Two-tower retriever gemini-3-8-flash-high-06
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<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app
Showing up to 200 items (files + folders).
/app/ └───solution/
</session_context>
Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search.
The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. 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 implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, 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 defines, trains, and applies both towers, along with a concise /app/solution/README.md, in /app/solution/. The README should state the model architecture, training objective, public validation score, and final submission command. 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.Exploring PyTorch CPU Constraints
I'm currently dissecting the specifics of our PyTorch CPU environment, focusing on version 2.8. My aim is to fully grasp all associated limitations as we embark on this competitive machine learning endeavor.
Analyzing CPU Execution Parameters
I'm currently solidifying my understanding of the operational constraints imposed by the CPU environment, specifically the 60-minute execution cap and strict read-only limitations on /task/ and the Python environment. I'm also prioritizing the precise output requirements, ensuring the /app/solution/submission.parquet file meets all specified row and column criteria, including rank uniqueness and candidate set adherence.
Developing Candidate Generation
My current focus is on candidate generation. I'm working on implementing a system where item embeddings are precomputed offline into a nearest-neighbor index. At request time, I'll compute a single user embedding and then use Maximum Inner-Product Search to retrieve relevant candidates, ensuring the model decomposes into independently trainable user and item towers.
{
"summary": "Starting the project to build a CPU PyTorch two-tower recommendation ranker. In this first phase, I will inspect the files in /task, examine data shapes, schemas, candidate eligibility format, and the public evaluation script.",
"strategic_intent": "Explore task data, evaluation script, and environment to understand data schemas, user counts, and candidate sets.",
"title": "Initial Exploration and Setup"
}Current topic: "Initial Exploration and Setup"
Topic summary: Starting the project to build a CPU PyTorch two-tower recommendation ranker. In this first phase, I will inspect the files in /task, examine data shapes, schemas, candidate eligibility format, and the public evaluation script.
Strategic Intent: Explore task data, evaluation script, and environment to understand data schemas, user counts, and candidate sets.{
"dir_path": "/task"
}Path not in workspace: Attempted path "/task" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app{
"description": "Inspect files in /task and /task/data",
"command": "ls -la /task && ls -la /task/data"
}<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 01:30 .
drwxr-xr-x 1 root root 4096 Sep 29 01:33 ..
-r--r--r-- 1 root root 0 Sep 29 01:17 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 data
-r--r--r-- 1 root root 5774 Sep 29 01:17 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:17 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:17 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:18 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:30 ..
-r--r--r-- 1 root root 349352 Sep 29 01:17 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:17 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:17 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:18 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:17 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:17 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:17 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:17 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:17 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:17 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:17 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:17 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:17 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:17 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:17 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:18 validation_preference_state.parquet
Process Group PGID: 1612
</untrusted_context>{
"description": "Inspect evaluate_public.py",
"command": "cat /task/evaluate_public.py"
}<untrusted_context>
Output: #!/usr/bin/env python3
"""Score a validation-stage ranking against public discovery labels.
``build_task.py`` publishes this module with the solver-visible task files. It
loads target users, validation eligibility, and validation-window events from
the adjacent ``data`` directory, then evaluates one ranking supplied on the
command line. Stage-independent mechanics come from the adjacent public
``ranking_evaluation.py`` module. The builder installs the same checked-in
module in the private judge, so public and private scoring mechanics cannot
drift. A relevant candidate receives grade 3 for an organic like or grade 1 for
an organic listen with at least 80 percent completion; a like takes precedence
when both events exist.
Before scoring, the ranking must contain exactly 100 distinct eligible items
for every target user, with integer-valued ``uid``, ``item_id``, and ``rank``
columns and ranks 1 through 100 exactly once per user. Extra columns are
ignored. Quality is graded NDCG@10, averaged only over users who have at least
one eligible relevant item in the validation window.
The evaluator prints a JSON object containing the model path and scores. It
does not apply the task's pass threshold; its purpose is model development and
selection. The private judge applies the threshold to the final submission.
Usage::
python evaluate_public.py <validation-ranking.parquet|csv>
"""
from __future__ import annotations
import json
import sys
from pathlib import Path
import pandas as pd
from ranking_evaluation import (
CandidateSet,
RankingValidationError,
ranked_items,
read_ranking,
score_rankings,
validate_ranking,
)
HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80
def validation_truth(
eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
"""Build cutoff-correct graded relevance from public validation events.
Only target-user events for eligible candidates are retained. Organic
listens completed to at least 80 percent receive grade 1, and organic likes
receive grade 3. Repeated user-item events are collapsed, and processing
likes second ensures that grade 3 wins over grade 1.
Args:
eligibility: Validation-cutoff candidate membership service.
targets: Complete set of target user IDs.
Returns:
A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
relevant event are omitted.
"""
listens = pd.read_parquet(DATA / "val_listens.parquet")
likes = pd.read_parquet(DATA / "val_likes.parquet")
strong = listens[
listens.uid.isin(targets)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= LISTEN_STRONG)
][["uid", "item_id"]].drop_duplicates()
organic_likes = likes[
likes.uid.isin(targets) & (likes.is_organic == 1)
][["uid", "item_id"]].drop_duplicates()
graded: dict[int, dict[int, int]] = {}
for raw_uid, raw_item in strong.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 1
for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
uid, item = int(raw_uid), int(raw_item)
if eligibility.is_eligible(uid, item):
graded.setdefault(uid, {})[item] = 3
return graded
def load_submission(
path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
"""Load and validate a public validation ranking.
Files ending in ``.parquet`` are read as parquet; every other suffix is
read as CSV. The required columns must be integer-valued, cover exactly the
target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
duplicate user-item pairs, and contain only cutoff-eligible items.
Args:
path: Ranking file to read.
eligibility: Validation-cutoff candidate membership service.
targets: Exact set of users the ranking must cover.
Returns:
A ``uid -> ranked item IDs`` mapping ordered by ascending rank.
Raises:
ValueError: If the ranking violates the required schema or invariants.
"""
frame = validate_ranking(read_ranking(path), targets)
if not all(
eligibility.is_eligible(uid, item)
for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None)
):
raise RankingValidationError(
"submission contains an item rejected by eligibility service"
)
return ranked_items(frame)
def score(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with public relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Eligible validation relevance keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
return score_rankings(ranked, graded)
def main() -> None:
"""Run the public evaluator CLI and print its JSON score report."""
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = validation_truth(eligibility, targets)
if len(sys.argv) != 2:
raise SystemExit(
"usage: evaluate_public.py <validation-ranking.parquet|csv>"
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1616
</untrusted_context>{
"description": "Inspect ranking_evaluation.py",
"command": "cat /task/ranking_evaluation.py"
}<untrusted_context>
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1619
</untrusted_context>{
"description": "Check CPU, memory, and disk space",
"command": "lscpu | grep -E \"Model name|CPU\\(s\\):|Thread|Core\" && free -h && df -h"
}<untrusted_context>
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
Thread(s) per core: 2
Core(s) per socket: 4
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1622
</untrusted_context>{
"command": "cat /proc/meminfo | head -n 5 && df -h /",
"description": "Check memory and disk from proc"
}<untrusted_context>
Output: MemTotal: 15988976 kB
MemFree: 1455876 kB
MemAvailable: 14541796 kB
Buffers: 247840 kB
Cached: 12512244 kB
Filesystem Size Used Avail Use% Mounted on
overlay 96G 14G 83G 15% /
Process Group PGID: 1627
</untrusted_context>{
"description": "Check installed requirements and torch config",
"command": "cat /task/requirements.txt && python3 -c \"import torch; print('PyTorch:', torch.__version__, 'threads:', torch.get_num_threads())\""
}<untrusted_context>
Output: # Exact distribution inventory for the Yambda two-tower task family.
#
# 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. 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
filelock==3.32.3
fsspec==2026.7.0
idna==3.18
Jinja2==3.1.6
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
MarkupSafe==3.0.3
mdit-py-plugins==0.6.1
mdurl==0.1.2
mpmath==1.3.0
nbformat==5.11.1
networkx==3.6.1
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
setuptools==84.0.0
six==1.17.0
sympy==1.14.0
text-unidecode==1.3
threadpoolctl==3.6.0
torch==2.8.0
tqdm==4.70.0
traitlets==5.16.1
typing-extensions==4.16.0
tzdata==2026.3
urllib3==2.7.0
webencodings==0.6.1
PyTorch: 2.8.0+cpu threads: 4
Process Group PGID: 1632
</untrusted_context>Inspecting Data Files
I'm currently setting up to leverage all eight processing cores using torch.set_num_threads(8). My immediate next step is to write a Python script to thoroughly examine our datasets, focusing on candidate_catalog.parquet to understand its columns, counts, and the minimum and maximum item_id values.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\n\nprint(\"--- candidate_catalog ---\")\ncc = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nprint(cc.info())\nprint(cc.head(3))\n\nprint(\"--- target_users ---\")\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(tu.info())\nprint(tu.head(3))\n\nprint(\"--- val eligible candidates ---\")\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as a:\n print(\"uids:\", a[\"uids\"].shape, \"item_ids:\", a[\"item_ids\"].shape, \"packed:\", a[\"packed_eligible\"].shape)\n\nprint(\"--- test eligible candidates ---\")\nwith np.load(DATA / \"test_eligible_candidates.npz\") as a:\n print(\"uids:\", a[\"uids\"].shape, \"item_ids:\", a[\"item_ids\"].shape, \"packed:\", a[\"packed_eligible\"].shape)\n\nprint(\"--- validation preference state ---\")\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(vps.info())\nprint(vps.head(3))\n\nprint(\"--- test preference state ---\")\ntps = pd.read_parquet(DATA / \"test_preference_state.parquet\")\nprint(tps.info())\nprint(tps.head(3))\n'",
"description": "Inspect catalog, target users, candidate sets, preference states"
}<untrusted_context>
Output: --- candidate_catalog ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 30000 entries, 0 to 29999
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 item_id 30000 non-null uint32
1 positive_listens 30000 non-null int64
2 organic_positive_listens 30000 non-null uint64
3 popularity_rank 30000 non-null int32
dtypes: int32(1), int64(1), uint32(1), uint64(1)
memory usage: 703.3 KB
None
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
--- target_users ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8456 entries, 0 to 8455
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 uid 8456 non-null uint32
1 recent_positive_listens 8456 non-null float64
2 history_positive_listens 8456 non-null int64
dtypes: float64(1), int64(1), uint32(1)
memory usage: 165.3 KB
None
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
--- val eligible candidates ---
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
--- test eligible candidates ---
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
--- validation preference state ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 469124 entries, 0 to 469123
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 uid 469124 non-null uint32
1 item_id 469124 non-null uint32
2 state 469124 non-null object
dtypes: object(1), uint32(2)
memory usage: 7.2+ MB
None
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
--- test preference state ---
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 484776 entries, 0 to 484775
Data columns (total 3 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 uid 484776 non-null uint32
1 item_id 484776 non-null uint32
2 state 484776 non-null object
dtypes: object(1), uint32(2)
memory usage: 7.4+ MB
None
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
Process Group PGID: 1643
</untrusted_context>Analyzing Data Shapes
My focus has shifted to understanding the dimensions of our candidate and test sets. It appears we have 8,456 users and 30,000 items, with the data shaped to accommodate this for validation and testing.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ncc = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as a:\n val_uids = a[\"uids\"]\n val_items = a[\"item_ids\"]\n\nwith np.load(DATA / \"test_eligible_candidates.npz\") as a:\n test_uids = a[\"uids\"]\n test_items = a[\"item_ids\"]\n\nprint(\"val_uids equal tu.uid?\", np.array_equal(val_uids, tu.uid.values))\nprint(\"test_uids equal tu.uid?\", np.array_equal(test_uids, tu.uid.values))\nprint(\"val_items equal cc.item_id?\", np.array_equal(val_items, cc.item_id.values))\nprint(\"test_items equal cc.item_id?\", np.array_equal(test_items, cc.item_id.values))\nprint(\"test_items equal val_items?\", np.array_equal(test_items, val_items))\n'",
"description": "Verify user and item alignment in candidate archives"
}<untrusted_context>
Output: val_uids equal tu.uid? True
test_uids equal tu.uid? True
val_items equal cc.item_id? True
test_items equal cc.item_id? True
test_items equal val_items? True
Process Group PGID: 1661
</untrusted_context>Confirming data alignment
My analysis confirms that val_uids and test_uids are both aligned with tu.uid.values. Similarly, val_items and test_items are consistent with cc.item_id.values. The next step involves examining the size and content of train_listens.parquet.
{
"description": "Inspect event tables statistics",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\n\nfor fname in [\"train_listens.parquet\", \"train_likes.parquet\", \"train_dislikes.parquet\", \"train_unlikes.parquet\", \"train_undislikes.parquet\", \"val_listens.parquet\", \"val_likes.parquet\", \"val_dislikes.parquet\"]:\n df = pd.read_parquet(DATA / fname)\n print(f\"{fname:26s}: rows={len(df):8d}, cols={list(df.columns)}, uids={df.uid.nunique():6d}, items={df.item_id.nunique():6d}, t_min={df.timestamp.min()}, t_max={df.timestamp.max()}\")\n'"
}<untrusted_context>
Output: train_listens.parquet : rows=43484159, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], uids= 9238, items=845395, t_min=0, t_max=24786795
train_likes.parquet : rows= 814831, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 8144, items=172303, t_min=80, t_max=24786790
train_dislikes.parquet : rows= 101075, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 5732, items= 50876, t_min=75, t_max=24786580
train_unlikes.parquet : rows= 292665, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 6197, items=113072, t_min=250, t_max=24786570
train_undislikes.parquet : rows= 19960, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 2769, items= 14725, t_min=15950, t_max=24785395
val_listens.parquet : rows= 1501697, cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'], uids= 8196, items=191747, t_min=24788600, t_max=25393395
val_likes.parquet : rows= 37795, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 4114, items= 22902, t_min=24788815, t_max=25393390
val_dislikes.parquet : rows= 3563, cols=['uid', 'timestamp', 'item_id', 'is_organic'], uids= 955, items= 3135, t_min=24789920, t_max=25393280
Process Group PGID: 1678
</untrusted_context>Analyzing Data Size
The sheer volume of train_listens.parquet, over 43 million rows, is striking. Despite this, reading the full dataset into pandas was surprisingly swift, a testament to efficient handling by pyarrow. My immediate focus now is to quantify the memory footprint of this loaded data.
{
"description": "Inspect validation truth stats",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nfrom evaluate_public import validation_truth\ntruth = validation_truth(eligibility, targets)\nprint(f\"Validation truth users: {len(truth)} / {len(targets)}\")\ngrade_counts = {1: 0, 3: 0}\nitems_per_user = []\nfor u, items in truth.items():\n items_per_user.append(len(items))\n for g in items.values():\n grade_counts[g] += 1\nprint(f\"Grade counts: {grade_counts}\")\nimport numpy as np\nprint(f\"Items per user with labels: min={min(items_per_user)}, median={np.median(items_per_user)}, max={max(items_per_user)}, mean={np.mean(items_per_user):.2f}\")\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: 1698
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\nimport numpy as np\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\ntruth = validation_truth(eligibility, targets)\nprint(f\"Validation truth users: {len(truth)} / {len(targets)}\")\ngrade_counts = {1: 0, 3: 0}\nitems_per_user = []\nfor u, items in truth.items():\n items_per_user.append(len(items))\n for g in items.values():\n grade_counts[g] += 1\nprint(f\"Grade counts: {grade_counts}\")\nprint(f\"Items per user with labels: min={min(items_per_user)}, median={np.median(items_per_user)}, max={max(items_per_user)}, mean={np.mean(items_per_user):.2f}\")\n'",
"description": "Inspect validation truth stats with PYTHONPATH=/task"
}<untrusted_context>
Output: Validation truth users: 4263 / 8456
Grade counts: {1: 33352, 3: 4417}
Items per user with labels: min=1, median=4.0, max=442, mean=8.86
Process Group PGID: 1709
</untrusted_context>Tracking User Engagement Metrics
My analysis shows significant progress, with over half of our target users exhibiting eligible organic listening or liking behavior. The validation window has yielded a substantial number of positive engagement events, with grade 1 events being particularly numerous. I am now shifting focus to further dissecting this data.
{
"description": "Compute popularity baseline score",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, ndcg_at_k\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_df.uid.astype(int))\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ntruth = validation_truth(eligibility, targets)\n\n# For each target user, find top 100 eligible items from catalog (which is sorted by popularity)\ncatalog_items = catalog.item_id.values\n\nuser_ranks = {}\nfor uid in targets:\n # get eligible items\n row = eligibility.uid_row[uid]\n mask = np.unpackbits(eligibility.packed[row], count=len(eligibility.item_ids), bitorder=\"little\").astype(bool)\n # eligibility item_ids are aligned with catalog_items?\n # let us check\n eligible_items = eligibility.item_ids[mask]\n top100 = eligible_items[:100]\n user_ranks[uid] = list(top100)\n\nndcgs = [ndcg_at_k(user_ranks[uid], truth[uid], 10) for uid in truth]\nprint(\"Popularity baseline NDCG@10:\", np.mean(ndcgs))\n'"
}<untrusted_context>
Output: Popularity baseline NDCG@10: 0.010709951701903768
Process Group PGID: 1729
</untrusted_context>Validating Submission Structure
I'm currently focused on ensuring my submission file adheres to the specified format. My immediate next step is to generate a baseline submission to /app/solution/submission.parquet and meticulously verify its structure. This ensures we have a valid output to work with right from the start, before diving into any performance optimizations beyond the 0.01071 NDCG@10 popularity baseline.
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_df.uid.astype(int))\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ntest_eligibility = CandidateSet(DATA / \"test_eligible_candidates.npz\")\n\nrecords = []\nfor uid in targets_df.uid.values:\n row = test_eligibility.uid_row[uid]\n mask = np.unpackbits(test_eligibility.packed[row], count=len(test_eligibility.item_ids), bitorder=\"little\").astype(bool)\n eligible_items = test_eligibility.item_ids[mask]\n top100 = eligible_items[:100]\n for r, item in enumerate(top100, 1):\n records.append((uid, item, r))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\nv = validate_ranking(df, targets)\nprint(\"Validated successfully! Shape:\", v.shape)\n'",
"description": "Verify baseline submission format against validate_ranking"
}<untrusted_context>
Output: Validated successfully! Shape: (845600, 3)
Process Group PGID: 1749
</untrusted_context>Verifying Eligibility Logic
I'm currently examining the test_eligibility.is_eligible function, specifically how item_ids filtered by the mask are determined to be eligible. My focus is on explicitly confirming this linkage.
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_df.uid.astype(int))\ntest_eligibility = CandidateSet(DATA / \"test_eligible_candidates.npz\")\n\nrecords = []\nfor uid in targets_df.uid.values:\n row = test_eligibility.uid_row[uid]\n mask = np.unpackbits(test_eligibility.packed[row], count=len(test_eligibility.item_ids), bitorder=\"little\").astype(bool)\n eligible_items = test_eligibility.item_ids[mask]\n top100 = eligible_items[:100]\n for r, item in enumerate(top100, 1):\n records.append((uid, item, r))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/submission.parquet\", index=False)\nprint(\"Saved /app/solution/submission.parquet\")\n'",
"description": "Save initial fallback submission.parquet"
}<untrusted_context>
Output: Saved /app/solution/submission.parquet
Process Group PGID: 1766
</untrusted_context>Verifying Baseline Ranking Output
I'm currently executing evaluate_public.py against a validation baseline ranking to observe its output format. My next step is to generate /app/solution/validation_popularity.parquet and then run the evaluation script with this generated file.
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_df.uid.astype(int))\nval_eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\nrecords = []\nfor uid in targets_df.uid.values:\n row = val_eligibility.uid_row[uid]\n mask = np.unpackbits(val_eligibility.packed[row], count=len(val_eligibility.item_ids), bitorder=\"little\").astype(bool)\n eligible_items = val_eligibility.item_ids[mask]\n top100 = eligible_items[:100]\n for r, item in enumerate(top100, 1):\n records.append((uid, item, r))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/validation_popularity.parquet\", index=False)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/validation_popularity.parquet",
"description": "Generate and evaluate popularity validation ranking"
}<untrusted_context>
Output: {
"model": "/app/solution/validation_popularity.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1782
</untrusted_context>Confirming Evaluation Workflow
The evaluation pipeline is now confirmed functional. My current focus is a deep dive into the project's requirements, architectural limitations, and specific domain details, informed by a re-reading of the prompt's initial directive to "Build a two-tower".
Refining User Tower Architecture
I'm now solidifying the design for the user tower. It will independently process user features to generate their embedding. My ongoing consideration is how to best leverage available user data within the CPU PyTorch constraints, ensuring it scales effectively for a large user base without global matrix construction.
Processing Interaction History
My focus has shifted to implementing the user tower based on interaction history, not direct user IDs. I'm actively exploring neural architectures like attention or pooling to aggregate item embeddings from a user's listening history, ensuring efficient processing within CPU PyTorch limits for scalability.
Integrating Interaction Metrics
I'm now integrating specific metrics like played_ratio_pct and track_length_seconds into my interaction processing. My goal is to define "listens" more precisely, distinguishing between conventional 50% completion and a higher 80% threshold for discovery-quality objectives. The two-window validation strategy is also being refined.
Analyzing Data Distributions
I'm now diving into the provided datasets, specifically analyzing the distributions and characteristics of user interactions and item features. My goal is to gain a deeper understanding of the data's nuances to inform feature engineering and model selection, especially concerning user popularity and candidate eligibility.
{
"description": "Inspect preference state and eligibility overlap",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"vps states:\", vps.state.value_counts())\nprint(\"vps target uids:\", vps.uid.nunique())\n\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_df.uid.astype(int))\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as a:\n packed = a[\"packed_eligible\"]\n items = a[\"item_ids\"]\n # Check number of eligible candidates per user\n # Unpack for a few users\n counts = []\n for r in range(min(500, len(packed))):\n m = np.unpackbits(packed[r], count=len(items), bitorder=\"little\")\n counts.append(m.sum())\n print(f\"Eligible count per user: min={min(counts)}, mean={np.mean(counts):.1f}, max={max(counts)}, total={len(items)}\")\n\n# Check overlap of eligible items with vps liked/disliked\n# Are disliked items eligible? Are already liked items eligible?\nsample_uid = targets_df.uid.iloc[0]\nsample_vps = vps[vps.uid == sample_uid]\nprint(\"sample uid:\", sample_uid, \"vps rows:\", len(sample_vps))\nrow = 0\nmask = np.unpackbits(packed[row], count=len(items), bitorder=\"little\").astype(bool)\neligible_set = set(items[mask])\nvps_liked_set = set(sample_vps[sample_vps.state == \"liked\"].item_id)\nvps_disliked_set = set(sample_vps[sample_vps.state == \"disliked\"].item_id)\nprint(\"sample liked in eligible:\", len(vps_liked_set & eligible_set), \"/\", len(vps_liked_set))\nprint(\"sample disliked in eligible:\", len(vps_disliked_set & eligible_set), \"/\", len(vps_disliked_set))\n'"
}<untrusted_context>
Output: vps states: state
liked 418435
disliked 50689
Name: count, dtype: int64
vps target uids: 7038
Eligible count per user: min=25547, mean=29057.7, max=29994, total=30000
sample uid: 100 vps rows: 15
sample liked in eligible: 0 / 13
sample disliked in eligible: 0 / 2
Process Group PGID: 1816
</untrusted_context>Analyzing Eligibility Data
I'm currently examining the eligibility metrics derived from validation_eligible_candidates.npz. It appears that a substantial majority, around 29,000 out of 30,000 items per user, are designated as eligible. These eligible items are specifically those identified by the platform as suitable for the user.
{
"command": "python3 -c '\nimport torch\nimport time\nimport numpy as np\n\ntorch.set_num_threads(8)\nu = torch.randn(8456, 128)\nv = torch.randn(30000, 128)\n\nt0 = time.time()\nscores = torch.mm(u, v.t())\nt1 = time.time()\nprint(f\"Matrix multiplication (8456, 128) x (128, 30000): {t1 - t0:.4f} s\")\n\n# Simulate masking and topk\nmask = torch.ones(8456, 30000, dtype=torch.bool)\n# randomly mask 1000 items per user\nmask[:, :1000] = False\n\nt0 = time.time()\nscores[~mask] = -float(\"inf\")\ntopk_vals, topk_indices = torch.topk(scores, 100, dim=1)\nt1 = time.time()\nprint(f\"Masking and topk(100): {t1 - t0:.4f} s\")\n'",
"description": "Benchmark MIPS matrix multiplication and topk on CPU"
}<untrusted_context>
Output: Matrix multiplication (8456, 128) x (128, 30000): 0.2637 s
Masking and topk(100): 0.2294 s
Process Group PGID: 1833
</untrusted_context>Analyzing Interaction Data
I'm currently deep-diving into the interaction history and candidate data, scrutinizing the 30,000-item catalog to understand its structure and feature set. My focus is on whether it's track-based or purely item-based.
Confirming Track Data
My initial scan of /task/data/ confirms only candidate_catalog.parquet and various interaction files exist. Crucially, train_listens.parquet contains track_length_seconds, strongly indicating candidates are indeed individual tracks, not abstract items.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"item_id\", \"track_length_seconds\"])\nsample = listens.drop_duplicates(\"item_id\").head(10)\nprint(sample)\n'",
"description": "Check track_length_seconds in train_listens"
}<untrusted_context>
Output: item_id track_length_seconds
0 8326270 170
1 1441281 105
2 286361 185
3 732449 240
4 3397170 130
5 7849270 205
6 1449307 205
7 6887232 145
8 2996197 95
9 6474571 245
Process Group PGID: 1857
</untrusted_context>Analyzing Catalog Overlap
I'm currently examining the intersection between the candidate catalog and the broader item sets. My focus is on quantifying how many of the 30,000 candidate items are present within the substantial train_listens (845,395 unique items) and val_listens (191,747 unique items) datasets.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncc = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncc_set = set(cc.item_id)\n\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\ntu_set = set(tu.uid)\n\nvl = pd.read_parquet(DATA / \"val_listens.parquet\")\nvl_tu = vl[vl.uid.isin(tu_set) & (vl.is_organic == 1) & (vl.played_ratio_pct >= 80)]\nprint(\"val_listens strong organic events by target users:\", len(vl_tu))\nprint(\"in cc_set:\", vl_tu.item_id.isin(cc_set).sum(), f\"({vl_tu.item_id.isin(cc_set).mean():.3%})\")\n\nvlikes = pd.read_parquet(DATA / \"val_likes.parquet\")\nvlikes_tu = vlikes[vlikes.uid.isin(tu_set) & (vlikes.is_organic == 1)]\nprint(\"val_likes organic events by target users:\", len(vlikes_tu))\nprint(\"in cc_set:\", vlikes_tu.item_id.isin(cc_set).sum(), f\"({vlikes_tu.item_id.isin(cc_set).mean():.3%})\")\n'",
"description": "Check overlap of validation organic events with candidate catalog"
}<untrusted_context>
Output: val_listens strong organic events by target users: 421615
in cc_set: 288828 (68.505%)
val_likes organic events by target users: 20702
in cc_set: 10406 (50.266%)
Process Group PGID: 1880
</untrusted_context>Analyzing Candidate Catalog Performance
My analysis shows that a significant portion of organic listens (68.5%) and likes (50%) from target users fall within the current candidate catalog of 30,000 items. This aligns with the validation_truth logic that prioritizes user-eligible items.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\ntl = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\"])\nprint(\"Target users in train_listens:\", tu.uid.isin(tl.uid).sum(), \"/\", len(tu))\n'",
"description": "Check target users coverage in train_listens"
}<untrusted_context>
Output: Target users in train_listens: 8456 / 8456
Process Group PGID: 1900
</untrusted_context>Analyzing Architectural Requirements
I'm currently re-reading the architectural specifications to ensure our two-tower neural network design fully aligns with all compliance and quality mandates, having confirmed all 8,456 target users possess interactions within train_listens.
Refining User and Item Representations
I'm focusing on how to independently generate user and item embeddings, ensuring they're compatible for dot-product relevance scoring. The key is to derive user representations from interaction history, avoiding global matrices and adhering to latency constraints.
Deconstructing Specification Constraints
I'm breaking down the provided requirements, focusing on how the dot-product scoring necessitates distinct user and item towers. My current emphasis is on ensuring the user tower derives its representation solely from interaction history, eschewing direct user-ID embeddings. The item tower will compute vectors offline for candidates.
Validating Item Tower Inputs
I've confirmed the item tower can indeed leverage pre-computed embeddings and feature projections into the R^D space. This means I can independently generate candidate item vectors offline, a significant efficiency gain for the real-time retrieval process.
Aggregating Interaction History
I'm now concentrating on how best to aggregate user interaction history. I'm weighing options like weighted averaging and multi-head attention over embedded historical items, considering the impact of explicit likes, dislikes, and recency.
Choosing Training Objectives
I'm evaluating training objectives suitable for large-scale two-tower models. My current focus is on in-batch negatives and InfoNCE loss, considering their effectiveness with sampled softmax for handling vast item catalogs and avoiding explicit matrix decomposition.
{
"reason": "Design two-tower neural network architecture, data pipeline, training objective, and validation/test workflow."
}Switching to Plan mode.{
"strategic_intent": "Analyze dataset characteristics, explore user history representations, and design two-tower architecture.",
"summary": "In Plan Mode, analyzing the exact interaction history data, training pairs generation, model architecture options (user tower and item tower), loss functions, and validation workflow to ensure high discovery NDCG@10 within CPU training constraints.",
"title": "Architecture Research & Planning"
}Current topic: "Architecture Research & Planning"
Topic summary: In Plan Mode, analyzing the exact interaction history data, training pairs generation, model architecture options (user tower and item tower), loss functions, and validation workflow to ensure high discovery NDCG@10 within CPU training constraints.
Strategic Intent: Analyze dataset characteristics, explore user history representations, and design two-tower architecture.Analyzing Item Overlap
I'm currently running a script to determine the overlap between the candidate catalog and user histories. This will help clarify which items should reside in each respective list, informing crucial design decisions.
{
"description": "Analyze target user listens in train_listens",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(targets_df.uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\n\nprint(f\"Total target users: {len(target_uids)}\")\nprint(f\"Total catalog items: {len(catalog_items)}\")\n\n# Check train_listens for target users\n# Read in chunks or specific columns\ndf = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"])\ntu_df = df[df.uid.isin(target_uids)]\nprint(f\"Total train listens for target users: {len(tu_df)} / {len(df)}\")\nprint(f\"Distinct items in target user listens: {tu_df.item_id.nunique()}\")\nin_cat = tu_df.item_id.isin(catalog_items)\nprint(f\"Listens in catalog: {in_cat.sum()} ({in_cat.mean():.2%})\")\n\n# Check distribution of positive listens per user\npos = tu_df[tu_df.played_ratio_pct >= 50]\nprint(f\"Positive listens (>=50%): {len(pos)}\")\nstrong = tu_df[tu_df.played_ratio_pct >= 80]\nprint(f\"Strong listens (>=80%): {len(strong)}\")\n'"
}Tool "run_shell_command" not found. Did you mean one of: "update_topic", "grep_search", "replace"?Exploring Plan Mode
I'm currently examining the available tools within Plan Mode. My focus is on whether run_shell_command is directly accessible or if there are alternative methods. So far, I've identified replace, exit_plan_mode, glob, invoke_agent, and read_file.
Investigating Execution Options
I'm considering how to execute commands within Plan Mode. While run_shell_command isn't immediately apparent, I'm exploring invoke_agent as a potential workaround for shell command execution. I also need to review previously gathered data, like the catalog and user information, to inform this decision.
Designing Model Architecture
I'm reviewing the task requirements to finalize the model architecture. Key considerations include building a two-tower PyTorch model on CPU, ensuring independent user and item vector computation via dot product, and deriving user representations from interaction history. The validation NDCG baseline is established, and the submission file shape has been verified.
Refining Recommendation Logic
I'm now focusing on the practical implementation details for generating recommendations. This involves precisely defining what constitutes "history" and "subsequent outcomes" for training, specifically by using train_* for history and val_* for outcomes. I'll then retrain the model on the combined train_* + val_* dataset for the final hidden-test ranking.
Processing Item Catalog
I am currently refining how to handle item IDs, specifically distinguishing between catalog items and those found only in user history. My approach involves creating a unified item ID mapping where catalog items are indexed sequentially, and frequent non-catalog items are assigned subsequent IDs, with rare items mapped to an "unknown" category.
Implementing Vector Calculations
I am focusing on the technical aspects of vector operations. Specifically, I am evaluating whether to use L2 normalization for user and item vectors to stabilize training and ensure stable gradient flow, considering the implications for similarity calculations. This step is critical before proceeding with offline precomputation of catalog item vectors.
Processing User History Data
I am currently analyzing user interaction data, specifically how to represent their listening history within the user tower. My focus is on extracting meaningful features from recent positive interactions, such as strong listens or organic likes, to capture evolving user preferences for music streaming.
Aggregating Interaction Sequences
I'm currently exploring methods to aggregate user interaction sequences. My focus is on how to best combine item embeddings with interaction type and recency features. I'm also evaluating attention pooling as a way to derive a user representation from this sequence, potentially using a query vector.
Combining Pooling Strategies
I am now evaluating different pooling strategies to combine recency-weighted average pooling, explicit preference pooling, and user context MLP. This approach offers computational efficiency on CPU, stability, and avoids Transformer complexity. I'm also considering how to formulate training instances from temporal sequences to maximize data utilization.
Finalizing Training Strategy
I'm now solidifying the training and validation methodology. I've confirmed that for tuning, I'll use train_* as history and val_* as outcomes. Crucially, for the final hidden-test ranking, I'll retrain on the combined train_* + val_* dataset, treating validation events as observed history.
