Basic recommender gemini-3-8-flash-high-09
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<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app
Showing up to 200 items (files + folders).
/app/ └───solution/
</session_context>
Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope.
All inputs are under /task/data/. Training data ends at timestamp 24786800. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap.
For each event family there is a train_*.parquet and val_*.parquet file:
| Family | Meaning | Columns |
|---|---|---|
listens | Playback events | uid, timestamp, item_id, is_organic, played_ratio_pct, track_length_seconds |
likes | Explicit positive actions | uid, timestamp, item_id, is_organic |
dislikes | Explicit negative actions | uid, timestamp, item_id, is_organic |
unlikes | Reversal of a prior like | uid, timestamp, item_id, is_organic |
undislikes | Reversal of a prior dislike | uid, timestamp, item_id, is_organic |
is_organic=1 means the listener sought out the track; 0 means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold.
The two public windows have different roles:
train_* as history and val_* as subsequent outcomes.train_* + val_*. At hidden-test time the validation events are observed history, not recommendable outcomes.Do not use val_* events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker.
Additional files under /task/data/:
candidate_catalog.parquet: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity.target_users.parquet: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels.validation_preference_state.parquet: active explicit state at the training cutoff, for validation ranking.test_preference_state.parquet: active explicit state after validation, for the final hidden-test ranking. State is liked or disliked; a pair may have both active channels.validation_eligible_candidates.npz and test_eligible_candidates.npz: cutoff-correct candidate sets for every target user. Each contains aligned arrays uids, item_ids, and packed_eligible. For user row r, recover the Boolean item mask with np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little").The exact available Python distribution inventory is listed in /task/requirements.txt. Future test interactions are hidden and used only by the separate Harbor verifier.
Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window:
If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain 2^grade - 1 and discount 1/log2(rank + 1), then averages per-user NDCG over users with at least one relevant item.
The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective.
The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift.
/task/evaluate_public.py scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable:
python /task/evaluate_public.py solution/validation.parquetStage-independent evaluation helpers are available in /task/ranking_evaluation.py. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds.
The public evaluator uses validation_eligible_candidates.npz; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above.
Write /app/solution/submission.parquet or /app/solution/submission.csv, with exactly 100 rows for every target user and columns uid, item_id, rank. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's test_eligible_candidates.npz candidate set.
Leave the code that builds the artifact and a concise /app/solution/README.md in /app/solution/. Keep the best valid submission saved throughout the attempt so it survives the deadline.
/app/solution/./task/ and the installed Python environment as read-only.Analyzing Prompt & Data
I'm currently dissecting the core prompt, focusing on the "Made for You" shelf objective and the specific training data cutoff. My aim is to grasp the environment and inherent constraints to build the most effective ranking model.
Exploring Task Environment
I'm now examining the available files and system constraints within the task environment. My focus is on understanding the provided data structures and any limitations that might impact model development and submission.
{
"summary": "Starting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy.",
"title": "Initial Task and Environment Research",
"strategic_intent": "Inspect task files, data schemas, hardware resources, and evaluation script."
}Current topic: "Initial Task and Environment Research"
Topic summary: Starting initial research into the music discovery ranker task. We will inspect the directory structure, available python packages, evaluation code, data sizes, and system hardware to plan our strategy.
Strategic Intent: Inspect task files, data schemas, hardware resources, and evaluation script.{
"command": "ls -la /task && ls -la /task/data",
"description": "List files in /task and /task/data"
}<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 01:20 .
drwxr-xr-x 1 root root 4096 Sep 29 01:22 ..
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 data
-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py
-r--r--r-- 1 root root 1278 Sep 29 01:16 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:20 ..
-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>{
"command": "lscpu; free -h; nvidia-smi || true",
"description": "Check CPU, memory, and GPU availability"
}<untrusted_context>
Output: Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7R13 Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 1
BogoMIPS: 5299.99
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 2 MiB (4 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Old microcode: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsa: Mitigation; Clear CPU buffers
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
/usr/bin/bash: line 4: free: command not found
/usr/bin/bash: line 4: nvidia-smi: command not found
Process Group PGID: 1616
</untrusted_context>{
"file_path": "/task/evaluate_public.py"
}Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/appChecking System Memory
I'm currently examining available system memory to understand resource constraints. My next step is to analyze the evaluate_public.py script in detail.
{
"description": "Check system memory and read evaluate_public.py",
"command": "head -n 5 /proc/meminfo && cat /task/evaluate_public.py"
}<untrusted_context>
Output: MemTotal: 15988968 kB
MemFree: 5363488 kB
MemAvailable: 14557848 kB
Buffers: 196276 kB
Cached: 8873924 kB
#!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py <validation-ranking.parquet|csv>"
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1621
</untrusted_context>{
"description": "Read requirements.txt and ranking_evaluation.py",
"command": "cat /task/requirements.txt && cat /task/ranking_evaluation.py"
}<untrusted_context>
Output: # Exact third-party distribution inventory for yambda-discovery-ranking-v01.
#
# This is an input to `uv pip sync`, not a list of minimum requirements. A
# conforming task environment contains exactly these third-party Python
# distributions and no others. The same inventory is used for source preparation,
# task building, reference scoring, judging, and solver attempts. Python itself
# is pinned by task.toml and both Harbor image Dockerfiles.
attrs==26.1.0
bleach==6.4.0
certifi==2026.7.22
chardet==7.6.0
charset-normalizer==3.5.1
fastjsonschema==2.22.2
idna==3.18
joblib==1.5.3
jsonschema==4.26.0
jsonschema-specifications==2025.9.1
jupyter-core==5.9.1
jupytext==1.19.5
kaggle==2.2.4
kagglesdk==0.1.37
markdown-it-py==4.2.0
mdit-py-plugins==0.6.1
mdurl==0.1.2
nbformat==5.11.1
numpy==2.2.6
packaging==26.3
pandas==2.3.3
pillow==12.3.0
pip==26.0.1
platformdirs==4.11.3
protobuf==7.35.1
pyarrow==18.1.0
python-dateutil==2.9.0.post0
python-dotenv==1.2.3
python-slugify==8.0.4
pytz==2026.3.post1
PyYAML==6.0.3
referencing==0.37.0
reportlab==4.2.5
requests==2.34.2
rpds-py==2026.6.3
scikit-learn==1.7.2
scipy==1.15.3
six==1.17.0
text-unidecode==1.3
threadpoolctl==3.6.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
"""Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1625
</untrusted_context>Checking Installed Libraries
I'm currently inventorying our available libraries, noting versions for numpy, scipy, pandas, and scikit-learn. My next step is to determine if lightgbm or xgboost are present.
{
"description": "Check available ML libraries",
"command": "python3 -c \"\nfor pkg in ['lightgbm', 'xgboost', 'catboost', 'torch', 'implicit', 'numba', 'scipy', 'sklearn']:\n try:\n __import__(pkg)\n print(f'{pkg}: available')\n except ImportError:\n print(f'{pkg}: NOT available')\n\""
}<untrusted_context>
Output: lightgbm: NOT available
xgboost: NOT available
catboost: NOT available
torch: NOT available
implicit: NOT available
numba: NOT available
scipy: available
sklearn: available
Process Group PGID: 1629
</untrusted_context>Exploring Available Libraries
I'm pleased to confirm that I have access to core scientific computing libraries including scipy, sklearn, numpy, and pandas. This is a crucial first step, and I will now focus on how best to leverage these tools to meet the project's objectives within the given constraints and time limit.
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nfor f in sorted(DATA.glob('*.parquet')):\n df = pd.read_parquet(f)\n print(f'{f.name:35s}: shape={df.shape}, cols={list(df.columns)}')\n if 'timestamp' in df.columns:\n print(f' timestamp min={df.timestamp.min()}, max={df.timestamp.max()}')\n\nfor f in sorted(DATA.glob('*.npz')):\n with np.load(f) as z:\n print(f'{f.name:35s}: files={z.files}')\n for k in z.files:\n print(f' {k}: shape={z[k].shape}, dtype={z[k].dtype}')\n\"",
"description": "Inspect dataset shapes, column names, timestamps, and npz contents"
}<untrusted_context>
Output: candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
test_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state']
train_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=75, max=24786580
train_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=80, max=24786790
train_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
timestamp min=0, max=24786795
train_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=15950, max=24785395
train_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=250, max=24786570
val_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=24789920, max=25393280
val_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=24788815, max=25393390
val_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
timestamp min=24788600, max=25393395
val_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=24810715, max=25393035
val_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
timestamp min=24789005, max=25393385
validation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']
test_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']
uids: shape=(8456,), dtype=uint32
item_ids: shape=(30000,), dtype=uint32
packed_eligible: shape=(8456, 3750), dtype=uint8
validation_eligible_candidates.npz : files=['uids', 'item_ids', 'packed_eligible']
uids: shape=(8456,), dtype=uint32
item_ids: shape=(30000,), dtype=uint32
packed_eligible: shape=(8456, 3750), dtype=uint8
Process Group PGID: 1647
</untrusted_context>Processing Input Data
I've begun by examining the provided data files. I've noted the total number of users in target_users.parquet and the item count along with its ordering and relevant columns from candidate_catalog.parquet. My next step is to understand the validation_ file.
Analyzing Data Sizes
I'm now processing the .npz files, noting the user and item counts, and investigating the memory implications of loading the massive train_listens.parquet file. I'm also meticulously dissecting the timestamp data to establish precise training, validation, and test windows, which are crucial for the upcoming analysis of target users and candidate eligibility.
{
"description": "Check eligible candidates per user statistics",
"command": "python3 -c \"\nimport numpy as np\n\nfor name in ['validation_eligible_candidates.npz', 'test_eligible_candidates.npz']:\n with np.load(f'/task/data/{name}') as z:\n packed = z['packed_eligible']\n # unpackbits\n unpacked = np.unpackbits(packed, axis=1)[:, :30000]\n sums = unpacked.sum(axis=1)\n print(f'{name}: eligible items per user: min={sums.min()}, max={sums.max()}, mean={sums.mean():.1f}, median={np.median(sums)}')\n\""
}<untrusted_context>
Output: validation_eligible_candidates.npz: eligible items per user: min=24657, max=30000, mean=29067.3, median=29242.0
test_eligible_candidates.npz: eligible items per user: min=24552, max=30000, mean=29041.3, median=29220.0
Process Group PGID: 1667
</untrusted_context>Investigating Ineligibility
I'm currently diving into the ~950 ineligible items per user, trying to pinpoint the exact criteria. My current hypothesis is that pre-existing states like 'listened' or 'disliked' might be contributing factors, and I'm examining validation_preference_state to confirm this.
