yambda-discovery-ranking-v01 / gemini-3-8-flash-high-basic-fixed-r01-d0433654
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T03:36:53.180Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T03:36:53.225Z
# Build a music-discovery ranker
Build the strongest ranking model you can for the "Made for You" shelf of a
music streaming service. Per-user candidate eligibility, cutoff-specific
explicit preference state, and a temporal backtest are provided. Candidate
eligibility is platform-owned; all modeling within that candidate boundary is
in scope.
## Data
All inputs are under `/task/data/`. Training data ends at timestamp `24786800`.
A 30-minute gap precedes a seven-day public validation window. The hidden test
is the following seven-day window after another 30-minute gap.
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| Family | Meaning | Columns |
| --- | --- | --- |
| `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` |
| `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` |
| `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` |
| `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` |
`is_organic=1` means the listener sought out the track; `0` means the action
came through a recommendation surface. A listen is a conventional positive at
50% completion. The discovery-quality objective below uses the stronger 80%
threshold.
The two public windows have different roles:
1. Build and tune a validation ranker using only `train_*` as history and
`val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.
## Discovery objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this objective for development, but its score is
not a separate acceptance stage. Final acceptance is determined on the hidden
test using the same objective.
The Yambda logs contain interactions, not impression propensities. Treat the
organic objective as a robust offline target, not as a causal estimate of
online lift.
## Public tools
`/task/evaluate_public.py` scores a validation-stage ranking against the public
window. Pass the path to a ranking with the same schema as the final
deliverable:
```sh
python /task/evaluate_public.py solution/validation.parquet
```
Stage-independent evaluation helpers are available in
`/task/ranking_evaluation.py`. The builder installs the same checked-in module
in the public agent image and private verifier image so scoring mechanics cannot
drift. The public copy contains no hidden labels, evaluator-owned data paths, or
pass thresholds.
The public evaluator uses `validation_eligible_candidates.npz`; it is not a
validator for the final artifact. No model, fitted representation, example
submission, or modeling architecture is supplied. Build and tune the best model
you can, then refit the selected approach as described above.
## Deliverable
Write `/app/solution/submission.parquet` or
`/app/solution/submission.csv`, with exactly 100 rows for every target user and
columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties
or duplicate items. Every submitted item must be present in that user's
`test_eligible_candidates.npz` candidate set.
Leave the code that builds the artifact and a concise
`/app/solution/README.md` in `/app/solution/`. Keep the best valid submission
saved throughout the attempt so it survives the deadline.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model provider
connection managed by Harbor.
- You have up to 30 minutes. Improve the model as much as possible within that
limit. Structural validity alone is not sufficient; the final submission is
also evaluated for ranking quality.
## Step 3 · agent · 2026-09-29T03:36:59.314Z
### Recorded reasoning summary
Understanding Core Requirements
I'm currently focusing on grasping the prompt, environment, mandates, and the tools at my disposal. My objective is to operate in YOLO mode, adhering strictly to the provided guidelines and ensuring all deliverables are placed within the `/app/solution/` directory.
### update_topic · 2026-09-29T03:36:59.314Z
{
"title": "Initial Exploration & Baseline Setup",
"strategic_intent": "Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.",
"summary": "Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early."
}
### Tool output · 2026-09-29T03:36:59.364Z
Current topic: "Initial Exploration & Baseline Setup"
Topic summary: Starting the music-discovery ranker project. First, inspect evaluation scripts, dataset sizes, hardware resources, and requirements. Then implement an initial baseline submission to ensure a valid artifact exists early.
Strategic Intent: Explore environment, inspect evaluation scripts and data sizes, and establish a baseline.
### read_file · 2026-09-29T03:36:59.314Z
{
"file_path": "/task/evaluate_public.py",
"end_line": 100,
"start_line": 1
}
### Tool output · 2026-09-29T03:36:59.364Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.
--- FILE CONTENT (truncated) ---
#!/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.
### read_file · 2026-09-29T03:36:59.314Z
{
"end_line": 150,
"file_path": "/task/ranking_evaluation.py",
"start_line": 1
}
### Tool output · 2026-09-29T03:36:59.364Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-150 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 151.
--- FILE CONTENT (truncated) ---
"""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"
### read_file · 2026-09-29T03:36:59.314Z
{
"start_line": 1,
"file_path": "/task/requirements.txt",
"end_line": 100
}
### Tool output · 2026-09-29T03:36:59.364Z
# 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
### run_shell_command · 2026-09-29T03:36:59.314Z
{
"description": "Check CPU, RAM, GPU availability and data file sizes",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi || true; ls -lh /task/data/"
}
### Tool output · 2026-09-29T03:36:59.364Z
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
/usr/bin/bash: line 4: nvidia-smi: command not found
total 340M
-r--r--r-- 1 root root 342K Sep 29 03:30 candidate_catalog.parquet
-r--r--r-- 1 root root 83K Sep 29 03:30 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 03:31 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 03:31 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 03:30 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 03:30 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 03:30 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 03:30 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 03:30 train_unlikes.parquet
-r--r--r-- 1 root root 36K Sep 29 03:30 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 03:30 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 03:30 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 03:30 val_undislikes.parquet
-r--r--r-- 1 root root 71K Sep 29 03:30 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 03:30 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 03:31 validation_preference_state.parquet
Process Group PGID: 1612
## Step 4 · agent · 2026-09-29T03:37:03.579Z
### run_shell_command · 2026-09-29T03:37:03.579Z
{
"description": "Check total RAM from /proc/meminfo",
"command": "cat /proc/meminfo | grep MemTotal"
}
### Tool output · 2026-09-29T03:37:03.595Z
Output: MemTotal: 15988968 kB
Process Group PGID: 1619
### read_file · 2026-09-29T03:37:03.579Z
{
"file_path": "/task/evaluate_public.py",
"end_line": 160,
"start_line": 100
}
### Tool output · 2026-09-29T03:37:03.595Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 100-158 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.
--- FILE CONTENT (truncated) ---
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()
### read_file · 2026-09-29T03:37:03.579Z
{
"file_path": "/task/ranking_evaluation.py",
"start_line": 150,
"end_line": 232
}
### Tool output · 2026-09-29T03:37:03.595Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 150-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.
--- FILE CONTENT (truncated) ---
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),
}
## Step 5 · agent · 2026-09-29T03:37:21.172Z
### Recorded reasoning summary
Analyzing Hardware Limitations
My current thinking is focused on optimizing for the available hardware. I'm noting the absence of GPUs and the substantial core count of AMD EPYC CPUs, alongside the 30-minute time constraint. The available libraries are also being considered as I plan the next steps.
### run_shell_command · 2026-09-29T03:37:21.172Z
{
"description": "Check available ML libraries in python3",
"command": "python3 -c \"for pkg in ['scipy', 'sklearn', 'lightgbm', 'xgboost', 'torch', 'implicit']:\n try:\n __import__(pkg)\n print(pkg, 'AVAILABLE')\n except ImportError:\n print(pkg, 'NOT available')\n\""
}
### Tool output · 2026-09-29T03:37:22.593Z
Output: scipy AVAILABLE
sklearn AVAILABLE
lightgbm NOT available
xgboost NOT available
torch NOT available
implicit NOT available
Process Group PGID: 1623
## Step 6 · agent · 2026-09-29T03:37:26.436Z
### run_shell_command · 2026-09-29T03:37:26.436Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('targets count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog count:', len(catalog), catalog.columns.tolist())\n\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val_npz keys:', list(val_npz.keys()), 'uids len:', len(val_npz['uids']), 'items len:', len(val_npz['item_ids']), 'packed shape:', val_npz['packed_eligible'].shape)\n\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\nprint('test_npz keys:', list(test_npz.keys()), 'uids len:', len(test_npz['uids']), 'items len:', len(test_npz['item_ids']), 'packed shape:', test_npz['packed_eligible'].shape)\n\"",
"description": "Inspect target users, catalog, and eligible candidate shapes"
}
### Tool output · 2026-09-29T03:37:27.360Z
Output: targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
catalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
val_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)
test_npz keys: ['uids', 'item_ids', 'packed_eligible'] uids len: 8456 items len: 30000 packed shape: (8456, 3750)
Process Group PGID: 1647
## Step 7 · agent · 2026-09-29T03:37:36.712Z
### Recorded reasoning summary
Verifying Data Integrity
I'm currently digging into the details of our user and item data. My immediate focus is on confirming the alignment between the user IDs in our NPZ files and the `target_users.parquet` file. I also need to determine the average number of eligible items per user. I'll be running an exploratory script shortly to gather these crucial metrics.
### run_shell_command · 2026-09-29T03:37:36.712Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\ntest_npz = np.load('/task/data/test_eligible_candidates.npz')\n\nprint('targets equals val uids:', np.array_equal(targets.uid.values, val_npz['uids']))\nprint('targets equals test uids:', np.array_equal(targets.uid.values, test_npz['uids']))\nprint('val items equals test items:', np.array_equal(val_npz['item_ids'], test_npz['item_ids']))\n\n# Unpack eligible bits count\npacked = val_npz['packed_eligible']\n# count bits set per user\nbits_per_user = np.unpackbits(packed, axis=1)[:, :30000].sum(axis=1)\nprint(f'Eligible items per user in val: min={bits_per_user.min()}, max={bits_per_user.max()}, mean={bits_per_user.mean():.1f}, median={np.median(bits_per_user)}')\n\"",
"description": "Check alignment and eligible candidate statistics"
}
### Tool output · 2026-09-29T03:37:37.630Z
Output: targets equals val uids: True
targets equals test uids: True
val items equals test items: True
Eligible items per user in val: min=24657, max=30000, mean=29067.3, median=29242.0
Process Group PGID: 1670
## Step 8 · agent · 2026-09-29T03:37:45.360Z
### Recorded reasoning summary
Confirming Data Consistency
I've confirmed the exact alignment across `targets.uid.values`, `val_npz['uids']`, and `test_npz['uids']`. Furthermore, `val_npz['item_ids']` and `test_npz['item_ids']` are identical, encompassing 30,000 items. The next step is to verify that the catalog's `item_id` aligns with these found in `val_npz['item_ids']`.
### run_shell_command · 2026-09-29T03:37:45.360Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_npz = np.load('/task/data/validation_eligible_candidates.npz')\nprint('catalog items equal val_npz item_ids:', np.array_equal(catalog.item_id.values, val_npz['item_ids']))\n\"",
"description": "Check if catalog items match val_npz item_ids"
}
### Tool output · 2026-09-29T03:37:46.116Z
Output: catalog items equal val_npz item_ids: True
Process Group PGID: 1686
## Step 9 · agent · 2026-09-29T03:37:50.544Z
### Recorded reasoning summary
Confirming Data Source
I'm verifying that the `item_id` values from the catalog directly correspond to the `item_ids` found within the `val_npz` data. My current focus is on understanding the `candidate_catalog.parquet` file, which appears to be a curated list of 30,000 tracks based on their listening popularity during training.
### run_shell_command · 2026-09-29T03:37:50.544Z
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Test top 100 eligible by catalog popularity\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\n# For each user, find first 100 set bits\nranked = {}\nfor i, uid in enumerate(uids):\n bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n eligible_indices = np.where(bits == 1)[0][:100]\n ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\"",
"description": "Evaluate catalog popularity baseline on validation set"
}
### Tool output · 2026-09-29T03:37:51.236Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1709
## Step 10 · agent · 2026-09-29T03:37:54.487Z
### run_shell_command · 2026-09-29T03:37:54.487Z
{
"description": "Evaluate catalog popularity baseline on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\n\npacked = eligibility.packed\nuids = eligibility.uids\n\nranked = {}\nfor i, uid in enumerate(uids):\n bits = np.unpackbits(packed[i], count=30000, bitorder='little')\n eligible_indices = np.where(bits == 1)[0][:100]\n ranked[int(uid)] = list(item_ids[eligible_indices])\n\nres = score_rankings(ranked, graded)\nprint('Catalog popularity baseline:', res)\n\""
}
### Tool output · 2026-09-29T03:37:56.119Z
Output: Catalog popularity baseline: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1720
## Step 11 · agent · 2026-09-29T03:38:01.704Z
### Recorded reasoning summary
Creating Baseline Submission
I've just achieved an NDCG@10 of 0.01071 with catalog popularity, involving 4263 users. My immediate next step is to generate a valid submission file, `/app/solution/submission.parquet`, based on this baseline, ensuring it adheres to the specified contract.
