Basic recommender gemini-3-8-flash-high-06
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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 Music Discovery Requirements
My current focus is on dissecting the core problem of building this music-discovery ranker. I'm meticulously reviewing all existing documentation, including directory structures, available files, and the specific constraints we're working under, alongside the tools at our disposal. I'm also paying close attention to the prompt's explicit instructions, ensuring each requirement is met.
{
"strategic_intent": "Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker.",
"summary": "Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.",
"title": "Initial Exploration and Environment Setup"
}Current topic: "Initial Exploration and Environment Setup"
Topic summary: Starting the music-discovery ranker task. First phase involves exploring the /task/ data directory, checking python environment and requirements, inspecting evaluation scripts, and understanding the candidate eligibility and metric computation.
Strategic Intent: Explore dataset, evaluation scripts, and baseline environment to plan the music-discovery ranker.{
"dir_path": "/task"
}Path not in workspace: Attempted path "/task" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"description": "Check files in /task/data",
"command": "ls -lh /task/data"
}<untrusted_context>
Output: total 340M
-r--r--r-- 1 root root 342K Sep 29 01:16 candidate_catalog.parquet
-r--r--r-- 1 root root 83K Sep 29 01:16 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 01:16 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 01:17 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 01:16 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 01:16 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 01:16 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 01:16 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 01:16 train_unlikes.parquet
-r--r--r-- 1 root root 36K Sep 29 01:16 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 01:16 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 01:16 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 01:16 val_undislikes.parquet
-r--r--r-- 1 root root 71K Sep 29 01:16 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 01:16 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 01:17 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>{
"command": "ls -la /task",
"description": "List contents of /task"
}<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
Process Group PGID: 1615
</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: 1618
</untrusted_context>{
"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: 1621
</untrusted_context>{
"description": "Check compute resources (CPU, RAM, GPU)",
"command": "lscpu | grep \"Model name\\|CPU(s):\"; free -h; nvidia-smi"
}<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: 1624
</untrusted_context>{
"description": "Check total memory",
"command": "cat /proc/meminfo | grep MemTotal"
}<untrusted_context>
Output: MemTotal: 15988968 kB
Process Group PGID: 1630
</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: 1634
</untrusted_context>Assessing Current Resource Allocation
My current focus is on understanding our resource landscape. I'm examining the available RAM and vCPUs, and I'll soon be inspecting the target_users and candidate_catalog datasets, including their dimensions and data types, to gauge overall size and inform our next steps.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Targets count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog count:', len(catalog), catalog.columns.tolist())\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Validation preference state:', len(val_state), val_state.columns.tolist())\nprint(val_state.head())\ntest_state = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test preference state:', len(test_state), test_state.columns.tolist())\n\"",
"description": "Check target users, catalog, preference state shapes"
}<untrusted_context>
Output: Targets count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens']
Catalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
Validation preference state: 469124 ['uid', 'item_id', 'state']
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
Test preference state: 484776 ['uid', 'item_id', 'state']
Process Group PGID: 1637
</untrusted_context>{
"description": "Check available libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'implicit', 'torch', 'scipy', 'sklearn', 'polars', 'pyarrow']:\n try:\n __import__(pkg)\n print(pkg, 'is AVAILABLE')\n except ImportError:\n print(pkg, 'is NOT available')\n\""
}<untrusted_context>
Output: lightgbm is NOT available
xgboost is NOT available
catboost is NOT available
implicit is NOT available
torch is NOT available
scipy is AVAILABLE
sklearn is AVAILABLE
polars is NOT available
pyarrow is AVAILABLE
Process Group PGID: 1654
</untrusted_context>{
"command": "python3 -c \"\nimport pandas as pd\nimport glob\n\nfor f in sorted(glob.glob('/task/data/*.parquet')):\n df = pd.read_parquet(f)\n ts_info = ''\n if 'timestamp' in df.columns:\n ts_info = f'min_ts: {df.timestamp.min()}, max_ts: {df.timestamp.max()}'\n print(f'{f.split(\\\"/\\\")[-1]}: {len(df):,} rows, cols: {df.columns.tolist()} {ts_info}')\n\"",
"description": "Summary of all parquet files"
}<untrusted_context>
Output: candidate_catalog.parquet: 30,000 rows, cols: ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet: 8,456 rows, cols: ['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet: 484,776 rows, cols: ['uid', 'item_id', 'state']
train_dislikes.parquet: 101,075 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 75, max_ts: 24786580
train_likes.parquet: 814,831 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 80, max_ts: 24786790
train_listens.parquet: 43,484,159 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 0, max_ts: 24786795
train_undislikes.parquet: 19,960 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 15950, max_ts: 24785395
train_unlikes.parquet: 292,665 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 250, max_ts: 24786570
val_dislikes.parquet: 3,563 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789920, max_ts: 25393280
val_likes.parquet: 37,795 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24788815, max_ts: 25393390
val_listens.parquet: 1,501,697 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] min_ts: 24788600, max_ts: 25393395
val_undislikes.parquet: 606 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24810715, max_ts: 25393035
val_unlikes.parquet: 9,346 rows, cols: ['uid', 'timestamp', 'item_id', 'is_organic'] min_ts: 24789005, max_ts: 25393385
validation_preference_state.parquet: 469,124 rows, cols: ['uid', 'item_id', 'state']
Process Group PGID: 1672
</untrusted_context>Analyzing Data Size
My focus has shifted to understanding the sheer volume of data. The training set alone has over 43 million entries, while the validation set contains a substantial 1.5 million. I'm currently examining the timestamp units within these datasets to get a clearer picture of the temporal scope.
{
"description": "Inspect candidate eligibility structures",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val elig keys:', val_elig.files)\nprint('uids:', len(val_elig['uids']), 'item_ids:', 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('uids:', len(test_elig['uids']), 'item_ids:', len(test_elig['item_ids']), 'packed shape:', test_elig['packed_eligible'].shape)\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('uids match targets?:', np.array_equal(val_elig['uids'], targets.uid.values), np.array_equal(test_elig['uids'], targets.uid.values))\n\n# 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 count val:', np.sum(row0))\n\n# Sample candidate counts across users\ncounts = [np.sum(np.unpackbits(val_elig['packed_eligible'][i], count=len(val_elig['item_ids']), bitorder='little')) for i in range(100)]\nprint('Sample candidate counts (min, mean, max):', min(counts), np.mean(counts), max(counts))\n\""
}<untrusted_context>
Output: Val elig keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456 item_ids: 30000 packed shape: (8456, 3750)
Test elig keys: ['uids', 'item_ids', 'packed_eligible']
uids: 8456 item_ids: 30000 packed shape: (8456, 3750)
uids match targets?: True True
User 0 eligible count val: 29318
Sample candidate counts (min, mean, max): 25547 29098.94 29971
Process Group PGID: 1692
</untrusted_context>Investigating Ineligibility
I'm trying to pinpoint why nearly 900 items are deemed ineligible for the 8,456 target users, given the vast pool of 30,000 candidate items. My focus is on understanding the specific criteria causing this discrepancy.
{
"description": "Check relationship between preference state and eligibility",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\n\n# Let's check for user 0\nuid0 = uids[0]\nuser0_state = val_state[val_state.uid == uid0]\nprint(f'User 0 (uid={uid0}) preference state:')\nprint(user0_state)\n\nrow0 = np.unpackbits(val_elig['packed_eligible'][0], count=len(item_ids), bitorder='little')\nitem_map = {item: i for i, item in enumerate(item_ids)}\nstate_in_elig = [row0[item_map[it]] for it in user0_state.item_id if it in item_map]\nprint('Are user 0 liked/disliked items in eligible?', state_in_elig[:10])\n\""
}<untrusted_context>
Output: User 0 (uid=100) preference state:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
5 100 3535902 liked
6 100 3633039 liked
7 100 4099299 liked
8 100 4221787 liked
9 100 4927727 disliked
10 100 4943655 liked
11 100 5590855 liked
12 100 6568592 liked
13 100 8200811 liked
14 100 8661238 disliked
Are user 0 liked/disliked items in eligible? [np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0), np.uint8(0)]
Process Group PGID: 1708
</untrusted_context>Analyzing User Preferences
I'm currently examining how user preference state items influence eligibility, specifically noting exclusions. My focus has shifted to understanding the evaluation metric's behavior, particularly its calculation of relevant items within the validation window and the resulting scores.
{
"command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head(10))\nprint(catalog.tail(5))\n\"",
"description": "Inspect candidate catalog"
}<untrusted_context>
Output: item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
5 5463340 22746 17398 6
6 8213481 22505 17071 7
7 906358 22238 17183 8
8 2859641 18380 13704 9
9 3033749 16682 12267 10
item_id positive_listens organic_positive_listens popularity_rank
29995 1275692 163 81 29996
29996 1280011 163 76 29997
29997 1309246 163 138 29998
29998 1634958 163 46 29999
29999 1670524 163 73 30000
Process Group PGID: 1726
</untrusted_context>Establishing Baseline Performance
I'm currently focused on establishing a quick popularity baseline for the validation set. I'll save this to /app/solution/val_pop.parquet and then use /task/evaluate_public.py to gauge our initial performance. I'm also taking a moment to understand the mechanics of this evaluation script.
{
"description": "Check if elig.item_ids matches catalog.item_id",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_items = catalog.item_id.values\n\n# For each user, take the top 100 eligible items in catalog order\nrows = []\nfor uid in uids:\n row = elig.uid_row[uid]\n packed = elig.packed[row]\n # Fast bitmask check\n mask = np.unpackbits(packed, count=len(elig.item_ids), bitorder='little')\n # Filter items that are eligible\n # Wait: elig.item_ids might have a different order than catalog_items!\n # Let's check if elig.item_ids is the same order or different!\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1743
</untrusted_context>{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Are item_ids identical to catalog?:', np.array_equal(elig.item_ids, catalog.item_id.values))\nif not np.array_equal(elig.item_ids, catalog.item_id.values):\n print('catalog[:5]:', catalog.item_id.values[:5])\n print('elig[:5]:', elig.item_ids[:5])\n\"",
"description": "Check item order between candidate_set and catalog"
}<untrusted_context>
Output: Are item_ids identical to catalog?: True
Process Group PGID: 1754
</untrusted_context>Confirming Item Order
I've confirmed that both validation and test item_ids precisely match the order in candidate_catalog.parquet, which is by popularity rank. Now, I'm proceeding to examine the item_ids within test_eligible_candidates.npz.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\n\ntest_elig = CandidateSet('/task/data/test_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Test elig item_ids identical to catalog?:', np.array_equal(test_elig.item_ids, catalog.item_id.values))\nprint('Test elig uids identical to targets?:', np.array_equal(test_elig.uids, pd.read_parquet('/task/data/target_users.parquet').uid.values))\n\"",
"description": "Check test elig item_ids and uids"
}<untrusted_context>
Output: Test elig item_ids identical to catalog?: True
Test elig uids identical to targets?: True
Process Group PGID: 1771
</untrusted_context>Confirming Data Integrity
My latest check confirms that both val_elig and test_elig datasets perfectly align with candidate_catalog.parquet and target_users.parquet in terms of item IDs and user IDs, respectively, maintaining the exact order. This rigorous verification reassures me of the data's foundational integrity.
{
"description": "Generate popularity baseline for validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\n# For popularity baseline, for each user take top 100 eligible items in catalog order\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # eligible item indices:\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/val_pop.parquet', index=False)\nprint(f'Done in {time.time()-t0:.2f}s, rows={len(df)}')\n\""
}<untrusted_context>
Output: Done in 1.13s, rows=845600
Process Group PGID: 1788
</untrusted_context>{
"description": "Evaluate popularity baseline on validation set",
"command": "python3 /task/evaluate_public.py /app/solution/val_pop.parquet"
}<untrusted_context>
Output: {
"model": "/app/solution/val_pop.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1799
</untrusted_context>Testing Submission Generation
I've confirmed the evaluation metrics, with quality_ndcg@10 at 0.01071 and over 4,000 users having relevant items. My next step is to see if I can swiftly produce a valid test submission using the current popularity baseline. This ensures we consistently have a functional deliverable.
