Basic recommender gemini-3-8-flash-high-08
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<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app
Showing up to 200 items (files + folders).
/app/ └───solution/
</session_context>
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.
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:
train_* as history and val_* as subsequent outcomes.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.
Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:
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.
/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:
python /task/evaluate_public.py solution/validation.parquetStage-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.
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.
/app/solution/./task/ and the installed Python environment as read-only.Analyzing Task Parameters
I'm currently dissecting the core requirements for this music discovery ranker. My immediate focus is on thoroughly understanding the prompt and all specified constraints to ensure a robust and compliant solution for the "Made for You" shelf.
Processing Input Data
My current focus is on understanding the structure and limits of the provided input data. I've noted the specific training data cutoff and the detailed timeline for validation and testing windows, including the crucial 30-minute gaps. I'm also scrutinizing the exact format required for the submission files and the strict constraints on candidate item inclusion.
{
"strategic_intent": "Explore the task environment, data files, evaluation scripts, and baseline setup.",
"title": "Initial Exploration and Environment Assessment",
"summary": "Starting the investigation into the music discovery ranker task. We will inspect the available data files, evaluator code, dependencies, and environment hardware to plan our modeling pipeline and validation strategy."
}Current topic: "Initial Exploration and Environment Assessment"
Topic summary: Starting the investigation into the music discovery ranker task. We will inspect the available data files, evaluator code, dependencies, and environment hardware to plan our modeling pipeline and validation strategy.
Strategic Intent: Explore the task environment, data files, evaluation scripts, and baseline setup.{
"command": "ls -la /task && ls -la /task/data",
"description": "Inspect contents of /task and /task/data"
}<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 01:20 .
drwxr-xr-x 1 root root 4096 Sep 29 01:22 ..
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 data
-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:20 ..
-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>{
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi 2>&1",
"description": "Check compute hardware (CPUs, RAM, GPU)"
}<untrusted_context>
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
Exit Code: 127
Process Group PGID: 1615
</untrusted_context>{
"description": "Check available system memory",
"command": "cat /proc/meminfo | grep \"MemTotal\\|MemAvailable\""
}<untrusted_context>
Output: MemTotal: 15988964 kB
MemAvailable: 14576240 kB
Process Group PGID: 1622
</untrusted_context>{
"file_path": "/task/requirements.txt"
}Path not in workspace: Attempted path "/task/requirements.txt" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"file_path": "/task/evaluate_public.py"
}Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"command": "cat /task/requirements.txt",
"description": "Read requirements.txt"
}<untrusted_context>
Output: # Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
Process Group PGID: 1626
</untrusted_context>{
"description": "Read evaluate_public.py",
"command": "cat /task/evaluate_public.py"
}<untrusted_context>
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py <validation-ranking.parquet|csv>"
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1629
</untrusted_context>{
"description": "Read ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
}<untrusted_context>
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1632
</untrusted_context>Clarifying Evaluation Logic
I'm examining the dataset sizes, specifically counting target users from target_users.parquet and items from candidate_catalog.parquet (which currently holds 30,000 items) to solidify the evaluation framework.
Analyzing Data Structures
I'm delving into the specifics of retrieval-catalog tracking. I'm also meticulously reviewing the alignment and unpacking mechanisms within validation_eligible_candidates.npz and test_eligible_candidates.npz to ensure accurate candidate selection.
{
"description": "Explore target users, catalog, preference states, and eligibility arrays",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(targets))\nprint('Target users head:\\n', targets.head())\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Candidate catalog count:', len(catalog))\nprint('Candidate catalog head:\\n', catalog.head())\nprint('Candidate catalog cols:', catalog.columns.tolist())\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state shape:', val_pref.shape, 'cols:', val_pref.columns.tolist())\nprint('Val pref head:\\n', val_pref.head())\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref state shape:', test_pref.shape, 'cols:', test_pref.columns.tolist())\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val elig keys:', val_elig.files)\nprint('Val elig uids:', len(val_elig['uids']), 'items:', len(val_elig['item_ids']), 'packed shape:', val_elig['packed_eligible'].shape)\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test elig keys:', test_elig.files)\nprint('Test elig uids:', len(test_elig['uids']), 'items:', len(test_elig['item_ids']), 'packed shape:', test_elig['packed_eligible'].shape)\n\n# Let's check candidate count per user\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(val_elig['item_ids']), bitorder='little')\nprint('User 0 eligible items:', row0.sum())\n\""
}<untrusted_context>
Output: Target users count: 8456
Target users head:
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
3 600 1985.0 2423
4 700 1776.0 6114
Candidate catalog count: 30000
Candidate catalog head:
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
Candidate catalog cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
Val pref state shape: (469124, 3) cols: ['uid', 'item_id', 'state']
Val pref head:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
Test pref state shape: (484776, 3) cols: ['uid', 'item_id', 'state']
Val elig keys: ['uids', 'item_ids', 'packed_eligible']
Val elig uids: 8456 items: 30000 packed shape: (8456, 3750)
Test elig keys: ['uids', 'item_ids', 'packed_eligible']
Test elig uids: 8456 items: 30000 packed shape: (8456, 3750)
User 0 eligible items: 29318
Process Group PGID: 1635
</untrusted_context>{
"description": "Check available ML/RecSys packages",
"command": "python3 -c \"\nfor pkg in ['implicit', 'lightgbm', 'xgboost', 'catboost', 'torch', 'scipy', 'sklearn', 'polars', 'duckdb']:\n try:\n mod = __import__(pkg)\n print(f'{pkg}: available, version {getattr(mod, \\\"__version__\\\", \\\"unknown\\\")}')\n except ImportError:\n print(f'{pkg}: NOT available')\n\""
}<untrusted_context>
Output: implicit: NOT available
lightgbm: NOT available
xgboost: NOT available
catboost: NOT available
torch: NOT available
scipy: available, version 1.15.3
sklearn: available, version 1.7.2
polars: NOT available
duckdb: NOT available
Process Group PGID: 1652
</untrusted_context>Analyzing Dependencies
My focus has been on cataloging the installed libraries. I've identified numpy, scipy, scikit-learn, pandas, pyarrow, joblib, and tqdm as essential components. Notably, no GBDT or deep learning packages are currently present, which is a key observation.
{
"description": "Check validation truth statistics",
"command": "python3 -c \"\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\nprint('Number of users with validation truth:', len(truth))\nitem_counts = [len(v) for v in truth.values()]\nprint('Mean relevant items per user:', sum(item_counts)/len(item_counts))\nprint('Max relevant items per user:', max(item_counts))\nprint('Min relevant items per user:', min(item_counts))\n\""
}<untrusted_context>
Output: Number of users with validation truth: 4263
Mean relevant items per user: 8.859723199624677
Max relevant items per user: 442
Min relevant items per user: 1
Process Group PGID: 1670
</untrusted_context>Calculating Baseline Popularity
I'm currently focusing on generating a baseline popularity ranking and score using the 4263 identified target users. My next step is to evaluate this baseline against the public validation data.
Testing Eligibility and Baseline
I'm now evaluating the preliminary popularity ranking against public validation data. My immediate plan is to determine the NDCG@10 score by selecting the top 100 eligible items for each user based on the candidate catalog's popularity rank, and then verify the submission format.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nDATA = Path('/task/data')\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\npacked = val_elig['packed_eligible']\n\nprint(f'Catalog items: {len(catalog)}, val_elig items: {len(item_ids)}')\n# Check if item_ids in catalog match item_ids in val_elig\nprint('Are item_ids identical:', np.array_equal(catalog['item_id'].values, item_ids))\n\"",
"description": "Check alignment between candidate_catalog and eligibility archives"
}<untrusted_context>
Output: Catalog items: 30000, val_elig items: 30000
Are item_ids identical: True
Process Group PGID: 1690
</untrusted_context>{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\ntest_elig = np.load(DATA / 'test_eligible_candidates.npz')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\n\nprint('Val uids == test uids:', np.array_equal(val_elig['uids'], test_elig['uids']))\nprint('Val uids == targets uids:', np.array_equal(val_elig['uids'], targets['uid'].values))\nprint('Val items == test items:', np.array_equal(val_elig['item_ids'], test_elig['item_ids']))\n\"",
"description": "Check UID and item ordering across validation and test"
}<untrusted_context>
Output: Val uids == test uids: True
Val uids == targets uids: True
Val items == test items: True
Process Group PGID: 1707
</untrusted_context>Confirming Data Alignment
I've confirmed that the user IDs and item IDs are perfectly synchronized across all relevant datasets (target_users, val_elig, and test_elig). This alignment, with all 8456 target users and 30000 items in identical order, is a crucial step.
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nt0 = time.time()\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\nprint(f'Unpacked in {time.time()-t0:.2f}s, shape: {mask.shape}, dtype: {mask.dtype}, memory: {mask.nbytes / 1e6:.1f} MB')\nprint('Fraction of eligible items:', mask.mean())\n\"",
"description": "Benchmark unpacking candidate mask"
}<untrusted_context>
Output: Unpacked in 0.19s, shape: (8456, 30000), dtype: uint8, memory: 253.7 MB
Fraction of eligible items: 0.9689102097130243
Process Group PGID: 1723
</untrusted_context>Investigating Eligibility Discrepancies
I'm working to understand why a small percentage of items are deemed ineligible for users. My current hypothesis is that it might be related to tracks the user has already listened to, liked, or perhaps other engagement signals.
{
"description": "Check contents of /app/solution",
"command": "ls -la /app/solution"
}<untrusted_context>
Output: total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 01:20 .
drwxr-xr-x 3 agent agent 4096 Sep 29 01:20 ..
Process Group PGID: 1733
</untrusted_context>Testing Popularity Scoring
I'm currently exploring how evaluate_public.py performs when solely focused on popularity metrics. My tests will specifically examine both positive_listens and organic_positive_listens to gauge their impact. I plan to implement these checks via Python tests within /app/solution/.
{
"description": "Evaluate popularity baseline on validation set",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, targets)\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nfor pop_col in ['positive_listens', 'organic_positive_listens']:\n pop_scores = catalog[pop_col].values # catalog order matches item_ids\n # For each user, get top 100 eligible items\n # pop_scores is shape (30000,)\n # If mask is 0, set score to -infinity\n ranked = {}\n # Since pop_scores is fixed, we can sort items once by pop_scores descending!\n sorted_item_indices = np.argsort(-pop_scores)\n \n t0 = time.time()\n for r, uid in enumerate(uids):\n user_mask = mask[r]\n # filter sorted items by user_mask\n user_ranked_idx = sorted_item_indices[user_mask[sorted_item_indices] == 1][:100]\n ranked[int(uid)] = item_ids[user_ranked_idx].tolist()\n \n s = score(ranked, graded)\n print(f'Pop col: {pop_col}, score: {s}, time: {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Pop col: positive_listens, score: {'quality_ndcg@10': 0.010695, 'quality_users': 4263}, time: 0.61s
Pop col: organic_positive_listens, score: {'quality_ndcg@10': 1.5e-05, 'quality_users': 4263}, time: 0.63s
Process Group PGID: 1736
</untrusted_context>Investigating Metrics Discrepancy
I'm analyzing the significant difference between positive_listens (NDCG@10 = 0.010695) and organic_positive_listens (0.000015). My next step is to examine the catalog data to understand the underlying definitions and reasons for this disparity.
{
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.describe())\nprint(cat.head(10))\n\"",
"description": "Inspect candidate_catalog distribution"
}<untrusted_context>
Output: item_id ... popularity_rank
count 3.000000e+04 ... 30000.000000
mean 4.698278e+06 ... 15000.500000
std 2.700274e+06 ... 8660.398374
min 2.060000e+02 ... 1.000000
25% 2.382368e+06 ... 7500.750000
50% 4.687992e+06 ... 15000.500000
75% 7.040711e+06 ... 22500.250000
max 9.390294e+06 ... 30000.000000
[8 rows x 4 columns]
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
Process Group PGID: 1756
</untrusted_context>Analyzing Score Discrepancies
I'm digging into why organic_positive_listens received a score of 1.5e-05. My current focus is examining the sorted_item_indices generated by np.argsort(-pop_scores) for both organic_positive_listens and positive_listens to pinpoint the source of this difference.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('positive_listens head top 5 indices:', np.argsort(-cat['positive_listens'].values)[:5])\nprint('organic top 5 indices:', np.argsort(-cat['organic_positive_listens'].values)[:5])\nprint('organic values head:', cat['organic_positive_listens'].head(5).values)\nprint('organic values max:', cat['organic_positive_listens'].max(), 'at index:', cat['organic_positive_listens'].idxmax())\nprint('organic values at top 5:', cat.iloc[np.argsort(-cat['organic_positive_listens'].values)[:5]])\n\"",
"description": "Check organic positive listens ordering"
}<untrusted_context>
Output: positive_listens head top 5 indices: [0 1 2 3 4]
organic top 5 indices: [17255 16200 21843 24628 29316]
organic values head: [20620 17347 17194 17699 18195]
organic values max: 20620 at index: 0
organic values at top 5: item_id positive_listens organic_positive_listens popularity_rank
17255 9062724 306 0 17256
16200 1659585 327 0 16201
21843 3743777 236 0 21844
24628 7639866 206 0 24629
29316 6275307 168 0 29317
Process Group PGID: 1773
</untrusted_context>Investigating Data Types
I'm currently examining the data types within candidate_catalog.parquet, specifically focusing on the organic_positive_listens column. My primary concern is whether it's represented as unsigned integer, object, or nullable integer, as this will impact subsequent operations, especially negation in numpy.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.dtypes)\nprint(type(cat['organic_positive_listens'].values[0]))\n\"",
"description": "Check catalog dtypes"
}<untrusted_context>
Output: item_id uint32
positive_listens int64
organic_positive_listens uint64
popularity_rank int32
dtype: object
<class 'numpy.uint64'>
Process Group PGID: 1790
</untrusted_context>Addressing Data Type Issues
I've identified a critical issue with uint64 data types causing underflow when negated in NumPy. My current focus is on implementing a robust solution, likely by casting to int64 before sorting, to ensure accurate ranking of organic positive listens.
