yambda-discovery-ranking-v01 / yambda-opus-high-2026-09-24-00-34-15-04330cf-2
Recorded model: claude-opus-5
## Step 1 · user · 2026-09-24T07:44:51.212Z
# 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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],
"removedNames": [],
"wireHiddenNames": [],
"readdedNames": [],
"pendingMcpServers": [],
"needsAuthMcpServers": [],
"failedMcpServers": []
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### Harness context
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"addedTypes": [
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"addedLines": [
"- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)",
"- 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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"removedTypes": [],
"isInitial": true,
"showConcurrencyNote": false
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### Harness context
{
"type": "skill_listing",
"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. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. 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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
{
"type": "total_tokens_reminder",
"text": "15000000 tokens left"
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## Step 2 · agent · 2026-09-24T07:44:52.697Z
I'll start by exploring the data and environment.
### Bash · 2026-09-24T07:44:53.583Z
{
"command": "ls -la /task/ /task/data/ && cat /task/requirements.txt | head -50 && nproc && free -g",
"description": "Explore task data directory"
}
### Tool output · 2026-09-24T07:44:53.651Z
/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 07:38 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 07:38 .
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 07:38 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 07:38 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
8
total used free shared buff/cache available
Mem: 15 1 4 0 9 14
Swap: 0 0 0
### Bash · 2026-09-24T07:44:54.372Z
{
"command": "cat /task/ranking_evaluation.py; echo ---- ; cat /task/evaluate_public.py",
"description": "Read evaluation code"
}
### Tool output · 2026-09-24T07:44:54.396Z
"""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
"""
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 "
)
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": "14982079 tokens left"
}
## Step 3 · agent · 2026-09-24T07:45:01.761Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:45:04.123Z
{
"command": "python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\ntu=pd.read_parquet('/task/data/target_users.parquet'); print(tu.shape, tu.columns.tolist(), tu.head())\ncc=pd.read_parquet('/task/data/candidate_catalog.parquet'); print(cc.shape, cc.columns.tolist(), cc.head())\nps=pd.read_parquet('/task/data/validation_preference_state.parquet'); print(ps.shape, ps.columns.tolist(), ps.head())\nimport pyarrow.parquet as pq\nf=pq.ParquetFile('/task/data/train_listens.parquet'); print(f.metadata.num_rows, f.schema_arrow)\nprint(time.time()-t)\n\""
}
### Tool output · 2026-09-24T07:45:04.940Z
(8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
3 600 1985.0 2423
4 700 1776.0 6114
(30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
(469124, 3) ['uid', 'item_id', 'state'] uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
43484159 uid: uint32
timestamp: uint32
item_id: uint32
is_organic: uint8
played_ratio_pct: uint16
track_length_seconds: uint32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797
0.07158136367797852
### Bash · 2026-09-24T07:45:06.560Z
{
"command": "python -c \"\nimport numpy as np\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint({k:(z[k].shape,z[k].dtype) for k in z.files})\np=z['packed_eligible']\nm=np.unpackbits(p,axis=1,bitorder='little')[:, :len(z['item_ids'])]\nprint('elig per user', m.sum(1)[:10], m.sum(1).mean())\nz2=np.load('/task/data/test_eligible_candidates.npz')\nprint({k:(z2[k].shape,z2[k].dtype) for k in z2.files})\nprint((z['uids']==z2['uids']).all(), (z['item_ids']==z2['item_ids']).all())\n\""
}
### Tool output · 2026-09-24T07:45:07.510Z
{'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
elig per user [29318 29832 29931 29320 28347 27476 29666 29501 28560 28736] 29067.306291390727
{'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))}
True True
### Harness context
{
"type": "total_tokens_reminder",
"text": "14973970 tokens left"
}
## Step 4 · agent · 2026-09-24T07:45:15.922Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:45:18.720Z
{
"command": "mkdir -p /app/solution && cd /app/solution && python -c \"\nimport pyarrow.parquet as pq, numpy as np, pandas as pd, time\nt=time.time()\ntb=pq.read_table('/task/data/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\nprint('read',time.time()-t, tb.num_rows)\nd=tb.to_pandas(); del tb\nprint('pd',time.time()-t)\nprint('uniq users', d.uid.nunique())\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nci=set(cat.item_id.values)\nm=d.item_id.isin(ci)\nprint('in catalog frac', m.mean())\nprint('ts range', d.timestamp.min(), d.timestamp.max())\nprint(time.time()-t)\n\""
}
### Tool output · 2026-09-24T07:45:25.034Z
read 0.3953073024749756 43484159
pd 0.512113094329834
uniq users 9238
in catalog frac 0.7186436099638032
ts range 0 24786795
5.475316762924194
### Harness context
{
"type": "total_tokens_reminder",
"text": "14972379 tokens left"
}
## Step 5 · agent · 2026-09-24T07:45:40.416Z
### Thinking unavailable — no text recorded
Only 9238 users total — small enough for dense matrix work. Let me check repeat-vs-novel structure fast.
### Bash · 2026-09-24T07:45:47.862Z
{
"command": "python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems=cat.item_id.values.astype(np.int64)\nz=np.load('/task/data/validation_eligible_candidates.npz')\nprint('catalog order == npz item order?', (z['item_ids'].astype(np.int64)==items).all())\ntl=pd.read_parquet('/task/data/train_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvl=pd.read_parquet('/task/data/val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nvlk=pd.read_parquet('/task/data/val_likes.parquet',columns=['uid','item_id','is_organic'])\ntu=set(pd.read_parquet('/task/data/target_users.parquet').uid)\nrel=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates()\nrel=rel[rel.uid.isin(tu)]\nhist=tl[(tl.is_organic==1)&(tl.played_ratio_pct>=50)][['uid','item_id']].drop_duplicates()\nh=set(map(tuple,hist.values))\nr=np.array([ (u,i) in h for u,i in rel.values])\nprint('val strong listens', len(rel),'repeat frac', r.mean())\nlk=vlk[(vlk.is_organic==1)&(vlk.uid.isin(tu))][['uid','item_id']].drop_duplicates()\nprint('val organic likes', len(lk), 'repeat frac', np.mean([ (u,i) in h for u,i in lk.values]))\nps=pd.read_parquet('/task/data/validation_preference_state.parquet')\npl=set(map(tuple, ps[ps.state=='liked'][['uid','item_id']].values))\nprint('rel in liked-state', np.mean([(u,i) in pl for u,i in rel.values]), 'likes in likedstate', np.mean([(u,i) in pl for u,i in lk.values]))\nprint(time.time()-t)\n\""
}
### Tool output · 2026-09-24T07:45:56.461Z
catalog order == npz item order? True
val strong listens 255608 repeat frac 0.6247457043598009
val organic likes 17195 repeat frac 0.09985460889793545
rel in liked-state 0.2168946198867015 likes in likedstate 0.03908112823495202
6.50438666343689
### Harness context
{
"type": "total_tokens_reminder",
"text": "14970363 tokens left"
}
## Step 6 · agent · 2026-09-24T07:46:20.036Z
### Thinking unavailable — no text recorded
Now building the main pipeline.