### run_shell_command · 2026-09-29T03:38:01.704Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntest_npz = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_set = set(targets.uid.astype(int))\n\nrows = []\nfor i, uid in enumerate(test_npz.uids):\n bits = np.unpackbits(test_npz.packed[i], count=30000, bitorder='little')\n chosen_items = test_npz.item_ids[np.where(bits == 1)[0][:100]]\n for rank, item in enumerate(chosen_items, 1):\n rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nvalidated = validate_ranking(df, target_set)\nprint('Validation passed! Row count:', len(validated))\nfor uid, item in df[['uid', 'item_id']].itertuples(index=False, name=None):\n if not test_npz.is_eligible(uid, item):\n raise ValueError(f'Ineligible item: {uid}, {item}')\nprint('All items verified eligible!')\nPath('/app/solution').mkdir(parents=True, exist_ok=True)\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission.parquet saved successfully!')\n\"",
"description": "Generate initial baseline submission and verify test eligibility"
}
### Tool output · 2026-09-29T03:38:04.590Z
Output: Validation passed! Row count: 845600
All items verified eligible!
Baseline submission.parquet saved successfully!
Process Group PGID: 1740
## Step 12 · agent · 2026-09-29T03:38:08.838Z
### Recorded reasoning summary
Establishing Initial Baseline
I've successfully generated a valid baseline submission file. My next step is to confirm the integrity of the `evaluate_public.py` script by running it against a validation version of this newly created baseline.
### run_shell_command · 2026-09-29T03:38:08.838Z
{
"description": "Run evaluate_public.py on validation baseline",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\nrows = []\nfor i, uid in enumerate(val_npz.uids):\n bits = np.unpackbits(val_npz.packed[i], count=30000, bitorder='little')\n chosen_items = val_npz.item_ids[np.where(bits == 1)[0][:100]]\n for rank, item in enumerate(chosen_items, 1):\n rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet && rm /app/solution/val_baseline.parquet"
}
### Tool output · 2026-09-29T03:38:13.321Z
Output: {
"model": "/app/solution/val_baseline.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1756
## Step 13 · agent · 2026-09-29T03:38:16.627Z
### update_topic · 2026-09-29T03:38:16.627Z
{
"title": "Data Analysis & Recommender Architecture Design",
"strategic_intent": "Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.",
"summary": "We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery."
}
### Tool output · 2026-09-29T03:38:16.630Z
Current topic: "Data Analysis & Recommender Architecture Design"
Topic summary: We established an initial valid baseline submission scoring 0.01071 on public validation. Now, we will analyze the interaction datasets (train_listens, likes, dislikes, preference states), examine timestamps, candidate sets, and design candidate ranking architectures (e.g. item-item collaborative filtering, matrix factorization, implicit feedback, linear models) suitable for music discovery.
Strategic Intent: Analyze interaction datasets, preference state, user history, and design collaborative filtering / recommender models.
## Step 14 · agent · 2026-09-29T03:38:24.240Z
### Recorded reasoning summary
Exploring Data Properties
I'm currently inspecting the `train_listens.parquet` file, focusing on its row and column counts, timestamp range, and distributions for `played_ratio_pct` and `is_organic`. This will help me understand the fundamental characteristics of this dataset.
### run_shell_command · 2026-09-29T03:38:24.240Z
{
"description": "Inspect timestamps, event counts, and preference state tables",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'val_listens', 'val_likes']:\n df = pd.read_parquet(f'/task/data/{name}.parquet')\n print(f'{name}: shape={df.shape}, min_ts={df.timestamp.min()}, max_ts={df.timestamp.max()}, unique_uids={df.uid.nunique()}, unique_items={df.item_id.nunique()}')\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', pref.shape, pref.head(5))\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_preference_state:', test_pref.shape, test_pref.head(5))\n\""
}
### Tool output · 2026-09-29T03:38:26.175Z
Output: train_listens: shape=(43484159, 6), min_ts=0, max_ts=24786795, unique_uids=9238, unique_items=845395
train_likes: shape=(814831, 4), min_ts=80, max_ts=24786790, unique_uids=8144, unique_items=172303
train_dislikes: shape=(101075, 4), min_ts=75, max_ts=24786580, unique_uids=5732, unique_items=50876
val_listens: shape=(1501697, 6), min_ts=24788600, max_ts=25393395, unique_uids=8196, unique_items=191747
val_likes: shape=(37795, 4), min_ts=24788815, max_ts=25393390, unique_uids=4114, unique_items=22902
validation_preference_state: (469124, 3) uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
test_preference_state: (484776, 3) uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
Process Group PGID: 1786
## Step 15 · agent · 2026-09-29T03:38:38.176Z
### Recorded reasoning summary
Analyzing User Data
I'm currently examining the `train_listens` dataset. It's quite large, with over 43 million entries, yet it represents a surprisingly small cohort of approximately 9,000 unique users, which closely matches the target user count. This suggests a deep dive into user behavior within this specific group is warranted.
### Recorded reasoning summary
Investigating Candidate Data
My analysis confirms the 30,000 candidate items are consistent across datasets. I'm now focused on understanding the criteria for "candidate eligibility" – why some items are not accessible to specific users, and I'm digging into the `ranking_evaluation.py` script for clues on this selective availability.
### run_shell_command · 2026-09-29T03:38:38.176Z
{
"description": "Check relationship between preference state and candidate eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = 0\nfor item in user_pref.item_id:\n if not val_npz.is_eligible(uid, item):\n ineligible_count += 1\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\""
}
### Tool output · 2026-09-29T03:38:38.882Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1806
## Step 16 · agent · 2026-09-29T03:38:43.258Z
### run_shell_command · 2026-09-29T03:38:43.258Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\nval_npz = CandidateSet('/task/data/validation_eligible_candidates.npz')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state values:\\n', pref.state.value_counts())\n\n# Check user 100\nuid = 100\nuser_pref = pref[pref.uid == uid]\nprint(f'User 100 pref count: {len(user_pref)}')\nineligible_count = sum(not val_npz.is_eligible(uid, item) for item in user_pref.item_id)\nprint(f'User 100 pref items ineligible: {ineligible_count} / {len(user_pref)}')\n\"",
"description": "Check preference state counts and eligibility"
}
### Tool output · 2026-09-29T03:38:44.158Z
Output: Preference state values:
state
liked 418435
disliked 50689
Name: count, dtype: int64
User 100 pref count: 15
User 100 pref items ineligible: 15 / 15
Process Group PGID: 1823
## Step 17 · agent · 2026-09-29T03:38:51.925Z
### Recorded reasoning summary
Refining Eligibility Filters
I'm focusing on how the existing candidate eligibility mask already handles exclusions. This means I don't need to reinvent eligibility logic, as the platform provides `validation_eligible_candidates.npz` which should cover it.
### Recorded reasoning summary
Prioritizing Eligibility Checks
I'm confirming that the existing candidate eligibility mask, specifically `validation_eligible_candidates.npz`, adequately covers exclusions. This means I can leverage this for ranking eligible candidates per user, rather than developing custom logic for it.
### run_shell_command · 2026-09-29T03:38:51.925Z
{
"description": "Inspect validation truth distributions",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ngrades_counts = []\ngrade3_counts = []\ngrade1_counts = []\nfor uid, items in graded.items():\n grades_counts.append(len(items))\n grade3_counts.append(sum(1 for g in items.values() if g == 3))\n grade1_counts.append(sum(1 for g in items.values() if g == 1))\n\nprint(f'Users with any graded item: {len(graded)} / {len(targets)} ({len(graded)/len(targets)*100:.1f}%)')\nprint(f'Items per user: mean={np.mean(grades_counts):.2f}, median={np.median(grades_counts)}, max={np.max(grades_counts)}')\nprint(f'Grade 3 per user: mean={np.mean(grade3_counts):.2f}, Grade 1 per user: mean={np.mean(grade1_counts):.2f}')\ntotal_g3 = sum(grade3_counts)\ntotal_g1 = sum(grade1_counts)\nprint(f'Total grade 3 items: {total_g3}, Total grade 1 items: {total_g1}')\n\""
}
### Tool output · 2026-09-29T03:38:53.178Z
Output: Users with any graded item: 4263 / 8456 (50.4%)
Items per user: mean=8.86, median=4.0, max=442
Grade 3 per user: mean=1.04, Grade 1 per user: mean=7.82
Total grade 3 items: 4417, Total grade 1 items: 33352
Process Group PGID: 1840
## Step 18 · agent · 2026-09-29T03:39:03.722Z
### Recorded reasoning summary
Analyzing User Engagement Metrics
My current focus is on understanding user engagement within the 7-day validation window. I've identified 4263 users with relevant items, averaging 8.86 items each, specifically tracking grade 3 (likes) and grade 1 (strong listens >= 80%). I'm evaluating performance using NDCG@10, aiming to quantify the gain for each grade.
### Recorded reasoning summary
Calculating Engagement Gains
My analysis now incorporates the calculated gains: grade 3 items offer a 7x gain over grade 1 items. This is critical as we target top positions, particularly within the NDCG@10 metric. I'm also starting to inventory the available training data, noting the substantial volume of listens data.
### Recorded reasoning summary
Evaluating Data Memory Usage
I've confirmed that the `train_listens` dataset, though 305MB compressed, will fit comfortably into RAM with pandas, around 1.2GB. This leaves ample room for modeling. My next step is to conduct EDA on this dataset to identify features correlating with future organic discovery. I'm curious how many of the 30,000 candidate items actually appear in `train_listens`.
### run_shell_command · 2026-09-29T03:39:03.722Z
{
"description": "Inspect candidate catalog data and distributions",
"command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(10))\nprint('Popularity rank min/max:', catalog.popularity_rank.min(), catalog.popularity_rank.max())\nprint('Positive listens min/max:', catalog.positive_listens.min(), catalog.positive_listens.max())\nprint('Organic positive listens min/max:', catalog.organic_positive_listens.min(), catalog.organic_positive_listens.max())\n\""
}
### Tool output · 2026-09-29T03:39:04.475Z
Output: 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
5 5463340 22746 17398 6
6 8213481 22505 17071 7
7 906358 22238 17183 8
8 2859641 18380 13704 9
9 3033749 16682 12267 10
item_id positive_listens organic_positive_listens popularity_rank
29990 1044085 163 83 29991
29991 1159301 163 2 29992
29992 1174892 163 122 29993
29993 1235235 163 29 29994
29994 1239621 163 86 29995
29995 1275692 163 81 29996
29996 1280011 163 76 29997
29997 1309246 163 138 29998
29998 1634958 163 46 29999
29999 1670524 163 73 30000
Popularity rank min/max: 1 30000
Positive listens min/max: 163 26615
Organic positive listens min/max: 0 20620
Process Group PGID: 1860
## Step 19 · agent · 2026-09-29T03:39:14.355Z
### Recorded reasoning summary
Confirming Catalog Inclusion
I'm verifying that all items designated in the validation ground truth are indeed present within the 30,000-item catalog. My current checks confirm this is the case, as the `validation_truth` logic inherently filters for catalog membership.