Defining Training Sequences
I'm now refining how to construct training and validation sequences. I'm considering temporal cutoffs, sliding windows for next-item prediction, and incorporating explicit preference states with higher grades to weight crucial interactions. This will directly inform how I generate positive pairs for model training.
Incorporating Negative Sampling
I am now refining the training loss by incorporating negative sampling strategies. My current focus is on leveraging in-batch negatives derived from the similarity matrix and exploring popularity-based debiasing to improve genuine relevance over mere popularity.
Adding Hard Negatives
I am now refining the training loss by incorporating explicit negative items, such as user dislikes, alongside in-batch negatives. I'm exploring margin ranking or BPR loss to leverage these hard negatives for better model performance.
Finalizing Submission Process
I am now preparing the final submission files for the hidden test set. This involves refitting the model on the combined train and validation datasets, generating top-100 predictions for all target users, and ensuring strict adherence to all validation requirements before saving the submission.
{
"file_path": "/home/agent/.gemini/tmp/app/46ea7e5c-8d67-43e9-9026-62146dcafefe/plans/plan.md",
"content": "# Latency-Efficient PyTorch Two-Tower Ranker Implementation Plan\n\n## 1. Objective and Constraints\nBuild a scalable two-tower neural network ranker in CPU PyTorch for the \"Made for You\" music shelf:\n- User tower computes user embedding $u \\in \\mathbb{R}^D$ from interaction history (no user-ID lookup).\n- Item tower independently computes candidate item embedding $v \\in \\mathbb{R}^D$.\n- Dot product $u \\cdot v$ determines ranking score (MIPS retrieval).\n- Strict separation: no global user-item interaction matrix construction or decomposition.\n- Evaluation metric: Graded NDCG@10 (grade 3 for organic like, grade 1 for organic listen >=80%).\n- Eligibility: Strictly respect cutoff-specific candidate eligibility from `validation_eligible_candidates.npz` (for validation) and `test_eligible_candidates.npz` (for test).\n- Deliverables:\n - `/app/solution/submission.parquet` (and `.csv`): 8,456 target users x 100 items, ranks 1-100, all test-eligible.\n - Complete, reproducible source code in `/app/solution/`.\n - Concise `/app/solution/README.md` detailing architecture, loss, validation score, and command.\n\n---\n\n## 2. Key Architecture & System Design\n\n### A. Item Tower (`ItemTower`)\n- **Input**: Candidate item ID (0 to 29,999 in catalog) + optional item features (log popularity, track length).\n- **Architecture**:\n - Item Embedding Table: `nn.Embedding(num_items, embed_dim)` (where $embed\\_dim = 64$ or $128$).\n - Dense Projection Layer: `Linear(embed_dim, hidden_dim) -> LayerNorm -> GELU -> Linear(hidden_dim, out_dim)`.\n - L2 Normalization: outputs unit-norm vector $v \\in \\mathbb{R}^D$.\n- **Offline Precomputation**:\n - Evaluated once for all 30,000 catalog items: $V \\in \\mathbb{R}^{30000 \\times D}$.\n\n### B. User Tower (`UserTower`)\n- **Input**: User interaction history:\n 1. Sequence of recent positive interactions (item IDs, interaction types: organic like, strong listen >=80%, completion ratio, recency time-delta).\n 2. Explicit preference state items (liked track IDs, disliked track IDs from preference state parquet).\n- **No User ID Embedding**: Derives representation purely from interaction events.\n- **Architecture**:\n - History Item Embedding: Shared or tied embedding table with item tower (or dedicated history table for catalog + top frequent non-catalog items).\n - Recency & Event-Type Encoding:\n - Event type embedding (like vs strong listen vs regular listen).\n - Recency decay weighting: $w_i = \\exp(-\\lambda \\cdot \\Delta t)$.\n - Multi-Component Aggregation:\n - Recency-weighted average pooling of recent positive interactions.\n - Explicit Likes pooling: mean embedding of explicitly liked items.\n - Explicit Dislikes pooling: mean embedding of explicitly disliked items.\n - Learned Attention Pooling over recent items.\n - User MLP:\n - Concat pooled components $\\to$ `Linear(concat_dim, hidden_dim) -> LayerNorm -> GELU -> Dropout -> Linear(hidden_dim, out_dim)`.\n - L2 Normalization: outputs unit-norm vector $u \\in \\mathbb{R}^D$.\n\n### C. Training Objective & Loss Formulation\n- **In-Batch Negatives Sampled Softmax with LogQ Popularity Correction**:\n - Cosine similarity: $S_{i, j} = u_i \\cdot v_j / \\tau$ (with temperature $\\tau \\approx 0.07$).\n - Corrected logits: $S'_{i, j} = S_{i, j} - \\log(p_j)$, where $p_j$ is candidate item background frequency.\n - Cross-entropy loss: maximizes score of true positive item $i$ against in-batch items $j \\neq i$.\n- **Graded Loss Weighting**:\n - Grade 3 (organic likes) have NDCG gain of 7 vs 1 for grade 1 listens.\n - Weight like instances $3\\times$ higher in loss to reflect the objective priority.\n- **Negative Preference Regularization**:\n - Push user embedding away from disliked items via margin penalty: $\\max(0, \\gamma + u_i \\cdot v_{\\text{disliked}} - u_i \\cdot v_{\\text{pos}})$.\n\n---\n\n## 3. Implementation Phases & Steps\n\n### Step 1: Data Preprocessing & Feature Pipeline (`data_pipeline.py`)\n- Load candidate catalog (30,000 items) and build item index mappings.\n- Parse user history up to cutoff:\n - For validation: cutoff at $T_{\\text{train\\_cutoff}} = 24786800$ using `train_*`.\n - For test: cutoff at $T_{\\text{val\\_cutoff}} = 25393400$ using `train_* + val_*`.\n- Extract per-user features:\n - Sequence of recent positive listens (item IDs, timestamps, played ratios).\n - Explicit likes and dislikes from preference state.\n- Create training dataset:\n - Positive interaction instances $(u\\_history, target\\_item, weight)$ formed via temporal sequence splitting.\n\n### Step 2: Model Implementation (`model.py`)\n- Define `ItemTower` and `UserTower` PyTorch `nn.Module` classes.\n- Define `TwoTowerRanker` container module.\n- Implement temperature-scaled In-Batch Negatives loss with popularity debiasing.\n\n### Step 3: Training & Validation Pipeline (`train.py`)\n- Train on CPU with multi-threading (`torch.set_num_threads(8)`).\n- Batch size: 512 or 1024 for efficient vectorized matrix operations.\n- Optimizer: AdamW with weight decay and cosine annealing / linear warmup learning rate schedule.\n- Public validation evaluation after each epoch using `/task/evaluate_public.py` and `ranking_evaluation.py`.\n- Benchmark against popularity baseline (0.01071) and iterate on hyperparameters (embedding dim, temperature, history length, loss weights).\n\n### Step 4: Final Refitting & Submission Generation (`generate_submission.py`)\n- Refit the best-performing architecture on `train_* + val_*` up to test cutoff.\n- Extract user embeddings for all 8,456 target users using test history.\n- Precompute candidate item embeddings for all 30,000 items.\n- Perform MIPS: compute scores $U V^\\top$, apply `test_eligible_candidates.npz` mask, select top 100 per user via `torch.topk`.\n- Validate submission with `ranking_evaluation.validate_ranking`:\n - Exactly 845,600 rows (8,456 users x 100 items).\n - Ranks 1 to 100 per user.\n - All items strictly verified eligible.\n - No duplicates or NaNs.\n- Save `/app/solution/submission.parquet` and `/app/solution/submission.csv`.\n\n### Step 5: Documentation & Verification (`README.md`)\n- Create `/app/solution/README.md` documenting:\n - Architecture description (User Tower & Item Tower).\n - Training objective and loss function.\n - Public validation NDCG@10 score.\n - Reproduction and inference command.\n- Verify all requirements and constraints are satisfied.\n"
}Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/46ea7e5c-8d67-43e9-9026-62146dcafefe/plans/plan.md. Here is the updated code:
# Latency-Efficient PyTorch Two-Tower Ranker Implementation Plan
## 1. Objective and Constraints
Build a scalable two-tower neural network ranker in CPU PyTorch for the "Made for You" music shelf:
- User tower computes user embedding $u \in \mathbb{R}^D$ from interaction history (no user-ID lookup).
- Item tower independently computes candidate item embedding $v \in \mathbb{R}^D$.
- Dot product $u \cdot v$ determines ranking score (MIPS retrieval).
- Strict separation: no global user-item interaction matrix construction or decomposition.
- Evaluation metric: Graded NDCG@10 (grade 3 for organic like, grade 1 for organic listen >=80%).
- Eligibility: Strictly respect cutoff-specific candidate eligibility from `validation_eligible_candidates.npz` (for validation) and `test_eligible_candidates.npz` (for test).
- Deliverables:
- `/app/solution/submission.parquet` (and `.csv`): 8,456 target users x 100 items, ranks 1-100, all test-eligible.
- Complete, reproducible source code in `/app/solution/`.
- Concise `/app/solution/README.md` detailing architecture, loss, validation score, and command.
---
## 2. Key Architecture & System Design
### A. Item Tower (`ItemTower`)
- **Input**: Candidate item ID (0 to 29,999 in catalog) + optional item features (log popularity, track length).
- **Architecture**:
- Item Embedding Table: `nn.Embedding(num_items, embed_dim)` (where $embed\_dim = 64$ or $128$).
- Dense Projection Layer: `Linear(embed_dim, hidden_dim) -> LayerNorm -> GELU -> Linear(hidden_dim, out_dim)`.
- L2 Normalization: outputs unit-norm vector $v \in \mathbb{R}^D$.
- **Offline Precomputation**:
- Evaluated once for all 30,000 catalog items: $V \in \mathbb{R}^{30000 \times D}$.
### B. User Tower (`UserTower`)
- **Input**: User interaction history:
1. Sequence of recent positive interactions (item IDs, interaction types: organic like, strong listen >=80%, completion ratio, recency time-delta).
2. Explicit preference state items (liked track IDs, disliked track IDs from preference state parquet).
- **No User ID Embedding**: Derives representation purely from interaction events.
- **Architecture**:
- History Item Embedding: Shared or tied embedding table with item tower (or dedicated history table for catalog + top frequent non-catalog items).
- Recency & Event-Type Encoding:
- Event type embedding (like vs strong listen vs regular listen).
- Recency decay weighting: $w_i = \exp(-\lambda \cdot \Delta t)$.
- Multi-Component Aggregation:
- Recency-weighted average pooling of recent positive interactions.
- Explicit Likes pooling: mean embedding of explicitly liked items.
- Explicit Dislikes pooling: mean embedding of explicitly disliked items.
- Learned Attention Pooling over recent items.
- User MLP:
- Concat pooled components $\to$ `Linear(concat_dim, hidden_dim) -> LayerNorm -> GELU -> Dropout -> Linear(hidden_dim, out_dim)`.
- L2 Normalization: outputs unit-norm vector $u \in \mathbb{R}^D$.
### C. Training Objective & Loss Formulation
- **In-Batch Negatives Sampled Softmax with LogQ Popularity Correction**:
- Cosine similarity: $S_{i, j} = u_i \cdot v_j / \tau$ (with temperature $\tau \approx 0.07$).
- Corrected logits: $S'_{i, j} = S_{i, j} - \log(p_j)$, where $p_j$ is candidate item background frequency.
- Cross-entropy loss: maximizes score of true positive item $i$ against in-batch items $j \neq i$.
- **Graded Loss Weighting**:
- Grade 3 (organic likes) have NDCG gain of 7 vs 1 for grade 1 listens.
- Weight like instances $3\times$ higher in loss to reflect the objective priority.
- **Negative Preference Regularization**:
- Push user embedding away from disliked items via margin penalty: $\max(0, \gamma + u_i \cdot v_{\text{disliked}} - u_i \cdot v_{\text{pos}})$.
---
## 3. Implementation Phases & Steps
### Step 1: Data Preprocessing & Feature Pipeline (`data_pipeline.py`)
- Load candidate catalog (30,000 items) and build item index mappings.
- Parse user history up to cutoff:
- For validation: cutoff at $T_{\text{train\_cutoff}} = 24786800$ using `train_*`.
- For test: cutoff at $T_{\text{val\_cutoff}} = 25393400$ using `train_* + val_*`.
- Extract per-user features:
- Sequence of recent positive listens (item IDs, timestamps, played ratios).
- Explicit likes and dislikes from preference state.
- Create training dataset:
- Positive interaction instances $(u\_history, target\_item, weight)$ formed via temporal sequence splitting.
### Step 2: Model Implementation (`model.py`)
- Define `ItemTower` and `UserTower` PyTorch `nn.Module` classes.
- Define `TwoTowerRanker` container module.
- Implement temperature-scaled In-Batch Negatives loss with popularity debiasing.
### Step 3: Training & Validation Pipeline (`train.py`)
- Train on CPU with multi-threading (`torch.set_num_threads(8)`).
- Batch size: 512 or 1024 for efficient vectorized matrix operations.
- Optimizer: AdamW with weight decay and cosine annealing / linear warmup learning rate schedule.
- Public validation evaluation after each epoch using `/task/evaluate_public.py` and `ranking_evaluation.py`.
- Benchmark against popularity baseline (0.01071) and iterate on hyperparameters (embedding dim, temperature, history length, loss weights).
### Step 4: Final Refitting & Submission Generation (`generate_submission.py`)
- Refit the best-performing architecture on `train_* + val_*` up to test cutoff.
- Extract user embeddings for all 8,456 target users using test history.
- Precompute candidate item embeddings for all 30,000 items.
- Perform MIPS: compute scores $U V^\top$, apply `test_eligible_candidates.npz` mask, select top 100 per user via `torch.topk`.
- Validate submission with `ranking_evaluation.validate_ranking`:
- Exactly 845,600 rows (8,456 users x 100 items).
- Ranks 1 to 100 per user.
- All items strictly verified eligible.
- No duplicates or NaNs.
- Save `/app/solution/submission.parquet` and `/app/solution/submission.csv`.
### Step 5: Documentation & Verification (`README.md`)
- Create `/app/solution/README.md` documenting:
- Architecture description (User Tower & Item Tower).
- Training objective and loss function.
- Public validation NDCG@10 score.
- Reproduction and inference command.
- Verify all requirements and constraints are satisfied.
Reviewing Process Constraints
I am currently reviewing the operational constraints to ensure adherence. My focus is on confirming I can execute tasks without user interaction in this headless environment. I will proceed with caution.
{
"plan_filename": "plan.md"
}Plan approved. Switching to YOLO mode (all tool calls auto-approved).
The approved implementation plan is stored at: /home/agent/.gemini/tmp/app/46ea7e5c-8d67-43e9-9026-62146dcafefe/plans/plan.md
Read and follow the plan strictly during implementation.{
"strategic_intent": "Implement data processing pipeline and PyTorch two-tower model components in /app/solution.",
"title": "Implementing Data Pipeline & Model Architecture",
"summary": "Following the approved plan, we are now implementing the data processing pipeline and neural two-tower architecture. We will extract user histories, explicit preferences, candidate mappings, and training pairs, then implement the UserTower and ItemTower modules with temperature-scaled in-batch negatives loss."
}Current topic: "Implementing Data Pipeline & Model Architecture"
Topic summary: Following the approved plan, we are now implementing the data processing pipeline and neural two-tower architecture. We will extract user histories, explicit preferences, candidate mappings, and training pairs, then implement the UserTower and ItemTower modules with temperature-scaled in-batch negatives loss.
Strategic Intent: Implement data processing pipeline and PyTorch two-tower model components in /app/solution.Analyzing Candidate Data
I'm currently dissecting the candidate_catalog.parquet file. My focus is on understanding the structure and content, specifically the item_id, positive_listens, organic_positive_listens, and popularity_rank fields for each of the 30,000 items. This analysis will inform how I assign each catalog item an identifier.
Mapping Items and Interactions
I'm now focusing on creating the item2idx and idx2item mappings. Simultaneously, I'm preparing to process user interaction data by extracting liked_items and disliked_items from validation_preference_state.parquet for the 8,456 target users. I'm also analyzing features like logged popularity and background probabilities.
{
"description": "Analyze target user listens and likes in time windows",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(targets_df.uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\n\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ntl_target = listens[listens.uid.isin(target_uids)]\nprint(f\"Target users listens count: {len(tl_target)} / {len(listens)}\")\nprint(f\"Min timestamp: {tl_target.timestamp.min()}, Max timestamp: {tl_target.timestamp.max()}\")\n\ncutoff = 24786800\nday = 86400\nprint(f\"Listens in last 7 days: {len(tl_target[tl_target.timestamp >= cutoff - 7*day])}\")\nprint(f\"Listens in last 14 days: {len(tl_target[tl_target.timestamp >= cutoff - 14*day])}\")\nprint(f\"Listens in last 30 days: {len(tl_target[tl_target.timestamp >= cutoff - 30*day])}\")\nprint(f\"Listens in last 90 days: {len(tl_target[tl_target.timestamp >= cutoff - 90*day])}\")\n\n# Check likes for target users\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_target = likes[likes.uid.isin(target_uids)]\nprint(f\"Target users train likes count: {len(likes_target)}\")\n'"
}<untrusted_context>
Output: Target users listens count: 43222297 / 43484159
Min timestamp: 5, Max timestamp: 24786795
Listens in last 7 days: 1486465
Listens in last 14 days: 2911931
Listens in last 30 days: 6113327
Listens in last 90 days: 16743307
Target users train likes count: 690968
Process Group PGID: 1920
</untrusted_context>Analyzing Listener Growth
My current focus is on dissecting recent listener engagement. I've noted approximately 1.48 million listens in the past week, building on a solid trend of around 6.11 million over the last 30 days. This provides a clear picture of current user activity.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\n\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\ntu_set = set(tu.uid)\n\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_tu = likes[likes.uid.isin(tu_set)]\nprint(\"Target likes total:\", len(likes_tu), \"in catalog:\", likes_tu.item_id.isin(catalog_items).sum(), f\"({likes_tu.item_id.isin(catalog_items).mean():.2%})\")\n\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nprint(\"vps liked total:\", len(vps[vps.state == \"liked\"]), \"in catalog:\", vps[vps.state == \"liked\"].item_id.isin(catalog_items).sum(), f\"({vps[vps.state == 'liked'].item_id.isin(catalog_items).mean():.2%})\")\nprint(\"vps disliked total:\", len(vps[vps.state == \"disliked\"]), \"in catalog:\", vps[vps.state == \"disliked\"].item_id.isin(catalog_items).sum(), f\"({vps[vps.state == 'disliked'].item_id.isin(catalog_items).mean():.2%})\")\n'",
"description": "Check preference state catalog overlap"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 16, in <module>
NameError: name 'liked' is not defined. Did you mean: 'likes'?
Target likes total: 690968 in catalog: 472889 (68.44%)
Exit Code: 1
Process Group PGID: 1941
</untrusted_context>{
"description": "Check vps catalog overlap",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\n\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nliked_vps = vps[vps.state == \"liked\"]\ndisliked_vps = vps[vps.state == \"disliked\"]\nprint(\"vps liked total:\", len(liked_vps), \"in catalog:\", liked_vps.item_id.isin(catalog_items).sum(), f\"({liked_vps.item_id.isin(catalog_items).mean():.2%})\")\nprint(\"vps disliked total:\", len(disliked_vps), \"in catalog:\", disliked_vps.item_id.isin(catalog_items).sum(), f\"({disliked_vps.item_id.isin(catalog_items).mean():.2%})\")\n'"
}<untrusted_context>
Output: vps liked total: 418435 in catalog: 418435 (100.00%)
vps disliked total: 50689 in catalog: 50689 (100.00%)
Process Group PGID: 1960
</untrusted_context>Analyzing Preference Data
I'm confirming that all items from validation_preference_state.parquet and test_preference_state.parquet have been fully processed regarding VPS liked and disliked counts. The validation set shows 100% coverage for both metrics.
{
"description": "Check tps catalog overlap",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\n\ntps = pd.read_parquet(DATA / \"test_preference_state.parquet\")\nliked_tps = tps[tps.state == \"liked\"]\ndisliked_tps = tps[tps.state == \"disliked\"]\nprint(\"tps liked total:\", len(liked_tps), \"in catalog:\", liked_tps.item_id.isin(catalog_items).sum(), f\"({liked_tps.item_id.isin(catalog_items).mean():.2%})\")\nprint(\"tps disliked total:\", len(disliked_tps), \"in catalog:\", disliked_tps.item_id.isin(catalog_items).sum(), f\"({disliked_tps.item_id.isin(catalog_items).mean():.2%})\")\n'"
}<untrusted_context>
Output: tps liked total: 432659 in catalog: 432659 (100.00%)
tps disliked total: 52117 in catalog: 52117 (100.00%)
Process Group PGID: 1977
</untrusted_context>Completing Cataloging
All active preference state items and candidate items for ranking are now successfully housed within the candidate_catalog. My next step involves examining the listens data specifically tied to these catalog items within train_listens.
{
"description": "Check last 60 days positive listens catalog overlap",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\ntu_set = set(tu.uid)\n\ncutoff = 24786800\nday = 86400\n# Load listens from last 60 days\ndf = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\", \"is_organic\"])\ndf = df[(df.uid.isin(tu_set)) & (df.timestamp >= cutoff - 60*day) & (df.played_ratio_pct >= 50)]\nprint(f\"Positive listens last 60 days: {len(df)}\")\nin_cat = df.item_id.isin(catalog_items)\nprint(f\"In catalog: {in_cat.sum()} ({in_cat.mean():.2%})\")\n'"
}<untrusted_context>
Output: Positive listens last 60 days: 7414396
In catalog: 5394858 (72.76%)
Process Group PGID: 1994
</untrusted_context>Analyzing Listen Trends
My current focus is on understanding the 27.2% of positive listens not within the candidate catalog. I'm exploring methods to index these non-catalog items, potentially by assigning them an Out-Of-Vocabulary index.
Refining Indexing Strategy
I'm leaning towards a compact embedding strategy. Indexing only the core catalog items, plus a single <UNK>/<PAD> token, seems most efficient for CPU performance and cache utilization, given the high percentage of catalog item engagement.
{
"description": "Check overlap between truth items and previous likes",
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ntruth = validation_truth(eligibility, targets)\n\n# For users in truth, check their history\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nuser_liked = vps[vps.state == \"liked\"].groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n# Check if ground truth items are items they previously liked (remember eligibility might filter already liked items!)\nliked_overlap = 0\ntotal_truth_items = 0\nfor uid, items in truth.items():\n user_likes = user_liked.get(uid, set())\n for item in items:\n total_truth_items += 1\n if item in user_likes:\n liked_overlap += 1\nprint(f\"Overlap between truth items and previous liked items: {liked_overlap} / {total_truth_items}\")\n'"
}<untrusted_context>
Output: Overlap between truth items and previous liked items: 0 / 37769
Process Group PGID: 2015
</untrusted_context>Analyzing Discovery Exclusion
I'm investigating why there's no overlap between truth items and previously liked items. It appears the platform's candidate eligibility specifically excludes liked items, which seems counterintuitive for a "Made for You" shelf focused on discovery-quality objectives. This warrants further examination.
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ntruth = validation_truth(eligibility, targets)\n\n# Check if target users had listened to truth items in train_listens\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntu_listens = listens[listens.uid.isin(targets)]\nuser_listened = tu_listens.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\nlistened_overlap = 0\ntotal_truth_items = 0\nfor uid, items in truth.items():\n listened = user_listened.get(uid, set())\n for item in items:\n total_truth_items += 1\n if item in listened:\n listened_overlap += 1\n\nprint(f\"Overlap between truth items and train_listens items: {listened_overlap} / {total_truth_items} ({listened_overlap / total_truth_items:.2%})\")\n'",
"description": "Check overlap between truth items and train_listens items"
}<untrusted_context>
Output: Overlap between truth items and train_listens items: 0 / 37769 (0.00%)
Process Group PGID: 2035
</untrusted_context>Discovering Untouched Gems
I've realized a crucial detail in my current thinking: my candidate eligibility filters are discarding all previously interacted-with items. This means I'm exclusively focusing on pure discovery, recommending content the user has absolutely no prior engagement with.
Refining Content Similarity
I'm now focusing on how items co-listened to or co-liked by other users can inform my understanding of future user preferences. The goal is to represent user tastes by aggregating embeddings of their interaction history, enabling the recommendation of items with high co-occurrence similarity.
Optimizing Recommendation Algorithms
I'm now considering how to train both the item and user embedding towers. The goal is to ensure co-occurring items are clustered closely in the embedding space and that the user tower effectively aggregates historical interactions to predict future or held-out items, specifically by training on sequential prefixes to predict the next item.
{
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\ntorch.set_num_threads(8)\n\ndim = 64\nnum_items = 30000\nbatch_size = 512\nseq_len = 50\n\n# Simulated User Tower: embedding lookup -> mean pool -> MLP\nclass UserTower(nn.Module):\n def __init__(self, num_items, dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items + 1, dim, padding_idx=num_items)\n self.mlp = nn.Sequential(\n nn.Linear(dim, dim),\n nn.LayerNorm(dim),\n nn.GELU(),\n nn.Linear(dim, dim)\n )\n def forward(self, history_ids, history_weights=None):\n # history_ids: (B, seq_len)\n emb = self.item_emb(history_ids) # (B, seq_len, dim)\n if history_weights is not None:\n # history_weights: (B, seq_len)\n w = history_weights.unsqueeze(-1)\n pooled = (emb * w).sum(dim=1) / (w.sum(dim=1).clamp(min=1e-6))\n else:\n pooled = emb.mean(dim=1)\n out = self.mlp(pooled)\n return F.normalize(out, p=2, dim=-1)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, dim)\n self.mlp = nn.Sequential(\n nn.Linear(dim, dim),\n nn.LayerNorm(dim),\n nn.GELU(),\n nn.Linear(dim, dim)\n )\n def forward(self, item_ids):\n emb = self.item_emb(item_ids)\n out = self.mlp(emb)\n return F.normalize(out, p=2, dim=-1)\n\nuser_tower = UserTower(num_items, dim)\nitem_tower = ItemTower(num_items, dim)\noptimizer = torch.optim.AdamW(list(user_tower.parameters()) + list(item_tower.parameters()), lr=1e-3)\n\n# Test 50 steps\nt0 = time.time()\nfor step in range(50):\n hist_ids = torch.randint(0, num_items, (batch_size, seq_len))\n hist_w = torch.rand(batch_size, seq_len)\n targets = torch.randint(0, num_items, (batch_size,))\n \n optimizer.zero_grad()\n u = user_tower(hist_ids, hist_w) # (B, dim)\n v = item_tower(targets) # (B, dim)\n \n # In-batch negatives similarity\n logits = torch.mm(u, v.t()) / 0.07 # (B, B)\n labels = torch.arange(batch_size)\n loss = F.cross_entropy(logits, labels)\n loss.backward()\n optimizer.step()\n\nt1 = time.time()\ndt = t1 - t0\nsamples = 50 * batch_size\nprint(f\"50 steps ({samples} samples) took {dt:.2f} s -> {samples / dt:.0f} samples/sec\")\n'",
"description": "Benchmark two-tower CPU training throughput"
}<untrusted_context>
Output: 50 steps (25600 samples) took 0.94 s -> 27304 samples/sec
Process Group PGID: 2058
</untrusted_context>Confirming CPU Training Speed
I'm pleased to report that my simulations confirm CPU training with PyTorch is remarkably swift. At 27,000+ samples per second, I'm confident we can achieve extensive training across complex architectures and multiple iterations well within our 60-minute timeframe.
Structuring Training Data
I'm now focusing on meticulously structuring the user history, features, and training samples. My immediate concern is defining the precise 7-day windows for both training and validation, mimicking the evaluation's setup to ensure accurate model development based on available signals.
Refining Sequential Training Data
I'm now exploring the possibility of leveraging sequence prediction across the entire user interaction history for training. This involves treating preceding item sequences as history for target items like organic likes or high-percentage listens, and I'm considering how to integrate train_likes and validation_preference_state effectively.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\ntlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\ntunlikes = pd.read_parquet(DATA / \"train_unlikes.parquet\")\n\nprint(\"vps liked:\", (vps.state == \"liked\").sum())\nprint(\"tlikes count:\", len(tlikes))\nprint(\"tunlikes count:\", len(tunlikes))\n# Is vps the net active state of (likes - unlikes)?\n# Let us check for a few users\n'",
"description": "Compare vps with train_likes and train_unlikes"
}<untrusted_context>
Output: vps liked: 418435
tlikes count: 814831
tunlikes count: 292665
Process Group PGID: 2082
</untrusted_context>Analyzing Training Data
My focus has been on examining the training data, specifically the vps (validation preference state). I've calculated net likes and identified the number of active liked states at the training cutoff. The hidden test set's preference data is also readily available.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\n\nlikes_per_user = vps[vps.state == \"liked\"].groupby(\"uid\").size()\nprint(f\"Users with active likes in vps: {len(likes_per_user)} / {len(tu)}\")\nprint(f\"Active likes per user: min={likes_per_user.min()}, median={likes_per_user.median()}, mean={likes_per_user.mean():.1f}, max={likes_per_user.max()}\")\n\ndislikes_per_user = vps[vps.state == \"disliked\"].groupby(\"uid\").size()\nprint(f\"Users with active dislikes in vps: {len(dislikes_per_user)} / {len(tu)}\")\nprint(f\"Active dislikes per user: min={dislikes_per_user.min()}, median={dislikes_per_user.median()}, mean={dislikes_per_user.mean():.1f}, max={dislikes_per_user.max()}\")\n'",
"description": "Inspect active likes and dislikes distribution per user"
}<untrusted_context>
Output: Users with active likes in vps: 6769 / 8456
Active likes per user: min=1, median=28.0, mean=61.8, max=1327
Users with active dislikes in vps: 4277 / 8456
Active dislikes per user: min=1, median=3.0, mean=11.9, max=1703
Process Group PGID: 2100
</untrusted_context>Analyzing User Engagement
I'm currently investigating the engagement patterns of our target user base. Specifically, I'm comparing users with active likes against those who, despite lacking likes, still possess listen history. My next step is to confirm if all target users show positive listens within the train_listens dataset.
{
"description": "Inspect target users positive listens stats",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = pd.read_parquet(DATA / \"target_users.parquet\")\nprint(tu.describe())\n'"
}<untrusted_context>
Output: uid recent_positive_listens history_positive_listens
count 8456.000000 8456.000000 8456.000000
mean 502289.108325 1251.076750 3236.440752
std 287953.395912 1414.340904 3697.575286
min 100.000000 20.000000 20.000000
25% 254375.000000 281.000000 630.000000
50% 503850.000000 784.500000 1920.500000
75% 751725.000000 1706.250000 4494.250000
max 1000000.000000 19592.000000 26597.000000
Process Group PGID: 2117
</untrusted_context>Confirming User Engagement
My analysis reveals a robust engagement level across all target users. Each user boasts a minimum of 20 positive listens in the last 90 days, with a median of 784.5. Furthermore, their overall listening history shows a median of 1,920 positive listens, indicating a consistently strong history of engagement.
Developing User Representation
I'm currently exploring how best to represent user history. My focus is on two key avenues: leveraging explicitly liked items for an average item embedding and analyzing recent positive listening sessions, weighting them by recency.
Refining Tower Architectures
I'm now investigating the feasibility of sharing item embeddings between user and item towers. My current hypothesis is that a concatenated representation of liked, recent, and disliked item embeddings, processed through an MLP, will form the user tower. The item tower will likely utilize its own embeddings, potentially from a similar MLP transformation.
Sharing Embeddings for Efficiency
I'm exploring shared item embeddings between user and item towers. This promises parameter efficiency and faster training by allowing item embedding updates to benefit both recommendation and history representation. I'm also considering an attention mechanism within the user tower.
Refining User Tower Composition
I'm now integrating attention mechanisms into the user tower, allowing it to dynamically weigh the importance of individual historical tracks. This, combined with mean-pooled and explicitly liked/disliked item embeddings, provides a comprehensive representation of user taste. I'm also examining the optimal positive targets for training from the extensive user listening data.
{
"description": "Check training positive counts in various windows",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = set(pd.read_parquet(DATA / \"candidate_catalog.parquet\").item_id)\n\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_tu = likes[likes.uid.isin(tu)]\nprint(\"Train likes organic:\", (likes_tu.is_organic == 1).sum(), \"in catalog:\", (likes_tu[likes_tu.is_organic == 1].item_id.isin(catalog)).sum())\n\n# Check listens in last 60 days\ncutoff = 24786800\nday = 86400\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ntl_tu = listens[(listens.uid.isin(tu)) & (listens.timestamp >= cutoff - 60*day)]\nstrong_org = tl_tu[(tl_tu.is_organic == 1) & (tl_tu.played_ratio_pct >= 80) & (tl_tu.item_id.isin(catalog))]\nprint(f\"Strong organic listens in catalog (last 60 days): {len(strong_org)}\")\n\nstrong_org_30d = tl_tu[(tl_tu.timestamp >= cutoff - 30*day) & (tl_tu.is_organic == 1) & (tl_tu.played_ratio_pct >= 80) & (tl_tu.item_id.isin(catalog))]\nprint(f\"Strong organic listens in catalog (last 30 days): {len(strong_org_30d)}\")\n\nstrong_org_14d = tl_tu[(tl_tu.timestamp >= cutoff - 14*day) & (tl_tu.is_organic == 1) & (tl_tu.played_ratio_pct >= 80) & (tl_tu.item_id.isin(catalog))]\nprint(f\"Strong organic listens in catalog (last 14 days): {len(strong_org_14d)}\")\n\nstrong_org_7d = tl_tu[(tl_tu.timestamp >= cutoff - 7*day) & (tl_tu.is_organic == 1) & (tl_tu.played_ratio_pct >= 80) & (tl_tu.item_id.isin(catalog))]\nprint(f\"Strong organic listens in catalog (last 7 days): {len(strong_org_7d)}\")\n'"
}<untrusted_context>
Output: Train likes organic: 342663 in catalog: 223046
Strong organic listens in catalog (last 60 days): 2422412
Strong organic listens in catalog (last 30 days): 1250037
Strong organic listens in catalog (last 14 days): 593945
Strong organic listens in catalog (last 7 days): 304885
Process Group PGID: 2133
</untrusted_context>Reviewing Recent Performance
My current focus is on analyzing recent performance metrics. Over the last week, we've seen over 300,000 organic listens, with a total of nearly 600,000 in two weeks and over 1.25 million in a month. Catalog likes are also strong, approaching 225,000.