{
"description": "Evaluate properly sorted popularity baseline",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, targets)\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nfor pop_col in ['positive_listens', 'organic_positive_listens']:\n pop_scores = catalog[pop_col].values.astype(np.float64)\n sorted_item_indices = np.argsort(-pop_scores)\n ranked = {}\n for r, uid in enumerate(uids):\n user_mask = mask[r]\n user_ranked_idx = sorted_item_indices[user_mask[sorted_item_indices] == 1][:100]\n ranked[int(uid)] = item_ids[user_ranked_idx].tolist()\n \n s = score(ranked, graded)\n print(f'Pop col: {pop_col}, score: {s}')\n\""
}<untrusted_context>
Output: Pop col: positive_listens, score: {'quality_ndcg@10': 0.010695, 'quality_users': 4263}
Pop col: organic_positive_listens, score: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 1807
</untrusted_context>Validating Baseline Generation
I'm currently focused on confirming if I can promptly produce a valid baseline submission file that adheres to the specified requirements. This step is crucial to ensure we have a starting point for further optimization and evaluation.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\nDATA = Path('/task/data')\ntest_elig_npz = DATA / 'test_eligible_candidates.npz'\ntest_elig = np.load(test_elig_npz)\nuids = test_elig['uids']\nitem_ids = test_elig['item_ids']\nmask = np.unpackbits(test_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\npop_scores = catalog['positive_listens'].values.astype(np.float64)\nsorted_items = np.argsort(-pop_scores)\n\nrows = []\nfor r, uid in enumerate(uids):\n user_mask = mask[r]\n chosen = sorted_items[user_mask[sorted_items] == 1][:100]\n for rank, idx in enumerate(chosen, start=1):\n rows.append((int(uid), int(item_ids[idx]), rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nout_path = Path('/app/solution/submission.parquet')\ndf.to_parquet(out_path, index=False)\n\n# Validate\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(test_elig_npz)\nvalidated = validate_ranking(pd.read_parquet(out_path), targets)\n\n# Check all eligible\nfor uid, item in validated[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f'Ineligible item {item} for user {uid}')\nprint('Baseline submission written and successfully validated! Total rows:', len(validated))\n\"",
"description": "Generate and validate baseline submission to /app/solution/submission.parquet"
}<untrusted_context>
Output: Baseline submission written and successfully validated! Total rows: 845600
Process Group PGID: 1827
</untrusted_context>Validating the Submission
I've successfully created and validated the submission.parquet file against both validate_ranking and elig.is_eligible. Now, I'm setting up a simple validation.parquet to test the public evaluator CLI.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nval_elig_npz = DATA / 'validation_eligible_candidates.npz'\nval_elig = np.load(val_elig_npz)\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\npop_scores = catalog['positive_listens'].values.astype(np.float64)\nsorted_items = np.argsort(-pop_scores)\n\nrows = []\nfor r, uid in enumerate(uids):\n user_mask = mask[r]\n chosen = sorted_items[user_mask[sorted_items] == 1][:100]\n for rank, idx in enumerate(chosen, start=1):\n rows.append((int(uid), int(item_ids[idx]), rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/validation.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Test evaluate_public.py on validation.parquet"
}<untrusted_context>
Output: {
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010695,
"quality_users": 4263
}
}
Process Group PGID: 1844
</untrusted_context>Refining Initial Solution
The foundational end-to-end functionality is now established, achieving a score of 0.010695. My focus is shifting to documenting this progress, starting with a preliminary README.md, ensuring it remains current as development continues.
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nfor f in ['train_likes.parquet', 'train_dislikes.parquet', 'train_unlikes.parquet', 'train_undislikes.parquet', 'validation_preference_state.parquet']:\n df = pd.read_parquet(DATA / f)\n print(f'{f}: shape={df.shape}, cols={df.columns.tolist()}')\n if 'timestamp' in df.columns:\n print(f' min_ts={df.timestamp.min()}, max_ts={df.timestamp.max()}')\n\"",
"description": "Inspect like/dislike files shape and timestamp ranges"
}<untrusted_context>
Output: train_likes.parquet: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
min_ts=80, max_ts=24786790
train_dislikes.parquet: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
min_ts=75, max_ts=24786580
train_unlikes.parquet: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
min_ts=250, max_ts=24786570
train_undislikes.parquet: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
min_ts=15950, max_ts=24785395
validation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']
Process Group PGID: 1879
</untrusted_context>{
"description": "Check row counts using parquet metadata",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nfor f in ['train_listens.parquet', 'val_listens.parquet', 'val_likes.parquet', 'val_dislikes.parquet']:\n meta = pq.read_metadata(DATA / f)\n print(f'{f}: num_rows={meta.num_rows}, num_row_groups={meta.num_row_groups}')\n\""
}<untrusted_context>
Output: train_listens.parquet: num_rows=43484159, num_row_groups=42
val_listens.parquet: num_rows=1501697, num_row_groups=2
val_likes.parquet: num_rows=37795, num_row_groups=1
val_dislikes.parquet: num_rows=3563, num_row_groups=1
Process Group PGID: 1899
</untrusted_context>Investigating Data Volumes
I'm currently examining the scale of our datasets. train_listens.parquet contains a significant 43.48 million entries, while val_listens.parquet holds 1.5 million. My next step is to explore the timestamp and user distributions within the training data, and then I'll turn my attention to target_users.parquet.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nfrom pathlib import Path\nimport pandas as pd\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\n\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nprint('Schema:', dataset.schema)\n\n# Let's inspect a sample or row group\nrg0 = dataset.get_fragment(0).to_table(columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ndf0 = rg0.to_pandas()\nprint('Sample rows:', len(df0))\nprint(df0.head())\nprint('Min ts:', df0.timestamp.min(), 'Max ts:', df0.timestamp.max())\nprint('Target users in rg0:', df0.uid.isin(targets).mean())\n\"",
"description": "Inspect schema and first row group of train_listens"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 13, in <module>
AttributeError: 'pyarrow._dataset.FileSystemDataset' object has no attribute 'get_fragment'. Did you mean: 'get_fragments'?
Schema: uid: uint32
timestamp: uint32
item_id: uint32
is_organic: uint8
played_ratio_pct: uint16
track_length_seconds: uint32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797
Exit Code: 1
Process Group PGID: 1910
</untrusted_context>{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nfrom pathlib import Path\nimport pandas as pd\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfrag0 = next(dataset.get_fragments())\ndf0 = frag0.to_table().to_pandas()\nprint('Frag0 rows:', len(df0))\nprint(df0.head())\nprint('Min ts:', df0.timestamp.min(), 'Max ts:', df0.timestamp.max())\nprint('Target users in frag0:', df0.uid.isin(targets).mean())\n\"",
"description": "Inspect first fragment of train_listens"
}<untrusted_context>
Output: Frag0 rows: 43484159
uid timestamp item_id is_organic played_ratio_pct track_length_seconds
0 100 39420 8326270 0 100 170
1 100 39420 1441281 0 100 105
2 100 39625 286361 0 100 185
3 100 40110 732449 0 100 240
4 100 40360 3397170 0 46 130
Min ts: 0 Max ts: 24786795
Target users in frag0: 0.9939779909276847
Process Group PGID: 1926
</untrusted_context>Investigating Dataset Size
I'm currently analyzing the train_listens.parquet file. It contains over 43 million rows, with a substantial 99.4% of those belonging to our target users. My next steps are to determine the total number of unique users and the timestamp unit used within this dataset.
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nval = pd.read_parquet('/task/data/val_listens.parquet')\nprint('Val listens min ts:', val.timestamp.min(), 'max ts:', val.timestamp.max())\nprint('Diff min - 24786800:', val.timestamp.min() - 24786800)\nprint('Diff max - min:', val.timestamp.max() - val.timestamp.min())\n\"",
"description": "Check validation timestamps"
}<untrusted_context>
Output: Val listens min ts: 24788600 max ts: 25393395
Diff min - 24786800: 1800
Diff max - min: 604795
Process Group PGID: 1946
</untrusted_context>Analyzing Timeframes
I'm currently focused on precisely calculating the 90-day window preceding the training cutoff, ensuring it aligns with the "users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels" criteria. This involves converting the provided timestamp in seconds to a more interpretable date range for accurate data subsetting.
Calculating Time Boundaries
My focus has shifted to establishing precise time boundaries. I've calculated that events occurring after timestamp 17,010,800 fall within the 90-day window leading up to the training cutoff, with even more recent 7-day and 30-day windows identified.
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state value counts:\\n', val_pref['state'].value_counts())\nprint('Number of unique users in pref state:', val_pref['uid'].nunique())\nprint('Number of unique items in pref state:', val_pref['item_id'].nunique())\n\n# Check how many of these items are eligible candidates for that user in validation_eligible_candidates!\nimport numpy as np\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nuid_to_row = {uid: i for i, uid in enumerate(val_elig['uids'])}\nitem_to_col = {item: i for i, item in enumerate(val_elig['item_ids'])}\npacked = val_elig['packed_eligible']\n\n# Sample 10000 rows\nsub = val_pref.sample(min(10000, len(val_pref)), random_state=42)\neligible_count = 0\nin_catalog_count = 0\nfor uid, item in zip(sub.uid, sub.item_id):\n if item in item_to_col:\n in_catalog_count += 1\n r = uid_to_row[uid]\n c = item_to_col[item]\n byte, bit = divmod(c, 8)\n if (packed[r, byte] >> bit) & 1:\n eligible_count += 1\nprint(f'Sample {len(sub)} pref states: in catalog = {in_catalog_count}, eligible = {eligible_count}')\n\"",
"description": "Check preference state eligibility"
}<untrusted_context>
Output: Val pref state value counts:
state
liked 418435
disliked 50689
Name: count, dtype: int64
Number of unique users in pref state: 7038
Number of unique items in pref state: 28814
Sample 10000 pref states: in catalog = 10000, eligible = 0
Process Group PGID: 1966
</untrusted_context>Refining Eligibility Logic
My current focus is on refining the eligibility criteria for track recommendations. I've observed that tracks already present in a user's preference state are being incorrectly considered as potential discoveries. This needs immediate correction, as the goal of "Made for You" is to surface novel content, not already-interacted-with items.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\npacked = val_elig['packed_eligible']\n\nuid0 = int(uids[0])\nrow0 = np.unpackbits(packed[0], count=30000, bitorder='little')\nineligible_items_0 = set(item_ids[row0 == 0])\nprint(f'User {uid0}: ineligible count = {len(ineligible_items_0)}')\n\n# Check user 0's history in train_listens, train_likes, train_dislikes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nu0_likes = set(likes[likes.uid == uid0].item_id)\nprint(f'User {uid0} likes count: {len(u0_likes)}, overlap with ineligible: {len(u0_likes & ineligible_items_0)}')\n\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nu0_pref = set(pref[pref.uid == uid0].item_id)\nprint(f'User {uid0} pref count: {len(u0_pref)}, overlap with ineligible: {len(u0_pref & ineligible_items_0)}')\n\"",
"description": "Check user 0 ineligible items overlap with history"
}<untrusted_context>
Output: User 100: ineligible count = 682
User 100 likes count: 19, overlap with ineligible: 13
User 100 pref count: 15, overlap with ineligible: 15
Process Group PGID: 1982
</untrusted_context>Refining
candidate filtering
I'm focusing on how the eligibility filter works. It's currently designed to automatically exclude items a user has already interacted with, whether positively or negatively. This ensures we're presenting fresh candidates from a catalog of approximately 30,000 items, with each user having around 29,000 of those potentially eligible for consideration.
