w, OUT / \"validation.parquet\")\n    sig.clear()\n    del small, gold, gold_s, CAND''')\nopen('rank.py','w').write(s)\nEOF\nnohup python -u rank.py > run.log 2>&1 & echo started; sleep 100; tail -20 run.log",
  "description": "Patch memory and rerun"
}
### Tool output · 2026-09-24T07:57:09.280Z
started
/bin/bash: line 131:  8298 Killed                  nohup python -u rank.py > run.log 2>&1
[    1.0s] users=10000 items=871816 catalog=30000
[    5.5s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.8s] val: knn done
[   48.9s] val: knn2 done
[   72.0s] val: svd done
[   74.1s] gold users=4263

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14942630 tokens left</total_tokens>"
}

## Step 16 · agent · 2026-09-24T07:57:18.610Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:57:25.863Z
{
  "command": "python - <<'EOF'\ns=open('rank.py').read()\ns=s.replace('''def build_signals(stage''','''def topk_idx(v, k, chunk=1000):\n    \"\"\"Chunked per-row top-k indices (descending) as int32.\"\"\"\n    n = v.shape[0]\n    out = np.empty((n, k), dtype=np.int32)\n    for st in range(0, n, chunk):\n        b = v[st:st + chunk]\n        p = np.argpartition(b, b.shape[1] - k, axis=1)[:, b.shape[1] - k:]\n        o = np.take_along_axis(b, p, 1).argsort(axis=1)[:, ::-1]\n        out[st:st + chunk] = np.take_along_axis(p, o, 1)\n    return out\n\n\ndef build_signals(stage''')\ns=s.replace('''            t = np.argpartition(-v[0], 60)[:60]\n            tops.append(np.tile(t, (nT, 1)))\n        else:\n            tops.append(np.argpartition(-v, 120, axis=1)[:, :120])''','''            t = np.argpartition(v[0], NCAT - 60)[NCAT - 60:]\n            tops.append(np.tile(t.astype(np.int32), (nT, 1)))\n        else:\n            tops.append(topk_idx(v, 120))''')\ns=s.replace('''        out[~valid] = NEG_INF\n        top = np.argpartition(-out, 10, axis=1)[:, :10]\n        o = np.take_along_axis(out, top, 1).argsort(axis=1)[:, ::-1]\n        top = np.take_along_axis(top, o, 1)\n        g''','''        out[~valid] = NEG_INF\n        top = np.argpartition(out, CANDW - 10, axis=1)[:, CANDW - 10:]\n        o = np.take_along_axis(out, top, 1).argsort(axis=1)[:, ::-1]\n        top = np.take_along_axis(top, o, 1)\n        g''')\ns=s.replace('''        top = np.argpartition(-s, 100, axis=1)[:, :100]\n        o = np.take_along_axis(s, top, 1).argsort(axis=1)[:, ::-1]\n        top = np.take_along_axis(top, o, 1)\n        pd''','''        top = topk_idx(s, 100)\n        del s\n        pd''')\ns=s.replace('CAND = np.concatenate(tops, axis=1)','CAND = np.concatenate(tops, axis=1).astype(np.int64)\\n    del tops')\nopen('rank.py','w').write(s)\nEOF\nnohup python -u rank.py > run.log 2>&1 & echo started; sleep 105; tail -30 run.log",
  "description": "Chunk top-k and rerun"
}
### Tool output · 2026-09-24T07:59:10.933Z
started
[    1.0s] users=10000 items=871816 catalog=30000
[    5.6s] val: A nnz=8660528
[    5.8s] val: popularity done
[   22.9s] val: knn done
[   49.1s] val: knn2 done
[   73.4s] val: svd done
[   75.5s] gold users=4263
[   78.4s] arena width=600, gold recall=0.197
[   78.5s] init blend ndcg@10=0.01558
[   78.5s] single pop      0.01059
[   78.6s] single pop7     0.02813
[   78.6s] single pop2     0.02070
[   78.7s] single poplike  0.02508
[   78.7s] single knn      0.00595
[   78.8s] single knn2     0.01689
[   78.8s] single svd      0.01397
[   83.9s] round 0: best=0.03576 w={'pop': 2.2, 'pop7': 2.2, 'pop2': 2.2, 'poplike': 2.2, 'knn': 0.8, 'knn2': 0.0, 'svd': 0.5}
[   88.9s] round 1: best=0.03826 w={'pop': 0.1, 'pop7': 1.3, 'pop2': 2.2, 'poplike': 2.2, 'knn': 0.8, 'knn2': 0.2, 'svd': 0.5}
[   93.8s] round 2: best=0.03856 w={'pop': 0.0, 'pop7': 1.7, 'pop2': 2.2, 'poplike': 2.2, 'knn': 0.8, 'knn2': 0.2, 'svd': 0.6}
[   93.8s] FINAL val ndcg@10=0.03856 weights={'pop': 0.0, 'pop7': 1.7, 'pop2': 2.2, 'poplike': 2.2, 'knn': 0.8, 'knn2': 0.2, 'svd': 0.6}
[   95.5s] wrote /app/solution/validation.parquet
[  100.2s] test: A nnz=8953922
[  100.5s] test: popularity done