Ensuring Temporal Validity
I'm currently deeply engaged with the integrity of our training data, specifically how we isolate historical user interactions from future targets. It’s paramount that the model only learns from past behavior when predicting future engagement, preventing any form of data leakage that could lead to spurious correlations or overfitting. This strict temporal separation is essential, particularly as our validation process already screens out previously heard tracks. I'm now exploring methods to implement multiple, distinct training time-windows to rigorously enforce this historical context.
Validating Temporal Separation
I'm refining the temporal segmentation of our training data. My current focus is on implementing distinct, sequential 7-day windows, each with its own historical user interaction cutoff, to rigorously ensure no future data influences past learning. This layered approach mirrors the validation window structure and aims to enhance the robustness of our model’s predictive capabilities by strictly isolating historical context.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = set(pd.read_parquet(DATA / \"candidate_catalog.parquet\").item_id)\n\ncutoff = 24786800\nday = 86400\nt1 = cutoff - 7 * day\n\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ntl_tu = listens[listens.uid.isin(tu)]\n\nhist = tl_tu[tl_tu.timestamp < t1]\ntarget = tl_tu[(tl_tu.timestamp >= t1) & (tl_tu.is_organic == 1) & (tl_tu.played_ratio_pct >= 80) & (tl_tu.item_id.isin(catalog))]\n\n# User history item sets before t1\nuser_hist_items = hist.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\ndiscovery_targets = 0\nrepeat_targets = 0\nfor uid, item in zip(target.uid, target.item_id):\n if item in user_hist_items.get(uid, set()):\n repeat_targets += 1\n else:\n discovery_targets += 1\n\nprint(f\"In 7-day window: discovery targets = {discovery_targets} ({discovery_targets / len(target):.2%}), repeat = {repeat_targets}\")\n'",
"description": "Check discovery vs repeat targets in 7-day window"
}<untrusted_context>
Output: In 7-day window: discovery targets = 67495 (22.14%), repeat = 237390
Process Group PGID: 2154
</untrusted_context>Analyzing Discovery Targets
My current focus is on the 7-day discovery rate, observing that a significant portion of listener engagement (67,495 targets) comes from entirely new content, while repeats (237,390) are accounted for by the eligibility mask at test time.
Prioritizing New Content
I'm prioritizing training on discovery targets to ensure the model learns to generalize to novel, unlistened items. This approach avoids an identity mapping where it would simply predict items already in a user's history, which would then be masked as ineligible during testing.
{
"description": "Check discovery vs repeat likes in 7-day window",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = set(pd.read_parquet(DATA / \"candidate_catalog.parquet\").item_id)\n\ncutoff = 24786800\nday = 86400\nt1 = cutoff - 7 * day\n\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\"])\ntl_tu = listens[listens.uid.isin(tu)]\nhist = tl_tu[tl_tu.timestamp < t1]\nuser_hist_items = hist.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_tu = likes[(likes.uid.isin(tu)) & (likes.timestamp >= t1) & (likes.is_organic == 1) & (likes.item_id.isin(catalog))]\n\ndisc_likes = 0\nrepeat_likes = 0\nfor uid, item in zip(likes_tu.uid, likes_tu.item_id):\n if item in user_hist_items.get(uid, set()):\n repeat_likes += 1\n else:\n disc_likes += 1\n\nprint(f\"Likes in 7-day window: discovery = {disc_likes} ({disc_likes / len(likes_tu):.2%}), repeat = {repeat_likes}\")\n'"
}<untrusted_context>
Output: Likes in 7-day window: discovery = 5418 (69.69%), repeat = 2356
Process Group PGID: 2175
</untrusted_context>Analyzing Discovery Data
My analysis confirms a significant portion, 69.7%, of recent organic likes stem from users discovering new music for the first time. This validates our focus on discovery behavior. My next step involves exploring optimal ways to represent user history within this context.
Designing Interaction Towers
I'm currently focused on architecting the item and user towers for our model. For the item tower, I'm considering using embeddings augmented with track-level features like listen counts and popularity rank, processed through a small MLP. This will inform how we represent items.
Refining Item Representation
My recent work involves finalizing the item tower, defining its output as a normalized vector derived from embeddings and track features via an MLP. This representation is fully precomputable offline, streamlining inference. My next focus is on how the user tower will integrate interaction history.
Developing User Representation
I'm exploring how to best represent users, specifically focusing on incorporating their recent positive listens. This involves considering recency weighting and attention mechanisms to capture the temporal dynamics of their listening behavior. My goal is to create a rich, informative user embedding.
Aggregating User Signals
I'm synthesizing various user interaction signals—likes, weighted recent listens, attention-weighted inputs, organic listens, and dislikes—into a unified representation. This combined embedding is then processed through a user MLP and L2 normalized to produce the final user vector for similarity calculations.
Validating Recommendation Architecture
I've confirmed that my architecture precisely matches the core requirements, decomposing into independent user and item vectors. Both towers are PyTorch modules, with the user tower solely relying on interaction history, ensuring no user-ID lookups. This setup supports offline item embedding computation and efficient online retrieval.
{
"description": "Test fast parquet filtering for target users listens",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\nfrom pathlib import Path\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncatalog_items = set(catalog.item_id)\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\n\n# Check reading listens for target users\n# To be fast and memory-efficient: only read needed columns\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 60 * day # last 60 days is plenty of recent history\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", list(tu)), (\"timestamp\", \">=\", t_start)]\n)\nprint(f\"Filtered listens loaded in {time.time() - t0:.2f} s: {len(listens)} rows\")\n'"
}<untrusted_context>
Output: Filtered listens loaded in 0.48 s: 11699019 rows
Process Group PGID: 2196
</untrusted_context>Analyzing Train Listens
My recent focus has been on the train_listens dataset. I've successfully loaded a substantial 11,699,019 rows, achieving this in an impressive 0.48 seconds, which highlights the efficiency of the Parquet row-group pushdown filtering. My next step is to delve into the specifics of what constitutes the train_listens data.
{
"description": "Test positive listens count with pushdown filtering",
"command": "python3 -c '\nimport pandas as pd\nimport time\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 60 * day\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", list(tu)), (\"timestamp\", \">=\", t_start), (\"played_ratio_pct\", \">=\", 50)]\n)\nprint(f\"Positive listens: {len(listens)} rows\")\n'"
}<untrusted_context>
Output: Positive listens: 7414396 rows
Process Group PGID: 2217
</untrusted_context>Constructing Training Samples
I'm currently focusing on how to best construct training samples for our two-tower model, considering the 7.4 million rows we have. The goal is to effectively train the model to predict user affinity for discovery tracks based on their historical engagement.
Exploring Temporal Windows
I'm now considering how to segment our 7.4 million rows into multiple temporal windows to generate training data. Specifically, I'm evaluating different prediction and history window durations, aiming to capture user engagement patterns across varied timeframes for effective model training.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\n\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nunlikes = pd.read_parquet(DATA / \"train_unlikes.parquet\")\n\n# Test for cutoff T = 24786800 - 7 * 86400\nt_cut = 24786800 - 7 * 86400\nl = likes[(likes.uid.isin(tu)) & (likes.timestamp < t_cut)]\nu = unlikes[(unlikes.uid.isin(tu)) & (unlikes.timestamp < t_cut)]\n\n# Group by (uid, item_id) and take the last action\ncomb = pd.concat([\n l.assign(action=\"like\"),\n u.assign(action=\"unlike\")\n]).sort_values(\"timestamp\")\nactive_likes = comb.groupby([\"uid\", \"item_id\"]).last()\nactive_likes = active_likes[active_likes.action == \"like\"]\nprint(f\"Active likes before t_cut: {len(active_likes)}\")\n'",
"description": "Verify reconstructing active likes at any timestamp"
}<untrusted_context>
Output: Active likes before t_cut: 597363
Process Group PGID: 2238
</untrusted_context>Reconstructing States
I can now reconstruct active likes and dislikes at any given timestamp. Crucially, validation_preference_state.parquet is directly provided at the training validation cutoff $T_{\text{train\_cutoff}} = 24786800$, simplifying my current stage of analysis.
Accessing Test Data
I've successfully obtained test_preference_state.parquet at timestamp $25393400$. I'm now exploring the potential of sequence prediction within user sessions, contrasting it with window-based discovery targets and its alignment with the evaluation structure.
Aligning Training
My current focus is aligning training windows with evaluation periods. I've realized that using multiple 7-day temporal windows, each with its own history cutoff, directly mirrors the train-to-test distribution. This approach should yield around 300,000 discovery training examples.
Exploring Repeat Listens
I'm now considering the inclusion of repeat positive listens in training, weighing them lower than organic likes. The concern is that overemphasizing known items might hinder discovery of new ones, a point I plan to test experimentally by comparing discovery-only targets against those incorporating repeat listens.
{
"description": "Benchmark EmbeddingBag on CPU",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb = nn.EmbeddingBag(30000, 128, mode=\"mean\")\nbatch_size = 1024\n# simulate 1024 users each with 30 likes\nindices = torch.randint(0, 30000, (batch_size * 30,))\noffsets = torch.arange(0, batch_size * 30, 30)\n\nt0 = time.time()\nfor _ in range(100):\n out = emb(indices, offsets)\nt1 = time.time()\nprint(f\"EmbeddingBag 100 iterations ({100 * batch_size} samples): {t1 - t0:.4f} s\")\n'"
}<untrusted_context>
Output: EmbeddingBag 100 iterations (102400 samples): 0.0264 s
Process Group PGID: 2255
</untrusted_context>Testing EmbeddingBag Capabilities
I've confirmed EmbeddingBag on CPU is remarkably swift, processing 100,000 samples in just 0.026 seconds. Further testing reveals it elegantly supports weights, either through mode='sum' or the per_sample_weights parameter.
{
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb = nn.EmbeddingBag(30000, 128, mode=\"sum\")\nbatch_size = 1024\nindices = torch.randint(0, 30000, (batch_size * 50,))\noffsets = torch.arange(0, batch_size * 50, 50)\nweights = torch.rand(batch_size * 50)\n\nt0 = time.time()\nfor _ in range(100):\n out = emb(indices, offsets, per_sample_weights=weights)\nt1 = time.time()\nprint(f\"EmbeddingBag with weights 100 iterations: {t1 - t0:.4f} s\")\n'",
"description": "Benchmark EmbeddingBag with weights on CPU"
}<untrusted_context>
Output: EmbeddingBag with weights 100 iterations: 0.0240 s
Process Group PGID: 2279
</untrusted_context>Optimizing Embedding Retrieval
I'm focusing on performance, noting a swift 0.0240 seconds for 100,000 samples. My current consideration involves handling edge cases where users have no likes or dislikes. Specifically, I'm investigating how nn.EmbeddingBag behaves with empty offset intervals, as it requires non-empty slices.
{
"description": "Test zero-padding token in EmbeddingBag",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\n\nemb = nn.EmbeddingBag(30001, 128, mode=\"sum\")\n# Set index 30000 to 0\nwith torch.no_grad():\n emb.weight[30000].zero_()\n\nindices = torch.tensor([1, 5, 30000, 2])\noffsets = torch.tensor([0, 2, 3]) # user 0 has [1, 5], user 1 has [30000], user 2 has [2]\nout = emb(indices, offsets)\nprint(\"User 1 is zero?\", (out[1] == 0).all().item())\n'"
}<untrusted_context>
Output: User 1 is zero? True
Process Group PGID: 2303
</untrusted_context>Processing Recent Items
I'm considering how to integrate an attention mechanism focusing on a user's recent $K$ interactions. My current focus is on preparing the recent_items tensor for this purpose, after confirming the initial user zero condition.
{
"description": "Benchmark attention pooling on CPU",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\ntorch.set_num_threads(8)\n\nemb = nn.Embedding(30001, 128, padding_idx=30000)\nq = nn.Parameter(torch.randn(128))\n\nB = 1024\nK = 30\nitems = torch.randint(0, 30000, (B, K))\nmask = torch.ones(B, K, dtype=torch.bool)\n\nt0 = time.time()\nfor _ in range(100):\n E = emb(items) # (B, K, D)\n scores = torch.matmul(E, q) / (128 ** 0.5)\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1).unsqueeze(-1)\n attn_out = (weights * E).sum(dim=1)\nt1 = time.time()\nprint(f\"Attention pooling 100 iterations ({100 * B} samples): {t1 - t0:.4f} s\")\n'"
}<untrusted_context>
Output: Attention pooling 100 iterations (102400 samples): 0.4094 s
Process Group PGID: 2316
</untrusted_context>Evaluating Model Performance
I've been analyzing the processing speeds, and both EmbeddingBag and Attention Pooling are proving exceptionally efficient for handling extensive and recent user data respectively. My focus now shifts to a thorough review of the UserTower input configurations.
Refining Input Features
I'm currently focused on the UserTower's input configurations. I've refined the feature extraction for likes and dislikes using mean pooling, and for recent listens, I'm exploring both recency-decay weighted and learned attention pooling. The next step is to integrate these into a concatenated vector before passing it through the User MLP.
Processing Item Features
I've completed the projection and MLP layers for candidate track features, resulting in unit item vectors. This allows for offline computation and efficient retrieval via MIPS at request time.
Incorporating Dislikes
I'm considering how to penalize disliked items. Since disliked items are already an input to the User MLP, it can likely learn to avoid them. I need to check the training loss, specifically the in-batch negatives with popularity debias using LogQ correction.
Refining User Representations
I'm considering how to explicitly penalize disliked tracks, perhaps by projecting user embeddings away from disliked track embeddings. The candidate eligibility mask already handles active dislikes, so I'll focus on data preparation and prototyping a training script. My next step is to define file structure for modularity.
Exploring Temporal Windows
I'm examining the temporal windows for constructing training samples, specifically analyzing positive listens and likes within different timeframes relative to a cutoff. This step is crucial for understanding the data's temporal dynamics and informing subsequent feature engineering.
{
"description": "Check training events per 7-day window",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = set(pd.read_parquet(DATA / \"candidate_catalog.parquet\").item_id)\n\ncutoff = 24786800\nday = 86400\n\n# Load listens from last 60 days\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", list(tu)), (\"timestamp\", \">=\", cutoff - 60*day)]\n)\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\")\nlikes_tu = likes[(likes.uid.isin(tu)) & (likes.timestamp >= cutoff - 60*day)]\n\nfor w in range(1, 6):\n t_end = cutoff - (w - 1) * 7 * day\n t_start = cutoff - w * 7 * day\n \n w_listens = listens[(listens.timestamp >= t_start) & (listens.timestamp < t_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.item_id.isin(catalog))]\n w_likes = likes_tu[(likes_tu.timestamp >= t_start) & (likes_tu.timestamp < t_end) & (likes_tu.is_organic == 1) & (likes_tu.item_id.isin(catalog))]\n \n print(f\"Window {w} ({t_start} - {t_end}): listens={len(w_listens)} (users={w_listens.uid.nunique()}), likes={len(w_likes)} (users={w_likes.uid.nunique()})\")\n'"
}<untrusted_context>
Output: Window 1 (24182000 - 24786800): listens=304885 (users=5842), likes=7774 (users=2185)
Window 2 (23577200 - 24182000): listens=289060 (users=5660), likes=7129 (users=2119)
Window 3 (22972400 - 23577200): listens=287015 (users=5593), likes=7451 (users=2026)
Window 4 (22367600 - 22972400): listens=289176 (users=5513), likes=7631 (users=2018)
Window 5 (21762800 - 22367600): listens=281021 (users=5450), likes=7492 (users=2002)
Process Group PGID: 2340
</untrusted_context>Observing Listen Data
I'm noticing a remarkable consistency in the listen and like metrics, with daily figures falling within tight ranges. This stability across recent seven-day periods is quite striking.
Precomputing User Features
I'm exploring the efficiency of precomputing user history features at each window cutoff. The initial analysis suggests a very streamlined process, where target users' features can be efficiently indexed by their UID for training batches.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\nfrom pathlib import Path\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(targets_df.uid)\nuid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 60 * day\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_start), (\"played_ratio_pct\", \">=\", 50)]\n)\nprint(f\"Listens loaded in {time.time() - t0:.2f} s: {len(listens)} rows\")\n\n# Filter listens to catalog items\nlistens_cat = listens[listens.item_id.isin(item2idx)].copy()\nlistens_cat[\"item_idx\"] = listens_cat.item_id.map(item2idx)\nlistens_cat[\"row_idx\"] = listens_cat.uid.map(uid2row)\n\n# Sort by timestamp descending\nlistens_cat = listens_cat.sort_values(\"timestamp\", ascending=False)\n\n# For each user, take top 50 recent listens\nt1 = time.time()\nrecent_grouped = listens_cat.groupby(\"row_idx\").head(50)\nprint(f\"Top 50 recent listens grouped in {time.time() - t1:.2f} s: {len(recent_grouped)} rows\")\n'",
"description": "Test fast user history grouping"
}<untrusted_context>
Output: Listens loaded in 0.46 s: 7414396 rows
Top 50 recent listens grouped in 0.22 s: 397117 rows
Process Group PGID: 2361
</untrusted_context>Analyzing Recent Listens
I'm currently evaluating the efficiency of grouping recent listens, aiming for improvements. Next, I'll be examining active likes and dislikes around the validation cutoff point to ensure data integrity.
{
"command": "python3 -c '\nimport pandas as pd\nimport time\nfrom pathlib import Path\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\nvps_liked = vps[vps.state == \"liked\"].copy()\nvps_liked[\"item_idx\"] = vps_liked.item_id.map(item2idx)\nliked_dict = vps_liked.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\nprint(f\"Liked dict built in {time.time() - t0:.2f} s: {len(liked_dict)} users\")\n'",
"description": "Test preference state grouping"
}<untrusted_context>
Output: Liked dict built in 0.28 s: 6769 users
Process Group PGID: 2382
</untrusted_context>Optimizing Feature Extraction
I've managed to extract features for all 8,456 users in under a second. My current focus is on evaluating the performance impact of using multiple training windows, specifically examining two or three distinct cutoffs.
Testing Window Strategies
I'm currently exploring the impact of using multiple training windows. My focus is on comparing the validation scores derived from training with discovery items versus incorporating repeat listens, aiming to quantify the performance difference. This will involve building a flexible evaluation framework.
Refining Feature Engineering
I'm now incorporating recency decay into item weights and exploring different grades for training pairs, specifically differentiating between likes and strong listens. This is in preparation for generating candidate item features like log positive listens and popularity ranks.
Integrating User Representation
I'm now exploring how to construct a robust user representation. My focus is on combining various historical item embeddings—liked, disliked, recent (with decay and attention), and organic—before passing them through a multi-layer perceptron to generate the final user embedding.
Implementing Loss Function
I've successfully implemented the InBatchNegativesLoss module, incorporating popularity corrections and weighted cross-entropy for training. I'm now focused on integrating this into the evaluation pipeline, particularly the matrix multiplication and top-k selection for generating final recommendations.
{
"description": "Check catalog track length coverage",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"item_id\", \"track_length_seconds\"])\nfirst_len = listens.drop_duplicates(\"item_id\").set_index(\"item_id\")[\"track_length_seconds\"]\ncatalog_len = catalog[\"item_id\"].map(first_len)\nprint(\"Catalog items with track length:\", catalog_len.notna().sum(), \"/\", len(catalog))\n'"
}<untrusted_context>
Output: Catalog items with track length: 30000 / 30000
Process Group PGID: 2399
</untrusted_context>Validating Catalog Data
All 30,000 catalog items now have their track lengths accounted for in train_listens. I've also successfully implemented a preprocessed cache for catalog features, stored in /app/solution/, to avoid redundant computation. My current focus is on examining the representation of user history for model training.
Defining Training Windows
I'm now defining distinct training windows based on specific cutoffs ($T_1, T_2, T_3$). For each, I'm clarifying target user history and item selection, and I'm curious about the active user count per window. My plan is to pre-extract user history features, including likes, dislikes, and recency-weighted listening data, mapping them via (window_idx, uid) to a unique history_idx for efficient training pair construction. I will then write a Python test script to validate this data structure.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\nfrom pathlib import Path\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\nuid2idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_cut = cutoff - 7 * day\nt_start = t_cut - 60 * day\n\n# 1. Active likes and dislikes before t_cut\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\", filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \"<\", t_cut)])\nunlikes = pd.read_parquet(DATA / \"train_unlikes.parquet\", filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \"<\", t_cut)])\nl_cat = likes[likes.item_id.isin(item2idx)][[\"uid\", \"item_id\", \"timestamp\"]].assign(action=1)\nu_cat = unlikes[unlikes.item_id.isin(item2idx)][[\"uid\", \"item_id\", \"timestamp\"]].assign(action=0)\ncomb_l = pd.concat([l_cat, u_cat]).sort_values(\"timestamp\")\nlast_l = comb_l.groupby([\"uid\", \"item_id\"]).last()\nactive_likes = last_l[last_l.action == 1].reset_index()\n\n# 2. Listens before t_cut\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_start), (\"timestamp\", \"<\", t_cut), (\"played_ratio_pct\", \">=\", 50)]\n)\nlistens_cat = listens[listens.item_id.isin(item2idx)].copy()\nlistens_cat[\"item_idx\"] = listens_cat.item_id.map(item2idx)\nlistens_cat[\"dt_days\"] = (t_cut - listens_cat.timestamp) / 86400.0\n\n# 3. Targets in [t_cut, cutoff]\ntarget_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_cut), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 80), (\"is_organic\", \"==\", 1)]\n)\ntarget_likes = pd.read_parquet(\n DATA / \"train_likes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_cut), (\"timestamp\", \"<\", cutoff), (\"is_organic\", \"==\", 1)]\n)\n\nprint(f\"Data parsed in {time.time() - t0:.2f} s: target listens={len(target_listens)}, target likes={len(target_likes)}\")\n'",
"description": "Test feature and target extraction timing"
}<untrusted_context>
Output: Data parsed in 1.77 s: target listens=428209, target likes=12022
Process Group PGID: 2422
</untrusted_context>Refining Data Processing
I've confirmed the rapid execution speed of the current implementation. My next step involves creating data.py with optimized functions for data preparation and dataset construction. I'm also meticulously reviewing the PyTorch representation of user history.
Optimizing Embedding Lookup
I'm focusing on the efficiency of my embedding lookups. The tensors for liked, disliked, and recent items appear surprisingly small, even for large batches. My current thought is to analyze self.liked_emb_bag to ensure it's being utilized optimally.
Investigating Edge Cases
I'm currently delving into scenarios where user interaction lists might be empty. Specifically, I'm examining how EmbeddingBag handles padding indices when encountering users with no liked or disliked items to ensure robust processing.
{
"description": "Verify EmbeddingBag zero padding handling",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nemb_bag = nn.EmbeddingBag(30001, 64, mode=\"mean\")\nwith torch.no_grad():\n emb_bag.weight[30000].zero_()\n\n# User 0 has likes [1, 2], User 1 has no likes (dummy [30000]), User 2 has [5]\nindices = torch.tensor([1, 2, 30000, 5], dtype=torch.long)\noffsets = torch.tensor([0, 2, 3], dtype=torch.long)\nout = emb_bag(indices, offsets)\nprint(\"Shape:\", out.shape)\nprint(\"User 1 is zeros?\", (out[1] == 0).all().item())\nprint(\"Any NaN?\", torch.isnan(out).any().item())\n'"
}<untrusted_context>
Output: Shape: torch.Size([3, 64])
User 1 is zeros? True
Any NaN? False
Process Group PGID: 2443
</untrusted_context>Testing EmbeddingBag Gradients
I'm currently investigating the gradient behavior of nn.EmbeddingBag. My recent focus has been on ensuring it correctly handles users with no likes or dislikes by producing exact zeros, avoiding any NaN values. I'm also actively testing what occurs when emb_bag.weight is updated during training and whether index 30,000 can receive gradients.
Exploring Padding Index
I'm now considering how to leverage padding_idx more directly. Two avenues are emerging: either zeroing out specific index gradients post-optimization or ensuring identical embedding layers are used across different components. I'm also exploring the efficiency of manual pooling versus EmbeddingBag and the implications of a fixed number of likes/dislikes per user for embedding lookup.
{
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb = nn.Embedding(30001, 128, padding_idx=30000)\nB = 1024\nliked = torch.randint(0, 30000, (B, 50))\nmask = (liked != 30000).unsqueeze(-1)\n\nt0 = time.time()\nfor _ in range(100):\n E = emb(liked)\n pooled = (E * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1)\nt1 = time.time()\nprint(f\"2D embedding lookup + masked mean for 100 iterations ({100 * B} samples): {t1 - t0:.4f} s\")\n'",
"description": "Benchmark 2D embedding lookup and masked mean on CPU"
}<untrusted_context>
Output: 2D embedding lookup + masked mean for 100 iterations (102400 samples): 0.3967 s
Process Group PGID: 2456
</untrusted_context>Achieving High Performance
I'm thrilled with the speed—100,000 samples processed in under half a second! The use of standard 2D tensors with padding has significantly simplified the process, eliminating the need for offsets and variable-length concatenations. This allows for efficient vectorized indexing, resulting in a torch.Tensor of shape (N_users, 50).
Incorporating Recency Weights
I'm now exploring how to apply recency weights to recent_listens. This feels like a natural next step to better capture evolving user preferences, especially considering how much historical data is already covered by the 50-item limit for most users.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\nuid2idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\n# Let us verify extracting user features for validation cutoff (24786800)\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 60 * day\n\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nliked_vps = vps[vps.state == \"liked\"]\ndisliked_vps = vps[vps.state == \"disliked\"]\n\n# Group likes and dislikes\nuser_likes = {u: [] for u in target_uids}\nfor u, item in zip(liked_vps.uid, liked_vps.item_id):\n if u in user_likes and item in item2idx:\n user_likes[u].append(item2idx[item])\n\nuser_dislikes = {u: [] for u in target_uids}\nfor u, item in zip(disliked_vps.uid, disliked_vps.item_id):\n if u in user_dislikes and item in item2idx:\n user_dislikes[u].append(item2idx[item])\n\nprint(\"Target users with likes:\", sum(len(l) > 0 for l in user_likes.values()))\nprint(\"Target users with dislikes:\", sum(len(d) > 0 for d in user_dislikes.values()))\n'",
"description": "Test validation preference state mapping"
}<untrusted_context>
Output: Target users with likes: 6769
Target users with dislikes: 4277
Process Group PGID: 2480
</untrusted_context>{
"description": "Test recent listens coverage per user",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 60 * day\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_start), (\"played_ratio_pct\", \">=\", 50)]\n)\n\nlistens = listens[listens.item_id.isin(item2idx)]\nuser_listens_count = listens.groupby(\"uid\").size()\nprint(\"Target users with positive catalog listens in last 60d:\", len(user_listens_count), \"/\", len(target_uids))\n'"
}<untrusted_context>
Output: Target users with positive catalog listens in last 60d: 8382 / 8456
Process Group PGID: 2498
</untrusted_context>Analyzing Remaining Users
I'm currently focused on understanding the remaining 74 users. I'm exploring extending the lookback window to 90 days or even all history to see if they meet the "at least 20 positive listens" threshold then. This should provide a clearer picture of their engagement.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 90 * day\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_start), (\"played_ratio_pct\", \">=\", 50)]\n)\nlistens = listens[listens.item_id.isin(item2idx)]\nuser_listens_count = listens.groupby(\"uid\").size()\nprint(\"Target users with positive catalog listens in last 90d:\", len(user_listens_count), \"/\", len(target_uids))\n'",
"description": "Test 90-day listens coverage"
}<untrusted_context>
Output: Target users with positive catalog listens in last 90d: 8443 / 8456
Process Group PGID: 2519
</untrusted_context>Analyzing User Data
My current focus is on processing recent user listening habits. I'm ensuring every user's most recent tracks are captured, drawing from up to 50 items per user for positive catalog listens. I'm now moving to writing the /app component.
Structuring Data Loading
I'm currently defining the data.py module to handle catalog and user data loading. This involves creating functions for loading the candidate catalog into tensors and dictionaries, and extracting user features like liked and disliked items based on various listening and preference paths.
Refining Data Processing
I'm now implementing the create_training_dataset function. This will generate training instances by sampling user features and positive target items across different temporal windows. A key feature is excluding previously seen items to ensure genuine discovery. I'm also defining the data structures for recent and organic items.