### run_shell_command · 2026-09-29T03:39:14.355Z
{
"description": "Check whether graded items appeared in user's prior train listens",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\n# Sample 100 users from graded\nsample_uids = list(graded.keys())[:100]\n\n# Check train_listens for these users\n# Read train_listens with columns uid, item_id\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_sample = train[train.uid.isin(sample_uids)]\n\n# For the sample users, what fraction of graded items were in train_sample?\nin_train_count = 0\ntotal_graded_sample = 0\nfor uid in sample_uids:\n user_train_items = set(train_sample[train_sample.uid == uid].item_id)\n user_graded_items = set(graded[uid].keys())\n in_train_count += len(user_graded_items.intersection(user_train_items))\n total_graded_sample += len(user_graded_items)\n\nprint(f'Sample: {in_train_count} / {total_graded_sample} ({in_train_count/total_graded_sample*100:.2f}%) of graded items appeared in user train listens')\n\""
}
### Tool output · 2026-09-29T03:39:18.518Z
Output: Sample: 0 / 858 (0.00%) of graded items appeared in user train listens
Process Group PGID: 1877
## Step 20 · agent · 2026-09-29T03:39:22.810Z
### Recorded reasoning summary
Verifying User Engagement
I'm currently cross-referencing eligible items with user listening history. My focus is on clarifying the precise definition of "eligible" – specifically, whether it encompasses items never listened to, or if it also excludes those with past like/dislike interactions. This will ensure accurate analysis of graded items.
### run_shell_command · 2026-09-29T03:39:22.810Z
{
"description": "Check if any user train items are marked eligible in validation candidate set",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nuid = 100\ntrain = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nuser_train = set(train[train.uid == uid].item_id)\nprint('User 100 total train items:', len(user_train))\n# Check how many user_train items are in eligibility.item_ids\noverlap = user_train.intersection(set(eligibility.item_ids))\nprint('User 100 train items in catalog:', len(overlap))\n# Check how many are eligible\neligible_overlap = sum(eligibility.is_eligible(uid, it) for it in overlap)\nprint('User 100 train items that are eligible:', eligible_overlap)\n\""
}
### Tool output · 2026-09-29T03:39:24.008Z
Output: User 100 total train items: 830
User 100 train items in catalog: 682
User 100 train items that are eligible: 0
Process Group PGID: 1900
## Step 21 · agent · 2026-09-29T03:39:33.819Z
### Recorded reasoning summary
Prioritizing Novelty
I'm focusing on building a robust ranking model for music discovery. My core insight is that every user has a history of listened-to items marked as ineligible. This means my primary objective is to ensure pure novelty and introduce users to entirely new content they haven't encountered before.
### Recorded reasoning summary
Refining Novelty Prediction
I'm now focused on predicting which entirely new tracks users will actively engage with. This involves a classic collaborative filtering challenge: ranking unseen items based on implicit and explicit feedback. I'm evaluating methods like Item-Item CF and implicit matrix factorization to identify top candidates.
### Recorded reasoning summary
Exploring Matrix Factorization
I'm investigating matrix factorization techniques like Truncated SVD and item-item similarity for predicting user engagement with novel music. The interaction matrix, even with 30,000 items, is surprisingly manageable, making these approaches computationally feasible.
### run_shell_command · 2026-09-29T03:39:33.819Z
{
"description": "Check interaction counts of target users with catalog items",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ntarget_likes = likes[likes.uid.isin(target_uids) & likes.item_id.isin(catalog_items)]\nprint(f'Target user likes in catalog: {len(target_likes)} rows, {target_likes.uid.nunique()} users, {target_likes.item_id.nunique()} items')\n\n# Check listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nprint(f'Target user listens in catalog: {len(target_listens)} rows, {target_listens.uid.nunique()} users, {target_listens.item_id.nunique()} items')\n\""
}
### Tool output · 2026-09-29T03:39:41.863Z
Output: Target user likes in catalog: 472889 rows, 6874 users, 28606 items
Target user listens in catalog: 31062540 rows, 8452 users, 30000 items
Process Group PGID: 1922
## Step 22 · agent · 2026-09-29T03:39:46.202Z
### Recorded reasoning summary
Analyzing Catalog Data
I'm examining the catalog listening data. It appears all 30,000 catalog items have been heard by our target users, and a significant majority, over 8400, of our target users have engaged with the catalog. This is quite promising.
### run_shell_command · 2026-09-29T03:39:46.202Z
{
"description": "Calculate unique user-item interaction pairs and matrix density",
"command": "python3 -c \"\nimport pandas as pd\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntarget_uids = set(targets.uid)\ncatalog_items = set(catalog.item_id)\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntarget_listens = listens[listens.uid.isin(target_uids) & listens.item_id.isin(catalog_items)]\nunique_pairs = len(target_listens.drop_duplicates())\nprint(f'Distinct (uid, item) pairs: {unique_pairs}')\nprint(f'Sparsity: {unique_pairs / (len(target_uids) * len(catalog_items)) * 100:.2f}% of user-catalog matrix is filled!')\n\""
}
### Tool output · 2026-09-29T03:39:55.411Z
Output: Distinct (uid, item) pairs: 7868664
Sparsity: 3.10% of user-catalog matrix is filled!
Process Group PGID: 1949
## Step 23 · agent · 2026-09-29T03:40:10.420Z
### Recorded reasoning summary
Analyzing Data Density
I've identified that the current matrix density for collaborative filtering is a remarkable 3.10%, significantly exceeding the typical 0.01%-0.1% range. This translates to approximately 7.87 million distinct (uid, item) pairs. I'm also noting the presence of `train_likes.parquet` and other training data files.
### Recorded reasoning summary
Exploring New Data Sources
I'm now considering additional data files such as `dislikes.parquet`, `train_unlikes.parquet`, and `train_undislikes.parquet`. I'm also examining `validation_preference_state.parquet` for explicit states and noting features like `played_ratio_pct` and `is_organic` within `train_listens.parquet`. The timing of data splits is also being refined.
### Recorded reasoning summary
Calculating Item Similarities
I'm calculating item similarities, considering both dense and sparse approaches for the similarity matrix $S$. A dense $30000 \times 30000$ matrix would fit in RAM, but I'm also exploring sparse representations using top-$k$ neighbors to significantly reduce memory usage. This could bring the memory footprint down to around 50 MB.
### Recorded reasoning summary
Testing Matrix Inversion
I'm evaluating the feasibility of inverting a large $30000 \times 30000$ matrix within 16 GB of RAM, a crucial step for the EASE algorithm. This inversion is a primary bottleneck I need to overcome.
### run_shell_command · 2026-09-29T03:40:10.420Z
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nA = A.T @ A + 100 * np.eye(8456, dtype=np.float32)\nt0 = time.time()\ninv = np.linalg.inv(A)\nt1 = time.time()\nprint(f'8456x8456 matrix inverse time: {t1 - t0:.2f} seconds')\n\"",
"description": "Benchmark matrix inverse time for 8456x8456 matrix"
}
### Tool output · 2026-09-29T03:40:31.539Z
Output: 8456x8456 matrix inverse time: 16.49 seconds
Process Group PGID: 1971
## Step 24 · agent · 2026-09-29T03:40:35.318Z
### Recorded reasoning summary
Exploring SVD Options
I'm currently evaluating different Singular Value Decomposition methods. Initial tests with full inversion on an $8456 \times 8456$ matrix were promising, achieving completion in under 17 seconds. My next step is to explore Truncated SVD and Randomized SVD via scikit-learn, particularly focusing on their performance with a sparse $8456 \times 30000$ matrix.
### run_shell_command · 2026-09-29T03:40:35.318Z
{
"command": "python3 -c \"\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport numpy as np\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nfor k in [64, 128, 256]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R)\n V = svd.components_\n t1 = time.time()\n print(f'TruncatedSVD k={k}: {t1 - t0:.2f} s, shape U={U.shape}, shape V={V.shape}')\n\"",
"description": "Benchmark TruncatedSVD on realistic matrix dimensions"
}
### Tool output · 2026-09-29T03:41:02.080Z
Output: TruncatedSVD k=64: 2.83 s, shape U=(8456, 64), shape V=(64, 30000)
TruncatedSVD k=128: 4.04 s, shape U=(8456, 128), shape V=(128, 30000)
TruncatedSVD k=256: 7.62 s, shape U=(8456, 256), shape V=(256, 30000)
Process Group PGID: 1981
## Step 25 · agent · 2026-09-29T03:41:09.300Z
### Recorded reasoning summary
Exploring Similarity Options
My current focus is on evaluating various item-item similarity approaches, noting that TruncatedSVD with k=128 is remarkably fast at 4 seconds. I'm now planning benchmarks for cosine similarity, BM25, and co-occurrence, aiming to compare their performance against the SVD results, especially considering the $8456 \times 30000$ dimensions of the rating matrix.
### run_shell_command · 2026-09-29T03:41:09.300Z
{
"command": "python3 -c \"\nimport scipy.sparse as sp\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\"",
"description": "Benchmark user-user similarity and prediction time"
}
### Tool output · 2026-09-29T03:41:09.989Z
Output: Traceback (most recent call last):
File "", line 5, in
NameError: name 'np' is not defined. Did you mean: 'sp'?
Exit Code: 1
Process Group PGID: 2005
## Step 26 · agent · 2026-09-29T03:41:15.827Z
### run_shell_command · 2026-09-29T03:41:15.827Z
{
"description": "Benchmark user-user similarity and prediction time with numpy import",
"command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\nt0 = time.time()\nU_sim = R @ R.T\nt1 = time.time()\nprint(f'R @ R.T took: {t1 - t0:.2f} s, shape={U_sim.shape}, nonzeros={U_sim.nnz}')\nt0 = time.time()\npreds = U_sim @ R\nt1 = time.time()\nprint(f'U_sim @ R took: {t1 - t0:.2f} s')\n\""
}
### Tool output · 2026-09-29T03:44:05.710Z
Output: R @ R.T took: 5.92 s, shape=(8456, 8456), nonzeros=71503936
U_sim @ R took: 152.54 s
Process Group PGID: 2022
## Step 27 · agent · 2026-09-29T03:44:09.636Z
### Recorded reasoning summary
Analyzing Performance Bottlenecks
I'm investigating why the `U_sim @ R` operation with a large, dense $8456 \times 8456$ matrix took 152 seconds. It seems dense matrix multiplication is the culprit here, significantly impacting performance. I'm now turning my attention to understanding the performance within TruncatedSVD.
### run_shell_command · 2026-09-29T03:44:09.636Z
{
"description": "Benchmark dense matrix multiplication time",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 128).astype(np.float32)\nV = np.random.randn(128, 30000).astype(np.float32)\nt0 = time.time()\nscores = U @ V\nt1 = time.time()\nprint(f'Dense U @ V took: {t1 - t0:.3f} s, shape={scores.shape}')\n\""
}
### Tool output · 2026-09-29T03:44:10.402Z
Output: Dense U @ V took: 0.408 s, shape=(8456, 30000)
Process Group PGID: 2057
## Step 28 · agent · 2026-09-29T03:44:14.842Z
### Recorded reasoning summary
Exploring Item-Item Efficiency
I'm currently investigating the computational feasibility of item-item similarity, specifically focusing on sparse matrix representations and their performance implications. My current analysis centers on the speed of scoring all users against items when only retaining the top-K most similar items per item, a critical step for optimizing recommendations.
### run_shell_command · 2026-09-29T03:44:14.842Z
{
"description": "Benchmark sparse R @ S matrix multiplication",
"command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.03, format='csr', dtype=np.float32)\n# sparse S with 100 nonzeros per column\ncols = np.repeat(np.arange(30000), 100)\nrows = np.random.randint(0, 30000, size=30000 * 100)\ndata = np.random.rand(30000 * 100).astype(np.float32)\nS = sp.csr_matrix((data, (rows, cols)), shape=(30000, 30000))\nt0 = time.time()\nscores = R @ S\nt1 = time.time()\nprint(f'Sparse R @ S (top-100 items) took: {t1 - t0:.3f} s, nonzeros={scores.nnz}')\n\""
}
### Tool output · 2026-09-29T03:44:33.077Z
Output: Sparse R @ S (top-100 items) took: 6.638 s, nonzeros=241554916
Process Group PGID: 2067
## Step 29 · agent · 2026-09-29T03:44:39.380Z
### Recorded reasoning summary
Analyzing Validation Data
I'm currently examining the validation data to define track relevance. My focus is on understanding what constitutes a "liked" organic track versus one that was simply listened to. This will inform how I construct the interaction matrix and associated weights.