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\nelig = np.load('/task/data/test_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nall_uids = []\nall_items = []\nall_ranks = []\n\nfor r in range(len(uids)):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_indices = np.where(mask)[0][:100]\n all_uids.extend([uids[r]] * 100)\n all_items.extend(item_ids[eligible_indices])\n all_ranks.extend(range(1, 101))\n\ndf = pd.DataFrame({\n 'uid': np.array(all_uids, dtype=np.int64),\n 'item_id': np.array(all_items, dtype=np.int64),\n 'rank': np.array(all_ranks, dtype=np.int64)\n})\ndf.to_parquet('/app/solution/submission.parquet', index=False)\nprint(f'Test submission created, rows={len(df)}')\n\"",
"description": "Create initial fallback test submission"
}<untrusted_context>
Output: Test submission created, rows=845600
Process Group PGID: 1819
</untrusted_context>{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nframe = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nall_valid = all(\n elig.is_eligible(uid, item)\n for uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None)\n)\nprint('Submission valid?:', all_valid)\n\"",
"description": "Verify initial test submission integrity"
}<untrusted_context>
Output: Submission valid?: True
Process Group PGID: 1830
</untrusted_context>Exploring Data for Optimization
I'm currently diving deep into the data, examining its structure and the nuances of the task. My focus is on identifying strategies to maximize the NDCG@10 score, considering various models and their potential impact.
Investigating Dataset Contents
I'm now examining the train_listens.parquet file. My immediate focus is on understanding the column definitions and the temporal distribution of listen events. I'm also keen to verify the presence of all target users and the nuances of positive listen definitions as per the problem description.
{
"description": "Analyze user presence in train files",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\nprint('Target users count:', len(target_uids))\n\n# Check target users in train_likes, train_dislikes, etc.\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Train likes:', len(likes), 'for targets:', likes.uid.isin(target_uids).sum())\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Train dislikes:', len(dislikes), 'for targets:', dislikes.uid.isin(target_uids).sum())\n\n# How big is train_listens for target users vs other users?\n# Let's inspect a chunk or pyarrow dataset\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint('Total train listens:', len(table))\ndf_sample = table.slice(0, 1000000).to_pandas()\nprint('Sample listens in targets:', df_sample.uid.isin(target_uids).mean())\n\""
}<untrusted_context>
Output: Target users count: 8456
Train likes: 814831 for targets: 690968
Train dislikes: 101075 for targets: 84266
Total train listens: 43484159
Sample listens in targets: 0.995437
Process Group PGID: 1848
</untrusted_context>Focusing on Target Users
I've confirmed that train_listens overwhelmingly comprises our target users, representing nearly all of its data. The sheer volume of listens per user is striking, and I'm now examining the temporal extent of this dataset.
{
"description": "Check listen timestamps distribution",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\nt_cutoff = 24786800\nt_90d = t_cutoff - 90 * 86400\nt_30d = t_cutoff - 30 * 86400\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'played_ratio_pct', 'is_organic'])\nts = table['timestamp']\n\nn_total = len(ts)\nn_90d = pc.sum(pc.greater_equal(ts, t_90d)).as_py()\nn_30d = pc.sum(pc.greater_equal(ts, t_30d)).as_py()\nprint(f'Total: {n_total:,}, in last 90d: {n_90d:,} ({n_90d/n_total:.1%}), in last 30d: {n_30d:,} ({n_30d/n_total:.1%})')\n\""
}<untrusted_context>
Output: Total: 43,484,159, in last 90d: 16,769,274 (38.6%), in last 30d: 6,126,103 (14.1%)
Process Group PGID: 1870
</untrusted_context>Analyzing Listen Patterns
My current focus is on dissecting listen patterns within specific timeframes. I'm particularly interested in understanding the "target window behavior" and identifying positive listens within the val_listens.parquet dataset, referencing the validation_truth definitions.
{
"description": "Inspect validation ground truth statistics",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\ntotal_users = len(truth)\ntotal_pairs = sum(len(items) for items in truth.values())\ngrade1_pairs = sum(sum(1 for g in items.values() if g == 1) for items in truth.values())\ngrade3_pairs = sum(sum(1 for g in items.values() if g == 3) for items in truth.values())\n\nprint(f'Users with truth: {total_users}')\nprint(f'Total user-item pairs: {total_pairs}')\nprint(f'Grade 1 pairs: {grade1_pairs}, Grade 3 pairs: {grade3_pairs}')\n\""
}<untrusted_context>
Output: Users with truth: 4263
Total user-item pairs: 37769
Grade 1 pairs: 33352, Grade 3 pairs: 4417
Process Group PGID: 1894
</untrusted_context>Exploring Item Engagement
I'm currently delving into the characteristics of items users interact with, specifically examining whether they've encountered these items previously in the training data. This analysis aims to uncover patterns in user engagement with new vs. familiar content.
{
"description": "Check if truth pairs appeared in train listens",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Check a sample of train listens for these users\n# Read train_listens for target users\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\ntrain_pairs = set(zip(train_df['uid'], train_df['item_id']))\n\ntruth_pairs = []\nfor u, items in truth.items():\n for item in items:\n truth_pairs.append((u, item))\n\nin_train = sum((u, item) in train_pairs for u, item in truth_pairs)\nprint(f'Total truth pairs: {len(truth_pairs)}')\nprint(f'Truth pairs already listened in train: {in_train} ({in_train/len(truth_pairs):.2%})')\nprint(f'Truth pairs NEVER listened in train (discovery!): {len(truth_pairs) - in_train} ({(len(truth_pairs) - in_train)/len(truth_pairs):.2%})')\n\""
}<untrusted_context>
Output: Total truth pairs: 37769
Truth pairs already listened in train: 0 (0.00%)
Truth pairs NEVER listened in train (discovery!): 37769 (100.00%)
Process Group PGID: 1914
</untrusted_context>Discovering Exclusion Logic
I've just realized something significant: my candidate selection process entirely prevents recommending tracks the user has already heard. This is a crucial design element, ensuring fresh discoveries are always presented.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom ranking_evaluation import CandidateSet\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ntrain_df = table.to_pandas()\n# sample 1000 pairs from train_df\nsample = train_df.sample(1000, random_state=42)\neligible_count = sum(elig.is_eligible(uid, item) for uid, item in zip(sample.uid, sample.item_id))\nprint('Sample of train listens eligible in validation:', eligible_count)\n\"",
"description": "Verify candidate eligibility excludes previous listens"
}<untrusted_context>
Output: Sample of train listens eligible in validation: 0
Process Group PGID: 1938
</untrusted_context>Discovering New Content
I'm focusing on how to ensure that all eligible candidates for a user are truly new discoveries. This means rigorously filtering out anything they've already engaged with.
Analyzing Candidate Discovery Methods
My current focus is on understanding how recommendation systems approach music discovery for users with absolutely no prior listening history. I'm exploring various collaborative filtering techniques and other methods, considering that I only have interaction data, not explicit metadata like artist or genre. I'm examining the available datasets to see what can be inferred.
Exploring Collaborative Filtering
I'm delving into collaborative filtering techniques. Specifically, I'm evaluating Item-Item CF and various Matrix Factorization models like EASE, considering the scale of 30,000 candidate items in relation to the training data.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_pandas().unique()\nprint('Unique items in train_listens:', len(items))\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\nprint('Catalog items count:', len(catalog_items))\nprint('Overlap:', len(set(items).intersection(catalog_items)))\n\"",
"description": "Check unique items in train_listens and overlap with catalog"
}<untrusted_context>
Output: Unique items in train_listens: 845395
Catalog items count: 30000
Overlap: 30000
Process Group PGID: 1962
</untrusted_context>Analyzing Catalog Discrepancy
I'm currently examining the significant difference between the 845,395 unique items in train_listens and the much smaller candidate_catalog of 30,000, noting that all catalog items are present in the training data. My next step is to quantify how many train_listens specifically pertain to those 30,000 items.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nin_catalog = np.isin(items, list(catalog_items))\nprint(f'Total listens: {len(items):,}')\nprint(f'Listens to candidate catalog items: {np.sum(in_catalog):,} ({np.mean(in_catalog):.2%})')\n\"",
"description": "Check listen count to candidate catalog items"
}<untrusted_context>
Output: Total listens: 43,484,159
Listens to candidate catalog items: 31,249,613 (71.86%)
Process Group PGID: 1983
</untrusted_context>Exploring Catalog Recommendations
I'm currently investigating how our recommendation engine handles items outside the core 30,000 catalog. My recent focus has been on testing and validation to see if recommendations can extend beyond this heavily utilized subset, aiming to ensure comprehensive suggestions.
Analyzing User Interaction Data
My recent focus has shifted to understanding the user-item interaction signals. I'm examining listens, likes, and dislikes data, and specifically checking if validation_preference_state accurately reflects all active explicit preferences at the training cutoff for validation.
{
"description": "Check state value counts",
"command": "python3 -c \"\nimport pandas as pd\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint(val_state.state.value_counts())\n\""
}<untrusted_context>
Output: state
liked 418435
disliked 50689
Name: count, dtype: int64
Process Group PGID: 2003
</untrusted_context>{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\ncatalog = pq.read_table('/task/data/candidate_catalog.parquet', columns=['item_id'])\ncatalog_items = set(catalog['item_id'].to_pandas())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\nprint(f'Read table in {time.time()-t0:.2f}s')\n\ndf_cat = df[df.item_id.isin(catalog_items)]\nprint(f'Filtered to catalog in {time.time()-t0:.2f}s, rows={len(df_cat):,}')\n\nunique_pairs = df_cat[['uid', 'item_id']].drop_duplicates()\nprint(f'Unique (user, item) pairs in catalog: {len(unique_pairs):,}')\nprint(f'Avg unique items per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Analyze unique user-item pairs in catalog"
}<untrusted_context>
Output: Read table in 0.85s
Filtered to catalog in 4.50s, rows=31,249,613
Unique (user, item) pairs in catalog: 7,965,936
Avg unique items per user: 942.0
Process Group PGID: 2020
</untrusted_context>Exploring User Interaction
My recent thoughts reveal that, on average, each user has interacted with approximately 942 unique items out of a potential 30,000. This suggests a significant pool of roughly 29,058 remaining candidates for deeper exploration per user. Surprisingly, processing the entirety of the train_listens.parquet dataset proved to be remarkably efficient.
Refining Candidate Selection
The processing speed of the dataset is exceptional. My current focus is on defining "relevance" for the validation and test windows. I'm re-examining the objective, specifically the criteria for grade 3 (likes) and grade 1 (listens to 80%), and how they influence graded NDCG@10. I aim to significantly surpass the baseline popularity score by leveraging personalized recommendation techniques.
Evaluating Item-Item Similarity
I'm shifting focus to evaluating item-item collaborative filtering approaches like cosine and BM25 similarity. I've confirmed the feasibility of computing the $30000 \times 30000$ item co-occurrence matrix, fitting within memory constraints. My next step is to analyze the sparsity of this matrix and the impact of keeping top-k neighbors.
Testing SVD Efficiency
I'm now investigating the computational feasibility and performance of Singular Value Decomposition (SVD) variants, specifically TruncatedSVD and RandomizedSVD, for reducing dimensionality. I'm keen to test their speed on the sparse interaction matrix and how quickly they can yield meaningful latent factors for user and item representations, aiming for dimensions like 64, 128, or 256.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K\nfrom evaluate_public import validation_truth\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Setup completed in {time.time()-t0:.2f}s')\n\"",
"description": "Test setup for evaluation script"
}<untrusted_context>
Output: Setup completed in 0.61s
Process Group PGID: 2042
</untrusted_context>Exploring Interaction Matrix Construction
I'm currently focused on building an interaction matrix, leveraging train_listens, train_likes, and validation_preference_state. My next step is to evaluate Item-Item Cosine Similarity combined with BM25. I'll be paying close attention to the details of this approach.
Refining Similarity Calculations
I'm refining the Item-Item Cosine Similarity calculation. I realized that by normalizing item columns first, the similarity matrix $S$ can be efficiently computed as $\tilde{R}^T \tilde{R}$. This also reveals a path to calculating user similarity vectors $v$ efficiently, which I'm exploring.
Evaluating Sparsity Strategies
I'm now considering whether a dense similarity matrix or a top-k approach with BM25 is superior. I'm analyzing how popular items might influence a dense matrix and exploring the effectiveness of TF-IDF or BM25 weighting on the interaction matrix $R$. EASE is also on my radar.
Optimizing EASE Computation
I've realized inverting the full $30000 \times 30000$ matrix for EASE is computationally prohibitive. Fortunately, because the number of users ($U=8456$) is significantly less than items ($I=30000$), I can leverage the Woodbury matrix identity. This allows me to invert a much smaller $U \times U$ matrix instead, drastically reducing the computational cost to seconds. This also provides an efficient way to compute the diagonal elements needed for predictions.
Deriving User-User Kernel
I'm verifying a mathematical identity for EASE, specifically that $X(X^T X + \lambda I)^{-1}$ is equivalent to $(X X^T + \lambda I)^{-1} X$. This confirms I can use the smaller user-user Gram matrix $K = X X^T$ for computation.
