<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
Workspace Directories:
/app
/task
Directory Structure:
Showing up to 200 items (files + folders).
/app/ └───solution/ Showing up to 200 items (files + folders).
Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.
Data
All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.
For each event family there is a train_*.parquet and val_*.parquet file:
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:
Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
After selecting the approach, refit it on train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.
Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.
Additional files under /task/data/:
candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.
target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.
validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.
test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.
validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").
The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.
Discovery objective
Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:
grade 3: the user organically likes the track;
grade 1: the user organically listens to at least 80% of the track;
grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.
The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.
The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.
Public tools
/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:
Stage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.
The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above.
Deliverable
Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.
Leave the code that builds the artifact and a concise /app/solution/README.md in /app/solution/. Keep the best valid submission saved throughout the attempt so it survives the deadline.
Constraints
Write deliverables and intermediate artifacts only under /app/solution/.
Treat /task/ and the installed Python environment as read-only.
Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths.
Do not install packages or use network access except for the model provider connection managed by Harbor.
You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality.
My current focus is on thoroughly grasping the intricacies of this music discovery ranking challenge. I'm dissecting the core requirements for the "Made for You" shelf, examining all associated constraints, and familiarizing myself with the project's directory structure, relevant files, and the given timeline. My priority is to establish a solid foundation before diving into model development.
Tool callupdate_topicTool callReturned
Tool call · Input
{
"strategic_intent": "Explore the environment, inspect the data files, evaluation scripts, and hardware resources.",
"summary": "Starting initial exploration of the task environment, data schemas, public evaluation script, candidate sets, and hardware availability.",
"title": "Environment and Data Exploration"
}
Tool response
Current topic: "Environment and Data Exploration"
Topic summary: Starting initial exploration of the task environment, data schemas, public evaluation script, candidate sets, and hardware availability.
Strategic Intent: Explore the environment, inspect the data files, evaluation scripts, and hardware resources.
04
Check hardware resources and inspect /task and /task/data directory
I'm currently examining the /task/ directory, specifically looking at evaluate_public.py and ranking_evaluation.py. Concurrently, I'm checking the available hardware resources like CPU, GPU, and RAM to understand the execution environment.
Tool callrun_shell_commandCheck hardware resources and inspect /task and /task/data directoryReturned
Tool call · Input
{
"command": "lscpu || true; free -h; nvidia-smi || true; ls -la /task; ls -lh /task/data",
"description": "Check hardware resources and inspect /task and /task/data directory"
}
#!/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()
"""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),
}
Tool callread_file/task/requirements.txtReturned
Tool call · Input
{
"file_path": "/task/requirements.txt"
}
Tool response
# 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
I'm currently reviewing the installed Python packages in my environment, noting the versions of numpy, pandas, pyarrow, scikit-learn, and scipy. I've also observed that lightgbm and xgboost are not present. My next step is to confirm the active Python environment and assess available RAM.
Tool callrun_shell_commandCheck python version and system memoryReturned
Tool call · Input
{
"description": "Check python version and system memory",
"command": "python3 -V && cat /proc/meminfo | head -n 5"
}
I'm initiating an exploration of the dataset, focusing on its dimensions and content. My immediate plan is to develop a script to inspect shapes, column types, and timestamp details, as the /app/solution directory is currently empty.
<untrusted_context>
Output: File "<string>", line 12
print(f"val_elig uids: {len(val_elig[\"uids\"])}, item_ids: {len(val_elig[\"item_ids\"])}, packed: {val_elig[\"packed_eligible\"].shape}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 1627
</untrusted_context>
I'm currently processing the user data, noting 8456 distinct users with their recent and historical positive listening counts. This information, combined with the 30,000 items in the candidate catalog and validation eligibility data, forms the foundation for our next steps.
Tool callrun_shell_commandCheck train row counts and preference statesReturned
Tool call · Input
{
"description": "Check train row counts and preference states",
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\np_meta = pq.read_metadata('/task/data/train_listens.parquet')\nprint('train_listens num rows:', p_meta.num_rows)\np_likes = pq.read_metadata('/task/data/train_likes.parquet')\nprint('train_likes num rows:', p_likes.num_rows)\np_dislikes = pq.read_metadata('/task/data/train_dislikes.parquet')\nprint('train_dislikes num rows:', p_dislikes.num_rows)\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val_pref states:', val_pref['state'].value_counts().to_dict())\n\""
}
Tool response
<untrusted_context>
Output: train_listens num rows: 43484159
train_likes num rows: 814831
train_dislikes num rows: 101075
val_pref states: {'liked': 418435, 'disliked': 50689}
Process Group PGID: 1650
</untrusted_context>
I'm currently examining the schemas of train_listens and target_users. Given the immense size of train_listens (over 43 million rows), I need to be mindful of resource consumption and efficiency. My focus is on understanding the structure and timestamp columns to inform how I'll proceed with data loading and initial analysis.
My current focus is on understanding the eligibility constraints for candidate items. It appears most items are eligible, with a small percentage marked ineligible, likely due to prior user interaction. I'm now digging into the is_eligible function to confirm these assumptions.
Recorded reasoning summary 33 words
Confirming Eligibility Constraints
I'm now confirming my earlier hunch regarding item eligibility. It seems the is_eligible function accurately filters out items with prior user engagement, ensuring only suitable candidates are considered for recommendations.
Tool callrun_shell_commandAnalyze validation truth statisticsReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint('Number of users with graded items in val:', len(graded), 'out of', len(targets))\nitem_counts = [len(items) for items in graded.values()]\nprint('Items per graded user: min, median, mean, max:', min(item_counts), pd.Series(item_counts).median(), pd.Series(item_counts).mean(), max(item_counts))\ngrade3_counts = [sum(1 for g in items.values() if g == 3) for items in graded.values()]\nprint('Grade 3 per user: mean:', pd.Series(grade3_counts).mean())\n\"",
"description": "Analyze validation truth statistics"
}
Tool response
<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 3, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1684
</untrusted_context>
12
Analyze validation truth statistics with path
+00:59Tool callrun_shell_commandAnalyze validation truth statistics with pathReturned
Tool call · Input
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\nprint('Number of users with graded items in val:', len(graded), 'out of', len(targets))\nitem_counts = [len(items) for items in graded.values()]\nprint('Items per graded user: min, median, mean, max:', min(item_counts), pd.Series(item_counts).median(), pd.Series(item_counts).mean(), max(item_counts))\ngrade3_counts = [sum(1 for g in items.values() if g == 3) for items in graded.values()]\nprint('Grade 3 per user: mean:', pd.Series(grade3_counts).mean())\n\"",
"description": "Analyze validation truth statistics with path"
}
Tool response
<untrusted_context>
Output: Number of users with graded items in val: 4263 out of 8456
Items per graded user: min, median, mean, max: 1 4.0 8.859723199624677 442
Grade 3 per user: mean: 1.0361247947454844
Process Group PGID: 1695
</untrusted_context>
I'm currently reviewing user data, focusing on those with eligible items in the validation window. It appears a significant portion, over 50%, meets this criterion, with an average of roughly 8.8 items per user. I'm also seeing about one grade 3 item per user on average.
Recorded reasoning summary 50 words
Developing Baseline Strategy
I'm now focusing on establishing a baseline by ranking eligible candidates. The plan is to select the top 100 items per user, sorted by popularity metrics like popularity_rank, organic_positive_listens, or positive_listens, from the candidate_catalog. I'll then script an evaluation using evaluate_public.py and prepare a preliminary submission file.
Tool callrun_shell_commandCheck candidate catalog vs item_ids in npzReturned
Tool call · Input
{
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = val_elig['uids']\nitem_ids = val_elig['item_ids']\npacked = val_elig['packed_eligible']\nprint(f'Load took {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Check if item_ids matches cat.item_id exactly\ncat_items = cat['item_id'].to_numpy()\nprint('item_ids match cat_items exactly?', np.array_equal(item_ids, cat_items))\n\"",
"description": "Check candidate catalog vs item_ids in npz"
}
Tool response
<untrusted_context>
Output: Load took 0.14s
item_ids match cat_items exactly? True
Process Group PGID: 1715
</untrusted_context>
I'm currently verifying that the item IDs within validation_eligible_candidates.npz and test_eligible_candidates.npz precisely mirror the ordered candidate_catalog (1-30000 by popularity rank). This confirmation is a crucial next step.
Tool callrun_shell_commandVerify test and val uids match target_usersReturned
Tool call · Input
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('test item_ids match cat?', np.array_equal(test_elig['item_ids'], cat['item_id'].to_numpy()))\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('test uids match target_users?', np.array_equal(test_elig['uids'], target_users['uid'].to_numpy()))\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('val uids match target_users?', np.array_equal(val_elig['uids'], target_users['uid'].to_numpy()))\n\"",
"description": "Verify test and val uids match target_users"
}
Tool response
<untrusted_context>
Output: test item_ids match cat? True
test uids match target_users? True
val uids match target_users? True
Process Group PGID: 1749
</untrusted_context>
My current focus is on confirming the perfect alignment between the uids, item_ids, target_users, and candidate_catalog sequences. I'm now investigating the performance implications of bit unpacking and efficient top-K eligible item retrieval given the scale of 30,000 items per user, aiming to optimize for speed.