### Recorded reasoning summary
Refining Interaction Signals
I'm now incorporating signals from the training data. Specifically, I'm analyzing `train_likes` and `train_listens` to better define taste profiles. This involves considering explicit likes, weighted listens based on `played_ratio_pct` and organic status, and recency through timestamps. Negative signals from `train_dislikes` are also being evaluated.
### run_shell_command · 2026-09-29T03:44:39.380Z
{
"description": "Measure load time of train_listens.parquet",
"command": "python3 -c \"\nimport pandas as pd\nimport time\n\nt0 = time.time()\ndf = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Loading train_listens took {t1 - t0:.2f} s. Rows: {len(df)}')\n\""
}
### Tool output · 2026-09-29T03:44:40.721Z
Output: Loading train_listens took 0.53 s. Rows: 43484159
Process Group PGID: 2090
## Step 30 · agent · 2026-09-29T03:44:54.312Z
### Recorded reasoning summary
Loading Data Rapidly
I've successfully loaded a substantial dataset of 43.4 million rows in a mere 0.53 seconds using pyarrow – impressive speed! My current focus is on analyzing the connection between user listening histories and catalog items, particularly within the `train_listens` dataset, which currently involves 8,456 target users.
### Recorded reasoning summary
Evaluating Model Viability
I'm currently exploring whether non-catalog items within `train_listens` offer significant value for our models, given the extensive interaction data target users already have with catalog items. I’m setting up an evaluation framework to test this experimentally, aiming to quickly assess model performance on the public validation set.
### Recorded reasoning summary
Designing Interaction Matrix
I'm now focusing on the core of collaborative filtering: defining the user-item interaction matrix. I'm weighing several options for entry values, from simple binary presence to weighted counts incorporating event type and even time decay, aiming for the most informative representation for my models.
### run_shell_command · 2026-09-29T03:44:54.312Z
{
"description": "Run TruncatedSVD baseline on public validation set",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nprint(f'Target users: {len(target_uids)}, Catalog items: {len(item_ids)}')\n\n# Load train listens for target users & catalog items\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s: {len(listens)} rows')\n\n# Build user-item positive counts\n# Only positive listens (played_ratio_pct >= 50)\npos_listens = listens[listens.played_ratio_pct >= 50]\nu_idx = pos_listens.uid.map(uid_to_idx).to_numpy()\ni_idx = pos_listens.item_id.map(item_to_idx).to_numpy()\n\n# Sparse matrix with counts\nR_counts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = R_counts.copy()\nR_log.data = np.log1p(R_log.data)\n\nprint(f'R_log constructed, nnz={R_log.nnz}')\n\n# Evaluate SVD for different k\nfor k in [64, 128, 256]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_log)\n V = svd.components_\n \n # Predict scores for all users\n # Batch predict top 100 eligible per user\n scores = U @ V # shape (8456, 30000)\n \n # Mask ineligible items by setting their score to -infinity\n # Use packed eligibility\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n \n res = score_rankings(ranked, graded)\n print(f'TruncatedSVD k={k}: {res} (took {time.time()-t0:.2f}s)')\n\""
}
### Tool output · 2026-09-29T03:45:21.811Z
Output: Target users: 8456, Catalog items: 30000
Filtered listens in 8.21s: 31062540 rows
R_log constructed, nnz=5514505
TruncatedSVD k=64: {'quality_ndcg@10': 0.017838, 'quality_users': 4263} (took 3.74s)
TruncatedSVD k=128: {'quality_ndcg@10': 0.019115, 'quality_users': 4263} (took 4.66s)
TruncatedSVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (took 7.17s)
Process Group PGID: 2111
## Step 31 · agent · 2026-09-29T03:45:40.145Z
### Recorded reasoning summary
Exploring SVD Improvements
I'm seeing significant gains with TruncatedSVD, with catalog popularity nearly doubling at k=256. This progress, achieved in just 7 seconds, is very encouraging. I'm now eager to investigate further model possibilities to build on these promising results.
### update_topic · 2026-09-29T03:45:40.145Z
{
"title": "Model Architecture Exploration & Feature Engineering",
"summary": "We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.",
"strategic_intent": "Develop, experiment with, and optimize diverse recommendation models and feature weighting."
}
### Tool output · 2026-09-29T03:45:41.660Z
Current topic: "Model Architecture Exploration & Feature Engineering"
Topic summary: We established a validation pipeline and verified that TruncatedSVD reaches 0.0203 NDCG@10 (vs 0.0107 popularity baseline). Now we will test and compare multiple collaborative filtering and ranking architectures: Item-Item Cosine / BM25 / TF-IDF, EASE (Embarrassingly Shallow Autoencoders), weighted implicit feedback (organic listens, likes, dislikes, time decay, played ratio), and model ensembling/blending.
Strategic Intent: Develop, experiment with, and optimize diverse recommendation models and feature weighting.
## Step 32 · agent · 2026-09-29T03:45:55.984Z
### Recorded reasoning summary
Exploring Recommendation Efficacy
I'm currently dissecting the effectiveness of recommendation systems for discovery. My focus is on analyzing the available signals, particularly user likes. I've noted that 'grade 3' in validation preference state signifies organic liking, a crucial insight for understanding user engagement.
### Recorded reasoning summary
Analyzing User Engagement Metrics
I'm now prioritizing the impact of organic listens and completion ratios on predicting taste. I'm also looking into how to properly interpret "skips" based on the `played_ratio_pct` distribution and the temporal relevance of data, considering potential taste drift over time.
### run_shell_command · 2026-09-29T03:45:55.984Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\n# Filter positive listens\npos = listens[listens.played_ratio_pct >= 50].copy()\nu_idx = pos.uid.map(uid_to_idx).to_numpy()\ni_idx = pos.item_id.map(item_to_idx).to_numpy()\ncounts = sp.csr_matrix((np.ones(len(u_idx), dtype=np.float32), (u_idx, i_idx)), shape=(len(target_uids), len(item_ids)))\nR_log = counts.copy()\nR_log.data = np.log1p(R_log.data)\n\n# Test SVD with k=384 and k=512\nfor k in [256, 384, 512]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_log)\n V = svd.components_\n scores = U @ V\n \n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n \n res = score_rankings(ranked, graded)\n print(f'SVD k={k}: {res} ({time.time()-t0:.2f}s)')\n\"",
"description": "Test SVD with larger latent dimensions (256, 384, 512)"
}
### Tool output · 2026-09-29T03:46:43.317Z
Output: SVD k=256: {'quality_ndcg@10': 0.020296, 'quality_users': 4263} (7.30s)
SVD k=384: {'quality_ndcg@10': 0.019694, 'quality_users': 4263} (12.11s)
SVD k=512: {'quality_ndcg@10': 0.020185, 'quality_users': 4263} (16.01s)
Process Group PGID: 2145
## Step 33 · agent · 2026-09-29T03:47:08.168Z
### Recorded reasoning summary
Exploring Collaborative Filtering
I'm now shifting focus from SVD to Item-Item Collaborative Filtering. My current goal is to evaluate several weighting schemes (Cosine, BM25, TFIDF, Co-occurrence) within this framework, starting with understanding its core mechanics using the provided user-item matrix.
### run_shell_command · 2026-09-29T03:47:08.168Z
{
"command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nR = sp.random(8456, 30000, density=0.02, format='csr', dtype=np.float32)\n# Normalize columns (items) for cosine similarity\nfrom sklearn.preprocessing import normalize\nR_norm = normalize(R, norm='l2', axis=0) # unit norm items\n\nt0 = time.time()\n# compute block by block or sparse dot\n# Let's test R.dot(R.T.dot(R)) or similar:\n# Note: user predictions = R @ S = R @ (R.T @ R) = (R @ R.T) @ R !!\n# Notice the associativity of matrix multiplication!\n# (R @ R.T) is (8456 x 8456)!\n# And ((R @ R.T) @ R) has shape (8456 x 30000)!\n\"",
"description": "Check matrix multiplication associativity for Item-Item vs User-User"
}
### Tool output · 2026-09-29T03:47:20.373Z
Output: (empty)
Process Group PGID: 2179
## Step 34 · agent · 2026-09-29T03:47:28.152Z
### Recorded reasoning summary
Exploring Mathematical Equivalence
I'm currently examining the mathematical equivalence in item-item collaborative filtering. Specifically, I'm interested in how the formula transforms with and without top-k truncation, highlighting the user-user co-occurrence aspect.
### run_shell_command · 2026-09-29T03:47:28.152Z
{
"description": "Benchmark fast block-wise top-K item similarity computation",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom sklearn.preprocessing import normalize\n\n# Create normalized item matrix R: shape (N_users, N_items)\nN_users, N_items = 8456, 30000\nR = sp.random(N_users, N_items, density=0.02, format='csr', dtype=np.float32)\nR_items = normalize(R, norm='l2', axis=0) # columns are unit norm\n# Convert to csc for fast column slicing or csr of R.T\nRt = R_items.T.tocsr() # shape (30000, 8456)\n\nt0 = time.time()\nK = 100\nrows, cols, vals = [], [], []\nbatch_size = 1000\nfor start in range(0, N_items, batch_size):\n end = min(start + batch_size, N_items)\n block = Rt[start:end] # (batch, 8456)\n sims = (block @ R_items).toarray() # (batch, 30000) dense\n # zero out self similarity\n for b in range(end - start):\n sims[b, start + b] = 0.0\n # top K\n top_k = np.argpartition(sims[b], -K)[-K:]\n top_k = top_k[sims[b, top_k] > 0]\n rows.extend([start + b] * len(top_k))\n cols.extend(top_k)\n vals.extend(sims[b, top_k])\n\nS = sp.csr_matrix((vals, (rows, cols)), shape=(N_items, N_items), dtype=np.float32)\nt1 = time.time()\nprint(f'Constructed top-{K} item-item similarity in {t1 - t0:.2f} s, nnz={S.nnz}')\n\""
}
### Tool output · 2026-09-29T03:48:09.616Z
Output: Constructed top-100 item-item similarity in 29.30 s, nnz=3000000
Process Group PGID: 2197
## Step 35 · agent · 2026-09-29T03:48:30.590Z
### Recorded reasoning summary
Exploring Faster Approaches
I'm currently investigating ways to accelerate our experimentation process, as the 29-second per run is proving to be a bottleneck. My focus is on evaluating the feasibility of techniques like user-user collaborative filtering, dual EASE, SVD, and TF-IDF/BM25 to achieve faster results, starting with an analysis of user-user CF given our $N_{\text{users}} = 8456$.
### Recorded reasoning summary
Calculating User-User Similarity
My initial exploration into user-user collaborative filtering is showing promise. The matrix multiplication to compute user-user similarity, given our user base size, completes in approximately 3 seconds. I am now focused on testing the speed of generating predictions using this dense similarity matrix.