{
"description": "Check preference state details and relationship with eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state counts:\\n', pref['state'].value_counts())\nprint('Users in pref:', pref['uid'].nunique())\n\n# Check overlap of preference state with eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids']\n items = z['item_ids']\n packed = z['packed_eligible']\n uid2idx = {u: i for i, u in enumerate(uids)}\n item2idx = {it: i for i, it in enumerate(items)}\n\nliked = pref[pref.state == 'liked']\ndisliked = pref[pref.state == 'disliked']\n\ndef check_elig(df):\n eligs = []\n for u, it in zip(df.uid[:1000], df.item_id[:1000]):\n if u in uid2idx and it in item2idx:\n r = uid2idx[u]\n c = item2idx[it]\n b = (packed[r, c // 8] >> (c % 8)) & 1\n eligs.append(b)\n return np.mean(eligs) if eligs else None\n\nprint('Fraction of liked in eligibility:', check_elig(liked))\nprint('Fraction of disliked in eligibility:', check_elig(disliked))\n\""
}<untrusted_context>
Output: Preference state counts:
state
liked 418435
disliked 50689
Name: count, dtype: int64
Users in pref: 7038
Fraction of liked in eligibility: 0.0
Fraction of disliked in eligibility: 0.0
Process Group PGID: 1677
</untrusted_context>Clarifying Eligibility Rules
I'm examining the platform's ownership of candidate eligibility. Specifically, I'm investigating why liked and disliked preference states currently register as 0.0 fraction within the eligibility framework, considering all modeling within that boundary is considered in scope for analysis.
{
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 4, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1695
</untrusted_context>{
"description": "Check validation truth stats",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Scored users in val:', len(truth))\ntot_items = sum(len(v) for v in truth.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in truth.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in truth.values())\nprint(f'Total positive user-item pairs in val: {tot_items} (grade 3: {grade3}, grade 1: {grade1})')\n\""
}<untrusted_context>
Output: Scored users in val: 4263
Total positive user-item pairs in val: 37769 (grade 3: 4417, grade 1: 33352)
Process Group PGID: 1706
</untrusted_context>Testing Baseline Performance
I'm currently evaluating a simple global popularity baseline against our validation set. Initial checks indicate that out of 8456 users, 4263 have relevant items, yielding 37,769 positive pairs. I'm scripting a quick comparison to see its performance.
{
"description": "Check candidate catalog details",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\n# Load candidate catalog\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog columns:', catalog.columns)\nprint(catalog.head())\n\""
}<untrusted_context>
Output: Catalog columns: Index(['item_id', 'positive_listens', 'organic_positive_listens',
'popularity_rank'],
dtype='object')
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Process Group PGID: 1726
</untrusted_context>Generating Initial Ranking
I'm currently developing a Python test to establish a baseline ranking. This involves processing a 30,000-item candidate catalog, ordered by popularity, and selecting the top 100 eligible items for each user based on this rank.
{
"description": "Evaluate popularity baseline on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\n# catalog items in order of popularity\n# Let's map item_ids in catalog to their column index in candidate npz\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\n# For each user, find top 100 eligible items\nranked = {}\nfor r, uid in enumerate(uids):\n # unpack bits for user r\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n # filter catalog_items where bits[catalog_cols] == 1\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now try organic_positive_listens popularity\ncatalog_org = catalog.sort_values('organic_positive_listens', ascending=False)\ncatalog_org_cols = np.array([col_map[item] for item in catalog_org.item_id], dtype=np.int32)\ncatalog_org_items = catalog_org.item_id.to_numpy(dtype=np.int64)\n\nranked_org = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_org_items[bits[catalog_org_cols] == 1]\n ranked_org[uid] = eligible_items[:100].tolist()\n\nprint('Catalog organic_positive_listens popularity NDCG@10:', score(ranked_org, truth))\n\""
}<untrusted_context>
Output: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Catalog organic_positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263}
Process Group PGID: 1743
</untrusted_context>Establishing Baseline Performance
My current focus is on establishing a solid baseline, with an initial NDCG@10 of approximately 1.07%. I'm also prioritizing the immediate generation of a valid submission file for testing to ensure structural integrity from the outset.
{
"description": "Generate initial baseline submission and validate it",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\n# Load test eligibility\nwith np.load('/task/data/test_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\nsub_df = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\nsub_df.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Submission saved! Shape:', sub_df.shape)\n\n# Verify with ranking_evaluation\ntargets = set(uids)\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nnorm = validate_ranking(pd.read_parquet('/app/solution/submission.parquet'), targets)\nfor uid, item in norm[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise ValueError(f'Item {item} for uid {uid} is not eligible!')\nprint('Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.')\n\""
}<untrusted_context>
Output: Submission saved! Shape: (845600, 3)
Submission validation SUCCESSFUL! Exactly 100 rows per user, all ranks 1..100, all items eligible.
Process Group PGID: 1763
</untrusted_context>{
"file_path": "/app/solution/README.md",
"content": "# Music Discovery Ranking Solution\n\n## Overview\nMusic Discovery candidate ranker for target streaming users.\nEvaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).\n\n## Methodology\n- Baseline popularity ranking verified and structurally valid.\n- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).\n"
}Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:
# Music Discovery Ranking Solution
## Overview
Music Discovery candidate ranker for target streaming users.
Evaluated on graded NDCG@10 on organic discovery events (grade 3 for organic likes, grade 1 for organic listens >= 80%).
## Methodology
- Baseline popularity ranking verified and structurally valid.
- Active development of personalized recommendation models (collaborative filtering, matrix factorization, item-item similarity, user profile matching, gradient boosting re-ranking).
Reviewing Data Parameters
I'm currently examining the provided data parameters. My focus is on understanding the training data cutoff and the subsequent validation window, specifically noting the 30-minute gap before the seven-day public validation period begins.
Analyzing Event Structures
I've been dissecting the event data, noting the distinct parquet files for 'listens', 'likes', 'dislikes', 'unlikes', and 'undislikes'. Each contains user, timestamp, and item information, with 'listens' offering additional playback details. I'm particularly focused on how these event types might function as features for the ranking model.
{
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(target_users))\nprint(target_users.head())\n\nt = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = t['uid'].to_numpy()\nunique_uids = set(uids)\nprint('Total unique uids in train_listens:', len(unique_uids))\ntarget_uid_set = set(target_users.uid)\nprint('Target uids in train_listens:', len(target_uid_set.intersection(unique_uids)))\n\"",
"description": "Inspect target users in train_listens"
}<untrusted_context>
Output: Target users count: 8456
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
3 600 1985.0 2423
4 700 1776.0 6114
Total unique uids in train_listens: 9238
Target uids in train_listens: 8456
Process Group PGID: 1780
</untrusted_context>Analyzing User Data
It's interesting to note the substantial overlap between our target users and those present in train_listens. With 8,456 target users all accounted for, and a candidate pool of 30,000 items, I'm now investigating the unique items within train_listens and candidate_catalog.
{
"description": "Inspect unique items in train_listens and candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Candidate catalog unique items:', cat['item_id'].nunique())\nt = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = t['item_id'].to_numpy()\nunique_items = set(item_ids)\nprint('Total unique item_ids in train_listens:', len(unique_items))\ncat_item_set = set(cat['item_id'])\nprint('Candidate items in train_listens:', len(cat_item_set.intersection(unique_items)))\n\""
}<untrusted_context>
Output: Candidate catalog unique items: 30000
Total unique item_ids in train_listens: 845395
Candidate items in train_listens: 30000
Process Group PGID: 1802
</untrusted_context>Analyzing Candidate Item Coverage
My analysis reveals that all 30,000 items in the candidate catalog are present within the training listen data, out of a total of 845,395 unique item IDs. I'm now investigating how target users have interacted with these specific 30,000 items.
{
"description": "Check if validation truth items appeared in user history",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\n# Check validation truth items: did the user listen to or like them in train?\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Load some train data for these users\n# Let's inspect a subset or load train_likes and train_listens\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Check how many (uid, item_id) in truth were in train_pref\ntruth_pairs = [(u, it) for u, items in truth.items() for it in items]\ntruth_df = pd.DataFrame(truth_pairs, columns=['uid', 'item_id'])\nprint(f'Total truth pairs: {len(truth_df)}')\n\npref_merged = pd.merge(truth_df, train_pref, on=['uid', 'item_id'])\nprint(f'Truth pairs in validation_preference_state: {len(pref_merged)}')\n\n# Now let us sample some users to see if their truth items were in train_listens\nsample_users = list(truth.keys())[:100]\nsample_truth = truth_df[truth_df.uid.isin(sample_users)]\nprint(f'Sample truth pairs: {len(sample_truth)}')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('uid', 'in', sample_users)],\n columns=['uid', 'item_id', 'played_ratio_pct'])\nlistens_sample = table.to_pandas()\nprint(f'Listens for sample users: {len(listens_sample)}')\n\nm = pd.merge(sample_truth, listens_sample, on=['uid', 'item_id'])\nprint(f'Sample truth pairs that appeared in train_listens: {len(m)} (unique pairs: {m[[\\\"uid\\\", \\\"item_id\\\"]].drop_duplicates().shape[0]}) out of {len(sample_truth)}')\n\""
}<untrusted_context>
Output: Total truth pairs: 37769
Truth pairs in validation_preference_state: 0
Sample truth pairs: 858
Listens for sample users: 591918
Sample truth pairs that appeared in train_listens: 0 (unique pairs: 0) out of 858
Process Group PGID: 1823
</untrusted_context>Investigating Training Pair Count
I'm currently digging into why zero truth pairs appeared in my training set. I'm planning to broaden my investigation across more users and examine the contents of validation_eligible_candidates.npz to understand the underlying issue.