I've confirmed candidate masking operates in effectively zero time for our 8456x30000 matrix. The next step is assessing the speed of retrieving top 100 items from user scores.
Tool callrun_shell_commandBenchmark top100 extraction for all usersReturned
I've achieved impressive speed, retrieving and sorting the top 100 items for all 8456 users in under a second. My current focus is on verifying a new strategy: assigning ineligibles to negative infinity should naturally position eligible items at the top using np.argpartition.
My focus is on ensuring the best valid submission is consistently saved, adhering strictly to the 100-item limit for /app/solution/submission.parquet or /app/solution/submission.csv. This ensures compliance with operational requirements.
My current focus is on thoroughly understanding the core problem and the nuances of the provided data. Simultaneously, I'm actively working on designing potential solutions, building upon the established structural integrity of the submission.
Tool callupdate_topicTool callReturned
Tool call · Input
{
"strategic_intent": "Investigate interactions, user preferences, item features, and modeling options for discovery ranking.",
"summary": "Generated an initial verified baseline submission to ensure deadline safety. Now conducting deep exploratory data analysis on the user interaction logs, preference states, timestamps, and candidate features to design an effective discovery recommendation model.",
"title": "Data Analysis and Modeling Strategy"
}
Tool response
Current topic: "Data Analysis and Modeling Strategy"
Topic summary: Generated an initial verified baseline submission to ensure deadline safety. Now conducting deep exploratory data analysis on the user interaction logs, preference states, timestamps, and candidate features to design an effective discovery recommendation model.
Strategic Intent: Investigate interactions, user preferences, item features, and modeling options for discovery ranking.
I'm currently dissecting the available data and the nuances of discovery ranking. My focus is on understanding how per-user candidate eligibility and cutoff-specific explicit preference states influence the "Made for You" shelf.
Tool callrun_shell_commandAnalyze target user listens in train_listensReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(target_users['uid'].to_numpy())\nprint(f'target_uids: {len(target_uids)}')\n\n# Read train_listens filtering or scanning\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read table in {time.time()-t0:.2f}s, total rows: {len(table)}')\n\n# Filter to target_uids\nt1 = time.time()\ntarget_uid_array = target_users['uid'].to_numpy()\nmask = pc.is_in(table['uid'], value_set=pc.scalar(target_uid_array))\nsub_table = table.filter(pc.is_in(table['uid'], value_set=target_uid_array))\nprint(f'Filtered table in {time.time()-t1:.2f}s, target user rows: {len(sub_table)}')\n\ndf_target = sub_table.to_pandas()\nprint('Target listens summary:')\nprint(df_target.head())\nprint('Min timestamp:', df_target['timestamp'].min(), 'Max timestamp:', df_target['timestamp'].max())\n\"",
"description": "Analyze target user listens in train_listens"
}
I've realized that pc.is_in in pyarrow requires a pa.array for value_set, not a pc.scalar. I'm adapting my filtering logic to use pa.array(target_users['uid'].to_numpy()) for proper data selection. It's also impressive how quickly train_listens.parquet loads – just 0.39s.
Tool callrun_shell_commandProper pyarrow filter for target user listensReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport time\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uid_array = pa.array(target_users['uid'].to_numpy())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read table in {time.time()-t0:.2f}s, total rows: {len(table)}')\n\nt1 = time.time()\nmask = pc.is_in(table['uid'], value_set=target_uid_array)\nsub_table = table.filter(mask)\nprint(f'Filtered table in {time.time()-t1:.2f}s, target user rows: {len(sub_table)}')\n\nt2 = time.time()\ndf_target = sub_table.to_pandas()\nprint(f'to_pandas in {time.time()-t2:.2f}s, target user rows: {len(df_target)}')\nprint('Min ts:', df_target['timestamp'].min(), 'Max ts:', df_target['timestamp'].max())\nprint('Organic ratio in target listens:', (df_target['is_organic'] == 1).mean())\nprint('>=80% ratio in target listens:', (df_target['played_ratio_pct'] >= 80).mean())\nprint('Number of unique items played by target users:', df_target['item_id'].nunique())\n\"",
"description": "Proper pyarrow filter for target user listens"
}
Tool response
<untrusted_context>
Output: Read table in 0.40s, total rows: 43484159
Filtered table in 0.65s, target user rows: 43222297
to_pandas in 0.10s, target user rows: 43222297
Min ts: 5 Max ts: 24786795
Organic ratio in target listens: 0.5171260564888535
>=80% ratio in target listens: 0.5930057812522088
Number of unique items played by target users: 839582
Process Group PGID: 1871
</untrusted_context>
24
Check how many listens are for candidate catalog items
My focus has shifted to understanding the composition of listens. It appears nearly all listens come from target users, which is a strong indicator. I'm now investigating the candidate catalog size, which seems remarkably small at 30,000 items, and will compare this to the number of listens within the training data.
Tool callrun_shell_commandCheck how many listens are for candidate catalog itemsReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport time\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = pa.array(cat['item_id'].to_numpy())\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask_cat = pc.is_in(table['item_id'], value_set=cat_items)\nsub_table = table.filter(mask_cat)\nprint(f'Filtered to catalog items in {time.time()-t0:.2f}s, rows: {len(sub_table)}')\n\"",
"description": "Check how many listens are for candidate catalog items"
}
Tool response
<untrusted_context>
Output: Filtered to catalog items in 1.37s, rows: 31249613
Process Group PGID: 1893
</untrusted_context>
I'm currently focused on understanding the "Made for You" discovery shelf. My current thought process is to analyze the relationship between user listening history and an item's eligibility for this shelf, specifically investigating if previously listened-to items can appear.
Tool callrun_shell_commandCheck if previously interacted items are eligibleReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nval_elig = CandidateSet('/task/data/validation_eligible_candidates.npz')\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('val_pref head:\\n', val_pref.head())\n\n# Check how many (uid, item_id) in val_pref are eligible in val_elig\npref_sample = val_pref.head(1000)\npref_elig = [val_elig.is_eligible(r.uid, r.item_id) for r in pref_sample.itertuples()]\nprint('Ratio of preference items that are eligible:', np.mean(pref_elig))\n\n# Check disliked vs liked in preference state\ndisliked = val_pref[val_pref.state == 'disliked'].head(1000)\ndisliked_elig = [val_elig.is_eligible(r.uid, r.item_id) for r in disliked.itertuples()]\nprint('Ratio of disliked items that are eligible:', np.mean(disliked_elig))\n\"",
"description": "Check if previously interacted items are eligible"
}
Tool response
<untrusted_context>
Output: val_pref head:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
Ratio of preference items that are eligible: 0.0
Ratio of disliked items that are eligible: 0.0
Process Group PGID: 1915
</untrusted_context>
26
Check if train listened items are eligible in validation
+03:25Tool callrun_shell_commandCheck if train listened items are eligible in validationReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nval_elig = CandidateSet('/task/data/validation_eligible_candidates.npz')\n# Take a user with some listens\nsample_listens = pd.read_parquet('/task/data/val_listens.parquet').head(2000)\n# val_listens is validation period, let's check train_listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\ndf_sample = table.slice(0, 10000).to_pandas()\nis_el = [val_elig.is_eligible(r.uid, r.item_id) for r in df_sample.itertuples()]\nprint('Ratio of train_listens items eligible in val_elig:', np.mean(is_el))\n\"",
"description": "Check if train listened items are eligible in validation"
}
Tool response
<untrusted_context>
Output: Ratio of train_listens items eligible in val_elig: 0.0
Process Group PGID: 1937
</untrusted_context>
I've stumbled upon a crucial finding: the ratio of eligible items for training within the validation set is zero. This points to a potential issue in how we're identifying candidates for the "Made for You" discovery shelf, where we aim to surface entirely new music for users.
Recorded reasoning summary 39 words
Refining Candidate Generation
I've realized the candidate mask already handles exclusions, so my initial zero-eligibility finding stems from that. Now I'm exploring how collaborative filtering, particularly Item-Item and Matrix Factorization methods like EASE, can effectively recommend genuinely novel items.
Recorded reasoning summary 46 words
Exploring Computational Constraints
My focus has shifted to the sheer scale of computations involved. The zero-eligibility issue might be intertwined with the impracticality of inverting large matrices. I'm now investigating more efficient methods like sparse item-item similarity or SVD to bypass these memory and time constraints.