Calculating EASE Predictions Efficiently
I've figured out how to efficiently calculate the diagonal elements $P_{ii}$ for EASE predictions. This involves a dot product between the $i$-th column of $X$ and the $i$-th column of $AX$. This then directly leads to the final prediction scores for unseen candidates, simplifying the scoring process considerably.
{
"description": "Verify mathematical equivalence of Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\n\n# Small test: U=10, I=20\nnp.random.seed(42)\nX = (np.random.rand(10, 20) > 0.7).astype(float)\nlam = 10.0\n\n# Method 1: Standard EASE\nG = X.T @ X + lam * np.eye(20)\nP1 = la.inv(G)\nB1 = np.eye(20) - P1 / np.diag(P1)\npred1 = X @ B1\n\n# Method 2: Dual EASE via Kernel\nK = X @ X.T + lam * np.eye(10)\n# A = inv(K)\n# M = A @ X\nM = la.solve(K, X, assume_a='pos')\ncol_dot = np.sum(X * M, axis=0)\ndiag_P2 = (1.0 - col_dot) / lam\n\n# Check diag_P2 vs np.diag(P1)\nprint('Max diff in diag(P):', np.max(np.abs(np.diag(P1) - diag_P2)))\n\n# pred2 = X - M / diag_P2\npred2 = X - M / diag_P2[None, :]\nprint('Max diff in pred:', np.max(np.abs(pred1 - pred2)))\n\""
}<untrusted_context>
Output: Max diff in diag(P): 1.3877787807814457e-17
Max diff in pred: 3.3306690738754696e-16
Process Group PGID: 2069
</untrusted_context>Confirming Exact Mathematical Equivalence
I've confirmed an exact mathematical equivalence in the results. The maximum differences in both the diagonal of P and the predictions are astonishingly small, on the order of $10^{-17}$ and $10^{-16}$ respectively. This level of precision strongly suggests the theoretical equivalence holds computationally as well.
{
"description": "Benchmark Cholesky solve for Dual EASE",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.linalg as la\nimport time\n\nU = 8456\nI = 30000\nprint(f'Testing with U={U}, I={I}...')\nt0 = time.time()\nK = np.random.randn(U, U).astype(np.float32)\nK = K @ K.T + 500.0 * np.eye(U, dtype=np.float32)\nprint(f'K created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nc, lower = la.cho_factor(K, overwrite_a=True)\nprint(f'Cholesky factor in {time.time()-t1:.2f}s')\n\nt2 = time.time()\nX = np.random.randn(U, I).astype(np.float32)\nM = la.cho_solve((c, lower), X)\nprint(f'Cho solve in {time.time()-t2:.2f}s')\n\""
}<untrusted_context>
Output: Testing with U=8456, I=30000...
K created in 4.44s
Cholesky factor in 1.14s
Cho solve in 25.03s
Process Group PGID: 2086
</untrusted_context>Optimizing Computation Time
My focus is on accelerating the cho_solve operation. It currently takes 25 seconds for 30,000 columns. I've broken down the process: K creation in 4.4s, Cholesky factor in 1.1s, and the cho_solve itself at 25s, for a total of around 30 seconds. I'm considering that X is actually a sparse matrix (CSR format) and exploring how that might be leveraged to improve efficiency.
{
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nt0 = time.time()\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\nprint(f'CSR created in {time.time()-t0:.2f}s')\n\nt1 = time.time()\nK = (X @ X.T).toarray()\nprint(f'K = X @ X.T in {time.time()-t1:.2f}s')\n\"",
"description": "Benchmark sparse X @ X.T"
}<untrusted_context>
Output: CSR created in 0.36s
K = X @ X.T in 7.17s
Process Group PGID: 2103
</untrusted_context>Optimizing Computational Bottlenecks
I'm exploring ways to accelerate the K = X @ X.T operation, which currently takes 7.17s. I'm also investigating the performance of cho_solve, which currently stands at 25s. I'm considering if processing all 30,000 columns simultaneously is necessary and examining alternative models or faster methods like TruncatedSVD.
{
"description": "Benchmark TruncatedSVD",
"command": "python3 -c \"\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\n\nU = 8456\nI = 30000\nnnz = 8000000\n\nrows = np.random.randint(0, U, nnz)\ncols = np.random.randint(0, I, nnz)\ndata = np.ones(nnz, dtype=np.float32)\n\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', n_iter=5, random_state=42)\nU_red = svd.fit_transform(X)\nV_red = svd.components_ # shape (128, I)\nprint(f'TruncatedSVD in {time.time()-t0:.2f}s, shapes: U={U_red.shape}, V={V_red.shape}')\n\n# Reconstructing scores: U_red @ V_red\nt1 = time.time()\nscores = U_red @ V_red # shape (U, I)\nprint(f'Matrix mult in {time.time()-t1:.2f}s, shape: {scores.shape}')\n\""
}<untrusted_context>
Output: TruncatedSVD in 4.70s, shapes: U=(8456, 128), V=(128, 30000)
Matrix mult in 0.42s, shape: (8456, 30000)
Process Group PGID: 2120
</untrusted_context>Analyzing SVD and EASE
I've finished running TruncatedSVD, which completed in 4.70 seconds, and the subsequent matrix multiplication at 0.42 seconds. My current focus is on evaluating both SVD and EASE performance on the validation set and exploring potential features for the user-item interaction matrix.
Investigating Interaction Signals
I'm currently examining the signals within train_listens.parquet. My attention is particularly drawn to played_ratio_pct and is_organic to define positive interactions, and I'm exploring how recent timestamps can be incorporated, perhaps through a decay function.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, ndcg_at_k, QUALITY_K, score_rankings\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\n# Test evaluation directly on val_pop ranking\nfrom evaluate_public import load_submission\nranked = load_submission('/app/solution/val_pop.parquet', elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\"",
"description": "Check local evaluation harness"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 15, in <module>
File "/task/evaluate_public.py", line 113, in load_submission
frame = validate_ranking(read_ranking(path), targets)
^^^^^^^^^^^^^^^^^^
File "/task/ranking_evaluation.py", line 98, in read_ranking
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'suffix'
Exit Code: 1
Process Group PGID: 2138
</untrusted_context>{
"description": "Check local evaluation harness with Path",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth, load_submission\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, targets)\n\nranked = load_submission(Path('/app/solution/val_pop.parquet'), elig, targets)\nres = score_rankings(ranked, truth)\nprint('Evaluation result:', res)\n\""
}<untrusted_context>
Output: Evaluation result: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 2158
</untrusted_context>Refining Evaluation Metrics
I'm currently focused on understanding the evaluation results, specifically the NDCG@10 score and the user count. I'm analyzing how the current evaluation methodology, which masks ineligible items per user from a score matrix, contributes to these figures.
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nU = 8456\nI = 30000\nscores = np.random.randn(U, I).astype(np.float32)\n\nt0 = time.time()\n# Test argpartition\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nrow_idx = np.arange(U)[:, None]\ntop100_scores = scores[row_idx, top100_idx]\nsort_order = np.argsort(-top100_scores, axis=1)\nfinal_ranks = top100_idx[row_idx, sort_order]\nprint(f'Top 100 for all {U} users computed in {time.time()-t0:.2f}s, shape: {final_ranks.shape}')\n\"",
"description": "Benchmark vectorized top 100 extraction"
}<untrusted_context>
Output: Top 100 for all 8456 users computed in 0.98s, shape: (8456, 100)
Process Group PGID: 2178
</untrusted_context>Refining Masking Logic
I've successfully ranked all users against items rapidly. My current focus is on the masking verification step. I'm examining val_elig.packed to ensure the bit unpacking and subsequent score assignment for masked items correctly handles the exclusion as intended.
{
"description": "Benchmark vectorized unpackbits",
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nelig = np.load('/task/data/validation_eligible_candidates.npz')\npacked = elig['packed_eligible']\nU, I = packed.shape[0], len(elig['item_ids'])\n\nt0 = time.time()\n# Unpack all at once\nmask = np.unpackbits(packed, axis=1, count=I, bitorder='little')\nprint(f'Unpacked all in {time.time()-t0:.2f}s, mask shape: {mask.shape}')\n\""
}<untrusted_context>
Output: Unpacked all in 0.03s, mask shape: (8456, 30000)
Process Group PGID: 2188
</untrusted_context>Optimizing Mask Processing
I've just identified a critical bottleneck: unpacking candidate masks for all users currently takes 0.03 seconds per mask. This delay significantly impacts the overall model evaluation, pushing it to roughly 1 second per model. My current focus is on optimizing this unpacking process.
{
"strategic_intent": "Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.",
"title": "Model Development and Validation Optimization",
"summary": "Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states."
}Current topic: "Model Development and Validation Optimization"
Topic summary: Completed initial environment and data analysis. Verified candidate eligibility contract, catalog structure, and established evaluation harness. Global popularity baseline scored 0.01071 NDCG@10. Initial valid submission saved. Now proceeding to build and evaluate personalized ranking models including SVD, BM25 item-item, and Dual EASE, optimizing temporal weights, interaction types, and explicit preference states.
Strategic Intent: Design, implement, and evaluate collaborative filtering and matrix factorization models on validation data.Testing Interaction Models
I'm now evaluating various user-item interaction representations and models using the validation set. My current focus is on defining what data populates the interaction matrix $X$. I'm examining the columns in train_listens such as uid, timestamp, item_id, is_organic, and played_.
Defining Interaction Weights
I'm exploring different ways to weight user-item interactions. Specifically, I'm considering factors like play duration, organic plays, explicit likes, and dislikes from the preference state data. Time decay will also be incorporated into these interaction weights.
{
"description": "Evaluate TruncatedSVD on validation set",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nprint(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf_listens = table.to_pandas()\nprint(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')\n\n# Only keep catalog items and target users\ndf_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]\nprint(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')\n\n# Read preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nprint(f'Preference state rows in catalog: {len(val_state):,}')\n\n# Baseline feature weights:\n# Weight listens by played_ratio and recency\ncutoff = 24786800\nhalf_life = 60 * 86400 # 60 days\nrecency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)\norganic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)\nratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)\nlisten_weights = ratio * organic * recency\n\n# Aggregate by (uid, item_id)\ndf_agg = pd.DataFrame({\n 'uid': df_listens.uid.values,\n 'item_id': df_listens.item_id.values,\n 'weight': listen_weights\n}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\n# Add likes (+3.0) and dislikes (-2.0)\nlikes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()\nlikes_df['weight'] = 3.0\n\ndislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()\ndislikes_df['weight'] = -2.0\n\nall_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)\nall_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()\n\nprint(f'All interactions aggregated: {len(all_interactions):,} pairs')\n\n# Construct sparse matrix X\nrows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)\ncols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)\ndata = all_interactions.weight.values.astype(np.float32)\n\nU = len(uids)\nI = len(item_ids)\nX = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\n\n# Test TruncatedSVD with different components\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X)\n scores = U_factor @ svd.components_\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\""
}<untrusted_context>
Output: /usr/bin/bash: line 107: 2200 Killed python3 -c "
import sys
sys.path.append('/task')
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from evaluate_public import validation_truth
from ranking_evaluation import CandidateSet, score_rankings
t0 = time.time()
targets = pd.read_parquet('/task/data/target_users.parquet')
uids = targets.uid.values
uid_to_idx = {u: i for i, u in enumerate(uids)}
catalog = pd.read_parquet('/task/data/candidate_catalog.parquet')
item_ids = catalog.item_id.values
item_to_idx = {it: i for i, it in enumerate(item_ids)}
elig = CandidateSet('/task/data/validation_eligible_candidates.npz')
truth = validation_truth(elig, set(uids))
print(f'Loaded targets, catalog, truth in {time.time()-t0:.2f}s')
# Read train listens
import pyarrow.parquet as pq
table = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])
df_listens = table.to_pandas()
print(f'Read listens in {time.time()-t0:.2f}s, filtering to catalog...')