{
"description": "Check if listened items in candidate catalog are ever eligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\n# Let us check 5 users completely: what is the overlap between their train listens/likes and eligible candidates?\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\nuid2row = {u: i for i, u in enumerate(uids)}\nitem2col = {it: i for i, it in enumerate(item_ids)}\n\nsample_uids = uids[:10].tolist()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n filters=[('uid', 'in', sample_uids)],\n columns=['uid', 'item_id'])\ndf_listens = table.to_pandas()\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes_sample = df_likes[df_likes.uid.isin(sample_uids)]\n\nfor u in sample_uids:\n r = uid2row[u]\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n user_listens = set(df_listens[df_listens.uid == u].item_id)\n user_likes = set(df_likes_sample[df_likes_sample.uid == u].item_id)\n \n # check how many listened items are in candidate catalog\n listened_in_cat = [it for it in user_listens if it in item2col]\n eligible_listened = [it for it in listened_in_cat if bits[item2col[it]] == 1]\n \n print(f'User {u}: listened_in_cat={len(listened_in_cat)}, of which eligible={len(eligible_listened)}')\n\""
}<untrusted_context>
Output: User 100: listened_in_cat=682, of which eligible=0
User 200: listened_in_cat=168, of which eligible=0
User 300: listened_in_cat=69, of which eligible=0
User 600: listened_in_cat=680, of which eligible=0
User 700: listened_in_cat=1653, of which eligible=0
User 800: listened_in_cat=2524, of which eligible=0
User 900: listened_in_cat=334, of which eligible=0
User 1000: listened_in_cat=499, of which eligible=0
User 1100: listened_in_cat=1440, of which eligible=0
User 1400: listened_in_cat=1262, of which eligible=0
Process Group PGID: 1849
</untrusted_context>Discovering New Music
I'm focusing on ensuring the recommendations I generate are truly novel. My current thinking prioritizes identifying and surfacing only those tracks the user hasn't encountered before, making this a genuine discovery engine.
Refining Ranking Strategies
My current focus is on refining the ranking algorithms. I'm exploring implicit feedback collaborative filtering methods, aiming for speed and effectiveness. The goal is to accurately predict organic listening and preference within the next week, considering a large user and item catalog. I'm beginning by analyzing the performance of loading the training data.
{
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\nt1 = time.time()\nprint(f'Reading train_listens took {t1 - t0:.2f}s, rows={len(table)}')\n\"",
"description": "Benchmark loading time of train_listens.parquet"
}<untrusted_context>
Output: Reading train_listens took 0.71s, rows=43484159
Process Group PGID: 1871
</untrusted_context>Analyzing Data Efficiently
I'm impressed by the speed of the data loading, processing 43.48 million rows in under a second. My current focus is on segmenting this data by key metrics: organic status, play ratio percentages, and differentiating between the 30,000 candidate items and the rest, to understand event distribution.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ntotal_rows = len(table)\nprint(f'Total rows: {total_rows}')\n\norg = pc.equal(table['is_organic'], 1)\nprint(f'Organic: {pc.sum(org).as_py()} ({pc.mean(org).as_py():.2%})')\n\nplayed_50 = pc.greater_equal(table['played_ratio_pct'], 50)\nprint(f'Played >= 50%: {pc.sum(played_50).as_py()} ({pc.mean(played_50).as_py():.2%})')\n\nplayed_80 = pc.greater_equal(table['played_ratio_pct'], 80)\nprint(f'Played >= 80%: {pc.sum(played_80).as_py()} ({pc.mean(played_80).as_py():.2%})')\n\norg_80 = pc.and_(org, played_80)\nprint(f'Organic & played >= 80%: {pc.sum(org_80).as_py()} ({pc.mean(org_80).as_py():.2%})')\n\n# Check timestamps range\nts = table['timestamp']\nmin_ts, max_ts = pc.min(ts).as_py(), pc.max(ts).as_py()\nprint(f'Timestamps: {min_ts} to {max_ts} (range: {(max_ts - min_ts) / 86400:.1f} days)')\n\"",
"description": "Analyze event statistics and time range in train_listens"
}<untrusted_context>
Output: Total rows: 43484159
Organic: 22477622 (51.69%)
Played >= 50%: 27500755 (63.24%)
Played >= 80%: 25755197 (59.23%)
Organic & played >= 80%: 12177583 (28.00%)
Timestamps: 0 to 24786795 (range: 286.9 days)
Process Group PGID: 1893
</untrusted_context>Analyzing User Data
I'm currently examining the target_users.parquet file. It contains user identifiers along with their recent and historical positive listening counts, spanning a period of approximately 287 days.
{
"command": "python3 -c \"\nimport pandas as pd\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes:', len(likes), likes.head())\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nprint('Unlikes:', len(unlikes))\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nprint('Dislikes:', len(dislikes))\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\nprint('Undislikes:', len(undislikes))\n\"",
"description": "Inspect feedback event counts"
}<untrusted_context>
Output: Likes: 814831 uid timestamp item_id is_organic
0 100 44755 732449 1
1 100 1155860 6568592 0
2 100 1259125 5411243 1
3 100 1260005 7371186 0
4 100 1263935 4943655 0
Unlikes: 292665
Dislikes: 101075
Undislikes: 19960
Process Group PGID: 1915
</untrusted_context>Analyzing Validation Data
I'm processing the validation_preference_state.parquet file. It contains 469,124 rows representing active preference states at the training cutoff, intended for validation ranking.
Exploring Collaborative Filtering
I'm considering matrix factorization techniques, specifically TruncatedSVD, iALS, and ALS, as potential models for this task. The goal is to leverage collaborative filtering to build effective recommendation systems.
{
"description": "Check target user listens in candidate catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct', 'is_organic', 'timestamp'])\ndf = table.to_pandas()\ntarget_users = set(pd.read_parquet('/task/data/target_users.parquet').uid)\ndf_target = df[df.uid.isin(target_users)]\nprint(f'Total target listens: {len(df_target)}')\n\nin_cat = df_target.item_id.isin(cat_set)\nprint(f'Listens in 30k catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\""
}<untrusted_context>
Output: Total target listens: 43222297
Listens in 30k catalog: 31062540 (71.87%)
Process Group PGID: 1932
</untrusted_context>Analyzing User Engagement
My analysis is honing in on target user engagement. It's clear nearly all listens come from this group, with a significant concentration within the 30,000-item candidate catalog. This suggests a highly engaged core audience.
Forming Interaction Matrix
I'm considering building a User-Item interaction matrix, exploring both dense and sparse formats. The memory requirements for even a large dense matrix appear manageable within our existing RAM, with sparse matrices being even more efficient. This approach seems promising for further analysis.
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\n# Check user-item stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ncat = set(pd.read_parquet('/task/data/candidate_catalog.parquet').item_id)\ndf_cat = df[df.item_id.isin(cat)]\n\n# Filter to positive listens: played_ratio_pct >= 50 or 80\ndf_pos = df_cat[df_cat.played_ratio_pct >= 50]\nprint(f'Positive listens in catalog: {len(df_pos)}')\nunique_pairs = df_pos.drop_duplicates(subset=['uid', 'item_id'])\nprint(f'Unique user-item positive pairs in catalog: {len(unique_pairs)}')\nprint(f'Average pairs per user: {len(unique_pairs) / 8456:.1f}')\n\"",
"description": "Check unique user-item pairs in catalog"
}<untrusted_context>
Output: Positive listens in catalog: 20056329
Unique user-item positive pairs in catalog: 5564926
Average pairs per user: 658.1
Process Group PGID: 1954
</untrusted_context>Analyzing User Data
My current focus is on understanding the scale of the user-item interaction data. It appears we have a substantial number of unique catalog items per user, averaging 658, leading to a total of 5.56 million unique user-item pairs. This volume is translating into significant memory considerations for sparse matrix representations.
{
"description": "Test evaluate_public.py on submission.parquet",
"command": "python3 /task/evaluate_public.py /app/solution/submission.parquet"
}<untrusted_context>
Output: {
"model": "/app/solution/submission.parquet",
"scores": {
"quality_ndcg@10": 0.0,
"quality_users": 4263
}
}
Process Group PGID: 1976
</untrusted_context>Correcting Evaluation Data
I've identified a critical discrepancy: my submission was evaluated against test_eligible_candidates.npz, but the evaluation script evaluate_public.py correctly uses validation_eligible_candidates.npz. This explains the zero NDCG score and necessitates a re-evaluation using the proper dataset.
Investigating Submission Discrepancies
I've confirmed the public evaluator uses validation_eligible_candidates.npz, not test_eligible_candidates.npz. The submission's items were all eligible for validation, meaning the issue isn't directly with eligibility checks but likely with how the ranked items align with validation criteria, especially since test eligibility is a subset of validation.
{
"description": "Check evaluation of submission.parquet against validation truth",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking('/app/solution/submission.parquet')\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 8, in <module>
File "/task/ranking_evaluation.py", line 98, in read_ranking
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
^^^^^^^^^^^
AttributeError: 'str' object has no attribute 'suffix'
Exit Code: 1
Process Group PGID: 1996
</untrusted_context>{
"description": "Check score on submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, CandidateSet, score_rankings, ranked_items\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\nprint('Evaluation of submission.parquet on val truth:', score_rankings(ranked, truth))\n\""
}<untrusted_context>
Output: Evaluation of submission.parquet on val truth: {'quality_ndcg@10': 0.0, 'quality_users': 4263}
Process Group PGID: 2007
</untrusted_context>{
"description": "Inspect submission.parquet ranking ordering",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import read_ranking, ranked_items\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nprint(sub.head(15))\nprint(sub.dtypes)\n\nordered = sub.sort_values(['uid', 'rank'])\nr = {int(uid): list(map(int, values)) for uid, values in ordered.groupby('uid').item_id}\nprint('User 100 top 10:', r[100][:10])\n\""
}<untrusted_context>
Output: uid item_id rank
0 100 5862961 1
1 100 6901374 2
2 100 5635052 3
3 100 3542184 4
4 100 8213481 5
5 100 906358 6
6 100 2859641 7
7 100 3033749 8
8 100 2651314 9
9 100 5252571 10
10 100 5436843 11
11 100 5927996 12
12 100 8902934 13
13 100 5734320 14
14 100 1464852 15
uid int64
item_id int64
rank int64
dtype: object
User 100 top 10: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]
Process Group PGID: 2027
</untrusted_context>Investigating Discrepancies
I'm currently digging into why the popularity baseline calculation yielded different results. It seems there might be a subtle difference in how the data is being processed between the two instances. I'm scrutinizing the earlier script for clues.
Revisiting Candidate Selection
I've identified a potential issue with how user IDs (uids) are being handled. It appears the user IDs used for candidate generation might not perfectly align with those in the submission file, leading to discrepancies. I'm now comparing these uids directly.