Analyzing
user engagement patterns
I'm diving into the validation data, specifically the validation_truth to uncover what signals correlate with users organically listening to or liking tracks. My goal is to predict the top 100 tracks each user will engage with positively within a week. I'm examining past listening habits, explicit likes/dislikes, and current preference states.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Let's see: for users in truth, how many items are in truth?\nall_true_items = set()\nfor items in truth.values():\n all_true_items.update(items.keys())\nprint(f'Total unique items in validation truth across all {len(truth)} users: {len(all_true_items)}')\n\n# How popular are these items in candidate_catalog?\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet').set_index('item_id')\nranks = [cat.loc[i, 'popularity_rank'] for i in all_true_items if i in cat.index]\nprint(f'Popularity rank stats of true items: mean={np.mean(ranks):.1f}, median={np.median(ranks):.1f}, min={np.min(ranks)}, max={np.max(ranks)}')\nprint(f'Fraction in top 1000 pop: {np.mean([r <= 1000 for r in ranks]):.3f}')\nprint(f'Fraction in top 5000 pop: {np.mean([r <= 5000 for r in ranks]):.3f}')\nprint(f'Fraction in top 10000 pop: {np.mean([r <= 10000 for r in ranks]):.3f}')\n\"",
"description": "Analyze properties of items in validation truth"
}<untrusted_context>
Output: Total unique items in validation truth across all 4263 users: 14124
Popularity rank stats of true items: mean=11727.8, median=10202.5, min=1, max=30000
Fraction in top 1000 pop: 0.067
Fraction in top 5000 pop: 0.284
Fraction in top 10000 pop: 0.493
Process Group PGID: 1999
</untrusted_context>Analyzing Popularity Ranks
I've identified that true discovery items aren't solely concentrated in the absolute top popularity tiers. Their median rank is actually around 10,000, indicating a broader distribution across the entire catalog rather than just the top 1,000.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nmax_ts = 24786800\n\n# Let's count rows in last 30 days, 60 days, 90 days, 180 days\nday = 86400\nfor days in [7, 14, 30, 60, 90, 180, 270]:\n cutoff = max_ts - days * day\n count = dataset.count_rows(filter=pc.field('timestamp') >= cutoff)\n print(f'Last {days:3d} days (ts >= {cutoff}): {count} rows')\n\"",
"description": "Check row counts by recency in train_listens"
}<untrusted_context>
Output: Last 7 days (ts >= 24182000): 1491699 rows
Last 14 days (ts >= 23577200): 2920227 rows
Last 30 days (ts >= 22194800): 6126103 rows
Last 60 days (ts >= 19602800): 11718758 rows
Last 90 days (ts >= 17010800): 16769274 rows
Last 180 days (ts >= 9234800): 30187038 rows
Last 270 days (ts >= 1458800): 41623586 rows
Process Group PGID: 2019
</untrusted_context>Analyzing Listen Data
My current focus is on analyzing listen data, noting recent volumes: approximately 1.5 million rows in the last seven days, mirroring previous patterns. Over the last thirty days, this figure reaches 6.1 million, and a ninety-day span shows 16.8 million rows. My next step is to investigate the overlap between train_listens and candidate_catalog.
{
"description": "Check fraction of listens that are in candidate catalog",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = set(cat.item_id)\nprint('Catalog size:', len(cat_items))\n\ndataset = ds.dataset(DATA / 'train_listens.parquet')\n# check a recent sample of 100,000 rows\ntable = dataset.scanner(columns=['item_id', 'played_ratio_pct', 'is_organic'], limit=100000).to_table()\nitems_in_cat = pc.is_in(table['item_id'], value_set=pa.array(list(cat_items), type=pa.uint32())) if False else None\ndf = table.to_pandas()\nprint('Fraction of sample listens in catalog:', df.item_id.isin(cat_items).mean())\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 15, in <module>
File "pyarrow/_dataset.pyx", line 283, in pyarrow._dataset.Dataset.scanner
TypeError: scanner() got an unexpected keyword argument 'limit'
Catalog size: 30000
Exit Code: 1
Process Group PGID: 2040
</untrusted_context>{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = set(cat.item_id)\n\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfrag = next(dataset.get_fragments())\ntab = frag.to_table(columns=['item_id']).slice(0, 100000)\ndf = tab.to_pandas()\nprint('Fraction of sample listens in catalog:', df.item_id.isin(cat_items).mean())\n\"",
"description": "Check fraction of sample listens in catalog"
}<untrusted_context>
Output: Fraction of sample listens in catalog: 0.70876
Process Group PGID: 2057
</untrusted_context>{
"description": "Check all unique uids in train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\n\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nprint('Reading all unique uids from train_listens...')\nuids = dataset.to_table(columns=['uid'])\nunique_uids = set(uids['uid'].to_pandas().unique())\nprint(f'Total unique uids in train_listens: {len(unique_uids)}')\nprint(f'Are all targets in unique_uids: {targets.issubset(unique_uids)}')\nprint(f'Unique uids that are not targets: {len(unique_uids - targets)}')\n\""
}<untrusted_context>
Output: Reading all unique uids from train_listens...
Total unique uids in train_listens: 9238
Are all targets in unique_uids: True
Unique uids that are not targets: 782
Process Group PGID: 2076
</untrusted_context>{
"description": "Check played_ratio_pct and is_organic distributions in train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nds_listens = ds.dataset(DATA / 'train_listens.parquet')\ntab = ds_listens.scanner(columns=['played_ratio_pct', 'is_organic', 'track_length_seconds'], batch_size=1000000).to_table()\ndf = tab.to_pandas()\nprint('Played ratio quantiles:\\n', df['played_ratio_pct'].quantile([0.1, 0.25, 0.5, 0.75, 0.8, 0.9, 1.0]))\nprint('is_organic mean:', df['is_organic'].mean())\nprint('Listens >= 80% ratio:', (df['played_ratio_pct'] >= 80).mean())\nprint('Organic & >= 80% ratio:', ((df['is_organic'] == 1) & (df['played_ratio_pct'] >= 80)).mean())\n\""
}<untrusted_context>
Output: Played ratio quantiles:
0.10 1.0
0.25 7.0
0.50 100.0
0.75 100.0
0.80 100.0
0.90 100.0
1.00 159.0
Name: played_ratio_pct, dtype: float64
is_organic mean: 0.5169151828370419
Listens >= 80% ratio: 0.5922891828263254
Organic & >= 80% ratio: 0.2800464187429726
Process Group PGID: 2096
</untrusted_context>Benchmarking Sparse Matrix Loading
I'm currently exploring the efficiency of loading train_listens into a SciPy sparse matrix, alongside methods for filtering it by recency and quality. My focus is on identifying the fastest approach through direct benchmarking.
{
"description": "Benchmark reading filtered train_listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ndataset = ds.dataset('/task/data/train_listens.parquet')\n# Filter for organic >= 80% or played >= 80% in the last 90 days\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read {len(table)} rows in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Read 10582861 rows in 0.46s
Process Group PGID: 2123
</untrusted_context>Testing PyArrow Speed
I'm impressed by PyArrow's speed, especially with C++ pushdown filtering; 10.58 million rows in just 0.46 seconds is remarkable. My next step is to evaluate performance when reading all rows with specific conditions (played_ratio_pct >= 50 or organic >= 80%) across the entire training set.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ndataset = ds.dataset('/task/data/train_listens.parquet')\nfilt = (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read all positive listens: {len(table)} rows in {time.time()-t0:.2f}s')\n\"",
"description": "Benchmark reading all positive listens in train_listens"
}<untrusted_context>
Output: Read all positive listens: 27500755 rows in 0.47s
Process Group PGID: 2145
</untrusted_context>Confirming Computational Capacity
My recent analysis confirms a robust 27.5 million rows processed in just 0.47 seconds. This indicates ample computational power and swift I/O for sophisticated model execution. The next step involves scrutinizing the public validation evaluation methodology against val_* labels.
{
"strategic_intent": "Design and validate collaborative filtering and ranking models on the public validation split.",
"title": "Collaborative Filtering & Recommendation Modeling Experiments",
"summary": "We explored the data, confirmed the target user set (8,456 users) and item catalog (30,000 items), benchmarked I/O speeds, and created a validated baseline submission. Now we are beginning modeling experiments: comparing Matrix Factorization, Item-Item Cosine/BM25/TF-IDF CF, EASE (Linear Model with L2 regularization), and preference-state features against the public validation benchmark."
}Current topic: "Collaborative Filtering & Recommendation Modeling Experiments"
Topic summary: We explored the data, confirmed the target user set (8,456 users) and item catalog (30,000 items), benchmarked I/O speeds, and created a validated baseline submission. Now we are beginning modeling experiments: comparing Matrix Factorization, Item-Item Cosine/BM25/TF-IDF CF, EASE (Linear Model with L2 regularization), and preference-state features against the public validation benchmark.
Strategic Intent: Design and validate collaborative filtering and ranking models on the public validation split.Analyzing Explicit Signals
I'm currently focused on understanding the explicit user feedback. The data shows a significant volume of explicitly liked items, with a much smaller, but still notable, number of disliked items, which I will explore further.
Incorporating User Signals
I'm now considering how explicit likes affect discovery; liked items are out, but similar ones are prime candidates. I'm also digging into listen data, noting play ratios, organic vs. recommendation sources, and time weighting. Negative feedback, like dislikes, is being analyzed to penalize similar items.
Evaluating Model Efficiency
I've been assessing the computational feasibility of different recommendation models. While item-item similarity matrices are straightforward, they are computationally demanding. EASE$^R$ presents an interesting challenge; item-based inversion is prohibitive, but user-based computation is quite efficient, as is low-rank SVD.
{
"description": "Check mapping setup speed",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\n\n# Load targets and catalog\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\n\nprint(f'Setup {len(uids)} users, {len(items)} items in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Setup 8456 users, 30000 items in 0.04s
Process Group PGID: 2167
</untrusted_context>Constructing Interaction Matrix
I'm currently focused on generating an interaction matrix from the training data. Specifically, I'm analyzing the 'likes' data, considering it a strong positive signal and assigning it a significant weight, perhaps 5.0 to 10.0.
Developing Interaction Weights
I'm refining the interaction matrix by assigning precise weights. Likes are confirmed as a high-value signal. Listens will be weighted based on recency, completion percentage, and organic vs. non-organic status. I'm also considering a strong negative weight for explicit dislikes. The next step is to implement these weights and test their impact.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\n\n# Target users and catalog items\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\n\nn_users = len(uids)\nn_items = len(items)\n\n# 1. Likes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\n# Filter for targets and catalog\nlikes_target = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nprint(f'Likes in catalog for target users: {len(likes_target)}')\n\n# 2. Preference state (liked)\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\nprint(f'Pref liked in catalog for target users: {len(pref_liked)}')\n\n# 3. Recent listens (last 90 days)\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\nprint(f'Recent listens in catalog: {len(df_listens)}')\nprint(f'Loaded all in {time.time()-t0:.2f}s')\n\"",
"description": "Test loading and filtering interaction data"
}<untrusted_context>
Output: Likes in catalog for target users: 472889
Pref liked in catalog for target users: 418435
Recent listens in catalog: 7752815
Loaded all in 3.40s
Process Group PGID: 2191
</untrusted_context>Weighting Interaction Data
I'm currently exploring methods to weight user-item interactions, specifically focusing on 'listens'. My approach involves recency weighting, calculating the time elapsed in days since the event relative to a maximum timestamp.
Refining Interaction Weighting
I'm refining interaction weights by incorporating recency and completion metrics for "listens," using exponential decay with a 30-day half-life and a base weight adjusted by listening ratio. Likes are assigned a high fixed weight. I'll then build a sparse matrix and test User-based CF and EASE models.
Testing EASE & Item-Item
I've confirmed the user-based EASE implementation using matrix inversion. Now, I'm exploring the relationship between user-user and item-item similarity matrices, noting how their score calculations can be equivalent. I'm also investigating efficient computation for normalized item-item cosine similarity.
{
"description": "Test TruncatedSVD on validation set",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# 1. Target users and items\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\n\nn_users = len(uids)\nn_items = len(items)\n\n# 2. Validation truth & eligibility\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# 3. Load events\nprint('Loading events...')\n# Likes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\n\n# Pref\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\n# Listens (last 90 days)\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n\n# Construct weights\nu_idx_list = []\ni_idx_list = []\nw_list = []\n\n# Likes: weight 5.0\nfor uid, item in zip(likes.uid, likes.item_id):\n u_idx_list.append(uid_to_idx[uid])\n i_idx_list.append(item_to_idx[item])\n w_list.append(5.0)\n\n# Pref liked: weight 5.0\nfor uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_idx_list.append(uid_to_idx[uid])\n i_idx_list.append(item_to_idx[item])\n w_list.append(5.0)\n\n# Listens: recency + completion\nt_max = 24786800\ndt_days = (t_max - df_listens.timestamp.values) / 86400.0\ndecay = np.exp(-dt_days / 30.0)\nbase = np.where(df_listens.is_organic == 1, 2.0, 1.0)\nlisten_weights = base * decay\n\nfor uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_weights):\n u_idx_list.append(uid_to_idx[uid])\n i_idx_list.append(item_to_idx[item])\n w_list.append(w)\n\nX = sp.csr_matrix((w_list, (u_idx_list, i_idx_list)), shape=(n_users, n_items), dtype=np.float32)\nprint(f'Matrix X constructed: nnz={X.nnz}, shape={X.shape}')\n\n# Test 1: SVD / TruncatedSVD\nfrom sklearn.decomposition import TruncatedSVD\nfor n_comp in [50, 100, 200]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U_sigma = svd.fit_transform(X) # (8456, n_comp)\n VT = svd.components_ # (n_comp, 30000)\n scores = U_sigma @ VT # (8456, 30000)\n \n # Evaluate\n # For each user, get top 100 eligible items\n # Mask ineligible by setting to -inf\n scores_masked = np.where(mask == 1, scores, -np.inf)\n \n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n # top 100\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n \n s = score(ranked, graded)\n print(f'TruncatedSVD({n_comp}): {s}, time={time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Loading events...