# Only keep catalog items and target users
df_listens = df_listens[df_listens.item_id.isin(item_to_idx) & df_listens.uid.isin(uid_to_idx)]
print(f'Filtered listens in {time.time()-t0:.2f}s, rows={len(df_listens):,}')
# Read preference state
val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')
val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]
print(f'Preference state rows in catalog: {len(val_state):,}')
# Baseline feature weights:
# Weight listens by played_ratio and recency
cutoff = 24786800
half_life = 60 * 86400 # 60 days
recency = np.exp(-np.log(2) * (cutoff - df_listens.timestamp.values) / half_life)
organic = np.where(df_listens.is_organic.values == 1, 1.5, 1.0)
ratio = np.clip(df_listens.played_ratio_pct.values / 100.0, 0, 1.5)
listen_weights = ratio * organic * recency
# Aggregate by (uid, item_id)
df_agg = pd.DataFrame({
'uid': df_listens.uid.values,
'item_id': df_listens.item_id.values,
'weight': listen_weights
}).groupby(['uid', 'item_id'], as_index=False)['weight'].sum()
# Add likes (+3.0) and dislikes (-2.0)
likes_df = val_state[val_state.state == 'liked'][['uid', 'item_id']].copy()
likes_df['weight'] = 3.0
dislikes_df = val_state[val_state.state == 'disliked'][['uid', 'item_id']].copy()
dislikes_df['weight'] = -2.0
all_interactions = pd.concat([df_agg, likes_df, dislikes_df], ignore_index=True)
all_interactions = all_interactions.groupby(['uid', 'item_id'], as_index=False)['weight'].sum()
print(f'All interactions aggregated: {len(all_interactions):,} pairs')
# Construct sparse matrix X
rows = np.array([uid_to_idx[u] for u in all_interactions.uid.values], dtype=np.int32)
cols = np.array([item_to_idx[it] for it in all_interactions.item_id.values], dtype=np.int32)
data = all_interactions.weight.values.astype(np.float32)
U = len(uids)
I = len(item_ids)
X = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)
# Test TruncatedSVD with different components
for n_comp in [64, 128, 256]:
t_svd = time.time()
svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)
U_factor = svd.fit_transform(X)
scores = U_factor @ svd.components_
# Mask ineligible
packed = elig.packed
mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0
scores[mask] = -1e9
# Rank top 100
top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
row_idx = np.arange(U)[:, None]
top100_scores = scores[row_idx, top100_idx]
sort_order = np.argsort(-top100_scores, axis=1)
ranked_top = top100_idx[row_idx, sort_order]
ranked_dict = {
int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]
for r in range(U)
}
res = score_rankings(ranked_dict, truth)
print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f} (took {time.time()-t_svd:.2f}s)')
"
Exit Code: 137
Process Group PGID: 2198
</untrusted_context>Detecting Memory Leaks
I've identified that an exit code of 137 signifies an Out Of Memory error. This seems to stem from the substantial size of df_listens, which, at 43.4 million rows, likely overloaded pandas during aggregation operations like groupby.
Filtering Data Effectively
I've realized that using only recent listen data, specifically the last 90 days, drastically reduces the dataset size by 62% and should prevent memory issues during processing. This approach also aligns with the hypothesis that recent user taste is more predictive.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport time\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Test reading with filters\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\nprint('Total rows:', pf.metadata.num_rows)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], filters=[('timestamp', '>=', t_min)])\nprint(f'Read filtered table in {time.time()-t0:.2f}s, rows={len(table):,}')\n\"",
"description": "Test filtered pyarrow read"
}<untrusted_context>
Output: Num row groups: 42
Total rows: 43484159
Read filtered table in 0.79s, rows=16,769,274
Process Group PGID: 2227
</untrusted_context>Exploring Efficient Aggregation
I'm currently focused on optimizing the aggregation of (uid, item_id) pairs. My primary concern is avoiding Out-of-Memory errors that arise from creating excessively large pandas DataFrames. I'm exploring alternative strategies to achieve this efficiency, leveraging existing mappings like uid_to_idx to streamline the process.
Testing Sparse Matrix Strategy
I'm investigating a new approach to aggregate (uid, item_id) pairs. By mapping UIDs and item IDs to integer indices, I can generate a unique pair_id. This allows for direct aggregation into a numpy array using np.bincount, potentially avoiding OOM errors and offering significant speed advantages for dense matrices.
{
"command": "python3 -c \"\nimport numpy as np\nimport time\n\nN = 15000000\nU = 8456\nI = 30000\ntotal_cells = U * I\n\nt0 = time.time()\nu_idx = np.random.randint(0, U, N, dtype=np.int32)\nit_idx = np.random.randint(0, I, N, dtype=np.int32)\nweights = np.random.rand(N).astype(np.float32)\npair_id = u_idx.astype(np.int64) * I + it_idx\n\nt1 = time.time()\nprint(f'Setup done in {t1-t0:.2f}s')\n\n# np.bincount\nflat = np.bincount(pair_id, weights=weights, minlength=total_cells).astype(np.float32)\nX = flat.reshape(U, I)\nprint(f'bincount done in {time.time()-t1:.2f}s, X shape: {X.shape}, nonzeros: {np.count_nonzero(X):,}')\n\"",
"description": "Benchmark bincount matrix construction"
}<untrusted_context>
Output: Setup done in 0.44s
bincount done in 0.40s, X shape: (8456, 30000), nonzeros: 14,565,541
Process Group PGID: 2249
</untrusted_context>Optimizing for speed
The bincount operation is complete, taking a mere 0.40 seconds with zero intermediate copies and no pandas groupby overhead. The outcome is a dense float32 matrix, ready for direct use or conversion to a sparse format. The next step is to test this with real data.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_arr = table['uid'].to_numpy()\nit_arr = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Map to indices\n# Filter out non-catalog items\n# Fast lookup using array if possible or np.isin\nin_catalog = np.isin(it_arr, item_ids)\nu_arr = u_arr[in_catalog]\nit_arr = it_arr[in_catalog]\npr_arr = pr_arr[in_catalog]\norg_arr = org_arr[in_catalog]\nts_arr = ts_arr[in_catalog]\n\n# Map to integer indices\nu_idx = np.array([uid_to_idx[u] for u in u_arr], dtype=np.int32)\n# Since item_to_idx is 30,000 items, let's create a direct lookup array or dict\nitem_map_arr = np.full(max(item_ids) + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n item_map_arr[it] = idx\nit_idx = item_map_arr[it_arr]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run memory-efficient SVD evaluation"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 53, in <module>
KeyError: np.uint32(500)
Exit Code: 1
Process Group PGID: 2259
</untrusted_context>Investigating User Data
I'm encountering a KeyError when trying to map user IDs to indices, specifically with np.uint32(500). This seems to stem from train_listens containing users who aren't part of our target 8456 group, indicating a small but present 0.5% of non-target users within the dataset.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\n\n# Fast direct array lookups for uids and items\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\n# Read filtered listens\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'], \n filters=[('timestamp', '>=', t_min)])\n\nu_raw = table['uid'].to_numpy()\nit_raw = table['item_id'].to_numpy()\npr_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\n\n# Mask valid\nvalid_u = (u_raw <= max_u)\nvalid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n\nvalid_it = (it_raw <= max_it)\nvalid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n\nvalid = valid_u & valid_it\n\nu_idx = u_map[u_raw[valid]]\nit_idx = it_map[it_raw[valid]]\npr_arr = pr_arr[valid]\norg_arr = org_arr[valid]\nts_arr = ts_arr[valid]\n\nhalf_life = 60 * 86400\nrecency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)\norganic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)\nratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)\nweights = ratio * organic * recency\n\npair_id = u_idx.astype(np.int64) * I + it_idx\nflat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX_dense = flat.reshape(U, I)\nX_sp = sp.csr_matrix(X_dense)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')\n\n# Run TruncatedSVD\nfor n_comp in [64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n scores = (U_factor @ svd.components_).astype(np.float32)\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n scores[mask] = -1e9\n \n # Rank top 100\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_scores, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run SVD evaluation with fixed filtering"
}<untrusted_context>
Output: /usr/bin/bash: line 119: 2290 Killed python3 -c "
import sys
sys.path.append('/task')
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
from sklearn.decomposition import TruncatedSVD
from evaluate_public import validation_truth
from ranking_evaluation import CandidateSet, score_rankings
t0 = time.time()
targets = pd.read_parquet('/task/data/target_users.parquet')
uids = targets.uid.values
uid_to_idx = {int(u): i for i, u in enumerate(uids)}
catalog = pd.read_parquet('/task/data/candidate_catalog.parquet')
item_ids = catalog.item_id.values
item_to_idx = {int(it): i for i, it in enumerate(item_ids)}
elig = CandidateSet('/task/data/validation_eligible_candidates.npz')
truth = validation_truth(elig, set(uids))
U = len(uids)
I = len(item_ids)
cutoff = 24786800
t_min = cutoff - 90 * 86400
# Fast direct array lookups for uids and items
max_u = max(uids)
u_map = np.full(max_u + 1, -1, dtype=np.int32)
for u, idx in uid_to_idx.items():
u_map[u] = idx
max_it = max(item_ids)
it_map = np.full(max_it + 1, -1, dtype=np.int32)
for it, idx in item_to_idx.items():
it_map[it] = idx
# Read filtered listens
table = pq.read_table('/task/data/train_listens.parquet',
columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'],
filters=[('timestamp', '>=', t_min)])
u_raw = table['uid'].to_numpy()
it_raw = table['item_id'].to_numpy()
pr_arr = table['played_ratio_pct'].to_numpy()
org_arr = table['is_organic'].to_numpy()
ts_arr = table['timestamp'].to_numpy()
# Mask valid
valid_u = (u_raw <= max_u)
valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)
valid_it = (it_raw <= max_it)
valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)
valid = valid_u & valid_it
u_idx = u_map[u_raw[valid]]
it_idx = it_map[it_raw[valid]]
pr_arr = pr_arr[valid]
org_arr = org_arr[valid]
ts_arr = ts_arr[valid]
half_life = 60 * 86400
recency = np.exp(-np.log(2) * (cutoff - ts_arr) / half_life).astype(np.float32)
organic = np.where(org_arr == 1, 1.5, 1.0).astype(np.float32)
ratio = np.clip(pr_arr / 100.0, 0, 1.5).astype(np.float32)
weights = ratio * organic * recency
pair_id = u_idx.astype(np.int64) * I + it_idx
flat = np.bincount(pair_id, weights=weights, minlength=U * I).astype(np.float32)
# Add preference state
val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')
val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]
state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)
state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)
state_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)
state_pairs = state_u.astype(np.int64) * I + state_it
np.add.at(flat, state_pairs, state_w)
X_dense = flat.reshape(U, I)
X_sp = sp.csr_matrix(X_dense)
print(f'Matrix X ready in {time.time()-t0:.2f}s, nonzeros: {X_sp.nnz:,}')
# Run TruncatedSVD
for n_comp in [64, 128, 256]:
t_svd = time.time()
svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)
U_factor = svd.fit_transform(X_sp)
scores = (U_factor @ svd.components_).astype(np.float32)
# Mask ineligible
packed = elig.packed
mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0
scores[mask] = -1e9
# Rank top 100
top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
row_idx = np.arange(U)[:, None]
top100_scores = scores[row_idx, top100_idx]
sort_order = np.argsort(-top100_scores, axis=1)
ranked_top = top100_idx[row_idx, sort_order]
ranked_dict = {
int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]
for r in range(U)
}
res = score_rankings(ranked_dict, truth)
print(f'SVD n_comp={n_comp}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f} (took {time.time()-t_svd:.2f}s)')
"
Exit Code: 137
Process Group PGID: 2288
</untrusted_context>Investigating Memory Consumption
My process was terminated due to excessive memory use. I'm now examining what might be consuming so much memory, starting by scrutinizing the max_u variable and its potential impact when used with np.full.
{
"description": "Check max uid and item_id",
"command": "python3 -c \"\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('max uid:', targets.uid.max(), 'min uid:', targets.uid.min())\nprint('max item_id:', catalog.item_id.max(), 'min item_id:', catalog.item_id.min())\n\""
}<untrusted_context>
Output: max uid: 1000000 min uid: 100
max item_id: 9390294 min item_id: 206
Process Group PGID: 2317
</untrusted_context>Exploring Memory Bottlenecks
I've confirmed that the uid and item_id ranges aren't the source of our memory woes, as both are surprisingly manageable. My current focus is now on the pq.read_table operation itself and what might be consuming excessive memory during that process.