{
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\n# Check user 100\nprint('User 100 in truth?', 100 in truth)\nif 100 in truth:\n print('User 100 truth items:', truth[100])\n\n# Check test eligibility vs val eligibility for user 100\nwith np.load('/task/data/validation_eligible_candidates.npz') as val_z, np.load('/task/data/test_eligible_candidates.npz') as test_z:\n val_items = val_z['item_ids']\n val_pack = val_z['packed_eligible']\n test_pack = test_z['packed_eligible']\n \n val_bits_0 = np.unpackbits(val_pack[0], count=len(val_items), bitorder='little')\n test_bits_0 = np.unpackbits(test_pack[0], count=len(val_items), bitorder='little')\n print('Diff between val and test bits for user 0:', np.sum(val_bits_0 != test_bits_0))\n print('Items in val but not test:', np.sum((val_bits_0 == 1) & (test_bits_0 == 0)))\n\"",
"description": "Check user 100 truth and bitmask difference"
}<untrusted_context>
Output: User 100 in truth? False
Diff between val and test bits for user 0: 24
Items in val but not test: 24
Process Group PGID: 2046
</untrusted_context>{
"description": "Inspect scores across users for submission.parquet",
"command": "python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, read_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nranked = ranked_items(sub)\n\nscores = [ndcg_at_k(ranked[uid], gold, 10) for uid, gold in truth.items()]\nprint('Number of truth users:', len(truth))\nprint('Number of nonzero scores:', sum(1 for s in scores if s > 0))\nprint('Max score:', max(scores))\nprint('Mean score:', np.mean(scores))\n\""
}<untrusted_context>
Output: Number of truth users: 4263
Number of nonzero scores: 0
Max score: 0.0
Mean score: 0.0
Process Group PGID: 2066
</untrusted_context>Investigating Catalog Data
I'm re-examining the initial data loading and processing, specifically why non-zero scores were reported as zero. I'm tracing back to the pd.read_parquet call and the subsequent handling of "Catalog positive" entries to pinpoint the discrepancy.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import score, validation_truth\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked = {}\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\nprint('Catalog positive_listens popularity NDCG@10:', score(ranked, truth))\n\n# Now let us compare ranked[uids[0]][:10] with submission.parquet\nsub = pd.read_parquet('/app/solution/submission.parquet')\nsub_u0 = sub[sub.uid == uids[0]].sort_values('rank').item_id.tolist()[:10]\nprint('ranked u0:', ranked[uids[0]][:10])\nprint('sub_u0: ', sub_u0)\n\"",
"description": "Compare ranked u0 with sub_u0"
}<untrusted_context>
Output: Catalog positive_listens popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
ranked u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]
sub_u0: [5862961, 6901374, 5635052, 3542184, 8213481, 906358, 2859641, 3033749, 2651314, 5252571]
Process Group PGID: 2086
</untrusted_context>Investigating Discrepancy
I'm examining why the ranked lists for u0 matched, yet the ndcg_at_k calculation yielded zero. I'm currently scrutinizing the data types of uids in the z array to pinpoint the source of this unexpected test result.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr = ranked_items(sub)\nprint('type of key in r:', type(list(r.keys())[0]))\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntargets = set(sub.uid.unique())\ntruth = validation_truth(eligibility, targets)\nprint('type of key in truth:', type(list(truth.keys())[0]))\nprint('First key in r:', list(r.keys())[0], 'First key in truth:', list(truth.keys())[0])\n\n# Now score_rankings(r, truth)\nprint(score_rankings(r, truth))\n\"",
"description": "Check score_rankings on submission.parquet again"
}<untrusted_context>
Output: type of key in r: <class 'int'>
type of key in truth: <class 'int'>
First key in r: 100 First key in truth: 200
{'quality_ndcg@10': 0.0, 'quality_users': 4263}
Process Group PGID: 2106
</untrusted_context>Investigating Metric Discrepancies
I'm currently analyzing a puzzling difference in scoring metrics. It appears that two functions, score and score_rankings, are producing distinct results for what I believed were identical inputs. My immediate next step is to inspect the actual rankings (ranked and r) at a specific index (200) to identify the precise divergence causing this unexpected outcome.
{
"description": "Compare ranked_val with r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet\nfrom pathlib import Path\n\n# Load ranked from first snippet:\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\nprint('ranked_val[200][:10]:', ranked_val[200][:10])\nprint('r_sub[200][:10]: ', r_sub[200][:10])\nprint('Are all users identical between ranked_val and r_sub?')\ndiff_count = 0\nfor u in uids:\n if ranked_val[u][:10] != r_sub[u][:10]:\n diff_count += 1\nprint('Users with diff top 10:', diff_count)\n\""
}<untrusted_context>
Output: ranked_val[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]
r_sub[200][:10]: [5862961, 6901374, 3542184, 9378983, 8213481, 906358, 3033749, 2651314, 5252571, 5436843]
Are all users identical between ranked_val and r_sub?
Users with diff top 10: 806
Process Group PGID: 2126
</untrusted_context>Analyzing Score Discrepancies
I'm currently scrutinizing a peculiar difference between ranked_val and r_sub in my evaluation. The quality_ndcg@10 score, which I expected to be consistent, is showing an unexpected 0.01071. I'm digging into why score(ranked_val, truth) differs from score(r_sub, truth) to pinpoint the source of this anomaly.
{
"description": "Evaluate both ranked_val and r_sub on truth",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, score_rankings, CandidateSet\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nprint('Score ranked_val:', score_rankings(ranked_val, truth))\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\nprint('Score r_sub: ', score_rankings(r_sub, truth))\n\""
}<untrusted_context>
Output: Score ranked_val: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Score r_sub: {'quality_ndcg@10': 0.0, 'quality_users': 4263}
Process Group PGID: 2143
</untrusted_context>Investigating Zero Values
I'm puzzled by a score of 0.01071 for ranked_val while r_sub registers as 0.0. I'm delving into the extraction logic for r_sub from sub and examining the ranked_items function within ranking_evaluation.py to understand this discrepancy.
{
"description": "Debug difference between ranked_val and r_sub",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\nfrom ranking_evaluation import read_ranking, ranked_items, CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\nfrom pathlib import Path\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nranked_val = {}\nfor r_idx, uid in enumerate(uids):\n bits = np.unpackbits(packed[r_idx], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1]\n ranked_val[uid] = eligible_items[:100].tolist()\n\ntargets = set(uids)\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, targets)\n\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nr_sub = ranked_items(sub)\n\n# Find a user where ndcg_at_k(ranked_val[uid], truth[uid], 10) > 0\nfor uid in truth:\n s_val = ndcg_at_k(ranked_val[uid], truth[uid], 10)\n s_sub = ndcg_at_k(r_sub[uid], truth[uid], 10)\n if s_val > 0:\n print(f'User {uid}: val_score={s_val}, sub_score={s_sub}')\n print(' truth:', truth[uid])\n print(' val top 10:', ranked_val[uid][:10])\n print(' sub top 10:', r_sub[uid][:10])\n break\n\""
}<untrusted_context>
Output: User 1400: val_score=0.09653121191524809, sub_score=0.0
truth: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}
val top 10: [5862961, 6901374, 5635052, 3542184, 9378983, 8213481, 906358, 2859641, 3033749, 2651314]
sub top 10: [5635052, 8213481, 906358, 2859641, 3033749, 2651314, 6853370, 3418493, 5927996, 6155741]
Process Group PGID: 2163
</untrusted_context>Confirming Accuracy Now
I'm reviewing the top 10 values and cross-referencing them with the ground truth. It's promising to see several identified values, like 6901374 and 9378983, present in the truth set. I'm specifically noting their original rankings within the top 10 list.
Uncovering Eligibility Logic
I've realized the core issue: items listened to during validation are correctly excluded from the test set's eligible candidates. This explains the zero hits when comparing the submission to validation truth, as the test set was inherently designed to omit these already-heard items.
{
"description": "Evaluate val_baseline.parquet with evaluate_public.py",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport pandas as pd\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n uids = z['uids'].astype(np.int64)\n item_ids = z['item_ids'].astype(np.int64)\n packed = z['packed_eligible']\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncol_map = {item: i for i, item in enumerate(item_ids)}\ncatalog_cols = np.array([col_map[item] for item in catalog.item_id], dtype=np.int32)\ncatalog_items = catalog.item_id.to_numpy(dtype=np.int64)\n\nrecords = []\nfor r, uid in enumerate(uids):\n bits = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n eligible_items = catalog_items[bits[catalog_cols] == 1][:100]\n for rank_idx, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank_idx))\n\ndf = pd.DataFrame(records, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/val_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/val_baseline.parquet"
}<untrusted_context>
Output: {
"model": "/app/solution/val_baseline.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 2183
</untrusted_context>{
"description": "Clean up temporary file",
"command": "rm -f /app/solution/val_baseline.parquet"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2218
</untrusted_context>{
"strategic_intent": "Design and experiment with personalized recommendation models on validation.",
"summary": "Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.",
"title": "Personalized Recommendation Modeling & Validation"
}Current topic: "Personalized Recommendation Modeling & Validation"
Topic summary: Completed initial exploration and baseline validation. We verified the evaluation pipeline, understanding of eligibility masks, and confirmed candidate catalog baseline NDCG@10 of 0.01071. Now we transition to modeling: researching item-item collaborative filtering, matrix factorization, and user-item interactions to significantly boost discovery NDCG@10.
Strategic Intent: Design and experiment with personalized recommendation models on validation.Analyzing Recommendation Drivers
My current focus is on dissecting the core components of an effective recommendation engine for our specific scenario. I'm meticulously examining user behavior patterns, particularly their listening history and associated timestamps, to identify key drivers for generating relevant suggestions.
Refining Feature Importance
I'm now delving into how specific user interactions, such as organic listens versus explicit likes and dislikes, will influence recommendation grades. My aim is to quantify their relative impact for NDCG@10, particularly focusing on achieving grade 3 and grade 1 organic outcomes within the week.
Evaluating Matrix Inversion
I'm currently assessing the computational feasibility of inverting a $30000 \times 30000$ matrix, as required by the EASE model. This large-scale inversion could be a bottleneck, and I'm exploring alternatives or optimizations if it proves impractical.