Tool callrun_shell_commandVerify number of users in train_listensReturned
Tool call · Input
{
"description": "Verify number of users in train_listens",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable_users = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = table_users['uid'].to_numpy()\nimport numpy as np\nunique_uids = np.unique(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_users = pq.read_table('/task/data/target_users.parquet')['uid'].to_numpy()\nprint('Target users count:', len(target_users))\nprint('Intersection:', len(set(unique_uids).intersection(set(target_users))))\n\""
}
Tool response
<untrusted_context>
Output: Total unique uids in train_listens: 9238
Target users count: 8456
Intersection: 8456
Process Group PGID: 1961
</untrusted_context>
28
Analyze train_listens timestamp and features distribution
My focus has shifted to understanding the user interaction landscape. It appears all 8,456 target users are present within the train_listens dataset, which comprises a surprisingly small 9,238 unique users in total. I'm now exploring the interaction matrix between these 8,456 target users and the 30,000 catalog items.
Recorded reasoning summary 42 words
Calculating SVD Performance
I've confirmed the feasibility of performing Truncated SVD on the user-item matrix. The dimensions are manageable, allowing for rapid computation of latent representations and subsequent prediction score generation. This opens avenues for exploring various weighting and dimensionality schemes quickly.
Recorded reasoning summary 50 words
Computing Item-Item Similarity
I've realized that calculating the full $30,000 \times 30,000$ item-item similarity matrix is unnecessary. Through careful matrix manipulation and associativity, I can achieve the desired item-item computations by first performing operations that result in an $8456 \times 8456$ matrix, avoiding the materialization of the larger, prohibitive matrix.
Recorded reasoning summary 52 words
Deriving Item Similarities
I've confirmed that the formula $(X \tilde{X}^T) \tilde{X}$ elegantly yields linear item-item similarity, aligning perfectly with user-user similarity applied to the item matrix. My next step is to meticulously examine the train_listens.parquet dataset for temporal features like timestamp and engagement metrics such as played_ratio_pct to inform non-linear similarity calculations.
Recorded reasoning summary 47 words
Refining Engagement Signals
I'm now focusing on refining the engagement signals. The discovery objective specifically rewards completions (played_ratio_pct >= 80), and I'm considering the distinction between organic listens versus radio/recommendations. Explicit actions like likes and dislikes from train_likes.parquet and train_dislikes.parquet are also crucial for deriving non-linear similarity.
Tool callrun_shell_commandAnalyze train_listens timestamp and features distributionReturned
Tool call · Input
{
"description": "Analyze train_listens timestamp and features distribution",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp', 'played_ratio_pct', 'is_organic'])\nts = table['timestamp'].to_numpy()\npct = table['played_ratio_pct'].to_numpy()\norg = table['is_organic'].to_numpy()\n\nprint(f'Train listens: min_ts={ts.min()}, max_ts={ts.max()}, span={(ts.max()-ts.min())/86400:.1f} days')\n# Quantiles of timestamp\nprint('Timestamp quantiles (days from end):')\nmax_t = ts.max()\nfor q in [0.01, 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.99]:\n val = np.quantile(ts, q)\n print(f'quantile {q:.2f}: {(max_t - val)/86400:.1f} days before cutoff')\n\nprint('Played ratio pct quantiles:', np.quantile(pct, [0.1, 0.25, 0.5, 0.75, 0.9]))\nprint('Organic fraction:', np.mean(org))\n\""
}
Tool response
<untrusted_context>
Output: Train listens: min_ts=0, max_ts=24786795, span=286.9 days
Timestamp quantiles (days from end):
quantile 0.01: 282.6 days before cutoff
quantile 0.05: 267.2 days before cutoff
quantile 0.10: 248.4 days before cutoff
quantile 0.25: 197.3 days before cutoff
quantile 0.50: 122.0 days before cutoff
quantile 0.75: 55.3 days before cutoff
quantile 0.90: 21.1 days before cutoff
quantile 0.99: 1.9 days before cutoff
Played ratio pct quantiles: [ 1. 7. 100. 100. 100.]
Organic fraction: 0.5169151828370419
Process Group PGID: 1981
</untrusted_context>
I've been examining the data, noticing how concentrated the listening activity is in recent periods. It's quite striking that half of all listens occurred within the last 122 days, with a quarter of them happening in the last 55. Additionally, I'm seeing that the 10% quantile for played_ratio_pct is 1%.
Recorded reasoning summary 36 words
Observing Engagement Dichotomy
My analysis reveals a striking pattern: listeners either abandon tracks very early, around 1-10% completion, or they engage fully, listening to 100%. This suggests a strong dichotomy in user interaction with the content.
Tool callrun_shell_commandCheck preference state items in candidate catalogReturned
Tool call · Input
{
"description": "Check preference state items in candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat['item_id'])\n\nprint('val_pref total rows:', len(val_pref))\nprint('val_pref items in candidate_catalog:', val_pref['item_id'].isin(cat_set).sum())\nprint('val_pref unique items:', val_pref['item_id'].nunique())\nprint('unique items in cat_set:', len(set(val_pref['item_id']).intersection(cat_set)))\n\""
}
Tool response
<untrusted_context>
Output: val_pref total rows: 469124
val_pref items in candidate_catalog: 469124
val_pref unique items: 28814
unique items in cat_set: 28814
Process Group PGID: 2005
</untrusted_context>
I've confirmed that every single entry within validation_preference_state corresponds to an item in our catalog. This means all 469,124 rows are indeed part of the candidate catalog. Now I'm shifting focus to train_likes and train_dislikes to see how they align.
Tool callrun_shell_commandCheck train likes in candidate catalogReturned
Tool call · Input
{
"description": "Check train likes in candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\n\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat['item_id'])\nprint('train_likes in catalog:', train_likes['item_id'].isin(cat_set).mean())\nprint('train_likes rows:', len(train_likes))\n\""
}
Tool response
<untrusted_context>
Output: train_likes in catalog: 0.6809068383505291
train_likes rows: 814831
Process Group PGID: 2022
</untrusted_context>
I'm now examining the candidate_catalog.parquet file. Initial checks reveal it contains item_id, positive_listens, organic_positive_listens, and popularity_rank. I'm currently investigating if any other related files or metadata exist.
Recorded reasoning summary 53 words
Refining Model Scope
I've confirmed the available data is solely interaction logs and preference states, excluding item features. This points to a classic collaborative filtering and sequential recommendation approach. My focus now is on identifying the most efficient and effective models for this type of problem, considering the dataset size and evaluation metric.
Recorded reasoning summary 42 words
Validating Recommendation Approach
I'm currently validating the setup for recommendation evaluation. The history includes training events and validation preference states, with outcomes being validation events scored by /task/evaluate_public.py. I need to ensure efficient testing of collaborative filtering architectures within the time constraints.
Recorded reasoning summary 38 words
Testing Matrix Factorization
I'm currently exploring Implicit Matrix Factorization, specifically TruncatedSVD. My focus is on defining the entries of the user-item interaction matrix, considering how to represent positive listens and likes, and then implementing SVD decomposition in Python.
Recorded reasoning summary 47 words
Evaluating Similarity Metrics
I'm now considering alternative similarity measures to TruncatedSVD. Specifically, I'm investigating the feasibility and performance of computing dense cosine similarity and top-k item similarities on the user-item interaction matrix, and comparing these against the speed of TruncatedSVD. I'm also exploring user-user collaborative filtering methods.
Recorded reasoning summary 39 words
Comparing User-User Baselines
I'm now comparing user-user KNN with power/top-k thresholding against EASE. User-user KNN offers impressive speed for predictions. I'm benchmarking matrix inversions for EASE and considering their practical implications. This comparison should highlight strong collaborative filtering baselines.
I've been focusing on performance optimizations. The Cholesky inverse now clocks in at a mere 6.94 seconds, and Gram matrix computations are a swift 0.23 seconds. User-User EASE is also efficiently solved in under 8 seconds. Next, I'm benchmarking TruncatedSVD on sparse matrices from scikit-learn.
Tool callrun_shell_commandBenchmark TruncatedSVD on sparse matrixReturned
Tool call · Input
{
"description": "Benchmark TruncatedSVD on sparse matrix",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# Simulate sparse matrix (8456, 30000) with ~3 million non-zeros (density ~1.2%)\nM = sp.random(8456, 30000, density=0.012, format='csr', dtype=np.float32)\nprint(f'Matrix nnz: {M.nnz}')\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=128, algorithm='randomized', random_state=42)\nU = svd.fit_transform(M)\nV = svd.components_\nprint(f'TruncatedSVD k=128 took {time.time()-t0:.2f}s, explained variance: {svd.explained_variance_ratio_.sum():.4f}')\n\""
}
Tool response
<untrusted_context>
Output: Matrix nnz: 3044160
TruncatedSVD k=128 took 2.48s, explained variance: 0.0312
Process Group PGID: 2057
</untrusted_context>
I'm focusing on building the user-item interaction matrix now, after a swift 2.48-second truncated SVD. Next steps involve examining available events and determining appropriate weighting for them. This work is a precursor to the experimental validation phase.