{
"file_path": "/app/solution/data.py",
"content": "\"\"\"Data loading, feature extraction, and dataset preparation for two-tower ranker.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nNUM_CATALOG_ITEMS = 30000\nPAD_INDEX = 30000\nMAX_LIKES = 50\nMAX_DISLIKES = 20\nMAX_RECENT = 50\nMAX_ORGANIC = 50\n\n\n@dataclass\nclass CatalogData:\n item2idx: Dict[int, int]\n idx2item: np.ndarray\n features: torch.Tensor # (30000, 4)\n log_p: torch.Tensor # (30000,)\n\n\n@dataclass\nclass UserHistoryTensors:\n liked_items: torch.Tensor # (N, MAX_LIKES) long\n disliked_items: torch.Tensor # (N, MAX_DISLIKES) long\n recent_items: torch.Tensor # (N, MAX_RECENT) long\n recent_weights: torch.Tensor # (N, MAX_RECENT) float\n recent_mask: torch.Tensor # (N, MAX_RECENT) bool\n organic_items: torch.Tensor # (N, MAX_ORGANIC) long\n\n\ndef load_catalog(data_dir: Path = DATA_DIR) -> CatalogData:\n \"\"\"Load candidate catalog, construct ID mappings, item features, and background priors.\"\"\"\n catalog = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n item2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n idx2item = catalog.item_id.to_numpy(dtype=np.int64)\n\n # Track length from train_listens\n listens = pd.read_parquet(data_dir / \"train_listens.parquet\", columns=[\"item_id\", \"track_length_seconds\"])\n track_lens = listens.drop_duplicates(\"item_id\").set_index(\"item_id\")[\"track_length_seconds\"]\n catalog_lens = catalog.item_id.map(track_lens).fillna(200.0).to_numpy(dtype=np.float32)\n\n # Extract catalog features\n pos_listens = catalog.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\n ranks = catalog.popularity_rank.to_numpy(dtype=np.float32)\n\n f1 = np.log1p(pos_listens)\n f2 = np.log1p(org_listens)\n f1 = (f1 - f1.mean()) / (f1.std() + 1e-6)\n f2 = (f2 - f2.mean()) / (f2.std() + 1e-6)\n f3 = 1.0 - (ranks / float(NUM_CATALOG_ITEMS))\n f4 = np.clip(catalog_lens / 300.0, 0.0, 5.0)\n\n features = torch.tensor(np.stack([f1, f2, f3, f4], axis=-1), dtype=torch.float32)\n\n # Popularity distribution for LogQ correction: p_j \\propto pos_listens^0.75\n weights = np.power(pos_listens, 0.75)\n probs = weights / weights.sum()\n log_p = torch.tensor(np.log(probs + 1e-12), dtype=torch.float32)\n\n return CatalogData(item2idx=item2idx, idx2item=idx2item, features=features, log_p=log_p)\n\n\ndef build_user_history_tensors(\n target_uids: List[int],\n item2idx: Dict[int, int],\n liked_dict: Dict[int, List[int]],\n disliked_dict: Dict[int, List[int]],\n recent_listens_dict: Dict[int, List[Tuple[int, float]]],\n organic_listens_dict: Dict[int, List[int]],\n) -> UserHistoryTensors:\n \"\"\"Pack user history dictionaries into dense 2D PyTorch tensors.\"\"\"\n N = len(target_uids)\n liked_items = torch.full((N, MAX_LIKES), PAD_INDEX, dtype=torch.long)\n disliked_items = torch.full((N, MAX_DISLIKES), PAD_INDEX, dtype=torch.long)\n recent_items = torch.full((N, MAX_RECENT), PAD_INDEX, dtype=torch.long)\n recent_weights = torch.zeros((N, MAX_RECENT), dtype=torch.float32)\n recent_mask = torch.zeros((N, MAX_RECENT), dtype=torch.bool)\n organic_items = torch.full((N, MAX_ORGANIC), PAD_INDEX, dtype=torch.long)\n\n for i, uid in enumerate(target_uids):\n # Liked items (up to MAX_LIKES)\n likes = liked_dict.get(uid, [])\n if likes:\n k = min(len(likes), MAX_LIKES)\n liked_items[i, :k] = torch.tensor(likes[:k], dtype=torch.long)\n\n # Disliked items (up to MAX_DISLIKES)\n dislikes = disliked_dict.get(uid, [])\n if dislikes:\n k = min(len(dislikes), MAX_DISLIKES)\n disliked_items[i, :k] = torch.tensor(dislikes[:k], dtype=torch.long)\n\n # Recent listens\n recents = recent_listens_dict.get(uid, [])\n if recents:\n k = min(len(recents), MAX_RECENT)\n items = [item for item, _ in recents[:k]]\n weights = [w for _, w in recents[:k]]\n recent_items[i, :k] = torch.tensor(items, dtype=torch.long)\n recent_weights[i, :k] = torch.tensor(weights, dtype=torch.float32)\n recent_mask[i, :k] = True\n\n # Organic listens\n organics = organic_listens_dict.get(uid, [])\n if organics:\n k = min(len(organics), MAX_ORGANIC)\n organic_items[i, :k] = torch.tensor(organics[:k], dtype=torch.long)\n\n return UserHistoryTensors(\n liked_items=liked_items,\n disliked_items=disliked_items,\n recent_items=recent_items,\n recent_weights=recent_weights,\n recent_mask=recent_mask,\n organic_items=organic_items,\n )\n\n\ndef extract_cutoff_history(\n cutoff_ts: int,\n target_uids: List[int],\n item2idx: Dict[int, int],\n preference_state_df: Optional[pd.DataFrame] = None,\n listens_df: Optional[pd.DataFrame] = None,\n likes_df: Optional[pd.DataFrame] = None,\n unlikes_df: Optional[pd.DataFrame] = None,\n dislikes_df: Optional[pd.DataFrame] = None,\n undislikes_df: Optional[pd.DataFrame] = None,\n lookback_days: int = 90,\n) -> Tuple[UserHistoryTensors, Dict[int, Set[int]]]:\n \"\"\"Extract user history up to cutoff_ts and return tensors and user history item sets.\"\"\"\n target_set = set(target_uids)\n day = 86400\n t_min = cutoff_ts - lookback_days * day\n\n # 1. Preferences\n liked_dict: Dict[int, List[int]] = {u: [] for u in target_uids}\n disliked_dict: Dict[int, List[int]] = {u: [] for u in target_uids}\n\n if preference_state_df is not None:\n liked_p = preference_state_df[preference_state_df.state == \"liked\"]\n for u, item in zip(liked_p.uid, liked_p.item_id):\n if u in target_set and item in item2idx:\n liked_dict[u].append(item2idx[item])\n disliked_p = preference_state_df[preference_state_df.state == \"disliked\"]\n for u, item in zip(disliked_p.uid, disliked_p.item_id):\n if u in target_set and item in item2idx:\n disliked_dict[u].append(item2idx[item])\n else:\n # Reconstruct from likes/unlikes and dislikes/undislikes before cutoff\n if likes_df is not None and unlikes_df is not None:\n l = likes_df[(likes_df.uid.isin(target_set)) & (likes_df.timestamp < cutoff_ts)][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=1)\n u = unlikes_df[(unlikes_df.uid.isin(target_set)) & (unlikes_df.timestamp < cutoff_ts)][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=0)\n comb = pd.concat([l, u]).sort_values(\"timestamp\")\n last_act = comb.groupby([\"uid\", \"item_id\"]).last()\n for (uid, item_id), row in last_act.iterrows():\n if row.act == 1 and item_id in item2idx:\n liked_dict[uid].append(item2idx[item_id])\n\n if dislikes_df is not None and undislikes_df is not None:\n d = dislikes_df[(dislikes_df.uid.isin(target_set)) & (dislikes_df.timestamp < cutoff_ts)][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=1)\n ud = undislikes_df[(undislikes_df.uid.isin(target_set)) & (undislikes_df.timestamp < cutoff_ts)][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=0)\n comb_d = pd.concat([d, ud]).sort_values(\"timestamp\")\n last_act_d = comb_d.groupby([\"uid\", \"item_id\"]).last()\n for (uid, item_id), row in last_act_d.iterrows():\n if row.act == 1 and item_id in item2idx:\n disliked_dict[uid].append(item2idx[item_id])\n\n # 2. Listens\n recent_listens_dict: Dict[int, List[Tuple[int, float]]] = {u: [] for u in target_uids}\n organic_listens_dict: Dict[int, List[int]] = {u: [] for u in target_uids}\n history_items: Dict[int, Set[int]] = {u: set(liked_dict[u]) for u in target_uids}\n\n if listens_df is not None:\n sub = listens_df[(listens_df.uid.isin(target_set)) & (listens_df.timestamp < cutoff_ts) & (listens_df.timestamp >= t_min)]\n # Filter to catalog items and positive listens\n sub = sub[(sub.item_id.isin(item2idx)) & (sub.played_ratio_pct >= 50)].copy()\n sub[\"item_idx\"] = sub.item_id.map(item2idx)\n sub = sub.sort_values(\"timestamp\", ascending=False)\n\n # Track all history items for discovery filtering\n for u, item in zip(sub.uid, sub.item_id):\n history_items[u].add(item)\n\n # Build recent listens with exponential recency decay\n for uid, grp in sub.groupby(\"uid\"):\n r_list = []\n org_list = []\n for _, row in grp.iterrows():\n item_idx = int(row.item_idx)\n dt_days = (cutoff_ts - row.timestamp) / 86400.0\n recency_weight = math.exp(-0.05 * dt_days)\n if row.is_organic == 1:\n recency_weight *= 1.5\n if len(org_list) < MAX_ORGANIC:\n org_list.append(item_idx)\n if len(r_list) < MAX_RECENT:\n r_list.append((item_idx, recency_weight))\n recent_listens_dict[uid] = r_list\n organic_listens_dict[uid] = org_list\n\n tensors = build_user_history_tensors(\n target_uids=target_uids,\n item2idx=item2idx,\n liked_dict=liked_dict,\n disliked_dict=disliked_dict,\n recent_listens_dict=recent_listens_dict,\n organic_listens_dict=organic_listens_dict,\n )\n return tensors, history_items\n\n\nclass TwoTowerDataset(Dataset):\n \"\"\"PyTorch Dataset for In-Batch Negatives training.\"\"\"\n\n def __init__(\n self,\n history_tensors: UserHistoryTensors,\n user_row_indices: np.ndarray,\n target_item_indices: np.ndarray,\n weights: np.ndarray,\n ):\n self.history_tensors = history_tensors\n self.user_rows = torch.tensor(user_row_indices, dtype=torch.long)\n self.target_items = torch.tensor(target_item_indices, dtype=torch.long)\n self.weights = torch.tensor(weights, dtype=torch.float32)\n\n def __len__(self) -> int:\n return len(self.user_rows)\n\n def __getitem__(self, idx: int):\n u_row = self.user_rows[idx]\n return (\n self.history_tensors.liked_items[u_row],\n self.history_tensors.disliked_items[u_row],\n self.history_tensors.recent_items[u_row],\n self.history_tensors.recent_weights[u_row],\n self.history_tensors.recent_mask[u_row],\n self.history_tensors.organic_items[u_row],\n self.target_items[idx],\n self.weights[idx],\n )\n"
}Successfully created and wrote to new file: /app/solution/data.py. Here is the updated code:
"""Data loading, feature extraction, and dataset preparation for two-tower ranker."""
from __future__ import annotations
import math
from dataclasses import dataclass
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
DATA_DIR = Path("/task/data")
NUM_CATALOG_ITEMS = 30000
PAD_INDEX = 30000
MAX_LIKES = 50
MAX_DISLIKES = 20
MAX_RECENT = 50
MAX_ORGANIC = 50
@dataclass
class CatalogData:
item2idx: Dict[int, int]
idx2item: np.ndarray
features: torch.Tensor # (30000, 4)
log_p: torch.Tensor # (30000,)
@dataclass
class UserHistoryTensors:
liked_items: torch.Tensor # (N, MAX_LIKES) long
disliked_items: torch.Tensor # (N, MAX_DISLIKES) long
recent_items: torch.Tensor # (N, MAX_RECENT) long
recent_weights: torch.Tensor # (N, MAX_RECENT) float
recent_mask: torch.Tensor # (N, MAX_RECENT) bool
organic_items: torch.Tensor # (N, MAX_ORGANIC) long
def load_catalog(data_dir: Path = DATA_DIR) -> CatalogData:
"""Load candidate catalog, construct ID mappings, item features, and background priors."""
catalog = pd.read_parquet(data_dir / "candidate_catalog.parquet")
item2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}
idx2item = catalog.item_id.to_numpy(dtype=np.int64)
# Track length from train_listens
listens = pd.read_parquet(data_dir / "train_listens.parquet", columns=["item_id", "track_length_seconds"])
track_lens = listens.drop_duplicates("item_id").set_index("item_id")["track_length_seconds"]
catalog_lens = catalog.item_id.map(track_lens).fillna(200.0).to_numpy(dtype=np.float32)
# Extract catalog features
pos_listens = catalog.positive_listens.to_numpy(dtype=np.float32)
org_listens = catalog.organic_positive_listens.to_numpy(dtype=np.float32)
ranks = catalog.popularity_rank.to_numpy(dtype=np.float32)
f1 = np.log1p(pos_listens)
f2 = np.log1p(org_listens)
f1 = (f1 - f1.mean()) / (f1.std() + 1e-6)
f2 = (f2 - f2.mean()) / (f2.std() + 1e-6)
f3 = 1.0 - (ranks / float(NUM_CATALOG_ITEMS))
f4 = np.clip(catalog_lens / 300.0, 0.0, 5.0)
features = torch.tensor(np.stack([f1, f2, f3, f4], axis=-1), dtype=torch.float32)
# Popularity distribution for LogQ correction: p_j \propto pos_listens^0.75
weights = np.power(pos_listens, 0.75)
probs = weights / weights.sum()
log_p = torch.tensor(np.log(probs + 1e-12), dtype=torch.float32)
return CatalogData(item2idx=item2idx, idx2item=idx2item, features=features, log_p=log_p)
def build_user_history_tensors(
target_uids: List[int],
item2idx: Dict[int, int],
liked_dict: Dict[int, List[int]],
disliked_dict: Dict[int, List[int]],
recent_listens_dict: Dict[int, List[Tuple[int, float]]],
organic_listens_dict: Dict[int, List[int]],
) -> UserHistoryTensors:
"""Pack user history dictionaries into dense 2D PyTorch tensors."""
N = len(target_uids)
liked_items = torch.full((N, MAX_LIKES), PAD_INDEX, dtype=torch.long)
disliked_items = torch.full((N, MAX_DISLIKES), PAD_INDEX, dtype=torch.long)
recent_items = torch.full((N, MAX_RECENT), PAD_INDEX, dtype=torch.long)
recent_weights = torch.zeros((N, MAX_RECENT), dtype=torch.float32)
recent_mask = torch.zeros((N, MAX_RECENT), dtype=torch.bool)
organic_items = torch.full((N, MAX_ORGANIC), PAD_INDEX, dtype=torch.long)
for i, uid in enumerate(target_uids):
# Liked items (up to MAX_LIKES)
likes = liked_dict.get(uid, [])
if likes:
k = min(len(likes), MAX_LIKES)
liked_items[i, :k] = torch.tensor(likes[:k], dtype=torch.long)
# Disliked items (up to MAX_DISLIKES)
dislikes = disliked_dict.get(uid, [])
if dislikes:
k = min(len(dislikes), MAX_DISLIKES)
disliked_items[i, :k] = torch.tensor(dislikes[:k], dtype=torch.long)
# Recent listens
recents = recent_listens_dict.get(uid, [])
if recents:
k = min(len(recents), MAX_RECENT)
items = [item for item, _ in recents[:k]]
weights = [w for _, w in recents[:k]]
recent_items[i, :k] = torch.tensor(items, dtype=torch.long)
recent_weights[i, :k] = torch.tensor(weights, dtype=torch.float32)
recent_mask[i, :k] = True
# Organic listens
organics = organic_listens_dict.get(uid, [])
if organics:
k = min(len(organics), MAX_ORGANIC)
organic_items[i, :k] = torch.tensor(organics[:k], dtype=torch.long)
return UserHistoryTensors(
liked_items=liked_items,
disliked_items=disliked_items,
recent_items=recent_items,
recent_weights=recent_weights,
recent_mask=recent_mask,
organic_items=organic_items,
)
def extract_cutoff_history(
cutoff_ts: int,
target_uids: List[int],
item2idx: Dict[int, int],
preference_state_df: Optional[pd.DataFrame] = None,
listens_df: Optional[pd.DataFrame] = None,
likes_df: Optional[pd.DataFrame] = None,
unlikes_df: Optional[pd.DataFrame] = None,
dislikes_df: Optional[pd.DataFrame] = None,
undislikes_df: Optional[pd.DataFrame] = None,
lookback_days: int = 90,
) -> Tuple[UserHistoryTensors, Dict[int, Set[int]]]:
"""Extract user history up to cutoff_ts and return tensors and user history item sets."""
target_set = set(target_uids)
day = 86400
t_min = cutoff_ts - lookback_days * day
# 1. Preferences
liked_dict: Dict[int, List[int]] = {u: [] for u in target_uids}
disliked_dict: Dict[int, List[int]] = {u: [] for u in target_uids}
if preference_state_df is not None:
liked_p = preference_state_df[preference_state_df.state == "liked"]
for u, item in zip(liked_p.uid, liked_p.item_id):
if u in target_set and item in item2idx:
liked_dict[u].append(item2idx[item])
disliked_p = preference_state_df[preference_state_df.state == "disliked"]
for u, item in zip(disliked_p.uid, disliked_p.item_id):
if u in target_set and item in item2idx:
disliked_dict[u].append(item2idx[item])
else:
# Reconstruct from likes/unlikes and dislikes/undislikes before cutoff
if likes_df is not None and unlikes_df is not None:
l = likes_df[(likes_df.uid.isin(target_set)) & (likes_df.timestamp < cutoff_ts)][["uid", "item_id", "timestamp"]].assign(act=1)
u = unlikes_df[(unlikes_df.uid.isin(target_set)) & (unlikes_df.timestamp < cutoff_ts)][["uid", "item_id", "timestamp"]].assign(act=0)
comb = pd.concat([l, u]).sort_values("timestamp")
last_act = comb.groupby(["uid", "item_id"]).last()
for (uid, item_id), row in last_act.iterrows():
if row.act == 1 and item_id in item2idx:
liked_dict[uid].append(item2idx[item_id])
if dislikes_df is not None and undislikes_df is not None:
d = dislikes_df[(dislikes_df.uid.isin(target_set)) & (dislikes_df.timestamp < cutoff_ts)][["uid", "item_id", "timestamp"]].assign(act=1)
ud = undislikes_df[(undislikes_df.uid.isin(target_set)) & (undislikes_df.timestamp < cutoff_ts)][["uid", "item_id", "timestamp"]].assign(act=0)
comb_d = pd.concat([d, ud]).sort_values("timestamp")
last_act_d = comb_d.groupby(["uid", "item_id"]).last()
for (uid, item_id), row in last_act_d.iterrows():
if row.act == 1 and item_id in item2idx:
disliked_dict[uid].append(item2idx[item_id])
# 2. Listens
recent_listens_dict: Dict[int, List[Tuple[int, float]]] = {u: [] for u in target_uids}
organic_listens_dict: Dict[int, List[int]] = {u: [] for u in target_uids}
history_items: Dict[int, Set[int]] = {u: set(liked_dict[u]) for u in target_uids}
if listens_df is not None:
sub = listens_df[(listens_df.uid.isin(target_set)) & (listens_df.timestamp < cutoff_ts) & (listens_df.timestamp >= t_min)]
# Filter to catalog items and positive listens
sub = sub[(sub.item_id.isin(item2idx)) & (sub.played_ratio_pct >= 50)].copy()
sub["item_idx"] = sub.item_id.map(item2idx)
sub = sub.sort_values("timestamp", ascending=False)
# Track all history items for discovery filtering
for u, item in zip(sub.uid, sub.item_id):
history_items[u].add(item)
# Build recent listens with exponential recency decay
for uid, grp in sub.groupby("uid"):
r_list = []
org_list = []
for _, row in grp.iterrows():
item_idx = int(row.item_idx)
dt_days = (cutoff_ts - row.timestamp) / 86400.0
recency_weight = math.exp(-0.05 * dt_days)
if row.is_organic == 1:
recency_weight *= 1.5
if len(org_list) < MAX_ORGANIC:
org_list.append(item_idx)
if len(r_list) < MAX_RECENT:
r_list.append((item_idx, recency_weight))
recent_listens_dict[uid] = r_list
organic_listens_dict[uid] = org_list
tensors = build_user_history_tensors(
target_uids=target_uids,
item2idx=item2idx,
liked_dict=liked_dict,
disliked_dict=disliked_dict,
recent_listens_dict=recent_listens_dict,
organic_listens_dict=organic_listens_dict,
)
return tensors, history_items
class TwoTowerDataset(Dataset):
"""PyTorch Dataset for In-Batch Negatives training."""
def __init__(
self,
history_tensors: UserHistoryTensors,
user_row_indices: np.ndarray,
target_item_indices: np.ndarray,
weights: np.ndarray,
):
self.history_tensors = history_tensors
self.user_rows = torch.tensor(user_row_indices, dtype=torch.long)
self.target_items = torch.tensor(target_item_indices, dtype=torch.long)
self.weights = torch.tensor(weights, dtype=torch.float32)
def __len__(self) -> int:
return len(self.user_rows)
def __getitem__(self, idx: int):
u_row = self.user_rows[idx]
return (
self.history_tensors.liked_items[u_row],
self.history_tensors.disliked_items[u_row],
self.history_tensors.recent_items[u_row],
self.history_tensors.recent_weights[u_row],
self.history_tensors.recent_mask[u_row],
self.history_tensors.organic_items[u_row],
self.target_items[idx],
self.weights[idx],
)
Exploring Data Combination
I'm currently investigating how to merge multiple training windows within the TwoTowerDataset. My focus is on combining individual UserHistoryTensors, user_row_indices, target_item_indices, and weights from each window into a unified structure for training.
Combining Datasets Seamlessly
I've confirmed that torch.utils.data.ConcatDataset is the ideal solution for merging my training windows. It elegantly handles the collation of tensors and labels from each sub-dataset, fitting perfectly with the DataLoader without requiring a custom collate function. I'm now proceeding to implement this in data.py.
Defining Training Windows
I am now structuring my training windows for validation, progressively shifting back in time. This approach will enable seamless refitting for the test set by simply realigning the existing validation windows. My next step involves implementing this in Python and adding prepare_training_datasets.
{
"description": "Verify extract_cutoff_history on validation cutoff",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nfrom data import load_catalog, extract_cutoff_history\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\nprint(\"Catalog loaded. Features shape:\", catalog_data.features.shape)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60*86400), (\"played_ratio_pct\", \">=\", 50)])\n\ntensors, hist_items = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=listens,\n)\nprint(\"Extracted tensors: liked shape:\", tensors.liked_items.shape, \"recent shape:\", tensors.recent_items.shape)\n'"
}<untrusted_context>
Output: Catalog loaded. Features shape: torch.Size([30000, 4])
Extracted tensors: liked shape: torch.Size([8456, 50]) recent shape: torch.Size([8456, 50])
Process Group PGID: 2540
</untrusted_context>Processing Data Swiftly
I've successfully loaded the catalog and extracted the tensors, completing this stage in under two seconds. My next focus is to finalize the data.py script by implementing the build_window_training_data function.
{
"description": "Test window target pair generation",
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = set(tu_df.uid)\nuid2row = {int(uid): idx for idx, uid in enumerate(tu_df.uid)}\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_end = cutoff\nt_start = cutoff - 7 * day\n\n# Previous history before t_start\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"played_ratio_pct\"], filters=[(\"uid\", \"in\", list(target_uids)), (\"timestamp\", \">=\", t_start - 60*day), (\"timestamp\", \"<\", t_start), (\"played_ratio_pct\", \">=\", 50)])\nhist_set = set(zip(listens.uid, listens.item_id))\n\n# Targets\ntarget_listens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"uid\", \"in\", list(target_uids)), (\"timestamp\", \">=\", t_start), (\"timestamp\", \"<\", t_end), (\"played_ratio_pct\", \">=\", 80), (\"is_organic\", \"==\", 1)])\ntarget_likes = pd.read_parquet(DATA / \"train_likes.parquet\", filters=[(\"uid\", \"in\", list(target_uids)), (\"timestamp\", \">=\", t_start), (\"timestamp\", \"<\", t_end), (\"is_organic\", \"==\", 1)])\n\n# Combine\npairs = {} # (uid, item_id) -> weight\nfor u, item in zip(target_listens.uid, target_listens.item_id):\n if item in item2idx and (u, item) not in hist_set:\n pairs[(u, item)] = 1.0\n\nfor u, item in zip(target_likes.uid, target_likes.item_id):\n if item in item2idx and (u, item) not in hist_set:\n pairs[(u, item)] = 3.0 # likes take precedence\n\nprint(f\"Window targets count: {len(pairs)}, unique users: {len(set(u for u, _ in pairs.keys()))}\")\n'"
}<untrusted_context>
Output: Window targets count: 64740, unique users: 5091
Process Group PGID: 2566
</untrusted_context>Analyzing Discovery Success
I'm currently processing a substantial influx of 64,740 discovery targets from 5,091 users over the past week. My next step is to explore incorporating recent positive listens as training targets for self-supervised next-item prediction, drawing inspiration from models like SASRec and YouTube DNN.
Developing Item Tower
I'm focusing on building the ItemTower module, designed to process item indices and candidate features to generate normalized embeddings. My immediate task is to implement the precomputation step, which will calculate and store these unit-normalized item representations for the entire catalog.
Refining UserTower Architecture
I'm currently refining the UserTower architecture, focusing on pooling strategies like attention and weighted sums for recent item interactions. The plan is to concatenate these aggregated user representations and pass them through a multi-layer perceptron before normalizing, aiming for more nuanced user embeddings.
{
"content": "\"\"\"Two-tower neural network ranker architecture for latency-efficient MIPS retrieval.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nPAD_INDEX = 30000\nNUM_CATALOG_ITEMS = 30000\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower producing independently precomputable candidate embeddings.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n feature_dim: int = 4,\n out_dim: int = 128,\n ):\n super().__init__()\n self.num_items = num_items\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n\n # Item embedding table (+1 for PAD_INDEX at 30000)\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[PAD_INDEX].zero_()\n\n # Item feature projection\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n\n # Output MLP\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(\n self,\n item_ids: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute item embeddings for a batch of item IDs.\n\n Args:\n item_ids: 1D tensor of item indices (0 to num_items-1).\n features: Optional tensor of shape (B, feature_dim) or (num_items, feature_dim).\n\n Returns:\n L2-normalized item vectors of shape (B, out_dim).\n \"\"\"\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n out = self.mlp(e)\n return F.normalize(out, p=2, dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all catalog items offline.\"\"\"\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n batch_ids = all_ids[start:end]\n batch_feats = catalog_features[start:end]\n vecs = self.forward(batch_ids, batch_feats)\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user representations from interaction history.\"\"\"\n\n def __init__(\n self,\n item_emb: nn.Embedding,\n embed_dim: int = 128,\n out_dim: int = 128,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n\n # Attention pooling query vector\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n\n # Multi-component MLP: inputs are [likes, dislikes, recent_weighted, recent_attn, organic]\n # Total input dimension = 5 * embed_dim\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, out_dim),\n )\n\n def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:\n \"\"\"Masked mean pooling over items tensor of shape (B, max_len).\"\"\"\n mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)\n emb = self.item_emb(items) # (B, max_len, embed_dim)\n counts = mask.sum(dim=1).clamp(min=1) # (B, 1)\n return (emb * mask).sum(dim=1) / counts # (B, embed_dim)\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute user representations from interaction histories.\"\"\"\n # 1. Explicit likes pool\n emb_liked = self._mean_pool(liked_items)\n\n # 2. Explicit dislikes pool\n emb_disliked = self._mean_pool(disliked_items)\n\n # 3. Recent listens: recency-weighted pool\n recent_emb = self.item_emb(recent_items) # (B, K, embed_dim)\n rw = recent_weights.unsqueeze(-1) # (B, K, 1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum\n\n # 4. Recent listens: learned attention pooling\n scores = torch.matmul(recent_emb, self.query_vector) / math.sqrt(self.embed_dim) # (B, K)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_weights = F.softmax(scores, dim=-1).unsqueeze(-1) # (B, K, 1)\n emb_recent_attn = (attn_weights * recent_emb).sum(dim=1)\n\n # 5. Organic positive listens pool\n emb_organic = self._mean_pool(organic_items)\n\n # Concatenate all representations\n h = torch.cat(\n [emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic],\n dim=-1,\n )\n\n out = self.mlp(h)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Container for ItemTower and UserTower with tied embeddings.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n out_dim: int = 128,\n feature_dim: int = 4,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_tower = ItemTower(\n num_items=num_items,\n embed_dim=embed_dim,\n feature_dim=feature_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_emb=self.item_tower.item_emb,\n embed_dim=embed_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n target_items: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n user_vecs = self.user_tower(\n liked_items=liked_items,\n disliked_items=disliked_items,\n recent_items=recent_items,\n recent_weights=recent_weights,\n recent_mask=recent_mask,\n organic_items=organic_items,\n )\n item_vecs = self.item_tower(target_items, features)\n return user_vecs, item_vecs\n\n\nclass InBatchNegativesLoss(nn.Module):\n \"\"\"Sampled Softmax In-Batch Negatives loss with LogQ popularity debiasing.\"\"\"\n\n def __init__(self, temperature: float = 0.07):\n super().__init__()\n self.temperature = temperature\n\n def forward(\n self,\n user_vecs: torch.Tensor,\n item_vecs: torch.Tensor,\n target_items: torch.Tensor,\n weights: torch.Tensor,\n log_p: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted in-batch negatives cross-entropy loss.\n\n Args:\n user_vecs: (B, D) unit-norm user vectors.\n item_vecs: (B, D) unit-norm item vectors.\n target_items: (B,) catalog item indices.\n weights: (B,) instance weights (e.g. 3.0 for like, 1.0 for listen).\n log_p: (num_items,) background log popularity distribution.\n\n Returns:\n Scalar loss.\n \"\"\"\n B = user_vecs.shape[0]\n # Cosine similarity matrix\n sim = torch.mm(user_vecs, item_vecs.t()) / self.temperature # (B, B)\n\n # LogQ correction: subtract log(p_j) from column j\n item_log_p = log_p[target_items] # (B,)\n sim = sim - item_log_p.unsqueeze(0)\n\n # Target label for user i is item i\n targets = torch.arange(B, device=user_vecs.device, dtype=torch.long)\n per_sample_loss = F.cross_entropy(sim, targets, reduction=\"none\")\n\n return (per_sample_loss * weights).mean()\n",
"file_path": "/app/solution/model.py"
}Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:
"""Two-tower neural network ranker architecture for latency-efficient MIPS retrieval."""
from __future__ import annotations
import math
from typing import Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
PAD_INDEX = 30000
NUM_CATALOG_ITEMS = 30000
class ItemTower(nn.Module):
"""Item Tower producing independently precomputable candidate embeddings."""
def __init__(
self,
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
feature_dim: int = 4,
out_dim: int = 128,
):
super().__init__()
self.num_items = num_items
self.embed_dim = embed_dim
self.out_dim = out_dim
# Item embedding table (+1 for PAD_INDEX at 30000)
self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)
nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)
with torch.no_grad():
self.item_emb.weight[PAD_INDEX].zero_()
# Item feature projection
self.feat_proj = nn.Sequential(
nn.Linear(feature_dim, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, embed_dim),
)
# Output MLP
self.mlp = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
def forward(
self,
item_ids: torch.Tensor,
features: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Compute item embeddings for a batch of item IDs.
Args:
item_ids: 1D tensor of item indices (0 to num_items-1).
features: Optional tensor of shape (B, feature_dim) or (num_items, feature_dim).
Returns:
L2-normalized item vectors of shape (B, out_dim).
"""
e = self.item_emb(item_ids)
if features is not None:
if features.shape[0] == self.num_items and item_ids.dim() == 1:
batch_feats = features[item_ids]
else:
batch_feats = features
f = self.feat_proj(batch_feats)
e = e + f
out = self.mlp(e)
return F.normalize(out, p=2, dim=-1)
@torch.no_grad()
def precompute_all_items(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute candidate item embeddings for all catalog items offline."""
self.eval()
all_ids = torch.arange(self.num_items, dtype=torch.long)
vectors = []
for start in range(0, self.num_items, batch_size):
end = min(start + batch_size, self.num_items)
batch_ids = all_ids[start:end]
batch_feats = catalog_features[start:end]
vecs = self.forward(batch_ids, batch_feats)
vectors.append(vecs)
return torch.cat(vectors, dim=0)
class UserTower(nn.Module):
"""User Tower deriving user representations from interaction history."""
def __init__(
self,
item_emb: nn.Embedding,
embed_dim: int = 128,
out_dim: int = 128,
dropout: float = 0.1,
):
super().__init__()
self.item_emb = item_emb
self.embed_dim = embed_dim
self.out_dim = out_dim
# Attention pooling query vector
self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)
# Multi-component MLP: inputs are [likes, dislikes, recent_weighted, recent_attn, organic]
# Total input dimension = 5 * embed_dim
in_dim = 5 * embed_dim
self.mlp = nn.Sequential(
nn.Linear(in_dim, 2 * embed_dim),
nn.LayerNorm(2 * embed_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(2 * embed_dim, out_dim),
)
def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:
"""Masked mean pooling over items tensor of shape (B, max_len)."""
mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)
emb = self.item_emb(items) # (B, max_len, embed_dim)
counts = mask.sum(dim=1).clamp(min=1) # (B, 1)
return (emb * mask).sum(dim=1) / counts # (B, embed_dim)
def forward(
self,
liked_items: torch.Tensor,
disliked_items: torch.Tensor,
recent_items: torch.Tensor,
recent_weights: torch.Tensor,
recent_mask: torch.Tensor,
organic_items: torch.Tensor,
) -> torch.Tensor:
"""Compute user representations from interaction histories."""
# 1. Explicit likes pool
emb_liked = self._mean_pool(liked_items)
# 2. Explicit dislikes pool
emb_disliked = self._mean_pool(disliked_items)
# 3. Recent listens: recency-weighted pool
recent_emb = self.item_emb(recent_items) # (B, K, embed_dim)
rw = recent_weights.unsqueeze(-1) # (B, K, 1)
rw_sum = rw.sum(dim=1).clamp(min=1e-6)
emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum
# 4. Recent listens: learned attention pooling
scores = torch.matmul(recent_emb, self.query_vector) / math.sqrt(self.embed_dim) # (B, K)
scores = scores.masked_fill(~recent_mask, -1e9)
attn_weights = F.softmax(scores, dim=-1).unsqueeze(-1) # (B, K, 1)
emb_recent_attn = (attn_weights * recent_emb).sum(dim=1)
# 5. Organic positive listens pool
emb_organic = self._mean_pool(organic_items)
# Concatenate all representations
h = torch.cat(
[emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic],
dim=-1,
)
out = self.mlp(h)
return F.normalize(out, p=2, dim=-1)
class TwoTowerRanker(nn.Module):
"""Container for ItemTower and UserTower with tied embeddings."""
def __init__(
self,
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
out_dim: int = 128,
feature_dim: int = 4,
dropout: float = 0.1,
):
super().__init__()
self.item_tower = ItemTower(
num_items=num_items,
embed_dim=embed_dim,
feature_dim=feature_dim,
out_dim=out_dim,
)
self.user_tower = UserTower(
item_emb=self.item_tower.item_emb,
embed_dim=embed_dim,
out_dim=out_dim,
dropout=dropout,
)
def forward(
self,
liked_items: torch.Tensor,
disliked_items: torch.Tensor,
recent_items: torch.Tensor,
recent_weights: torch.Tensor,
recent_mask: torch.Tensor,
organic_items: torch.Tensor,
target_items: torch.Tensor,
features: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
user_vecs = self.user_tower(
liked_items=liked_items,
disliked_items=disliked_items,
recent_items=recent_items,
recent_weights=recent_weights,
recent_mask=recent_mask,
organic_items=organic_items,
)
item_vecs = self.item_tower(target_items, features)
return user_vecs, item_vecs
class InBatchNegativesLoss(nn.Module):
"""Sampled Softmax In-Batch Negatives loss with LogQ popularity debiasing."""
def __init__(self, temperature: float = 0.07):
super().__init__()
self.temperature = temperature
def forward(
self,
user_vecs: torch.Tensor,
item_vecs: torch.Tensor,
target_items: torch.Tensor,
weights: torch.Tensor,
log_p: torch.Tensor,
) -> torch.Tensor:
"""Compute weighted in-batch negatives cross-entropy loss.
Args:
user_vecs: (B, D) unit-norm user vectors.
item_vecs: (B, D) unit-norm item vectors.
target_items: (B,) catalog item indices.
weights: (B,) instance weights (e.g. 3.0 for like, 1.0 for listen).
log_p: (num_items,) background log popularity distribution.
Returns:
Scalar loss.
"""
B = user_vecs.shape[0]
# Cosine similarity matrix
sim = torch.mm(user_vecs, item_vecs.t()) / self.temperature # (B, B)
# LogQ correction: subtract log(p_j) from column j
item_log_p = log_p[target_items] # (B,)
sim = sim - item_log_p.unsqueeze(0)
# Target label for user i is item i
targets = torch.arange(B, device=user_vecs.device, dtype=torch.long)
per_sample_loss = F.cross_entropy(sim, targets, reduction="none")
return (per_sample_loss * weights).mean()
{
"description": "Test TwoTowerRanker forward and backward pass",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nfrom model import TwoTowerRanker, InBatchNegativesLoss\nfrom data import PAD_INDEX, NUM_CATALOG_ITEMS\n\nranker = TwoTowerRanker(num_items=NUM_CATALOG_ITEMS, embed_dim=128, out_dim=128)\ncriterion = InBatchNegativesLoss(temperature=0.07)\n\nB = 16\nliked = torch.randint(0, NUM_CATALOG_ITEMS, (B, 50))\ndisliked = torch.randint(0, NUM_CATALOG_ITEMS, (B, 20))\nrecent = torch.randint(0, NUM_CATALOG_ITEMS, (B, 50))\nrecent_w = torch.rand(B, 50)\nrecent_m = torch.ones(B, 50, dtype=torch.bool)\norganic = torch.randint(0, NUM_CATALOG_ITEMS, (B, 50))\ntargets = torch.randint(0, NUM_CATALOG_ITEMS, (B,))\nweights = torch.ones(B)\nlog_p = torch.randn(NUM_CATALOG_ITEMS)\nfeats = torch.randn(NUM_CATALOG_ITEMS, 4)\n\nu_vecs, i_vecs = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, feats)\nprint(\"u_vecs shape:\", u_vecs.shape, \"norm:\", torch.norm(u_vecs, dim=-1))\nprint(\"i_vecs shape:\", i_vecs.shape, \"norm:\", torch.norm(i_vecs, dim=-1))\n\nloss = criterion(u_vecs, i_vecs, targets, weights, log_p)\nprint(\"Loss:\", loss.item())\n\nloss.backward()\nprint(\"Backward successful!\")\n'"
}<untrusted_context>
Output: u_vecs shape: torch.Size([16, 128]) norm: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
grad_fn=<LinalgVectorNormBackward0>)
i_vecs shape: torch.Size([16, 128]) norm: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000,
1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000, 1.0000],
grad_fn=<LinalgVectorNormBackward0>)
Loss: 3.365292549133301
Backward successful!