Exploring Alternative Strategies
I'm shifting focus to computational efficiency. Instead of direct matrix inversion for EASE, I'm now testing item-item similarity methods, including cosine and BM25. I'm also evaluating truncated SVD for latent factor models, as it shows potential for significantly faster computation on validation sets.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\nprint('Loading data...')\n\n# Load target users and candidates\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_ids = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(item_ids)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_col_map = np.array([uid_to_idx[u] for u in val_uids])\nval_item_map = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\nprint(f'Truth loaded ({len(truth)} users). Time: {time.time() - t0:.2f}s')\n\n# Read train listens\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', \n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\nprint(f'Train listens loaded: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\n# Filter to target users and catalog items\ndf_listens = df_listens[df_listens.uid.isin(uid_to_idx) & df_listens.item_id.isin(item_to_idx)]\nprint(f'Filtered listens: {len(df_listens)}. Time: {time.time() - t0:.2f}s')\n\"",
"description": "Load and filter train listens in python"
}<untrusted_context>
Output: Loading data...
Truth loaded (4263 users). Time: 0.69s
Train listens loaded: 43484159. Time: 1.17s
Filtered listens: 31062540. Time: 11.08s
Process Group PGID: 2221
</untrusted_context>Optimizing Data Filtering
I'm analyzing the performance of filtering 43 million rows, which currently takes about 10 seconds. I've identified the df_listens.uid.isin(uid_to_idx) operation as a key area. Given that the unique UIDs in train_listens (9,238) largely overlap with the target users (8,456), a significant portion, approximately 99.4%, of the rows are already relevant. I'm now exploring strategies to further accelerate this process.
{
"description": "Benchmark fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pyarrow.compute.SetLookupOptions(value_set=pyarrow.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\n# Filter using pc.is_in\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 10, in <module>
NameError: name 'pyarrow' is not defined
Exit Code: 1
Process Group PGID: 2250
</untrusted_context>{
"description": "Test fast PyArrow filtering",
"command": "python3 -c \"\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat.item_id.to_numpy()))\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nt1 = time.time()\nprint(f'Read table: {t1 - t0:.2f}s')\n\nmask = pc.is_in(table['item_id'], options=item_set)\nt2 = time.time()\nprint(f'pc.is_in took: {t2 - t1:.2f}s, matches: {pc.sum(mask).as_py()}')\n\nfiltered = table.filter(mask)\nt3 = time.time()\nprint(f'Filter table took: {t3 - t2:.2f}s, len={len(filtered)}')\n\""
}<untrusted_context>
Output: Read table: 0.41s
pc.is_in took: 0.56s, matches: 31249613
Filter table took: 0.41s, len=31249613
Process Group PGID: 2267
</untrusted_context>Optimizing Data Processing
I've successfully optimized the data reading and filtering stages, achieving a significant 10x speedup. My next steps involve rigorously testing the construction of the sparse user-item interaction matrix, the SVD computations (both Truncated and randomized), candidate score prediction, and finally, evaluating NDCG@10 on the validation data.
{
"description": "Test TruncatedSVD model on validation",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\nt0 = time.time()\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Load validation eligibility\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\n\n# Map val_items index to cat_items index\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Read train listens\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\n# Map to matrix indices\n# Only keep uids that are in uid_to_idx\nu_mask = np.isin(u_arr, uids)\nu_arr = u_arr[u_mask]\nit_arr = it_arr[u_mask]\npct_arr = pct_arr[u_mask]\norg_arr = org_arr[u_mask]\nts_arr = ts_arr[u_mask]\n\nu_idx = np.vectorize(uid_to_idx.get)(u_arr)\nit_idx = np.vectorize(item_to_idx.get)(it_arr)\n\n# Weights: base weight for completion + bonus for organic + time decay\n# Max timestamp in train is 24786800\nCUTOFF = 24786800.0\n# Half-life of 60 days (60 * 86400 = 5,184,000s)\ntime_weight = np.exp((ts_arr - CUTOFF) / (60.0 * 86400.0))\n\n# Positive listen weight: >=50%: 1.0, >=80%: 2.0, organic bonus: +1.0\nlisten_weight = np.where(pct_arr >= 80, 2.0, np.where(pct_arr >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nprint(f'Building sparse matrix, elapsed: {time.time() - t0:.2f}s')\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nprint(f'Matrix shape: {X.shape}, nnz: {X.nnz}')\n\n# Add likes data\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.vectorize(uid_to_idx.get)(likes.uid.to_numpy())\nl_it = np.vectorize(item_to_idx.get)(likes.item_id.to_numpy())\nl_ts = likes.timestamp.to_numpy()\nl_org = likes.is_organic.to_numpy()\nl_tw = np.exp((l_ts - CUTOFF) / (60.0 * 86400.0))\nl_w = (np.where(l_org == 1, 5.0, 3.0)) * l_tw\n\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X + X_likes\n\nprint(f'Total matrix nnz: {X.nnz}, elapsed: {time.time() - t0:.2f}s')\n\n# Run SVD\nprint('Computing SVD...')\nsvd = TruncatedSVD(n_components=64, random_state=42)\nU = svd.fit_transform(X) # (8456, 64)\nV = svd.components_ # (64, 30000)\nprint(f'SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f}, elapsed: {time.time() - t0:.2f}s')\n\n# Predict scores: S = U @ V\n# Let's score target users\nprint('Scoring users and evaluating...')\nranked = {}\n# Process in batches of 500 users\nBATCH_SIZE = 500\nfor start in range(0, len(val_uids), BATCH_SIZE):\n end = min(start + BATCH_SIZE, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_u_idx = [uid_to_idx[u] for u in batch_uids]\n \n # Compute scores for this batch: (batch_size, 30000)\n batch_scores = U[batch_u_idx] @ V\n \n # For each user, get top 100 eligible items\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n # Mask out ineligible items\n user_scores = batch_scores[i].copy()\n # Eligible mask in terms of cat_items:\n # val_items mapped to cat_items:\n # Ineligible items get -inf\n mask = np.ones(len(cat_items), dtype=bool)\n # where bits == 0, mark ineligible\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nres = score(ranked, truth)\nprint(f'TruncatedSVD(64) NDCG@10: {res}')\n\""
}<untrusted_context>
Output: Building sparse matrix, elapsed: 12.91s
Matrix shape: (8456, 30000), nnz: 7868664
Total matrix nnz: 7876748, elapsed: 14.76s
Computing SVD...
SVD explained variance ratio sum: 0.4563, elapsed: 19.15s
Scoring users and evaluating...
TruncatedSVD(64) NDCG@10: {'quality_ndcg@10': 0.021702, 'quality_users': 4263}
Process Group PGID: 2289
</untrusted_context>Improving SVD Performance
My latest experiments with TruncatedSVD, specifically using 64 components, yielded impressive results, achieving a score of 0.021702, more than double the baseline. This significant improvement was also realized in a remarkably short time, just 19 seconds. I'm now eager to explore the impact of varying the number of components used in the SVD process to further refine these outcomes.
{
"description": "Evaluate SVD(128) raw vs cosine",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast matrix builder\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\nCUTOFF = 24786800.0\n# Try half-life of 45 days\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (45.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (45.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\nX = X + sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n\n# Test SVD with 128 components\nprint('Computing SVD(128)...')\nsvd = TruncatedSVD(n_components=128, random_state=42)\nU = svd.fit_transform(X)\nV = svd.components_\n\ndef eval_preds(U_mat, V_mat, name):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_scores = U_mat[[uid_to_idx[u] for u in batch_uids]] @ V_mat\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n print(f'{name} NDCG@10: {score(ranked, truth)}')\n\neval_preds(U, V, 'SVD(128) raw')\n\n# Normalized embeddings (cosine similarity)\nU_norm = U / (np.linalg.norm(U, axis=1, keepdims=True) + 1e-9)\nV_norm = V / (np.linalg.norm(V, axis=0, keepdims=True) + 1e-9)\neval_preds(U_norm, V_norm, 'SVD(128) cosine')\n\""
}<untrusted_context>
Output: Computing SVD(128)...
SVD(128) raw NDCG@10: {'quality_ndcg@10': 0.020271, 'quality_users': 4263}
SVD(128) cosine NDCG@10: {'quality_ndcg@10': 0.005956, 'quality_users': 4263}
Process Group PGID: 2318
</untrusted_context>Exploring SVD Performance
I'm observing an intriguing trend where the raw SVD score for 128 dimensions is lower than for 64. This suggests potential overfitting or that a higher dimensionality isn't necessarily improving the raw metric. I'm now turning my attention to item-item collaborative filtering approaches like cosine and BM25 to see how they perform in comparison and if they reveal different insights into item similarity.
Investigating Item-Item CF
I'm delving into the mechanics of item-item collaborative filtering, specifically exploring how TF-IDF or BM25 normalization impacts the item similarity matrix. The current focus is on optimizing the calculation of predicted scores to avoid materializing a massive similarity matrix. I'm evaluating whether computing user-user similarities first offers a more memory-efficient pathway.
{
"description": "Test User-User Collaborative Filtering",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\n# Fast load likes + recent listens\n# Let us test loading likes and recent listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nCUTOFF = 24786800.0\n# Likes weights\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# Let us also load listens from last 90 days\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\n# 90 days before cutoff = CUTOFF - 90 * 86400 = 17010800\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', 17010800)],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.0, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Matrix built, nnz={X.nnz}')\n\n# User-User CF:\n# Normalize rows of X (user vectors)\nX_norm = normalize(X, norm='l2', axis=1) # (8456, 30000)\n\nt0 = time.time()\n# Compute user-user similarity: S_user = X_norm @ X_norm.T (8456 x 8456)\nprint('Computing user-user similarity...')\nS_user = (X_norm @ X_norm.T).toarray()\n# Zero diagonal\nnp.fill_diagonal(S_user, 0.0)\nprint(f'User-user similarity computed: {time.time() - t0:.2f}s, shape={S_user.shape}')\n\n# Power transform on similarities to sharpen neighbors\n# S_user = np.power(np.maximum(S_user, 0), 2.0)\n# Optional: keep top-K neighbors per user\nK = 200\nprint(f'Keeping top-{K} neighbors...')\nfor i in range(len(uids)):\n row = S_user[i]\n if len(row) > K:\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n\n# Predicted scores: R = S_user @ X\nprint('Computing predicted scores...')\nt1 = time.time()\n# Compute in batches\nranked = {}\nBATCH = 500\nfor start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n \n # Dense @ Sparse -> Dense (batch_size, 30000)\n batch_scores = S_user[batch_indices] @ X # or S_user @ X.toarray()\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n\nprint(f'Scoring finished: {time.time() - t1:.2f}s')\nres = score(ranked, truth)\nprint(f'User-User CF (top-{K}) NDCG@10: {res}')\n\""
}<untrusted_context>
Output: Matrix built, nnz=4395173
Computing user-user similarity...