Tool callupdate_topicTool callReturned
Tool call · Input
{
"strategic_intent": "Build data pipeline for training interaction matrix and test collaborative filtering models on validation set.",
"title": "Collaborative Filtering Model Exploration",
"summary": "Completed initial benchmarks and mathematical formulations for User-User EASE, Item-Item CF, and TruncatedSVD. Now building data extraction pipelines to create weighted user-item interaction matrices from listens, preference states, and likes, and evaluating models on the public validation window."
}
Tool response
Current topic: "Collaborative Filtering Model Exploration"
Topic summary: Completed initial benchmarks and mathematical formulations for User-User EASE, Item-Item CF, and TruncatedSVD. Now building data extraction pipelines to create weighted user-item interaction matrices from listens, preference states, and likes, and evaluating models on the public validation window.
Strategic Intent: Build data pipeline for training interaction matrix and test collaborative filtering models on validation set.
34
Check item frequency distribution in train_listens
I'm currently examining the target_users.parquet file, which contains 8,456 users. My immediate task is to establish a mapping of each uid to its corresponding row index, from 0 to 8455. Subsequently, I will turn my attention to the candidate_catalog.parquet file, which holds 30,000 items.
Tool callrun_shell_commandCheck item frequency distribution in train_listensReturned
Tool call · Input
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nu_items, counts = np.unique(items, return_counts=True)\nprint(f'Total items: {len(u_items)}')\nfor min_c in [1, 5, 10, 50, 100]:\n print(f'Items with >= {min_c} listens: {(counts >= min_c).sum()}')\n\"",
"description": "Check item frequency distribution in train_listens"
}
Tool response
<untrusted_context>
Output: Total items: 845395
Items with >= 1 listens: 845395
Items with >= 5 listens: 339975
Items with >= 10 listens: 240543
Items with >= 50 listens: 96719
Items with >= 100 listens: 61133
Process Group PGID: 2081
</untrusted_context>
35
Benchmark filtering train_listens to catalog items
My focus has shifted to understanding listen distributions, specifically comparing catalog items against non-catalog items for target users. I'm currently examining a dataset of 845,395 listens against a catalog of 30,000 items.
Recorded reasoning summary 39 words
Examining Listen Patterns
I've discovered that a significant majority of listens, approximately 72.3%, are for catalog items. This concentration is important as my recommendation system only scores these catalog items. I am now proceeding to analyze validation event data.
Recorded reasoning summary 34 words
Investigating User Engagement
I'm now scrutinizing how user engagement, specifically is_organic, differentiates between direct listens and dislikes/undislikes. I'm also verifying that validation preference states accurately reflect prior explicit feedback up to the training cutoff.
Recorded reasoning summary 37 words
Testing Collaborative Filtering Baseline
I'm now focused on implementing a basic collaborative filtering model. My goal is to construct a sparse matrix representing user-item interactions, incorporating recent positive listens and preference states for a robust baseline test.
Tool callrun_shell_commandBenchmark filtering train_listens to catalog itemsReturned
Tool call · Input
{
"description": "Benchmark filtering train_listens to catalog items",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users['uid'].to_numpy()\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat['item_id'].to_numpy()\nitem_to_idx = {item: j for j, item in enumerate(items)}\n\nprint(f'Index mapping done in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Read train_listens with only relevant columns\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time()-t0:.2f}s')\n\nt0 = time.time()\nu_arr = table['uid'].to_numpy()\ni_arr = table['item_id'].to_numpy()\nts_arr = table['timestamp'].to_numpy()\npct_arr = table['played_ratio_pct'].to_numpy()\norg_arr = table['is_organic'].to_numpy()\n\n# Filter to catalog items and target users\n# Using fast lookup:\ncat_set = set(items)\n# We can do boolean indexing in numpy\nmask = np.isin(i_arr, items)\nprint(f'np.isin mask done in {time.time()-t0:.2f}s, matching listens: {mask.sum()}')\n\""
}
Tool response
<untrusted_context>
Output: Index mapping done in 0.02s
Read table in 0.41s
np.isin mask done in 0.52s, matching listens: 31249613
Process Group PGID: 2103
</untrusted_context>
The recent focus has been on precisely quantifying the computational cost of constructing the sparse interaction matrix $R$. Initial benchmarks for indexing, reading tables, and masking are promisingly rapid, paving the way to examine the per-pair computation of $R$.
Recorded reasoning summary 49 words
Developing Event Weighting Logic
I'm currently refining the weighting system for individual listen events, incorporating factors like play duration, organic listening, and time decay. My next step is to test these parameters, especially the preference state and log-count compression, through a dedicated Python script to evaluate recommender algorithm performance.
Tool callwrite_file/app/solution/exp1.pyReturned
Tool call · Input
{
"file_path": "/app/solution/exp1.py",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport scipy.linalg\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n t_start = time.time()\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n uids = targets_frame.uid.to_numpy(dtype=np.int64)\n uid_to_idx = {uid: i for i, uid in enumerate(uids)}\n num_users = len(uids)\n\n cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n items = cat.item_id.to_numpy(dtype=np.int64)\n item_to_idx = {item: j for j, item in enumerate(items)}\n num_items = len(items)\n\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\")\n\n # 1. Load train listens\n t0 = time.time()\n table = pq.read_table(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n )\n print(f\"Read train_listens in {time.time()-t0:.2f}s\")\n\n t0 = time.time()\n u_raw = table[\"uid\"].to_numpy()\n i_raw = table[\"item_id\"].to_numpy()\n ts_raw = table[\"timestamp\"].to_numpy()\n pct_raw = table[\"played_ratio_pct\"].to_numpy()\n org_raw = table[\"is_organic\"].to_numpy()\n\n # Map to indices\n # Filter only catalog items\n in_cat = np.isin(i_raw, items)\n u_filt = u_raw[in_cat]\n i_filt = i_raw[in_cat]\n ts_filt = ts_raw[in_cat]\n pct_filt = pct_raw[in_cat]\n org_filt = org_raw[in_cat]\n\n # Map uids to idx\n u_idx = np.array([uid_to_idx.get(u, -1) for u in u_filt], dtype=np.int32)\n # Map item_ids to idx\n i_idx = np.array([item_to_idx.get(it, -1) for it in i_filt], dtype=np.int32)\n\n valid_mask = (u_idx >= 0) & (i_idx >= 0)\n u_idx = u_idx[valid_mask]\n i_idx = i_idx[valid_mask]\n ts_filt = ts_filt[valid_mask]\n pct_filt = pct_filt[valid_mask]\n org_filt = org_filt[valid_mask]\n print(f\"Mapped {len(u_idx)} listens in {time.time()-t0:.2f}s\")\n\n # Experiment with weights:\n # Strong listens: pct >= 80 get 1.0, 50 <= pct < 80 get 0.5, skips get 0\n cutoff_ts = 24786800\n days_before = (cutoff_ts - ts_filt) / 86400.0\n # Half life of 60 days\n time_decay = np.exp(-days_before / 60.0)\n\n listen_quality = np.where(pct_filt >= 80, 1.0, np.where(pct_filt >= 50, 0.5, 0.1))\n organic_boost = np.where(org_filt == 1, 1.5, 1.0)\n w_listens = listen_quality * organic_boost * time_decay\n\n # Build sparse matrix of listens\n R_listens = sp.csr_matrix(\n (w_listens, (u_idx, i_idx)), shape=(num_users, num_items), dtype=np.float32\n )\n\n # 2. Add validation preference state (likes/dislikes)\n val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n pref_u = np.array([uid_to_idx.get(u, -1) for u in val_pref.uid], dtype=np.int32)\n pref_i = np.array([item_to_idx.get(it, -1) for it in val_pref.item_id], dtype=np.int32)\n pref_valid = (pref_u >= 0) & (pref_i >= 0)\n pref_u = pref_u[pref_valid]\n pref_i = pref_i[pref_valid]\n pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 3.0, -2.0).astype(np.float32)\n\n R_pref = sp.csr_matrix(\n (pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32\n )\n\n # Combine listens and preference state\n # Apply log1p to listens to dampen extreme frequencies\n R_listens.data = np.log1p(R_listens.data)\n R = (R_listens + R_pref).tocsr()\n\n print(f\"Interaction matrix R built. Shape: {R.shape}, nnz: {R.nnz}\")\n\n def evaluate_scores(scores, name):\n # Mask ineligible items\n scores = scores.copy()\n scores[~mask] = -np.inf\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(num_users)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n ranked = {\n int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n for i in range(num_users)\n }\n res = score_rankings(ranked, graded)\n print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\")\n return res[\"quality_ndcg@10\"]\n\n # Test 1: User-User EASE\n print(\"Testing User-User EASE...\")\n t0 = time.time()\n # Normalize rows of R for Gram matrix\n # Row L2 normalization\n row_norms = sp.linalg.norm(R, axis=1)\n row_norms[row_norms == 0] = 1.0\n R_norm = sp.diags(1.0 / row_norms) @ R\n\n # Gram matrix G = R_norm @ R_norm.T\n G = (R_norm @ R_norm.T).toarray()\n print(f\"Gram matrix computed in {time.time()-t0:.2f}s\")\n\n for reg in [50.0, 100.0, 200.0, 500.0, 1000.0]:\n t_inv = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + reg)\n c, lower = scipy.linalg.cho_factor(G_reg)\n P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n # Compute scores: B @ R\n scores = B @ R.toarray()\n print(f\"Solved EASE reg={reg} in {time.time()-t_inv:.2f}s\")\n evaluate_scores(scores, f\"User-EASE reg={reg}\")\n\nif __name__ == \"__main__\":\n main()\n",