Process Group PGID: 2589
</untrusted_context>Building Evaluation Script
I've just confirmed the last test passed successfully. My next step is to begin constructing the evaluate.py script. I'm currently defining its core functionality, focusing on how it will utilize the trained TwoTowerRanker, CatalogData (including features, item2idx, and idx2item), and validation set.
Developing Evaluation Logic
I'm now working on the evaluate_model function within evaluate.py. My focus is on computing item and user vectors, then calculating their dot product for scoring. I'm also integrating the eligibility mask and will soon implement the top-k selection.
{
"description": "Benchmark vectorized mask unpacking",
"command": "python3 -c '\nimport numpy as np\nimport torch\nimport time\n\n# Test unpacking 8456 rows of packed bits\nwith np.load(\"/task/data/validation_eligible_candidates.npz\") as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\n\nt0 = time.time()\nmask = np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool)\nt1 = time.time()\nprint(f\"Unpacked mask shape: {mask.shape} in {t1 - t0:.4f} s\")\n'"
}<untrusted_context>
Output: Unpacked mask shape: (8456, 30000) in 0.0563 s
Process Group PGID: 2603
</untrusted_context>Optimizing Performance Now
I've successfully optimized the retrieval and ranking process, achieving a remarkable 0.0563 seconds for 8,456 users. The dataframe construction is now incredibly swift.
{
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport time\n\nt0 = time.time()\nN = 8456\ntarget_uids = np.arange(N)\ntopk = np.random.randint(0, 30000, (N, 100))\nuid_col = np.repeat(target_uids, 100)\nitem_col = topk.flatten()\nrank_col = np.tile(np.arange(1, 101, dtype=np.int64), N)\n\ndf = pd.DataFrame({\"uid\": uid_col, \"item_id\": item_col, \"rank\": rank_col})\ndf.to_parquet(\"/app/solution/test_speed.parquet\", index=False)\nt1 = time.time()\nprint(f\"Dataframe created and saved in {t1 - t0:.4f} s\")\n'",
"description": "Benchmark dataframe construction and parquet saving"
}<untrusted_context>
Output: Dataframe created and saved in 0.1095 s
Process Group PGID: 2613
</untrusted_context>{
"command": "rm -f /app/solution/test_speed.parquet",
"description": "Remove temporary speed test file"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2624
</untrusted_context>Writing Evaluation Script
I am currently focused on developing the /app/solution/evaluate.py script. My immediate task is to import the ranking_evaluation module from the /task directory. Following that, I will implement the logic to score the output against the validation ground truth and then proceed with validating the ranking itself.
{
"content": "\"\"\"Evaluation module for two-tower ranker.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, score_rankings, validate_ranking\nfrom evaluate_public import validation_truth\n\nfrom data import CatalogData, UserHistoryTensors\nfrom model import TwoTowerRanker\n\nDATA_DIR = Path(\"/task/data\")\n\n\[email protected]_grad()\ndef generate_ranking_dataframe(\n ranker: TwoTowerRanker,\n catalog_data: CatalogData,\n user_tensors: UserHistoryTensors,\n target_uids: List[int],\n eligibility_npz_path: Path,\n batch_size: int = 1024,\n) -> pd.DataFrame:\n \"\"\"Generate top-100 ranking dataframe for all target users using MIPS retrieval.\"\"\"\n ranker.eval()\n\n # 1. Precompute item embeddings offline\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features) # (30000, D)\n\n # 2. Compute user embeddings in batches\n N = len(target_uids)\n user_vec_list = []\n for start in range(0, N, batch_size):\n end = min(start + batch_size, N)\n u_vec = ranker.user_tower(\n liked_items=user_tensors.liked_items[start:end],\n disliked_items=user_tensors.disliked_items[start:end],\n recent_items=user_tensors.recent_items[start:end],\n recent_weights=user_tensors.recent_weights[start:end],\n recent_mask=user_tensors.recent_mask[start:end],\n organic_items=user_tensors.organic_items[start:end],\n )\n user_vec_list.append(u_vec)\n user_vecs = torch.cat(user_vec_list, dim=0) # (N, D)\n\n # 3. MIPS: dot product scores\n scores = torch.mm(user_vecs, item_vecs.t()) # (N, 30000)\n\n # 4. Apply eligibility mask\n with np.load(eligibility_npz_path, allow_pickle=False) as artifact:\n packed = artifact[\"packed_eligible\"]\n item_ids = artifact[\"item_ids\"]\n # Ensure candidate items are aligned\n assert np.array_equal(item_ids, catalog_data.idx2item), \"Candidate items misaligned!\"\n\n mask = np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool)\n torch_mask = torch.from_numpy(mask)\n scores[~torch_mask] = -float(\"inf\")\n\n # 5. Top 100 items per user\n _, topk_indices = torch.topk(scores, 100, dim=1)\n topk_np = topk_indices.numpy()\n\n uid_col = np.repeat(np.array(target_uids, dtype=np.int64), 100)\n item_col = catalog_data.idx2item[topk_np.flatten()]\n rank_col = np.tile(np.arange(1, 101, dtype=np.int64), N)\n\n df = pd.DataFrame({\"uid\": uid_col, \"item_id\": item_col, \"rank\": rank_col})\n return df\n\n\ndef evaluate_on_validation(\n ranker: TwoTowerRanker,\n catalog_data: CatalogData,\n val_user_tensors: UserHistoryTensors,\n target_uids: List[int],\n output_parquet_path: Optional[Path] = None,\n) -> Dict[str, float | int]:\n \"\"\"Evaluate current model on public validation set and return score dict.\"\"\"\n val_eligibility_path = DATA_DIR / \"validation_eligible_candidates.npz\"\n targets_set = set(target_uids)\n eligibility = CandidateSet(val_eligibility_path)\n graded = validation_truth(eligibility, targets_set)\n\n ranking_df = generate_ranking_dataframe(\n ranker=ranker,\n catalog_data=catalog_data,\n user_tensors=val_user_tensors,\n target_uids=target_uids,\n eligibility_npz_path=val_eligibility_path,\n )\n\n # Validate ranking contract\n validated_df = validate_ranking(ranking_df, targets_set)\n\n if output_parquet_path is not None:\n validated_df.to_parquet(output_parquet_path, index=False)\n\n # Extract ordered items per user\n ordered = validated_df.sort_values([\"uid\", \"rank\"])\n ranked_dict = {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n scores = score_rankings(ranked_dict, graded)\n return scores\n",
"file_path": "/app/solution/evaluate.py"
}Successfully created and wrote to new file: /app/solution/evaluate.py. Here is the updated code:
"""Evaluation module for two-tower ranker."""
from __future__ import annotations
import sys
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import numpy as np
import pandas as pd
import torch
sys.path.append("/task")
from ranking_evaluation import CandidateSet, score_rankings, validate_ranking
from evaluate_public import validation_truth
from data import CatalogData, UserHistoryTensors
from model import TwoTowerRanker
DATA_DIR = Path("/task/data")
@torch.no_grad()
def generate_ranking_dataframe(
ranker: TwoTowerRanker,
catalog_data: CatalogData,
user_tensors: UserHistoryTensors,
target_uids: List[int],
eligibility_npz_path: Path,
batch_size: int = 1024,
) -> pd.DataFrame:
"""Generate top-100 ranking dataframe for all target users using MIPS retrieval."""
ranker.eval()
# 1. Precompute item embeddings offline
item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features) # (30000, D)
# 2. Compute user embeddings in batches
N = len(target_uids)
user_vec_list = []
for start in range(0, N, batch_size):
end = min(start + batch_size, N)
u_vec = ranker.user_tower(
liked_items=user_tensors.liked_items[start:end],
disliked_items=user_tensors.disliked_items[start:end],
recent_items=user_tensors.recent_items[start:end],
recent_weights=user_tensors.recent_weights[start:end],
recent_mask=user_tensors.recent_mask[start:end],
organic_items=user_tensors.organic_items[start:end],
)
user_vec_list.append(u_vec)
user_vecs = torch.cat(user_vec_list, dim=0) # (N, D)
# 3. MIPS: dot product scores
scores = torch.mm(user_vecs, item_vecs.t()) # (N, 30000)
# 4. Apply eligibility mask
with np.load(eligibility_npz_path, allow_pickle=False) as artifact:
packed = artifact["packed_eligible"]
item_ids = artifact["item_ids"]
# Ensure candidate items are aligned
assert np.array_equal(item_ids, catalog_data.idx2item), "Candidate items misaligned!"
mask = np.unpackbits(packed, count=len(item_ids), bitorder="little", axis=1).astype(bool)
torch_mask = torch.from_numpy(mask)
scores[~torch_mask] = -float("inf")
# 5. Top 100 items per user
_, topk_indices = torch.topk(scores, 100, dim=1)
topk_np = topk_indices.numpy()
uid_col = np.repeat(np.array(target_uids, dtype=np.int64), 100)
item_col = catalog_data.idx2item[topk_np.flatten()]
rank_col = np.tile(np.arange(1, 101, dtype=np.int64), N)
df = pd.DataFrame({"uid": uid_col, "item_id": item_col, "rank": rank_col})
return df
def evaluate_on_validation(
ranker: TwoTowerRanker,
catalog_data: CatalogData,
val_user_tensors: UserHistoryTensors,
target_uids: List[int],
output_parquet_path: Optional[Path] = None,
) -> Dict[str, float | int]:
"""Evaluate current model on public validation set and return score dict."""
val_eligibility_path = DATA_DIR / "validation_eligible_candidates.npz"
targets_set = set(target_uids)
eligibility = CandidateSet(val_eligibility_path)
graded = validation_truth(eligibility, targets_set)
ranking_df = generate_ranking_dataframe(
ranker=ranker,
catalog_data=catalog_data,
user_tensors=val_user_tensors,
target_uids=target_uids,
eligibility_npz_path=val_eligibility_path,
)
# Validate ranking contract
validated_df = validate_ranking(ranking_df, targets_set)
if output_parquet_path is not None:
validated_df.to_parquet(output_parquet_path, index=False)
# Extract ordered items per user
ordered = validated_df.sort_values(["uid", "rank"])
ranked_dict = {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
scores = score_rankings(ranked_dict, graded)
return scores
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom evaluate import evaluate_on_validation\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60*86400), (\"played_ratio_pct\", \">=\", 50)])\n\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=listens,\n)\n\nranker = TwoTowerRanker()\nscores = evaluate_on_validation(\n ranker=ranker,\n catalog_data=catalog_data,\n val_user_tensors=val_tensors,\n target_uids=target_uids,\n output_parquet_path=Path(\"/app/solution/val_test.parquet\"),\n)\nprint(\"Untrained model validation score:\", scores)\n'",
"description": "Test validation evaluation pipeline with TwoTowerRanker"
}<untrusted_context>
Output: Untrained model validation score: {'quality_ndcg@10': 0.000177, 'quality_users': 4263}
Process Group PGID: 2627
</untrusted_context>{
"description": "Remove temporary evaluation test file",
"command": "rm -f /app/solution/val_test.parquet"
}<untrusted_context>
Output: (empty)
Process Group PGID: 2653
</untrusted_context>{
"title": "Model Training & Validation Tuning",
"summary": "Implemented data extraction, PyTorch model architecture, and validation evaluation pipeline. Now building the full multi-window training script, training the two-tower model on CPU, and measuring NDCG@10 on the public validation window to establish and optimize validation performance.",
"strategic_intent": "Train and evaluate two-tower model on validation split, tune loss parameters and data generation."
}Current topic: "Model Training & Validation Tuning"
Topic summary: Implemented data extraction, PyTorch model architecture, and validation evaluation pipeline. Now building the full multi-window training script, training the two-tower model on CPU, and measuring NDCG@10 on the public validation window to establish and optimize validation performance.
Strategic Intent: Train and evaluate two-tower model on validation split, tune loss parameters and data generation.Designing Multi-Window Datasets
I'm currently detailing the multi-window dataset creation. My focus is on the training data, specifically defining the outcome window. I'm setting the training data cutoff at $T_{\text{train\_end}} = 24786800$ and considering a day as 86400 seconds, beginning with Window 1's outcome window ending 7 days prior to this cutoff.
Defining Training Windows
I've established the timeline for my multi-window dataset. I'm currently defining the specific outcome and history cutoff times for Windows 2 through 4, each structured with a 7-day outcome window preceding its respective cutoff. I've also outlined the data extraction and filtering logic for each window, emphasizing the discovery of new items.
{
"description": "Benchmark multi-window dataset construction",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history, TwoTowerDataset\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\nuid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\ncatalog_data = load_catalog(DATA)\nitem2idx = catalog_data.item2idx\n\ncutoff = 24786800\nday = 86400\n\n# Load all required events for target users in range [cutoff - 120*day, cutoff]\nt_min_load = cutoff - 120 * day\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)]\n)\nlikes = pd.read_parquet(\n DATA / \"train_likes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)]\n)\nunlikes = pd.read_parquet(\n DATA / \"train_unlikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)]\n)\ndislikes = pd.read_parquet(\n DATA / \"train_dislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)]\n)\nundislikes = pd.read_parquet(\n DATA / \"train_undislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)]\n)\n\nprint(f\"Loaded raw data in {time.time() - t0:.2f} s\")\n\n# Build 3 windows\ndatasets = []\ntotal_samples = 0\nfor w in range(1, 4):\n tw_end = cutoff - (w - 1) * 7 * day\n tw_start = cutoff - w * 7 * day\n \n tensors, hist_items = extract_cutoff_history(\n cutoff_ts=tw_start,\n target_uids=target_uids,\n item2idx=item2idx,\n preference_state_df=None,\n listens_df=listens,\n likes_df=likes,\n unlikes_df=unlikes,\n dislikes_df=dislikes,\n undislikes_df=undislikes,\n lookback_days=90,\n )\n \n # Target events in [tw_start, tw_end]\n w_listens = listens[(listens.timestamp >= tw_start) & (listens.timestamp < tw_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n w_likes = likes[(likes.timestamp >= tw_start) & (likes.timestamp < tw_end) & (likes.is_organic == 1)]\n \n pairs = {}\n for u, item in zip(w_listens.uid, w_listens.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 1.0\n \n for u, item in zip(w_likes.uid, w_likes.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 3.0\n \n u_rows = [uid2row[u] for u, _ in pairs.keys()]\n t_items = [item2idx[item] for _, item in pairs.keys()]\n weights = list(pairs.values())\n \n print(f\"Window {w}: {len(pairs)} target pairs\")\n total_samples += len(pairs)\n\nprint(f\"Total dataset build time: {time.time() - t0:.2f} s, Total samples: {total_samples}\")\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>Investigating Timeout Cause
I'm currently scrutinizing why a command timed out. My focus is on identifying the specific operation that incurred the excessive delay. Preliminary examination points to extract_cutoff_history when preference_state_df is None, particularly within the conditional block involving likes_df and unlikes_df.
Optimizing DataFrame Iteration
I've identified significant performance bottlenecks stemming from extensive iterrows() usage on large DataFrames. My current focus is on implementing vectorized operations to replace these slow iterations, aiming for a substantial reduction in execution time. I'll be benchmarking these vectorized approaches shortly.
{
"description": "Benchmark fast vectorized extraction",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\n# Create 500k sample df\ndf = pd.DataFrame({\n \"uid\": np.random.randint(0, 8000, 500000),\n \"item_id\": np.random.randint(0, 30000, 500000),\n \"act\": np.random.randint(0, 2, 500000)\n})\n\nt0 = time.time()\n# Fast vectorized extraction:\nactive = df[df.act == 1]\n# group into dict of lists via groupby\ngrouped = active.groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\nt1 = time.time()\nprint(f\"Vectorized groupby.apply(list): {t1 - t0:.4f} s for 500k rows!\")\n'"
}<untrusted_context>
Output: Vectorized groupby.apply(list): 0.1500 s for 500k rows!
Process Group PGID: 2683
</untrusted_context>Optimizing Recent Listens
I've achieved impressive speed with groupby.apply(list). My current focus is on efficiently identifying the top 50 most recent positive listens for each user. I'm exploring vectorized approaches to accomplish this in under 0.5 seconds, with testing underway.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nN = 2000000\nuids = np.random.randint(0, 8456, N)\ntimestamps = np.random.randint(1000000, 2000000, N)\nitem_indices = np.random.randint(0, 30000, N)\nis_organic = np.random.randint(0, 2, N)\n\ndf = pd.DataFrame({\n \"uid\": uids,\n \"timestamp\": timestamps,\n \"item_idx\": item_indices,\n \"is_organic\": is_organic\n})\n\nt0 = time.time()\ndf = df.sort_values(\"timestamp\", ascending=False)\ntop50 = df.groupby(\"uid\").head(50).copy()\n\ndt_days = (2000000 - top50[\"timestamp\"].values) / 86400.0\nweights = np.exp(-0.05 * dt_days)\nweights = np.where(top50[\"is_organic\"].values == 1, weights * 1.5, weights)\ntop50[\"weight\"] = weights\n\nitems_dict = top50.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\nweights_dict = top50.groupby(\"uid\")[\"weight\"].apply(list).to_dict()\n\nt1 = time.time()\nprint(f\"2 million rows processed in {t1 - t0:.4f} s! Users: {len(items_dict)}\")\n'",
"description": "Benchmark fully vectorized recent listens grouping"
}<untrusted_context>
Output: 2 million rows processed in 0.5561 s! Users: 8456
Process Group PGID: 2694
</untrusted_context>Optimizing Tensor Creation
I'm seeing impressive speedups, processing 2 million rows in under a second. My current focus is on efficiently packing these user interactions into tensors. I'm exploring how to best extract and format likes data, ensuring we don't exceed MAX_LIKES and accurately populate the liked_items tensor for each user.
{
"description": "Benchmark tensor packing loop",
"command": "python3 -c '\nimport torch\nimport numpy as np\nimport time\n\nN = 8456\ntarget_uids = list(range(N))\nitems_dict = {u: list(np.random.randint(0, 30000, 40)) for u in target_uids}\nweights_dict = {u: list(np.random.rand(40)) for u in target_uids}\n\nt0 = time.time()\nrecent_items = torch.full((N, 50), 30000, dtype=torch.long)\nrecent_weights = torch.zeros((N, 50), dtype=torch.float32)\nrecent_mask = torch.zeros((N, 50), dtype=torch.bool)\n\nfor i, uid in enumerate(target_uids):\n items = items_dict.get(uid)\n if items:\n k = len(items)\n recent_items[i, :k] = torch.tensor(items, dtype=torch.long)\n recent_weights[i, :k] = torch.tensor(weights_dict[uid], dtype=torch.float32)\n recent_mask[i, :k] = True\n\nt1 = time.time()\nprint(f\"Packed 8456 users in {t1 - t0:.4f} s!\")\n'"
}<untrusted_context>
Output: Packed 8456 users in 0.2298 s!
Process Group PGID: 2705
</untrusted_context>{
"content": "\"\"\"Data loading, feature extraction, and dataset preparation for two-tower ranker.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nNUM_CATALOG_ITEMS = 30000\nPAD_INDEX = 30000\nMAX_LIKES = 50\nMAX_DISLIKES = 20\nMAX_RECENT = 50\nMAX_ORGANIC = 50\n\n\n@dataclass\nclass CatalogData:\n item2idx: Dict[int, int]\n idx2item: np.ndarray\n features: torch.Tensor # (30000, 4)\n log_p: torch.Tensor # (30000,)\n\n\n@dataclass\nclass UserHistoryTensors:\n liked_items: torch.Tensor # (N, MAX_LIKES) long\n disliked_items: torch.Tensor # (N, MAX_DISLIKES) long\n recent_items: torch.Tensor # (N, MAX_RECENT) long\n recent_weights: torch.Tensor # (N, MAX_RECENT) float\n recent_mask: torch.Tensor # (N, MAX_RECENT) bool\n organic_items: torch.Tensor # (N, MAX_ORGANIC) long\n\n\ndef load_catalog(data_dir: Path = DATA_DIR) -> CatalogData:\n \"\"\"Load candidate catalog, construct ID mappings, item features, and background priors.\"\"\"\n catalog = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n item2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n idx2item = catalog.item_id.to_numpy(dtype=np.int64)\n\n # Track length from train_listens\n listens = pd.read_parquet(data_dir / \"train_listens.parquet\", columns=[\"item_id\", \"track_length_seconds\"])\n track_lens = listens.drop_duplicates(\"item_id\").set_index(\"item_id\")[\"track_length_seconds\"]\n catalog_lens = catalog.item_id.map(track_lens).fillna(200.0).to_numpy(dtype=np.float32)\n\n # Extract catalog features\n pos_listens = catalog.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog.organic_positive_listens.to_numpy(dtype=np.float32)\n ranks = catalog.popularity_rank.to_numpy(dtype=np.float32)\n\n f1 = np.log1p(pos_listens)\n f2 = np.log1p(org_listens)\n f1 = (f1 - f1.mean()) / (f1.std() + 1e-6)\n f2 = (f2 - f2.mean()) / (f2.std() + 1e-6)\n f3 = 1.0 - (ranks / float(NUM_CATALOG_ITEMS))\n f4 = np.clip(catalog_lens / 300.0, 0.0, 5.0)\n\n features = torch.tensor(np.stack([f1, f2, f3, f4], axis=-1), dtype=torch.float32)\n\n # Popularity distribution for LogQ correction: p_j \\propto pos_listens^0.75\n weights = np.power(pos_listens, 0.75)\n probs = weights / weights.sum()\n log_p = torch.tensor(np.log(probs + 1e-12), dtype=torch.float32)\n\n return CatalogData(item2idx=item2idx, idx2item=idx2item, features=features, log_p=log_p)\n\n\ndef build_user_history_tensors(\n target_uids: List[int],\n liked_dict: Dict[int, List[int]],\n disliked_dict: Dict[int, List[int]],\n recent_items_dict: Dict[int, List[int]],\n recent_weights_dict: Dict[int, List[float]],\n organic_dict: Dict[int, List[int]],\n) -> UserHistoryTensors:\n \"\"\"Pack user history dictionaries into dense 2D PyTorch tensors quickly.\"\"\"\n N = len(target_uids)\n liked_items = torch.full((N, MAX_LIKES), PAD_INDEX, dtype=torch.long)\n disliked_items = torch.full((N, MAX_DISLIKES), PAD_INDEX, dtype=torch.long)\n recent_items = torch.full((N, MAX_RECENT), PAD_INDEX, dtype=torch.long)\n recent_weights = torch.zeros((N, MAX_RECENT), dtype=torch.float32)\n recent_mask = torch.zeros((N, MAX_RECENT), dtype=torch.bool)\n organic_items = torch.full((N, MAX_ORGANIC), PAD_INDEX, dtype=torch.long)\n\n for i, uid in enumerate(target_uids):\n # Liked items\n likes = liked_dict.get(uid)\n if likes:\n k = min(len(likes), MAX_LIKES)\n liked_items[i, :k] = torch.tensor(likes[:k], dtype=torch.long)\n\n # Disliked items\n dislikes = disliked_dict.get(uid)\n if dislikes:\n k = min(len(dislikes), MAX_DISLIKES)\n disliked_items[i, :k] = torch.tensor(dislikes[:k], dtype=torch.long)\n\n # Recent items & weights\n recents = recent_items_dict.get(uid)\n if recents:\n k = min(len(recents), MAX_RECENT)\n recent_items[i, :k] = torch.tensor(recents[:k], dtype=torch.long)\n recent_weights[i, :k] = torch.tensor(recent_weights_dict[uid][:k], dtype=torch.float32)\n recent_mask[i, :k] = True\n\n # Organic items\n organics = organic_dict.get(uid)\n if organics:\n k = min(len(organics), MAX_ORGANIC)\n organic_items[i, :k] = torch.tensor(organics[:k], dtype=torch.long)\n\n return UserHistoryTensors(\n liked_items=liked_items,\n disliked_items=disliked_items,\n recent_items=recent_items,\n recent_weights=recent_weights,\n recent_mask=recent_mask,\n organic_items=organic_items,\n )\n\n\ndef extract_cutoff_history(\n cutoff_ts: int,\n target_uids: List[int],\n item2idx: Dict[int, int],\n preference_state_df: Optional[pd.DataFrame] = None,\n listens_df: Optional[pd.DataFrame] = None,\n likes_df: Optional[pd.DataFrame] = None,\n unlikes_df: Optional[pd.DataFrame] = None,\n dislikes_df: Optional[pd.DataFrame] = None,\n undislikes_df: Optional[pd.DataFrame] = None,\n lookback_days: int = 90,\n) -> Tuple[UserHistoryTensors, Dict[int, Set[int]]]:\n \"\"\"Extract user history up to cutoff_ts using vectorized operations.\"\"\"\n target_set = set(target_uids)\n day = 86400\n t_min = cutoff_ts - lookback_days * day\n\n # 1. Preferences\n liked_dict: Dict[int, List[int]] = {}\n disliked_dict: Dict[int, List[int]] = {}\n\n if preference_state_df is not None:\n p_sub = preference_state_df[preference_state_df.uid.isin(target_set) & preference_state_df.item_id.isin(item2idx)].copy()\n p_sub[\"item_idx\"] = p_sub.item_id.map(item2idx)\n liked_sub = p_sub[p_sub.state == \"liked\"]\n disliked_sub = p_sub[p_sub.state == \"disliked\"]\n liked_dict = liked_sub.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n disliked_dict = disliked_sub.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n else:\n # Reconstruct from likes/unlikes and dislikes/undislikes before cutoff\n if likes_df is not None and unlikes_df is not None:\n l = likes_df[(likes_df.uid.isin(target_set)) & (likes_df.timestamp < cutoff_ts) & (likes_df.item_id.isin(item2idx))][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=1)\n u = unlikes_df[(unlikes_df.uid.isin(target_set)) & (unlikes_df.timestamp < cutoff_ts) & (unlikes_df.item_id.isin(item2idx))][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=0)\n comb = pd.concat([l, u]).sort_values(\"timestamp\")\n # Drop duplicates keeping last action\n last_act = comb.drop_duplicates(subset=[\"uid\", \"item_id\"], keep=\"last\")\n active_l = last_act[last_act.act == 1].copy()\n active_l[\"item_idx\"] = active_l.item_id.map(item2idx)\n liked_dict = active_l.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n if dislikes_df is not None and undislikes_df is not None:\n d = dislikes_df[(dislikes_df.uid.isin(target_set)) & (dislikes_df.timestamp < cutoff_ts) & (dislikes_df.item_id.isin(item2idx))][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=1)\n ud = undislikes_df[(undislikes_df.uid.isin(target_set)) & (undislikes_df.timestamp < cutoff_ts) & (undislikes_df.item_id.isin(item2idx))][[\"uid\", \"item_id\", \"timestamp\"]].assign(act=0)\n comb_d = pd.concat([d, ud]).sort_values(\"timestamp\")\n last_act_d = comb_d.drop_duplicates(subset=[\"uid\", \"item_id\"], keep=\"last\")\n active_d = last_act_d[last_act_d.act == 1].copy()\n active_d[\"item_idx\"] = active_d.item_id.map(item2idx)\n disliked_dict = active_d.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # 2. Listens\n recent_items_dict: Dict[int, List[int]] = {}\n recent_weights_dict: Dict[int, List[float]] = {}\n organic_dict: Dict[int, List[int]] = {}\n history_items: Dict[int, Set[int]] = {u: set(liked_dict.get(u, [])) for u in target_uids}\n\n if listens_df is not None:\n sub = listens_df[(listens_df.uid.isin(target_set)) & (listens_df.timestamp < cutoff_ts) & (listens_df.timestamp >= t_min)]\n # Filter to catalog items and positive listens\n sub = sub[(sub.item_id.isin(item2idx)) & (sub.played_ratio_pct >= 50)].copy()\n sub[\"item_idx\"] = sub.item_id.map(item2idx)\n sub = sub.sort_values(\"timestamp\", ascending=False)\n\n # Track history items for discovery filtering\n # Group unique items per user\n hist_u = sub.groupby(\"uid\")[\"item_id\"].unique().to_dict()\n for u, items in hist_u.items():\n history_items[u].update(items)\n\n # Top 50 recent listens\n top50 = sub.groupby(\"uid\").head(MAX_RECENT).copy()\n dt_days = (cutoff_ts - top50[\"timestamp\"].values) / 86400.0\n weights = np.exp(-0.05 * dt_days)\n weights = np.where(top50[\"is_organic\"].values == 1, weights * 1.5, weights)\n top50[\"weight\"] = weights\n\n recent_items_dict = top50.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n recent_weights_dict = top50.groupby(\"uid\")[\"weight\"].apply(list).to_dict()\n\n # Top 50 organic positive listens\n org_sub = sub[sub.is_organic == 1].groupby(\"uid\").head(MAX_ORGANIC)\n organic_dict = org_sub.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n tensors = build_user_history_tensors(\n target_uids=target_uids,\n liked_dict=liked_dict,\n disliked_dict=disliked_dict,\n recent_items_dict=recent_items_dict,\n recent_weights_dict=recent_weights_dict,\n organic_dict=organic_dict,\n )\n return tensors, history_items\n\n\nclass TwoTowerDataset(Dataset):\n \"\"\"PyTorch Dataset for In-Batch Negatives training.\"\"\"\n\n def __init__(\n self,\n history_tensors: UserHistoryTensors,\n user_row_indices: np.ndarray,\n target_item_indices: np.ndarray,\n weights: np.ndarray,\n ):\n self.history_tensors = history_tensors\n self.user_rows = torch.tensor(user_row_indices, dtype=torch.long)\n self.target_items = torch.tensor(target_item_indices, dtype=torch.long)\n self.weights = torch.tensor(weights, dtype=torch.float32)\n\n def __len__(self) -> int:\n return len(self.user_rows)\n\n def __getitem__(self, idx: int):\n u_row = self.user_rows[idx]\n return (\n self.history_tensors.liked_items[u_row],\n self.history_tensors.disliked_items[u_row],\n self.history_tensors.recent_items[u_row],\n self.history_tensors.recent_weights[u_row],\n self.history_tensors.recent_mask[u_row],\n self.history_tensors.organic_items[u_row],\n self.target_items[idx],\n self.weights[idx],\n )\n",
"file_path": "/app/solution/data.py"
}Successfully overwrote file: /app/solution/data.py. Here is the updated code:
...
return CatalogData(item2idx=item2idx, idx2item=idx2item, features=features, log_p=log_p)
def build_user_history_tensors(
target_uids: List[int],
liked_dict: Dict[int, List[int]],
disliked_dict: Dict[int, List[int]],
recent_items_dict: Dict[int, List[int]],
recent_weights_dict: Dict[int, List[float]],
organic_dict: Dict[int, List[int]],
) -> UserHistoryTensors:
"""Pack user history dictionaries into dense 2D PyTorch tensors quickly."""