User-user similarity computed: 5.07s, shape=(8456, 8456)
Keeping top-200 neighbors...
Computing predicted scores...
Scoring finished: 21.51s
User-User CF (top-200) NDCG@10: {'quality_ndcg@10': 0.027614, 'quality_users': 4263}
Process Group PGID: 2347
</untrusted_context>Analyzing User-User CF Success
I'm thrilled to report a significant leap in our User-User CF performance for top-200 recommendations, hitting an NDCG@10 of 0.027614. This represents almost a threefold improvement over the baseline. My next step is to dissect the underlying reasons for this remarkable effectiveness.
Refining Similarity Metrics
I'm exploring ways to enhance our similarity calculations. Specifically, I’m investigating applying power exponents to the similarity matrix and testing various $K$ values. Additionally, I'm considering incorporating IDF weighting for items to better reflect niche taste shared between users, alongside a deeper dive into how different user interaction weights like likes, dislikes, and organic plays impact performance.
{
"description": "Grid search alpha and K on User-User CF with IDF",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\n# Compute IDF for each item\n# Number of users interacting with item\nitem_doc_freq = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_doc_freq + 1.0)) + 1.0\n# Apply IDF to X\nX_idf = X.multiply(idf).tocsr()\n\n# Compute user similarities with X_idf\nX_norm = normalize(X_idf, norm='l2', axis=1)\nS_raw = (X_norm @ X_norm.T).toarray()\nnp.fill_diagonal(S_raw, 0.0)\n# Zero negative\nS_raw = np.maximum(S_raw, 0.0)\n\ndef test_config(alpha, K):\n S = np.power(S_raw, alpha)\n for i in range(len(uids)):\n row = S[i]\n thresh = np.partition(row, -K)[-K]\n row[row < thresh] = 0.0\n \n # R = S @ X\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_indices = [uid_to_idx[u] for u in batch_uids]\n batch_scores = S[batch_indices] @ X\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = np.asarray(batch_scores[i]).ravel().copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'alpha={alpha}, K={K} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor alpha in [1.0, 1.5, 2.0, 2.5]:\n for K in [50, 100, 200]:\n test_config(alpha, K)\n\""
}<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>Optimizing Calculation Loop
I've been analyzing the computational load from the recent loop execution. It ran through 4 alphas and 3 Ks, totaling 12 iterations. Each iteration involved a significant matrix multiplication for 8456 users across 17 batches, leading to an unexpectedly long processing time exceeding 5 minutes. I'm now focused on identifying specific bottlenecks within this process.
{
"description": "Benchmark dense float32 GEMM",
"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 GEMM (8456x8456 @ 8456x30000) took: {time.time() - t0:.2f}s')\n\""
}<untrusted_context>
Output: Dense GEMM (8456x8456 @ 8456x30000) took: 19.82s
Process Group PGID: 2406
</untrusted_context>Calculating Validation Users
I've identified that dense GEMM took 19.82 seconds and realized we only need to score validation or target users. A key question now is how many users actually fall into the validation set. It appears we only need to score 4263 users, or a subset for NDCG@10 evaluation.
Refining Computation Times
I've realized that the bulk of the computation isn't the user scoring itself, but rather the matrix operations. SVD(64) is surprisingly fast, taking only 4 seconds for decomposition, and scoring all users is a mere 0.02 seconds. Now I'm exploring why SVD yielded 0.021702 and how to improve matrix factorization performance beyond 0.030.
Testing Transformations
I'm now prioritizing testing TF-IDF transformations before SVD, as this approach is known to significantly improve recommendations. I'll also be investigating the impact of PPMI, explicit preference states, distinguishing organic from non-organic interactions, exploring different rank dimensions, and incorporating item popularity priors.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\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(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# 1. Load likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\nl_tw = np.exp((likes.timestamp.to_numpy() - CUTOFF) / (60.0 * 86400.0))\nl_w = np.where(likes.is_organic.to_numpy() == 1, 5.0, 3.0) * l_tw\n\n# 2. Load listens from last 120 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ntime_weight = np.exp((ts_arr[u_mask] - CUTOFF) / (60.0 * 86400.0))\nlisten_weight = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\nlisten_weight += np.where(org_arr[u_mask] == 1, 1.5, 0.0)\nweights = listen_weight * time_weight\n\nX_listens = sp.csr_matrix((weights, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\nX_likes = sp.csr_matrix((l_w, (l_u, l_it)), shape=(len(uids), len(cat_items)))\nX = X_listens + X_likes\n\nprint(f'Data prepared in {time.time() - t0:.2f}s, X shape={X.shape}, nnz={X.nnz}')\n\ndef evaluate_model(pred_fn, name):\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = pred_fn(batch_idx) # (batch_size, 30000)\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'{name} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n return res[\\\"quality_ndcg@10\\\"]\n\n# 1. Raw SVD\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Raw SVD({n_comp})')\n\n# 2. Log1p(X) + SVD\nX_log = X.copy()\nX_log.data = np.log1p(X_log.data)\nfor n_comp in [64, 100]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_log)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'Log1p SVD({n_comp})')\n\n# 3. TF-IDF / BM25 + SVD\nitem_df = np.diff(X.tocsc().indptr)\nidf = np.log((len(uids) + 1.0) / (item_df + 1.0)) + 1.0\nX_tfidf = X_log.multiply(idf).tocsr()\nfor n_comp in [64, 100, 150]:\n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X_tfidf)\n V = svd.components_\n evaluate_model(lambda b_idx, U=U, V=V: U[b_idx] @ V, f'TFIDF SVD({n_comp})')\n\"",
"description": "Test various SVD transformations and dimensions"
}<untrusted_context>
Output: Data prepared in 8.27s, X shape=(8456, 30000), nnz=5114107
Raw SVD(64) -> NDCG@10 = 0.021653
Raw SVD(100) -> NDCG@10 = 0.019723
Log1p SVD(64) -> NDCG@10 = 0.032032
Log1p SVD(100) -> NDCG@10 = 0.031072
TFIDF SVD(64) -> NDCG@10 = 0.029062
TFIDF SVD(100) -> NDCG@10 = 0.029837
TFIDF SVD(150) -> NDCG@10 = 0.030893
Process Group PGID: 2416
</untrusted_context>Improving Recommendation Performance
My current focus is on dramatically enhancing recommendation efficacy. I've just observed a substantial improvement with Log1p SVD(64), achieving an NDCG@10 of 0.032032, a significant jump from baseline methods and completed in a remarkably short time.
Exploring Transform Options
I am now investigating alternative power transformations beyond log1p, specifically exploring square root and scaled log transforms. Simultaneously, I'm refining SVD component counts around 48, 64, and 80. Additionally, I'm considering adjustments to the listening window and time decay parameters, along with the impact of organic listen weighting. The explicit preference state data is also being re-examined to ensure all relevant user interactions are captured.
{
"description": "Check preference state overlap with train_likes and candidate catalog",
"command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\npref_cat = pref[pref.item_id.isin(cat_set)]\nprint(f'Preference state in catalog: total={len(pref_cat)}, liked={(pref_cat.state==\\\"liked\\\").sum()}, disliked={(pref_cat.state==\\\"disliked\\\").sum()}')\n\n# Check overlap with train_likes\npref_liked_pairs = set(zip(pref_cat[pref_cat.state == 'liked'].uid, pref_cat[pref_cat.state == 'liked'].item_id))\ntrain_likes_pairs = set(zip(likes.uid, likes.item_id))\nprint(f'Liked in pref but not in train_likes: {len(pref_liked_pairs - train_likes_pairs)}')\nprint(f'Disliked in pref: {len(pref_cat[pref_cat.state == \\\"disliked\\\"])}')\n\""
}<untrusted_context>
Output: Preference state in catalog: total=469124, liked=418435, disliked=50689
Liked in pref but not in train_likes: 0
Disliked in pref: 50689
Process Group PGID: 2445
</untrusted_context>Analyzing Preference Data
I'm currently processing a significant volume of user preference data, specifically noting the 50,689 disliked tracks within validation_preference_state. My focus is on how this past preference impacts eligibility for new recommendations, as candidates must not be currently liked or disliked.
Incorporating Active Preferences
My thoughts are now deeply focused on integrating the active preference state. I'm considering assigning significant positive weight to actively liked tracks and substantial negative weight to actively disliked ones. SVD's capacity to handle negative entries is a key consideration, and I'm preparing to test various parameter combinations for active likes/dislikes, time decay, SVD components, and data transformations.
{
"description": "Test half-life and dislike weights with SVD",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\n# Preference state:\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\n# Train likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\n# Train listens from last 180 days\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=45.0, org_weight=2.0, dis_weight=2.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n # completion weight\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n # organic bonus\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n # Active preferences (at cutoff, so time weight = 1.0)\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n # Apply log1p on positive part\n X.data = np.log1p(X.data)\n \n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [30.0, 45.0, 60.0]:\n for dis in [0.0, 1.0, 3.0]:\n X = build_matrix(half_life_days=hl, dis_weight=dis)\n ndcg = evaluate_svd(X, n_components=64)\n print(f'hl={hl:4.1f}, dis={dis:3.1f}, n_comp=64 -> NDCG@10 = {ndcg:.6f}')\n\""
}<untrusted_context>
Output: hl=30.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.035042
hl=30.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.035127
hl=30.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.034566
hl=45.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.033303
hl=45.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.033362
hl=45.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.032999
hl=60.0, dis=0.0, n_comp=64 -> NDCG@10 = 0.031963
hl=60.0, dis=1.0, n_comp=64 -> NDCG@10 = 0.031900
hl=60.0, dis=3.0, n_comp=64 -> NDCG@10 = 0.031689
Process Group PGID: 2463
</untrusted_context>Testing Half-Life Impacts
I've observed that a shorter half-life significantly boosts NDCG@10 performance, with hl=30 achieving 0.0351. I'm now exploring even shorter durations like 20, 14, and 25 days to pinpoint the optimal recency setting.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=20.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef evaluate_svd(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n ranked = {}\n BATCH = 1000\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [14.0, 21.0, 28.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n for n_comp in [48, 64, 80]:\n ndcg = evaluate_svd(X, n_components=n_comp)\n print(f'hl={hl:4.1f}, n_comp={n_comp:2d} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Tune half-life and components around sweet spot"
}<untrusted_context>
Output: hl=14.0, n_comp=48 -> NDCG@10 = 0.037210
hl=14.0, n_comp=64 -> NDCG@10 = 0.037352
hl=14.0, n_comp=80 -> NDCG@10 = 0.037183
hl=21.0, n_comp=48 -> NDCG@10 = 0.036251
hl=21.0, n_comp=64 -> NDCG@10 = 0.036416
hl=21.0, n_comp=80 -> NDCG@10 = 0.036135
hl=28.0, n_comp=48 -> NDCG@10 = 0.035093
hl=28.0, n_comp=64 -> NDCG@10 = 0.035299
hl=28.0, n_comp=80 -> NDCG@10 = 0.035341
Process Group PGID: 2492
</untrusted_context>Refining Parameters and Tastes
I've achieved a score of 0.037352 with hl=14.0, n_comp=64. I'm now investigating whether 7 or 10 days provides superior results and exploring how to best integrate short-term and medium-term user taste components within the recommender system.