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14941009 tokens left</total_tokens>"
}

## Step 17 · agent · 2026-09-24T07:59:14.618Z

### Thinking unavailable — no text recorded


### Bash · 2026-09-24T07:59:14.835Z
{
  "command": "sleep 95; tail -6 run.log; ls -la /app/solution/",
  "description": "Wait for test stage"
}
### Tool output · 2026-09-24T08:00:49.953Z
[   93.8s] FINAL val ndcg@10=0.03856 weights={'pop': 0.0, 'pop7': 1.7, 'pop2': 2.2, 'poplike': 2.2, 'knn': 0.8, 'knn2': 0.2, 'svd': 0.6}
[   95.5s] wrote /app/solution/validation.parquet
[  100.2s] test: A nnz=8953922
[  100.5s] test: popularity done
[  117.9s] test: knn done
[  144.7s] test: knn2 done
total 1524
drwxr-xr-x 1 agent agent    4096 Sep 24 07:59 .
drwxr-xr-x 1 agent agent    4096 Sep 24 07:41 ..
-rw-r--r-- 1 agent agent   13819 Sep 24 07:57 rank.py
-rw-r--r-- 1 agent agent    1229 Sep 24 07:59 run.log
-rw-r--r-- 1 agent agent 1520405 Sep 24 07:59 validation.parquet
-rw-r--r-- 1 agent agent     181 Sep 24 07:59 weights.json

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14940101 tokens left</total_tokens>"
}

## Step 18 · agent · 2026-09-24T08:00:53.062Z

### Bash · 2026-09-24T08:00:53.062Z
{
  "command": "sleep 40; tail -3 run.log; ls -la /app/solution/submission.parquet 2>&1",
  "description": "Check submission written"
}
### Tool error · 2026-09-24T08:01:33.143Z
Exit code 2
[  100.5s] test: popularity done
[  117.9s] test: knn done
[  144.7s] test: knn2 done
ls: cannot access '/app/solution/submission.parquet': No such file or directory

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14939571 tokens left</total_tokens>"
}

## Step 19 · agent · 2026-09-24T08:01:35.881Z

### Bash · 2026-09-24T08:01:35.881Z
{
  "command": "sleep 45; tail -4 run.log; ls -la /app/solution/submission.parquet 2>&1 | tail -1",
  "description": "Check submission written"
}
### Tool output · 2026-09-24T08:02:20.904Z
[  100.2s] test: A nnz=8953922
[  100.5s] test: popularity done
[  117.9s] test: knn done
[  144.7s] test: knn2 done
ls: cannot access '/app/solution/submission.parquet': No such file or directory

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14939347 tokens left</total_tokens>"
}

## Step 20 · agent · 2026-09-24T08:02:29.289Z

### Thinking unavailable — no text recorded


Process died again on the test stage. Writing a lean accumulate-in-place variant to produce the submission:

### Bash · 2026-09-24T08:02:49.147Z
{
  "command": "ps aux|grep -c \"[r]ank.py\"; python - <<'EOF'\ns=open('/app/solution/rank.py').read()\n# accumulate signals one at a time instead of holding all dense matrices\ns=s.replace('''def main():''','''def build_blend(stage, target_uids, elig, w, keys):\n    \"\"\"Memory-lean scoring: standardise and accumulate each signal in turn.\"\"\"\n    nT = len(target_uids)\n    acc = np.zeros((nT, NCAT), dtype=np.float32)\n    ncand = np.maximum(elig.sum(1, keepdims=True), 1).astype(np.float32)\n\n    def add(v, weight):\n        if weight == 0.0:\n            return\n        if v.ndim == 1:\n            acc.__iadd__(weight * ((v - v.mean()) / (v.std() + 1e-6)).astype(np.float32))\n            return\n        v *= elig\n        mu = v.sum(1, dtype=np.float64, keepdims=True) / ncand\n        sq = np.einsum(\"ij,ij->i\", v, v).reshape(-1, 1).astype(np.float64) / ncand\n        sd = (np.sqrt(np.maximum(sq - mu * mu, 0)) + 1e-6).astype(np.float32)\n        v -= mu.astype(np.float32)\n        v /= sd\n        v *= np.float32(weight)\n        acc.__iadd__(v)\n\n    ls, lk, dk = history(stage)\n    cutoff = int(ls.timestamp.max())\n    pr, org = ls.played_ratio_pct.values, ls.is_organic.values == 1\n    pos, strong = pr >= 50, pr >= 80\n    lr = UROW.reindex(ls.uid.values).values\n    lc = ICOL.reindex(ls.item_id.values).values\n    ts = ls.timestamp.values\n    A_pos = mat(lr[pos], lc[pos], np.ones(pos.sum()), log1p=True)\n    sel = strong & org\n    A = (A_pos + 0.7 * mat(lr[sel], lc[sel], np.ones(sel.sum()), log1p=True)\n         + 2.0 * mat(UROW.reindex(lk.uid.values).values,\n                     ICOL.reindex(lk.item_id.values).values, np.ones(len(lk)), clip=1.0)\n         - 1.0 * mat(UROW.reindex(dk.uid.values).values,\n                     ICOL.reindex(dk.item_id.values).values, np.ones(len(dk)), clip=1.0)).tocsr()\n    A.data = np.maximum(A.data, 0.0).astype(np.float32)\n    A.eliminate_zeros()\n    del A_pos\n    trow = UROW.reindex(target_uids).values\n\n    def popvec(mask, tmin, tvec, cols):\n        m = mask if tmin is None else (mask & (tvec >= tmin))\n        c = cols[m]\n        c = c[c < NCAT]\n        return np.log1p(np.bincount(c, minlength=NCAT).astype(np.float32))\n\n    add(popvec(pos & org, None, ts, lc), w[\"pop\"])\n    add(popvec(strong & org, cutoff - 7 * DAY, ts, lc), w[\"pop7\"])\n    add(popvec(strong & org, cutoff - 2 * DAY, ts, lc), w[\"pop2\"])\n    add(popvec(lk.is_organic.values == 1, cutoff - 14 * DAY, lk.timestamp.values,\n               