{
"command": "python3 -c \"\nimport os\nfor path in ['/sys/fs/cgroup/memory.max', '/sys/fs/cgroup/memory/memory.limit_in_bytes']:\n if os.path.exists(path):\n with open(path) as f:\n print(path, f.read().strip())\n\"",
"description": "Check container cgroup memory limit"
}<untrusted_context>
Output: /sys/fs/cgroup/memory.max 8589934592
Process Group PGID: 2334
</untrusted_context>Discovering Memory Constraints
I've realized the container's cgroup memory limit is set to 8 GB, significantly less than the host's 16 GB. This explains why processes are being OOM killed when memory usage spikes. I'll be implementing stricter memory management and ensuring temporary variables are promptly deleted.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nimport pandas as pd\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor batch in pf.iter_batches(batch_size=2_000_000, \n columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'],\n filters=[('timestamp', '>=', t_min)]):\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)\n ts_arr = batch['timestamp'].to_numpy(zero_copy_only=False)\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Listens accumulated in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test streaming parquet read and accumulation"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 36, in <module>
TypeError: ParquetFile.iter_batches() got an unexpected keyword argument 'filters'
Exit Code: 1
Process Group PGID: 2337
</untrusted_context>Exploring Filter Options
I'm currently investigating how to best apply filters when iterating through Parquet batches in pyarrow, specifically noting the absence of a filters argument in ParquetFile.iter_batches() for version 18.1.0. My focus is on exploring alternative approaches, such as utilizing pq.read_table with filters or examining the capabilities of pq.ParquetDataset, potentially by reading specific row groups.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport time\n\nt0 = time.time()\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\n# Read 1 row group\nt = pf.read_row_group(0, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nprint(f'1 row group read in {time.time()-t0:.3f}s, rows={len(t):,}')\n\"",
"description": "Test reading single row group"
}<untrusted_context>
Output: Num row groups: 42
1 row group read in 0.021s, rows=1,048,576
Process Group PGID: 2354
</untrusted_context>Benchmarking Row Group Performance
I'm impressed by the speed; each row group processes in under 0.03 seconds, with minimal memory overhead. I'm now focusing on optimizing the complete accumulation across all row groups, incorporating row skipping based on timestamp < t_min.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nimport time\nimport gc\n\nt0 = time.time()\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\n# 1.01 GB accumulation array\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n b = np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n flat += b\n\nprint(f'Accumulation completed in {time.time()-t0:.2f}s, nonzeros: {np.count_nonzero(flat):,}')\n\"",
"description": "Test complete streaming accumulation across row groups"
}<untrusted_context>
Output: Accumulation completed in 9.48s, nonzeros: 4,156,376
Process Group PGID: 2370
</untrusted_context>Analyzing Data Interactions
My recent focus has been on integrating explicit user preferences, specifically liked and disliked items, with the accumulated listening interaction matrix (flat). This step is crucial for refining the model's understanding of user tastes.
Integrating Explicit Preferences
I've been verifying the inclusion of train_likes and train_dislikes by examining validation_preference_state to ensure it captures active explicit preferences at the training cutoff. This is a key step for improving recommendation accuracy by directly incorporating user feedback.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nflat = np.zeros(U * I, dtype=np.float32)\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n pairs = u_idx.astype(np.int64) * I + it_idx\n flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)\n\n# Preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\nstate_pairs = state_u.astype(np.int64) * I + state_it\nnp.add.at(flat, state_pairs, state_w)\n\nX = flat.reshape(U, I)\nprint(f'Matrix X ready in {time.time()-t0:.2f}s')\n\n# Dual EASE:\n# K = X @ X.T\nt_k = time.time()\nK = X @ X.T\nprint(f'K computed in {time.time()-t_k:.2f}s')\n\nfor lam in [500.0, 1000.0, 2000.0]:\n t_solve = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n M = la.cho_solve((c, lower), X)\n \n # col_dot = sum_u (X * M)\n col_dot = np.sum(X * M, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)\n \n # Score for unseen items: -M / diag_P\n # So higher is better: -M / diag_P\n ease_scores = -M / diag_P[None, :]\n \n # Mask ineligible\n packed = elig.packed\n mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0\n ease_scores[mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(U)[:, None]\n top100_s = ease_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n ranked_dict = {\n int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]\n for r in range(U)\n }\n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_solve:.2f}s)')\n\"",
"description": "Test Dual EASE model on validation set"
}<untrusted_context>
Output: /usr/bin/bash: line 130: 2389 Killed python3 -c "
import sys
sys.path.append('/task')
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import scipy.linalg as la
import pyarrow.parquet as pq
from evaluate_public import validation_truth
from ranking_evaluation import CandidateSet, score_rankings
t0 = time.time()
targets = pd.read_parquet('/task/data/target_users.parquet')
uids = targets.uid.values
uid_to_idx = {int(u): i for i, u in enumerate(uids)}
max_u = max(uids)
u_map = np.full(max_u + 1, -1, dtype=np.int32)
for u, idx in uid_to_idx.items():
u_map[u] = idx
catalog = pd.read_parquet('/task/data/candidate_catalog.parquet')
item_ids = catalog.item_id.values
item_to_idx = {int(it): i for i, it in enumerate(item_ids)}
max_it = max(item_ids)
it_map = np.full(max_it + 1, -1, dtype=np.int32)
for it, idx in item_to_idx.items():
it_map[it] = idx
U = len(uids)
I = len(item_ids)
elig = CandidateSet('/task/data/validation_eligible_candidates.npz')
truth = validation_truth(elig, set(uids))
cutoff = 24786800
t_min = cutoff - 90 * 86400
half_life = 60 * 86400
flat = np.zeros(U * I, dtype=np.float32)
pf = pq.ParquetFile('/task/data/train_listens.parquet')
for rg in range(pf.num_row_groups):
batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])
ts = batch['timestamp'].to_numpy(zero_copy_only=False)
time_mask = (ts >= t_min)
if not np.any(time_mask):
continue
u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]
it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]
pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]
org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]
ts_arr = ts[time_mask]
valid_u = (u_raw <= max_u)
valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)
valid_it = (it_raw <= max_it)
valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)
valid = valid_u & valid_it
if not np.any(valid):
continue
u_idx = u_map[u_raw[valid]]
it_idx = it_map[it_raw[valid]]
rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)
org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)
rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)
w = rat * org * rec
pairs = u_idx.astype(np.int64) * I + it_idx
flat += np.bincount(pairs, weights=w, minlength=U * I).astype(np.float32)
# Preference state
val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')
val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]
state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)
state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)
state_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)
state_pairs = state_u.astype(np.int64) * I + state_it
np.add.at(flat, state_pairs, state_w)
X = flat.reshape(U, I)
print(f'Matrix X ready in {time.time()-t0:.2f}s')
# Dual EASE:
# K = X @ X.T
t_k = time.time()
K = X @ X.T
print(f'K computed in {time.time()-t_k:.2f}s')
for lam in [500.0, 1000.0, 2000.0]:
t_solve = time.time()
K_reg = K.copy()
np.fill_diagonal(K_reg, K_reg.diagonal() + lam)
c, lower = la.cho_factor(K_reg, overwrite_a=True)
M = la.cho_solve((c, lower), X)
# col_dot = sum_u (X * M)
col_dot = np.sum(X * M, axis=0)
diag_P = np.maximum((1.0 - col_dot) / lam, 1e-6)
# Score for unseen items: -M / diag_P
# So higher is better: -M / diag_P
ease_scores = -M / diag_P[None, :]
# Mask ineligible
packed = elig.packed
mask = np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0
ease_scores[mask] = -1e9
# Top 100
top100_idx = np.argpartition(-ease_scores, 100, axis=1)[:, :100]
row_idx = np.arange(U)[:, None]
top100_s = ease_scores[row_idx, top100_idx]
sort_order = np.argsort(-top100_s, axis=1)
ranked_top = top100_idx[row_idx, sort_order]
ranked_dict = {
int(uids[r]): [int(item_ids[c]) for c in ranked_top[r]]
for r in range(U)
}
res = score_rankings(ranked_dict, truth)
print(f'Dual EASE lam={lam}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f} (took {time.time()-t_solve:.2f}s)')
"
Exit Code: 137
Process Group PGID: 2387
</untrusted_context>Investigating Memory Allocation
I'm currently probing memory usage, as it appears the K = X @ X.T operation was unexpectedly terminated. My suspicion is memory constraints, given that X and its flat representation each consume roughly 1GB. I'm examining how X's reshape operation might be impacting available memory.
{
"description": "Test sparse X and K memory usage",
"command": "python3 -c \"\nimport resource\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nprint_mem('Start')\n# Create sparse matrix with 4.2M nonzeros\nU, I = 8456, 30000\nnnz = 4200000\nrows = np.random.randint(0, U, nnz, dtype=np.int32)\ncols = np.random.randint(0, I, nnz, dtype=np.int32)\ndata = np.random.rand(nnz).astype(np.float32)\n\nX_sp = sp.csr_matrix((data, (rows, cols)), shape=(U, I), dtype=np.float32)\ndel rows, cols, data\ngc.collect()\nprint_mem('X_sp created')\n\nt0 = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint_mem(f'K = X_sp @ X_sp.T in {time.time()-t0:.2f}s')\n\""
}<untrusted_context>
Output: [Start] Peak RSS: 65.1 MB
[X_sp created] Peak RSS: 146.3 MB
[K = X_sp @ X_sp.T in 3.22s] Peak RSS: 916.4 MB
Process Group PGID: 2414
</untrusted_context>Optimizing Memory Allocation
I'm focusing on reducing memory usage by exploring ways to construct X_sp directly, bypassing the large 1 GB flat array. My recent calculations show peak RSS at just 916 MB, which is encouraging. I'm currently analyzing the train_listens row groups, specifically u_idx, it_idx, and w, to achieve this efficiency.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport gc\n\ndef print_mem(step=''):\n usage = resource.getrusage(resource.RUSAGE_SELF).ru_maxrss\n print(f'[{step}] Peak RSS: {usage / 1024:.1f} MB')\n\nt0 = time.time()\nprint_mem('Init')\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\n# Add preference state\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\ngc.collect()\n\nprint_mem('Concatenated')\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\nprint_mem('X_sp built')\nprint(f'X_sp ready in {time.time()-t0:.2f}s, shape={X_sp.shape}, nnz={X_sp.nnz:,}')\n\"",
"description": "Test ultra-lean sparse matrix construction"
}<untrusted_context>
Output: [Init] Peak RSS: 131.1 MB
[Concatenated] Peak RSS: 628.9 MB
[X_sp built] Peak RSS: 694.2 MB
X_sp ready in 2.23s, shape=(8456, 30000), nnz=4,412,668
Process Group PGID: 2431
</untrusted_context>Achieving Optimization Goals
My recent focus has been on optimizing performance, and I'm thrilled to report significant gains. I've successfully reduced X_sp execution time to an impressive 2.23 seconds while keeping peak RSS below 700 MB. Now, I'm delving into the specifics of SVD and EASE, specifically testing TruncatedSVD to further refine our scoring mechanisms.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nfor n_comp in [32, 64, 128, 256]:\n t_svd = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=5, random_state=42)\n U_factor = svd.fit_transform(X_sp)\n V_factor = svd.components_\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = U_factor[b_start:b_end] @ V_factor\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_svd:.2f}s)')\n\"",
"description": "Run batched SVD evaluation on validation"
}<untrusted_context>
Output: SVD n_comp= 32: NDCG@10 = 0.018734 (took 3.94s)
SVD n_comp= 64: NDCG@10 = 0.018742 (took 4.49s)
SVD n_comp=128: NDCG@10 = 0.018088 (took 6.06s)
SVD n_comp=256: NDCG@10 = 0.016818 (took 8.90s)
Process Group PGID: 2456
</untrusted_context>Testing New Algorithms
I've successfully boosted NDCG@10 significantly with SVD and am now exploring Dual EASE and other methods. My current focus is on identifying algorithms that can offer even greater improvements beyond SVD's performance.
Implementing Dual EASE
I'm analyzing Dual EASE's implementation with our batched, low-memory constraints. The Cholesky factorization of $K_{reg}$ is manageable at 286 MB. I'm now exploring solving for $M$ in column blocks to further optimize memory usage, which appears feasible.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport resource\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u = []\nall_it = []\nall_w = []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\ngc.collect()\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\nprint(f'Building K...')\nt_k = time.time()\nK = (X_sp @ X_sp.T).toarray()\nprint(f'K ready in {time.time()-t_k:.2f}s')\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0]:\n t_ease = time.time()\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n \n # Solve in item chunks to keep memory low\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n \n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X_sp[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n \n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n \n # Predicted scores for unseen items: -M * inv_diag_P\n # Scale M by -inv_diag_P\n M *= -inv_diag_P[None, :]\n \n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = M[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n print(f'Dual EASE lam={lam:6.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f} (took {time.time()-t_ease:.2f}s)')\n\"",
"description": "Evaluate Dual EASE on validation set"
}<untrusted_context>
Output: Building K...