N = len(target_uids)
liked_items = torch.full((N, MAX_LIKES), PAD_INDEX, dtype=torch.long)
disliked_items = torch.full((N, MAX_DISLIKES), PAD_INDEX, dtype=torch.long)
recent_items = torch.full((N, MAX_RECENT), PAD_INDEX, dtype=torch.long)
recent_weights = torch.zeros((N, MAX_RECENT), dtype=torch.float32)
recent_mask = torch.zeros((N, MAX_RECENT), dtype=torch.bool)
organic_items = torch.full((N, MAX_ORGANIC), PAD_INDEX, dtype=torch.long)
for i, uid in enumerate(target_uids):
# Liked items
likes = liked_dict.get(uid)
if likes:
k = min(len(likes), MAX_LIKES)
liked_items[i, :k] = torch.tensor(likes[:k], dtype=torch.long)
# Disliked items
dislikes = disliked_dict.get(uid)
if dislikes:
k = min(len(dislikes), MAX_DISLIKES)
disliked_items[i, :k] = torch.tensor(dislikes[:k], dtype=torch.long)
# Recent items & weights
recents = recent_items_dict.get(uid)
if recents:
k = min(len(recents), MAX_RECENT)
recent_items[i, :k] = torch.tensor(recents[:k], dtype=torch.long)
recent_weights[i, :k] = torch.tensor(recent_weights_dict[uid][:k], dtype=torch.float32)
recent_mask[i, :k] = True
# Organic items
organics = organic_dict.get(uid)
if organics:
k = min(len(organics), MAX_ORGANIC)
organic_items[i, :k] = torch.tensor(organics[:k], dtype=torch.long)
return UserHistoryTensors(
...
unlikes_df: Optional[pd.DataFrame] = None,
dislikes_df: Optional[pd.DataFrame] = None,
undislikes_df: Optional[pd.DataFrame] = None,
lookback_days: int = 90,
) -> Tuple[UserHistoryTensors, Dict[int, Set[int]]]:
"""Extract user history up to cutoff_ts using vectorized operations."""
target_set = set(target_uids)
day = 86400
t_min = cutoff_ts - lookback_days * day
# 1. Preferences
liked_dict: Dict[int, List[int]] = {}
disliked_dict: Dict[int, List[int]] = {}
if preference_state_df is not None:
p_sub = preference_state_df[preference_state_df.uid.isin(target_set) & preference_state_df.item_id.isin(item2idx)].copy()
p_sub["item_idx"] = p_sub.item_id.map(item2idx)
liked_sub = p_sub[p_sub.state == "liked"]
disliked_sub = p_sub[p_sub.state == "disliked"]
liked_dict = liked_sub.groupby("uid")["item_idx"].apply(list).to_dict()
disliked_dict = disliked_sub.groupby("uid")["item_idx"].apply(list).to_dict()
else:
# Reconstruct from likes/unlikes and dislikes/undislikes before cutoff
if likes_df is not None and unlikes_df is not None:
l = likes_df[(likes_df.uid.isin(target_set)) & (likes_df.timestamp < cutoff_ts) & (likes_df.item_id.isin(item2idx))][["uid", "item_id", "timestamp"]].assign(act=1)
u = unlikes_df[(unlikes_df.uid.isin(target_set)) & (unlikes_df.timestamp < cutoff_ts) & (unlikes_df.item_id.isin(item2idx))][["uid", "item_id", "timestamp"]].assign(act=0)
comb = pd.concat([l, u]).sort_values("timestamp")
# Drop duplicates keeping last action
last_act = comb.drop_duplicates(subset=["uid", "item_id"], keep="last")
active_l = last_act[last_act.act == 1].copy()
active_l["item_idx"] = active_l.item_id.map(item2idx)
liked_dict = active_l.groupby("uid")["item_idx"].apply(list).to_dict()
if dislikes_df is not None and undislikes_df is not None:
d = dislikes_df[(dislikes_df.uid.isin(target_set)) & (dislikes_df.timestamp < cutoff_ts) & (dislikes_df.item_id.isin(item2idx))][["uid", "item_id", "timestamp"]].assign(act=1)
ud = undislikes_df[(undislikes_df.uid.isin(target_set)) & (undislikes_df.timestamp < cutoff_ts) & (undislikes_df.item_id.isin(item2idx))][["uid", "item_id", "timestamp"]].assign(act=0)
comb_d = pd.concat([d, ud]).sort_values("timestamp")
last_act_d = comb_d.drop_duplicates(subset=["uid", "item_id"], keep="last")
active_d = last_act_d[last_act_d.act == 1].copy()
active_d["item_idx"] = active_d.item_id.map(item2idx)
disliked_dict = active_d.groupby("uid")["item_idx"].apply(list).to_dict()
# 2. Listens
recent_items_dict: Dict[int, List[int]] = {}
recent_weights_dict: Dict[int, List[float]] = {}
organic_dict: Dict[int, List[int]] = {}
history_items: Dict[int, Set[int]] = {u: set(liked_dict.get(u, [])) for u in target_uids}
if listens_df is not None:
sub = listens_df[(listens_df.uid.isin(target_set)) & (listens_df.timestamp < cutoff_ts) & (listens_df.timestamp >= t_min)]
# Filter to catalog items and positive listens
sub = sub[(sub.item_id.isin(item2idx)) & (sub.played_ratio_pct >= 50)].copy()
sub["item_idx"] = sub.item_id.map(item2idx)
sub = sub.sort_values("timestamp", ascending=False)
# Track history items for discovery filtering
# Group unique items per user
hist_u = sub.groupby("uid")["item_id"].unique().to_dict()
for u, items in hist_u.items():
history_items[u].update(items)
# Top 50 recent listens
top50 = sub.groupby("uid").head(MAX_RECENT).copy()
dt_days = (cutoff_ts - top50["timestamp"].values) / 86400.0
weights = np.exp(-0.05 * dt_days)
weights = np.where(top50["is_organic"].values == 1, weights * 1.5, weights)
top50["weight"] = weights
recent_items_dict = top50.groupby("uid")["item_idx"].apply(list).to_dict()
recent_weights_dict = top50.groupby("uid")["weight"].apply(list).to_dict()
# Top 50 organic positive listens
org_sub = sub[sub.is_organic == 1].groupby("uid").head(MAX_ORGANIC)
organic_dict = org_sub.groupby("uid")["item_idx"].apply(list).to_dict()
tensors = build_user_history_tensors(
target_uids=target_uids,
liked_dict=liked_dict,
disliked_dict=disliked_dict,
recent_items_dict=recent_items_dict,
recent_weights_dict=recent_weights_dict,
organic_dict=organic_dict,
)
return tensors, history_items
class TwoTowerDataset(Dataset):
...{
"description": "Benchmark updated vectorized extract_cutoff_history",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\nitem2idx = catalog_data.item2idx\n\ncutoff = 24786800\nday = 86400\ntw_start = cutoff - 7 * day\n\nlikes = pd.read_parquet(DATA / \"train_likes.parquet\", filters=[(\"uid\", \"in\", target_uids)])\nunlikes = pd.read_parquet(DATA / \"train_unlikes.parquet\", filters=[(\"uid\", \"in\", target_uids)])\nlistens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", tw_start - 60*day)])\n\nt1 = time.time()\ntensors, hist_items = extract_cutoff_history(\n cutoff_ts=tw_start,\n target_uids=target_uids,\n item2idx=item2idx,\n preference_state_df=None,\n listens_df=listens,\n likes_df=likes,\n unlikes_df=unlikes,\n lookback_days=60,\n)\nt2 = time.time()\nprint(f\"Extraction took {t2 - t1:.2f} s! Liked tensor: {tensors.liked_items.shape}, Recent tensor: {tensors.recent_items.shape}\")\n'"
}<untrusted_context>
Output: Extraction took 4.73 s! Liked tensor: torch.Size([8456, 50]), Recent tensor: torch.Size([8456, 50])
Process Group PGID: 2718
</untrusted_context>Developing Training Script
My focus has shifted to constructing the train.py script. I’ve successfully extracted all 8,456 users in just 4.73 seconds, and now I’m outlining the script’s core functionalities, starting with loading the catalog and then proceeding to load the target users.
Refining Training Logic
I'm now preparing validation history tensors and constructing training datasets from multiple 7-day windows. I'm also exploring the inclusion of high-affinity positive items via a self-supervised task, aiming for around 260,000 training instances. Batch sizes of 512 or 1024 seem feasible.
{
"file_path": "/app/solution/train.py",
"content": "\"\"\"Training script for Two-Tower Neural Ranker with public validation evaluation.\"\"\"\nfrom __future__ import annotations\n\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import List, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import ConcatDataset, DataLoader\n\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\n\nfrom data import (\n DATA_DIR,\n NUM_CATALOG_ITEMS,\n TwoTowerDataset,\n extract_cutoff_history,\n load_catalog,\n)\nfrom evaluate import evaluate_on_validation\nfrom model import InBatchNegativesLoss, TwoTowerRanker\n\n\ndef build_window_datasets(\n catalog_data,\n target_uids: List[int],\n num_windows: int = 3,\n lookback_days: int = 60,\n) -> Tuple[ConcatDataset, int]:\n \"\"\"Build multi-window discovery training datasets from training event logs.\"\"\"\n t0 = time.time()\n cutoff = 24786800\n day = 86400\n uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\n target_set = set(target_uids)\n item2idx = catalog_data.item2idx\n\n # Load all needed events\n t_min_load = cutoff - (num_windows * 7 + lookback_days) * day\n print(f\"Loading event logs from t >= {t_min_load}...\")\n listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n likes = pd.read_parquet(\n DATA_DIR / \"train_likes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n unlikes = pd.read_parquet(\n DATA_DIR / \"train_unlikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n dislikes = pd.read_parquet(\n DATA_DIR / \"train_dislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n undislikes = pd.read_parquet(\n DATA_DIR / \"train_undislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n print(f\"Loaded raw data in {time.time() - t0:.2f} s\")\n\n datasets = []\n total_pairs = 0\n\n for w in range(1, num_windows + 1):\n tw_end = cutoff - (w - 1) * 7 * day\n tw_start = cutoff - w * 7 * day\n print(f\"Building window {w}: history < {tw_start}, outcome in [{tw_start}, {tw_end}]...\")\n\n tensors, hist_items = extract_cutoff_history(\n cutoff_ts=tw_start,\n target_uids=target_uids,\n item2idx=item2idx,\n preference_state_df=None,\n listens_df=listens,\n likes_df=likes,\n unlikes_df=unlikes,\n dislikes_df=dislikes,\n undislikes_df=undislikes,\n lookback_days=lookback_days,\n )\n\n w_listens = listens[\n (listens.timestamp >= tw_start)\n & (listens.timestamp < tw_end)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n ]\n w_likes = likes[\n (likes.timestamp >= tw_start)\n & (likes.timestamp < tw_end)\n & (likes.is_organic == 1)\n ]\n\n # Discovery positive pairs\n pairs = {} # (uid, item_id) -> weight\n for u, item in zip(w_listens.uid, w_listens.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 1.0\n\n for u, item in zip(w_likes.uid, w_likes.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 3.0 # like takes precedence\n\n u_rows = np.array([uid2row[u] for u, _ in pairs.keys()], dtype=np.int64)\n t_items = np.array([item2idx[item] for _, item in pairs.keys()], dtype=np.int64)\n weights = np.array(list(pairs.values()), dtype=np.float32)\n\n ds = TwoTowerDataset(tensors, u_rows, t_items, weights)\n datasets.append(ds)\n total_pairs += len(pairs)\n print(f\"Window {w} pairs: {len(pairs)}\")\n\n return ConcatDataset(datasets), total_pairs\n\n\ndef main():\n torch.set_num_threads(8)\n print(f\"PyTorch threads: {torch.get_num_threads()}\")\n\n # 1. Load catalog & target users\n print(\"Loading catalog and target users...\")\n catalog_data = load_catalog(DATA_DIR)\n tu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = list(tu_df.uid)\n print(f\"Target users: {len(target_uids)}, Catalog items: {len(catalog_data.idx2item)}\")\n\n # 2. Extract validation user history tensors at cutoff 24786800\n print(\"Extracting validation user history at cutoff 24786800...\")\n cutoff = 24786800\n vps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\n val_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n )\n val_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n )\n print(\"Validation tensors ready.\")\n\n # 3. Build multi-window training dataset\n train_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n )\n print(f\"Total training pairs across windows: {total_pairs}\")\n\n batch_size = 512\n train_loader = DataLoader(\n train_dataset,\n batch_size=batch_size,\n shuffle=True,\n drop_last=True,\n )\n\n # 4. Initialize model, optimizer, loss\n embed_dim = 128\n out_dim = 128\n ranker = TwoTowerRanker(\n num_items=NUM_CATALOG_ITEMS,\n embed_dim=embed_dim,\n out_dim=out_dim,\n feature_dim=4,\n dropout=0.1,\n )\n criterion = InBatchNegativesLoss(temperature=0.07)\n optimizer = torch.optim.AdamW(ranker.parameters(), lr=1e-3, weight_decay=1e-4)\n epochs = 5\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs * len(train_loader), eta_min=1e-5)\n\n best_score = 0.0\n best_parquet_path = Path(\"/app/solution/validation.parquet\")\n\n # 5. Training loop\n catalog_features = catalog_data.features\n log_p = catalog_data.log_p\n\n print(\"\\n--- Starting Training ---\")\n for epoch in range(1, epochs + 1):\n ranker.train()\n total_loss = 0.0\n num_batches = 0\n t_epoch_start = time.time()\n\n for step, batch in enumerate(train_loader):\n liked, disliked, recent, recent_w, recent_m, organic, target_items, weights = batch\n\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(\n liked_items=liked,\n disliked_items=disliked,\n recent_items=recent,\n recent_weights=recent_w,\n recent_mask=recent_m,\n organic_items=organic,\n target_items=target_items,\n features=catalog_features,\n )\n\n loss = criterion(\n user_vecs=u_vecs,\n item_vecs=i_vecs,\n target_items=target_items,\n weights=weights,\n log_p=log_p,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n avg_loss = total_loss / max(num_batches, 1)\n epoch_dur = time.time() - t_epoch_start\n\n # Evaluate after epoch\n print(f\"Epoch {epoch}/{epochs} finished in {epoch_dur:.1f} s | Avg Loss: {avg_loss:.4f} | Evaluating validation...\")\n val_eval_path = Path(f\"/app/solution/val_epoch_{epoch}.parquet\")\n scores = evaluate_on_validation(\n ranker=ranker,\n catalog_data=catalog_data,\n val_user_tensors=val_tensors,\n target_uids=target_uids,\n output_parquet_path=val_eval_path,\n )\n ndcg = scores[\"quality_ndcg@10\"]\n print(f\"Epoch {epoch} Public Validation NDCG@10: {ndcg:.6f} (Users: {scores['quality_users']})\")\n\n if ndcg > best_score:\n best_score = ndcg\n torch.save(ranker.state_dict(), \"/app/solution/best_ranker.pt\")\n # Copy or save best validation parquet\n import shutil\n shutil.copyfile(val_eval_path, best_parquet_path)\n print(f\"*** New best validation NDCG@10: {best_score:.6f}! Saved model and {best_parquet_path} ***\")\n\n print(f\"\\nTraining complete! Best Public Validation NDCG@10: {best_score:.6f}\")\n\n\nif __name__ == \"__main__\":\n main()\n"
}Successfully created and wrote to new file: /app/solution/train.py. Here is the updated code:
"""Training script for Two-Tower Neural Ranker with public validation evaluation."""
from __future__ import annotations
import sys
import time
from pathlib import Path
from typing import List, Tuple
import numpy as np
import pandas as pd
import torch
from torch.utils.data import ConcatDataset, DataLoader
sys.path.append("/task")
sys.path.append("/app/solution")
from data import (
DATA_DIR,
NUM_CATALOG_ITEMS,
TwoTowerDataset,
extract_cutoff_history,
load_catalog,
)
from evaluate import evaluate_on_validation
from model import InBatchNegativesLoss, TwoTowerRanker
def build_window_datasets(
catalog_data,
target_uids: List[int],
num_windows: int = 3,
lookback_days: int = 60,
) -> Tuple[ConcatDataset, int]:
"""Build multi-window discovery training datasets from training event logs."""
t0 = time.time()
cutoff = 24786800
day = 86400
uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}
target_set = set(target_uids)
item2idx = catalog_data.item2idx
# Load all needed events
t_min_load = cutoff - (num_windows * 7 + lookback_days) * day
print(f"Loading event logs from t >= {t_min_load}...")
listens = pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=[("uid", "in", target_uids), ("timestamp", ">=", t_min_load)],
)
likes = pd.read_parquet(
DATA_DIR / "train_likes.parquet",
filters=[("uid", "in", target_uids), ("timestamp", ">=", t_min_load)],
)
unlikes = pd.read_parquet(
DATA_DIR / "train_unlikes.parquet",
filters=[("uid", "in", target_uids), ("timestamp", ">=", t_min_load)],
)
dislikes = pd.read_parquet(
DATA_DIR / "train_dislikes.parquet",
filters=[("uid", "in", target_uids), ("timestamp", ">=", t_min_load)],
)
undislikes = pd.read_parquet(
DATA_DIR / "train_undislikes.parquet",
filters=[("uid", "in", target_uids), ("timestamp", ">=", t_min_load)],
)
print(f"Loaded raw data in {time.time() - t0:.2f} s")
datasets = []
total_pairs = 0
for w in range(1, num_windows + 1):
tw_end = cutoff - (w - 1) * 7 * day
tw_start = cutoff - w * 7 * day
print(f"Building window {w}: history < {tw_start}, outcome in [{tw_start}, {tw_end}]...")
tensors, hist_items = extract_cutoff_history(
cutoff_ts=tw_start,
target_uids=target_uids,
item2idx=item2idx,
preference_state_df=None,
listens_df=listens,
likes_df=likes,
unlikes_df=unlikes,
dislikes_df=dislikes,
undislikes_df=undislikes,
lookback_days=lookback_days,
)
w_listens = listens[
(listens.timestamp >= tw_start)
& (listens.timestamp < tw_end)
& (listens.is_organic == 1)
& (listens.played_ratio_pct >= 80)
]
w_likes = likes[
(likes.timestamp >= tw_start)
& (likes.timestamp < tw_end)
& (likes.is_organic == 1)
]
# Discovery positive pairs
pairs = {} # (uid, item_id) -> weight
for u, item in zip(w_listens.uid, w_listens.item_id):
if item in item2idx and item not in hist_items.get(u, set()):
pairs[(u, item)] = 1.0
for u, item in zip(w_likes.uid, w_likes.item_id):
if item in item2idx and item not in hist_items.get(u, set()):
pairs[(u, item)] = 3.0 # like takes precedence
u_rows = np.array([uid2row[u] for u, _ in pairs.keys()], dtype=np.int64)
t_items = np.array([item2idx[item] for _, item in pairs.keys()], dtype=np.int64)
weights = np.array(list(pairs.values()), dtype=np.float32)
ds = TwoTowerDataset(tensors, u_rows, t_items, weights)
datasets.append(ds)
total_pairs += len(pairs)
print(f"Window {w} pairs: {len(pairs)}")
return ConcatDataset(datasets), total_pairs
def main():
torch.set_num_threads(8)
print(f"PyTorch threads: {torch.get_num_threads()}")
# 1. Load catalog & target users
print("Loading catalog and target users...")
catalog_data = load_catalog(DATA_DIR)
tu_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = list(tu_df.uid)
print(f"Target users: {len(target_uids)}, Catalog items: {len(catalog_data.idx2item)}")
# 2. Extract validation user history tensors at cutoff 24786800
print("Extracting validation user history at cutoff 24786800...")
cutoff = 24786800
vps = pd.read_parquet(DATA_DIR / "validation_preference_state.parquet")
val_listens = pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=[("uid", "in", target_uids), ("timestamp", ">=", cutoff - 60 * 86400)],
)
val_tensors, _ = extract_cutoff_history(
cutoff_ts=cutoff,
target_uids=target_uids,
item2idx=catalog_data.item2idx,
preference_state_df=vps,
listens_df=val_listens,
lookback_days=60,
)
print("Validation tensors ready.")
# 3. Build multi-window training dataset
train_dataset, total_pairs = build_window_datasets(
catalog_data=catalog_data,
target_uids=target_uids,
num_windows=3,
lookback_days=60,
)
print(f"Total training pairs across windows: {total_pairs}")
batch_size = 512
train_loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
drop_last=True,
)
# 4. Initialize model, optimizer, loss
embed_dim = 128
out_dim = 128
ranker = TwoTowerRanker(
num_items=NUM_CATALOG_ITEMS,
embed_dim=embed_dim,
out_dim=out_dim,
feature_dim=4,
dropout=0.1,
)
criterion = InBatchNegativesLoss(temperature=0.07)
optimizer = torch.optim.AdamW(ranker.parameters(), lr=1e-3, weight_decay=1e-4)
epochs = 5
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs * len(train_loader), eta_min=1e-5)
best_score = 0.0
best_parquet_path = Path("/app/solution/validation.parquet")
# 5. Training loop
catalog_features = catalog_data.features
log_p = catalog_data.log_p
print("\n--- Starting Training ---")
for epoch in range(1, epochs + 1):
ranker.train()
total_loss = 0.0
num_batches = 0
t_epoch_start = time.time()
for step, batch in enumerate(train_loader):
liked, disliked, recent, recent_w, recent_m, organic, target_items, weights = batch
optimizer.zero_grad()
u_vecs, i_vecs = ranker(
liked_items=liked,
disliked_items=disliked,
recent_items=recent,
recent_weights=recent_w,
recent_mask=recent_m,
organic_items=organic,
target_items=target_items,
features=catalog_features,
)
loss = criterion(
user_vecs=u_vecs,
item_vecs=i_vecs,
target_items=target_items,
weights=weights,
log_p=log_p,
)
loss.backward()
torch.nn.utils.clip_grad_norm_(ranker.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
total_loss += loss.item()
num_batches += 1
avg_loss = total_loss / max(num_batches, 1)
epoch_dur = time.time() - t_epoch_start
# Evaluate after epoch
print(f"Epoch {epoch}/{epochs} finished in {epoch_dur:.1f} s | Avg Loss: {avg_loss:.4f} | Evaluating validation...")
val_eval_path = Path(f"/app/solution/val_epoch_{epoch}.parquet")
scores = evaluate_on_validation(
ranker=ranker,
catalog_data=catalog_data,
val_user_tensors=val_tensors,
target_uids=target_uids,
output_parquet_path=val_eval_path,
)
ndcg = scores["quality_ndcg@10"]
print(f"Epoch {epoch} Public Validation NDCG@10: {ndcg:.6f} (Users: {scores['quality_users']})")
if ndcg > best_score:
best_score = ndcg
torch.save(ranker.state_dict(), "/app/solution/best_ranker.pt")
# Copy or save best validation parquet
import shutil
shutil.copyfile(val_eval_path, best_parquet_path)
print(f"*** New best validation NDCG@10: {best_score:.6f}! Saved model and {best_parquet_path} ***")
print(f"\nTraining complete! Best Public Validation NDCG@10: {best_score:.6f}")
if __name__ == "__main__":
main()
{
"description": "Train two-tower ranker and evaluate on public validation",
"command": "python3 /app/solution/train.py"
}<untrusted_context>
Output: PyTorch threads: 8
Loading catalog and target users...
Target users: 8456, Catalog items: 30000
Extracting validation user history at cutoff 24786800...
Validation tensors ready.
Loading event logs from t >= 17788400...
Loaded raw data in 0.38 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Total training pairs across windows: 189438
--- Starting Training ---
Epoch 1/5 finished in 21.2 s | Avg Loss: 7.1398 | Evaluating validation...
Epoch 1 Public Validation NDCG@10: 0.009772 (Users: 4263)
*** New best validation NDCG@10: 0.009772! Saved model and /app/solution/validation.parquet ***
Epoch 2/5 finished in 20.4 s | Avg Loss: 6.6309 | Evaluating validation...
Epoch 2 Public Validation NDCG@10: 0.010382 (Users: 4263)
*** New best validation NDCG@10: 0.010382! Saved model and /app/solution/validation.parquet ***
Epoch 3/5 finished in 20.6 s | Avg Loss: 6.1483 | Evaluating validation...
Epoch 3 Public Validation NDCG@10: 0.009378 (Users: 4263)
Epoch 4/5 finished in 20.4 s | Avg Loss: 5.7447 | Evaluating validation...
Epoch 4 Public Validation NDCG@10: 0.007435 (Users: 4263)
Epoch 5/5 finished in 20.6 s | Avg Loss: 5.4824 | Evaluating validation...
Epoch 5 Public Validation NDCG@10: 0.006637 (Users: 4263)
Training complete! Best Public Validation NDCG@10: 0.010382
Process Group PGID: 2744
</untrusted_context>Observing Metric Fluctuations
My recent training run showed an initial boost in validation NDCG@10 to 0.010382 at Epoch 2. While the loss metric continued to decrease throughout subsequent epochs, the NDCG metric unfortunately began to decline after its initial peak.
Investigating Metric Divergence
I'm focusing on the post-epoch 2 dip in NDCG. I suspect the learning rate might be too aggressive, causing overfitting, or there's an issue with how item_log_p is applied during training versus inference, significantly altering the similarity scores.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\npop_map = catalog.set_index(\"item_id\")[\"popularity_rank\"]\n\ndf_ep1 = pd.read_parquet(\"/app/solution/val_epoch_1.parquet\")\ndf_ep2 = pd.read_parquet(\"/app/solution/val_epoch_2.parquet\")\ndf_ep4 = pd.read_parquet(\"/app/solution/val_epoch_4.parquet\")\n\nprint(\"Epoch 1 avg popularity rank of top 10 items:\", df_ep1[df_ep1[\"rank\"] <= 10][\"item_id\"].map(pop_map).mean())\nprint(\"Epoch 2 avg popularity rank of top 10 items:\", df_ep2[df_ep2[\"rank\"] <= 10][\"item_id\"].map(pop_map).mean())\nprint(\"Epoch 4 avg popularity rank of top 10 items:\", df_ep4[df_ep4[\"rank\"] <= 10][\"item_id\"].map(pop_map).mean())\n'",
"description": "Inspect average popularity rank of recommended items"
}<untrusted_context>
Output: Epoch 1 avg popularity rank of top 10 items: 1402.983183538316
Epoch 2 avg popularity rank of top 10 items: 1634.5238883632924
Epoch 4 avg popularity rank of top 10 items: 3957.141166035951
Process Group PGID: 2781
</untrusted_context>Observing Ranking Shifts
My latest analysis reveals a significant trend: the average popularity rank of recommended top 10 items has steadily increased, escalating from around 1,400 in Epoch 1 to nearly 4,000 by Epoch 4. This indicates a noticeable shift in the model's selection towards less popular items over time.
{
"command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntargets = set(pd.read_parquet(DATA / \"target_users.parquet\").uid.astype(int))\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\npop_map = catalog.set_index(\"item_id\")[\"popularity_rank\"].to_dict()\n\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ntruth = validation_truth(eligibility, targets)\n\ntruth_ranks = []\nfor u, items in truth.items():\n for it in items:\n if it in pop_map:\n truth_ranks.append(pop_map[it])\n\nprint(f\"Truth items count: {len(truth_ranks)}\")\nprint(f\"Truth items popularity rank: mean={np.mean(truth_ranks):.1f}, median={np.median(truth_ranks)}, 25%={np.percentile(truth_ranks, 25)}, 75%={np.percentile(truth_ranks, 75)}\")\n'",
"description": "Inspect truth items popularity rank distribution"
}<untrusted_context>
Output: Truth items count: 37769
Truth items popularity rank: mean=8463.1, median=5622.0, 25%=1733.0, 75%=13374.0
Process Group PGID: 2800
</untrusted_context>Analyzing Popularity Metrics
My current focus is on understanding the discrepancies between the popularity baseline and my calculated scores. I'm delving into the specific items the baseline recommended to pinpoint the factors contributing to this difference.
Investigating Embedding Initialization
I've identified a key discrepancy: while the popularity baseline performs slightly better, my model's user embeddings are initialized randomly and trained on a limited dataset. This could be hindering its ability to learn meaningful taste similarity, impacting its recommendation effectiveness.
{
"command": "python3 -c '\nimport torch\nranker_state = torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\")\nemb = ranker_state[\"item_tower.item_emb.weight\"]\nnorm = torch.norm(emb, dim=-1)\nprint(\"Item emb norms: min=\", norm.min().item(), \"mean=\", norm.mean().item(), \"max=\", norm.max().item())\n'",
"description": "Check trained item embedding norms"
}<untrusted_context>
Output: Item emb norms: min= 0.0 mean= 0.6469931602478027 max= 1.012860894203186
Process Group PGID: 2820
</untrusted_context>Observing Embedding Norms
I'm tracking the norm of our item embeddings. Initially set to a standard deviation of 0.05, their average norm is now 0.64 after two epochs, up from an expected 0.56. I'm investigating this increase, particularly in light of the 43.4 million listens in the training data.
Exploring Data Scale
I'm realizing the immense potential of our 43.4 million listens; we've only tapped into 0.4% of it with 189,000 pairs. This vast dataset offers a rich co-occurrence matrix, hinting at deeper connections through shared artist, genre, tempo, and community. I'm now exploring if Word2Vec or session-based co-occurrence can be used for pretraining embeddings, ensuring it adheres to scaling constraints without global matrix decomposition.
Training Item Embeddings
I'm considering training item embeddings directly on user listening sessions. This approach, akin to Item2Vec, leverages sequences to capture item relationships. Given the millions of potential pairs from user histories and the speed of PyTorch, we can train on a substantial portion of the data efficiently.
Testing Initialization Hypothesis
I'm testing the idea that initializing item embeddings with pre-computed, high-quality representations could directly align user and item latent spaces. This would allow immediate retrieval of relevant tracks via inner-product search. I'm exploring Item2Vec on listening sequences as a potential method for generating these initial embeddings, checking for the availability of necessary libraries like Gensim.
Refining Embedding Training
I'm realizing we can directly train our Two-Tower ranker on user interaction sequences. This bypasses the need for separate Item2Vec pretraining. The User Tower already models sequences and predicts the next item, effectively acting as a neural recommender. I'm now investigating why our initial training run used such a small sample size.
Expanding Training Scope
I'm realizing the significant underutilization of our data. Instead of the current limited approach, I'm planning to leverage user listening sessions more comprehensively. This involves training on user session sequences for next-item prediction, generating millions of potential training pairs, and supplementing these with the held-out discovery pairs. I'm also revisiting the competitiveness of our popularity baseline to understand its effectiveness.
Validating Bias Incorporation
I'm confirming that the dot product between user and item embeddings can indeed capture item biases. By augmenting the user embedding with a bias term and the item embedding with its own bias, the inner product naturally incorporates these factors. This means the Item Tower can learn popularity directly, simplifying retrieval through maximum inner-product search on pre-computed item embeddings.
{
"description": "Test popularity blending with two-tower model",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom evaluate import generate_ranking_dataframe, evaluate_on_validation\nfrom ranking_evaluation import CandidateSet, score_rankings, validate_ranking\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nitem_vecs = ranker.item_tower.precompute_all_items(catalog_data.features) # (30000, 128)\n\n# Compute user vectors\nu_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n) # (8456, 128)\n\nscores_raw = torch.mm(u_vecs, item_vecs.t()).numpy()\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool)\n\n# Catalog popularity score\npop = catalog_data.features[:, 0].numpy() # log popularity standardized\n\nfor alpha in [0.0, 0.2, 0.5, 0.8, 1.0, 1.5, 2.0]:\n scores = scores_raw + alpha * pop.reshape(1, -1)\n scores[~mask] = -1e9\n \n # top 100\n topk_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n # sort top 100\n row_indices = np.arange(len(scores))[:, None]\n topk_scores = scores[row_indices, topk_idx]\n order = np.argsort(-topk_scores, axis=1)\n sorted_topk = topk_idx[row_indices, order]\n \n ranked_dict = {}\n for i, uid in enumerate(target_uids):\n ranked_dict[uid] = [int(catalog_data.idx2item[idx]) for idx in sorted_topk[i]]\n \n res = score_rankings(ranked_dict, graded)\n print(f\"Alpha {alpha:4.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}\")\n'"
}<untrusted_context>
Output: File "<string>", line 83
print(f"Alpha {alpha:4.1f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 2833
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nitem_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n\nu_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n)\n\nscores_raw = torch.mm(u_vecs, item_vecs.t()).numpy()\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool)\n\npop = catalog_data.features[:, 0].numpy()\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0]:\n scores = scores_raw + alpha * pop.reshape(1, -1)\n scores[~mask] = -1e9\n \n topk_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_indices = np.arange(len(scores))[:, None]\n topk_scores = scores[row_indices, topk_idx]\n order = np.argsort(-topk_scores, axis=1)\n sorted_topk = topk_idx[row_indices, order]\n \n ranked_dict = {}\n for i, uid in enumerate(target_uids):\n ranked_dict[uid] = [int(catalog_data.idx2item[idx]) for idx in sorted_topk[i]]\n \n res = score_rankings(ranked_dict, graded)\n score_val = res[\"quality_ndcg@10\"]\n print(f\"Alpha {alpha:4.1f}: NDCG@10 = {score_val:.6f}\")\n'",
"description": "Test popularity bias blending score"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 49, in <module>
RuntimeError: Can't call numpy() on Tensor that requires grad. Use tensor.detach().numpy() instead.
Exit Code: 1
Process Group PGID: 2836
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_raw = torch.mm(u_vecs, item_vecs.t()).cpu().numpy()\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool)\n\npop = catalog_data.features[:, 0].numpy()\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0]:\n scores = scores_raw + alpha * pop.reshape(1, -1)\n scores[~mask] = -1e9\n \n topk_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\n row_indices = np.arange(len(scores))[:, None]\n topk_scores = scores[row_indices, topk_idx]\n order = np.argsort(-topk_scores, axis=1)\n sorted_topk = topk_idx[row_indices, order]\n \n ranked_dict = {}\n for i, uid in enumerate(target_uids):\n ranked_dict[uid] = [int(catalog_data.idx2item[idx]) for idx in sorted_topk[i]]\n \n res = score_rankings(ranked_dict, graded)\n score_val = res[\"quality_ndcg@10\"]\n print(f\"Alpha {alpha:4.1f}: NDCG@10 = {score_val:.6f}\")\n'",
"description": "Test popularity bias blending score with torch.no_grad"
}<untrusted_context>
Output: /usr/bin/bash: line 83: 2864 Killed PYTHONPATH=/task python3 -c '
import sys
sys.path.append("/app/solution")
import torch
import numpy as np
import pandas as pd
from pathlib import Path
from data import load_catalog, extract_cutoff_history
from model import TwoTowerRanker
from ranking_evaluation import CandidateSet, score_rankings
from evaluate_public import validation_truth
DATA = Path("/task/data")
tu_df = pd.read_parquet(DATA / "target_users.parquet")
target_uids = list(tu_df.uid)
catalog_data = load_catalog(DATA)
cutoff = 24786800
vps = pd.read_parquet(DATA / "validation_preference_state.parquet")
val_listens = pd.read_parquet(
DATA / "train_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=[("uid", "in", target_uids), ("timestamp", ">=", cutoff - 60 * 86400)],
)
val_tensors, _ = extract_cutoff_history(
cutoff_ts=cutoff,
target_uids=target_uids,
item2idx=catalog_data.item2idx,
preference_state_df=vps,
listens_df=val_listens,
lookback_days=60,
)
ranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)
ranker.load_state_dict(torch.load("/app/solution/best_ranker.pt", map_location="cpu"))
ranker.eval()
with torch.no_grad():
item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)
u_vecs = ranker.user_tower(
liked_items=val_tensors.liked_items,
disliked_items=val_tensors.disliked_items,
recent_items=val_tensors.recent_items,
recent_weights=val_tensors.recent_weights,
recent_mask=val_tensors.recent_mask,
organic_items=val_tensors.organic_items,
)
scores_raw = torch.mm(u_vecs, item_vecs.t()).cpu().numpy()
val_eligibility_path = DATA / "validation_eligible_candidates.npz"
targets_set = set(target_uids)
eligibility = CandidateSet(val_eligibility_path)
graded = validation_truth(eligibility, targets_set)
with np.load(val_eligibility_path) as a:
packed = a["packed_eligible"]
item_ids = a["item_ids"]
mask = np.unpackbits(packed, count=len(item_ids), bitorder="little", axis=1).astype(bool)
pop = catalog_data.features[:, 0].numpy()
for alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.8, 1.0]:
scores = scores_raw + alpha * pop.reshape(1, -1)
scores[~mask] = -1e9
topk_idx = np.argpartition(-scores, 100, axis=1)[:, :100]
row_indices = np.arange(len(scores))[:, None]
topk_scores = scores[row_indices, topk_idx]
order = np.argsort(-topk_scores, axis=1)
sorted_topk = topk_idx[row_indices, order]
ranked_dict = {}
for i, uid in enumerate(target_uids):
ranked_dict[uid] = [int(catalog_data.idx2item[idx]) for idx in sorted_topk[i]]
res = score_rankings(ranked_dict, graded)
score_val = res["quality_ndcg@10"]
print(f"Alpha {alpha:4.1f}: NDCG@10 = {score_val:.6f}")
'
Exit Code: 137
Process Group PGID: 2862
</untrusted_context>Investigating Memory Leaks
I'm currently dissecting a critical memory issue, pinpointed to the OOM killer. My focus is on a specific line of code involving array manipulation (scores = scores_raw + alpha * pop.reshape(1, -1)) and its impact on memory allocation, especially within a loop structure that appears to be compounding the problem.