"instruction": "Create initial collaborative filtering exploration script"
}
Tool response
Successfully created and wrote to new file: /app/solution/exp1.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import scipy.linalg
import pyarrow.parquet as pq
import sys
from pathlib import Path
sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings
def main():
t_start = time.time()
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
uids = targets_frame.uid.to_numpy(dtype=np.int64)
uid_to_idx = {uid: i for i, uid in enumerate(uids)}
num_users = len(uids)
cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
items = cat.item_id.to_numpy(dtype=np.int64)
item_to_idx = {item: j for j, item in enumerate(items)}
num_items = len(items)
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)
print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}")
# 1. Load train listens
t0 = time.time()
table = pq.read_table(
DATA / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"],
)
print(f"Read train_listens in {time.time()-t0:.2f}s")
t0 = time.time()
u_raw = table["uid"].to_numpy()
i_raw = table["item_id"].to_numpy()
ts_raw = table["timestamp"].to_numpy()
pct_raw = table["played_ratio_pct"].to_numpy()
org_raw = table["is_organic"].to_numpy()
# Map to indices
# Filter only catalog items
in_cat = np.isin(i_raw, items)
u_filt = u_raw[in_cat]
i_filt = i_raw[in_cat]
ts_filt = ts_raw[in_cat]
pct_filt = pct_raw[in_cat]
org_filt = org_raw[in_cat]
# Map uids to idx
u_idx = np.array([uid_to_idx.get(u, -1) for u in u_filt], dtype=np.int32)
# Map item_ids to idx
i_idx = np.array([item_to_idx.get(it, -1) for it in i_filt], dtype=np.int32)
valid_mask = (u_idx >= 0) & (i_idx >= 0)
u_idx = u_idx[valid_mask]
i_idx = i_idx[valid_mask]
ts_filt = ts_filt[valid_mask]
pct_filt = pct_filt[valid_mask]
org_filt = org_filt[valid_mask]
print(f"Mapped {len(u_idx)} listens in {time.time()-t0:.2f}s")
# Experiment with weights:
# Strong listens: pct >= 80 get 1.0, 50 <= pct < 80 get 0.5, skips get 0
cutoff_ts = 24786800
days_before = (cutoff_ts - ts_filt) / 86400.0
# Half life of 60 days
time_decay = np.exp(-days_before / 60.0)
listen_quality = np.where(pct_filt >= 80, 1.0, np.where(pct_filt >= 50, 0.5, 0.1))
organic_boost = np.where(org_filt == 1, 1.5, 1.0)
w_listens = listen_quality * organic_boost * time_decay
# Build sparse matrix of listens
R_listens = sp.csr_matrix(
(w_listens, (u_idx, i_idx)), shape=(num_users, num_items), dtype=np.float32
)
# 2. Add validation preference state (likes/dislikes)
val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
pref_u = np.array([uid_to_idx.get(u, -1) for u in val_pref.uid], dtype=np.int32)
pref_i = np.array([item_to_idx.get(it, -1) for it in val_pref.item_id], dtype=np.int32)
pref_valid = (pref_u >= 0) & (pref_i >= 0)
pref_u = pref_u[pref_valid]
pref_i = pref_i[pref_valid]
pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 3.0, -2.0).astype(np.float32)
R_pref = sp.csr_matrix(
(pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32
)
# Combine listens and preference state
# Apply log1p to listens to dampen extreme frequencies
R_listens.data = np.log1p(R_listens.data)
R = (R_listens + R_pref).tocsr()
print(f"Interaction matrix R built. Shape: {R.shape}, nnz: {R.nnz}")
def evaluate_scores(scores, name):
# Mask ineligible items
scores = scores.copy()
scores[~mask] = -np.inf
top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
row_idx = np.arange(num_users)[:, None]
top100_scores = scores[row_idx, top100_idx]
order = np.argsort(-top100_scores, axis=1)
sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
ranked = {
int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
for i in range(num_users)
}
res = score_rankings(ranked, graded)
print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)")
return res["quality_ndcg@10"]
# Test 1: User-User EASE
print("Testing User-User EASE...")
t0 = time.time()
# Normalize rows of R for Gram matrix
# Row L2 normalization
row_norms = sp.linalg.norm(R, axis=1)
row_norms[row_norms == 0] = 1.0
R_norm = sp.diags(1.0 / row_norms) @ R
# Gram matrix G = R_norm @ R_norm.T
G = (R_norm @ R_norm.T).toarray()
print(f"Gram matrix computed in {time.time()-t0:.2f}s")
for reg in [50.0, 100.0, 200.0, 500.0, 1000.0]:
t_inv = time.time()
G_reg = G.copy()
np.fill_diagonal(G_reg, G_reg.diagonal() + reg)
c, lower = scipy.linalg.cho_factor(G_reg)
P = scipy.linalg.cho_solve((c, lower), np.eye(num_users, dtype=np.float32))
B = -P / np.diag(P)[:, None]
np.fill_diagonal(B, 0.0)
# Compute scores: B @ R
scores = B @ R.toarray()
print(f"Solved EASE reg={reg} in {time.time()-t_inv:.2f}s")
evaluate_scores(scores, f"User-EASE reg={reg}")
if __name__ == "__main__":
main()
37
Run exp1.py to test User-User EASE
+07:17Tool callrun_shell_commandRun exp1.py to test User-User EASEReturned
Tool call · Input
{
"command": "python3 /app/solution/exp1.py",
"description": "Run exp1.py to test User-User EASE"
}
Tool response
<untrusted_context>
Output: /usr/bin/bash: line 5: 2140 Killed python3 /app/solution/exp1.py
Exit Code: 137
Process Group PGID: 2138
</untrusted_context>
I'm currently digging into the out-of-memory error, specifically analyzing the memory footprint of reading the train_listens.parquet table and its conversion to a NumPy array for uid.
Recorded reasoning summary 40 words
Analyzing Memory Leaks
I'm pinpointing memory usage during data loading and processing. The creation of large NumPy arrays and Python lists for uid, items, and indices appears to be a significant contributor to the memory spike, alongside intermediate matrix calculations.
Tool callrun_shell_commandCheck min and max of uid and item_idReturned
Tool call · Input
{
"description": "Check min and max of uid and item_id",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('uid min, max:', target_users['uid'].min(), target_users['uid'].max())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('item_id min, max:', cat['item_id'].min(), cat['item_id'].max())\n\""
}
Tool response
<untrusted_context>
Output: uid min, max: 100 1000000
item_id min, max: 206 9390294
Process Group PGID: 2179
</untrusted_context>
I'm currently optimizing memory allocation. I've determined the necessary size for uid_map and item_map arrays, realizing they'll only consume a small portion of memory, around 4 MB and 37 MB respectively. This is a promising step towards efficient resource utilization.
Tool callrun_shell_commandTest lookup maps and inspect row groupsReturned
Tool call · Input
{
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users['uid'].to_numpy(dtype=np.int64)\nuid_map = np.full(1000001, -1, dtype=np.int32)\nuid_map[uids] = np.arange(len(uids), dtype=np.int32)\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitems = cat['item_id'].to_numpy(dtype=np.int64)\nitem_map = np.full(9390295, -1, dtype=np.int32)\nitem_map[items] = np.arange(len(items), dtype=np.int32)\nprint(f'Maps built in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Read parquet file in batches\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint(f'Num row groups: {pf.num_row_groups}')\n\"",
"description": "Test lookup maps and inspect row groups"
}
Tool response
<untrusted_context>
Output: Maps built in 0.03s
Num row groups: 42
Process Group PGID: 2202
</untrusted_context>
40
Benchmark batched processing of train_listens
+09:58Tool callrun_shell_commandBenchmark batched processing of train_listensReturned
I've just processed an astonishing 15.5 million listens in a mere 1.58 seconds, with impressively low memory usage. My current focus is on efficiently combining row_list, col_list, and data_list for matrix construction, exploring np.concatenate and the creation of coo_matrix or csr_matrix.
I'm thrilled to report that the User-KNN model with a power of 3.0 has significantly surpassed the baseline popularity metric, achieving more than double the original NDCG@10 score. My current focus is on understanding the underlying reasons for this impressive improvement.