### run_shell_command · 2026-09-29T03:48:30.590Z
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = np.random.randn(8456, 8456).astype(np.float32)\nR = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nout = U @ R\nt1 = time.time()\nprint(f'Dense 8456x8456 @ 8456x30000 took {t1 - t0:.2f} s')\n\"",
"description": "Benchmark dense BLAS multiplication of User similarity by R"
}
### Tool output · 2026-09-29T03:48:56.786Z
Output: Dense 8456x8456 @ 8456x30000 took 19.73 s
Process Group PGID: 2221
## Step 36 · agent · 2026-09-29T03:49:14.486Z
### Recorded reasoning summary
Exploring SVD Efficiency
I've been exploring Singular Value Decomposition and found that truncated SVD, specifically with matrices U and V of dimensions (8456 x 256) and (256 x 30000) respectively, is remarkably fast. The fitting process took approximately 5 seconds, and the subsequent matrix multiplication of U and V was completed in just 0.4 seconds, for a total of around 6 seconds. My next step is to investigate randomized SVD, PCA, and NMF for potentially even greater speed.
### Recorded reasoning summary
Improving Input Matrix
My current focus is on enhancing the input matrix $R$ by incorporating various weighting schemes. I'm particularly interested in how TF-IDF, BM25, time decay, and explicit signals like likes and dislikes will impact validation NDCG. I'll begin by experimenting with these feature weightings, comparing them to the previous `R_log` baseline.
### Recorded reasoning summary
Investigating Decay Parameters
I'm currently evaluating different half-lives for exponential decay, specifically considering 30, 60, and 90 days to see their impact on training. The maximum timestamp observed is 24,786,800, which will be a key reference point.
### run_shell_command · 2026-09-29T03:49:14.486Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Load listens\nprint('Loading train data...')\nt0 = time.time()\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.uid.isin(targets) & dislikes.item_id.isin(item_to_idx)].copy()\nprint(f'Data loaded in {time.time()-t0:.2f}s')\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n t0 = time.time()\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]} (eval took {time.time()-t0:.2f}s)')\n return res[\\\"quality_ndcg@10\\\"]\n\n# Experiment 1: Baseline log counts\npos = listens[listens.played_ratio_pct >= 50].copy()\nu = pos.uid.map(uid_to_idx).to_numpy()\nit = pos.item_id.map(item_to_idx).to_numpy()\nR_base = sp.csr_matrix((np.ones(len(u), dtype=np.float32), (u, it)), shape=(len(target_uids), len(item_ids)))\nR_base.data = np.log1p(R_base.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_base)\nV = svd.components_\nevaluate_predictions(U @ V, 'Baseline pos >= 50, k=256')\n\n# Experiment 2: Event weighting (likes, organic, completion)\n# Let's assign weights to events:\n# like: 5.0\n# organic listen >= 80%: 3.0\n# organic listen >= 50%: 2.0\n# non-organic listen >= 80%: 1.5\n# non-organic listen >= 50%: 1.0\n\n# Calculate listen weights\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\n\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nweights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_w = np.concatenate([weights, weights_lk])\n\nR_weighted = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_w_log = R_weighted.copy()\nR_w_log.data = np.log1p(R_w_log.data)\n\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_w_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Weighted events (likes + organic + strong)')\n\"",
"description": "Run event weighting experiment and evaluate NDCG@10"
}
### Tool output · 2026-09-29T03:49:44.872Z
Output: Loading train data...
Data loaded in 8.38s
Baseline pos >= 50, k=256 -> NDCG@10: 0.020296 (eval took 1.07s)
Weighted events (likes + organic + strong) -> NDCG@10: 0.022618 (eval took 1.07s)
Process Group PGID: 2231
## Step 37 · agent · 2026-09-29T03:49:53.854Z
### Recorded reasoning summary
Improving NDCG Performance
I'm excited to report a significant NDCG improvement, a jump from 0.020296 to 0.022618, which is an 11.4% gain attributed to event weighting. My next step involves investigating the impact of incorporating time decay into these events. I'll be examining how this adjustment might influence user engagement, particularly considering the dynamic nature of preferences in contexts like music streaming.
### run_shell_command · 2026-09-29T03:49:53.854Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [15, 30, 45, 60, 90, 120, 180]:\n decay = np.power(0.5, age_days / H).astype(np.float32)\n w = all_w_base * decay\n R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R)\n V = svd.components_\n evaluate_predictions(U @ V, f'Half-life H={H} days')\n\"",
"description": "Test time decay half-lives from 15 to 180 days"
}
### Tool output · 2026-09-29T03:51:20.470Z
Output: Half-life H=15 days -> NDCG@10: 0.035707
Half-life H=30 days -> NDCG@10: 0.033335
Half-life H=45 days -> NDCG@10: 0.031045
Half-life H=60 days -> NDCG@10: 0.029109
Half-life H=90 days -> NDCG@10: 0.026911
Half-life H=120 days -> NDCG@10: 0.026084
Half-life H=180 days -> NDCG@10: 0.024919
Process Group PGID: 2265
## Step 38 · agent · 2026-09-29T03:51:29.868Z
### run_shell_command · 2026-09-29T03:51:29.868Z
{
"description": "Test fine-grained half-lives from 3 to 18 days",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\nfor H in [3, 5, 7, 10, 12, 14, 18]:\n decay = np.power(0.5, age_days / H).astype(np.float32)\n w = all_w_base * decay\n R = sp.csr_matrix((w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n svd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R)\n V = svd.components_\n evaluate_predictions(U @ V, f'Half-life H={H} days')\n\""
}
### Tool output · 2026-09-29T03:52:56.074Z
Output: Half-life H=3 days -> NDCG@10: 0.033782
Half-life H=5 days -> NDCG@10: 0.036387
Half-life H=7 days -> NDCG@10: 0.037852
Half-life H=10 days -> NDCG@10: 0.037834
Half-life H=12 days -> NDCG@10: 0.036565
Half-life H=14 days -> NDCG@10: 0.036082
Half-life H=18 days -> NDCG@10: 0.035692
Process Group PGID: 2305
## Step 39 · agent · 2026-09-29T03:53:08.228Z
### Recorded reasoning summary
Optimizing Exposure Duration
I've found that a 7 to 10-day window significantly boosts performance, exceeding the baseline by nearly 3.8 times. My current focus is exploring how to integrate this short-term insight with longer-term taste preferences to further refine our approach.
### run_shell_command · 2026-09-29T03:53:08.228Z
{
"description": "Test blending short-term and medium-term models",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nlistens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()\n\nT_max = 24786800\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nbase_weights_l = np.ones(len(listens), dtype=np.float32)\nstrong = listens.played_ratio_pct >= 80\norganic = listens.is_organic == 1\nbase_weights_l[~strong & ~organic] = 1.0\nbase_weights_l[strong & ~organic] = 1.5\nbase_weights_l[~strong & organic] = 2.0\nbase_weights_l[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nts_l = listens.timestamp.to_numpy()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\nts_lk = likes.timestamp.to_numpy()\nbase_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk])\nall_it = np.concatenate([it_l, it_lk])\nall_ts = np.concatenate([ts_l, ts_lk])\nall_w_base = np.concatenate([base_weights_l, base_weights_lk])\nage_days = (T_max - all_ts) / 86400.0\n\n# Model 1: H=7\ndecay7 = np.power(0.5, age_days / 7.0).astype(np.float32)\nR7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR7.data = np.log1p(R7.data)\nsvd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU7 = svd7.fit_transform(R7)\nV7 = svd7.components_\nscore7 = U7 @ V7\n\n# Model 2: H=45\ndecay45 = np.power(0.5, age_days / 45.0).astype(np.float32)\nR45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR45.data = np.log1p(R45.data)\nsvd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU45 = svd45.fit_transform(R45)\nV45 = svd45.components_\nscore45 = U45 @ V45\n\nevaluate_predictions(score7, 'Model 1 (H=7)')\nevaluate_predictions(score45, 'Model 2 (H=45)')\n\n# Test blend weights\nfor alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:\n # Normalize standard deviations or scale\n std7 = np.std(score7)\n std45 = np.std(score45)\n blend = (score7 / std7) + alpha * (score45 / std45)\n evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')\n\""
}
### Tool output · 2026-09-29T03:53:43.236Z
Output: /usr/bin/bash: line 98: 2341 Killed python3 -c "
import sys
sys.path.insert(0, '/task')
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
# 1. Load targets, catalog, and eligibility
targets_frame = pd.read_parquet('/task/data/target_users.parquet')
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets)
target_uids = eligibility.uids
item_ids = eligibility.item_ids
uid_to_idx = {uid: i for i, uid in enumerate(target_uids)}
item_to_idx = {item: i for i, item in enumerate(item_ids)}
listens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
listens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)].copy()
likes = pd.read_parquet('/task/data/train_likes.parquet')
likes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)].copy()
T_max = 24786800
def evaluate_predictions(scores, name=''):
ranked = {}
for i, uid in enumerate(target_uids):
user_scores = scores[i].copy()
bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')
user_scores[bits == 0] = -np.inf
top100_idx = np.argpartition(user_scores, -100)[-100:]
top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]
ranked[int(uid)] = list(item_ids[top100_sorted])
res = score_rankings(ranked, graded)
print(f'{name} -> NDCG@10: {res[\"quality_ndcg@10\"]}')
return res[\"quality_ndcg@10\"]
base_weights_l = np.ones(len(listens), dtype=np.float32)
strong = listens.played_ratio_pct >= 80
organic = listens.is_organic == 1
base_weights_l[~strong & ~organic] = 1.0
base_weights_l[strong & ~organic] = 1.5
base_weights_l[~strong & organic] = 2.0
base_weights_l[strong & organic] = 3.0
u_l = listens.uid.map(uid_to_idx).to_numpy()
it_l = listens.item_id.map(item_to_idx).to_numpy()
ts_l = listens.timestamp.to_numpy()
u_lk = likes.uid.map(uid_to_idx).to_numpy()
it_lk = likes.item_id.map(item_to_idx).to_numpy()
ts_lk = likes.timestamp.to_numpy()
base_weights_lk = np.full(len(likes), 5.0, dtype=np.float32)
all_u = np.concatenate([u_l, u_lk])
all_it = np.concatenate([it_l, it_lk])
all_ts = np.concatenate([ts_l, ts_lk])
all_w_base = np.concatenate([base_weights_l, base_weights_lk])
age_days = (T_max - all_ts) / 86400.0
# Model 1: H=7
decay7 = np.power(0.5, age_days / 7.0).astype(np.float32)
R7 = sp.csr_matrix((all_w_base * decay7, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
R7.data = np.log1p(R7.data)
svd7 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)
U7 = svd7.fit_transform(R7)
V7 = svd7.components_
score7 = U7 @ V7
# Model 2: H=45
decay45 = np.power(0.5, age_days / 45.0).astype(np.float32)
R45 = sp.csr_matrix((all_w_base * decay45, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
R45.data = np.log1p(R45.data)
svd45 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)
U45 = svd45.fit_transform(R45)
V45 = svd45.components_
score45 = U45 @ V45
evaluate_predictions(score7, 'Model 1 (H=7)')
evaluate_predictions(score45, 'Model 2 (H=45)')
# Test blend weights
for alpha in [0.1, 0.2, 0.3, 0.4, 0.5]:
# Normalize standard deviations or scale
std7 = np.std(score7)
std45 = np.std(score45)
blend = (score7 / std7) + alpha * (score45 / std45)
evaluate_predictions(blend, f'Blend H=7 + {alpha} * H=45')
"
Exit Code: 137
Process Group PGID: 2339
## Step 40 · agent · 2026-09-29T03:53:52.246Z
### Recorded reasoning summary
Investigating Memory Bottleneck
I'm currently dissecting the root cause of the "Process was killed" exit code, specifically noting the substantial memory footprint from large sparse and dense matrices, coupled with the `svd.fit_transform` operation, which I suspect triggered the OOM killer.
### run_shell_command · 2026-09-29T03:53:52.246Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport gc\n\nt_cut = 24786800\nt_90d = t_cut - 90 * 86400\nt_120d = t_cut - 120 * 86400\n\n# Read with pyarrow filter\ndf_90 = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nprint(f'Listens in last 90 days: {len(df_90)} rows (vs 43.4M total)!')\n\"",
"description": "Check row count of train_listens filtered to last 90 days"
}
### Tool output · 2026-09-29T03:53:53.512Z
Output: Listens in last 90 days: 16769274 rows (vs 43.4M total)!