{
"description": "Test popularity blending with memory-efficient PyTorch tensors",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_base = torch.mm(u_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\npop = catalog_data.features[:, 0].unsqueeze(0)\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.5]:\n scores = scores_base + alpha * pop\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n \n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(f\"Alpha {alpha:.1f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}\")\n'"
}<untrusted_context>
Output: File "<string>", line 75
print(f"Alpha {alpha:.1f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 2888
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_base = torch.mm(u_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\npop = catalog_data.features[:, 0].unsqueeze(0)\n\nfor alpha in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n scores = scores_base + alpha * pop\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n \n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n val = res[\"quality_ndcg@10\"]\n print(\"Alpha:\", alpha, \"NDCG@10:\", val)\n'",
"description": "Test popularity blend with proper quoting"
}<untrusted_context>
Output: Alpha: 0.0 NDCG@10: 0.010382
Alpha: 0.05 NDCG@10: 0.012155
Alpha: 0.1 NDCG@10: 0.011319
Alpha: 0.15 NDCG@10: 0.011555
Alpha: 0.2 NDCG@10: 0.011293
Alpha: 0.3 NDCG@10: 0.011078
Process Group PGID: 2891
</untrusted_context>Improving NDCG Score
My thoughts are focused on the significant improvement observed in the NDCG@10 score, now at 0.012155. This represents a notable increase from both the popularity baseline (0.01071) and the unblended model (0.01038), demonstrating a substantial gain. I'm now considering how to ensure the model's decomposition meets the exact prompt requirement.
Refining Score Calculation
I'm exploring how to ensure the final relevance score is strictly the dot product of independently generated user and item vectors from separate PyTorch towers. The current approach seems to allow for this if both towers output vectors of the same dimensionality, with adjustments for item bias potentially integrated.
Verifying Relevance Calculation
I'm confirming that the relevance score will indeed be the precise dot product of independently computed, same-dimensionality user and item vectors from separate PyTorch towers. This allows for efficient offline precomputation of item vectors and online computation of user vectors, enabling fast retrieval via Maximum Inner Product Search.
Adjusting Vector Normalization
I've realized that normalizing both user and item vectors to unit norm negates any ability to represent item popularity or global priors. I'm now exploring ways to incorporate item bias or popularity into the item vector itself, potentially by modifying the last dimension to carry this information while keeping the initial dimensions normalized.
Incorporating Popularity Data
I've formulated a method to combine user and item taste vectors with item popularity. This ensures the dot product represents both similarity and bias, while adhering to the two-tower MIPS architecture. I'm now considering training with more extensive data, specifically focusing on session-based training pairs from train_listens.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\nfrom pathlib import Path\n\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\n\n# Load listens from last 30 days\nt0 = time.time()\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", list(tu)), (\"timestamp\", \">=\", cutoff - 30*day), (\"played_ratio_pct\", \">=\", 50)]\n)\nlistens = listens[listens.item_id.isin(item2idx)].sort_values([\"uid\", \"timestamp\"])\nprint(f\"Loaded {len(listens)} positive listens in {time.time() - t0:.2f} s\")\n'",
"description": "Test loading last 30 days positive listens"
}<untrusted_context>
Output: Loaded 2787729 positive listens in 0.98 s
Process Group PGID: 2917
</untrusted_context>Refining Discovery Training
My recent thoughts are focused on optimizing user tower training for the "Made for You" discovery shelf. I'm analyzing the rapid consumption of 2.78 million listens in under a second, aiming to refine training for organic listening and candidate eligibility filters, prioritizing a high organic like rate.
Improving Recommendation Quality
I'm re-evaluating our training process. The recent run with multiple historical windows, despite a large number of pairs, yielded a suboptimal NDCG@10. I'm investigating potential issues in the loss calculation, particularly the interaction with item popularity bias, to boost neural similarity.
Adjusting Popularity Bias
I've identified a significant issue where the popularity correction was overly penalizing popular tracks. This forced model embeddings downwards, causing it to suppress items users actually enjoy. I'm now testing alternative approaches, like scaling down or removing the popularity correction entirely, and standard in-batch negatives, to rectify this.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker, InBatchNegativesLoss\nfrom evaluate import evaluate_on_validation\nfrom train import build_window_datasets\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n)\n\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\n# Train without LogQ correction (log_p = zeros)\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128, feature_dim=4, dropout=0.1)\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\ncriterion = InBatchNegativesLoss(temperature=0.07)\nzero_log_p = torch.zeros(30000)\n\nfor epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n loss = criterion(u_vecs, i_vecs, targets, weights, zero_log_p)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n scores = evaluate_on_validation(ranker, catalog_data, val_tensors, target_uids)\n print(f\"Epoch {epoch} without LogQ: NDCG@10 = {scores[\\\"quality_ndcg@10\\\"]:.6f}\")\n'",
"description": "Test training without LogQ correction"
}<untrusted_context>
Output: File "<string>", line 65
print(f"Epoch {epoch} without LogQ: NDCG@10 = {scores[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 2938
</untrusted_context>{
"description": "Test training without LogQ correction with proper print",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker, InBatchNegativesLoss\nfrom evaluate import evaluate_on_validation\nfrom train import build_window_datasets\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n)\n\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128, feature_dim=4, dropout=0.1)\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\ncriterion = InBatchNegativesLoss(temperature=0.07)\nzero_log_p = torch.zeros(30000)\n\nfor epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n loss = criterion(u_vecs, i_vecs, targets, weights, zero_log_p)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n scores = evaluate_on_validation(ranker, catalog_data, val_tensors, target_uids)\n val = scores[\"quality_ndcg@10\"]\n print(\"Epoch\", epoch, \"without LogQ: NDCG@10 =\", val)\n'"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 23, in <module>
NameError: name 'DATA' is not defined
Exit Code: 1
Process Group PGID: 2941
</untrusted_context>{
"description": "Run test without LogQ correction",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker, InBatchNegativesLoss\nfrom evaluate import evaluate_on_validation\nfrom train import build_window_datasets\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n)\n\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128, feature_dim=4, dropout=0.1)\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\ncriterion = InBatchNegativesLoss(temperature=0.07)\nzero_log_p = torch.zeros(30000)\n\nfor epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n loss = criterion(u_vecs, i_vecs, targets, weights, zero_log_p)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n scores = evaluate_on_validation(ranker, catalog_data, val_tensors, target_uids)\n val = scores[\"quality_ndcg@10\"]\n print(\"Epoch\", epoch, \"without LogQ: NDCG@10 =\", val)\n'"
}<untrusted_context>
Output: Loading event logs from t >= 17788400...
Loaded raw data in 0.37 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Epoch 1 without LogQ: NDCG@10 = 0.00514
Epoch 2 without LogQ: NDCG@10 = 0.005152
Epoch 3 without LogQ: NDCG@10 = 0.003833
Process Group PGID: 2978
</untrusted_context>Analyzing LogQ Impact
I'm seeing a significant uplift in NDCG@10 when incorporating LogQ, moving from a baseline of 0.0051 to 0.010382, and further to 0.012155 with an alpha of 0.05. This suggests LogQ effectively mitigates the sampling bias inherent in in-batch negative sampled softmax, which previously led to over-recommendation of popular items.
Integrating Item Bias
I'm exploring a new approach to integrate item bias directly into the Two-Tower model. The goal is to learn the balance between personalization and discovery end-to-end, achieving an exact dot product relevance score. I've begun designing the architecture for the item tower, focusing on combining item embeddings with a learned bias component.
Refining Ranking Score
I'm refining the ranking score calculation within the Two-Tower model, specifically focusing on a novel dot product formulation. This new approach integrates normalized user and item tower outputs, incorporating learned prior weights ($s_u, s_v$) through parameters $\alpha$ and $\beta$ to balance personalization and discovery. I'm now preparing to implement and test this in Python.
Implementing Vector Output
I've successfully implemented the modifications in ItemTower and UserTower to output a $D$-dimensional vector directly. This adjustment is crucial for testing the refined ranking score formulation in model.py, aiming to achieve the promising NDCG@10 score of 0.012155 observed previously.
Achieving Exact Dot Product
I've successfully integrated the user and item towers to output vectors that, when their dot product is taken, precisely mirror the desired formulation $taste_u \cdot taste_v + bias\_scale \cdot item\_bias$. This architectural alignment allows for precomputing item embeddings offline and using efficient MIPS for retrieval at request time, fulfilling all constraints.
{
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom train import build_window_datasets\nfrom evaluate import evaluate_on_validation\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n)\n\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\n# Define Custom TwoTower with bias coordinate\nclass CustomItemTower(nn.Module):\n def __init__(self, num_items=30000, embed_dim=128, feature_dim=4, out_dim=128):\n super().__init__()\n self.num_items = num_items\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=30000)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[30000].zero_()\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim - 1),\n )\n self.bias_mlp = nn.Sequential(\n nn.Linear(feature_dim, 32),\n nn.GELU(),\n nn.Linear(32, 1),\n )\n def forward(self, item_ids, features=None):\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n bias = self.bias_mlp(batch_feats)\n else:\n bias = torch.zeros((e.shape[0], 1), device=e.device)\n taste = F.normalize(self.mlp(e), p=2, dim=-1)\n return torch.cat([taste, bias], dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(self, catalog_features, batch_size=4096):\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n vecs = self.forward(all_ids[start:end], catalog_features[start:end])\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\nclass CustomUserTower(nn.Module):\n def __init__(self, item_emb, embed_dim=128, out_dim=128, dropout=0.1):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n self.bias_scale = nn.Parameter(torch.tensor([0.05]))\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, out_dim - 1),\n )\n def _mean_pool(self, items):\n mask = (items != 30000).unsqueeze(-1)\n emb = self.item_emb(items)\n counts = mask.sum(dim=1).clamp(min=1)\n return (emb * mask).sum(dim=1) / counts\n def forward(self, liked_items, disliked_items, recent_items, recent_weights, recent_mask, organic_items):\n emb_liked = self._mean_pool(liked_items)\n emb_disliked = self._mean_pool(disliked_items)\n recent_emb = self.item_emb(recent_items)\n rw = recent_weights.unsqueeze(-1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum\n scores = torch.matmul(recent_emb, self.query_vector) / (self.embed_dim ** 0.5)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_w = F.softmax(scores, dim=-1).unsqueeze(-1)\n emb_recent_attn = (attn_w * recent_emb).sum(dim=1)\n emb_organic = self._mean_pool(organic_items)\n h = torch.cat([emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic], dim=-1)\n taste = F.normalize(self.mlp(h), p=2, dim=-1)\n b = self.bias_scale.expand(taste.shape[0], 1)\n return torch.cat([taste, b], dim=-1)\n\nclass CustomRanker(nn.Module):\n def __init__(self):\n super().__init__()\n self.item_tower = CustomItemTower()\n self.user_tower = CustomUserTower(self.item_tower.item_emb)\n def forward(self, liked, disliked, recent, recent_w, recent_m, organic, target_items, features):\n u = self.user_tower(liked, disliked, recent, recent_w, recent_m, organic)\n v = self.item_tower(target_items, features)\n return u, v\n\nranker = CustomRanker()\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=1e-3, weight_decay=1e-4)\n\n# Scaled LogQ\nlog_p = catalog_data.log_p * 0.5\n\nfor epoch in range(1, 5):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n \n sim = torch.mm(u_vecs, i_vecs.t()) / 0.07\n item_log_p = log_p[targets]\n sim = sim - item_log_p.unsqueeze(0)\n \n targets_label = torch.arange(len(u_vecs))\n loss = (F.cross_entropy(sim, targets_label, reduction=\"none\") * weights).mean()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n scores = evaluate_on_validation(ranker, catalog_data, val_tensors, target_uids)\n val = scores[\"quality_ndcg@10\"]\n print(\"Epoch\", epoch, \"NDCG@10 =\", val, \"bias_scale =\", ranker.user_tower.bias_scale.item())\n'",
"description": "Train custom ranker with joint taste and bias representation"
}<untrusted_context>
Output: Loading event logs from t >= 17788400...
Loaded raw data in 0.38 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Epoch 1 NDCG@10 = 0.006353 bias_scale = 0.049787212163209915
Epoch 2 NDCG@10 = 0.009257 bias_scale = 0.05678049474954605
Epoch 3 NDCG@10 = 0.006972 bias_scale = 0.056780025362968445
Epoch 4 NDCG@10 = 0.005919 bias_scale = 0.06052014231681824
Process Group PGID: 3015
</untrusted_context>Exploring Embedding Initialization
My current focus is on understanding the performance gap. I'm investigating why a randomly initialized two-tower model, trained from scratch, yields a specific accuracy, and how improving item embeddings could significantly boost this.
Developing Item Representations
I'm exploring how to generate richer item embeddings beyond catalog features. The core idea is to leverage track co-occurrence from user listening history. My current thinking is on methods like SVD or Skip-gram applied to co-occurrence data, but I need to ensure scalability and avoid a global user-item matrix as per the constraints.
Refining Item Embedding Generation
I'm reconsidering how to construct item embeddings, specifically moving away from global user-item matrices to ensure scalability. My current direction involves exploring session-based next-track prediction to generate richer item representations, aiming to test this approach directly.
Generating Sequential Training Pairs
I'm excited about the prospect of generating a substantial number of high-quality sequential training examples for our model. The approach of using preceding listens and active likes to predict the next track appears very promising, and I'm eager to test its implementation in Python to assess its efficiency.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\nfrom pathlib import Path\n\nt0 = time.time()\nDATA = Path(\"/task/data\")\ntu = set(pd.read_parquet(DATA / \"target_users.parquet\").uid)\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem2idx = {int(item): idx for idx, item in enumerate(catalog.item_id)}\n\ncutoff = 24786800\nday = 86400\nt_start = cutoff - 45 * day\n\nlistens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", list(tu)), (\"timestamp\", \">=\", t_start), (\"played_ratio_pct\", \">=\", 50)]\n)\nlistens = listens[listens.item_id.isin(item2idx)].sort_values([\"uid\", \"timestamp\"])\nprint(f\"Loaded {len(listens)} positive catalog listens in {time.time() - t0:.2f} s\")\n\n# Group by user and create sequential training samples\nt1 = time.time()\nsamples = []\nfor uid, grp in listens.groupby(\"uid\"):\n items = grp.item_idx.values if \"item_idx\" in grp else grp.item_id.map(item2idx).values\n timestamps = grp.timestamp.values\n is_org = grp.is_organic.values\n # For every 5th item after index 10:\n for idx in range(10, len(items), 5):\n target = items[idx]\n weight = 2.0 if is_org[idx] == 1 else 1.0\n # History prefix\n hist_prefix = items[max(0, idx - 50):idx]\n samples.append((uid, target, weight, len(hist_prefix)))\n\nprint(f\"Generated {len(samples)} sequential training samples in {time.time() - t1:.2f} s!\")\n'",
"description": "Test sequential training samples generation speed"
}<untrusted_context>
Output: Loaded 4109923 positive catalog listens in 1.37 s
Generated 808850 sequential training samples in 111.98 s!
Process Group PGID: 3052
</untrusted_context>Optimizing data generation
I'm re-evaluating my training data generation process. The current Python loop is too slow, taking about two minutes for a substantial chunk of data. My next steps involve exploring NumPy vectorization and chunking techniques to dramatically improve speed and efficiency.
Improving recommendation relevance
My current focus is on enhancing NDCG@10. I'm dissecting why the TwoTower model combined with popularity bias significantly boosted performance to 0.012155. The synergy lies in leveraging both personalized neural similarity and a strong prior on universally liked items.
Refining recommendation scoring
I've realized the relevance score is a dot product of user and item embeddings, where item embeddings are precomputed. I'm now focused on how tuning the popularity weight alpha and exploring which catalog features improve the neural tower's performance might further boost NDCG@10.
{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_base = torch.mm(u_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\n# Test different feature coordinates:\n# f0: log pos listens (standardized)\n# f1: log organic pos listens (standardized)\n# f2: popularity rank (1 - rank/30000)\n# f3: track length\n\nfor f_idx, name in enumerate([\"pos_listens\", \"org_listens\", \"pop_rank\", \"track_length\"]):\n feat = catalog_data.features[:, f_idx].unsqueeze(0)\n for alpha in [0.03, 0.05, 0.08, 0.1]:\n scores = scores_base + alpha * feat\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Feature:\", name, \"Alpha:\", alpha, \"NDCG@10:\", res[\"quality_ndcg@10\"])\n'",
"description": "Test different catalog feature coordinates"
}<untrusted_context>
Output: Feature: pos_listens Alpha: 0.03 NDCG@10: 0.011948
Feature: pos_listens Alpha: 0.05 NDCG@10: 0.012155
Feature: pos_listens Alpha: 0.08 NDCG@10: 0.011417
Feature: pos_listens Alpha: 0.1 NDCG@10: 0.011319
Feature: org_listens Alpha: 0.03 NDCG@10: 0.012761
Feature: org_listens Alpha: 0.05 NDCG@10: 0.012708
Feature: org_listens Alpha: 0.08 NDCG@10: 0.012549
Feature: org_listens Alpha: 0.1 NDCG@10: 0.012246
Feature: pop_rank Alpha: 0.03 NDCG@10: 0.010473
Feature: pop_rank Alpha: 0.05 NDCG@10: 0.010384
Feature: pop_rank Alpha: 0.08 NDCG@10: 0.010534
Feature: pop_rank Alpha: 0.1 NDCG@10: 0.01053
Feature: track_length Alpha: 0.03 NDCG@10: 0.010053
Feature: track_length Alpha: 0.05 NDCG@10: 0.009784
Feature: track_length Alpha: 0.08 NDCG@10: 0.007299
Feature: track_length Alpha: 0.1 NDCG@10: 0.005415
Process Group PGID: 3073
</untrusted_context>Analyzing Organic Discovery
I'm currently focused on understanding the significance of the org_listens metric, particularly its low Alpha and NDCG@10 scores. My current thinking is that this metric is key to achieving the objective of organic discovery, with relevance directly tied to observed organic behavior.
{
"description": "Fine grid test on organic listens prior",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_base = torch.mm(u_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nfeat = catalog_data.features[:, 1].unsqueeze(0) # org_listens\n\nfor alpha in [0.01, 0.015, 0.02, 0.025, 0.03, 0.035, 0.04]:\n scores = scores_base + alpha * feat\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Alpha:\", alpha, \"NDCG@10:\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: Alpha: 0.01 NDCG@10: 0.011193
Alpha: 0.015 NDCG@10: 0.011493
Alpha: 0.02 NDCG@10: 0.011992
Alpha: 0.025 NDCG@10: 0.012427
Alpha: 0.03 NDCG@10: 0.012761
Alpha: 0.035 NDCG@10: 0.01298
Alpha: 0.04 NDCG@10: 0.013111
Process Group PGID: 3099
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(num_items=30000, embed_dim=128, out_dim=128)\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n item_vecs = ranker.item_tower.precompute_all_items(catalog_data.features)\n u_vecs = ranker.user_tower(\n liked_items=val_tensors.liked_items,\n disliked_items=val_tensors.disliked_items,\n recent_items=val_tensors.recent_items,\n recent_weights=val_tensors.recent_weights,\n recent_mask=val_tensors.recent_mask,\n organic_items=val_tensors.organic_items,\n )\n scores_base = torch.mm(u_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nfeat = catalog_data.features[:, 1].unsqueeze(0)\n\nfor alpha in [0.04, 0.045, 0.05, 0.055, 0.06, 0.07]:\n scores = scores_base + alpha * feat\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Alpha:\", alpha, \"NDCG@10:\", res[\"quality_ndcg@10\"])\n'",
"description": "Test alpha up to 0.07"
}<untrusted_context>
Output: Alpha: 0.04 NDCG@10: 0.013111
Alpha: 0.045 NDCG@10: 0.012933
Alpha: 0.05 NDCG@10: 0.012708
Alpha: 0.055 NDCG@10: 0.01274
Alpha: 0.06 NDCG@10: 0.01286
Alpha: 0.07 NDCG@10: 0.013033
Process Group PGID: 3125
</untrusted_context>Testing Alpha's Impact
I've found that an alpha of 0.04 yields a discovery NDCG@10 of 0.013111, representing a 22.4% relative improvement over the baseline's 0.010710. My next step is to determine if this can be directly integrated into the PyTorch neural network towers.
Validating Model Architecture
I've confirmed that the proposed architecture aligns with the dot product calculation: UserTower and ItemTower outputs, when concatenated with the scaled org_listens and a bias term, precisely reproduce the desired scoring function. This validates the mathematical formulation.
{
"description": "Verify TwoTowerNetwork dot product matches exactly",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nclass TwoTowerNetwork(nn.Module):\n def __init__(self, base_ranker, alpha=0.04):\n super().__init__()\n self.base_ranker = base_ranker\n self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)\n \n def get_item_vectors(self, catalog_features):\n taste_v = self.base_ranker.item_tower.precompute_all_items(catalog_features) # (30000, 128)\n prior_v = self.alpha * catalog_features[:, 1].unsqueeze(-1) # (30000, 1)\n return torch.cat([taste_v, prior_v], dim=-1) # (30000, 129)\n \n def get_user_vectors(self, liked, disliked, recent, recent_w, recent_m, organic):\n taste_u = self.base_ranker.user_tower(liked, disliked, recent, recent_w, recent_m, organic) # (N, 128)\n ones_u = torch.ones((taste_u.shape[0], 1)) # (N, 1)\n return torch.cat([taste_u, ones_u], dim=-1) # (N, 129)\n\nfrom model import TwoTowerRanker\nbase = TwoTowerRanker()\nbase.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nnet = TwoTowerNetwork(base, alpha=0.04)\n\nwith torch.no_grad():\n item_vecs = net.get_item_vectors(catalog_data.features) # (30000, 129)\n user_vecs = net.get_user_vectors(\n val_tensors.liked_items,\n val_tensors.disliked_items,\n val_tensors.recent_items,\n val_tensors.recent_weights,\n val_tensors.recent_mask,\n val_tensors.organic_items,\n ) # (8456, 129)\n scores = torch.mm(user_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nscores.masked_fill_(~mask, -1e9)\n_, topk_idx = torch.topk(scores, 100, dim=1)\ntopk_np = topk_idx.numpy()\n\nranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n}\nres = score_rankings(ranked_dict, graded)\nprint(\"TwoTowerNetwork dot product NDCG@10:\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 11, in <module>
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 3151
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nclass TwoTowerNetwork(nn.Module):\n def __init__(self, base_ranker, alpha=0.04):\n super().__init__()\n self.base_ranker = base_ranker\n self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)\n \n def get_item_vectors(self, catalog_features):\n taste_v = self.base_ranker.item_tower.precompute_all_items(catalog_features) # (30000, 128)\n prior_v = self.alpha * catalog_features[:, 1].unsqueeze(-1) # (30000, 1)\n return torch.cat([taste_v, prior_v], dim=-1) # (30000, 129)\n \n def get_user_vectors(self, liked, disliked, recent, recent_w, recent_m, organic):\n taste_u = self.base_ranker.user_tower(liked, disliked, recent, recent_w, recent_m, organic) # (N, 128)\n ones_u = torch.ones((taste_u.shape[0], 1)) # (N, 1)\n return torch.cat([taste_u, ones_u], dim=-1) # (N, 129)\n\nfrom model import TwoTowerRanker\nbase = TwoTowerRanker()\nbase.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nnet = TwoTowerNetwork(base, alpha=0.04)\n\nwith torch.no_grad():\n item_vecs = net.get_item_vectors(catalog_data.features) # (30000, 129)\n user_vecs = net.get_user_vectors(\n val_tensors.liked_items,\n val_tensors.disliked_items,\n val_tensors.recent_items,\n val_tensors.recent_weights,\n val_tensors.recent_mask,\n val_tensors.organic_items,\n ) # (8456, 129)\n scores = torch.mm(user_vecs, item_vecs.t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nscores.masked_fill_(~mask, -1e9)\n_, topk_idx = torch.topk(scores, 100, dim=1)\ntopk_np = topk_idx.numpy()\n\nranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n}\nres = score_rankings(ranked_dict, graded)\nprint(\"TwoTowerNetwork dot product NDCG@10:\", res[\"quality_ndcg@10\"])\n'",
"description": "Verify TwoTowerNetwork dot product with sys.path"
}<untrusted_context>
Output: TwoTowerNetwork dot product NDCG@10: 0.013077
Process Group PGID: 3162
</untrusted_context>Verifying Relevance Metrics
I'm currently focusing on the quality_ndcg@10 metric, which stands at 0.013077. My next step is to confirm that the TwoTowerNetwork is correctly generating user and item vectors of dimension D, and that their dot product is being computed to yield the relevance score.
Confirming Vector Calculations
My recent focus has been on verifying the calculation of user and item vectors and their subsequent dot product for relevance scoring. I've confirmed this step is indeed correct, leading me to consider training on the combined train_* and val_* datasets for final submission.
Integrating Validation Data
I've confirmed that the validation data is crucial for updating user history. My next step is to meticulously inspect the val_*.parquet files to understand their structure and how best to integrate them with the train_* dataset for a more robust model.
{
"command": "python3 -c '\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path(\"/task/data\")\ntl = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"timestamp\"])\nvl = pd.read_parquet(DATA / \"val_listens.parquet\", columns=[\"uid\", \"timestamp\"])\nprint(f\"train_listens max ts: {tl.timestamp.max()}, val_listens min ts: {vl.timestamp.min()}, val_listens max ts: {vl.timestamp.max()}\")\n\ntps = pd.read_parquet(DATA / \"test_preference_state.parquet\")\nprint(f\"test_preference_state rows: {len(tps)}, states: {tps.state.value_counts().to_dict()}\")\n'",
"description": "Check val_listens and test_preference_state timestamps"
}<untrusted_context>
Output: train_listens max ts: 24786795, val_listens min ts: 24788600, val_listens max ts: 25393395
test_preference_state rows: 484776, states: {'liked': 432659, 'disliked': 52117}
Process Group PGID: 3188
</untrusted_context>Analyzing Data Distribution
My current focus is on understanding the temporal and preference data. The training data extends up to timestamp 24786795, while validation data begins shortly after at 24788600 and goes until 25393395. The preference dataset shows a notable imbalance, with significantly more liked items (432,659) than disliked items (52,117).
Refining Model Integration
I'm ensuring the ItemTower and UserTower in model.py are fully PyTorch modules. Their forward passes will directly output vectors of the same dimensionality, with the dot product yielding the precise relevance score. This confirms seamless integration.
Implementing Feature Integration
I am currently implementing the feature integration logic within the forward pass. This involves handling both direct item embeddings and projecting external features to the embedding dimension, ensuring compatibility with subsequent layers.
Combining Features and Bias
I'm integrating external features into the item embeddings and adding a learned bias term, scaled by alpha. The taste embedding is normalized, and then concatenated with this bias for the final output. This allows for a more nuanced representation.
Processing Recent Embeddings
I'm currently processing the embeddings for recent items, applying weights and calculating a weighted average. This involves careful handling of masking and ensuring the sums are properly normalized to avoid division by zero. This step is crucial for incorporating temporal relevance.
Integrating Model Components
I am now integrating the ItemTower and UserTower. The goal is to ensure their outputs are compatible, specifically that the ItemTower's out_dim matches the UserTower's expected item_emb dimension. This merge is crucial for the overall model architecture.
{
"content": "\"\"\"Two-tower neural network ranker architecture for latency-efficient MIPS retrieval.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nPAD_INDEX = 30000\nNUM_CATALOG_ITEMS = 30000\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower producing independently precomputable candidate embeddings.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n feature_dim: int = 4,\n out_dim: int = 128,\n alpha: float = 0.04,\n ):\n super().__init__()\n self.num_items = num_items\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)\n\n # Item embedding table (+1 for PAD_INDEX at 30000)\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[PAD_INDEX].zero_()\n\n # Item feature projection\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n\n # Output MLP producing (out_dim - 1) taste representation\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim - 1),\n )\n\n def forward(\n self,\n item_ids: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute item embeddings for a batch of item IDs.\n\n Returns:\n Item vectors of shape (B, out_dim).\n \"\"\"\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n # Feature column 1 is organic_positive_listens\n prior = self.alpha * batch_feats[:, 1].unsqueeze(-1)\n else:\n prior = torch.zeros((e.shape[0], 1), device=e.device)\n\n taste = F.normalize(self.mlp(e), p=2, dim=-1)\n return torch.cat([taste, prior], dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all catalog items offline.\"\"\"\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n batch_ids = all_ids[start:end]\n batch_feats = catalog_features[start:end]\n vecs = self.forward(batch_ids, batch_feats)\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user representations from interaction history.\"\"\"\n\n def __init__(\n self,\n item_emb: nn.Embedding,\n embed_dim: int = 128,\n out_dim: int = 128,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n\n # Attention pooling query vector\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n\n # Multi-component MLP: inputs are [likes, dislikes, recent_weighted, recent_attn, organic]\n # Total input dimension = 5 * embed_dim\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, out_dim - 1),\n )\n\n def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:\n \"\"\"Masked mean pooling over items tensor of shape (B, max_len).\"\"\"\n mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)\n emb = self.item_emb(items) # (B, max_len, embed_dim)\n counts = mask.sum(dim=1).clamp(min=1) # (B, 1)\n return (emb * mask).sum(dim=1) / counts # (B, embed_dim)\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute user representations from interaction histories.\"\"\"\n # 1. Explicit likes pool\n emb_liked = self._mean_pool(liked_items)\n\n # 2. Explicit dislikes pool\n emb_disliked = self._mean_pool(disliked_items)\n\n # 3. Recent listens: recency-weighted pool\n recent_emb = self.item_emb(recent_items) # (B, K, embed_dim)\n rw = recent_weights.unsqueeze(-1) # (B, K, 1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum\n\n # 4. Recent listens: learned attention pooling\n scores = torch.matmul(recent_emb, self.query_vector) / math.sqrt(self.embed_dim) # (B, K)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_weights = F.softmax(scores, dim=-1).unsqueeze(-1) # (B, K, 1)\n emb_recent_attn = (attn_weights * recent_emb).sum(dim=1)\n\n # 5. Organic positive listens pool\n emb_organic = self._mean_pool(organic_items)\n\n # Concatenate all representations\n h = torch.cat(\n [emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic],\n dim=-1,\n )\n\n taste = F.normalize(self.mlp(h), p=2, dim=-1)\n ones = torch.ones((taste.shape[0], 1), device=taste.device)\n return torch.cat([taste, ones], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Container for ItemTower and UserTower with tied embeddings.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n out_dim: int = 128,\n feature_dim: int = 4,\n dropout: float = 0.1,\n alpha: float = 0.04,\n ):\n super().__init__()\n self.item_tower = ItemTower(\n num_items=num_items,\n embed_dim=embed_dim,\n feature_dim=feature_dim,\n out_dim=out_dim,\n alpha=alpha,\n )\n self.user_tower = UserTower(\n item_emb=self.item_tower.item_emb,\n embed_dim=embed_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n target_items: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n user_vecs = self.user_tower(\n liked_items=liked_items,\n disliked_items=disliked_items,\n recent_items=recent_items,\n recent_weights=recent_weights,\n recent_mask=recent_mask,\n organic_items=organic_items,\n )\n item_vecs = self.item_tower(target_items, features)\n return user_vecs, item_vecs\n\n\nclass InBatchNegativesLoss(nn.Module):\n \"\"\"Sampled Softmax In-Batch Negatives loss with LogQ popularity debiasing.\"\"\"\n\n def __init__(self, temperature: float = 0.07):\n super().__init__()\n self.temperature = temperature\n\n def forward(\n self,\n user_vecs: torch.Tensor,\n item_vecs: torch.Tensor,\n target_items: torch.Tensor,\n weights: torch.Tensor,\n log_p: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted in-batch negatives cross-entropy loss.\"\"\"\n B = user_vecs.shape[0]\n # Cosine / dot product similarity matrix\n sim = torch.mm(user_vecs, item_vecs.t()) / self.temperature # (B, B)\n\n # LogQ correction: subtract log(p_j) from column j\n item_log_p = log_p[target_items] # (B,)\n sim = sim - item_log_p.unsqueeze(0)\n\n # Target label for user i is item i\n targets = torch.arange(B, device=user_vecs.device, dtype=torch.long)\n per_sample_loss = F.cross_entropy(sim, targets, reduction=\"none\")\n\n return (per_sample_loss * weights).mean()\n",
"file_path": "/app/solution/model.py"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
self,
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
feature_dim: int = 4,
out_dim: int = 128,
alpha: float = 0.04,
):
super().__init__()
self.num_items = num_items
self.embed_dim = embed_dim
self.out_dim = out_dim
self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)
# Item embedding table (+1 for PAD_INDEX at 30000)
self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)
nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)
with torch.no_grad():
...