Recorded reasoning summary 50 words
Exploring Model Strengths
I'm now delving into the theoretical underpinnings of why User-User KNN with elevated powers, like 3.0, excels. The current hypothesis suggests that this amplifies genuine user similarities while suppressing spurious ones in sparse, high-dimensional data. I am also considering the impact of top-K filtering on this phenomenon.
Recorded reasoning summary 49 words
Investigating Item Similarity Computation
I'm now exploring the computational feasibility of item-item similarity calculations, specifically how quickly we can derive top-k similarities. I'm considering matrix multiplications like $R C^T$ and then $(R C^T) C$, and also assessing the performance of sparse matrix multiplication for $C^T C$ given $C$'s sparsity.
My recent focus has been on observing the sparsity of matrices. The current analysis highlights an unexpectedly high number of non-zero elements (nnz=29547668) for just 1,000 items, suggesting a near-dense structure due to overlapping columns in the random matrix. This starkly contrasts with the expected sparsity of the User-User matrix.
Recorded reasoning summary 71 words
Exploring Matrix Efficiency
I've realized the $8456 \times 8456$ matrix, while large, is actually quite manageable in dense format, fitting into about 286 MB. This means dense operations are fast and exact, and I've already computed $G$ in just over 8 seconds. I'm now considering further analysis on $G$, including power scaling and exploring Top-K nearest neighbor approaches, as well as incorporating TF-IDF and BM25 item weighting to enhance similarity metrics.
Recorded reasoning summary 63 words
Refining Similarity Calculations
I'm now considering how to better refine similarity calculations. Specifically, I'm investigating the impact of weighting columns by IDF before computing User-User cosine similarity, hypothesizing that this will significantly boost the influence of shared obscure tracks over popular ones. I'm also exploring how item popularity might influence candidate scoring and potential damping or prior combination techniques for the final scores.
Tool callupdate_topicTool callReturned
Tool call · Input
{
"strategic_intent": "Experiment with IDF weighting, Top-K neighbors, Power tuning, and TruncatedSVD on validation set.",
"title": "Advanced CF and Feature Engineering",
"summary": "User-User KNN with power=3.0 achieved 0.023824 NDCG@10, more than double the popularity baseline. Now testing IDF column weighting, neighborhood top-K sparsification, power tuning, TruncatedSVD, and hybrid scoring on the public validation window."
}
Tool response
Current topic: "Advanced CF and Feature Engineering"
Topic summary: User-User KNN with power=3.0 achieved 0.023824 NDCG@10, more than double the popularity baseline. Now testing IDF column weighting, neighborhood top-K sparsification, power tuning, TruncatedSVD, and hybrid scoring on the public validation window.
Strategic Intent: Experiment with IDF weighting, Top-K neighbors, Power tuning, and TruncatedSVD on validation set.
I'm currently focused on creating exp2.py to evaluate IDF weighting for user similarity. The formula $IDF_j = \log((N + 1) / (n_j + 1)) + 1$ is being integrated for systematic testing.
Tool callwrite_file/app/solution/exp2.pyReturned
Tool call · Input
{
"instruction": "Systematic experimentation with IDF, powers, top-k, and SVD",
"file_path": "/app/solution/exp2.py",
"content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport pyarrow.parquet as pq\nimport sys\nfrom pathlib import Path\nfrom sklearn.decomposition import TruncatedSVD\n\nsys.path.append(\"/task\")\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\n\ndef main():\n t_start = time.time()\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n uids = targets_frame.uid.to_numpy(dtype=np.int64)\n num_users = len(uids)\n\n uid_map = np.full(1000001, -1, dtype=np.int32)\n uid_map[uids] = np.arange(num_users, dtype=np.int32)\n\n cat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n items = cat.item_id.to_numpy(dtype=np.int64)\n pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)\n num_items = len(items)\n\n item_map = np.full(9390295, -1, dtype=np.int32)\n item_map[items] = np.arange(num_items, dtype=np.int32)\n\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n\n print(f\"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}\")\n\n t0 = time.time()\n cutoff_ts = 24786800\n # Include up to 180 days of history\n min_ts = cutoff_ts - 180 * 86400\n\n pf = pq.ParquetFile(DATA / \"train_listens.parquet\")\n row_list, col_list, data_list = [], [], []\n\n for batch in pf.iter_batches(columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"]):\n u_raw = batch[\"uid\"].to_numpy()\n i_raw = batch[\"item_id\"].to_numpy()\n ts = batch[\"timestamp\"].to_numpy()\n\n m_ts = ts >= min_ts\n if not np.any(m_ts):\n continue\n u_raw = u_raw[m_ts]\n i_raw = i_raw[m_ts]\n ts = ts[m_ts]\n pct = batch[\"played_ratio_pct\"].to_numpy()[m_ts]\n org = batch[\"is_organic\"].to_numpy()[m_ts]\n\n m_item = (i_raw < len(item_map))\n i_idx = np.full(len(i_raw), -1, dtype=np.int32)\n i_idx[m_item] = item_map[i_raw[m_item]]\n\n m_user = (u_raw < len(uid_map))\n u_idx = np.full(len(u_raw), -1, dtype=np.int32)\n u_idx[m_user] = uid_map[u_raw[m_user]]\n\n valid = (u_idx >= 0) & (i_idx >= 0)\n if not np.any(valid):\n continue\n\n u_idx = u_idx[valid]\n i_idx = i_idx[valid]\n ts = ts[valid]\n pct = pct[valid]\n org = org[valid]\n\n days = (cutoff_ts - ts) / 86400.0\n # Time decay with half-life ~60 days\n decay = np.exp(-days / 60.0)\n # Quality: >= 80% is high, 50-80% is medium, < 50% is low\n quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))\n boost = np.where(org == 1, 1.5, 1.0)\n w = (quality * boost * decay).astype(np.float32)\n\n row_list.append(u_idx)\n col_list.append(i_idx)\n data_list.append(w)\n\n row_arr = np.concatenate(row_list)\n col_arr = np.concatenate(col_list)\n data_arr = np.concatenate(data_list)\n del row_list, col_list, data_list\n\n R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()\n del row_arr, col_arr, data_arr\n R_listens.data = np.log1p(R_listens.data)\n\n # Add validation preference state\n val_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n pref_u = uid_map[val_pref.uid.to_numpy()]\n pref_i = item_map[val_pref.item_id.to_numpy()]\n pref_valid = (pref_u >= 0) & (pref_i >= 0)\n pref_u = pref_u[pref_valid]\n pref_i = pref_i[pref_valid]\n pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == \"liked\", 4.0, -2.0).astype(np.float32)\n\n R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)\n\n R = (R_listens + R_pref).tocsr()\n del R_listens, R_pref\n print(f\"R combined: shape={R.shape}, nnz={R.nnz}\")\n\n # Compute IDF per item\n # Item user frequency (number of users who have > 0 interaction)\n R_bin = (R > 0).astype(np.float32)\n user_counts = np.array(R_bin.sum(axis=0)).ravel()\n idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0\n idf_diag = sp.diags(idf.astype(np.float32))\n\n # IDF-weighted matrix for similarity\n R_idf = (R @ idf_diag).tocsr()\n print(\"Computed item IDF weights.\")\n\n def evaluate_scores(scores, name):\n scores = scores.copy()\n scores[~mask] = -np.inf\n top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_idx = np.arange(num_users)[:, None]\n top100_scores = scores[row_idx, top100_idx]\n order = np.argsort(-top100_scores, axis=1)\n sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)\n\n ranked = {\n int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]\n for i in range(num_users)\n }\n res = score_rankings(ranked, graded)\n print(f\"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)\")\n return res[\"quality_ndcg@10\"], scores\n\n # 1. Cosine similarity on R vs R_idf\n for use_idf in [False, True]:\n label = \"IDF\" if use_idf else \"NoIDF\"\n mat = R_idf if use_idf else R\n row_norms = sp.linalg.norm(mat, axis=1)\n row_norms[row_norms == 0] = 1.0\n mat_norm = sp.diags(1.0 / row_norms) @ mat\n\n t0 = time.time()\n G = (mat_norm @ mat_norm.T).toarray()\n np.fill_diagonal(G, 0.0)\n print(f\"Gram matrix ({label}) computed in {time.time()-t0:.2f}s\")\n\n for power in [2.0, 3.0, 4.0, 5.0]:\n G_p = np.maximum(G, 0.0) ** power\n # Try predicting with R.toarray()\n scores = G_p @ R.toarray()\n evaluate_scores(scores, f\"User-KNN ({label}) power={power}\")\n\n # 2. Test Top-K neighbors filtering\n print(\"\\nTesting Top-K neighbors filtering on best Gram matrix (IDF)...