K ready in 5.45s
Dual EASE lam= 100.0: NDCG@10 = 0.008167 (took 24.86s)
Dual EASE lam= 500.0: NDCG@10 = 0.009555 (took 24.92s)
Dual EASE lam=1000.0: NDCG@10 = 0.010752 (took 24.87s)
Dual EASE lam=2000.0: NDCG@10 = 0.012705 (took 24.89s)
Process Group PGID: 2483
</untrusted_context>Analyzing SVD Performance
I'm focusing on why SVD significantly outperformed Dual EASE in the recent test. It appears SVD's lower rank latent space effectively captures broader user preferences, enabling generalization beyond direct co-occurrences, which is crucial for music discovery where user taste is nuanced.
Optimizing SVD Parameters
I'm now investigating hyperparameter tuning for SVD. Specifically, I'm testing the impact of different normalization techniques like TF-IDF and BM25 on the user-item matrix, alongside exploring various numbers of latent components and their influence on performance metrics.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\n\n# Function to build X given half_life, t_min_days, organic_mult\ndef build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n if min_ratio > 0:\n ratio_mask = (pr_arr >= min_ratio)\n if not np.any(ratio_mask):\n continue\n pr_arr = pr_arr[ratio_mask]\n time_mask_idx = np.where(time_mask)[0][ratio_mask]\n else:\n time_mask_idx = np.where(time_mask)[0]\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Pre-unpack eligibility mask\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Evaluate function\ndef eval_factors(U_factor, V_factor, pop_weight=0.0):\n log_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_factor[b_start:b_end] @ V_factor).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Test base X\nX_sp = build_X(half_life_days=60, t_min_days=90, organic_mult=1.5, min_ratio=0)\nfor n_comp in [48, 64, 80]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n score_pure = eval_factors(U_f, V_f, pop_weight=0.0)\n print(f'SVD n_comp={n_comp}, pure NDCG@10 = {score_pure:.6f}')\n for pw in [0.01, 0.03, 0.05, 0.1]:\n score_pop = eval_factors(U_f, V_f, pop_weight=pw)\n print(f' + pop_weight={pw}: NDCG@10 = {score_pop:.6f}')\n\"",
"description": "Evaluate SVD hyperparameters and popularity blending"
}<untrusted_context>
Output: SVD n_comp=48, pure NDCG@10 = 0.019382
+ pop_weight=0.01: NDCG@10 = 0.019535
+ pop_weight=0.03: NDCG@10 = 0.019844
+ pop_weight=0.05: NDCG@10 = 0.019932
+ pop_weight=0.1: NDCG@10 = 0.019699
SVD n_comp=64, pure NDCG@10 = 0.019016
+ pop_weight=0.01: NDCG@10 = 0.019301
+ pop_weight=0.03: NDCG@10 = 0.019635
+ pop_weight=0.05: NDCG@10 = 0.019690
+ pop_weight=0.1: NDCG@10 = 0.019813
SVD n_comp=80, pure NDCG@10 = 0.019169
+ pop_weight=0.01: NDCG@10 = 0.019459
+ pop_weight=0.03: NDCG@10 = 0.019739
+ pop_weight=0.05: NDCG@10 = 0.020100
+ pop_weight=0.1: NDCG@10 = 0.020543
Process Group PGID: 2510
</untrusted_context>Improving NDCG@10 Score
My NDCG@10 has now surpassed 0.02! I'm seeing positive trends as I increase n_comp to 80 and pop_weight. I'll now explore further increases to pop_weight and experiment with organic popularity.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\n# Let's run a test comparing organic_pop vs positive_listens pop and different weights\n\"",
"description": "Test script setup"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2537
</untrusted_context>Exploring Normalization Strategies
I'm currently investigating various normalization techniques for the raw listen counts. Specifically, I'm considering options like TF-IDF and BM25, and their potential application to address the wide dynamic range observed in user-item interaction weights, aiming for a more balanced data representation.
Testing Log-Scaling and Normalization
I'm now exploring a log-scaling transformation np.log1p(X_sp.data). Following this, I'm assessing the impact of L2 normalizing user rows before SVD to mitigate high-activity user influence. I'm also considering item-item collaborative filtering with cosine similarity and top-K neighbors.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\nt_min = cutoff - 90 * 86400\nhalf_life = 60 * 86400\n\nall_u, all_it, all_w = [], [], []\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask]\n ts_arr = ts[time_mask]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 1.5, 1.0).astype(np.float32)\n rat = np.clip(pr_arr[valid] / 100.0, 0, 1.5).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n\nval_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\nstate_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\nstate_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\nstate_w = np.where(val_state.state.values == 'liked', 3.0, -2.0).astype(np.float32)\n\nall_u.append(state_u)\nall_it.append(state_it)\nall_w.append(state_w)\n\nu_all = np.concatenate(all_u)\nit_all = np.concatenate(all_it)\nw_all = np.concatenate(all_w)\ndel all_u, all_it, all_w\n\nX_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\ndel u_all, it_all, w_all\nX_sp.sum_duplicates()\n\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Build Item-Item Cosine Similarity Matrix\n# We can normalize columns of X_sp (items)\n# X_csc for fast column ops\nX_csc = X_sp.tocsc()\n# For cosine, clip negative weights to 0 for similarity\nX_pos = X_csc.copy()\nX_pos.data = np.maximum(0, X_pos.data)\nX_norm = normalize(X_pos, norm='l2', axis=0) # unit norm columns\n\nprint(f'Computing Item-Item similarity in chunks...')\nt_sim = time.time()\n# S_sparse: for each chunk of items, compute dot with all items, keep top K\nK_nn = 100\nrows_s, cols_s, data_s = [], [], []\n\nchunk_size = 2000\nfor c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n # items in chunk: (c_end - c_start)\n chunk_items = X_norm[:, c_start:c_end]\n # sim matrix: (c_end - c_start) x I\n sim_chunk = (chunk_items.T @ X_norm).toarray() # dense chunk\n # zero out diagonal\n for idx_i in range(c_end - c_start):\n sim_chunk[idx_i, c_start + idx_i] = 0.0\n \n # top K for each item in chunk\n top_k_idx = np.argpartition(-sim_chunk, K_nn, axis=1)[:, :K_nn]\n for idx_i in range(c_end - c_start):\n top_cols = top_k_idx[idx_i]\n top_vals = sim_chunk[idx_i, top_cols]\n # filter positive\n pos_mask = top_vals > 0\n top_cols = top_cols[pos_mask]\n top_vals = top_vals[pos_mask]\n \n rows_s.extend([c_start + idx_i] * len(top_cols))\n cols_s.extend(top_cols)\n data_s.extend(top_vals)\n\nS_sparse = sp.csr_matrix((data_s, (rows_s, cols_s)), shape=(I, I), dtype=np.float32)\nprint(f'Item similarity S built in {time.time()-t_sim:.2f}s, nnz={S_sparse.nnz:,}')\n\n# User scores: X_sp @ S_sparse\nt_score = time.time()\n# Multiply in batches\nranked_dict = {}\nbatch_size = 1000\nlog_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\nfor b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (X_sp[b_start:b_end] @ S_sparse).toarray()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n\nres = score_rankings(ranked_dict, truth)\nprint(f'Item-Item CF pure NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate Item-Item CF on validation set"
}<untrusted_context>
Output: Computing Item-Item similarity in chunks...
Item similarity S built in 24.33s, nnz=2,997,411
Item-Item CF pure NDCG@10 = 0.011673
Process Group PGID: 2564
</untrusted_context>Analyzing SVD Dominance
I'm focusing on understanding why SVD is outperforming item-item collaborative filtering so significantly. The sparsity of the interaction matrix appears to be a key factor contributing to SVD's superior performance.
Refining SVD Input
I'm now exploring the impact of different time windows and decay factors on SVD's performance, specifically testing 180, 90, and 45-day windows with various half-lives. I'm also investigating the inclusion of train_likes.parquet and clarifying the scope of validation_preference_state.parquet.
{
"description": "Check organic and played ratio distributions",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\norg = table['is_organic'].to_numpy()\npr = table['played_ratio_pct'].to_numpy()\nprint(f'Organic listen fraction: {org.mean():.2%}')\nprint(f'Played >= 80% fraction: {(pr >= 80).mean():.2%}')\nprint(f'Organic and >= 80%: {((org == 1) & (pr >= 80)).mean():.2%}')\n\""
}<untrusted_context>
Output: Organic listen fraction: 51.69%
Played >= 80% fraction: 59.23%
Organic and >= 80%: 28.00%
Process Group PGID: 2591
</untrusted_context>Analyzing Listen Data
I'm focusing on the low organic listen percentage and its implications. Specifically, I'm questioning the weighting of skips. If a listen has a played ratio below 30%, it's a clear skip. I'm considering if skips should have negative or zero weight, as positively weighting a track a user skipped after only 5 seconds seems counterproductive.
{
"description": "Test discrete ratio weights and organic pop weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\nt0 = time.time()\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\n\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\n# Preload candidate catalog popularity signals\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\nlog_tot_pop = np.log1p(catalog.positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=0.0):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * pop_vec[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\n# Function to build X given parameters\ndef build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0, like_w=4.0, dislike_w=-2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n \n all_u, all_it, all_w = [], [], []\n \n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n \n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n \n valid_u = (u_raw <= max_u)\n valid_u[valid_u] = (u_map[u_raw[valid_u]] >= 0)\n valid_it = (it_raw <= max_it)\n valid_it[valid_it] = (it_map[it_raw[valid_it]] >= 0)\n valid = valid_u & valid_it\n if not np.any(valid):\n continue\n \n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n \n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n \n # Played ratio scoring\n pr = pr_arr[valid]\n rat = np.where(pr >= 80, 1.0, np.where(pr >= 50, 0.4, -skip_penalty)).astype(np.float32)\n w = rat * org * rec\n \n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(w)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n\n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n\n X_sp = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X_sp.sum_duplicates()\n return X_sp\n\n# Test half_life_days=45, organic_mult=2.0\nX_sp = build_matrix(half_life_days=45, t_min_days=90, organic_mult=2.0, skip_penalty=0.0)\n\nfor n_comp in [64, 80, 96]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_sp)\n V_f = svd.components_\n for pw in [0.05, 0.1, 0.15, 0.2]:\n score_org = evaluate(U_f, V_f, pop_vec=log_org_pop, pop_weight=pw)\n print(f'n_comp={n_comp}, pw_org={pw:.2f}: NDCG@10 = {score_org:.6f}')\n\""
}<untrusted_context>
Output: n_comp=64, pw_org=0.05: NDCG@10 = 0.019745
n_comp=64, pw_org=0.10: NDCG@10 = 0.020004
n_comp=64, pw_org=0.15: NDCG@10 = 0.019890
n_comp=64, pw_org=0.20: NDCG@10 = 0.019773
n_comp=80, pw_org=0.05: NDCG@10 = 0.020798
n_comp=80, pw_org=0.10: NDCG@10 = 0.021160
n_comp=80, pw_org=0.15: NDCG@10 = 0.021262
n_comp=80, pw_org=0.20: NDCG@10 = 0.021121
n_comp=96, pw_org=0.05: NDCG@10 = 0.020271
n_comp=96, pw_org=0.10: NDCG@10 = 0.021136
n_comp=96, pw_org=0.15: NDCG@10 = 0.020850
n_comp=96, pw_org=0.20: NDCG@10 = 0.020710
Process Group PGID: 2613
</untrusted_context>Improving NDCG Performance
I've achieved a near doubling of NDCG@10 to 0.021262 with n_comp=80 and pw_org=0.15. Now, I'm investigating further gains by exploring normalization techniques for user vectors in $X_{\text{sp}}$, specifically L2 and L1 norms prior to SVD.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\n\n# Test IDF weighting on X_sp\n# Let's import the previous setup logic and test IDF scaling\n\"",
"description": "Prepare IDF test"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2640
</untrusted_context>Exploring Ranking Factors
I'm currently evaluating the impact of L2 user normalization, varying item IDF weighting (0.25, 0.5, and 1.0), and applying log1p transformations to weights. My aim is to refine our ranking algorithm by testing these parameters.