### Write · 2026-09-24T07:46:56.145Z
{
"file_path": "/app/solution/build.py",
"content": "\"\"\"Discovery ranker for the \"Made for You\" shelf.\n\nTwo stages:\n 1. Feature construction from event history (repeat-consumption, explicit\n preference state, user-user CF, SVD item-item CF, popularity).\n 2. A blend whose weights are tuned on the public validation window, then\n refit on train+val for the hidden test.\n\"\"\"\nfrom __future__ import annotations\n\nimport sys\nimport time\n\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\n\nDATA = \"/task/data\"\nTRAIN_CUTOFF = 24786800\nWEEK = 7 * 24 * 3600\nT0 = time.time()\n\n\ndef log(*a):\n print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n\n\ndef load_events(stage):\n \"\"\"Return history frames for the given stage ('val' builds from train only).\"\"\"\n parts = [\"train\"] if stage == \"val\" else [\"train\", \"val\"]\n out = {}\n for fam, cols in [\n (\"listens\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]),\n (\"likes\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]),\n (\"dislikes\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]),\n ]:\n out[fam] = pd.concat(\n [pd.read_parquet(f\"{DATA}/{p}_{fam}.parquet\", columns=cols) for p in parts],\n ignore_index=True,\n )\n return out\n\n\ndef build(stage):\n \"\"\"Build the dense (n_users x n_items) score-feature stack for one stage.\"\"\"\n cat = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\n items = cat.item_id.values.astype(np.int64)\n n_items = len(items)\n item_pos = pd.Series(np.arange(n_items), index=items)\n\n npz = np.load(f\"{DATA}/{'validation' if stage=='val' else 'test'}_eligible_candidates.npz\")\n tgt_uids = npz[\"uids\"].astype(np.int64)\n assert (npz[\"item_ids\"].astype(np.int64) == items).all()\n elig = np.unpackbits(npz[\"packed_eligible\"], axis=1, bitorder=\"little\")[:, :n_items].astype(bool)\n n_tgt = len(tgt_uids)\n\n ev = load_events(stage)\n cutoff = TRAIN_CUTOFF if stage == \"val\" else TRAIN_CUTOFF + WEEK + 1800\n log(stage, \"events loaded\")\n\n li = ev[\"listens\"]\n li = li[li.item_id.isin(item_pos.index)]\n li = li.assign(col=item_pos.reindex(li.item_id.values).values)\n\n all_uids = np.union1d(li.uid.unique().astype(np.int64), tgt_uids)\n urow = pd.Series(np.arange(len(all_uids)), index=all_uids)\n li = li.assign(row=urow.reindex(li.uid.values).values)\n\n strong = li[(li.is_organic == 1) & (li.played_ratio_pct >= 80)]\n pos = li[li.played_ratio_pct >= 50]\n\n # ---- global popularity (organic strong listens) ----\n pop = np.bincount(strong.col.values, minlength=n_items).astype(np.float32)\n pop_l = np.log1p(pop)\n pop_l /= pop_l.max()\n\n # ---- user x item history matrices ----\n def mat(df, w=None, n_rows=len(all_uids)):\n d = np.ones(len(df), np.float32) if w is None else w.astype(np.float32)\n m = sp.coo_matrix((d, (df.row.values, df.col.values)), shape=(n_rows, n_items)).tocsr()\n m.sum_duplicates()\n return m\n\n X_strong = mat(strong) # counts of strong organic listens\n X_pos = mat(pos) # any >=50% listen\n age = (cutoff - strong.timestamp.values).astype(np.float64)\n X_rec = mat(strong, np.exp(-age / (14 * 86400.0))) # 14-day half-life-ish\n X_rec2 = mat(strong, np.exp(-age / (3 * 86400.0))) # short-term\n log(stage, \"history matrices\", X_strong.nnz, X_pos.nnz)\n\n tgt_rows = urow.reindex(tgt_uids).values\n\n # ---- explicit preference state ----\n ps = pd.read_parquet(f\"{DATA}/{'validation' if stage=='val' else 'test'}_preference_state.parquet\")\n ps = ps[ps.item_id.isin(item_pos.index) & ps.uid.isin(set(tgt_uids))]\n trow = pd.Series(np.arange(n_tgt), index=tgt_uids)\n pr = trow.reindex(ps.uid.values).values\n pc = item_pos.reindex(ps.item_id.values).values\n liked = np.zeros((n_tgt, n_items), np.float32)\n disliked = np.zeros((n_tgt, n_items), np.float32)\n m = (ps.state == \"liked\").values\n liked[pr[m], pc[m]] = 1.0\n disliked[pr[~m], pc[~m]] = 1.0\n\n # ---- CF: user-user on cosine-normalised strong history ----\n Xn = X_strong.copy()\n Xn.data = np.log1p(Xn.data).astype(np.float32)\n norms = np.sqrt(Xn.multiply(Xn).sum(1)).A.ravel()\n norms[norms == 0] = 1.0\n Xn = sp.diags((1.0 / norms).astype(np.float32)) @ Xn\n Xn = Xn.astype(np.float32).tocsr()\n\n Xd = np.asarray(Xn.todense(), dtype=np.float32)\n sim = Xd[tgt_rows] @ Xd.T # (n_tgt x n_users)\n log(stage, \"user-user sim\", sim.shape)\n np.put_along_axis(sim, np.arange(len(tgt_rows))[:, None] * 0 + tgt_rows[:, None], 0.0, axis=1)\n K = 200\n idx = np.argpartition(-sim, K, axis=1)[:, :K]\n top = np.take_along_axis(sim, idx, axis=1)\n top = np.maximum(top, 0) ** 2\n rows = np.repeat(np.arange(n_tgt), K)\n Sk = sp.csr_matrix((top.ravel(), (rows, idx.ravel())), shape=sim.shape)\n del sim\n cf_uu = np.asarray((Sk @ Xn).todense(), dtype=np.float32)\n cf_uu /= cf_uu.max(1, keepdims=True).clip(1e-9)\n log(stage, \"user-user CF done\")\n\n # ---- CF: item-item over the same normalised matrix, low-rank ----\n from sklearn.utils.extmath import randomized_svd\n\n U, S, Vt = randomized_svd(Xn, n_components=192, n_iter=4, random_state=0)\n ii = (U[tgt_rows] * S) @ Vt\n ii /= np.abs(ii).max(1, keepdims=True).clip(1e-9)\n log(stage, \"svd done\")\n\n del Xd, Sk\n feats = {\n \"pop\": np.broadcast_to(pop_l, (n_tgt, n_items)).astype(np.float32),\n \"rep\": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),\n \"rec\": np.asarray(X_rec[tgt_rows].todense(), np.float32),\n \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"posl\": np.log1p(np.asarray(X_pos[tgt_rows].todense(), np.float32)),\n \"liked\": liked,\n \"disliked\": disliked,\n \"cf_uu\": cf_uu,\n \"ii\": ii.astype(np.float32),\n }\n for k in (\"rep\", \"rec\", \"rec2\", \"posl\"):\n feats[k] = feats[k] / feats[k].max(1, keepdims=True).clip(1e-9)\n log(stage, \"features built\")\n return dict(items=items, tgt_uids=tgt_uids, elig=elig, feats=feats)\n\n\ndef val_truth(items, tgt_uids, elig):\n item_pos = pd.Series(np.arange(len(items)), index=items)\n trow = pd.Series(np.arange(len(tgt_uids)), index=tgt_uids)\n grade = np.zeros((len(tgt_uids), len(items)), np.int8)\n vl = pd.read_parquet(f\"{DATA}/val_listens.parquet\")\n vk = pd.read_parquet(f\"{DATA}/val_likes.parquet\")\n for df, g in ((vl[(vl.is_organic == 1) & (vl.played_ratio_pct >= 80)], 1),\n (vk[vk.is_organic == 1], 3)):\n df = df[df.uid.isin(set(tgt_uids)) & df.item_id.isin(item_pos.index)]\n grade[trow.reindex(df.uid.values).values, item_pos.reindex(df.item_id.values).values] = g\n grade[~elig] = 0\n return grade\n\n\ndef ndcg(score, grade, elig, k=10):\n s = np.where(elig, score, -np.inf)\n idx = np.argpartition(-s, k, axis=1)[:, :k]\n ordr = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n idx = np.take_along_axis(idx, ordr, axis=1)\n g = np.take_along_axis(grade, idx, axis=1).astype(np.float64)\n disc = 1.0 / np.log2(np.arange(2, k + 2))\n dcg = ((2.0 ** g - 1) * disc).sum(1)\n gs = -np.sort(-grade, axis=1)[:, :k].astype(np.float64)\n idcg = ((2.0 ** gs - 1) * disc).sum(1)\n m = idcg > 0\n return float(np.mean(dcg[m] / idcg[m])), int(m.sum())\n\n\ndef combine(feats, w):\n out = np.zeros_like(feats[\"pop\"])\n for k, v in w.items():\n if v:\n out += np.float32(v) * feats[k]\n return out\n\n\ndef write_sub(res, score, path):\n elig, tgt_uids, items = res[\"elig\"], res[\"tgt_uids\"], res[\"items\"]\n s = np.where(elig, score, -np.inf)\n idx = np.argpartition(-s, 100, axis=1)[:, :100]\n ordr = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n idx = np.take_along_axis(idx, ordr, axis=1)\n n = len(tgt_uids)\n df = pd.DataFrame({\n \"uid\": np.repeat(tgt_uids, 100),\n \"item_id\": items[idx.ravel()],\n \"rank\": np.tile(np.arange(1, 101), n),\n })\n assert np.take_along_axis(elig, idx, axis=1).all()\n df.to_parquet(path, index=False)\n log(\"wrote\", path)\n\n\nif __name__ == \"__main__\":\n np.save(\"/app/solution/_noop.npy\", np.zeros(1))\n"
}
### Tool output · 2026-09-24T07:46:56.159Z
File created successfully at: /app/solution/build.py (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14964467 tokens left"
}
## Step 7 · agent · 2026-09-24T07:46:59.119Z
### Bash · 2026-09-24T07:46:59.119Z
{
"command": "python -c \"\nimport build as B, numpy as np, itertools, pickle\nr=B.build('val')\ng=B.val_truth(r['items'],r['tgt_uids'],r['elig'])\nB.log('truth users', (g.max(1)>0).sum())\nf=r['feats']\nfor k in f: \n print(k, B.ndcg(np.ascontiguousarray(f[k]),g,r['elig']))\nnp.save('/app/solution/_valgrade.npy', g)\n\" 2>&1 | tail -30"
}
### Tool output · 2026-09-24T07:47:07.561Z
[ 0.7s] val events loaded
[ 5.3s] val history matrices 1950643 5564926
### Harness context
{
"type": "total_tokens_reminder",
"text": "14964179 tokens left"
}
## Step 8 · agent · 2026-09-24T07:47:12.482Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:47:14.271Z
{
"command": "python -c \"\nimport build as B, numpy as np\nr=B.build('val')\ng=B.val_truth(r['items'],r['tgt_uids'],r['elig'])\nB.log('truth users', (g.max(1)>0).sum())\nf=r['feats']\nfor k in f:\n print(k, B.ndcg(np.ascontiguousarray(f[k]),g,r['elig']), flush=True)\nnp.save('/app/solution/_valgrade.npy', g)\nimport pickle; pickle.dump(r, open('/app/solution/_val.pkl','wb'), protocol=4)\n\"; echo EXIT=$?"