Process Group PGID: 2373
## Step 41 · agent · 2026-09-29T03:53:59.271Z
### Recorded reasoning summary
Refining Target Users
I'm focusing on defining our target user group. The initial filtering based on a 90-day window and positive listens has loaded a substantial dataset, efficiently discarding stale records. My current thought is to solidify this 90-day criteria for defining users with at least 20 positive listens prior to the training cutoff.
### run_shell_command · 2026-09-29T03:53:59.271Z
{
"description": "Check validation preference state overlap with catalog",
"command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\ncat_set = set(catalog.item_id)\ntarget_set = set(targets.uid)\n\npref_cat = pref[pref.uid.isin(target_set) & pref.item_id.isin(cat_set)]\nprint('Preference state in catalog:', len(pref_cat))\nprint(pref_cat.state.value_counts())\n\""
}
### Tool output · 2026-09-29T03:54:00.147Z
Output: Preference state in catalog: 469124
state
liked 418435
disliked 50689
Name: count, dtype: int64
Process Group PGID: 2394
## Step 42 · agent · 2026-09-29T03:54:17.427Z
### Recorded reasoning summary
Cataloging preferences complete
I’ve successfully cataloged all 469,124 preference state entries. They accurately reflect the precise net active likes and dislikes at the cutoff, presenting a clean, explicit signal.
### Recorded reasoning summary
Refining the approach
My current focus is on validating the impact of incorporating `validation_preference_state` and refining our weighting strategies. I'm exploring how to best integrate organic status, completion, and time decay into listen counts, while simultaneously assessing the efficacy of different matrix factorization techniques like SVD against ALS, and considering pre-processing with TF-IDF/BM25.
### run_shell_command · 2026-09-29T03:54:17.427Z
{
"description": "Evaluate effect of Item IDF weighting on SVD",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\n# Load filtered listens\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet(\n '/task/data/train_likes.parquet',\n filters=[('timestamp', '>=', t_90d)]\n)\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nprint(f'Filtered loaded: listens={len(listens)}, likes={len(likes)}, pref={len(pref)}')\n\n# Compute weights\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\n# Likes\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\n# Active preference state (give base weight 3.0 for liked, -2.0 for disliked)\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Without IDF\nsvd = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_log)\nV = svd.components_\nevaluate_predictions(U @ V, 'Standard log1p, k=256')\n\n# 2. With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr) # count of nonzeros per item\nidf = np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0\nR_idf = R_log.multiply(sp.diags(idf))\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# 3. With IDF power 0.5 (smoother)\nidf_half = np.power(idf, 0.5)\nR_idf_half = R_log.multiply(sp.diags(idf_half))\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\""
}
### Tool output · 2026-09-29T03:54:30.973Z
Output: Traceback (most recent call last):
File "", line 109, in
File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py", line 440, in multiply
raise ValueError("inconsistent shapes")
ValueError: inconsistent shapes
Filtered loaded: listens=12099707, likes=193175, pref=469124
Standard log1p, k=256 -> NDCG@10: 0.037856
Exit Code: 1
Process Group PGID: 2411
## Step 43 · agent · 2026-09-29T03:54:43.560Z
### Recorded reasoning summary
Clarifying Matrix Multiplication
I've realized my previous approach for multiplying columns by `idf` was incorrect. The `sp.diags(idf)` function generates a large sparse matrix, which is incompatible with elementwise multiplication in `R_log.multiply`. I now understand the correct method is to use matrix multiplication: `R_log @ sp.diags(idf)`.
### run_shell_command · 2026-09-29T03:54:43.560Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# With IDF\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_idf = R_log @ sp.diags(idf)\n\nsvd_idf = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf = svd_idf.fit_transform(R_idf)\nV_idf = svd_idf.components_\nevaluate_predictions(U_idf @ V_idf, 'Log1p + Item IDF, k=256')\n\n# With IDF^0.5\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf_half = R_log @ sp.diags(idf_half)\nsvd_idf2 = TruncatedSVD(n_components=256, algorithm='randomized', random_state=42)\nU_idf2 = svd_idf2.fit_transform(R_idf_half)\nV_idf2 = svd_idf2.components_\nevaluate_predictions(U_idf2 @ V_idf2, 'Log1p + Item IDF^0.5, k=256')\n\"",
"description": "Run corrected IDF test"
}
### Tool output · 2026-09-29T03:55:03.844Z
Output: Log1p + Item IDF, k=256 -> NDCG@10: 0.038861
Log1p + Item IDF^0.5, k=256 -> NDCG@10: 0.040174
Process Group PGID: 2445
## Step 44 · agent · 2026-09-29T03:55:18.488Z
### Recorded reasoning summary
Improving NDCG@10
I'm pleased to report our NDCG@10 has reached 0.040174. This reflects a consistent upward trend from our initial baseline, with our latest IDF-scaled time-decayed SVD model showing significant improvement. I'm now focused on further enhancements.
### Recorded reasoning summary
Refining Latent Factor Scaling
I'm exploring strategies to better balance user and item influences within the latent space. Specifically, I'm investigating the impact of normalizing user vectors before SVD and experimenting with different singular value scaling exponents ($\alpha$) beyond the standard SVD, aiming to reduce the dominance of top eigenvalues. The goal is to improve the model's ability to capture nuanced user preferences.
### run_shell_command · 2026-09-29T03:55:18.488Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nsvd = TruncatedSVD(n_components=300, algorithm='randomized', random_state=42)\nU_sigma = svd.fit_transform(R_idf) # U * Sigma\nsigma = svd.singular_values_ # Sigma\nV = svd.components_ # V^T\nU_ortho = U_sigma / sigma # orthonormal U\n\nfor alpha in [1.0, 0.8, 0.6, 0.5, 0.4, 0.2, 0.0]:\n scores = (U_ortho * (sigma ** alpha)) @ V\n evaluate_predictions(scores, f'Sigma power alpha={alpha}')\n\"",
"description": "Test singular value exponent alpha on validation NDCG"
}
### Tool output · 2026-09-29T03:55:44.463Z
Output: Sigma power alpha=1.0 -> NDCG@10: 0.039454
Sigma power alpha=0.8 -> NDCG@10: 0.037045
Sigma power alpha=0.6 -> NDCG@10: 0.035139
Sigma power alpha=0.5 -> NDCG@10: 0.034337
Sigma power alpha=0.4 -> NDCG@10: 0.033474
Sigma power alpha=0.2 -> NDCG@10: 0.031411
Sigma power alpha=0.0 -> NDCG@10: 0.029817
Process Group PGID: 2479
## Step 45 · agent · 2026-09-29T03:55:56.395Z
### Recorded reasoning summary
Exploring Dimension Effects
My current focus is on the impact of the dimension parameter, $k$. I've observed an improvement in performance by increasing it from 256 to 300, with the score dropping from 0.040174 to 0.039454. I'm now considering further increases to ascertain the optimal value.
### run_shell_command · 2026-09-29T03:55:56.395Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\nage_days = (T_cut - listens.timestamp.to_numpy()) / 86400.0\ndecay = np.power(0.5, age_days / 8.0).astype(np.float32)\n\nweights = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights[~strong & ~organic] = 1.0\nweights[strong & ~organic] = 1.5\nweights[~strong & organic] = 2.0\nweights[strong & organic] = 3.0\nweights *= decay\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_lk = np.power(0.5, age_days_lk / 8.0).astype(np.float32)\nweights_lk = 5.0 * decay_lk\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nall_u = np.concatenate([u_l, u_lk, u_pref])\nall_it = np.concatenate([it_l, it_lk, it_pref])\nall_w = np.concatenate([weights, weights_lk, weights_pref])\ndel u_l, it_l, weights, u_lk, it_lk, weights_lk, u_pref, it_pref, weights_pref; gc.collect()\n\nR_raw = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR_log = R_raw.copy()\nR_log.data = np.log1p(R_log.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R_raw.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nidf_half = np.power(idf, 0.5).astype(np.float32)\nR_idf = R_log @ sp.diags(idf_half)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [128, 160, 192, 224, 256]:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_idf)\n V = svd.components_\n evaluate_predictions(U @ V, f'R_idf, k={k}')\n\n# Test L2 row normalization\nR_norm = normalize(R_idf, norm='l2', axis=1)\nfor k in [160, 224, 256]:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_norm)\n V = svd.components_\n evaluate_predictions(U @ V, f'R_norm (L2), k={k}')\n\"",
"description": "Test dimensions k and row normalization"
}
### Tool output · 2026-09-29T03:56:47.657Z
Output: R_idf, k=128 -> NDCG@10: 0.040999
R_idf, k=160 -> NDCG@10: 0.041634
R_idf, k=192 -> NDCG@10: 0.039876
R_idf, k=224 -> NDCG@10: 0.040027
R_idf, k=256 -> NDCG@10: 0.040174
R_norm (L2), k=160 -> NDCG@10: 0.037777
R_norm (L2), k=224 -> NDCG@10: 0.036706
R_norm (L2), k=256 -> NDCG@10: 0.036614
Process Group PGID: 2513
## Step 46 · agent · 2026-09-29T03:56:58.590Z
### Recorded reasoning summary
Evaluating R_idf Parameters
I'm excited by the R_idf results, hitting NDCG@10 at 0.041634 with k=160. It's interesting that L2 normalization actually decreased performance, suggesting that unnormalized user magnitude offers a valuable insight into engagement levels. I'm now considering the next steps to refine this.
### run_shell_command · 2026-09-29T03:56:58.590Z
{
"description": "Grid search on half-life H and IDF power p",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref = pref_liked.item_id.map(item_to_idx).to_numpy()\nweights_pref = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nfor H in [5.0, 7.0, 8.0, 9.0, 10.0]:\n decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n \n all_u = np.concatenate([u_l, u_lk, u_pref])\n all_it = np.concatenate([it_l, it_lk, it_pref])\n all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, weights_pref])\n \n R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n \n N_users = len(target_uids)\n doc_freq = np.diff(R.tocsc().indptr)\n idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n \n for p in [0.3, 0.4, 0.5, 0.6]:\n R_p = R @ sp.diags(np.power(idf, p).astype(np.float32))\n svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n evaluate_predictions(U @ V, f'H={H}, p={p}')\n\""
}
### Tool output · 2026-09-29T03:58:48.093Z
Output: H=5.0, p=0.3 -> NDCG@10: 0.04074
H=5.0, p=0.4 -> NDCG@10: 0.040963
H=5.0, p=0.5 -> NDCG@10: 0.041177
H=5.0, p=0.6 -> NDCG@10: 0.041378
H=7.0, p=0.3 -> NDCG@10: 0.040681
H=7.0, p=0.4 -> NDCG@10: 0.040945
H=7.0, p=0.5 -> NDCG@10: 0.041128
H=7.0, p=0.6 -> NDCG@10: 0.041682
H=8.0, p=0.3 -> NDCG@10: 0.040347
H=8.0, p=0.4 -> NDCG@10: 0.040697
H=8.0, p=0.5 -> NDCG@10: 0.041634
H=8.0, p=0.6 -> NDCG@10: 0.04126
H=9.0, p=0.3 -> NDCG@10: 0.039766
H=9.0, p=0.4 -> NDCG@10: 0.040537
H=9.0, p=0.5 -> NDCG@10: 0.04086
H=9.0, p=0.6 -> NDCG@10: 0.040916
H=10.0, p=0.3 -> NDCG@10: 0.03948
H=10.0, p=0.4 -> NDCG@10: 0.040259
H=10.0, p=0.5 -> NDCG@10: 0.039998
H=10.0, p=0.6 -> NDCG@10: 0.040176
Process Group PGID: 2547
## Step 47 · agent · 2026-09-29T03:59:12.174Z
### Recorded reasoning summary
Exploring Dislikes' Impact
I'm now investigating the effect of incorporating dislikes as negative signals. Specifically, I'm examining the `train_dislikes.parquet` dataset and `validation_preference_state` where the state is 'disliked' to see how this penalization influences performance.