{
"description": "Test fine-grained half-life values",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nfor hl in [5.0, 7.0, 10.0, 14.0]:\n X = build_matrix(half_life_days=hl, dis_weight=1.0)\n scores_mat = get_scores(X, 64)\n # Evaluate\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = scores_mat[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'hl={hl:4.1f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}<untrusted_context>
Output: hl= 5.0 -> NDCG@10 = 0.037882
hl= 7.0 -> NDCG@10 = 0.039199
hl=10.0 -> NDCG@10 = 0.039379
hl=14.0 -> NDCG@10 = 0.037352
Process Group PGID: 2521
</untrusted_context>Refining the algorithm
I've achieved a hl=10.0 score of 0.039379, nearing 0.040 which is nearly four times the baseline. I'm now exploring combinations of hl values (9.0, 10.0, 11.0) and integrating short-term recency (hl=10.0), medium-term preferences (hl=40.0), and item popularity priors.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\n\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\n\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\n\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndef build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n return X\n\ndef get_scores(X, n_components):\n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X)\n V = svd.components_\n return U @ V\n\nscores_10 = get_scores(build_matrix(10.0), 64)\nscores_35 = get_scores(build_matrix(35.0), 64)\n\n# Normalize scores per user by std dev\nscores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)\nscores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)\n\ndef test_blend(w1, w2):\n blended = w1 * scores_10_norm + w2 * scores_35_norm\n ranked = {}\n for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = blended[uid_to_idx[uid]].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:\n test_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend (short + medium)"
}<untrusted_context>
Output: /usr/bin/bash: line 121: 2552 Killed python3 -c "
import sys
sys.path.append('/task')
import numpy as np
import scipy.sparse as sp
from sklearn.decomposition import TruncatedSVD
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet
import pandas as pd
import pyarrow.parquet as pq
import pyarrow.compute as pc
import pyarrow as pa
target_users = pd.read_parquet('/task/data/target_users.parquet')
uids = target_users.uid.to_numpy(dtype=np.int64)
uid_to_idx = {u: i for i, u in enumerate(uids)}
cat = pd.read_parquet('/task/data/candidate_catalog.parquet')
cat_items = cat.item_id.to_numpy(dtype=np.int64)
item_to_idx = {it: i for i, it in enumerate(cat_items)}
with np.load('/task/data/validation_eligible_candidates.npz') as z:
val_uids = z['uids'].astype(np.int64)
val_items = z['item_ids'].astype(np.int64)
val_packed = z['packed_eligible']
val_to_cat = np.array([item_to_idx[it] for it in val_items])
eligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')
truth = validation_truth(eligibility, set(val_uids))
CUTOFF = 24786800.0
pref = pd.read_parquet('/task/data/validation_preference_state.parquet')
pref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]
p_liked = pref[pref.state == 'liked']
p_disliked = pref[pref.state == 'disliked']
pref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))
pref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))
pref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))
pref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))
likes = pd.read_parquet('/task/data/train_likes.parquet')
likes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]
l_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))
l_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))
item_set = pc.SetLookupOptions(value_set=pa.array(cat_items))
table = pq.read_table('/task/data/train_listens.parquet',
filters=[('timestamp', '>=', int(CUTOFF - 180 * 86400))],
columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])
filtered = table.filter(pc.is_in(table['item_id'], options=item_set))
u_arr = filtered['uid'].to_numpy()
it_arr = filtered['item_id'].to_numpy()
pct_arr = filtered['played_ratio_pct'].to_numpy()
org_arr = filtered['is_organic'].to_numpy()
ts_arr = filtered['timestamp'].to_numpy()
u_mask = np.isin(u_arr, uids)
u_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))
it_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))
def build_matrix(half_life_days=10.0, org_weight=2.0, dis_weight=1.0):
tau = half_life_days * 86400.0
tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)
cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))
cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)
w_listens = cw * tw_listens
tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)
w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes
X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))
X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))
X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))
X = X1 + X2 + X3
X.data = np.log1p(X.data)
if dis_weight > 0:
X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))
X = X + X_dis
return X
def get_scores(X, n_components):
svd = TruncatedSVD(n_components=n_components, random_state=42)
U = svd.fit_transform(X)
V = svd.components_
return U @ V
scores_10 = get_scores(build_matrix(10.0), 64)
scores_35 = get_scores(build_matrix(35.0), 64)
# Normalize scores per user by std dev
scores_10_norm = scores_10 / (np.std(scores_10, axis=1, keepdims=True) + 1e-9)
scores_35_norm = scores_35 / (np.std(scores_35, axis=1, keepdims=True) + 1e-9)
def test_blend(w1, w2):
blended = w1 * scores_10_norm + w2 * scores_35_norm
ranked = {}
for i, (r_idx, uid) in enumerate(zip(range(len(val_uids)), val_uids)):
bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')
user_scores = blended[uid_to_idx[uid]].copy()
mask = np.ones(len(cat_items), dtype=bool)
mask[val_to_cat[bits == 0]] = False
user_scores[~mask] = -1e9
top100_idx = np.argpartition(user_scores, -100)[-100:]
top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]
ranked[uid] = cat_items[top100_sorted].tolist()
res = score(ranked, truth)
print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}')
for w2 in [0.0, 0.1, 0.2, 0.3, 0.5]:
test_blend(1.0, w2)
"
Exit Code: 137
Process Group PGID: 2550
</untrusted_context>Analyzing Memory Leaks
I'm encountering an "Out of Memory" error (exit code 137). It appears that intermediate matrix multiplications, specifically scores_10 = U @ V and scores_35 = U @ V, each consuming approximately 2GB of memory, are the primary culprits. I'm also seeing significant memory usage from scores_10_norm.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\n# Clean up arrow table\ndel table, filtered\ngc.collect()\n\ndef fit_svd(half_life_days=10.0, n_components=64, dis_weight=1.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n if dis_weight > 0:\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), dis_weight), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n return U, V\n\nprint('Fitting model 1 (hl=10)...')\nU1, V1 = fit_svd(10.0, 64)\nprint('Fitting model 2 (hl=35)...')\nU2, V2 = fit_svd(35.0, 64)\n\ndef evaluate_blend(w1, w2):\n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n s1 = U1[batch_idx] @ V1\n s2 = U2[batch_idx] @ V2\n # Normalize each per row\n s1 = s1 / (np.std(s1, axis=1, keepdims=True) + 1e-9)\n s2 = s2 / (np.std(s2, axis=1, keepdims=True) + 1e-9)\n batch_scores = w1 * s1 + w2 * s2\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n print(f'w1={w1:.2f}, w2={w2:.2f} -> NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\nfor w2 in [0.0, 0.15, 0.25, 0.35, 0.5]:\n evaluate_blend(1.0, w2)\n\"",
"description": "Test multi-timescale blend with safe memory footprint"
}<untrusted_context>
Output: Fitting model 1 (hl=10)...
Fitting model 2 (hl=35)...
w1=1.00, w2=0.00 -> NDCG@10 = 0.039379
w1=1.00, w2=0.15 -> NDCG@10 = 0.038790
w1=1.00, w2=0.25 -> NDCG@10 = 0.038580
w1=1.00, w2=0.35 -> NDCG@10 = 0.038450
w1=1.00, w2=0.50 -> NDCG@10 = 0.038535
Process Group PGID: 2579
</untrusted_context>Exploring SVD Components
I'm currently investigating the impact of increasing SVD component counts while keeping hl=10.0 constant. My focus is on how these higher counts influence NDCG@10, and I'm also considering the roles of org_weight and likes weight in our objective.
{
"description": "Test n_components and popularity gamma",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\n# Popularity feature\norg_pop = cat.organic_positive_listens.to_numpy(dtype=np.float32)\nlog_org_pop = np.log1p(org_pop)\nlog_org_pop = (log_org_pop - log_org_pop.mean()) / (log_org_pop.std() + 1e-9)\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef build_and_evaluate(half_life_days=10.0, n_components=64, org_weight=2.0, pop_gamma=0.0):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_weight, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_components, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n \n batch_scores = U[batch_idx] @ V\n if pop_gamma > 0:\n batch_scores = batch_scores + pop_gamma * log_org_pop\n \n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor n_c in [64, 96, 128]:\n for gamma in [0.0, 0.05, 0.1]:\n ndcg = build_and_evaluate(half_life_days=10.0, n_components=n_c, pop_gamma=gamma)\n print(f'n_comp={n_c:3d}, gamma={gamma:4.2f} -> NDCG@10 = {ndcg:.6f}')\n\""
}<untrusted_context>
Output: n_comp= 64, gamma=0.00 -> NDCG@10 = 0.039379
n_comp= 64, gamma=0.05 -> NDCG@10 = 0.033986
n_comp= 64, gamma=0.10 -> NDCG@10 = 0.030370
n_comp= 96, gamma=0.00 -> NDCG@10 = 0.038020
n_comp= 96, gamma=0.05 -> NDCG@10 = 0.034237
n_comp= 96, gamma=0.10 -> NDCG@10 = 0.030247
n_comp=128, gamma=0.00 -> NDCG@10 = 0.038432
n_comp=128, gamma=0.05 -> NDCG@10 = 0.034034
n_comp=128, gamma=0.10 -> NDCG@10 = 0.030205
Process Group PGID: 2608
</untrusted_context>Refining Popularity Logic
I've discovered the popularity bonus is counterproductive, likely because SVD inherently captures item affinities. Including global popularity seems to dilute individual user preferences by boosting generic tracks. My next step is to investigate further, starting with the half parameter.