}
### Tool output · 2026-09-24T07:47:21.315Z
[ 0.8s] val events loaded
[ 5.1s] val history matrices 1950643 5564926
/bin/bash: line 21: 8204 Killed python -c "
import build as B, numpy as np
r=B.build('val')
g=B.val_truth(r['items'],r['tgt_uids'],r['elig'])
B.log('truth users', (g.max(1)>0).sum())
f=r['feats']
for k in f:
print(k, B.ndcg(np.ascontiguousarray(f[k]),g,r['elig']), flush=True)
np.save('/app/solution/_valgrade.npy', g)
import pickle; pickle.dump(r, open('/app/solution/_val.pkl','wb'), protocol=4)
"
EXIT=137
### Harness context
{
"type": "total_tokens_reminder",
"text": "14963584 tokens left"
}
## Step 9 · agent · 2026-09-24T07:47:27.466Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:47:37.125Z
{
"command": "python - <<'EOF'\nimport re\ns=open('build.py').read()\ns=s.replace(\"\"\" X_strong = mat(strong) # counts of strong organic listens\n X_pos = mat(pos) # any >=50% listen\n age = (cutoff - strong.timestamp.values).astype(np.float64)\n X_rec = mat(strong, np.exp(-age / (14 * 86400.0))) # 14-day half-life-ish\n X_rec2 = mat(strong, np.exp(-age / (3 * 86400.0))) # short-term\n log(stage, \"history matrices\", X_strong.nnz, X_pos.nnz)\n\"\"\",\"\"\" X_strong = mat(strong) # counts of strong organic listens\n X_pos = mat(pos) # any >=50% listen\n age = (cutoff - strong.timestamp.values).astype(np.float64)\n X_rec = mat(strong, np.exp(-age / (14 * 86400.0))) # 14-day half-life-ish\n X_rec2 = mat(strong, np.exp(-age / (3 * 86400.0))) # short-term\n log(stage, \"history matrices\", X_strong.nnz, X_pos.nnz)\n del ev, li, strong, pos\n\"\"\")\ns=s.replace(\"\"\" feats = {\n \"pop\": np.broadcast_to(pop_l, (n_tgt, n_items)).astype(np.float32),\n \"rep\": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),\n \"rec\": np.asarray(X_rec[tgt_rows].todense(), np.float32),\n \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"posl\": np.log1p(np.asarray(X_pos[tgt_rows].todense(), np.float32)),\n \"liked\": liked,\n \"disliked\": disliked,\n \"cf_uu\": cf_uu,\n \"ii\": ii.astype(np.float32),\n }\n for k in (\"rep\", \"rec\", \"rec2\", \"posl\"):\n feats[k] = feats[k] / feats[k].max(1, keepdims=True).clip(1e-9)\n\"\"\",\"\"\" feats = {\n \"pop\": np.broadcast_to(pop_l, (n_tgt, n_items)),\n \"rep\": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),\n \"rec\": np.asarray(X_rec[tgt_rows].todense(), np.float32),\n \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"posl\": np.log1p(np.asarray(X_pos[tgt_rows].todense(), np.float32)),\n \"liked\": liked,\n \"disliked\": disliked,\n \"cf_uu\": cf_uu,\n \"ii\": ii.astype(np.float32),\n }\n del X_strong, X_pos, X_rec, X_rec2, Xn, U, S, Vt, ii\n for k in (\"rep\", \"rec\", \"rec2\", \"posl\"):\n feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)\n\"\"\")\ns=s.replace(\"\"\" del Xd, Sk\n\"\"\",\"\"\" del Xd, Sk, U, S, Vt\n\"\"\").replace(\" del X_strong, X_pos, X_rec, X_rec2, Xn, U, S, Vt, ii\",\" del X_strong, X_pos, X_rec, X_rec2, Xn, ii\")\nopen('build.py','w').write(s)\nEOF\ngrep -n \"del \" build.py"
}
### Tool output · 2026-09-24T07:47:37.296Z
90: del ev, li, strong, pos
124: del sim
137: del Xd, Sk, U, S, Vt
149: del X_strong, X_pos, X_rec, X_rec2, Xn, ii
### Harness context
{
"type": "edited_text_file",
"filename": "/app/solution/build.py",
"snippet": "1\t\"\"\"Discovery ranker for the \"Made for You\" shelf.\n2\t\n3\tTwo stages:\n4\t 1. Feature construction from event history (repeat-consumption, explicit\n5\t preference state, user-user CF, SVD item-item CF, popularity).\n6\t 2. A blend whose weights are tuned on the public validation window, then\n7\t refit on train+val for the hidden test.\n8\t\"\"\"\n9\tfrom __future__ import annotations\n10\t\n11\timport sys\n12\timport time\n13\t\n14\timport numpy as np\n15\timport pandas as pd\n16\timport scipy.sparse as sp\n17\t\n18\tDATA = \"/task/data\"\n19\tTRAIN_CUTOFF = 24786800\n20\tWEEK = 7 * 24 * 3600\n21\tT0 = time.time()\n22\t\n23\t\n24\tdef log(*a):\n25\t print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n26\t\n27\t\n28\tdef load_events(stage):\n29\t \"\"\"Return history frames for the given stage ('val' builds from train only).\"\"\"\n30\t parts = [\"train\"] if stage == \"val\" else [\"train\", \"val\"]\n31\t out = {}\n32\t for fam, cols in [\n33\t (\"listens\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]),\n34\t (\"likes\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]),\n35\t (\"dislikes\", [\"uid\", \"timestamp\", \"item_id\", \"is_organic\"]),\n36\t ]:\n37\t out[fam] = pd.concat(\n38\t [pd.read_parquet(f\"{DATA}/{p}_{fam}.parquet\", columns=cols) for p in parts],\n39\t ignore_index=True,\n40\t )\n41\t return out\n42\t\n43\t\n44\tdef build(stage):\n45\t \"\"\"Build the dense (n_users x n_items) score-feature stack for one stage.\"\"\"\n46\t cat = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\n47\t items = cat.item_id.values.astype(np.int64)\n48\t n_items = len(items)\n49\t item_pos = pd.Series(np.arange(n_items), index=items)\n50\t\n51\t npz = np.load(f\"{DATA}/{'validation' if stage=='val' else 'test'}_eligible_candidates.npz\")\n52\t tgt_uids = npz[\"uids\"].astype(np.int64)\n53\t assert (npz[\"item_ids\"].astype(np.int64) == items).all()\n54\t elig = np.unpackbits(npz[\"packed_eligible\"], axis=1, bitorder=\"little\")[:, :n_items].astype(bool)\n55\t n_tgt = len(tgt_uids)\n56\t\n57\t ev = load_events(stage)\n58\t cutoff = TRAIN_CUTOFF if stage == \"val\" else TRAIN_CUTOFF + WEEK + 1800\n59\t log(stage, \"events loaded\")\n60\t\n61\t li = ev[\"listens\"]\n62\t li = li[li.item_id.isin(item_pos.index)]\n63\t li = li.assign(col=item_pos.reindex(li.item_id.values).values)\n64\t\n65\t all_uids = np.union1d(li.uid.unique().astype(np.int64), tgt_uids)\n66\t urow = pd.Series(np.arange(len(all_uids)), index=all_uids)\n67\t li = li.assign(row=urow.reindex(li.uid.values).values)\n68\t\n69\t strong = li[(li.is_organic == 1) & (li.played_ratio_pct >= 80)]\n70\t pos = li[li.played_ratio_pct >= 50]\n71\t\n72\t # ---- global popularity (organic strong listens) ----\n73\t pop = np.bincount(strong.col.values, minlength=n_items).astype(np.float32)\n74\t pop_l = np.log1p(pop)\n75\t pop_l /= pop_l.max()\n76\t\n77\t # ---- user x item history matrices ----\n78\t def mat(df, w=None, n_rows=len(all_uids)):\n79\t d = np.ones(len(df), np.float32) if w is None else w.astype(np.float32)\n80\t m = sp.coo_matrix((d, (df.row.values, df.col.values)), shape=(n_rows, n_items)).tocsr()\n81\t m.sum_duplicates()\n82\t return m\n83\t\n84\t X_strong = mat(strong) # counts of strong organic listens\n85\t X_pos = mat(pos) # any >=50% listen\n86\t age = (cutoff - strong.timestamp.values).astype(np.float64)\n87\t X_rec = mat(strong, np.exp(-age / (14 * 86400.0))) # 14-day half-life-ish\n88\t X_rec2 = mat(strong, np.exp(-age / (3 * 86400.0))) # short-term\n89\t log(stage, \"history matrices\", X_strong.nnz, X_pos.nnz)\n90\t del ev, li, strong, pos\n91\t\n92\t tgt_rows = urow.reindex(tgt_uids).values\n93\t\n94\t # ---- explicit preference state ----\n95\t ps = pd.read_parquet(f\"{DATA}/{'validation' if stage=='val' else 'test'}_preference_state.parquet\")\n96\t ps = ps[ps.item_id.isin(item_pos.index) & ps.uid.isin(set(tgt_uids))]\n97\t trow = pd.Series(np.arange(n_tgt), index=tgt_uids)\n98\t pr = trow.reindex(ps.uid.values).values\n99\t pc = item_pos.reindex(ps.item_id.values).values\n100\t liked = np.zeros((n_tgt, n_items), np.float32)\n101\t disliked = np.zeros((n_tgt, n_items), np.float32)\n102\t m = (ps.state == \"liked\").values\n103\t liked[pr[m], pc[m]] = 1.0\n104\t disliked[pr[~m], pc[~m]] = 1.0\n105\t\n106\t # ---- CF: user-user on cosine-normalised strong history ----\n107\t Xn = X_strong.copy()\n108\t Xn.data = np.log1p(Xn.data).astype(np.float32)\n109\t norms = np.sqrt(Xn.multiply(Xn).sum(1)).A.ravel()\n110\t norms[norms == 0] = 1.0\n111\t Xn = sp.diags((1.0 / norms).astype(np.float32)) @ Xn\n112\t Xn = Xn.astype(np.float32).tocsr()\n113\t\n114\t Xd = np.asarray(Xn.todense(), dtype=np.float32)\n115\t sim = Xd[tgt_rows] @ Xd.T # (n_tgt x n_users)\n116\t log(stage, \"user-user sim\", sim.shape)\n117\t np.put_along_axis(sim, np.arange(len(tgt_rows))[:, None] * 0 + tgt_rows[:, None], 0.0, axis=1)\n118\t K = 200\n119\t idx = np.argpartition(-sim, K, axis=1)[:, :K]\n120\t top = np.take_along_axis(sim, idx, axis=1)\n121\t top = np.maximum(top, 0) ** 2\n122\t rows = np.repeat(np.arange(n_tgt), K)\n123\t Sk = sp.csr_matrix((top.ravel(), (rows, idx.ravel())), shape=sim.shape)\n124\t del sim\n125\t cf_uu = np.asarray((Sk @ Xn).todense(), dtype=np.float32)\n126\t cf_uu /= cf_uu.max(1, keepdims=True).clip(1e-9)\n127\t log(stage, \"user-user CF done\")\n128\t\n129\t # ---- CF: item-item over the same normalised matrix, low-rank ----\n130\t from sklearn.utils.extmath import randomized_svd\n131\t\n132\t U, S, Vt = randomized_svd(Xn, n_components=192, n_iter=4, random_state=0)\n133\t ii = (U[tgt_rows] * S) @ Vt\n134\t ii /= np.abs(ii).max(1, keepdims=True).clip(1e-9)\n135\t log(stage, \"svd done\")\n136\t\n137\t del Xd, Sk, U, S, Vt\n138\t feats = {\n139\t \"pop\": np.broadcast_to(pop_l, (n_tgt, n_items)),\n140\t \"rep\": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),\n141\t \"rec\": np.asarray(X_rec[tgt_rows].todense(), np.float32),\n142\t \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n143\t \"posl\": np.log1p(np.asarray(X_pos[tgt_rows].todense(), np.float32)),\n144\t \"liked\": liked,\n145\t \"disliked\": disliked,\n146\t \"cf_uu\": cf_uu,\n147\t \"ii\": ii.astype(np.float32),\n148\t }\n149\t del X_strong, X_pos, X_rec, X_rec2, Xn, ii\n150\t for k in (\"rep\", \"rec\", \"rec2\", \"posl\"):\n151\t feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)\n152\t log(stage, \"features built\")\n153\t return dict(items=items, tgt_uids=tgt_uids, elig=elig, feats=feats)\n154\t\n155\t\n156\tdef val_truth(items, tgt_uids, elig):\n157\t item_pos = pd.Series(np.arange(len(items)), index=items)\n158\t trow = pd.Series(np.arange(len(tgt_uids)), index=tgt_uids)\n159\t grade = np.zeros((len(tgt_uids), len(items)), np.int8)\n160\t vl = pd.read_parquet(f\"{DATA}/val_listens.parquet\")\n161\t vk = pd.read_parquet(f\"{DATA}/val_likes.parquet\")\n162\t for df, g in ((vl[(vl.is_organic == 1) & (vl.played_ratio_pct >= 80)], 1),\n163\t (vk[vk.is_organic == 1], 3)):\n164\t df = df[df.uid.isin(set(tgt_uids)) & df.item_id.isin(item_pos.index)]\n165\t grade[trow.reindex(df.uid.values).values, item_pos.reindex(df.item_id.values).values] = g\n166\t grade[~elig] = 0\n167\t return grade\n168\t\n169\t\n170\tdef ndcg(score, grade, elig, k=10):\n171\t s = np.where(elig, score, -np.inf)\n172\t idx = np.argpartition(-s, k, axis=1)[:, :k]\n173\t ordr = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n174\t idx = np.take_along_axis(idx, ordr, axis=1)\n175\t g = np.take_along_axis(grade, idx, axis=1).astype(np.float64)\n176\t disc = 1.0 / np.log2(np.arange(2, k + 2))\n177\t dcg = ((2.0 ** g - 1) * disc).sum(1)\n178\t gs = -np.sort(-grade, axis=1)[:, :k].astype(np.float64)\n179\t idcg = ((2.0 ** gs - 1) * disc).sum(1)\n180\t m = idcg > 0\n181\t return float(np.mean(dcg[m] / idcg[m])), int(m.sum())\n182\t\n183\t\n184\tdef combine(feats, w):\n185\t out = np.zeros_like(feats[\"pop\"])\n186\t for k, v in w.items():\n187\t if v:\n188\t out += np.float32(v) * feats[k]\n189\t return out\n190\t\n191\t\n\n... [19 lines truncated] ..."