### run_shell_command · 2026-09-29T03:59:12.174Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\npref_disliked = pref[pref.state == 'disliked']\nu_pref_d = pref_disliked.uid.map(uid_to_idx).to_numpy()\nit_pref_d = pref_disliked.item_id.map(item_to_idx).to_numpy()\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nfor neg_w in [0.0, -1.0, -3.0, -5.0]:\n if neg_w == 0.0:\n cur_u, cur_it, cur_w = all_u, all_it, all_w\n else:\n w_pref_d = np.full(len(pref_disliked), neg_w, dtype=np.float32)\n cur_u = np.concatenate([all_u, u_pref_d])\n cur_it = np.concatenate([all_it, it_pref_d])\n cur_w = np.concatenate([all_w, w_pref_d])\n \n R = sp.csr_matrix((cur_w, (cur_u, cur_it)), shape=(len(target_uids), len(item_ids)))\n # Sign-preserving log1p: sign(x) * log1p(abs(x))\n R.data = np.sign(R.data) * np.log1p(np.abs(R.data))\n \n N_users = len(target_uids)\n doc_freq = np.diff(R.tocsc().indptr)\n idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n \n svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n evaluate_predictions(U @ V, f'neg_weight={neg_w}')\n\"",
"description": "Test negative preference weights for dislikes"
}
### Tool output · 2026-09-29T03:59:40.575Z
Output: neg_weight=0.0 -> NDCG@10: 0.041682
neg_weight=-1.0 -> NDCG@10: 0.041229
neg_weight=-3.0 -> NDCG@10: 0.040782
neg_weight=-5.0 -> NDCG@10: 0.041575
Process Group PGID: 2587
## Step 48 · agent · 2026-09-29T03:59:53.247Z
### Recorded reasoning summary
Refining Preference Matrix
I've adjusted the `neg_weight` to 0.0, which effectively excludes disliked items from the positive preference matrix. This approach is cleaner and has shown a slight improvement in results, as explicit dislikes are already filtered out of candidate eligibility.
### run_shell_command · 2026-09-29T03:59:53.247Z
{
"description": "Evaluate popularity prior blended with CF scores",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\n# Prepare catalog popularity vector aligned with item_ids\ncat_map = {row.item_id: row for row in catalog.itertuples()}\norg_pop = np.array([cat_map[it].organic_positive_listens for it in item_ids], dtype=np.float32)\ntot_pop = np.array([cat_map[it].positive_listens for it in item_ids], dtype=np.float32)\nlog_org = np.log1p(org_pop)\nlog_tot = np.log1p(tot_pop)\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nH = 7.0\ndecay_l = np.power(0.5, age_days_l / H).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\nsvd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\nU = svd.fit_transform(R_p)\nV = svd.components_\ncf_scores = U @ V # (8456, 30000)\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nevaluate_predictions(cf_scores, 'Pure CF scores')\n\n# Standardize CF scores\ncf_std = np.std(cf_scores)\npop_std = np.std(log_org)\n\nfor beta in [0.01, 0.02, 0.05, 0.1, 0.2]:\n comb = (cf_scores / cf_std) + beta * (log_org / pop_std)\n evaluate_predictions(comb, f'CF + {beta} * organic_pop')\n\""
}
### Tool output · 2026-09-29T04:00:11.430Z
Output: Pure CF scores -> NDCG@10: 0.041682
CF + 0.01 * organic_pop -> NDCG@10: 0.041632
CF + 0.02 * organic_pop -> NDCG@10: 0.041607
CF + 0.05 * organic_pop -> NDCG@10: 0.041483
CF + 0.1 * organic_pop -> NDCG@10: 0.04117
CF + 0.2 * organic_pop -> NDCG@10: 0.041172
Process Group PGID: 2621
## Step 49 · agent · 2026-09-29T04:00:31.748Z
### Recorded reasoning summary
Refining Recommendations
My focus has shifted to exploring ensembling and multi-scale representations. I've confirmed that pure CF effectively captures personalization and discovery, but I'm seeing how pure popularity can negatively impact discovery metrics.
### run_shell_command · 2026-09-29T04:00:31.748Z
{
"description": "Test multi-timescale model blending",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):\n decay_l = np.power(0.5, age_days_l / H).astype(np.float32)\n decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)\n \n all_u = np.concatenate([u_l, u_lk, u_pref_l])\n all_it = np.concatenate([it_l, it_lk, it_pref_l])\n all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])\n \n R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n \n N_users = len(target_uids)\n doc_freq = np.diff(R.tocsc().indptr)\n idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))\n \n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)\n U = svd.fit_transform(R_p)\n V = svd.components_\n return U @ V\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\ns_short = get_scores_for_H(5.0, k=160, rand_state=42)\ns_mid = get_scores_for_H(12.0, k=160, rand_state=43)\ns_long = get_scores_for_H(30.0, k=160, rand_state=44)\n\nevaluate_predictions(s_short, 'Short H=5')\nevaluate_predictions(s_mid, 'Mid H=12')\nevaluate_predictions(s_long, 'Long H=30')\n\n# Standardize scores before blending\ns_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)\ns_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)\ns_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)\n\nfor w_m in [0.2, 0.4, 0.6, 0.8]:\n for w_l in [0.0, 0.1, 0.2, 0.3]:\n blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm\n evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')\n\""
}
### Tool output · 2026-09-29T04:00:56.395Z
Output: :102: RuntimeWarning: invalid value encountered in divide
:103: RuntimeWarning: invalid value encountered in divide
/usr/bin/bash: line 114: 2657 Killed python3 -c "
import sys
sys.path.insert(0, '/task')
import gc
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
targets_frame = pd.read_parquet('/task/data/target_users.parquet')
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
graded = validation_truth(eligibility, targets)
target_uids = eligibility.uids
item_ids = eligibility.item_ids
uid_to_idx = {uid: i for i, uid in enumerate(target_uids)}
item_to_idx = {item: i for i, item in enumerate(item_ids)}
T_cut = 24786800
t_90d = T_cut - 90 * 86400
listens = pd.read_parquet(
'/task/data/train_listens.parquet',
columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],
filters=[('timestamp', '>=', t_90d)]
)
listens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]
likes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])
likes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]
pref = pd.read_parquet('/task/data/validation_preference_state.parquet')
pref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]
pref_liked = pref[pref.state == 'liked']
u_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()
it_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()
w_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)
age_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0
age_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0
weights_l_base = np.ones(len(listens), dtype=np.float32)
strong = (listens.played_ratio_pct >= 80).to_numpy()
organic = (listens.is_organic == 1).to_numpy()
weights_l_base[~strong & ~organic] = 1.0
weights_l_base[strong & ~organic] = 1.5
weights_l_base[~strong & organic] = 2.0
weights_l_base[strong & organic] = 3.0
u_l = listens.uid.map(uid_to_idx).to_numpy()
it_l = listens.item_id.map(item_to_idx).to_numpy()
u_lk = likes.uid.map(uid_to_idx).to_numpy()
it_lk = likes.item_id.map(item_to_idx).to_numpy()
def get_scores_for_H(H, k=160, idf_p=0.6, rand_state=42):
decay_l = np.power(0.5, age_days_l / H).astype(np.float32)
decay_lk = np.power(0.5, age_days_lk / H).astype(np.float32)
all_u = np.concatenate([u_l, u_lk, u_pref_l])
all_it = np.concatenate([it_l, it_lk, it_pref_l])
all_w = np.concatenate([weights_l_base * decay_l, 5.0 * decay_lk, w_pref_l])
R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))
R.data = np.log1p(R.data)
N_users = len(target_uids)
doc_freq = np.diff(R.tocsc().indptr)
idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)
R_p = R @ sp.diags(np.power(idf, idf_p).astype(np.float32))
svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=rand_state)
U = svd.fit_transform(R_p)
V = svd.components_
return U @ V
def evaluate_predictions(scores, name=''):
ranked = {}
for i, uid in enumerate(target_uids):
user_scores = scores[i].copy()
bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')
user_scores[bits == 0] = -np.inf
top100_idx = np.argpartition(user_scores, -100)[-100:]
top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]
ranked[int(uid)] = list(item_ids[top100_sorted])
res = score_rankings(ranked, graded)
print(f'{name} -> NDCG@10: {res[\"quality_ndcg@10\"]}')
return res[\"quality_ndcg@10\"]
s_short = get_scores_for_H(5.0, k=160, rand_state=42)
s_mid = get_scores_for_H(12.0, k=160, rand_state=43)
s_long = get_scores_for_H(30.0, k=160, rand_state=44)
evaluate_predictions(s_short, 'Short H=5')
evaluate_predictions(s_mid, 'Mid H=12')
evaluate_predictions(s_long, 'Long H=30')
# Standardize scores before blending
s_short_norm = (s_short - np.mean(s_short, axis=1, keepdims=True)) / np.std(s_short, axis=1, keepdims=True)
s_mid_norm = (s_mid - np.mean(s_mid, axis=1, keepdims=True)) / np.std(s_mid, axis=1, keepdims=True)
s_long_norm = (s_long - np.mean(s_long, axis=1, keepdims=True)) / np.std(s_long, axis=1, keepdims=True)
for w_m in [0.2, 0.4, 0.6, 0.8]:
for w_l in [0.0, 0.1, 0.2, 0.3]:
blend = s_short_norm + w_m * s_mid_norm + w_l * s_long_norm
evaluate_predictions(blend, f'Blend: 1.0*short + {w_m}*mid + {w_l}*long')
"
Exit Code: 137
Process Group PGID: 2655
## Step 50 · agent · 2026-09-29T04:01:11.788Z
### Recorded reasoning summary
Exploring Memory Bottlenecks
I'm currently investigating memory usage patterns with large arrays, specifically observing issues with mean calculations on substantial datasets. My focus is on optimizing these operations to prevent exhaustion, especially when dealing with arrays that have a standard deviation of zero for certain users, which is triggering runtime warnings.
### run_shell_command · 2026-09-29T04:01:11.788Z
{
"description": "Test multi-timescale decay kernel with gamma values",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\n\nweights_l_base = np.ones(len(listens), dtype=np.float32)\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nweights_l_base[~strong & ~organic] = 1.0\nweights_l_base[strong & ~organic] = 1.5\nweights_l_base[~strong & organic] = 2.0\nweights_l_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\ndel listens; gc.collect()\n\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\ndel likes; gc.collect()\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# Test multi-timescale decay kernel: 2^(-age/7) + gamma * 2^(-age/30)\nfor gamma in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n kernel_l = (np.power(0.5, age_days_l / 7.0) + gamma * np.power(0.5, age_days_l / 30.0)).astype(np.float32)\n kernel_lk = (np.power(0.5, age_days_lk / 7.0) + gamma * np.power(0.5, age_days_lk / 30.0)).astype(np.float32)\n \n all_u = np.concatenate([u_l, u_lk, u_pref_l])\n all_it = np.concatenate([it_l, it_lk, it_pref_l])\n all_w = np.concatenate([weights_l_base * kernel_l, 5.0 * kernel_lk, w_pref_l])\n \n R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n \n N_users = len(target_uids)\n doc_freq = np.diff(R.tocsc().indptr)\n idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n \n svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n scores = U @ V\n evaluate_predictions(scores, f'gamma={gamma}')\n\""
}
### Tool output · 2026-09-29T04:01:53.369Z
Output: gamma=0.0 -> NDCG@10: 0.041682
gamma=0.05 -> NDCG@10: 0.041469
gamma=0.1 -> NDCG@10: 0.040599
gamma=0.15 -> NDCG@10: 0.040384
gamma=0.2 -> NDCG@10: 0.039918
gamma=0.3 -> NDCG@10: 0.039588
Process Group PGID: 2683
## Step 51 · agent · 2026-09-29T04:02:14.980Z
### Recorded reasoning summary
Confirming Gamma Value
I've confirmed that setting Gamma to 0.0, which prioritizes recent decay patterns, yields the optimal performance. This suggests users are most effectively exploring new music based on their immediate listening phase or mood.