{
"description": "Test normalizations and transforms",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 4.0, -2.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n return X\n\nX_base = build_matrix(half_life_days=45, t_min_days=90)\n\n# Test 1: User L2 Normalization\nX_user_norm = normalize(X_base, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_user_norm)\nV_f = svd.components_\nprint('User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 2: Log1p transform on positive values\nX_log = X_base.copy()\npos_idx = X_log.data > 0\nX_log.data[pos_idx] = np.log1p(X_log.data[pos_idx])\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log)\nV_f = svd.components_\nprint('Log1p NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 3: Log1p + User L2 Normalization\nX_log_norm = normalize(X_log, norm='l2', axis=1)\nsvd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\nU_f = svd.fit_transform(X_log_norm)\nV_f = svd.components_\nprint('Log1p + User L2 normalized NDCG@10:', evaluate(U_f, V_f, 0.15))\n\n# Test 4: Item IDF weighting\n# df per item\ndf_item = np.diff(X_base.tocsc().indptr)\nidf = np.log((U + 1.0) / (df_item + 1.0)) + 1.0\nfor p in [0.25, 0.5]:\n idf_diag = sp.diags(idf ** p)\n X_idf = X_base @ idf_diag\n svd = TruncatedSVD(n_components=80, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X_idf)\n V_f = svd.components_\n print(f'IDF^{p} NDCG@10:', evaluate(U_f, V_f, 0.15))\n\""
}<untrusted_context>
Output: User L2 normalized NDCG@10: 0.010972
Log1p NDCG@10: 0.026368
Log1p + User L2 normalized NDCG@10: 0.011793
IDF^0.25 NDCG@10: 0.02251
IDF^0.5 NDCG@10: 0.021536
Process Group PGID: 2658
</untrusted_context>Analyzing Log1p Impact
I'm seeing a significant jump in NDCG@10 to 0.026368 after applying Log1p compression. This improvement, up from 0.021262, suggests that accounting for the power-law distribution of listen frequencies, where some tracks are vastly more popular, is a beneficial strategy.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\nlog_org_pop = np.log1p(catalog.organic_positive_listens.values).astype(np.float32)\n\ndef evaluate(U_f, V_f, pop_weight=0.15):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n if pop_weight != 0.0:\n b_scores += pop_weight * log_org_pop[None, :]\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [64, 80, 100, 128]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n for pw in [0.0, 0.05, 0.1, 0.15, 0.2]:\n score = evaluate(U_f, V_f, pop_weight=pw)\n print(f'n_comp={n_comp}, pw={pw:.2f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Sweep components and popularity weights for log1p SVD"
}<untrusted_context>
Output: n_comp=64, pw=0.00: NDCG@10 = 0.031635
n_comp=64, pw=0.05: NDCG@10 = 0.029762
n_comp=64, pw=0.10: NDCG@10 = 0.028191
n_comp=64, pw=0.15: NDCG@10 = 0.026146
n_comp=64, pw=0.20: NDCG@10 = 0.023952
n_comp=80, pw=0.00: NDCG@10 = 0.031213
n_comp=80, pw=0.05: NDCG@10 = 0.029205
n_comp=80, pw=0.10: NDCG@10 = 0.027956
n_comp=80, pw=0.15: NDCG@10 = 0.026271
n_comp=80, pw=0.20: NDCG@10 = 0.024379
n_comp=100, pw=0.00: NDCG@10 = 0.031578
n_comp=100, pw=0.05: NDCG@10 = 0.029687
n_comp=100, pw=0.10: NDCG@10 = 0.028371
n_comp=100, pw=0.15: NDCG@10 = 0.026645
n_comp=100, pw=0.20: NDCG@10 = 0.024692
n_comp=128, pw=0.00: NDCG@10 = 0.031919
n_comp=128, pw=0.05: NDCG@10 = 0.029963
n_comp=128, pw=0.10: NDCG@10 = 0.028236
n_comp=128, pw=0.15: NDCG@10 = 0.026469
n_comp=128, pw=0.20: NDCG@10 = 0.024727
Process Group PGID: 2685
</untrusted_context>Discovering Pure SVD Power
I've hit on something significant! It turns out that after log-scaling, pure SVD, even with just 128 components, surprisingly outperforms models that incorporate popularity. The log-scaling seems to amplify the true personalized signal, pushing popularity into a detrimental role.
{
"description": "Evaluate SVD 160/200, Dual EASE on log1p, and Ensembling",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg as la\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_scores(scores_matrix):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = scores_matrix[b_start:b_end].copy()\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\n# Higher n_components for SVD\nfor n_comp in [128, 160, 200]:\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n scores_svd = (U_f @ svd.components_).astype(np.float32)\n print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')\n\n# Dual EASE on log1p matrix\nK = (X @ X.T).toarray()\nfor lam in [100.0, 300.0, 500.0, 1000.0]:\n K_reg = K.copy()\n np.fill_diagonal(K_reg, K_reg.diagonal() + lam)\n c, lower = la.cho_factor(K_reg, overwrite_a=True)\n chunk_size = 5000\n M = np.empty((U, I), dtype=np.float32)\n col_dot = np.empty(I, dtype=np.float32)\n for c_start in range(0, I, chunk_size):\n c_end = min(c_start + chunk_size, I)\n X_chunk = X[:, c_start:c_end].toarray()\n M_chunk = la.cho_solve((c, lower), X_chunk)\n M[:, c_start:c_end] = M_chunk\n col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)\n diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)\n inv_diag_P = 1.0 / diag_P\n scores_ease = -M * inv_diag_P[None, :]\n print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')\n \n # Check ensemble with SVD-160\n # Normalize each score matrix (z-score per user)\n scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)\n scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)\n for w_ease in [0.2, 0.4, 0.6]:\n blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm\n print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')\n\""
}<untrusted_context>
Output: /usr/bin/bash: line 149: 2714 Killed python3 -c "
import sys
sys.path.append('/task')
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import scipy.linalg as la
import pyarrow.parquet as pq
from sklearn.decomposition import TruncatedSVD
from evaluate_public import validation_truth
from ranking_evaluation import CandidateSet, score_rankings
targets = pd.read_parquet('/task/data/target_users.parquet')
uids = targets.uid.values
uid_to_idx = {int(u): i for i, u in enumerate(uids)}
max_u = max(uids)
u_map = np.full(max_u + 1, -1, dtype=np.int32)
for u, idx in uid_to_idx.items():
u_map[u] = idx
catalog = pd.read_parquet('/task/data/candidate_catalog.parquet')
item_ids = catalog.item_id.values
item_to_idx = {int(it): i for i, it in enumerate(item_ids)}
max_it = max(item_ids)
it_map = np.full(max_it + 1, -1, dtype=np.int32)
for it, idx in item_to_idx.items():
it_map[it] = idx
U = len(uids)
I = len(item_ids)
elig = CandidateSet('/task/data/validation_eligible_candidates.npz')
truth = validation_truth(elig, set(uids))
cutoff = 24786800
packed = elig.packed
elig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)
def evaluate_scores(scores_matrix):
ranked_dict = {}
batch_size = 1000
for b_start in range(0, U, batch_size):
b_end = min(b_start + batch_size, U)
b_len = b_end - b_start
b_scores = scores_matrix[b_start:b_end].copy()
b_scores[elig_mask[b_start:b_end]] = -1e9
top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]
row_idx = np.arange(b_len)[:, None]
top100_s = b_scores[row_idx, top100_idx]
sort_order = np.argsort(-top100_s, axis=1)
ranked_top = top100_idx[row_idx, sort_order]
for i in range(b_len):
u_real = int(uids[b_start + i])
ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]
res = score_rankings(ranked_dict, truth)
return res['quality_ndcg@10']
def build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):
half_life = half_life_days * 86400
t_min = cutoff - t_min_days * 86400
all_u, all_it, all_w = [], [], []
pf = pq.ParquetFile('/task/data/train_listens.parquet')
for rg in range(pf.num_row_groups):
batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])
ts = batch['timestamp'].to_numpy(zero_copy_only=False)
time_mask = (ts >= t_min)
if not np.any(time_mask):
continue
time_mask_idx = np.where(time_mask)[0]
u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]
it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]
pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]
org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]
ts_arr = ts[time_mask_idx]
valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)
if not np.any(valid):
continue
u_idx = u_map[u_raw[valid]]
it_idx = it_map[it_raw[valid]]
rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)
org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)
rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)
all_u.append(u_idx)
all_it.append(it_idx)
all_w.append(rat * org * rec)
val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')
val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]
state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)
state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)
state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)
all_u.append(state_u)
all_it.append(state_it)
all_w.append(state_w)
u_all = np.concatenate(all_u)
it_all = np.concatenate(all_it)
w_all = np.concatenate(all_w)
X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)
X.sum_duplicates()
pos_idx = X.data > 0
X.data[pos_idx] = np.log1p(X.data[pos_idx])
return X
X = build_matrix(half_life_days=45, t_min_days=90)
# Higher n_components for SVD
for n_comp in [128, 160, 200]:
svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)
U_f = svd.fit_transform(X)
scores_svd = (U_f @ svd.components_).astype(np.float32)
print(f'SVD n_comp={n_comp}: NDCG@10 = {evaluate_scores(scores_svd):.6f}')
# Dual EASE on log1p matrix
K = (X @ X.T).toarray()
for lam in [100.0, 300.0, 500.0, 1000.0]:
K_reg = K.copy()
np.fill_diagonal(K_reg, K_reg.diagonal() + lam)
c, lower = la.cho_factor(K_reg, overwrite_a=True)
chunk_size = 5000
M = np.empty((U, I), dtype=np.float32)
col_dot = np.empty(I, dtype=np.float32)
for c_start in range(0, I, chunk_size):
c_end = min(c_start + chunk_size, I)
X_chunk = X[:, c_start:c_end].toarray()
M_chunk = la.cho_solve((c, lower), X_chunk)
M[:, c_start:c_end] = M_chunk
col_dot[c_start:c_end] = np.sum(X_chunk * M_chunk, axis=0)
diag_P = np.maximum((1.0 - col_dot) / lam, 1e-7)
inv_diag_P = 1.0 / diag_P
scores_ease = -M * inv_diag_P[None, :]
print(f'Dual EASE on log1p, lam={lam}: NDCG@10 = {evaluate_scores(scores_ease):.6f}')
# Check ensemble with SVD-160
# Normalize each score matrix (z-score per user)
scores_svd_norm = (scores_svd - scores_svd.mean(axis=1, keepdims=True)) / (scores_svd.std(axis=1, keepdims=True) + 1e-6)
scores_ease_norm = (scores_ease - scores_ease.mean(axis=1, keepdims=True)) / (scores_ease.std(axis=1, keepdims=True) + 1e-6)
for w_ease in [0.2, 0.4, 0.6]:
blend = (1 - w_ease) * scores_svd_norm + w_ease * scores_ease_norm
print(f' Blend (1-{w_ease})*SVD + {w_ease}*EASE(lam={lam}): NDCG@10 = {evaluate_scores(blend):.6f}')
"
Exit Code: 137
Process Group PGID: 2712
</untrusted_context>Analyzing Memory Footprint
I'm currently scrutinizing the memory demands of my calculations. Specifically, I'm noting that scores_svd, a substantial $8456 \times 30000$ matrix, consumes about 1 GB. This is a key factor as I move into the second loop, where it's joined by several other large arrays like K, M, scores_ease, and their normalized counterparts.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 2.0, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nX = build_matrix(half_life_days=45, t_min_days=90)\n\nfor n_comp in [96, 128, 160, 200, 256]:\n t1 = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n V_f = svd.components_\n score = evaluate_factors(U_f, V_f)\n print(f'SVD n_comp={n_comp:3d}: NDCG@10 = {score:.6f} (took {time.time()-t1:.2f}s)')\n del U_f, V_f, svd\n gc.collect()\n\"",
"description": "Evaluate SVD components with lean batched memory"
}<untrusted_context>
Output: SVD n_comp= 96: NDCG@10 = 0.032038 (took 5.82s)
SVD n_comp=128: NDCG@10 = 0.031919 (took 7.34s)
SVD n_comp=160: NDCG@10 = 0.031208 (took 8.52s)
SVD n_comp=200: NDCG@10 = 0.030494 (took 10.06s)
SVD n_comp=256: NDCG@10 = 0.030458 (took 12.72s)
Process Group PGID: 2739
</untrusted_context>Tuning Data Parameters
I'm currently exploring how different half_life_days and t_min_days values impact performance. Initial runs with n_comp=96 yielded an NDCG@10 of 0.032038. I'm systematically testing a range of values for both parameters to identify optimal settings.
{
"description": "Evaluate half-life variations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=45, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [20, 35, 50, 70]:\n X = build_matrix(half_life_days=hl, t_min_days=90, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
}<untrusted_context>
Output: half_life=20d: NDCG@10 = 0.033544
half_life=35d: NDCG@10 = 0.032493
half_life=50d: NDCG@10 = 0.031782
half_life=70d: NDCG@10 = 0.031005
Process Group PGID: 2766
</untrusted_context>{
"description": "Fine-grain search for optimal half-life",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor hl in [7, 10, 14, 20]:\n X = build_matrix(half_life_days=hl, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'half_life={hl:2d}d: NDCG@10 = {score:.6f}')\n\""
}<untrusted_context>
Output: half_life= 7d: NDCG@10 = 0.032981
half_life=10d: NDCG@10 = 0.033948
half_life=14d: NDCG@10 = 0.034023
half_life=20d: NDCG@10 = 0.033817
Process Group PGID: 2793
</untrusted_context>Testing Parameters
I've successfully evaluated the NDCG@10 for a 14-day half-life, achieving 0.034023. My next step is to explore the interplay of organic_mult and like_w under this established half-life. I'm currently testing variations of like_w (3, 5, 8, 12) and organic_mult (1.5, 2.0).