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14961725 tokens left"
}
## Step 10 · agent · 2026-09-24T07:47:46.278Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:47:53.879Z
{
"command": "python - <<'EOF'\ns=open('build.py').read()\ns=s.replace(\"\"\" liked = np.zeros((n_tgt, n_items), np.float32)\n disliked = np.zeros((n_tgt, n_items), np.float32)\n m = (ps.state == \"liked\").values\n liked[pr[m], pc[m]] = 1.0\n disliked[pr[~m], pc[~m]] = 1.0\"\"\",\n\"\"\" m = (ps.state == \"liked\").values\n liked = (pr[m], pc[m])\n disliked = (pr[~m], pc[~m])\"\"\")\ns=s.replace(\"\"\" \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"posl\": np.log1p(np.asarray(X_pos[tgt_rows].todense(), np.float32)),\n \"liked\": liked,\n \"disliked\": disliked,\n \"cf_uu\": cf_uu,\"\"\",\"\"\" \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"cf_uu\": cf_uu,\"\"\")\ns=s.replace(' for k in (\"rep\", \"rec\", \"rec2\", \"posl\"):',' for k in (\"rep\", \"rec\", \"rec2\"):')\ns=s.replace(' return dict(items=items, tgt_uids=tgt_uids, elig=elig, feats=feats)',\n ' return dict(items=items, tgt_uids=tgt_uids, elig=elig, feats=feats,\\n liked=liked, disliked=disliked, pop=pop_l)')\ns=s.replace('''def combine(feats, w):\n out = np.zeros_like(feats[\"pop\"])\n for k, v in w.items():\n if v:\n out += np.float32(v) * feats[k]\n return out''','''def combine(res, w, out=None):\n \"\"\"Linear blend of the dense features plus sparse explicit-state terms.\"\"\"\n f = res[\"feats\"]\n if out is None:\n out = np.zeros((len(res[\"tgt_uids\"]), len(res[\"items\"])), np.float32)\n else:\n out[:] = 0\n out += np.float32(w.get(\"pop\", 0.0)) * res[\"pop\"]\n for k in (\"rep\", \"rec\", \"rec2\", \"cf_uu\", \"ii\"):\n v = w.get(k, 0.0)\n if v:\n out += np.float32(v) * f[k]\n if w.get(\"liked\"):\n np.add.at(out, res[\"liked\"], np.float32(w[\"liked\"]))\n if w.get(\"disliked\"):\n np.add.at(out, res[\"disliked\"], np.float32(w[\"disliked\"]))\n return out''')\nopen('build.py','w').write(s)\nEOF\ngrep -n \"feats = {\" -A 12 build.py"
}
### Tool output · 2026-09-24T07:47:53.917Z
136: feats = {
137- "pop": np.broadcast_to(pop_l, (n_tgt, n_items)),
138- "rep": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),
139- "rec": np.asarray(X_rec[tgt_rows].todense(), np.float32),
140- "rec2": np.asarray(X_rec2[tgt_rows].todense(), np.float32),
141- "cf_uu": cf_uu,
142- "ii": ii.astype(np.float32),
143- }
144- del X_strong, X_pos, X_rec, X_rec2, Xn, ii
145- for k in ("rep", "rec", "rec2"):
146- feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)
147- log(stage, "features built")
148- return dict(items=items, tgt_uids=tgt_uids, elig=elig, feats=feats,
### Harness context
{
"type": "total_tokens_reminder",
"text": "14955792 tokens left"
}
## Step 11 · agent · 2026-09-24T07:47:56.582Z
### Thinking unavailable — no text recorded
### Write · 2026-09-24T07:48:07.605Z
{
"file_path": "/app/solution/tune.py",
"content": "\"\"\"Tune blend weights on the public validation window, then refit for test.\"\"\"\nimport gc\nimport json\n\nimport numpy as np\n\nimport build as B\n\nres = B.build(\"val\")\ngrade = B.val_truth(res[\"items\"], res[\"tgt_uids\"], res[\"elig\"])\nB.log(\"labelled users\", int((grade.max(1) > 0).sum()))\ngc.collect()\n\nbuf = np.zeros((len(res[\"tgt_uids\"]), len(res[\"items\"])), np.float32)\ndisc = 1.0 / np.log2(np.arange(2, 12))\ngs = -np.sort(-grade, axis=1)[:, :10].astype(np.float64)\nIDCG = ((2.0 ** gs - 1) * disc).sum(1)\nMASK = IDCG > 0\ndel gs\n\n\ndef score_of(w):\n s = B.combine(res, w, out=buf)\n s[~res[\"elig\"]] = -np.inf\n idx = np.argpartition(-s, 10, axis=1)[:, :10]\n ordr = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n idx = np.take_along_axis(idx, ordr, axis=1)\n g = np.take_along_axis(grade, idx, axis=1).astype(np.float64)\n dcg = ((2.0 ** g - 1) * disc).sum(1)\n return float(np.mean(dcg[MASK] / IDCG[MASK]))\n\n\nfor k in (\"pop\", \"rep\", \"rec\", \"rec2\", \"cf_uu\", \"ii\", \"liked\"):\n B.log(\"solo\", k, round(score_of({k: 1.0}), 5))\n\nw = {\"pop\": 0.05, \"rep\": 1.0, \"rec\": 0.5, \"rec2\": 0.3, \"cf_uu\": 0.3, \"ii\": 0.2,\n \"liked\": 0.3, \"disliked\": -0.5}\nbest = score_of(w)\nB.log(\"init\", best, w)\nGRID = {\n \"pop\": [0.0, 0.02, 0.05, 0.1, 0.2, 0.4],\n \"rep\": [0.0, 0.25, 0.5, 1.0, 2.0],\n \"rec\": [0.0, 0.25, 0.5, 1.0, 2.0, 4.0],\n \"rec2\": [0.0, 0.15, 0.3, 0.6, 1.2, 2.5],\n \"cf_uu\": [0.0, 0.1, 0.2, 0.4, 0.8, 1.6],\n \"ii\": [0.0, 0.05, 0.1, 0.2, 0.4, 0.8],\n \"liked\": [0.0, 0.1, 0.2, 0.4, 0.8],\n \"disliked\": [0.0, -0.2, -0.5, -2.0],\n}\nfor sweep in range(3):\n improved = False\n for k, vals in GRID.items():\n cur = w[k]\n for v in vals:\n if v == cur:\n continue\n w[k] = v\n s = score_of(w)\n if s > best + 1e-6:\n best, cur, improved = s, v, True\n w[k] = cur\n w[k] = cur\n B.log(\"sweep\", sweep, round(best, 5), w)\n if not improved:\n break\n\njson.dump({\"weights\": w, \"val_ndcg@10\": best}, open(\"/app/solution/weights.json\", \"w\"), indent=2)\nB.log(\"BEST\", best, w)\n\ns = B.combine(res, w, out=buf)\nB.write_sub(res, s, \"/app/solution/validation.parquet\")\ndel res, grade, buf, s\ngc.collect()\n\ntest = B.build(\"test\")\ns = B.combine(test, w)\nB.write_sub(test, s, \"/app/solution/submission.parquet\")\nB.log(\"done\")\n"
}
### Tool output · 2026-09-24T07:48:07.618Z
File created successfully at: /app/solution/tune.py (file state is current in your context — no need to Read it back)
### Harness context
{
"type": "total_tokens_reminder",
"text": "14953965 tokens left"
}
## Step 12 · agent · 2026-09-24T07:48:09.303Z
### Bash · 2026-09-24T07:48:09.303Z
{
"command": "nohup python -u tune.py > tune.log 2>&1 &\necho started; sleep 60; tail -20 tune.log; free -g | head -2"
}
### Tool output · 2026-09-24T07:49:09.433Z
started
/bin/bash: line 3: 8242 Killed nohup python -u tune.py > tune.log 2>&1
[ 0.7s] val events loaded
[ 5.1s] val history matrices 1950643 5564926
[ 27.3s] val user-user sim (8456, 9221)
[ 31.6s] val user-user CF done
[ 34.8s] val svd done
[ 36.1s] val features built
[ 36.5s] labelled users 4263
total used free shared buff/cache available
Mem: 15 1 8 0 5 14
### Harness context
{
"type": "total_tokens_reminder",
"text": "14953798 tokens left"
}
## Step 13 · agent · 2026-09-24T07:49:19.240Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:49:20.138Z
{
"command": "cat /sys/fs/cgroup/memory.max 2>/dev/null || cat /sys/fs/cgroup/memory/memory.limit_in_bytes 2>/dev/null; echo; cat /sys/fs/cgroup/memory.peak 2>/dev/null"
}
### Tool output · 2026-09-24T07:49:20.185Z
8589934592
8589934592
### Harness context
{
"type": "total_tokens_reminder",
"text": "14952850 tokens left"
}
## Step 14 · agent · 2026-09-24T07:49:26.469Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:49:32.056Z
{
"command": "python - <<'EOF'\ns=open('build.py').read()\ns=s.replace(''' for k in (\"rep\", \"rec\", \"rec2\"):\n feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)''',\n''' for k in (\"rep\", \"rec\", \"rec2\"):\n feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)\n for k in (\"rep\", \"rec\", \"rec2\", \"cf_uu\", \"ii\"):\n feats[k] = feats[k].astype(np.float16) # 8 GiB cgroup budget\n gc.collect()''')\ns=s.replace(''' Xd = np.asarray(Xn.todense(), dtype=np.float32)\n sim = Xd[tgt_rows] @ Xd.T''',''' Xd = np.asarray(Xn.todense(), dtype=np.float32)\n sim = np.ascontiguousarray(Xd[tgt_rows]) @ Xd.T''')\ns=s.replace(\"import sys\\nimport time\",\"import gc\\nimport time\")\ns=s.replace(''' U, S, Vt = randomized_svd(Xn, n_components=192, n_iter=4, random_state=0)''',\n''' del Xd\n gc.collect()\n U, S, Vt = randomized_svd(Xn, n_components=192, n_iter=4, random_state=0)''')\ns=s.replace(\" del Xd, Sk, U, S, Vt\",\" del Sk, U, S, Vt\\n gc.collect()\")\nopen('build.py','w').write(s)\nEOF\nsed -i 's/^import json$/import json\\nimport gc/' tune.py; python -c \"import ast;ast.parse(open('/app/solution/build.py').read())\" && echo OK\nnohup python -u tune.py > tune.log 2>&1 &\nsleep 75; tail -25 tune.log"
}
### Tool output · 2026-09-24T07:50:47.196Z
OK
[ 0.7s] val events loaded
[ 5.1s] val history matrices 1950643 5564926
[ 27.4s] val user-user sim (8456, 9221)
[ 31.7s] val user-user CF done
[ 34.9s] val svd done
[ 41.0s] val features built
[ 41.3s] labelled users 4263
[ 45.0s] solo pop 0.01055
[ 53.0s] solo rep 7e-05
[ 61.0s] solo rec 7e-05
[ 69.0s] solo rec2 7e-05
[ 71.7s] solo cf_uu 0.0219
[ 73.9s] solo ii 0.02245
### Harness context
{
"type": "edited_text_file",
"filename": "/app/solution/tune.py",
"snippet": "1\t\"\"\"Tune blend weights on the public validation window, then refit for test.\"\"\"\n2\timport gc\n3\timport json\n4\timport gc\n5\t\n6\timport numpy as np\n7\t\n8\timport build as B\n9\t\n10\tres = B.build(\"val\")\n11\tgrade = B.val_truth(res[\"items\"], res[\"tgt_uids\"], res[\"elig\"])\n12\tB.log(\"labelled users\", int((grade.max(1) > 0).sum()))"
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14951728 tokens left"
}
## Step 15 · agent · 2026-09-24T07:51:02.183Z
### Thinking unavailable — no text recorded
Important finding: eligibility excludes the user's own history — this is a pure *discovery* task, so repeat features are worthless and CF quality is everything.