Matrix X constructed: nnz=3148790, shape=(8456, 30000)
TruncatedSVD(50): {'quality_ndcg@10': 0.024506, 'quality_users': 4263}, time=3.29s
TruncatedSVD(100): {'quality_ndcg@10': 0.02146, 'quality_users': 4263}, time=3.92s
TruncatedSVD(200): {'quality_ndcg@10': 0.020177, 'quality_users': 4263}, time=5.30s
Process Group PGID: 2220
</untrusted_context>Exploring Collaborative Filtering Methods
My recent focus has been on exploring collaborative filtering techniques. TruncatedSVD showed impressive gains, more than doubling the popularity baseline. I'm now pivoting to investigate User-based CF and Item-Item CF, alongside EASE, BM25/TFIDF weighting, and the impact of negative feedback, starting with User-based Cosine CF and BM25 CF.
{
"description": "Test User-CF and User-EASE on validation set",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport scipy.linalg as la\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# Target users and items\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\n\nn_users = len(uids)\nn_items = len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# Likes & Pref\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\n# Listens (last 90 days)\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n\nu_idx_list, i_idx_list, w_list = [], [], []\n\nfor uid, item in zip(likes.uid, likes.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\n\nfor uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\n\nt_max = 24786800\ndt_days = (t_max - df_listens.timestamp.values) / 86400.0\nlisten_weights = np.where(df_listens.is_organic == 1, 2.0, 1.0) * np.exp(-dt_days / 30.0)\nfor uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_weights):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(w)\n\nX = sp.csr_matrix((w_list, (u_idx_list, i_idx_list)), shape=(n_users, n_items), dtype=np.float32)\n\ndef evaluate_scores(scores, name):\n scores_masked = np.where(mask == 1, scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'{name}: {s}')\n return scores\n\n# 1. User-based Cosine CF\nt0 = time.time()\nX_norm = normalize(X, norm='l2', axis=1) # shape (8456, 30000)\n# User similarity\nS_user = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_user, 0)\n# Optional power\nfor power in [1.0, 2.0, 3.0]:\n if power == 1.0:\n S = S_user\n else:\n S = np.sign(S_user) * np.abs(S_user) ** power\n scores = S @ X.toarray()\n evaluate_scores(scores, f'User-CF cosine power={power}')\nprint(f'User-CF finished in {time.time()-t0:.2f}s')\n\n# 2. User-EASE\nt0 = time.time()\nG = (X @ X.T).toarray() # (8456, 8456)\nfor reg in [100.0, 500.0, 1000.0, 2000.0]:\n G_reg = G.copy()\n G_reg[np.diag_indices_from(G_reg)] += reg\n P = la.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0)\n scores = B @ X.toarray()\n evaluate_scores(scores, f'User-EASE reg={reg}')\nprint(f'User-EASE finished in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: User-CF cosine power=1.0: {'quality_ndcg@10': 0.022527, 'quality_users': 4263}
User-CF cosine power=2.0: {'quality_ndcg@10': 0.028902, 'quality_users': 4263}
User-CF cosine power=3.0: {'quality_ndcg@10': 0.029033, 'quality_users': 4263}
User-CF finished in 67.73s
User-EASE reg=100.0: {'quality_ndcg@10': 0.009281, 'quality_users': 4263}
User-EASE reg=500.0: {'quality_ndcg@10': 0.011472, 'quality_users': 4263}
User-EASE reg=1000.0: {'quality_ndcg@10': 0.012565, 'quality_users': 4263}
User-EASE reg=2000.0: {'quality_ndcg@10': 0.013533, 'quality_users': 4263}
User-EASE finished in 121.20s
Process Group PGID: 2249
</untrusted_context>Evaluating User-CF Performance
I'm seeing some promising results with User-CF, particularly with power values of 2.0 and 3.0, yielding scores around 0.029. The current implementation uses a specific similarity matrix calculation and applies a power transformation to it. I'm now considering exploring Item-Item CF as an alternative approach to see how it compares.
Investigating Item-Item CF
I'm now exploring Item-Item Collaborative Filtering. This approach leverages item-user interaction vectors and calculates item similarity based on shared user engagement. I'm assessing its computational speed and performance.
Refining Similarity Calculations
I'm realizing the mask applied to user-interacted items makes the diagonal of the similarity matrix irrelevant. Even if an interacted item were eligible, subtracting its diagonal contribution would nullify it. The matrix multiplication $X X_{\text{col\_norm}}^T X_{\text{col\_norm}}$ is computationally efficient, completing in just one second.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nprint('Catalog positive_listens quantiles:\\n', cat['positive_listens'].quantile([0.1, 0.25, 0.5, 0.75, 0.9, 0.99, 1.0]))\n\"",
"description": "Check distribution of listens per item in candidate catalog"
}<untrusted_context>
Output: Catalog positive_listens quantiles:
0.10 185.00
0.25 228.00
0.50 354.00
0.75 669.25
0.90 1367.00
0.99 5052.16
1.00 26615.00
Name: positive_listens, dtype: float64
Process Group PGID: 2278
</untrusted_context>Exploring Item-Item CF
I'm currently investigating the impact of incorporating non-target users' listening data into our Item-Item Collaborative Filtering model's training set. This approach deviates from the current methodology and I'm curious to see how it affects our recommendation quality, especially considering the substantial positive listening engagement we're observing across items.
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nA = np.random.randn(2000, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\n\nt0 = time.time()\nC = A @ B\nprint(f'Batch matmul 2000x8456 by 8456x30000 took {time.time()-t0:.3f}s')\n\n# Top 100 per row\nt1 = time.time()\nidx = np.argpartition(-C, 100, axis=1)[:, :100]\nprint(f'Top 100 per row for 2000 items took {time.time()-t1:.3f}s')\n\"",
"description": "Benchmark batch item-item similarity and top-100 partition"
}<untrusted_context>
Output: Batch matmul 2000x8456 by 8456x30000 took 4.778s
Top 100 per row for 2000 items took 0.309s
Process Group PGID: 2302
</untrusted_context>Exploring Sparse Operations
I've been analyzing the performance of batch matrix multiplication with dense matrices, but a crucial realization has emerged: my matrices are actually sparse. This changes everything, as the standard @ operation on SciPy CSR matrices inherently optimizes for sparsity, only calculating non-zero elements. This fundamental shift will dramatically alter my approach and expected performance.
{
"command": "python3 -c \"\nimport scipy.sparse as sp\nimport numpy as np\nimport time\n\nX = sp.random(8456, 30000, density=0.012, format='csr', dtype=np.float32)\nt0 = time.time()\nXT = X.T.tocsr()\nprint('Transpose took:', time.time()-t0)\n\nt0 = time.time()\n# compute 2000 rows of XT @ X\nchunk = XT[:2000] @ X\nprint('Chunk 2000 sparse matmul took:', time.time()-t0, 'nnz in chunk:', chunk.nnz)\n\"",
"description": "Benchmark sparse matrix multiplication for item similarities"
}<untrusted_context>
Output: Transpose took: 0.022045135498046875
Chunk 2000 sparse matmul took: 0.9425482749938965 nnz in chunk: 42253861
Process Group PGID: 2319
</untrusted_context>Analyzing User-CF
I'm currently exploring the User-CF approach, specifically noting the size of the $S_{user}$ matrix ($8456 \times 8456$, approximately 285 MB). I'm also considering the potential benefits of retaining only the top-K nearest neighbors for each user within this framework.
Refining User-CF
I'm now focusing on optimizing the User-CF approach. I'm investigating the impact of limiting neighbors to top-K users and exploring different event weighting strategies to enhance recommendations, especially considering the significance of organic listens for discovery relevance.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet\n\nDATA = Path('/task/data')\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet').uid.astype(int))\ntruth = validation_truth(elig, targets)\n\n# Let's inspect users in truth and see what predicts validation items\n# Look at a sample of users\nsample_uids = list(truth.keys())[:50]\nsample_items = {uid: set(items.keys()) for uid, items in truth.items() if uid in sample_uids}\n\n# Check: are validation items from artists the user has listened to before?\n# Wait! Do we have artist metadata?\n# Let's check if there is any metadata file!\n\"",
"description": "Check if there are metadata files"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2336
</untrusted_context>Identifying Interaction Data
I'm focusing on cataloging the available interaction data. Currently, my attention is on the .parquet files, specifically train_listens, train_likes, train_dislikes, train_unlikes, and train_undislikes. I'm confirming that these are indeed the only interaction tables present, with no associated metadata files.
Analyzing Interaction Datasets
I've identified additional .parquet files: validation_preference_state, test_preference_state, candidate_catalog, and target_users. My current focus shifts to user-based collaborative filtering (User-CF). I'm investigating tuning the user similarity metric, specifically exploring BM25/IDF weighting to better capture nuanced taste rather than just popular items.
{
"description": "Test User-CF with IDF weighting on validation set",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# Target users and items\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\n\nn_users = len(uids)\nn_items = len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# Likes & Pref\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\n# Listens (last 90 days)\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n\nu_idx_list, i_idx_list, w_list = [], [], []\n\nfor uid, item in zip(likes.uid, likes.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\n\nfor uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\n\nt_max = 24786800\ndt_days = (t_max - df_listens.timestamp.values) / 86400.0\nlisten_weights = np.where(df_listens.is_organic == 1, 2.0, 1.0) * np.exp(-dt_days / 30.0)\nfor uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_weights):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(w)\n\nX = sp.csr_matrix((w_list, (u_idx_list, i_idx_list)), shape=(n_users, n_items), dtype=np.float32)\n\n# Compute item document frequencies (how many users interacted with each item)\ndf = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\n# BM25 IDF: log((N - df + 0.5) / (df + 0.5) + 1.0)\nidf = np.log((n_users - df + 0.5) / (df + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\n\n# Apply IDF to X\n# X_idf = X * diag(idf)\nX_idf = X.multiply(idf).tocsr()\n\ndef eval_matrix(scores, desc):\n scores_masked = np.where(mask == 1, scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'{desc}: {s}')\n\n# Test Cosine with IDF\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_user, 0)\n\nfor power in [1.0, 2.0, 3.0, 4.0]:\n S = np.sign(S_user) * (np.abs(S_user) ** power)\n scores = S @ X.toarray()\n eval_matrix(scores, f'User-CF with IDF, power={power}')\n\""
}<untrusted_context>
Output: User-CF with IDF, power=1.0: {'quality_ndcg@10': 0.025017, 'quality_users': 4263}
User-CF with IDF, power=2.0: {'quality_ndcg@10': 0.030119, 'quality_users': 4263}
User-CF with IDF, power=3.0: {'quality_ndcg@10': 0.027531, 'quality_users': 4263}
User-CF with IDF, power=4.0: {'quality_ndcg@10': 0.023131, 'quality_users': 4263}
Process Group PGID: 2356
</untrusted_context>Exploring Top-K Neighbors
I'm currently investigating the impact of limiting recommendations to the top-K nearest neighbors in User-CF. My initial User-CF with IDF, power=2.0 yielded a value of 0.030119, and I'm now exploring how this metric shifts with varying K values (50, 100, 200, 500, 1000).
{
"description": "Test top-K neighbor filtering in User-CF",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\nimport pandas as pd\n\n# Load saved test from previous step or recompute S_user quickly\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n\nu_idx_list, i_idx_list, w_list = [], [], []\nfor uid, item in zip(likes.uid, likes.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\nfor uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\nt_max = 24786800\ndt_days = (t_max - df_listens.timestamp.values) / 86400.0\nlisten_weights = np.where(df_listens.is_organic == 1, 2.0, 1.0) * np.exp(-dt_days / 30.0)\nfor uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_weights):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(w)\n\nX = sp.csr_matrix((w_list, (u_idx_list, i_idx_list)), shape=(n_users, n_items), dtype=np.float32)\ndf = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\nidf = np.log((n_users - df + 0.5) / (df + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\nX_idf = X.multiply(idf).tocsr()\n\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_user, 0)\n\ndef eval_matrix(scores, desc):\n scores_masked = np.where(mask == 1, scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'{desc}: {s}')\n\nfor K in [100, 200, 300, 500]:\n # Keep only top K per row\n S_topk = np.zeros_like(S_user)\n row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\n for r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = S_user[r, cols]\n \n for power in [1.5, 2.0]:\n S = (S_topk ** power)\n scores = S @ X.toarray()\n eval_matrix(scores, f'Top-K={K}, power={power}')\n\""
}<untrusted_context>
Output: Top-K=100, power=1.5: {'quality_ndcg@10': 0.030883, 'quality_users': 4263}
Top-K=100, power=2.0: {'quality_ndcg@10': 0.029248, 'quality_users': 4263}
Top-K=200, power=1.5: {'quality_ndcg@10': 0.032103, 'quality_users': 4263}
Top-K=200, power=2.0: {'quality_ndcg@10': 0.030905, 'quality_users': 4263}
Top-K=300, power=1.5: {'quality_ndcg@10': 0.031681, 'quality_users': 4263}
Top-K=300, power=2.0: {'quality_ndcg@10': 0.031029, 'quality_users': 4263}
Top-K=500, power=1.5: {'quality_ndcg@10': 0.03052, 'quality_users': 4263}
Top-K=500, power=2.0: {'quality_ndcg@10': 0.03084, 'quality_users': 4263}
Process Group PGID: 2385
</untrusted_context>Testing Hyperparameters Now
I'm currently exploring how different power values impact performance, focusing on those around 1.2, 1.4, and 1.5. I'm also investigating alternative scoring methods beyond raw interaction data to refine the approach. The initial 0.032103 result with K=200 and power=1.5 is quite promising!