{
"description": "Search over organic multiplier and like weights",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=5.0, dislike_w=-3.0, organic_mult=2.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [1.5, 2.0, 3.0]:\n for lw in [3.0, 6.0, 10.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=lw, dislike_w=-3.0, organic_mult=org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org_mult={org_m:.1f}, like_w={lw:.1f}: NDCG@10 = {score:.6f}')\n\""
}<untrusted_context>
Output: org_mult=1.5, like_w=3.0: NDCG@10 = 0.033124
org_mult=1.5, like_w=6.0: NDCG@10 = 0.032162
org_mult=1.5, like_w=10.0: NDCG@10 = 0.032840
org_mult=2.0, like_w=3.0: NDCG@10 = 0.033992
org_mult=2.0, like_w=6.0: NDCG@10 = 0.034090
org_mult=2.0, like_w=10.0: NDCG@10 = 0.034108
org_mult=3.0, like_w=3.0: NDCG@10 = 0.035300
org_mult=3.0, like_w=6.0: NDCG@10 = 0.035780
org_mult=3.0, like_w=10.0: NDCG@10 = 0.034943
Process Group PGID: 2820
</untrusted_context>Testing Multiplier Impact
I'm exploring how altering the organic multiplier affects NDCG@10. Initial findings with a multiplier of 3.0 yielded 0.035780. Now, I'm investigating higher multipliers (4.0, 5.0) and the impact of downweighting non-organic listens.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=3.0, non_org_mult=1.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, non_org_mult).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor org_m in [3.0, 4.0, 5.0]:\n for non_org_m in [0.2, 0.5, 1.0]:\n X = build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=org_m, non_org_mult=non_org_m)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'org={org_m:.1f}, non_org={non_org_m:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test organic vs non-organic relative weighting"
}<untrusted_context>
Output: org=3.0, non_org=0.2: NDCG@10 = 0.034776
org=3.0, non_org=0.5: NDCG@10 = 0.036133
org=3.0, non_org=1.0: NDCG@10 = 0.035780
org=4.0, non_org=0.2: NDCG@10 = 0.035782
org=4.0, non_org=0.5: NDCG@10 = 0.036248
org=4.0, non_org=1.0: NDCG@10 = 0.036237
org=5.0, non_org=0.2: NDCG@10 = 0.035872
org=5.0, non_org=0.5: NDCG@10 = 0.036697
org=5.0, non_org=1.0: NDCG@10 = 0.036811
Process Group PGID: 2847
</untrusted_context>Observing Performance Gains
My recent evaluations show a continued upward trend in NDCG@10, currently at 0.036811. This represents a significant 3.44x improvement over the baseline. I'm now exploring the impact of increasing the 'org' parameter to 6.0.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef evaluate_factors(U_f, V_f):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n b_scores = (U_f[b_start:b_end] @ V_f).astype(np.float32)\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\ndef build_matrix(ratio_low=0.0, ratio_mid=0.3, ratio_high=1.0):\n half_life = 14 * 86400\n t_min = cutoff - 60 * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, 5.0, 1.0).astype(np.float32)\n \n pr = pr_arr[valid]\n rat = np.where(pr >= 80, ratio_high, np.where(pr >= 50, ratio_mid, ratio_low)).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', 6.0, -3.0).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\nfor r_low in [0.0, -0.5, 0.1]:\n for r_mid in [0.2, 0.4, 0.6]:\n X = build_matrix(ratio_low=r_low, ratio_mid=r_mid, ratio_high=1.0)\n svd = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\n U_f = svd.fit_transform(X)\n score = evaluate_factors(U_f, svd.components_)\n print(f'r_low={r_low:.1f}, r_mid={r_mid:.1f}: NDCG@10 = {score:.6f}')\n\"",
"description": "Test ratio scoring thresholds"
}<untrusted_context>
Output: r_low=0.0, r_mid=0.2: NDCG@10 = 0.035598
r_low=0.0, r_mid=0.4: NDCG@10 = 0.035635
r_low=0.0, r_mid=0.6: NDCG@10 = 0.035516
r_low=-0.5, r_mid=0.2: NDCG@10 = 0.020860
r_low=-0.5, r_mid=0.4: NDCG@10 = 0.021453
r_low=-0.5, r_mid=0.6: NDCG@10 = 0.022195
r_low=0.1, r_mid=0.2: NDCG@10 = 0.036099
r_low=0.1, r_mid=0.4: NDCG@10 = 0.035879
r_low=0.1, r_mid=0.6: NDCG@10 = 0.036049
Process Group PGID: 2874
</untrusted_context>Testing Ratio Calculations
My recent explorations involved tweaking the r_low parameter, finding a value of 0.1 to be marginally more effective than 0.0 or negative alternatives. This builds upon prior experimentation where a threshold of 0.4 for ratios below 80 resulted in a score of 0.036811. I am now investigating clipping the ratio.
Exploring Blending Strategies
I'm now considering multi-model ensembling. Specifically, I want to investigate blending different SVD models, perhaps trained with varying random seeds or half-lives, to see if averaging their predictions yields improvements. I'm also tentatively looking into iALS but am concerned about implementation speed.
{
"description": "Evaluate multi-temporal SVD ensemble",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet, score_rankings\nimport gc\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nuids = targets.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\nmax_u = max(uids)\nu_map = np.full(max_u + 1, -1, dtype=np.int32)\nfor u, idx in uid_to_idx.items():\n u_map[u] = idx\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = catalog.item_id.values\nitem_to_idx = {int(it): i for i, it in enumerate(item_ids)}\nmax_it = max(item_ids)\nit_map = np.full(max_it + 1, -1, dtype=np.int32)\nfor it, idx in item_to_idx.items():\n it_map[it] = idx\n\nU = len(uids)\nI = len(item_ids)\n\nelig = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(elig, set(uids))\ncutoff = 24786800\npacked = elig.packed\nelig_mask = (np.unpackbits(packed, axis=1, count=I, bitorder='little') == 0)\n\ndef build_matrix(half_life_days=14, t_min_days=60, like_w=6.0, dislike_w=-3.0, organic_mult=5.0):\n half_life = half_life_days * 86400\n t_min = cutoff - t_min_days * 86400\n all_u, all_it, all_w = [], [], []\n pf = pq.ParquetFile('/task/data/train_listens.parquet')\n for rg in range(pf.num_row_groups):\n batch = pf.read_row_group(rg, columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\n ts = batch['timestamp'].to_numpy(zero_copy_only=False)\n time_mask = (ts >= t_min)\n if not np.any(time_mask):\n continue\n time_mask_idx = np.where(time_mask)[0]\n u_raw = batch['uid'].to_numpy(zero_copy_only=False)[time_mask_idx]\n it_raw = batch['item_id'].to_numpy(zero_copy_only=False)[time_mask_idx]\n pr_arr = batch['played_ratio_pct'].to_numpy(zero_copy_only=False)[time_mask_idx]\n org_arr = batch['is_organic'].to_numpy(zero_copy_only=False)[time_mask_idx]\n ts_arr = ts[time_mask_idx]\n valid = (u_raw <= max_u) & (u_map[np.minimum(u_raw, max_u)] >= 0) & (it_raw <= max_it) & (it_map[np.minimum(it_raw, max_it)] >= 0)\n if not np.any(valid):\n continue\n u_idx = u_map[u_raw[valid]]\n it_idx = it_map[it_raw[valid]]\n rec = np.exp(-np.log(2) * (cutoff - ts_arr[valid]) / half_life).astype(np.float32)\n org = np.where(org_arr[valid] == 1, organic_mult, 1.0).astype(np.float32)\n rat = np.where(pr_arr[valid] >= 80, 1.0, 0.4).astype(np.float32)\n all_u.append(u_idx)\n all_it.append(it_idx)\n all_w.append(rat * org * rec)\n \n val_state = pd.read_parquet('/task/data/validation_preference_state.parquet')\n val_state = val_state[val_state.item_id.isin(item_to_idx) & val_state.uid.isin(uid_to_idx)]\n state_u = np.array([uid_to_idx[u] for u in val_state.uid], dtype=np.int32)\n state_it = np.array([item_to_idx[it] for it in val_state.item_id], dtype=np.int32)\n state_w = np.where(val_state.state.values == 'liked', like_w, dislike_w).astype(np.float32)\n all_u.append(state_u)\n all_it.append(state_it)\n all_w.append(state_w)\n \n u_all = np.concatenate(all_u)\n it_all = np.concatenate(all_it)\n w_all = np.concatenate(all_w)\n X = sp.csr_matrix((w_all, (u_all, it_all)), shape=(U, I), dtype=np.float32)\n X.sum_duplicates()\n \n pos_idx = X.data > 0\n X.data[pos_idx] = np.log1p(X.data[pos_idx])\n return X\n\n# Model 1: half_life = 10 days\nX1 = build_matrix(half_life_days=10, t_min_days=45)\nsvd1 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=42)\nU1 = svd1.fit_transform(X1)\nV1 = svd1.components_\n\n# Model 2: half_life = 25 days\nX2 = build_matrix(half_life_days=25, t_min_days=90)\nsvd2 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=123)\nU2 = svd2.fit_transform(X2)\nV2 = svd2.components_\n\n# Model 3: half_life = 60 days\nX3 = build_matrix(half_life_days=60, t_min_days=120)\nsvd3 = TruncatedSVD(n_components=96, algorithm='randomized', n_iter=7, random_state=456)\nU3 = svd3.fit_transform(X3)\nV3 = svd3.components_\n\ndef eval_ensemble(weights):\n ranked_dict = {}\n batch_size = 1000\n for b_start in range(0, U, batch_size):\n b_end = min(b_start + batch_size, U)\n b_len = b_end - b_start\n s1 = (U1[b_start:b_end] @ V1).astype(np.float32)\n s2 = (U2[b_start:b_end] @ V2).astype(np.float32)\n s3 = (U3[b_start:b_end] @ V3).astype(np.float32)\n \n # Standardize per user within batch\n s1 = (s1 - s1.mean(axis=1, keepdims=True)) / (s1.std(axis=1, keepdims=True) + 1e-6)\n s2 = (s2 - s2.mean(axis=1, keepdims=True)) / (s2.std(axis=1, keepdims=True) + 1e-6)\n s3 = (s3 - s3.mean(axis=1, keepdims=True)) / (s3.std(axis=1, keepdims=True) + 1e-6)\n \n b_scores = weights[0] * s1 + weights[1] * s2 + weights[2] * s3\n b_scores[elig_mask[b_start:b_end]] = -1e9\n \n top100_idx = np.argpartition(-b_scores, 100, axis=1)[:, :100]\n row_idx = np.arange(b_len)[:, None]\n top100_s = b_scores[row_idx, top100_idx]\n sort_order = np.argsort(-top100_s, axis=1)\n ranked_top = top100_idx[row_idx, sort_order]\n \n for i in range(b_len):\n u_real = int(uids[b_start + i])\n ranked_dict[u_real] = [int(item_ids[c]) for c in ranked_top[i]]\n \n res = score_rankings(ranked_dict, truth)\n return res['quality_ndcg@10']\n\nprint('Model 1 alone:', eval_ensemble([1.0, 0.0, 0.0]))\nprint('Model 2 alone:', eval_ensemble([0.0, 1.0, 0.0]))\nprint('Model 3 alone:', eval_ensemble([0.0, 0.0, 1.0]))\nprint('Ensemble 1+2 (0.6, 0.4):', eval_ensemble([0.6, 0.4, 0.0]))\nprint('Ensemble 1+2+3 (0.5, 0.35, 0.15):', eval_ensemble([0.5, 0.35, 0.15]))\nprint('Ensemble 1+2+3 (0.6, 0.3, 0.1):', eval_ensemble([0.6, 0.3, 0.1]))\n\""
}<untrusted_context>
Output: Model 1 alone: 0.037002
Model 2 alone: 0.035264
Model 3 alone: 0.031669
Ensemble 1+2 (0.6, 0.4): 0.036813
Ensemble 1+2+3 (0.5, 0.35, 0.15): 0.03658
Ensemble 1+2+3 (0.6, 0.3, 0.1): 0.036885
Process Group PGID: 2901
</untrusted_context>[
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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.