### Bash · 2026-09-24T07:51:03.187Z
{
"command": "sleep 50; tail -8 tune.log"
}
### Tool output · 2026-09-24T07:51:53.202Z
[ 45.0s] solo pop 0.01055
[ 53.0s] solo rep 7e-05
[ 61.0s] solo rec 7e-05
[ 69.0s] solo rec2 7e-05
[ 71.7s] solo cf_uu 0.0219
[ 73.9s] solo ii 0.02245
[ 81.2s] solo liked 7e-05
[ 86.7s] init 0.023518388985170727 {'pop': 0.05, 'rep': 1.0, 'rec': 0.5, 'rec2': 0.3, 'cf_uu': 0.3, 'ii': 0.2, 'liked': 0.3, 'disliked': -0.5}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14950021 tokens left"
}
## Step 16 · agent · 2026-09-24T07:52:00.480Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:52:08.840Z
{
"command": "pkill -f tune.py; cat > /app/solution/tune2.py <<'EOF'\n\"\"\"Lean weight search (discovery-only features) + final artifacts.\"\"\"\nimport gc, json\nimport numpy as np\nimport build as B\n\ndef prep(res, grade):\n disc = 1.0 / np.log2(np.arange(2, 12))\n gs = -np.sort(-grade, axis=1)[:, :10].astype(np.float64)\n idcg = ((2.0 ** gs - 1) * disc).sum(1); m = idcg > 0\n del gs; gc.collect()\n return disc, idcg, m\n\nres = B.build(\"val\")\ngrade = B.val_truth(res[\"items\"], res[\"tgt_uids\"], res[\"elig\"])\ndisc, IDCG, MASK = prep(res, grade)\nbuf = np.zeros(grade.shape, np.float32)\nNEG = res[\"elig\"] == False\n\ndef score_of(w):\n s = B.combine(res, w, out=buf)\n s[NEG] = -np.inf\n idx = np.argpartition(-s, 10, axis=1)[:, :10]\n o = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n g = np.take_along_axis(grade, np.take_along_axis(idx, o, axis=1), axis=1).astype(np.float64)\n return float(np.mean(((2.0 ** g - 1) * disc).sum(1)[MASK] / IDCG[MASK]))\n\nw = {\"pop\": 0.05, \"cf_uu\": 0.5, \"ii\": 0.5, \"disliked\": -1.0}\nbest = score_of(w); B.log(\"init\", round(best, 5))\nGRID = {\"cf_uu\": [0.2, 0.35, 0.5, 0.8, 1.2], \"ii\": [0.2, 0.35, 0.5, 0.8, 1.2],\n \"pop\": [0.0, 0.02, 0.05, 0.12, 0.25], \"disliked\": [0.0, -1.0]}\nfor sweep in range(2):\n for k, vals in GRID.items():\n cur = w[k]\n for v in vals:\n if v == cur: continue\n w[k] = v; s = score_of(w)\n if s > best + 1e-6: best, cur = s, v\n w[k] = cur\n w[k] = cur\n B.log(\"sweep\", sweep, round(best, 5), w)\njson.dump({\"weights\": w, \"val_ndcg@10\": best}, open(\"/app/solution/weights.json\", \"w\"), indent=2)\nB.log(\"BEST\", best, w)\nB.write_sub(res, B.combine(res, w, out=buf), \"/app/solution/validation.parquet\")\ndel res, grade, buf, NEG; gc.collect()\ntest = B.build(\"test\")\nB.write_sub(test, B.combine(test, w), \"/app/solution/submission.parquet\")\nB.log(\"done\")\nEOF\nnohup python -u tune2.py > tune2.log 2>&1 &\nsleep 100; tail -12 tune2.log"
}
### Tool error · 2026-09-24T07:52:08.896Z
Exit code 144
### Harness context
{
"type": "total_tokens_reminder",
"text": "14948232 tokens left"
}
## Step 17 · agent · 2026-09-24T07:52:10.499Z
### Bash · 2026-09-24T07:52:10.499Z
{
"command": "sleep 45; tail -12 /app/solution/tune2.log"
}
### Tool error · 2026-09-24T07:52:55.513Z
Exit code 1
tail: cannot open '/app/solution/tune2.log' for reading: No such file or directory
### Harness context
{
"type": "total_tokens_reminder",
"text": "14948123 tokens left"
}
## Step 18 · agent · 2026-09-24T07:52:58.694Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:52:59.151Z
{
"command": "pgrep -af python | head; ls /app/solution/"
}
### Tool output · 2026-09-24T07:52:59.171Z
8327 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790235893615-teb25c.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af python | head; ls /app/solution/' < /dev/null && pwd -P >| /tmp/claude-8d43-cwd
build.py
tune.log
tune.py
### Harness context
{
"type": "total_tokens_reminder",
"text": "14947877 tokens left"
}
## Step 19 · agent · 2026-09-24T07:53:07.641Z
### Bash · 2026-09-24T07:53:07.641Z
{
"command": "cat > tune2.py <<'PYEOF'\n\"\"\"Lean weight search (discovery-only features) + final artifacts.\"\"\"\nimport gc, json\nimport numpy as np\nimport build as B\n\nres = B.build(\"val\")\ngrade = B.val_truth(res[\"items\"], res[\"tgt_uids\"], res[\"elig\"])\ndisc = 1.0 / np.log2(np.arange(2, 12))\ngs = -np.sort(-grade, axis=1)[:, :10].astype(np.float64)\nIDCG = ((2.0 ** gs - 1) * disc).sum(1); MASK = IDCG > 0\ndel gs; gc.collect()\nbuf = np.zeros(grade.shape, np.float32)\nNEG = ~res[\"elig\"]\n\ndef score_of(w):\n s = B.combine(res, w, out=buf)\n s[NEG] = -np.inf\n idx = np.argpartition(-s, 10, axis=1)[:, :10]\n o = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n g = np.take_along_axis(grade, np.take_along_axis(idx, o, axis=1), axis=1).astype(np.float64)\n return float(np.mean(((2.0 ** g - 1) * disc).sum(1)[MASK] / IDCG[MASK]))\n\nw = {\"pop\": 0.05, \"cf_uu\": 0.5, \"ii\": 0.5, \"disliked\": -1.0}\nbest = score_of(w); B.log(\"init\", round(best, 5))\nGRID = {\"cf_uu\": [0.2, 0.35, 0.8, 1.2], \"ii\": [0.2, 0.35, 0.8, 1.2],\n \"pop\": [0.0, 0.02, 0.12, 0.25], \"disliked\": [0.0]}\nfor sweep in range(2):\n for k, vals in GRID.items():\n cur = w[k]\n for v in vals:\n if v == cur: continue\n w[k] = v; s = score_of(w)\n if s > best + 1e-6: best, cur = s, v\n w[k] = cur\n w[k] = cur\n B.log(\"sweep\", sweep, round(best, 5), w)\njson.dump({\"weights\": w, \"val_ndcg@10\": best}, open(\"/app/solution/weights.json\", \"w\"), indent=2)\nB.log(\"BEST\", best, w)\nB.write_sub(res, B.combine(res, w, out=buf), \"/app/solution/validation.parquet\")\ndel res, grade, buf, NEG; gc.collect()\ntest = B.build(\"test\")\nB.write_sub(test, B.combine(test, w), \"/app/solution/submission.parquet\")\nB.log(\"done\")\nPYEOF\nnohup python -u tune2.py > tune2.log 2>&1 &\nsleep 90; tail -12 tune2.log"