\")\n row_norms = sp.linalg.norm(R_idf, axis=1)\n row_norms[row_norms == 0] = 1.0\n R_norm = sp.diags(1.0 / row_norms) @ R_idf\n G = (R_norm @ R_norm.T).toarray()\n np.fill_diagonal(G, 0.0)\n\n for top_k in [100, 200, 500]:\n for power in [2.0, 3.0, 4.0]:\n # Keep top-k per row\n G_topk = np.zeros_like(G)\n # Find threshold\n idx = np.argpartition(-G, top_k, axis=1)[:, :top_k]\n row_idx = np.arange(num_users)[:, None]\n val = G[row_idx, idx]\n val_p = np.maximum(val, 0.0) ** power\n np.put_along_axis(G_topk, idx, val_p, axis=1)\n\n scores = G_topk @ R.toarray()\n evaluate_scores(scores, f\"User-KNN (IDF) topK={top_k} power={power}\")\n\n # 3. Test TruncatedSVD\n print(\"\\nTesting TruncatedSVD...\")\n for n_comp in [128, 256, 512]:\n t0 = time.time()\n svd = TruncatedSVD(n_components=n_comp, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_idf)\n V = svd.components_\n scores_svd = U @ V\n print(f\"SVD n_comp={n_comp} took {time.time()-t0:.2f}s\")\n evaluate_scores(scores_svd, f\"TruncatedSVD n_comp={n_comp}\")\n\nif __name__ == \"__main__\":\n main()\n"
}
Tool response
Successfully created and wrote to new file: /app/solution/exp2.py. Here is the updated code:
import time
import numpy as np
import pandas as pd
import scipy.sparse as sp
import pyarrow.parquet as pq
import sys
from pathlib import Path
from sklearn.decomposition import TruncatedSVD
sys.path.append("/task")
from evaluate_public import validation_truth, DATA
from ranking_evaluation import CandidateSet, score_rankings
def main():
t_start = time.time()
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
uids = targets_frame.uid.to_numpy(dtype=np.int64)
num_users = len(uids)
uid_map = np.full(1000001, -1, dtype=np.int32)
uid_map[uids] = np.arange(num_users, dtype=np.int32)
cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
items = cat.item_id.to_numpy(dtype=np.int64)
pop_listens = cat.positive_listens.to_numpy(dtype=np.float32)
num_items = len(items)
item_map = np.full(9390295, -1, dtype=np.int32)
item_map[items] = np.arange(num_items, dtype=np.int32)
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
mask = np.unpackbits(eligibility.packed, axis=1, count=num_items, bitorder="little").astype(bool)
print(f"Setup loaded in {time.time()-t_start:.2f}s. Scored users: {len(graded)}")
t0 = time.time()
cutoff_ts = 24786800
# Include up to 180 days of history
min_ts = cutoff_ts - 180 * 86400
pf = pq.ParquetFile(DATA / "train_listens.parquet")
row_list, col_list, data_list = [], [], []
for batch in pf.iter_batches(columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"]):
u_raw = batch["uid"].to_numpy()
i_raw = batch["item_id"].to_numpy()
ts = batch["timestamp"].to_numpy()
m_ts = ts >= min_ts
if not np.any(m_ts):
continue
u_raw = u_raw[m_ts]
i_raw = i_raw[m_ts]
ts = ts[m_ts]
pct = batch["played_ratio_pct"].to_numpy()[m_ts]
org = batch["is_organic"].to_numpy()[m_ts]
m_item = (i_raw < len(item_map))
i_idx = np.full(len(i_raw), -1, dtype=np.int32)
i_idx[m_item] = item_map[i_raw[m_item]]
m_user = (u_raw < len(uid_map))
u_idx = np.full(len(u_raw), -1, dtype=np.int32)
u_idx[m_user] = uid_map[u_raw[m_user]]
valid = (u_idx >= 0) & (i_idx >= 0)
if not np.any(valid):
continue
u_idx = u_idx[valid]
i_idx = i_idx[valid]
ts = ts[valid]
pct = pct[valid]
org = org[valid]
days = (cutoff_ts - ts) / 86400.0
# Time decay with half-life ~60 days
decay = np.exp(-days / 60.0)
# Quality: >= 80% is high, 50-80% is medium, < 50% is low
quality = np.where(pct >= 80, 1.0, np.where(pct >= 50, 0.4, 0.05))
boost = np.where(org == 1, 1.5, 1.0)
w = (quality * boost * decay).astype(np.float32)
row_list.append(u_idx)
col_list.append(i_idx)
data_list.append(w)
row_arr = np.concatenate(row_list)
col_arr = np.concatenate(col_list)
data_arr = np.concatenate(data_list)
del row_list, col_list, data_list
R_listens = sp.coo_matrix((data_arr, (row_arr, col_arr)), shape=(num_users, num_items), dtype=np.float32).tocsr()
del row_arr, col_arr, data_arr
R_listens.data = np.log1p(R_listens.data)
# Add validation preference state
val_pref = pd.read_parquet(DATA / "validation_preference_state.parquet")
pref_u = uid_map[val_pref.uid.to_numpy()]
pref_i = item_map[val_pref.item_id.to_numpy()]
pref_valid = (pref_u >= 0) & (pref_i >= 0)
pref_u = pref_u[pref_valid]
pref_i = pref_i[pref_valid]
pref_w = np.where(val_pref.state.to_numpy()[pref_valid] == "liked", 4.0, -2.0).astype(np.float32)
R_pref = sp.csr_matrix((pref_w, (pref_u, pref_i)), shape=(num_users, num_items), dtype=np.float32)
R = (R_listens + R_pref).tocsr()
del R_listens, R_pref
print(f"R combined: shape={R.shape}, nnz={R.nnz}")
# Compute IDF per item
# Item user frequency (number of users who have > 0 interaction)
R_bin = (R > 0).astype(np.float32)
user_counts = np.array(R_bin.sum(axis=0)).ravel()
idf = np.log((num_users + 1.0) / (user_counts + 1.0)) + 1.0
idf_diag = sp.diags(idf.astype(np.float32))
# IDF-weighted matrix for similarity
R_idf = (R @ idf_diag).tocsr()
print("Computed item IDF weights.")
def evaluate_scores(scores, name):
scores = scores.copy()
scores[~mask] = -np.inf
top100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
row_idx = np.arange(num_users)[:, None]
top100_scores = scores[row_idx, top100_idx]
order = np.argsort(-top100_scores, axis=1)
sorted_top100_idx = np.take_along_axis(top100_idx, order, axis=1)
ranked = {
int(uids[i]): [int(items[idx]) for idx in sorted_top100_idx[i]]
for i in range(num_users)
}
res = score_rankings(ranked, graded)
print(f"[{name}] NDCG@10: {res['quality_ndcg@10']:.6f} ({res['quality_users']} users)")
return res["quality_ndcg@10"], scores
# 1. Cosine similarity on R vs R_idf
for use_idf in [False, True]:
label = "IDF" if use_idf else "NoIDF"
mat = R_idf if use_idf else R
row_norms = sp.linalg.norm(mat, axis=1)
row_norms[row_norms == 0] = 1.0
mat_norm = sp.diags(1.0 / row_norms) @ mat
t0 = time.time()
G = (mat_norm @ mat_norm.T).toarray()
np.fill_diagonal(G, 0.0)
print(f"Gram matrix ({label}) computed in {time.time()-t0:.2f}s")
for power in [2.0, 3.0, 4.0, 5.0]:
G_p = np.maximum(G, 0.0) ** power
# Try predicting with R.toarray()
scores = G_p @ R.toarray()
evaluate_scores(scores, f"User-KNN ({label}) power={power}")
# 2. Test Top-K neighbors filtering
print("\nTesting Top-K neighbors filtering on best Gram matrix (IDF)...")
row_norms = sp.linalg.norm(R_idf, axis=1)
row_norms[row_norms == 0] = 1.0
R_norm = sp.diags(1.0 / row_norms) @ R_idf
G = (R_norm @ R_norm.T).toarray()
np.fill_diagonal(G, 0.0)
for top_k in [100, 200, 500]:
for power in [2.0, 3.0, 4.0]:
# Keep top-k per row
G_topk = np.zeros_like(G)
# Find threshold
idx = np.argpartition(-G, top_k, axis=1)[:, :top_k]
row_idx = np.arange(num_users)[:, None]
val = G[row_idx, idx]
val_p = np.maximum(val, 0.0) ** power
np.put_along_axis(G_topk, idx, val_p, axis=1)
scores = G_topk @ R.toarray()
evaluate_scores(scores, f"User-KNN (IDF) topK={top_k} power={power}")
# 3. Test TruncatedSVD
print("\nTesting TruncatedSVD...")
for n_comp in [128, 256, 512]:
t0 = time.time()
svd = TruncatedSVD(n_components=n_comp, algorithm="randomized", random_state=42)
U = svd.fit_transform(R_idf)
V = svd.components_
scores_svd = U @ V
print(f"SVD n_comp={n_comp} took {time.time()-t0:.2f}s")
evaluate_scores(scores_svd, f"TruncatedSVD n_comp={n_comp}")
if __name__ == "__main__":
main()
46
Run exp2.py to compare IDF, power, top-k, and SVD
+14:32Tool callrun_shell_commandRun exp2.py to compare IDF, power, top-k, and SVDReturned
Tool call · Input
{
"description": "Run exp2.py to compare IDF, power, top-k, and SVD",
"command": "python3 /app/solution/exp2.py"
}
Tool response
<untrusted_context>
Command was automatically cancelled because it exceeded the timeout of 5.0 minutes without output. There was no output before it was cancelled.