Exploring Parameter Variations
I'm currently investigating the impact of different life_days values on model performance, specifically testing integers between 8.0 and 12.0. Concurrently, I'm evaluating alternative power transformations beyond log1p, like log1p(c*X) with varying c and power(X, p) with p as 0.4 or 0.5.
{
"description": "Test fine half-lives and power transformations",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef run_test(half_life_days, transform_type, param, n_comp=64):\n tau = half_life_days * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, 2.0, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 5.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n if transform_type == 'log1p':\n X.data = np.log1p(param * X.data)\n elif transform_type == 'power':\n X.data = np.power(X.data, param)\n \n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=n_comp, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor hl in [8.0, 9.0, 10.0, 11.0, 12.0]:\n ndcg = run_test(hl, 'log1p', 1.0, 64)\n print(f'hl={hl:4.1f}, log1p(1.0) -> NDCG@10 = {ndcg:.6f}')\n\nfor p in [0.3, 0.4, 0.5, 0.6]:\n ndcg = run_test(9.0, 'power', p, 64)\n print(f'hl=9.0, power({p:.1f}) -> NDCG@10 = {ndcg:.6f}')\n\""
}<untrusted_context>
Output: hl= 8.0, log1p(1.0) -> NDCG@10 = 0.039347
hl= 9.0, log1p(1.0) -> NDCG@10 = 0.039269
hl=10.0, log1p(1.0) -> NDCG@10 = 0.039379
hl=11.0, log1p(1.0) -> NDCG@10 = 0.038408
hl=12.0, log1p(1.0) -> NDCG@10 = 0.037942
hl=9.0, power(0.3) -> NDCG@10 = 0.037865
hl=9.0, power(0.4) -> NDCG@10 = 0.038516
hl=9.0, power(0.5) -> NDCG@10 = 0.039264
hl=9.0, power(0.6) -> NDCG@10 = 0.037893
Process Group PGID: 2637
</untrusted_context>Exploring Unlikes/Undislikes
I'm currently investigating the impact of unlikes and undislikes. My initial thought was to directly add these, but I've paused to confirm if validation_preference_state.parquet already accounts for them, as the "active explicit" label suggests.
{
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus, org_like_w, pref_like_w):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, org_like_w, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), pref_like_w), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor org_b in [1.0, 2.0, 3.0, 4.0]:\n for org_l in [6.0, 10.0]:\n for pref_l in [3.0, 5.0, 8.0]:\n ndcg = test_weights(org_b, org_l, pref_l)\n print(f'org_b={org_b:.1f}, org_l={org_l:.1f}, pref_l={pref_l:.1f} -> NDCG@10 = {ndcg:.6f}')\n\"",
"description": "Test interaction event weights grid"
}<untrusted_context>
Output: org_b=1.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038661
org_b=1.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038202
org_b=1.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.036613
org_b=1.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038810
org_b=1.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038652
org_b=1.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.036688
org_b=2.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038739
org_b=2.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.039379
org_b=2.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.037763
org_b=2.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038921
org_b=2.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.039163
org_b=2.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.037839
org_b=3.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.038768
org_b=3.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038589
org_b=3.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.038598
org_b=3.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.038939
org_b=3.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038756
org_b=3.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.038819
org_b=4.0, org_l=6.0, pref_l=3.0 -> NDCG@10 = 0.039789
org_b=4.0, org_l=6.0, pref_l=5.0 -> NDCG@10 = 0.038557
org_b=4.0, org_l=6.0, pref_l=8.0 -> NDCG@10 = 0.039228
org_b=4.0, org_l=10.0, pref_l=3.0 -> NDCG@10 = 0.039756
org_b=4.0, org_l=10.0, pref_l=5.0 -> NDCG@10 = 0.038666
org_b=4.0, org_l=10.0, pref_l=8.0 -> NDCG@10 = 0.039095
Process Group PGID: 2666
</untrusted_context>Testing Organic Boost
My focus is on optimizing the 'org_b' parameter to maximize NDCG@10. I've observed that increasing it from 4.0 to 5.0 and then to 6.0 shows promise. My next step is to thoroughly evaluate these higher values, as organic listens are the critical metric.
{
"description": "Test higher organic listen bonus",
"command": "python3 -c \"\nimport sys\nsys.path.append('/task')\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pyarrow as pa\nimport gc\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nuids = target_users.uid.to_numpy(dtype=np.int64)\nuid_to_idx = {u: i for i, u in enumerate(uids)}\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy(dtype=np.int64)\nitem_to_idx = {it: i for i, it in enumerate(cat_items)}\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as z:\n val_uids = z['uids'].astype(np.int64)\n val_items = z['item_ids'].astype(np.int64)\n val_packed = z['packed_eligible']\nval_to_cat = np.array([item_to_idx[it] for it in val_items])\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ntruth = validation_truth(eligibility, set(val_uids))\n\nCUTOFF = 24786800.0\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref = pref[pref.uid.isin(uid_to_idx) & pref.item_id.isin(item_to_idx)]\np_liked = pref[pref.state == 'liked']\np_disliked = pref[pref.state == 'disliked']\npref_u_liked = np.fromiter((uid_to_idx[x] for x in p_liked.uid), dtype=np.int32, count=len(p_liked))\npref_it_liked = np.fromiter((item_to_idx[x] for x in p_liked.item_id), dtype=np.int32, count=len(p_liked))\npref_u_dis = np.fromiter((uid_to_idx[x] for x in p_disliked.uid), dtype=np.int32, count=len(p_disliked))\npref_it_dis = np.fromiter((item_to_idx[x] for x in p_disliked.item_id), dtype=np.int32, count=len(p_disliked))\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.uid.isin(uid_to_idx) & likes.item_id.isin(item_to_idx)]\nl_u = np.fromiter((uid_to_idx[x] for x in likes.uid), dtype=np.int32, count=len(likes))\nl_it = np.fromiter((item_to_idx[x] for x in likes.item_id), dtype=np.int32, count=len(likes))\n\nitem_set = pc.SetLookupOptions(value_set=pa.array(cat_items))\ntable = pq.read_table('/task/data/train_listens.parquet', \n filters=[('timestamp', '>=', int(CUTOFF - 120 * 86400))],\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nfiltered = table.filter(pc.is_in(table['item_id'], options=item_set))\n\nu_arr = filtered['uid'].to_numpy()\nit_arr = filtered['item_id'].to_numpy()\npct_arr = filtered['played_ratio_pct'].to_numpy()\norg_arr = filtered['is_organic'].to_numpy()\nts_arr = filtered['timestamp'].to_numpy()\n\nu_mask = np.isin(u_arr, uids)\nu_idx = np.fromiter((uid_to_idx[x] for x in u_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\nit_idx = np.fromiter((item_to_idx[x] for x in it_arr[u_mask]), dtype=np.int32, count=int(u_mask.sum()))\n\ndel table, filtered\ngc.collect()\n\ndef test_weights(org_listen_bonus):\n tau = 10.0 * 86400.0\n tw_listens = np.exp((ts_arr[u_mask] - CUTOFF) / tau)\n cw = np.where(pct_arr[u_mask] >= 80, 2.0, np.where(pct_arr[u_mask] >= 50, 1.0, 0.2))\n cw += np.where(org_arr[u_mask] == 1, org_listen_bonus, 0.0)\n w_listens = cw * tw_listens\n \n tw_likes = np.exp((likes.timestamp.to_numpy() - CUTOFF) / tau)\n w_likes = (np.where(likes.is_organic.to_numpy() == 1, 6.0, 4.0)) * tw_likes\n \n X1 = sp.csr_matrix((w_listens, (u_idx, it_idx)), shape=(len(uids), len(cat_items)))\n X2 = sp.csr_matrix((w_likes, (l_u, l_it)), shape=(len(uids), len(cat_items)))\n X3 = sp.csr_matrix((np.full(len(pref_u_liked), 3.0), (pref_u_liked, pref_it_liked)), shape=(len(uids), len(cat_items)))\n \n X = X1 + X2 + X3\n X.data = np.log1p(X.data)\n X_dis = sp.csr_matrix((-np.full(len(pref_u_dis), 1.0), (pref_u_dis, pref_it_dis)), shape=(len(uids), len(cat_items)))\n X = X + X_dis\n \n svd = TruncatedSVD(n_components=64, random_state=42)\n U = svd.fit_transform(X).astype(np.float32)\n V = svd.components_.astype(np.float32)\n \n ranked = {}\n BATCH = 500\n for start in range(0, len(val_uids), BATCH):\n end = min(start + BATCH, len(val_uids))\n batch_uids = val_uids[start:end]\n batch_idx = [uid_to_idx[u] for u in batch_uids]\n batch_scores = U[batch_idx] @ V\n for i, (r_idx, uid) in enumerate(zip(range(start, end), batch_uids)):\n bits = np.unpackbits(val_packed[r_idx], count=len(val_items), bitorder='little')\n user_scores = batch_scores[i].copy()\n mask = np.ones(len(cat_items), dtype=bool)\n mask[val_to_cat[bits == 0]] = False\n user_scores[~mask] = -1e9\n top100_idx = np.argpartition(user_scores, -100)[-100:]\n top100_sorted = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = cat_items[top100_sorted].tolist()\n res = score(ranked, truth)\n return res['quality_ndcg@10']\n\nfor ob in [3.0, 4.0, 5.0, 6.0, 8.0]:\n ndcg = test_weights(ob)\n print(f'org_listen_bonus={ob:.1f} -> NDCG@10 = {ndcg:.6f}')\n\""
}<untrusted_context>
Output: org_listen_bonus=3.0 -> NDCG@10 = 0.038768
org_listen_bonus=4.0 -> NDCG@10 = 0.039789
org_listen_bonus=5.0 -> NDCG@10 = 0.039753
org_listen_bonus=6.0 -> NDCG@10 = 0.039323
org_listen_bonus=8.0 -> NDCG@10 = 0.039805
Process Group PGID: 2695
</untrusted_context>[
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]The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.
Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.
Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.