ICOL.reindex(lk.item_id.values).values), w[\"poplike\"])\n    log(f\"{stage}: pop accumulated\")\n    Cn = normalise(A, col_pow=1.0, row_norm=True)\n    if w[\"knn\"]:\n        C_cat = normalise(A, col_pow=1.0, row_norm=False)[:, :NCAT].tocsr()\n        add(neighbour_score(Cn, trow, C_cat, K=250, power=3.0), w[\"knn\"])\n        del C_cat\n        log(f\"{stage}: knn accumulated\")\n    if w[\"knn2\"]:\n        Ch = normalise(A, col_pow=0.4, row_norm=True)\n        add(neighbour_score(Ch, trow, Ch[:, :NCAT].tocsr(), K=800, power=1.0), w[\"knn2\"])\n        del Ch\n        log(f\"{stage}: knn2 accumulated\")\n    if w[\"svd\"]:\n        U, S, Vt = randomized_svd(Cn, n_components=160, n_iter=4, random_state=0)\n        Z = np.ascontiguousarray((U[trow] * S) @ Vt[:, :NCAT], dtype=np.float32)\n        del U, S, Vt, Cn, A\n        add(Z, w[\"svd\"])\n        del Z\n        log(f\"{stage}: svd accumulated\")\n    acc[~elig] = NEG_INF\n    return acc\n\n\ndef write_ranking(scores, target_uids, path):\n    top = topk_idx(scores, 100)\n    pd.DataFrame({\n        \"uid\": np.repeat(target_uids, 100).astype(np.int64),\n        \"item_id\": ITEMS[top].ravel().astype(np.int64),\n        \"rank\": np.tile(np.arange(1, 101), len(target_uids)).astype(np.int64),\n    }).to_parquet(path, index=False)\n    log(f\"wrote {path}\")\n\n\ndef main_test(weights):\n    target_uids = TARGETS.uid.values.astype(np.int64)\n    em = load_elig(\"test\", target_uids)\n    keys = [\"pop\", \"pop7\", \"pop2\", \"poplike\", \"knn\", \"knn2\", \"svd\"]\n    acc = build_blend(\"test\", target_uids, em, weights, keys)\n    write_ranking(acc, target_uids, OUT / \"submission.parquet\")\n\n\ndef main():''')\ns=s.replace('''if __name__ == \"__main__\":\n    main()''','''if __name__ == \"__main__\":\n    import sys\n    if len(sys.argv) > 1 and sys.argv[1] == \"test\":\n        main_test(json.loads((OUT / \"weights.json\").read_text())[\"weights\"])\n    else:\n        main()''')\nopen('/app/solution/rank.py','w').write(s)\nEOF\nnohup python -u rank.py test > run_test.log 2>&1 & echo started; sleep 90; tail -12 run_test.log",
  "description": "Run lean test-stage scoring"
}
### Tool output · 2026-09-24T08:04:19.229Z
2
started
[1]+  Done                    nohup python -u rank.py test > run_test.log 2>&1
[    1.0s] users=10000 items=871816 catalog=30000
[    6.4s] test: pop accumulated
[   24.3s] test: knn accumulated
[   51.4s] test: knn2 accumulated
[   75.7s] test: svd accumulated
[   76.7s] wrote /app/solution/submission.parquet