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\nimport pandas as pd\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\ndataset = ds.dataset(DATA / 'train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n\nu_idx_list, i_idx_list, w_list = [], [], []\nfor uid, item in zip(likes.uid, likes.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\nfor uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(5.0)\nt_max = 24786800\ndt_days = (t_max - df_listens.timestamp.values) / 86400.0\nlisten_weights = np.where(df_listens.is_organic == 1, 2.0, 1.0) * np.exp(-dt_days / 30.0)\nfor uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_weights):\n u_idx_list.append(uid_to_idx[uid]); i_idx_list.append(item_to_idx[item]); w_list.append(w)\n\nX = sp.csr_matrix((w_list, (u_idx_list, i_idx_list)), shape=(n_users, n_items), dtype=np.float32)\ndf = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\nidf = np.log((n_users - df + 0.5) / (df + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\nX_idf = X.multiply(idf).tocsr()\n\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_user, 0)\n\nK = 200\nS_topk = np.zeros_like(S_user)\nrow_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\nfor r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = S_user[r, cols]\n\nS = (S_topk ** 1.5)\nbase_scores = S @ X.toarray()\n\n# Item popularity counts\nitem_pop = np.array(X.sum(axis=0)).ravel() + 1.0\n\ndef eval_matrix(scores, desc):\n scores_masked = np.where(mask == 1, scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'{desc}: {s}')\n\neval_matrix(base_scores, 'Base scores (gamma=0)')\nfor gamma in [0.05, 0.1, 0.2, 0.3, 0.5]:\n scores = base_scores / (item_pop ** gamma)\n eval_matrix(scores, f'gamma={gamma}')\n\"",
"description": "Test item popularity damping on User-CF scores"
}<untrusted_context>
Output: Base scores (gamma=0): {'quality_ndcg@10': 0.032103, 'quality_users': 4263}
gamma=0.05: {'quality_ndcg@10': 0.032614, 'quality_users': 4263}
gamma=0.1: {'quality_ndcg@10': 0.032888, 'quality_users': 4263}
gamma=0.2: {'quality_ndcg@10': 0.032417, 'quality_users': 4263}
gamma=0.3: {'quality_ndcg@10': 0.029914, 'quality_users': 4263}
gamma=0.5: {'quality_ndcg@10': 0.018475, 'quality_users': 4263}
Process Group PGID: 2414
</untrusted_context>Exploring Negative Interactions
I'm now considering how to integrate negative interaction data, specifically looking into "dislikes" and "unlikes" to enrich the model's understanding. This adds another layer to the learning process.
Integrating Negative Feedback
I'm now exploring how to leverage negative interactions, like "dislikes," to refine recommendations. The idea is to assign negative weights within the feature matrix $X$, effectively penalizing items that similar users have disliked. This should naturally reduce their prominence in recommendations.
Expanding Historical Data
I'm now contemplating the full user history beyond the last 90 days, considering an exponential time decay. This will give older, less recent listens a weighted contribution, ensuring richer profiles for less active users while prioritizing recent engagement. I'm also thinking about weighting organic listens more heavily.
Implementing Item-Item CF
I'm now focused on implementing and testing Item-Item Collaborative Filtering. My current line of thought is on efficiently computing the item similarity matrix and exploring matrix operations like $X S_{\text{item}}$ to blend it with existing User-CF approaches. I believe this can significantly enhance recommendation quality.
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\n\nX = sp.random(8456, 30000, density=0.015, format='csr', dtype=np.float32)\nVT = normalize(X.T, norm='l2', axis=1).T # (30000, 8456)\n\nt0 = time.time()\nM = X @ VT # (8456, 8456)\nprint(f'Step 1: X @ VT took {time.time()-t0:.3f}s')\n\nt0 = time.time()\n# M is shape (8456, 8456), VT.T is shape (8456, 30000)\n# Convert M to dense float32\nM_dense = M.toarray() if sp.issparse(M) else M\nscores = M_dense @ VT.T.toarray()\nprint(f'Step 2: M @ V took {time.time()-t0:.3f}s, scores shape={scores.shape}')\n\"",
"description": "Benchmark fast item-item matrix multiplication via associativity"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 11, in <module>
File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py", line 732, in __matmul__
return self._matmul_dispatch(other)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py", line 633, in _matmul_dispatch
raise ValueError(
ValueError: matmul: dimension mismatch with signature (n,k=30000),(k=8456,m)->(n,m)
Exit Code: 1
Process Group PGID: 2443
</untrusted_context>Analyzing Matrix Dimensions
My current focus is on understanding the dimensional implications of our operations. I'm examining how the initial $(N, M)$ shape of $X$ transforms through transposition and normalization, resulting in a $(30000, 8456)$ matrix for $V$. This directly impacts the final similarity matrix $S$.
{
"description": "Verify exact dimension match for fast Item-Item CF",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\n\nX = sp.random(8456, 30000, density=0.015, format='csr', dtype=np.float32)\nV = normalize(X.T.tocsr(), norm='l2', axis=1) # (30000, 8456)\n\nt0 = time.time()\nXV = (X @ V).toarray() # (8456, 8456)\nprint(f'Step 1: X @ V took {time.time()-t0:.3f}s')\n\nt0 = time.time()\nVT = V.T.toarray() # (8456, 30000)\nscores = XV @ VT # (8456, 30000)\nprint(f'Step 2: XV @ VT took {time.time()-t0:.3f}s, scores shape={scores.shape}')\n\""
}<untrusted_context>
Output: Step 1: X @ V took 2.779s
Step 2: XV @ VT took 19.801s, scores shape=(8456, 30000)
Process Group PGID: 2461
</untrusted_context>Normalizing Item Vectors
I'm currently exploring the implications of normalizing the transposed input matrix $X$. Specifically, I'm focusing on how this operation, represented as $V = \text{normalize}(X^T, \text{norm}='l2', \text{axis}=1)$, defines $V$ as a matrix where each row corresponds to a normalized item vector, effectively dividing each item's vector by its L2 norm. This is equivalent to multiplying $X^T$ by a diagonal matrix $D_{\text{item}}^{-1}$.
Analyzing Item-Item Relationships
I'm analyzing how normalizing item vectors impacts user-user interaction matrices. This normalization essentially weights item co-occurrences inversely by their L2 norms, creating an IDF-like effect where rarer items are upweighted. This suggests a direct link between item-item collaborative filtering and user-based methods with specific inverse-norm weighting.
Experimenting with Temporal Dynamics
I'm now investigating the influence of various time decay factors, specifically $\tau$ values ranging from 15 to 180 days. Concurrently, I'm refining event weighting strategies, considering distinctions between organic and recommendation-based likes, active versus passive preferences, and exploring the impact of engagement thresholds like 80% completion. I'm also evaluating different user similarity metrics, including BM25, IDF, and cosine, with a focus on logarithmic or sublinear transformations for interaction counts to manage heavy-tailed distributions and stabilize variance.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_to_idx = {item: i for i, item in enumerate(items)}\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# Test different history cutoffs and sublinear TF\nfor history_days in [60, 90, 180]:\n t0 = time.time()\n cutoff = 24786800 - history_days * 86400\n dataset = ds.dataset(DATA / 'train_listens.parquet')\n filt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\n table = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n df_listens = table.to_pandas()\n df_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\n \n # Likes & pref\n likes = pd.read_parquet(DATA / 'train_likes.parquet')\n likes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\n pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\n pref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n \n u_list, i_list, w_list = [], [], []\n for uid, item, org in zip(likes.uid, likes.item_id, likes.is_organic):\n w = 6.0 if org == 1 else 4.0\n u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(w)\n for uid, item in zip(pref_liked.uid, pref_liked.item_id):\n u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(5.0)\n \n t_max = 24786800\n dt_days = (t_max - df_listens.timestamp.values) / 86400.0\n decay = np.exp(-dt_days / 45.0)\n base = np.where(df_listens.is_organic == 1, 2.5, 1.0)\n listen_w = base * decay\n for uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_w):\n u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(w)\n \n # Aggregate duplicate (user, item) by summing weights\n # scipy.sparse coo_matrix automatically sums duplicate entries!\n X = sp.csr_matrix((w_list, (u_list, i_list)), shape=(n_users, n_items), dtype=np.float32)\n # Sublinear scaling: log1p(X)\n X.data = np.log1p(X.data)\n \n df_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\n idf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)\n idf = np.maximum(idf, 0.0)\n X_idf = X.multiply(idf).tocsr()\n \n X_norm = normalize(X_idf, norm='l2', axis=1)\n S_user = (X_norm @ X_norm.T).toarray()\n np.fill_diagonal(S_user, 0)\n \n K = 200\n S_topk = np.zeros_like(S_user)\n row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\n for r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)\n \n S = (S_topk ** 1.5)\n scores = S @ X.toarray()\n \n item_pop = np.array(X.sum(axis=0)).ravel() + 1.0\n for gamma in [0.08, 0.12, 0.16]:\n adj_scores = scores / (item_pop ** gamma)\n scores_masked = np.where(mask == 1, adj_scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'days={history_days}, gamma={gamma}: {s}')\n\"",
"description": "Test history window, sublinear scaling, and gamma"
}<untrusted_context>
Output: /usr/bin/bash: line 100: 2481 Killed python3 -c "
import pyarrow.dataset as ds
import pyarrow.compute as pc
import pandas as pd
import numpy as np
import scipy.sparse as sp
from sklearn.preprocessing import normalize
import time
from pathlib import Path
import sys
sys.path.insert(0, '/task')
from evaluate_public import validation_truth, CandidateSet, score
DATA = Path('/task/data')
targets = pd.read_parquet(DATA / 'target_users.parquet')
uids = targets['uid'].values
uid_to_idx = {uid: i for i, uid in enumerate(uids)}
catalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')
items = catalog['item_id'].values
item_to_idx = {item: i for i, item in enumerate(items)}
n_users, n_items = len(uids), len(items)
elig = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = validation_truth(elig, set(uids))
val_elig = np.load(DATA / 'validation_eligible_candidates.npz')
mask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')
# Test different history cutoffs and sublinear TF
for history_days in [60, 90, 180]:
t0 = time.time()
cutoff = 24786800 - history_days * 86400
dataset = ds.dataset(DATA / 'train_listens.parquet')
filt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)
table = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
df_listens = table.to_pandas()
df_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]
# Likes & pref
likes = pd.read_parquet(DATA / 'train_likes.parquet')
likes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]
pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')
pref_liked = pref[(pref.state == 'liked') & pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]
u_list, i_list, w_list = [], [], []
for uid, item, org in zip(likes.uid, likes.item_id, likes.is_organic):
w = 6.0 if org == 1 else 4.0
u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(w)
for uid, item in zip(pref_liked.uid, pref_liked.item_id):
u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(5.0)
t_max = 24786800
dt_days = (t_max - df_listens.timestamp.values) / 86400.0
decay = np.exp(-dt_days / 45.0)
base = np.where(df_listens.is_organic == 1, 2.5, 1.0)
listen_w = base * decay
for uid, item, w in zip(df_listens.uid, df_listens.item_id, listen_w):
u_list.append(uid_to_idx[uid]); i_list.append(item_to_idx[item]); w_list.append(w)
# Aggregate duplicate (user, item) by summing weights
# scipy.sparse coo_matrix automatically sums duplicate entries!
X = sp.csr_matrix((w_list, (u_list, i_list)), shape=(n_users, n_items), dtype=np.float32)
# Sublinear scaling: log1p(X)
X.data = np.log1p(X.data)
df_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)
idf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)
idf = np.maximum(idf, 0.0)
X_idf = X.multiply(idf).tocsr()
X_norm = normalize(X_idf, norm='l2', axis=1)
S_user = (X_norm @ X_norm.T).toarray()
np.fill_diagonal(S_user, 0)
K = 200
S_topk = np.zeros_like(S_user)
row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]
for r in range(n_users):
cols = row_topk[r]
S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)
S = (S_topk ** 1.5)
scores = S @ X.toarray()
item_pop = np.array(X.sum(axis=0)).ravel() + 1.0
for gamma in [0.08, 0.12, 0.16]:
adj_scores = scores / (item_pop ** gamma)
scores_masked = np.where(mask == 1, adj_scores, -np.inf)
ranked = {}
for r in range(n_users):
user_scores = scores_masked[r]
top100_idx = np.argpartition(-user_scores, 100)[:100]
top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]
ranked[int(uids[r])] = items[top100_idx].tolist()
s = score(ranked, graded)
print(f'days={history_days}, gamma={gamma}: {s}')
"
Exit Code: 137
Process Group PGID: 2479
</untrusted_context>Investigating Memory Usage
I'm currently pinpointing the source of the Out of Memory error. My analysis reveals the matrix multiplication S @ X.toarray() is the primary culprit, consuming over 1.5 GB of memory due to the large dimensions of both matrices.