### Recorded reasoning summary
Adjusting Skip Weighting
I'm re-evaluating our event weights, specifically how short, incomplete listens (skips) are handled. It appears tracks played for less than 30% are currently assigned a weight of 1.0, which seems too high given their nature as skips. I'm investigating this further.
### run_shell_command · 2026-09-29T04:02:14.980Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\n# 1. Load targets, catalog, and eligibility\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# Test different completion thresholds:\n# What if minimum played ratio is 0%, 30%, 50%, 60%, 80%?\nfor min_pct in [0, 20, 30, 40, 50]:\n mask = listens.played_ratio_pct >= min_pct\n cur_u_l = u_l[mask]\n cur_it_l = it_l[mask]\n cur_decay = decay_l[mask]\n \n strong = (listens.played_ratio_pct[mask] >= 80).to_numpy()\n organic = (listens.is_organic[mask] == 1).to_numpy()\n w_base = np.ones(len(cur_u_l), dtype=np.float32)\n w_base[~strong & ~organic] = 1.0\n w_base[strong & ~organic] = 1.5\n w_base[~strong & organic] = 2.0\n w_base[strong & organic] = 3.0\n \n all_u = np.concatenate([cur_u_l, u_lk, u_pref_l])\n all_it = np.concatenate([cur_it_l, it_lk, it_pref_l])\n all_w = np.concatenate([w_base * cur_decay, 5.0 * decay_lk, w_pref_l])\n \n R = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\n R.data = np.log1p(R.data)\n \n N_users = len(target_uids)\n doc_freq = np.diff(R.tocsc().indptr)\n idf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\n R_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n \n svd = TruncatedSVD(n_components=160, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n evaluate_predictions(U @ V, f'min_played_ratio >= {min_pct}%')\n\"",
"description": "Evaluate minimum played ratio threshold"
}
### Tool output · 2026-09-29T04:02:46.953Z
Output: min_played_ratio >= 0% -> NDCG@10: 0.041682
min_played_ratio >= 20% -> NDCG@10: 0.042088
min_played_ratio >= 30% -> NDCG@10: 0.042272
min_played_ratio >= 40% -> NDCG@10: 0.04133
min_played_ratio >= 50% -> NDCG@10: 0.041569
Process Group PGID: 2717
## Step 52 · agent · 2026-09-29T04:02:57.954Z
### Recorded reasoning summary
Analyzing Performance Boosts
My analysis shows a slight but positive increase in the `min_played_ratio >= 30%` metric, suggesting that refining playback thresholds is indeed reducing noise and improving NDCG. I'm now exploring the impact of `n_components` around 160 in conjunction with this improved filtering.
### run_shell_command · 2026-09-29T04:02:57.954Z
{
"description": "Scan k around 150 for min_played_ratio >= 30%",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [100, 120, 140, 150, 160, 170, 180, 200]:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n evaluate_predictions(U @ V, f'k={k}')\n\""
}
### Tool output · 2026-09-29T04:03:36.780Z
Output: k=100 -> NDCG@10: 0.041147
k=120 -> NDCG@10: 0.041725
k=140 -> NDCG@10: 0.041758
k=150 -> NDCG@10: 0.04131
k=160 -> NDCG@10: 0.042272
k=170 -> NDCG@10: 0.040027
k=180 -> NDCG@10: 0.040822
k=200 -> NDCG@10: 0.04042
Process Group PGID: 2751
## Step 53 · agent · 2026-09-29T04:03:47.264Z
### Recorded reasoning summary
Exploring Randomized SVD
I'm currently investigating the impact of the random seed on Randomized SVD, particularly its relationship with the `n_iter` parameter. My recent tests with `k=160` yielded a promising result of 0.042272, and I'm now keen to explore variations in `n_iter` to optimize performance.
### run_shell_command · 2026-09-29T04:03:47.264Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nfor n_iter in [3, 5, 7, 10]:\n svd = TruncatedSVD(n_components=160, algorithm='randomized', n_iter=n_iter, random_state=42)\n U = svd.fit_transform(R_p)\n V = svd.components_\n evaluate_predictions(U @ V, f'n_iter={n_iter}')\n\"",
"description": "Test n_iter power iterations in TruncatedSVD"
}
### Tool output · 2026-09-29T04:04:11.717Z
Output: n_iter=3 -> NDCG@10: 0.042084
n_iter=5 -> NDCG@10: 0.042272
n_iter=7 -> NDCG@10: 0.041766
n_iter=10 -> NDCG@10: 0.041552
Process Group PGID: 2785
## Step 54 · agent · 2026-09-29T04:04:23.146Z
### Recorded reasoning summary
Testing ensemble gains
I'm exploring ensemble methods to boost performance. My current focus is on averaging predictions from different random seeds and varying the `k` parameter. This should reveal if combining these slight variations yields a higher NDCG@10.
### run_shell_command · 2026-09-29T04:04:23.146Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport gc\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# Ensemble 3 SVDs with different seeds / k\nsum_scores = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nconfigs = [(160, 42), (160, 101), (140, 202), (160, 303)]\nfor k, seed in configs:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n U = svd.fit_transform(R_p)\n V = svd.components_\n score_k = U @ V\n evaluate_predictions(score_k, f'Single model k={k}, seed={seed}')\n sum_scores += score_k\n\nevaluate_predictions(sum_scores, 'Ensemble average of 4 SVD models')\n\"",
"description": "Test multi-seed/multi-k ensemble average"
}
### Tool output · 2026-09-29T04:04:47.155Z
Output: Single model k=160, seed=42 -> NDCG@10: 0.042272
Single model k=160, seed=101 -> NDCG@10: 0.041304
Single model k=140, seed=202 -> NDCG@10: 0.04302
Single model k=160, seed=303 -> NDCG@10: 0.042018
Ensemble average of 4 SVD models -> NDCG@10: 0.042823
Process Group PGID: 2813
## Step 55 · agent · 2026-09-29T04:05:12.494Z
### run_shell_command · 2026-09-29T04:05:12.494Z
{
"description": "Evaluate 3-seed ensemble for k around 140",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\nfor k in [130, 135, 140, 145]:\n scores_k = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\n for seed in [42, 101, 202]:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n U = svd.fit_transform(R_p)\n V = svd.components_\n scores_k += (U @ V) / 3.0\n evaluate_predictions(scores_k, f'Ensemble of 3 seeds for k={k}')\n\""
}
### Tool output · 2026-09-29T04:06:01.052Z
Output: Ensemble of 3 seeds for k=130 -> NDCG@10: 0.041599
Ensemble of 3 seeds for k=135 -> NDCG@10: 0.041803
Ensemble of 3 seeds for k=140 -> NDCG@10: 0.043033
Ensemble of 3 seeds for k=145 -> NDCG@10: 0.042618
Process Group PGID: 2853
## Step 56 · agent · 2026-09-29T04:06:21.475Z
### Recorded reasoning summary
Achieving Enhanced NDCG
I've successfully developed an ensemble of three seeds for k=140, achieving an NDCG@10 score of 0.043033. This represents a significant improvement, more than quadrupling the baseline of 0.01071, and this impressive gain was realized in a mere 10 seconds of computation. My next step is to investigate variations in k, specifically exploring k=14.
### run_shell_command · 2026-09-29T04:06:21.475Z
{
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ntarget_uids = eligibility.uids\nitem_ids = eligibility.item_ids\nuid_to_idx = {uid: i for i, uid in enumerate(target_uids)}\nitem_to_idx = {item: i for i, item in enumerate(item_ids)}\n\nT_cut = 24786800\nt_90d = T_cut - 90 * 86400\n\nlistens = pd.read_parquet(\n '/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_90d), ('played_ratio_pct', '>=', 30)]\n)\nlistens = listens[listens.uid.isin(targets) & listens.item_id.isin(item_to_idx)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet', filters=[('timestamp', '>=', t_90d)])\nlikes = likes[likes.uid.isin(targets) & likes.item_id.isin(item_to_idx)]\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(targets) & pref.item_id.isin(item_to_idx)]\n\npref_liked = pref[pref.state == 'liked']\nu_pref_l = pref_liked.uid.map(uid_to_idx).to_numpy()\nit_pref_l = pref_liked.item_id.map(item_to_idx).to_numpy()\nw_pref_l = np.full(len(pref_liked), 3.0, dtype=np.float32)\n\nage_days_l = (T_cut - listens.timestamp.to_numpy()) / 86400.0\nage_days_lk = (T_cut - likes.timestamp.to_numpy()) / 86400.0\ndecay_l = np.power(0.5, age_days_l / 7.0).astype(np.float32)\ndecay_lk = np.power(0.5, age_days_lk / 7.0).astype(np.float32)\n\nstrong = (listens.played_ratio_pct >= 80).to_numpy()\norganic = (listens.is_organic == 1).to_numpy()\nw_base = np.ones(len(listens), dtype=np.float32)\nw_base[~strong & ~organic] = 1.0\nw_base[strong & ~organic] = 1.5\nw_base[~strong & organic] = 2.0\nw_base[strong & organic] = 3.0\n\nu_l = listens.uid.map(uid_to_idx).to_numpy()\nit_l = listens.item_id.map(item_to_idx).to_numpy()\nu_lk = likes.uid.map(uid_to_idx).to_numpy()\nit_lk = likes.item_id.map(item_to_idx).to_numpy()\n\nall_u = np.concatenate([u_l, u_lk, u_pref_l])\nall_it = np.concatenate([it_l, it_lk, it_pref_l])\nall_w = np.concatenate([w_base * decay_l, 5.0 * decay_lk, w_pref_l])\n\nR = sp.csr_matrix((all_w, (all_u, all_it)), shape=(len(target_uids), len(item_ids)))\nR.data = np.log1p(R.data)\n\nN_users = len(target_uids)\ndoc_freq = np.diff(R.tocsc().indptr)\nidf = (np.log((N_users + 1.0) / (doc_freq + 1.0)) + 1.0).astype(np.float32)\nR_p = R @ sp.diags(np.power(idf, 0.6).astype(np.float32))\n\ndef evaluate_predictions(scores, name=''):\n ranked = {}\n for i, uid in enumerate(target_uids):\n user_scores = scores[i].copy()\n bits = np.unpackbits(eligibility.packed[i], count=30000, bitorder='little')\n user_scores[bits == 0] = -np.inf\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uid)] = list(item_ids[top100_sorted])\n res = score_rankings(ranked, graded)\n print(f'{name} -> NDCG@10: {res[\\\"quality_ndcg@10\\\"]}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# Multi-rank ensemble: k in [120, 140, 160] with different seeds\nscores_multi = np.zeros((len(target_uids), len(item_ids)), dtype=np.float32)\nfor k, seed in [(120, 42), (140, 101), (140, 202), (160, 303), (140, 404)]:\n svd = TruncatedSVD(n_components=k, algorithm='randomized', random_state=seed)\n U = svd.fit_transform(R_p)\n V = svd.components_\n scores_multi += (U @ V) / 5.0\n\nevaluate_predictions(scores_multi, 'Multi-rank ensemble (5 components)')\n\"",
"description": "Evaluate multi-rank 5-component ensemble"
}
### Tool output · 2026-09-29T04:06:44.028Z
Output: Multi-rank ensemble (5 components) -> NDCG@10: 0.042633
Process Group PGID: 2887
## Recorded session usage
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