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14936565 tokens left</total_tokens>"
}

## Step 21 · agent · 2026-09-24T08:04:23.727Z

Submission written. Validating it against the structural contract and eligibility:

### Bash · 2026-09-24T08:04:26.768Z
{
  "command": "python -c \"\nimport pandas as pd,numpy as np,sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking, read_ranking\nfrom pathlib import Path\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(read_ranking(Path('submission.parquet')),t)\ncs=CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nrow=np.array([cs.uid_row[u] for u in f.uid]); col=np.array([cs.item_column[i] for i in f.item_id])\nbits=(cs.packed[row,col//8]>>(col%8))&1\nprint('rows',len(f),'all eligible',bool(bits.all()))\n\" && python /task/evaluate_public.py validation.parquet",
  "description": "Validate submission and score validation ranking"
}
### Tool output · 2026-09-24T08:04:30.793Z
rows 845600 all eligible True
{
  "model": "validation.parquet",
  "scores": {
    "quality_ndcg@10": 0.038596,
    "quality_users": 4263
  }
}

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14935994 tokens left</total_tokens>"
}

## Step 22 · agent · 2026-09-24T08:04:35.890Z

### Thinking unavailable — no text recorded


### Write · 2026-09-24T08:04:50.488Z
{
  "file_path": "/app/solution/README.md",
  "content": "# Made-for-You discovery ranker\n\n`rank.py` builds both artifacts. Public validation score of the selected\nconfiguration: **graded NDCG@10 = 0.038596** over 4263 scored users\n(`python /task/evaluate_public.py solution/validation.parquet`).\n\n## Run\n\n```sh\npython rank.py        # fits on train_*, tunes blend weights, writes validation.parquet + weights.json\npython rank.py test   # refits every signal on train_* + val_*, writes submission.parquet\n```\n\nThe two entry points exist because the full-signal tuning path holds several\ndense `8456 x 30000` score matrices; `rank.py test` uses `build_blend`, which\nstandardises and accumulates one signal at a time so the refit fits in memory.\n\n## Model\n\nOne affinity matrix per stage over all 10,000 users and 871,816 items (the\nsimilarity space is the full item space, only the scoring target is the 30k\nretrieval catalog):\n\n```\nA = log1p(positive listens) + 0.7*log1p(strong organic listens) + 2*like - dislike   (clipped at 0)\n```\n\nHistory is `train_*` for the validation ranker and `train_* + val_*` for the\nhidden-test ranker; validation events are never used as features when scoring\nthe validation window.\n\nSignals, each per-user standardised over that user's eligible candidates only:\n\n| signal | definition |\n| --- | --- |\n| `pop` | log organic positive-listen count over all history |\n| `pop7` | log strong-organic-listen count in the last 7 days before the cutoff |\n| `pop2` | same over the last 2 days (trending) |\n| `poplike` | log organic-like count over the last 14 days |\n| `knn` | user-kNN: exact cosine neighbours on the item-normalised matrix, top-250, similarity cubed, aggregating un-row-normalised catalog affinity |\n| `knn2` | user-kNN variant with weaker item-popularity damping (`col_pow=0.4`), top-800, linear similarity |\n| `svd` | rank-160 randomized SVD reconstruction of the normalised affinity matrix |\n\nWeights are hill-climbed directly on validation NDCG@10 (3 coordinate rounds\nover a 12-point grid) inside a reduced arena: the union of each signal's\nper-user top items, with duplicate and ineligible columns masked. Tuning on the\narena reproduced the evaluator's score to 4 decimals (0.03856 vs 0.038596).\nSelected weights are in `weights.json`.\n\n## Notes\n\n- Explicit preference state is informative but not needed for filtering: every\n  `liked`/`disliked` pair in both state files is already ineligible, so\n  eligibility masks are used verbatim and dislikes only enter `A` as a negative\n  affinity term.\n- Trending popularity carries most of the signal on this objective\n  (`pop2`/`poplike` get the largest weights); collaborative signals add on top\n  of it, while all-time popularity (`pop`) is tuned to zero once the recent\n  windows are present.\n- Not attempted for lack of time: a learned reranker (sklearn\n  `HistGradientBoostingClassifier`) over the top-200 blended candidates with\n  the signals above as features, and item-item kNN with an explicitly truncated\n  catalog similarity matrix.\n"
}
### Tool output · 2026-09-24T08:04:50.502Z
File created successfully at: /app/solution/README.md (file state is current in your context — no need to Read it back)

### Harness context
{
  "type": "total_tokens_reminder",
  "text": "<total_tokens>14934656 tokens left</total_tokens>"
}

## Step 23 · agent · 2026-09-24T08:05:00.758Z

Done. `/app/solution/submission.parquet` is written and verified.

**Result:** public validation graded NDCG@10 = **0.038596** (4263 scored users). The submission passes `validate_ranking` — 845,600 rows, 100 ranks per target user, no duplicates — and every item is in that user's `test_eligible_candidates.npz` set (checked directly against the packed bits).

**Approach:** a single per-stage user×item affinity matrix (positive listens + strong organic listens + likes, minus dislikes) over all 10k users and the full 872k item space, scored onto the 30k catalog through seven signals — all-time popularity, 7-day and 2-day trending, 14-day like velocity, two user-kNN variants (exact cosine neighbours, top-250 cubed and top-800 linear with different popularity damping), and a rank-160 SVD. Each signal is standardised per user over that user's eligible candidates, then blended with weights hill-climbed on validation NDCG@10.

Two things worth flagging:

- Trending popularity dominates (`pop2` and `poplike` take the largest weights, all-time `pop` tunes to zero); the CF signals contribute on top but are weaker alone. That's consistent with a 7-day discovery window on never-interacted items.
- The tuning ran in a reduced "arena" (union of each signal's per-user top items) for speed. It reproduced the evaluator's score to four decimals, so the shortcut cost nothing measurable.

Two deviations from what I'd have liked to deliver, both time-driven: the first two full-pipeline runs were OOM-killed (holding several dense 8456×30000 matrices at once), which cost most of the remaining budget — hence the split `rank.py` / `rank.py test` entry points, where the refit accumulates one signal at a time. And I did not get to the learned reranker (sklearn `HistGradientBoostingClassifier` over the top-200 blended candidates using these signals as features), which I'd expect to be the largest available gain from here. Both are noted in the README.