Refining Memory Management
I've identified that keeping multiple large, dense arrays in memory simultaneously, coupled with the substantial size of the df_listens DataFrame, is triggering the Out of Memory errors. Optimizing matrix types and considering batch processing for the DataFrame should resolve this.
{
"description": "Test fast memory-efficient loading of listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport gc\nimport time\n\nDATA = '/task/data'\ntargets = pd.read_parquet(f'{DATA}/target_users.parquet')\nuids = targets['uid'].values\nuid_set = set(uids)\n\ncatalog = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitems = catalog['item_id'].values\nitem_set = set(items)\n\nt0 = time.time()\ndataset = ds.dataset(f'{DATA}/train_listens.parquet')\ncutoff_90d = 24786800 - 90 * 86400\nfilt = (pc.field('timestamp') >= cutoff_90d) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Loaded {len(table)} rows in {time.time()-t0:.2f}s')\n\n# Convert to numpy arrays directly or filtered\nu_arr = table['uid'].to_numpy()\ni_arr = table['item_id'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\npct_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\ndel table\ngc.collect()\nprint(f'Arrays extracted in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Loaded 10582861 rows in 0.58s
Arrays extracted in 0.68s
Process Group PGID: 2508
</untrusted_context>Optimizing Data Structures
I've successfully extracted the user and item data in under a second, keeping memory usage remarkably low. My current focus is on refining the mapping between raw IDs and internal indices, ensuring efficient lookups for the 8456 target users and 30000 catalog items.
{
"description": "Check if uids and items are sorted",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nDATA = '/task/data'\ntargets = pd.read_parquet(f'{DATA}/target_users.parquet')\nuids = targets['uid'].values\nprint('Are uids sorted?', np.all(np.diff(uids) > 0))\n\ncat = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitems = cat['item_id'].values\nprint('Are items sorted?', np.all(np.diff(items) > 0))\n\""
}<untrusted_context>
Output: Are uids sorted? True
Are items sorted? True
Process Group PGID: 2537
</untrusted_context>Optimizing Search Operations
I'm focusing on leveraging the sorted nature of both uids and items. My current investigation centers on the efficiency of np.searchsorted, which appears to be a remarkably fast, $O(\log N)$ vectorized operation. Initial tests show it handles 10 million elements in approximately 0.05 seconds, and I'm now verifying this performance.
{
"description": "Benchmark np.searchsorted on 10M elements",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nuids = np.arange(8456)\nquery = np.random.randint(0, 10000, size=10000000)\nt0 = time.time()\nidx = np.searchsorted(uids, query)\nprint('np.searchsorted on 10M took:', time.time()-t0, 'seconds')\n\""
}<untrusted_context>
Output: np.searchsorted on 10M took: 0.6383545398712158 seconds
Process Group PGID: 2554
</untrusted_context>Achieving Sub-Second Filtering
I've successfully filtered and mapped 10 million rows to (u_idx, i_idx) in under a second, completely avoiding Python loops. This demonstrates significant performance gains.
{
"description": "Vectorized filtering and mapping for 180 days of listens",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nDATA = '/task/data'\ntargets = pd.read_parquet(f'{DATA}/target_users.parquet')\nuids = targets['uid'].values\ncat = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nitems = cat['item_id'].values\n\nt0 = time.time()\ndataset = ds.dataset(f'{DATA}/train_listens.parquet')\n# 180 days\ncutoff = 24786800 - 180 * 86400\nfilt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n\nu_arr = table['uid'].to_numpy()\ni_arr = table['item_id'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\npct_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\n\nu_idx = np.searchsorted(uids, u_arr)\nu_valid = (u_idx < len(uids)) & (uids[np.minimum(u_idx, len(uids)-1)] == u_arr)\n\ni_idx = np.searchsorted(items, i_arr)\ni_valid = (i_idx < len(items)) & (items[np.minimum(i_idx, len(items)-1)] == i_arr)\n\nvalid = u_valid & i_valid\nprint(f'Total rows read: {len(u_arr)}, valid rows: {valid.sum()} in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: Total rows read: 19038702, valid rows: 10682 in 2.20s
Process Group PGID: 2564
</untrusted_context>Investigating Data Validity
I'm digging into why the valid row count is so low. My suspicion is that the item_id values within cat['item_id'].values might not be sorted as expected, which could be a crucial factor affecting processing. I need to verify this.
{
"description": "Check if item_id and uid are monotonic increasing",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nDATA = '/task/data'\ncat = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nprint('cat item_id head:', cat['item_id'].head().tolist())\nprint('is cat item_id sorted?', cat['item_id'].is_monotonic_increasing)\n\ntargets = pd.read_parquet(f'{DATA}/target_users.parquet')\nprint('targets uid head:', targets['uid'].head().tolist())\nprint('is targets uid sorted?', targets['uid'].is_monotonic_increasing)\n\""
}<untrusted_context>
Output: cat item_id head: [5862961, 6901374, 5635052, 3542184, 9378983]
is cat item_id sorted? False
targets uid head: [100, 200, 300, 600, 700]
is targets uid sorted? True
Process Group PGID: 2593
</untrusted_context>Identifying Sorting Discrepancies
I've pinpointed an inconsistency: while targets['uid'] is confirmed as sorted, cat['item_id'] is not. A previous script's unexpected True output for np.all(np.diff(items) > 0) has been resolved by realizing the uids were directly extracted from targets['uid'].values.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = '/task/data'\ncat = pd.read_parquet(f'{DATA}/candidate_catalog.parquet')\nmax_item = cat['item_id'].max()\nitem_lookup = np.full(max_item + 1, -1, dtype=np.int32)\nitem_lookup[cat['item_id'].values] = np.arange(len(cat))\n\ntargets = pd.read_parquet(f'{DATA}/target_users.parquet')\nmax_uid = targets['uid'].max()\nuid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)\nuid_lookup[targets['uid'].values] = np.arange(len(targets))\n\nprint(f'Lookup arrays created: max_item={max_item}, max_uid={max_uid}')\n\n# Benchmark lookup on 20M random IDs\nq_items = np.random.randint(0, max_item + 1, size=20000000)\nt0 = time.time()\nres = item_lookup[q_items]\nprint(f'20M direct lookups in {time.time()-t0:.3f}s')\n\"",
"description": "Benchmark O(1) direct array lookup"
}<untrusted_context>
Output: Lookup arrays created: max_item=9390294, max_uid=1000000
20M direct lookups in 0.154s
Process Group PGID: 2610
</untrusted_context>Achieving Rapid Lookups
I've successfully optimized lookups to an astonishing 0.154 seconds for 20 million entries. The memory footprint is remarkably small, with item and UID lookups combined requiring only around 42MB. This efficiency will make processing millions of events feel instantaneous. My next step is to leverage this speed for constructing the interaction matrix $X$.
Defining Interaction Matrix Events
I'm currently defining the specific events that will populate the interaction matrix $X$. My focus is on consolidating data from likes, dislikes, unlikes, listens, and preference states, considering organic interactions, timestamps for recency decay, and listening duration thresholds.
Testing Time Decay
I'm now designing experiments to evaluate the impact of different time decay parameters, specifically half-lives of 30, 45, 60, and 90 days, on validation NDCG@10. I'll also explore varying the inclusion window of historical events.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# Setup targets and catalog\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nmax_uid = targets['uid'].max()\nuid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)\nuid_lookup[uids] = np.arange(len(uids))\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nmax_item = catalog['item_id'].max()\nitem_lookup = np.full(max_item + 1, -1, dtype=np.int32)\nitem_lookup[items] = np.arange(len(items))\n\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# Load likes and pref\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\n\n# Read listens from train_listens\n# Let's test 180 days cutoff\nt_max = 24786800\ncutoff = t_max - 180 * 86400\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfilt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read {len(table)} listens')\n\nu_l = table['uid'].to_numpy()\ni_l = table['item_id'].to_numpy()\nts_l = table['timestamp'].to_numpy()\npct_l = table['played_ratio_pct'].to_numpy()\norg_l = table['is_organic'].to_numpy()\n\n# Map to indices\nu_idx_l = uid_lookup[np.clip(u_l, 0, max_uid)]\ni_idx_l = item_lookup[np.clip(i_l, 0, max_item)]\nvalid_l = (u_idx_l >= 0) & (i_idx_l >= 0)\nu_idx_l, i_idx_l, ts_l, pct_l, org_l = u_idx_l[valid_l], i_idx_l[valid_l], ts_l[valid_l], pct_l[valid_l], org_l[valid_l]\nprint(f'Valid listens in catalog & targets: {len(u_idx_l)}')\n\n# Map likes\nu_k = uid_lookup[np.clip(likes.uid.to_numpy(), 0, max_uid)]\ni_k = item_lookup[np.clip(likes.item_id.to_numpy(), 0, max_item)]\nvalid_k = (u_k >= 0) & (i_k >= 0)\nu_idx_k, i_idx_k, org_k = u_k[valid_k], i_k[valid_k], likes.is_organic.to_numpy()[valid_k]\n\n# Map pref liked\npref_liked = pref[pref.state == 'liked']\nu_p = uid_lookup[np.clip(pref_liked.uid.to_numpy(), 0, max_uid)]\ni_p = item_lookup[np.clip(pref_liked.item_id.to_numpy(), 0, max_item)]\nvalid_p = (u_p >= 0) & (i_p >= 0)\nu_idx_p, i_idx_p = u_p[valid_p], i_p[valid_p]\n\n# Test half-lives: 30, 45, 60\nfor tau in [30.0, 45.0, 60.0]:\n dt = (t_max - ts_l) / 86400.0\n decay = np.exp(-dt / tau)\n base_listen = np.where(org_l == 1, 2.5, 1.0) * np.where(pct_l >= 80, 1.5, 1.0)\n w_l = (base_listen * decay).astype(np.float32)\n \n w_k = np.where(org_k == 1, 6.0, 4.0).astype(np.float32)\n w_p = np.full(len(u_idx_p), 5.0, dtype=np.float32)\n \n all_u = np.concatenate([u_idx_l, u_idx_k, u_idx_p])\n all_i = np.concatenate([i_idx_l, i_idx_k, i_idx_p])\n all_w = np.concatenate([w_l, w_k, w_p])\n \n X = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\n X.data = np.log1p(X.data)\n \n df_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\n idf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)\n idf = np.maximum(idf, 0.0)\n X_idf = X.multiply(idf).tocsr()\n \n X_norm = normalize(X_idf, norm='l2', axis=1)\n S_user = (X_norm @ X_norm.T).toarray().astype(np.float32)\n np.fill_diagonal(S_user, 0)\n \n K = 200\n S_topk = np.zeros_like(S_user)\n row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\n for r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)\n \n S_topk = (S_topk ** 1.5).astype(np.float32)\n scores = S_topk @ X.toarray()\n \n item_pop = np.array(X.sum(axis=0)).ravel() + 1.0\n for gamma in [0.08, 0.12, 0.16]:\n adj_scores = scores / (item_pop ** gamma)\n scores_masked = np.where(mask == 1, adj_scores, -np.inf)\n ranked = {}\n for r in range(n_users):\n user_scores = scores_masked[r]\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[int(uids[r])] = items[top100_idx].tolist()\n s = score(ranked, graded)\n print(f'tau={tau}, gamma={gamma}: {s}')\n\"",
"description": "Test tau (decay half-life) and gamma with full mapped events"
}<untrusted_context>
Output: /usr/bin/bash: line 127: 2629 Killed python3 -c "
import pyarrow.dataset as ds
import pyarrow.compute as pc
import pandas as pd
import numpy as np
import scipy.sparse as sp
from sklearn.preprocessing import normalize
import time
from pathlib import Path
import sys
sys.path.insert(0, '/task')
from evaluate_public import validation_truth, CandidateSet, score
DATA = Path('/task/data')
# Setup targets and catalog
targets = pd.read_parquet(DATA / 'target_users.parquet')
uids = targets['uid'].values
max_uid = targets['uid'].max()
uid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)
uid_lookup[uids] = np.arange(len(uids))
catalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')
items = catalog['item_id'].values
max_item = catalog['item_id'].max()
item_lookup = np.full(max_item + 1, -1, dtype=np.int32)
item_lookup[items] = np.arange(len(items))
n_users, n_items = len(uids), len(items)
elig = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = validation_truth(elig, set(uids))
val_elig = np.load(DATA / 'validation_eligible_candidates.npz')
mask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')
# Load likes and pref
likes = pd.read_parquet(DATA / 'train_likes.parquet')
pref = pd.read_parquet(DATA / 'validation_preference_state.parquet')
# Read listens from train_listens
# Let's test 180 days cutoff
t_max = 24786800
cutoff = t_max - 180 * 86400
dataset = ds.dataset(DATA / 'train_listens.parquet')
filt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)
table = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
print(f'Read {len(table)} listens')
u_l = table['uid'].to_numpy()
i_l = table['item_id'].to_numpy()
ts_l = table['timestamp'].to_numpy()
pct_l = table['played_ratio_pct'].to_numpy()
org_l = table['is_organic'].to_numpy()
# Map to indices
u_idx_l = uid_lookup[np.clip(u_l, 0, max_uid)]
i_idx_l = item_lookup[np.clip(i_l, 0, max_item)]
valid_l = (u_idx_l >= 0) & (i_idx_l >= 0)
u_idx_l, i_idx_l, ts_l, pct_l, org_l = u_idx_l[valid_l], i_idx_l[valid_l], ts_l[valid_l], pct_l[valid_l], org_l[valid_l]
print(f'Valid listens in catalog & targets: {len(u_idx_l)}')
# Map likes
u_k = uid_lookup[np.clip(likes.uid.to_numpy(), 0, max_uid)]
i_k = item_lookup[np.clip(likes.item_id.to_numpy(), 0, max_item)]
valid_k = (u_k >= 0) & (i_k >= 0)
u_idx_k, i_idx_k, org_k = u_k[valid_k], i_k[valid_k], likes.is_organic.to_numpy()[valid_k]
# Map pref liked
pref_liked = pref[pref.state == 'liked']
u_p = uid_lookup[np.clip(pref_liked.uid.to_numpy(), 0, max_uid)]
i_p = item_lookup[np.clip(pref_liked.item_id.to_numpy(), 0, max_item)]
valid_p = (u_p >= 0) & (i_p >= 0)
u_idx_p, i_idx_p = u_p[valid_p], i_p[valid_p]
# Test half-lives: 30, 45, 60
for tau in [30.0, 45.0, 60.0]:
dt = (t_max - ts_l) / 86400.0
decay = np.exp(-dt / tau)
base_listen = np.where(org_l == 1, 2.5, 1.0) * np.where(pct_l >= 80, 1.5, 1.0)
w_l = (base_listen * decay).astype(np.float32)
w_k = np.where(org_k == 1, 6.0, 4.0).astype(np.float32)
w_p = np.full(len(u_idx_p), 5.0, dtype=np.float32)
all_u = np.concatenate([u_idx_l, u_idx_k, u_idx_p])
all_i = np.concatenate([i_idx_l, i_idx_k, i_idx_p])
all_w = np.concatenate([w_l, w_k, w_p])
X = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)
X.data = np.log1p(X.data)
df_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)
idf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)
idf = np.maximum(idf, 0.0)
X_idf = X.multiply(idf).tocsr()
X_norm = normalize(X_idf, norm='l2', axis=1)
S_user = (X_norm @ X_norm.T).toarray().astype(np.float32)
np.fill_diagonal(S_user, 0)
K = 200
S_topk = np.zeros_like(S_user)
row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]
for r in range(n_users):
cols = row_topk[r]
S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)
S_topk = (S_topk ** 1.5).astype(np.float32)
scores = S_topk @ X.toarray()
item_pop = np.array(X.sum(axis=0)).ravel() + 1.0
for gamma in [0.08, 0.12, 0.16]:
adj_scores = scores / (item_pop ** gamma)
scores_masked = np.where(mask == 1, adj_scores, -np.inf)
ranked = {}
for r in range(n_users):
user_scores = scores_masked[r]
top100_idx = np.argpartition(-user_scores, 100)[:100]
top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]
ranked[int(uids[r])] = items[top100_idx].tolist()
s = score(ranked, graded)
print(f'tau={tau}, gamma={gamma}: {s}')
"
Exit Code: 137
Process Group PGID: 2627
</untrusted_context>Refining Memory Usage
I'm currently investigating memory allocation bottlenecks. Specifically, I've identified that the intermediate dense matrix creation in S_topk @ X.toarray() is consuming a significant 1.01 GB, and this is doubled by the subsequent adj_scores calculation. My focus is on optimizing these operations to reduce the overall memory footprint.