}
### Tool output · 2026-09-24T07:54:37.750Z
/bin/bash: line 93: 8335 Killed nohup python -u tune2.py > tune2.log 2>&1
[ 0.7s] val events loaded
[ 5.1s] val history matrices 1950643 5564926
[ 27.2s] val user-user sim (8456, 9221)
[ 31.6s] val user-user CF done
[ 34.8s] val svd done
[ 41.1s] val features built
### Harness context
{
"type": "total_tokens_reminder",
"text": "14946639 tokens left"
}
## Step 20 · agent · 2026-09-24T07:54:48.608Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:54:54.228Z
{
"command": "python - <<'EOF'\ns=open('build.py').read()\ns=s.replace(''' X_pos = mat(pos) # any >=50% listen\n age = (cutoff - strong.timestamp.values).astype(np.float64)\n X_rec = mat(strong, np.exp(-age / (14 * 86400.0))) # 14-day half-life-ish\n X_rec2 = mat(strong, np.exp(-age / (3 * 86400.0))) # short-term\n log(stage, \"history matrices\", X_strong.nnz, X_pos.nnz)\n del ev, li, strong, pos''',''' log(stage, \"history matrix\", X_strong.nnz)\n del ev, li, strong, pos\n gc.collect()''')\ns=s.replace(''' \"rep\": np.log1p(np.asarray(X_strong[tgt_rows].todense(), np.float32)),\n \"rec\": np.asarray(X_rec[tgt_rows].todense(), np.float32),\n \"rec2\": np.asarray(X_rec2[tgt_rows].todense(), np.float32),\n \"cf_uu\"''',''' \"cf_uu\"''')\ns=s.replace(''' del X_strong, X_pos, X_rec, X_rec2, Xn, ii\n for k in (\"rep\", \"rec\", \"rec2\"):\n feats[k] /= feats[k].max(1, keepdims=True).clip(1e-9)\n for k in (\"rep\", \"rec\", \"rec2\", \"cf_uu\", \"ii\"):''',''' del X_strong, Xn, ii\n for k in (\"cf_uu\", \"ii\"):''')\ns=s.replace(' for k in (\"rep\", \"rec\", \"rec2\", \"cf_uu\", \"ii\"):\\n v = w.get',' for k in (\"cf_uu\", \"ii\"):\\n v = w.get')\nopen('build.py','w').write(s)\nEOF\ngrep -n \"X_pos\\|X_rec\\|rep\\|rec2\" build.py; python -c \"import ast;ast.parse(open('build.py').read())\" && echo SYNTAX_OK"
}
### Tool output · 2026-09-24T07:54:54.487Z
4: 1. Feature construction from event history (repeat-consumption, explicit
117: rows = np.repeat(np.arange(n_tgt), K)
205: "uid": np.repeat(tgt_uids, 100),
SYNTAX_OK
### Harness context
{
"type": "total_tokens_reminder",
"text": "14945071 tokens left"
}
## Step 21 · agent · 2026-09-24T07:55:04.169Z
### Bash · 2026-09-24T07:55:04.169Z
{
"command": "cat > final.py <<'PYEOF'\n\"\"\"Fit the discovery ranker and write the hidden-test submission (test first).\"\"\"\nimport gc, json, sys\nimport numpy as np\nimport build as B\n\nW = {\"pop\": 0.05, \"cf_uu\": 0.5, \"ii\": 0.5, \"disliked\": -1.0}\ntest = B.build(\"test\")\nB.write_sub(test, B.combine(test, W), \"/app/solution/submission.parquet\")\ndel test; gc.collect()\nB.log(\"submission written\")\n\nres = B.build(\"val\")\ngrade = B.val_truth(res[\"items\"], res[\"tgt_uids\"], res[\"elig\"])\ndisc = 1.0 / np.log2(np.arange(2, 12))\nIDCG = ((2.0 ** (-np.sort(-grade, axis=1)[:, :10].astype(np.float64)) - 1) * disc).sum(1)\nMASK = IDCG > 0\ngc.collect()\nbuf = np.zeros(grade.shape, np.float32)\nNEG = ~res[\"elig\"]\n\ndef score_of(w):\n s = B.combine(res, w, out=buf)\n s[NEG] = -np.inf\n idx = np.argpartition(-s, 10, axis=1)[:, :10]\n o = np.take_along_axis(s, idx, axis=1).argsort(axis=1)[:, ::-1]\n g = np.take_along_axis(grade, np.take_along_axis(idx, o, axis=1), axis=1).astype(np.float64)\n return float(np.mean(((2.0 ** g - 1) * disc).sum(1)[MASK] / IDCG[MASK]))\n\nw = dict(W); best = score_of(w); B.log(\"init\", round(best, 5))\nfor k, vals in {\"cf_uu\": [0.2, 0.35, 0.8, 1.5], \"ii\": [0.2, 0.35, 0.8, 1.5],\n \"pop\": [0.0, 0.02, 0.12, 0.3]}.items():\n cur = w[k]\n for v in vals:\n w[k] = v; s = score_of(w)\n B.log(\" \", k, v, round(s, 5))\n if s > best + 1e-6: best, cur = s, v\n w[k] = cur\n w[k] = cur\njson.dump({\"weights\": w, \"val_ndcg@10\": best, \"baseline_weights\": W},\n open(\"/app/solution/weights.json\", \"w\"), indent=2)\nB.log(\"BEST\", best, w)\nB.write_sub(res, B.combine(res, w, out=buf), \"/app/solution/validation.parquet\")\nif w != W:\n del res, grade, buf, NEG; gc.collect()\n test = B.build(\"test\")\n B.write_sub(test, B.combine(test, w), \"/app/solution/submission.parquet\")\nB.log(\"done\")\nPYEOF\nnohup python -u final.py > final.log 2>&1 &\nsleep 80; tail -15 final.log; ls -la /app/solution/"
}
### Tool output · 2026-09-24T07:56:24.200Z
[ 1.0s] test events loaded
[ 3.6s] test history matrix 2008203
[ 25.8s] test user-user sim (8456, 9223)
[ 30.2s] test user-user CF done
[ 33.4s] test svd done
[ 35.7s] test features built
[ 39.1s] wrote /app/solution/submission.parquet
[ 39.1s] submission written
[ 39.6s] val events loaded
[ 42.2s] val history matrix 1950643
[ 64.4s] val user-user sim (8456, 9221)
[ 68.7s] val user-user CF done
[ 71.5s] val svd done
[ 73.8s] val features built
total 1660
drwxr-xr-x 1 agent agent 4096 Sep 24 07:55 .
drwxr-xr-x 1 agent agent 4096 Sep 24 07:41 ..
-rw-r--r-- 1 agent agent 8036 Sep 24 07:54 build.py
-rw-r--r-- 1 agent agent 478 Sep 24 07:56 final.log
-rw-r--r-- 1 agent agent 1809 Sep 24 07:55 final.py
-rw-r--r-- 1 agent agent 1652876 Sep 24 07:55 submission.parquet
-rw-r--r-- 1 agent agent 573 Sep 24 07:50 tune.log
-rw-r--r-- 1 agent agent 2358 Sep 24 07:49 tune.py
-rw-r--r-- 1 agent agent 206 Sep 24 07:53 tune2.log
-rw-r--r-- 1 agent agent 1705 Sep 24 07:53 tune2.py
### Harness context
{
"type": "total_tokens_reminder",
"text": "14943909 tokens left"
}
## Step 22 · agent · 2026-09-24T07:56:25.908Z
### Thinking unavailable — no text recorded
Submission is on disk. Writing the README while the weight sweep runs.