</untrusted_context>
47
Benchmark A @ B matmul with pre-allocated dense array
I'm currently pinpointing the cause of the timeout. My attention is focused on why no output was generated prior to cancellation, suspecting Python's output buffering behavior might be at play. I'm also examining the R.toarray() computation within the loop for potential performance bottlenecks.
Recorded reasoning summary 48 words
Optimizing Matrix Multiplication
I've identified the core issue: repeated, costly matrix multiplications are exceeding the timeout. Specifically, the conversion of the sparse matrix R to a dense array before multiplication is the bottleneck. I’m now exploring direct sparse-dense multiplication using (R.T @ G_p.T).T or other optimized scipy methods.
Tool callrun_shell_commandBenchmark A @ B matmul with pre-allocated dense arrayReturned
Tool call · Input
{
"description": "Benchmark A @ B matmul with pre-allocated dense array",
"command": "python3 -c \"\nimport time\nimport numpy as np\n\nA = np.random.randn(8456, 8456).astype(np.float32)\nB = np.random.randn(8456, 30000).astype(np.float32)\nt0 = time.time()\nC = A @ B\nprint(f'Dense matmul took {time.time()-t0:.2f}s')\n\""
}
Tool response
<untrusted_context>
Output: Dense matmul took 19.84s
Process Group PGID: 2357
</untrusted_context>
I'm analyzing the recent matrix multiplication performance. It appears a single operation is taking approximately 20 seconds, largely due to the immense number of floating-point operations involved ($4.3 \times 10^{12}$). This means executing the planned 17 matrix multiplications would consume roughly 340 seconds. I am now re-evaluating the necessity of running all 17 operations.
Tool callrun_shell_commandBenchmark sparse G @ sparse R matmulReturned
Tool call · Input
{
"description": "Benchmark sparse G @ sparse R matmul",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# G_sparse has 100 neighbors per row\nn = 8456\nrows = np.repeat(np.arange(n), 100)\ncols = np.random.randint(0, n, size=n*100)\ndata = np.random.rand(n*100).astype(np.float32)\nG_sp = sp.csr_matrix((data, (rows, cols)), shape=(n, n))\n\n# R_csr has ~5.7M nonzeros\nR_rows = np.random.randint(0, n, size=5700000)\nR_cols = np.random.randint(0, 30000, size=5700000)\nR_data = np.random.rand(5700000).astype(np.float32)\nR_sp = sp.csr_matrix((R_data, (R_rows, R_cols)), shape=(n, 30000))\n\nt0 = time.time()\nscores = G_sp @ R_sp\nprint(f'Sparse matmul took {time.time()-t0:.2f}s, nnz={scores.nnz}')\n\""
}
Tool response
<untrusted_context>
Output: Sparse matmul took 5.55s, nnz=226502931
Process Group PGID: 2367
</untrusted_context>
I'm excited to report that sparse matrix multiplication is now incredibly fast, clocking in at just 5.55 seconds. My current focus is on TruncatedSVD. I've identified the dimensions of U and V as (8456, k) and (k, 30000) respectively, and I'm now benchmarking the speed of their matrix product U @ V.
I'm focusing on evaluating TruncatedSVD against User-User KNN. A dedicated script, test_models.py, is being prepared with immediate print output for clear validation score tracking.
My recent runs with TruncatedSVD on R_idf are showing promising speed, completing in just under 8 seconds. The NDCG@10 score of 0.025775 indicates a baseline level of effectiveness. This approach is notably efficient in terms of memory and prediction time.
Recorded reasoning summary 42 words
Adding Explicit Likes
I'm now considering incorporating explicit likes from train_likes.parquet into the interaction matrix. This explicit user feedback, including timestamps, could significantly enhance the model's ability to capture user preferences and push NDCG@10 scores higher, complementing the existing listening behavior features.
Recorded reasoning summary 82 words
Incorporating Organic Likes
I'm realizing that not all likes are equal; organic likes, as noted in train_likes.parquet, represent a strong positive signal. I need to ensure this "grade 3" event is properly handled to better reflect user preference, especially considering multiple or recent organic likes.
Testing BM25 Weighting
Furthermore, I'm exploring BM25 weighting as a transformation for my interaction matrix. This approach, standard in information retrieval, should help by saturating high-frequency counts and normalizing for varying user activity lengths before applying SVD.
Tool callrun_shell_commandAnalyze ground truth relevance vs item popularity rankReturned
Tool call · Input
{
"description": "Analyze ground truth relevance vs item popularity rank",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet\n\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop = dict(zip(cat.item_id, cat.popularity_rank))\n\nall_graded_items = []\ngrades = []\nfor uid, items in graded.items():\n for item, grade in items.items():\n all_graded_items.append(item)\n grades.append(grade)\n\npop_ranks = [item_pop.get(it, 30000) for it in all_graded_items]\ngrades = np.array(grades)\npop_ranks = np.array(pop_ranks)\n\nprint('Total relevant events in validation:', len(pop_ranks))\nprint('Popularity rank percentiles for all relevant items:')\nfor q in [0.1, 0.25, 0.5, 0.75, 0.9]:\n print(f'quantile {q:.2f}: rank {np.quantile(pop_ranks, q):.0f}')\n\nprint('\\nPopularity rank percentiles for Grade 3 (likes):')\np3 = pop_ranks[grades == 3]\nfor q in [0.1, 0.25, 0.5, 0.75, 0.9]:\n print(f'quantile {q:.2f}: rank {np.quantile(p3, q):.0f}')\n\""
}
Tool response
<untrusted_context>
Output: Total relevant events in validation: 37769
Popularity rank percentiles for all relevant items:
quantile 0.10: rank 428
quantile 0.25: rank 1733
quantile 0.50: rank 5622
quantile 0.75: rank 13374
quantile 0.90: rank 21680
Popularity rank percentiles for Grade 3 (likes):
quantile 0.10: rank 618
quantile 0.25: rank 2107
quantile 0.50: rank 6371
quantile 0.75: rank 14181
quantile 0.90: rank 21802
Process Group PGID: 2427
</untrusted_context>
My current focus is on dissecting the popularity distribution of relevant discovery items. I've noted a significant skew towards the upper half, with a median rank of 5,622 and a substantial portion residing within the top percentiles, suggesting a power-law characteristic.
Recorded reasoning summary 46 words
Testing Blended Recommendations
I'm now exploring how to blend SVD predictions with catalog popularity. My current experiments involve a script to test different weighting factors for this blend, alongside variations in feature engineering for the SVD model itself, including different data window sizes and weighting schemes.
My recent focus has been on optimizing the Singular Value Decomposition (SVD) model. I've successfully boosted NDCG@10 to 0.028123 with n_comp=200 and pop gamma=0.2, a significant leap from the baseline. I also identified a critical memory leak in evaluate_scores related to unmanaged arrays, which is now being addressed.
I've found that n_comp=200 with gamma=1.0 yielded a score of 0.028583. Intriguingly, gamma=1.0 outperformed gamma=0.7. I'm now exploring higher gamma values (1.2 to 2.5) and different component counts (128, 160, 200) to see if further improvements are possible, and I'm also considering rank-normalization and combining multiple SVDs with varying random seeds.
I'm currently investigating the memory footprint of our SVD implementation. Specifically, I'm pinpointing the impact of the scores_knn_norm and scores_svd_norm arrays, which together consume 2 GB. My focus is on understanding the internal memory allocations made by scikit-learn's randomized SVD during the fit_transform process, especially when handling large input matrices like R_idf.
I've made significant progress, achieving a nearly threefold improvement with SVD n_comp=200 + org gamma=1.2, reaching 0.028772 compared to the baseline of 0.0107. I'm now investigating further avenues to enhance performance.
My latest runs are showing promising NDCG@10 scores, nearing 0.0290. I'm now exploring variations like org=1.5 all=0.5 and org=1.6 all=0.4, as well as org=1.8, to push this further. The speed is also impressive, with each seed completing in about 6.6 seconds.
Tool callrun_shell_commandCheck fast eval setupReturned
Tool call · Input
{
"description": "Check fast eval setup",
"command": "python3 -c \"\nimport numpy as np\nimport pyarrow.parquet as pq\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth, DATA\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom sklearn.decomposition import TruncatedSVD\nimport scipy.sparse as sp\nimport pandas as pd\n\n# Let's import the fitted components or run a fast eval\n\""
}
Tool response
<untrusted_context>
Output: (empty)
Process Group PGID: 2618
</untrusted_context>
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