Reducing Memory Overhead
My recent focus has been on aggressively cutting memory consumption within the loop. I've realized that recomputing dense matrices repeatedly and loading extensive tables were the primary culprits. My strategy now involves converting S_topk to a sparse CSR format and processing user scores in smaller chunks to prevent memory spikes.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nimport gc\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# Setup targets and catalog\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nmax_uid = targets['uid'].max()\nuid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)\nuid_lookup[uids] = np.arange(len(uids))\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nmax_item = catalog['item_id'].max()\nitem_lookup = np.full(max_item + 1, -1, dtype=np.int32)\nitem_lookup[items] = np.arange(len(items))\n\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# 90 days cutoff\nt_max = 24786800\ncutoff = t_max - 90 * 86400\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfilt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n\nu_l = table['uid'].to_numpy()\ni_l = table['item_id'].to_numpy()\nts_l = table['timestamp'].to_numpy()\npct_l = table['played_ratio_pct'].to_numpy()\norg_l = table['is_organic'].to_numpy()\ndel table\ngc.collect()\n\nu_idx_l = uid_lookup[np.clip(u_l, 0, max_uid)]\ni_idx_l = item_lookup[np.clip(i_l, 0, max_item)]\nvalid_l = (u_idx_l >= 0) & (i_idx_l >= 0)\nu_idx_l, i_idx_l, ts_l, pct_l, org_l = u_idx_l[valid_l], i_idx_l[valid_l], ts_l[valid_l], pct_l[valid_l], org_l[valid_l]\ndel u_l, i_l\ngc.collect()\n\n# Likes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nu_k = uid_lookup[np.clip(likes.uid.to_numpy(), 0, max_uid)]\ni_k = item_lookup[np.clip(likes.item_id.to_numpy(), 0, max_item)]\nvalid_k = (u_k >= 0) & (i_k >= 0)\nu_idx_k, i_idx_k, org_k = u_k[valid_k], i_k[valid_k], likes.is_organic.to_numpy()[valid_k]\n\n# Pref\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[pref.state == 'liked']\nu_p = uid_lookup[np.clip(pref_liked.uid.to_numpy(), 0, max_uid)]\ni_p = item_lookup[np.clip(pref_liked.item_id.to_numpy(), 0, max_item)]\nvalid_p = (u_p >= 0) & (i_p >= 0)\nu_idx_p, i_idx_p = u_p[valid_p], i_p[valid_p]\n\n# Weights\ndt = (t_max - ts_l) / 86400.0\ndecay = np.exp(-dt / 35.0)\nbase_listen = np.where(org_l == 1, 2.5, 1.0) * np.where(pct_l >= 80, 1.5, 1.0)\nw_l = (base_listen * decay).astype(np.float32)\n\nw_k = np.where(org_k == 1, 6.0, 4.0).astype(np.float32)\nw_p = np.full(len(u_idx_p), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx_l, u_idx_k, u_idx_p])\nall_i = np.concatenate([i_idx_l, i_idx_k, i_idx_p])\nall_w = np.concatenate([w_l, w_k, w_p])\n\nX = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nX.data = np.log1p(X.data)\n\ndf_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\nidf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\nX_idf = X.multiply(idf).tocsr()\n\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray().astype(np.float32)\nnp.fill_diagonal(S_user, 0)\n\nfor K in [150, 200, 250]:\n S_topk = np.zeros_like(S_user)\n row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\n for r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)\n \n for power in [1.3, 1.5, 1.7]:\n S_power = sp.csr_matrix((S_topk ** power).astype(np.float32))\n item_pop = np.array(X.sum(axis=0)).ravel() + 1.0\n \n for gamma in [0.08, 0.10, 0.12]:\n damping = (item_pop ** gamma).astype(np.float32)\n ranked = {}\n for start in range(0, n_users, 1000):\n end = min(start + 1000, n_users)\n chunk_scores = (S_power[start:end] @ X).toarray()\n chunk_scores /= damping\n chunk_mask = mask[start:end]\n chunk_scores = np.where(chunk_mask == 1, chunk_scores, -np.inf)\n \n for r_sub in range(end - start):\n r_idx = start + r_sub\n row = chunk_scores[r_sub]\n top100 = np.argpartition(-row, 100)[:100]\n top100 = top100[np.argsort(-row[top100])]\n ranked[int(uids[r_idx])] = items[top100].tolist()\n s = score(ranked, graded)\n print(f'K={K}, power={power}, gamma={gamma}: {s}')\n\"",
"description": "Chunked scoring: parameter sweep over K, power, gamma"
}<untrusted_context>
Output: K=150, power=1.3, gamma=0.08: {'quality_ndcg@10': 0.035319, 'quality_users': 4263}
K=150, power=1.3, gamma=0.1: {'quality_ndcg@10': 0.035866, 'quality_users': 4263}
K=150, power=1.3, gamma=0.12: {'quality_ndcg@10': 0.036216, 'quality_users': 4263}
K=150, power=1.5, gamma=0.08: {'quality_ndcg@10': 0.035588, 'quality_users': 4263}
K=150, power=1.5, gamma=0.1: {'quality_ndcg@10': 0.035899, 'quality_users': 4263}
K=150, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.036341, 'quality_users': 4263}
K=150, power=1.7, gamma=0.08: {'quality_ndcg@10': 0.035763, 'quality_users': 4263}
K=150, power=1.7, gamma=0.1: {'quality_ndcg@10': 0.03623, 'quality_users': 4263}
K=150, power=1.7, gamma=0.12: {'quality_ndcg@10': 0.036312, 'quality_users': 4263}
K=200, power=1.3, gamma=0.08: {'quality_ndcg@10': 0.034764, 'quality_users': 4263}
K=200, power=1.3, gamma=0.1: {'quality_ndcg@10': 0.035246, 'quality_users': 4263}
K=200, power=1.3, gamma=0.12: {'quality_ndcg@10': 0.035697, 'quality_users': 4263}
K=200, power=1.5, gamma=0.08: {'quality_ndcg@10': 0.034874, 'quality_users': 4263}
K=200, power=1.5, gamma=0.1: {'quality_ndcg@10': 0.035249, 'quality_users': 4263}
K=200, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.035709, 'quality_users': 4263}
K=200, power=1.7, gamma=0.08: {'quality_ndcg@10': 0.035272, 'quality_users': 4263}
K=200, power=1.7, gamma=0.1: {'quality_ndcg@10': 0.035695, 'quality_users': 4263}
K=200, power=1.7, gamma=0.12: {'quality_ndcg@10': 0.036099, 'quality_users': 4263}
K=250, power=1.3, gamma=0.08: {'quality_ndcg@10': 0.034218, 'quality_users': 4263}
K=250, power=1.3, gamma=0.1: {'quality_ndcg@10': 0.034623, 'quality_users': 4263}
K=250, power=1.3, gamma=0.12: {'quality_ndcg@10': 0.034609, 'quality_users': 4263}
K=250, power=1.5, gamma=0.08: {'quality_ndcg@10': 0.034863, 'quality_users': 4263}
K=250, power=1.5, gamma=0.1: {'quality_ndcg@10': 0.034847, 'quality_users': 4263}
K=250, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.035045, 'quality_users': 4263}
K=250, power=1.7, gamma=0.08: {'quality_ndcg@10': 0.035003, 'quality_users': 4263}
K=250, power=1.7, gamma=0.1: {'quality_ndcg@10': 0.034963, 'quality_users': 4263}
K=250, power=1.7, gamma=0.12: {'quality_ndcg@10': 0.035342, 'quality_users': 4263}
Process Group PGID: 2656
</untrusted_context>Improving NDCG@10
I've successfully boosted NDCG@10 to 0.036341 without encountering memory constraints. My next step is to investigate the impact of varying K values, specifically exploring thresholds of 50, 80, 100, 120, and 150 to refine our results further.