### Write · 2026-09-24T07:56:38.040Z
{
"file_path": "/app/solution/README.md",
"content": "# \"Made for You\" discovery ranker\n\n`python final.py` builds `submission.parquet` (hidden test) and\n`validation.parquet` (public window); `build.py` holds the model.\n\n## Key observation\n\nThe supplied candidate eligibility masks out each user's *own* interaction\nhistory (~900 items/user). Measured on the public window, repeat-consumption\nfeatures (past strong-listen counts, recency-decayed counts, `liked`\npreference state) score NDCG@10 ≈ 7e-5 on their own — they rank items that are\nnever eligible. This is a pure **discovery** problem, so the model is entirely\ncollaborative-filtering driven and those features were dropped.\n\n## Model\n\nHistory = organic listens completed to ≥80 % (the objective's own positive\ndefinition), as a user × item count matrix `X` over the 30 000-track catalog,\n`log1p`-damped and L2-row-normalised (`Xn`).\n\nThree scores are blended linearly:\n\n| Feature | Definition |\n| --- | --- |\n| `cf_uu` | User-user CF: cosine neighbours over `Xn`, self-similarity zeroed, top-200 neighbours, weights squared to sharpen, then propagated back through `Xn`. Row-max normalised. |\n| `ii` | Low-rank item-item CF: rank-192 randomised SVD of `Xn`, reconstructed scores. Row-max normalised. |\n| `pop` | Global organic strong-listen popularity, `log1p`, max-normalised — a prior for thin users. |\n\nPlus a `disliked` penalty applied to active explicit-dislike pairs from the\nstage's preference-state file.\n\nWeights are chosen by coordinate ascent directly on validation NDCG@10\n(`weights.json` records the result). Solo validation NDCG@10: `ii` 0.0225,\n`cf_uu` 0.0219, `pop` 0.0106 — the blend beats all three.\n\n## Stage handling\n\n`build(\"val\")` uses only `train_*` as history and the validation preference\nstate / eligibility. `build(\"test\")` refits the identical pipeline on\n`train_* + val_*` with the test preference state and test eligibility, so the\nvalidation week is observed history rather than a label source. Recency decay\nis anchored to each stage's own cutoff.\n\n## Caveats\n\nFit under a 30-minute wall clock and an 8 GiB memory cap: dense\n(8456 × 30 000) feature planes are stored as float16, which bounds the feature\ncount. Given more time the obvious next steps are a full item-item cosine with\npopularity damping / EASE-style closed form, and a GBDT reranker over a top-300\nprefilter trained on a pseudo-validation split carved from the tail of train.\n"
}
### Tool output · 2026-09-24T07:56:38.069Z
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": "14942287 tokens left"
}
## Step 23 · agent · 2026-09-24T07:56:39.644Z
### Bash · 2026-09-24T07:56:39.644Z
{
"command": "sleep 60; tail -20 final.log"
}
### Tool output · 2026-09-24T07:57:39.662Z
[ 79.6s] init 0.02438
[ 82.8s] cf_uu 0.2 0.02418
[ 86.0s] cf_uu 0.35 0.02407
[ 89.1s] cf_uu 0.8 0.02401
[ 92.3s] cf_uu 1.5 0.02308
[ 95.5s] ii 0.2 0.0234
[ 98.7s] ii 0.35 0.02416
[ 101.9s] ii 0.8 0.02406
[ 105.0s] ii 1.5 0.02391
[ 108.4s] pop 0.0 0.02442
[ 111.6s] pop 0.02 0.02461
[ 114.8s] pop 0.12 0.02402
[ 118.0s] pop 0.3 0.02226
[ 118.0s] BEST 0.02461460816228665 {'pop': 0.02, 'cf_uu': 0.5, 'ii': 0.5, 'disliked': -1.0}
[ 121.4s] wrote /app/solution/validation.parquet
[ 122.1s] test events loaded
[ 124.8s] test history matrix 2008203
[ 146.8s] test user-user sim (8456, 9223)
[ 151.2s] test user-user CF done
[ 154.1s] test svd done
### Harness context
{
"type": "total_tokens_reminder",
"text": "14942165 tokens left"
}
## Step 24 · agent · 2026-09-24T07:57:41.579Z
### Thinking unavailable — no text recorded
### Bash · 2026-09-24T07:57:42.209Z
{
"command": "sleep 20; tail -3 final.log && python /task/evaluate_public.py /app/solution/validation.parquet"
}
### Tool output · 2026-09-24T07:58:04.808Z
[ 156.4s] test features built
[ 159.7s] wrote /app/solution/submission.parquet
[ 159.7s] done
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.024615,
"quality_users": 4263
}
}
### Harness context
{
"type": "total_tokens_reminder",
"text": "14941556 tokens left"
}
## Step 25 · agent · 2026-09-24T07:58:09.646Z
### Bash · 2026-09-24T07:58:09.646Z
{
"command": "rm -f tune.py tune2.py tune.log tune2.log _noop.npy && python -c \"\nimport pandas as pd,numpy as np\nfrom ranking_evaluation import CandidateSet, validate_ranking, read_ranking\nimport sys; sys.path.insert(0,'/task')\nd=pd.read_parquet('submission.parquet'); print(d.shape, d.dtypes.to_dict())\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(d,t)\ncs=CandidateSet('/task/data/test_eligible_candidates.npz')\nel=np.unpackbits(cs.packed,axis=1,bitorder='little')[:,:30000].astype(bool)\nrow={int(u):r for r,u in enumerate(cs.uids)}; col={int(i):c for c,i in enumerate(cs.item_ids)}\nr=np.array([row[u] for u in f.uid]); c=np.array([col[i] for i in f.item_id])\nprint('all eligible:', el[r,c].all())\nprint(d.head(3))\n\" 2>&1 | tail -20; ls -la /app/solution"
}
### Tool output · 2026-09-24T07:58:10.360Z
Traceback (most recent call last):
File "", line 3, in
ModuleNotFoundError: No module named 'ranking_evaluation'
total 3276
drwxr-xr-x 1 agent agent 4096 Sep 24 07:58 .
drwxr-xr-x 1 agent agent 4096 Sep 24 07:41 ..
-rw-r--r-- 1 agent agent 2390 Sep 24 07:56 README.md
-rw-r--r-- 1 agent agent 8036 Sep 24 07:54 build.py
-rw-r--r-- 1 agent agent 1268 Sep 24 07:57 final.log
-rw-r--r-- 1 agent agent 1809 Sep 24 07:55 final.py
-rw-r--r-- 1 agent agent 1656496 Sep 24 07:57 submission.parquet
-rw-r--r-- 1 agent agent 1656816 Sep 24 07:57 validation.parquet
-rw-r--r-- 1 agent agent 231 Sep 24 07:57 weights.json
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940973 tokens left"
}
## Step 26 · agent · 2026-09-24T07:58:14.881Z
### Bash · 2026-09-24T07:58:14.881Z
{
"command": "PYTHONPATH=/task python -c \"\nimport pandas as pd,numpy as np\nfrom ranking_evaluation import CandidateSet, validate_ranking\nd=pd.read_parquet('/app/solution/submission.parquet'); print(d.shape, d.dtypes.to_dict())\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nf=validate_ranking(d,t); print('schema OK')\ncs=CandidateSet('/task/data/test_eligible_candidates.npz')\nel=np.unpackbits(cs.packed,axis=1,bitorder='little')[:,:30000].astype(bool)\nrow={int(u):r for r,u in enumerate(cs.uids)}; col={int(i):c for c,i in enumerate(cs.item_ids)}\nr=np.array([row[u] for u in f.uid]); c=np.array([col[i] for i in f.item_id])\nprint('all eligible:', bool(el[r,c].all()))\n\""
}
### Tool output · 2026-09-24T07:58:16.408Z
(845600, 3) {'uid': dtype('int64'), 'item_id': dtype('int64'), 'rank': dtype('int64')}
schema OK
all eligible: True
### Harness context
{
"type": "total_tokens_reminder",
"text": "14940198 tokens left"
}
## Step 27 · agent · 2026-09-24T07:58:27.007Z
Done. `/app/solution/submission.parquet` is written and validated: 845,600 rows (8,456 users × 100), ranks 1–100 with no ties or duplicates, and every item eligible under `test_eligible_candidates.npz`.
**Public validation NDCG@10: 0.024615** (4,263 scored users).
The decisive finding shaped the whole model: the supplied eligibility masks exclude each user's own interaction history (~900 items/user). Repeat-consumption features — past strong-listen counts, recency-decayed counts, `liked` preference state — each score ≈7e-5 solo, because they rank items that are never eligible. This is a pure discovery problem, so I dropped them and put everything into collaborative filtering.
Final model is a linear blend over organic ≥80%-completion listens (the objective's own positive definition), log1p-damped and L2-row-normalized:
| Feature | Solo val NDCG@10 | Weight |
| --- | --- | --- |
| `ii` — rank-192 randomized SVD item-item | 0.0225 | 0.5 |
| `cf_uu` — top-200 user-user cosine neighbors, squared weights | 0.0219 | 0.5 |
| `pop` — global organic popularity prior | 0.0106 | 0.02 |
| `disliked` penalty from preference state | — | −1.0 |
Weights came from coordinate ascent on validation NDCG@10. The blend beats every component alone. Validation features use `train_*` only; the test artifact refits the identical pipeline on `train_* + val_*` with test-stage preference state and eligibility.
Two things worth flagging:
- The absolute score is low because the task is genuinely hard — with the user's own history excluded, the model must predict which of ~29k unseen tracks a user will organically seek out in a seven-day window.
- I ran against an 8 GiB cgroup cap (not the 15 GiB the host reports), which forced float16 storage for the dense 8456×30000 feature planes and bounded how many features I could hold at once. Two OOM kills cost time mid-attempt.
Not attempted, in priority order if there were more time: a full item-item cosine with popularity damping or EASE-style closed form (the SVD is a low-rank approximation of this and was the strongest single feature), and a GBDT reranker over a top-300 prefilter trained on a pseudo-validation split carved from the tail of train. Both are noted in the README.