{
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport time\nfrom pathlib import Path\nimport sys\nimport gc\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\n# Setup targets and catalog\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nmax_uid = targets['uid'].max()\nuid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)\nuid_lookup[uids] = np.arange(len(uids))\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nmax_item = catalog['item_id'].max()\nitem_lookup = np.full(max_item + 1, -1, dtype=np.int32)\nitem_lookup[items] = np.arange(len(items))\n\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# 90 days cutoff\nt_max = 24786800\ncutoff = t_max - 90 * 86400\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfilt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n\nu_l = table['uid'].to_numpy()\ni_l = table['item_id'].to_numpy()\nts_l = table['timestamp'].to_numpy()\npct_l = table['played_ratio_pct'].to_numpy()\norg_l = table['is_organic'].to_numpy()\ndel table\ngc.collect()\n\nu_idx_l = uid_lookup[np.clip(u_l, 0, max_uid)]\ni_idx_l = item_lookup[np.clip(i_l, 0, max_item)]\nvalid_l = (u_idx_l >= 0) & (i_idx_l >= 0)\nu_idx_l, i_idx_l, ts_l, pct_l, org_l = u_idx_l[valid_l], i_idx_l[valid_l], ts_l[valid_l], pct_l[valid_l], org_l[valid_l]\ndel u_l, i_l\ngc.collect()\n\n# Likes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nu_k = uid_lookup[np.clip(likes.uid.to_numpy(), 0, max_uid)]\ni_k = item_lookup[np.clip(likes.item_id.to_numpy(), 0, max_item)]\nvalid_k = (u_k >= 0) & (i_k >= 0)\nu_idx_k, i_idx_k, org_k = u_k[valid_k], i_k[valid_k], likes.is_organic.to_numpy()[valid_k]\n\n# Pref\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[pref.state == 'liked']\nu_p = uid_lookup[np.clip(pref_liked.uid.to_numpy(), 0, max_uid)]\ni_p = item_lookup[np.clip(pref_liked.item_id.to_numpy(), 0, max_item)]\nvalid_p = (u_p >= 0) & (i_p >= 0)\nu_idx_p, i_idx_p = u_p[valid_p], i_p[valid_p]\n\n# Weights\ndt = (t_max - ts_l) / 86400.0\ndecay = np.exp(-dt / 35.0)\nbase_listen = np.where(org_l == 1, 2.5, 1.0) * np.where(pct_l >= 80, 1.5, 1.0)\nw_l = (base_listen * decay).astype(np.float32)\n\nw_k = np.where(org_k == 1, 6.0, 4.0).astype(np.float32)\nw_p = np.full(len(u_idx_p), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx_l, u_idx_k, u_idx_p])\nall_i = np.concatenate([i_idx_l, i_idx_k, i_idx_p])\nall_w = np.concatenate([w_l, w_k, w_p])\n\nX = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nX.data = np.log1p(X.data)\n\ndf_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\nidf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\nX_idf = X.multiply(idf).tocsr()\n\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray().astype(np.float32)\nnp.fill_diagonal(S_user, 0)\n\nfor K in [60, 80, 100, 120, 150]:\n S_topk = np.zeros_like(S_user)\n row_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\n for r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)\n \n for power in [1.5, 1.6]:\n S_power = sp.csr_matrix((S_topk ** power).astype(np.float32))\n item_pop = np.array(X.sum(axis=0)).ravel() + 1.0\n \n for gamma in [0.12, 0.14, 0.16]:\n damping = (item_pop ** gamma).astype(np.float32)\n ranked = {}\n for start in range(0, n_users, 1000):\n end = min(start + 1000, n_users)\n chunk_scores = (S_power[start:end] @ X).toarray()\n chunk_scores /= damping\n chunk_mask = mask[start:end]\n chunk_scores = np.where(chunk_mask == 1, chunk_scores, -np.inf)\n \n for r_sub in range(end - start):\n r_idx = start + r_sub\n row = chunk_scores[r_sub]\n top100 = np.argpartition(-row, 100)[:100]\n top100 = top100[np.argsort(-row[top100])]\n ranked[int(uids[r_idx])] = items[top100].tolist()\n s = score(ranked, graded)\n print(f'K={K:3d}, power={power}, gamma={gamma:.2f}: {s}')\n\"",
"description": "Sweep K from 60 to 150 and gamma from 0.12 to 0.16"
}<untrusted_context>
Output: K= 60, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.035756, 'quality_users': 4263}
K= 60, power=1.5, gamma=0.14: {'quality_ndcg@10': 0.035778, 'quality_users': 4263}
K= 60, power=1.5, gamma=0.16: {'quality_ndcg@10': 0.035504, 'quality_users': 4263}
K= 60, power=1.6, gamma=0.12: {'quality_ndcg@10': 0.035805, 'quality_users': 4263}
K= 60, power=1.6, gamma=0.14: {'quality_ndcg@10': 0.035747, 'quality_users': 4263}
K= 60, power=1.6, gamma=0.16: {'quality_ndcg@10': 0.035465, 'quality_users': 4263}
K= 80, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.035896, 'quality_users': 4263}
K= 80, power=1.5, gamma=0.14: {'quality_ndcg@10': 0.035862, 'quality_users': 4263}
K= 80, power=1.5, gamma=0.16: {'quality_ndcg@10': 0.035757, 'quality_users': 4263}
K= 80, power=1.6, gamma=0.12: {'quality_ndcg@10': 0.035998, 'quality_users': 4263}
K= 80, power=1.6, gamma=0.14: {'quality_ndcg@10': 0.035986, 'quality_users': 4263}
K= 80, power=1.6, gamma=0.16: {'quality_ndcg@10': 0.035789, 'quality_users': 4263}
K=100, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.036799, 'quality_users': 4263}
K=100, power=1.5, gamma=0.14: {'quality_ndcg@10': 0.036934, 'quality_users': 4263}
K=100, power=1.5, gamma=0.16: {'quality_ndcg@10': 0.036836, 'quality_users': 4263}
K=100, power=1.6, gamma=0.12: {'quality_ndcg@10': 0.03691, 'quality_users': 4263}
K=100, power=1.6, gamma=0.14: {'quality_ndcg@10': 0.036983, 'quality_users': 4263}
K=100, power=1.6, gamma=0.16: {'quality_ndcg@10': 0.03703, 'quality_users': 4263}
K=120, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.036409, 'quality_users': 4263}
K=120, power=1.5, gamma=0.14: {'quality_ndcg@10': 0.036834, 'quality_users': 4263}
K=120, power=1.5, gamma=0.16: {'quality_ndcg@10': 0.037082, 'quality_users': 4263}
K=120, power=1.6, gamma=0.12: {'quality_ndcg@10': 0.036406, 'quality_users': 4263}
K=120, power=1.6, gamma=0.14: {'quality_ndcg@10': 0.036977, 'quality_users': 4263}
K=120, power=1.6, gamma=0.16: {'quality_ndcg@10': 0.036923, 'quality_users': 4263}
K=150, power=1.5, gamma=0.12: {'quality_ndcg@10': 0.036341, 'quality_users': 4263}
K=150, power=1.5, gamma=0.14: {'quality_ndcg@10': 0.036806, 'quality_users': 4263}
K=150, power=1.5, gamma=0.16: {'quality_ndcg@10': 0.036866, 'quality_users': 4263}
K=150, power=1.6, gamma=0.12: {'quality_ndcg@10': 0.036356, 'quality_users': 4263}
K=150, power=1.6, gamma=0.14: {'quality_ndcg@10': 0.03671, 'quality_users': 4263}
K=150, power=1.6, gamma=0.16: {'quality_ndcg@10': 0.036909, 'quality_users': 4263}
Process Group PGID: 2685
</untrusted_context>Improving NDCG Score
My recent explorations have pushed the NDCG@10 score above 0.037, with promising results at K=120, power=1.5, gamma=0.16 and K=100, power=1.6, gamma=0.16. I'm now investigating the impact of incorporating train_dislikes and validation_preference_state into the disliked data.
{
"description": "Test blending User-CF with SVD",
"command": "python3 -c \"\nimport pyarrow.dataset as ds\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nfrom pathlib import Path\nimport sys\nimport gc\nsys.path.insert(0, '/task')\nfrom evaluate_public import validation_truth, CandidateSet, score\n\nDATA = Path('/task/data')\n\ntargets = pd.read_parquet(DATA / 'target_users.parquet')\nuids = targets['uid'].values\nmax_uid = targets['uid'].max()\nuid_lookup = np.full(max_uid + 1, -1, dtype=np.int32)\nuid_lookup[uids] = np.arange(len(uids))\n\ncatalog = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitems = catalog['item_id'].values\nmax_item = catalog['item_id'].max()\nitem_lookup = np.full(max_item + 1, -1, dtype=np.int32)\nitem_lookup[items] = np.arange(len(items))\n\nn_users, n_items = len(uids), len(items)\n\nelig = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(elig, set(uids))\nval_elig = np.load(DATA / 'validation_eligible_candidates.npz')\nmask = np.unpackbits(val_elig['packed_eligible'], axis=1, count=30000, bitorder='little')\n\n# 90 days cutoff\nt_max = 24786800\ncutoff = t_max - 90 * 86400\ndataset = ds.dataset(DATA / 'train_listens.parquet')\nfilt = (pc.field('timestamp') >= cutoff) & (pc.field('played_ratio_pct') >= 50)\ntable = dataset.to_table(filter=filt, columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\n\nu_l = table['uid'].to_numpy()\ni_l = table['item_id'].to_numpy()\nts_l = table['timestamp'].to_numpy()\npct_l = table['played_ratio_pct'].to_numpy()\norg_l = table['is_organic'].to_numpy()\ndel table\ngc.collect()\n\nu_idx_l = uid_lookup[np.clip(u_l, 0, max_uid)]\ni_idx_l = item_lookup[np.clip(i_l, 0, max_item)]\nvalid_l = (u_idx_l >= 0) & (i_idx_l >= 0)\nu_idx_l, i_idx_l, ts_l, pct_l, org_l = u_idx_l[valid_l], i_idx_l[valid_l], ts_l[valid_l], pct_l[valid_l], org_l[valid_l]\ndel u_l, i_l\ngc.collect()\n\n# Likes\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nu_k = uid_lookup[np.clip(likes.uid.to_numpy(), 0, max_uid)]\ni_k = item_lookup[np.clip(likes.item_id.to_numpy(), 0, max_item)]\nvalid_k = (u_k >= 0) & (i_k >= 0)\nu_idx_k, i_idx_k, org_k = u_k[valid_k], i_k[valid_k], likes.is_organic.to_numpy()[valid_k]\n\n# Pref\npref = pd.read_parquet(DATA / 'validation_preference_state.parquet')\npref_liked = pref[pref.state == 'liked']\nu_p = uid_lookup[np.clip(pref_liked.uid.to_numpy(), 0, max_uid)]\ni_p = item_lookup[np.clip(pref_liked.item_id.to_numpy(), 0, max_item)]\nvalid_p = (u_p >= 0) & (i_p >= 0)\nu_idx_p, i_idx_p = u_p[valid_p], i_p[valid_p]\n\n# Weights\ndt = (t_max - ts_l) / 86400.0\ndecay = np.exp(-dt / 35.0)\nbase_listen = np.where(org_l == 1, 2.5, 1.0) * np.where(pct_l >= 80, 1.5, 1.0)\nw_l = (base_listen * decay).astype(np.float32)\n\nw_k = np.where(org_k == 1, 6.0, 4.0).astype(np.float32)\nw_p = np.full(len(u_idx_p), 5.0, dtype=np.float32)\n\nall_u = np.concatenate([u_idx_l, u_idx_k, u_idx_p])\nall_i = np.concatenate([i_idx_l, i_idx_k, i_idx_p])\nall_w = np.concatenate([w_l, w_k, w_p])\n\nX = sp.csr_matrix((all_w, (all_u, all_i)), shape=(n_users, n_items), dtype=np.float32)\nX.data = np.log1p(X.data)\n\n# SVD\nprint('Fitting SVD...')\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU_svd = svd.fit_transform(X).astype(np.float32)\nVT_svd = svd.components_.astype(np.float32)\nprint(f'SVD fitted in {time.time()-t0:.2f}s')\n\n# User-CF setup\ndf_cnt = np.bincount(X.nonzero()[1], minlength=n_items).astype(np.float32)\nidf = np.log((n_users - df_cnt + 0.5) / (df_cnt + 0.5) + 1.0)\nidf = np.maximum(idf, 0.0)\nX_idf = X.multiply(idf).tocsr()\n\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_user = (X_norm @ X_norm.T).toarray().astype(np.float32)\nnp.fill_diagonal(S_user, 0)\n\nK = 110\nS_topk = np.zeros_like(S_user)\nrow_topk = np.argpartition(-S_user, K, axis=1)[:, :K]\nfor r in range(n_users):\n cols = row_topk[r]\n S_topk[r, cols] = np.maximum(S_user[r, cols], 0.0)\n\nS_power = sp.csr_matrix((S_topk ** 1.55).astype(np.float32))\nitem_pop = np.array(X.sum(axis=0)).ravel() + 1.0\ndamping = (item_pop ** 0.16).astype(np.float32)\n\n# Test blend weights alpha for SVD\nfor alpha in [0.0, 0.005, 0.01, 0.02, 0.05]:\n ranked = {}\n for start in range(0, n_users, 1000):\n end = min(start + 1000, n_users)\n chunk_cf = (S_power[start:end] @ X).toarray()\n chunk_cf /= damping\n \n if alpha > 0:\n chunk_svd = U_svd[start:end] @ VT_svd\n # Normalize svd chunk or scale\n chunk_scores = chunk_cf + alpha * chunk_svd\n else:\n chunk_scores = chunk_cf\n \n chunk_mask = mask[start:end]\n chunk_scores = np.where(chunk_mask == 1, chunk_scores, -np.inf)\n \n for r_sub in range(end - start):\n r_idx = start + r_sub\n row = chunk_scores[r_sub]\n top100 = np.argpartition(-row, 100)[:100]\n top100 = top100[np.argsort(-row[top100])]\n ranked[int(uids[r_idx])] = items[top100].tolist()\n s = score(ranked, graded)\n print(f'alpha={alpha}: {s}')\n\""
}<untrusted_context>
Output: Fitting SVD...
SVD fitted in 1.82s
alpha=0.0: {'quality_ndcg@10': 0.037184, 'quality_users': 4263}
alpha=0.005: {'quality_ndcg@10': 0.037195, 'quality_users': 4263}
alpha=0.01: {'quality_ndcg@10': 0.037266, 'quality_users': 4263}
alpha=0.02: {'quality_ndcg@10': 0.037241, 'quality_users': 4263}
alpha=0.05: {'quality_ndcg@10': 0.037456, 'quality_users': 4263}
Process Group PGID: 2714
</untrusted_context>Exploring Alpha Values
I'm continuing to refine the SVD blending. After a promising improvement with alpha=0.05, I'm now investigating the impact of higher alpha values like 0.08, 0.10, 0.15, and 0.20. Additionally, I'm considering normalizing both CF and SVD scores using methods like z-scoring or rank normalization, applied per user, to see if that further enhances performance.
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]The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.
Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.
Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.