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, embed_dim),
)
# Output MLP producing (out_dim - 1) taste representation
self.mlp = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim - 1),
)
def forward(
self,
item_ids: torch.Tensor,
features: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Compute item embeddings for a batch of item IDs.
Returns:
Item vectors of shape (B, out_dim).
"""
e = self.item_emb(item_ids)
if features is not None:
if features.shape[0] == self.num_items and item_ids.dim() == 1:
batch_feats = features[item_ids]
else:
batch_feats = features
f = self.feat_proj(batch_feats)
e = e + f
# Feature column 1 is organic_positive_listens
prior = self.alpha * batch_feats[:, 1].unsqueeze(-1)
else:
prior = torch.zeros((e.shape[0], 1), device=e.device)
taste = F.normalize(self.mlp(e), p=2, dim=-1)
return torch.cat([taste, prior], dim=-1)
@torch.no_grad()
def precompute_all_items(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
...
self.mlp = nn.Sequential(
nn.Linear(in_dim, 2 * embed_dim),
nn.LayerNorm(2 * embed_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(2 * embed_dim, out_dim - 1),
)
def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:
"""Masked mean pooling over items tensor of shape (B, max_len)."""
mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)
...
h = torch.cat(
[emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic],
dim=-1,
)
taste = F.normalize(self.mlp(h), p=2, dim=-1)
ones = torch.ones((taste.shape[0], 1), device=taste.device)
return torch.cat([taste, ones], dim=-1)
class TwoTowerRanker(nn.Module):
"""Container for ItemTower and UserTower with tied embeddings."""
...
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
out_dim: int = 128,
feature_dim: int = 4,
dropout: float = 0.1,
alpha: float = 0.04,
):
super().__init__()
self.item_tower = ItemTower(
num_items=num_items,
embed_dim=embed_dim,
feature_dim=feature_dim,
out_dim=out_dim,
alpha=alpha,
)
self.user_tower = UserTower(
item_emb=self.item_tower.item_emb,
embed_dim=embed_dim,
out_dim=out_dim,
...
item_vecs: torch.Tensor,
target_items: torch.Tensor,
weights: torch.Tensor,
log_p: torch.Tensor,
) -> torch.Tensor:
"""Compute weighted in-batch negatives cross-entropy loss."""
B = user_vecs.shape[0]
# Cosine / dot product similarity matrix
sim = torch.mm(user_vecs, item_vecs.t()) / self.temperature # (B, B)
# LogQ correction: subtract log(p_j) from column j
item_log_p = log_p[target_items] # (B,)
sim = sim - item_log_p.unsqueeze(0)
...{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/app/solution\")\nfrom train import main\nmain()\n'",
"description": "Run train.py with integrated TwoTowerRanker"
}<untrusted_context>
Output: PyTorch threads: 8
Loading catalog and target users...
Target users: 8456, Catalog items: 30000
Extracting validation user history at cutoff 24786800...
Validation tensors ready.
Loading event logs from t >= 17788400...
Loaded raw data in 0.39 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Total training pairs across windows: 189438
--- Starting Training ---
Epoch 1/5 finished in 21.5 s | Avg Loss: 7.1397 | Evaluating validation...
Epoch 1 Public Validation NDCG@10: 0.008634 (Users: 4263)
*** New best validation NDCG@10: 0.008634! Saved model and /app/solution/validation.parquet ***
Epoch 2/5 finished in 20.4 s | Avg Loss: 6.6307 | Evaluating validation...
Epoch 2 Public Validation NDCG@10: 0.010005 (Users: 4263)
*** New best validation NDCG@10: 0.010005! Saved model and /app/solution/validation.parquet ***
Epoch 3/5 finished in 21.0 s | Avg Loss: 6.1550 | Evaluating validation...
Epoch 3 Public Validation NDCG@10: 0.008777 (Users: 4263)
Epoch 4/5 finished in 20.9 s | Avg Loss: 5.7596 | Evaluating validation...
Epoch 4 Public Validation NDCG@10: 0.007285 (Users: 4263)
Epoch 5/5 finished in 20.9 s | Avg Loss: 5.4894 | Evaluating validation...
Epoch 5 Public Validation NDCG@10: 0.006999 (Users: 4263)
Training complete! Best Public Validation NDCG@10: 0.010005
Process Group PGID: 3208
</untrusted_context>Refining Feature Interaction
I'm currently focusing on how the prior feature, specifically batch_feats[:, 1].unsqueeze(-1), interacts within the ItemTower during training. My current line of inquiry is understanding its influence on the final sim calculation, especially when i_vecs incorporates this prior.
Integrating Prior Knowledge
My current focus is on how the prior is applied, noting its addition to all in-batch negatives. I'm exploring its interaction with log_p within the sampled softmax denominator. Previously, when taste embeddings were learned independently and the prior applied separately, I achieved a notable score. I'm now investigating alternative integration methods, such as a separate learned head or direct addition to item vectors, to discern their impact on the final relevance score calculation and inference efficiency.
{
"description": "Generate validation.parquet and run evaluate_public.py",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom evaluate import generate_ranking_dataframe, evaluate_on_validation\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker(alpha=0.04)\n# Load best base taste weights\nstate = torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\")\n# filter out any non-matching keys if any\nranker.load_state_dict(state, strict=False)\nranker.eval()\n\nscores = evaluate_on_validation(\n ranker=ranker,\n catalog_data=catalog_data,\n val_user_tensors=val_tensors,\n target_uids=target_uids,\n output_parquet_path=Path(\"/app/solution/validation.parquet\"),\n)\nprint(\"Saved /app/solution/validation.parquet with score:\", scores)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/validation.parquet"
}<untrusted_context>
Output: Saved /app/solution/validation.parquet with score: {'quality_ndcg@10': 0.010005, 'quality_users': 4263}
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010005,
"quality_users": 4263
}
}
Process Group PGID: 3245
</untrusted_context>Investigating metric discrepancy
I'm digging into why quality_ndcg@10 dropped. It seems the train.py script might have overwritten the best model with one trained at alpha=0.04 instead of the previous higher-performing one, impacting the current evaluation.
{
"description": "Check existing val_epoch files",
"command": "ls -lh /app/solution/val_epoch_*.parquet"
}<untrusted_context>
Output: -rw-r--r-- 1 agent agent 1.5M Sep 29 02:21 /app/solution/val_epoch_1.parquet
-rw-r--r-- 1 agent agent 1.6M Sep 29 02:21 /app/solution/val_epoch_2.parquet
-rw-r--r-- 1 agent agent 1.8M Sep 29 02:21 /app/solution/val_epoch_3.parquet
-rw-r--r-- 1 agent agent 1.8M Sep 29 02:22 /app/solution/val_epoch_4.parquet
-rw-r--r-- 1 agent agent 1.8M Sep 29 02:22 /app/solution/val_epoch_5.parquet
Process Group PGID: 3289
</untrusted_context>Investigating Training Discrepancy
I'm currently dissecting the training process to understand why val_epoch_2.parquet was overwritten. My initial investigation points to a dimension mismatch in taste_v and an incorrect out_dim calculation within ItemTower.mlp during the first run. I'm now scrutinizing model.py for the root cause.
{
"description": "Train taste and evaluate combined 129-dim dot product",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom train import build_window_datasets\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=3,\n lookback_days=60,\n)\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items=30000, embed_dim=128, feature_dim=4, taste_dim=128, alpha=0.04):\n super().__init__()\n self.num_items = num_items\n self.embed_dim = embed_dim\n self.taste_dim = taste_dim\n self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=30000)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[30000].zero_()\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, taste_dim),\n )\n def forward(self, item_ids, features=None):\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n bias = self.alpha * batch_feats[:, 1].unsqueeze(-1)\n else:\n bias = torch.zeros((e.shape[0], 1), device=e.device)\n taste = F.normalize(self.mlp(e), p=2, dim=-1)\n return torch.cat([taste, bias], dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(self, catalog_features, batch_size=4096):\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n vecs = self.forward(all_ids[start:end], catalog_features[start:end])\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\nclass UserTower(nn.Module):\n def __init__(self, item_emb, embed_dim=128, taste_dim=128, dropout=0.1):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.taste_dim = taste_dim\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, taste_dim),\n )\n def _mean_pool(self, items):\n mask = (items != 30000).unsqueeze(-1)\n emb = self.item_emb(items)\n counts = mask.sum(dim=1).clamp(min=1)\n return (emb * mask).sum(dim=1) / counts\n def forward(self, liked_items, disliked_items, recent_items, recent_weights, recent_mask, organic_items):\n emb_l = self._mean_pool(liked_items)\n emb_d = self._mean_pool(disliked_items)\n recent_emb = self.item_emb(recent_items)\n rw = recent_weights.unsqueeze(-1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_rw = (recent_emb * rw).sum(dim=1) / rw_sum\n scores = torch.matmul(recent_emb, self.query_vector) / (self.embed_dim ** 0.5)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_w = F.softmax(scores, dim=-1).unsqueeze(-1)\n emb_attn = (attn_w * recent_emb).sum(dim=1)\n emb_org = self._mean_pool(organic_items)\n h = torch.cat([emb_l, emb_d, emb_rw, emb_attn, emb_org], dim=-1)\n taste = F.normalize(self.mlp(h), p=2, dim=-1)\n ones = torch.ones((taste.shape[0], 1), device=taste.device)\n return torch.cat([taste, ones], dim=-1)\n\nclass Ranker(nn.Module):\n def __init__(self):\n super().__init__()\n self.item_tower = ItemTower()\n self.user_tower = UserTower(self.item_tower.item_emb)\n def forward(self, liked, disliked, recent, recent_w, recent_m, organic, target_items, features):\n u = self.user_tower(liked, disliked, recent, recent_w, recent_m, organic)\n v = self.item_tower(target_items, features)\n return u, v\n\nranker = Ranker()\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=1e-3, weight_decay=1e-4)\n\nval_eligibility_path = DATA_DIR / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nlog_p = catalog_data.log_p\n\nfor epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u, v = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n \n # Train taste dimensions (first 128 dims)\n u_taste = u[:, :128]\n v_taste = v[:, :128]\n sim = torch.mm(u_taste, v_taste.t()) / 0.07 - log_p[targets].unsqueeze(0)\n loss = (F.cross_entropy(sim, torch.arange(len(u)), reduction=\"none\") * weights).mean()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n ranker.eval()\n with torch.no_grad():\n all_v = ranker.item_tower.precompute_all_items(catalog_data.features) # (30000, 129)\n all_u = ranker.user_tower(val_tensors.liked_items, val_tensors.disliked_items, val_tensors.recent_items, val_tensors.recent_weights, val_tensors.recent_mask, val_tensors.organic_items) # (8456, 129)\n scores = torch.mm(all_u, all_v.t()) # dot product of 129-dim vectors!\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Epoch\", epoch, \"NDCG@10 =\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: Loading event logs from t >= 17788400...
Loaded raw data in 0.39 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Epoch 1 NDCG@10 = 0.01258
Epoch 2 NDCG@10 = 0.012488
Epoch 3 NDCG@10 = 0.01215
Process Group PGID: 3292
</untrusted_context>Testing Hyperparameters Now
I'm currently exploring the impact of adjusting the learning rate, specifically trying 5e-4 and 3e-4 with warmup and cosine decay. I'm also investigating the effect of using 4 or 5 windows, with initial results for 4 windows showing promising progress.
{
"description": "Test 4 windows with lr=5e-4",
"command": "python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom train import build_window_datasets\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\ntrain_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=4,\n lookback_days=60,\n)\ntrain_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items=30000, embed_dim=128, feature_dim=4, taste_dim=128, alpha=0.04):\n super().__init__()\n self.num_items = num_items\n self.embed_dim = embed_dim\n self.taste_dim = taste_dim\n self.alpha = nn.Parameter(torch.tensor([alpha]), requires_grad=False)\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=30000)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[30000].zero_()\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, taste_dim),\n )\n def forward(self, item_ids, features=None):\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n bias = self.alpha * batch_feats[:, 1].unsqueeze(-1)\n else:\n bias = torch.zeros((e.shape[0], 1), device=e.device)\n taste = F.normalize(self.mlp(e), p=2, dim=-1)\n return torch.cat([taste, bias], dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(self, catalog_features, batch_size=4096):\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n vecs = self.forward(all_ids[start:end], catalog_features[start:end])\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\nclass UserTower(nn.Module):\n def __init__(self, item_emb, embed_dim=128, taste_dim=128, dropout=0.1):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.taste_dim = taste_dim\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, taste_dim),\n )\n def _mean_pool(self, items):\n mask = (items != 30000).unsqueeze(-1)\n emb = self.item_emb(items)\n counts = mask.sum(dim=1).clamp(min=1)\n return (emb * mask).sum(dim=1) / counts\n def forward(self, liked_items, disliked_items, recent_items, recent_weights, recent_mask, organic_items):\n emb_l = self._mean_pool(liked_items)\n emb_d = self._mean_pool(disliked_items)\n recent_emb = self.item_emb(recent_items)\n rw = recent_weights.unsqueeze(-1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_rw = (recent_emb * rw).sum(dim=1) / rw_sum\n scores = torch.matmul(recent_emb, self.query_vector) / (self.embed_dim ** 0.5)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_w = F.softmax(scores, dim=-1).unsqueeze(-1)\n emb_attn = (attn_w * recent_emb).sum(dim=1)\n emb_org = self._mean_pool(organic_items)\n h = torch.cat([emb_l, emb_d, emb_rw, emb_attn, emb_org], dim=-1)\n taste = F.normalize(self.mlp(h), p=2, dim=-1)\n ones = torch.ones((taste.shape[0], 1), device=taste.device)\n return torch.cat([taste, ones], dim=-1)\n\nclass Ranker(nn.Module):\n def __init__(self):\n super().__init__()\n self.item_tower = ItemTower()\n self.user_tower = UserTower(self.item_tower.item_emb)\n def forward(self, liked, disliked, recent, recent_w, recent_m, organic, target_items, features):\n u = self.user_tower(liked, disliked, recent, recent_w, recent_m, organic)\n v = self.item_tower(target_items, features)\n return u, v\n\nranker = Ranker()\noptimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\n\nval_eligibility_path = DATA_DIR / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nlog_p = catalog_data.log_p\n\nfor epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u, v = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n \n u_taste = u[:, :128]\n v_taste = v[:, :128]\n sim = torch.mm(u_taste, v_taste.t()) / 0.07 - log_p[targets].unsqueeze(0)\n loss = (F.cross_entropy(sim, torch.arange(len(u)), reduction=\"none\") * weights).mean()\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n ranker.eval()\n with torch.no_grad():\n all_v = ranker.item_tower.precompute_all_items(catalog_data.features)\n all_u = ranker.user_tower(val_tensors.liked_items, val_tensors.disliked_items, val_tensors.recent_items, val_tensors.recent_weights, val_tensors.recent_mask, val_tensors.organic_items)\n scores = torch.mm(all_u, all_v.t())\n scores.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(scores, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Epoch\", epoch, \"NDCG@10 =\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: Loading event logs from t >= 17183600...
Loaded raw data in 0.38 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Building window 4: history < 22367600, outcome in [22367600, 22972400]...
Window 4 pairs: 60166
Epoch 1 NDCG@10 = 0.011641
Epoch 2 NDCG@10 = 0.012918
Epoch 3 NDCG@10 = 0.011108
Process Group PGID: 3329
</untrusted_context>Observing Performance Metrics
I'm seeing promising results with NDCG@10 hitting 0.012918 in Epoch 2. This training duration appears to be a sweet spot across configurations, consistently yielding optimal performance before overfitting becomes a concern, while also keeping training time efficient.
Analyzing Refitting Logic
I'm re-evaluating the prompt's refitting instructions carefully. My current focus is on distinguishing the roles of public windows for validation versus final refitting, ensuring val_* events are used appropriately. I also need to finalize the submission format, generating 100 ranked items per user from the candidate set.
Finalizing Submission Artifacts
I'm confirming that the refitted model utilizes the latest validation data for training. My focus is now on ensuring the submission file, /app/solution/submission.parquet, correctly reflects the top 100 ranked items per user, incorporating explicit preferences and eligible candidates for the test cutoff.
Structuring Code Repository
I'm organizing the codebase into logical components within /app/solution/. This includes defining modules for item and user towers, the ranking mechanism, and the loss function. Data loading and dataset creation are also being streamlined for efficient training.
Implementing Model Architecture
I'm focusing on a clean implementation of model.py, specifically defining the TwoTowerRanker with a 128-dimensional embedding size. This will facilitate the subsequent refitting and submission steps as outlined.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"Two-tower neural network ranker architecture for latency-efficient MIPS retrieval.\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nPAD_INDEX = 30000\nNUM_CATALOG_ITEMS = 30000\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower producing independently precomputable candidate embeddings of dimension D.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n feature_dim: int = 4,\n out_dim: int = 128,\n alpha: float = 0.04,\n ):\n super().__init__()\n self.num_items = num_items\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n self.taste_dim = out_dim - 1\n self.alpha = nn.Parameter(torch.tensor([alpha], dtype=torch.float32), requires_grad=False)\n\n # Item embedding table (+1 for PAD_INDEX at 30000)\n self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)\n nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)\n with torch.no_grad():\n self.item_emb.weight[PAD_INDEX].zero_()\n\n # Item feature projection\n self.feat_proj = nn.Sequential(\n nn.Linear(feature_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, embed_dim),\n )\n\n # Output MLP producing taste_dim representation\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, self.taste_dim),\n )\n\n def forward(\n self,\n item_ids: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute item embeddings for a batch of item IDs.\n\n Returns:\n Item vectors of shape (B, out_dim).\n \"\"\"\n e = self.item_emb(item_ids)\n if features is not None:\n if features.shape[0] == self.num_items and item_ids.dim() == 1:\n batch_feats = features[item_ids]\n else:\n batch_feats = features\n f = self.feat_proj(batch_feats)\n e = e + f\n # Feature column 1 is standardized log organic_positive_listens\n prior = self.alpha * batch_feats[:, 1].unsqueeze(-1)\n else:\n prior = torch.zeros((e.shape[0], 1), device=e.device)\n\n taste = F.normalize(self.mlp(e), p=2, dim=-1)\n return torch.cat([taste, prior], dim=-1)\n\n @torch.no_grad()\n def precompute_all_items(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all catalog items offline.\"\"\"\n self.eval()\n all_ids = torch.arange(self.num_items, dtype=torch.long)\n vectors = []\n for start in range(0, self.num_items, batch_size):\n end = min(start + batch_size, self.num_items)\n batch_ids = all_ids[start:end]\n batch_feats = catalog_features[start:end]\n vecs = self.forward(batch_ids, batch_feats)\n vectors.append(vecs)\n return torch.cat(vectors, dim=0)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user representations from interaction history.\"\"\"\n\n def __init__(\n self,\n item_emb: nn.Embedding,\n embed_dim: int = 128,\n out_dim: int = 128,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_emb = item_emb\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n self.taste_dim = out_dim - 1\n\n # Attention pooling query vector\n self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)\n\n # Multi-component MLP: inputs are [likes, dislikes, recent_weighted, recent_attn, organic]\n # Total input dimension = 5 * embed_dim\n in_dim = 5 * embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, 2 * embed_dim),\n nn.LayerNorm(2 * embed_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(2 * embed_dim, self.taste_dim),\n )\n\n def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:\n \"\"\"Masked mean pooling over items tensor of shape (B, max_len).\"\"\"\n mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)\n emb = self.item_emb(items) # (B, max_len, embed_dim)\n counts = mask.sum(dim=1).clamp(min=1) # (B, 1)\n return (emb * mask).sum(dim=1) / counts # (B, embed_dim)\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute user representations from interaction histories.\"\"\"\n # 1. Explicit likes pool\n emb_liked = self._mean_pool(liked_items)\n\n # 2. Explicit dislikes pool\n emb_disliked = self._mean_pool(disliked_items)\n\n # 3. Recent positive listens: recency-weighted pool\n recent_emb = self.item_emb(recent_items) # (B, K, embed_dim)\n rw = recent_weights.unsqueeze(-1) # (B, K, 1)\n rw_sum = rw.sum(dim=1).clamp(min=1e-6)\n emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum\n\n # 4. Recent positive listens: learned attention pooling\n scores = torch.matmul(recent_emb, self.query_vector) / math.sqrt(self.embed_dim) # (B, K)\n scores = scores.masked_fill(~recent_mask, -1e9)\n attn_weights = F.softmax(scores, dim=-1).unsqueeze(-1) # (B, K, 1)\n emb_recent_attn = (attn_weights * recent_emb).sum(dim=1)\n\n # 5. Organic positive listens pool\n emb_organic = self._mean_pool(organic_items)\n\n # Concatenate all representations\n h = torch.cat(\n [emb_liked, emb_disliked, emb_recent_w, emb_recent_attn, emb_organic],\n dim=-1,\n )\n\n taste = F.normalize(self.mlp(h), p=2, dim=-1)\n ones = torch.ones((taste.shape[0], 1), device=taste.device)\n return torch.cat([taste, ones], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Container for ItemTower and UserTower with tied embeddings and exact dot product scoring.\"\"\"\n\n def __init__(\n self,\n num_items: int = NUM_CATALOG_ITEMS,\n embed_dim: int = 128,\n out_dim: int = 128,\n feature_dim: int = 4,\n dropout: float = 0.1,\n alpha: float = 0.04,\n ):\n super().__init__()\n self.item_tower = ItemTower(\n num_items=num_items,\n embed_dim=embed_dim,\n feature_dim=feature_dim,\n out_dim=out_dim,\n alpha=alpha,\n )\n self.user_tower = UserTower(\n item_emb=self.item_tower.item_emb,\n embed_dim=embed_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n\n def forward(\n self,\n liked_items: torch.Tensor,\n disliked_items: torch.Tensor,\n recent_items: torch.Tensor,\n recent_weights: torch.Tensor,\n recent_mask: torch.Tensor,\n organic_items: torch.Tensor,\n target_items: torch.Tensor,\n features: Optional[torch.Tensor] = None,\n ) -> Tuple[torch.Tensor, torch.Tensor]:\n u = self.user_tower(\n liked_items=liked_items,\n disliked_items=disliked_items,\n recent_items=recent_items,\n recent_weights=recent_weights,\n recent_mask=recent_mask,\n organic_items=organic_items,\n )\n v = self.item_tower(target_items, features)\n return u, v\n\n\nclass InBatchNegativesLoss(nn.Module):\n \"\"\"Sampled Softmax In-Batch Negatives loss with LogQ popularity debiasing.\"\"\"\n\n def __init__(self, temperature: float = 0.07, taste_dim: int = 127):\n super().__init__()\n self.temperature = temperature\n self.taste_dim = taste_dim\n\n def forward(\n self,\n user_vecs: torch.Tensor,\n item_vecs: torch.Tensor,\n target_items: torch.Tensor,\n weights: torch.Tensor,\n log_p: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted in-batch negatives cross-entropy loss on taste embeddings.\"\"\"\n B = user_vecs.shape[0]\n u_taste = user_vecs[:, :self.taste_dim]\n v_taste = item_vecs[:, :self.taste_dim]\n\n sim = torch.mm(u_taste, v_taste.t()) / self.temperature # (B, B)\n item_log_p = log_p[target_items] # (B,)\n sim = sim - item_log_p.unsqueeze(0)\n\n targets = torch.arange(B, device=user_vecs.device, dtype=torch.long)\n per_sample_loss = F.cross_entropy(sim, targets, reduction=\"none\")\n\n return (per_sample_loss * weights).mean()\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
PAD_INDEX = 30000
NUM_CATALOG_ITEMS = 30000
class ItemTower(nn.Module):
"""Item Tower producing independently precomputable candidate embeddings of dimension D."""
def __init__(
self,
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
...
):
super().__init__()
self.num_items = num_items
self.embed_dim = embed_dim
self.out_dim = out_dim
self.taste_dim = out_dim - 1
self.alpha = nn.Parameter(torch.tensor([alpha], dtype=torch.float32), requires_grad=False)
# Item embedding table (+1 for PAD_INDEX at 30000)
self.item_emb = nn.Embedding(num_items + 1, embed_dim, padding_idx=PAD_INDEX)
nn.init.normal_(self.item_emb.weight, mean=0.0, std=0.05)
with torch.no_grad():
...
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, embed_dim),
)
# Output MLP producing taste_dim representation
self.mlp = nn.Sequential(
nn.Linear(embed_dim, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, self.taste_dim),
)
def forward(
self,
item_ids: torch.Tensor,
...
batch_feats = features[item_ids]
else:
batch_feats = features
f = self.feat_proj(batch_feats)
e = e + f
# Feature column 1 is standardized log organic_positive_listens
prior = self.alpha * batch_feats[:, 1].unsqueeze(-1)
else:
prior = torch.zeros((e.shape[0], 1), device=e.device)
taste = F.normalize(self.mlp(e), p=2, dim=-1)
...
):
super().__init__()
self.item_emb = item_emb
self.embed_dim = embed_dim
self.out_dim = out_dim
self.taste_dim = out_dim - 1
# Attention pooling query vector
self.query_vector = nn.Parameter(torch.randn(embed_dim) * 0.05)
# Multi-component MLP: inputs are [likes, dislikes, recent_weighted, recent_attn, organic]
...
self.mlp = nn.Sequential(
nn.Linear(in_dim, 2 * embed_dim),
nn.LayerNorm(2 * embed_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(2 * embed_dim, self.taste_dim),
)
def _mean_pool(self, items: torch.Tensor) -> torch.Tensor:
"""Masked mean pooling over items tensor of shape (B, max_len)."""
mask = (items != PAD_INDEX).unsqueeze(-1) # (B, max_len, 1)
...
emb_liked = self._mean_pool(liked_items)
# 2. Explicit dislikes pool
emb_disliked = self._mean_pool(disliked_items)
# 3. Recent positive listens: recency-weighted pool
recent_emb = self.item_emb(recent_items) # (B, K, embed_dim)
rw = recent_weights.unsqueeze(-1) # (B, K, 1)
rw_sum = rw.sum(dim=1).clamp(min=1e-6)
emb_recent_w = (recent_emb * rw).sum(dim=1) / rw_sum
# 4. Recent positive listens: learned attention pooling
scores = torch.matmul(recent_emb, self.query_vector) / math.sqrt(self.embed_dim) # (B, K)
scores = scores.masked_fill(~recent_mask, -1e9)
attn_weights = F.softmax(scores, dim=-1).unsqueeze(-1) # (B, K, 1)
emb_recent_attn = (attn_weights * recent_emb).sum(dim=1)
...
ones = torch.ones((taste.shape[0], 1), device=taste.device)
return torch.cat([taste, ones], dim=-1)
class TwoTowerRanker(nn.Module):
"""Container for ItemTower and UserTower with tied embeddings and exact dot product scoring."""
def __init__(
self,
num_items: int = NUM_CATALOG_ITEMS,
embed_dim: int = 128,
...
recent_mask: torch.Tensor,
organic_items: torch.Tensor,
target_items: torch.Tensor,
features: Optional[torch.Tensor] = None,
) -> Tuple[torch.Tensor, torch.Tensor]:
u = self.user_tower(
liked_items=liked_items,
disliked_items=disliked_items,
recent_items=recent_items,
recent_weights=recent_weights,
recent_mask=recent_mask,
organic_items=organic_items,
)
v = self.item_tower(target_items, features)
return u, v
class InBatchNegativesLoss(nn.Module):
"""Sampled Softmax In-Batch Negatives loss with LogQ popularity debiasing."""
def __init__(self, temperature: float = 0.07, taste_dim: int = 127):
super().__init__()
self.temperature = temperature
self.taste_dim = taste_dim
def forward(
self,
user_vecs: torch.Tensor,
item_vecs: torch.Tensor,
target_items: torch.Tensor,
weights: torch.Tensor,
log_p: torch.Tensor,
) -> torch.Tensor:
"""Compute weighted in-batch negatives cross-entropy loss on taste embeddings."""
B = user_vecs.shape[0]
u_taste = user_vecs[:, :self.taste_dim]
v_taste = item_vecs[:, :self.taste_dim]
sim = torch.mm(u_taste, v_taste.t()) / self.temperature # (B, B)
item_log_p = log_p[target_items] # (B,)
sim = sim - item_log_p.unsqueeze(0)
targets = torch.arange(B, device=user_vecs.device, dtype=torch.long)
per_sample_loss = F.cross_entropy(sim, targets, reduction="none")
return (per_sample_loss * weights).mean()
{
"file_path": "/app/solution/train.py",
"content": "\"\"\"Training script for Two-Tower Neural Ranker with public validation evaluation.\"\"\"\nfrom __future__ import annotations\n\nimport shutil\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import List, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import ConcatDataset, DataLoader\n\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\n\nfrom data import (\n DATA_DIR,\n NUM_CATALOG_ITEMS,\n TwoTowerDataset,\n extract_cutoff_history,\n load_catalog,\n)\nfrom evaluate import evaluate_on_validation\nfrom model import InBatchNegativesLoss, TwoTowerRanker\n\n\ndef build_window_datasets(\n catalog_data,\n target_uids: List[int],\n num_windows: int = 4,\n lookback_days: int = 60,\n) -> Tuple[ConcatDataset, int]:\n \"\"\"Build multi-window discovery training datasets from training event logs.\"\"\"\n t0 = time.time()\n cutoff = 24786800\n day = 86400\n uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\n item2idx = catalog_data.item2idx\n\n # Load all needed events\n t_min_load = cutoff - (num_windows * 7 + lookback_days) * day\n print(f\"Loading event logs from t >= {t_min_load}...\")\n listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n likes = pd.read_parquet(\n DATA_DIR / \"train_likes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n unlikes = pd.read_parquet(\n DATA_DIR / \"train_unlikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n dislikes = pd.read_parquet(\n DATA_DIR / \"train_dislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n undislikes = pd.read_parquet(\n DATA_DIR / \"train_undislikes.parquet\",\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", t_min_load)],\n )\n print(f\"Loaded raw data in {time.time() - t0:.2f} s\")\n\n datasets = []\n total_pairs = 0\n\n for w in range(1, num_windows + 1):\n tw_end = cutoff - (w - 1) * 7 * day\n tw_start = cutoff - w * 7 * day\n print(f\"Building window {w}: history < {tw_start}, outcome in [{tw_start}, {tw_end}]...\")\n\n tensors, hist_items = extract_cutoff_history(\n cutoff_ts=tw_start,\n target_uids=target_uids,\n item2idx=item2idx,\n preference_state_df=None,\n listens_df=listens,\n likes_df=likes,\n unlikes_df=unlikes,\n dislikes_df=dislikes,\n undislikes_df=undislikes,\n lookback_days=lookback_days,\n )\n\n w_listens = listens[\n (listens.timestamp >= tw_start)\n & (listens.timestamp < tw_end)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= 80)\n ]\n w_likes = likes[\n (likes.timestamp >= tw_start)\n & (likes.timestamp < tw_end)\n & (likes.is_organic == 1)\n ]\n\n pairs = {} # (uid, item_id) -> weight\n for u, item in zip(w_listens.uid, w_listens.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 1.0\n\n for u, item in zip(w_likes.uid, w_likes.item_id):\n if item in item2idx and item not in hist_items.get(u, set()):\n pairs[(u, item)] = 3.0 # like takes precedence\n\n u_rows = np.array([uid2row[u] for u, _ in pairs.keys()], dtype=np.int64)\n t_items = np.array([item2idx[item] for _, item in pairs.keys()], dtype=np.int64)\n weights = np.array(list(pairs.values()), dtype=np.float32)\n\n ds = TwoTowerDataset(tensors, u_rows, t_items, weights)\n datasets.append(ds)\n total_pairs += len(pairs)\n print(f\"Window {w} pairs: {len(pairs)}\")\n\n return ConcatDataset(datasets), total_pairs\n\n\ndef main():\n torch.set_num_threads(8)\n print(f\"PyTorch threads: {torch.get_num_threads()}\")\n\n # 1. Load catalog & target users\n print(\"Loading catalog and target users...\")\n catalog_data = load_catalog(DATA_DIR)\n tu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = list(tu_df.uid)\n print(f\"Target users: {len(target_uids)}, Catalog items: {len(catalog_data.idx2item)}\")\n\n # 2. Extract validation user history tensors at cutoff 24786800\n print(\"Extracting validation user history at cutoff 24786800...\")\n cutoff = 24786800\n vps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\n val_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n )\n val_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n )\n del val_listens\n del vps\n print(\"Validation tensors ready.\")\n\n # 3. Build multi-window training dataset\n train_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=4,\n lookback_days=60,\n )\n print(f\"Total training pairs across windows: {total_pairs}\")\n\n batch_size = 512\n train_loader = DataLoader(\n train_dataset,\n batch_size=batch_size,\n shuffle=True,\n drop_last=True,\n )\n\n # 4. Initialize model, optimizer, loss\n embed_dim = 128\n out_dim = 128\n alpha = 0.04\n ranker = TwoTowerRanker(\n num_items=NUM_CATALOG_ITEMS,\n embed_dim=embed_dim,\n out_dim=out_dim,\n feature_dim=4,\n dropout=0.1,\n alpha=alpha,\n )\n criterion = InBatchNegativesLoss(temperature=0.07, taste_dim=out_dim - 1)\n optimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\n epochs = 4\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs * len(train_loader), eta_min=1e-5)\n\n best_score = 0.0\n best_parquet_path = Path(\"/app/solution/validation.parquet\")\n\n catalog_features = catalog_data.features\n log_p = catalog_data.log_p\n\n print(\"\\n--- Starting Training ---\")\n for epoch in range(1, epochs + 1):\n ranker.train()\n total_loss = 0.0\n num_batches = 0\n t_epoch_start = time.time()\n\n for step, batch in enumerate(train_loader):\n liked, disliked, recent, recent_w, recent_m, organic, target_items, weights = batch\n\n optimizer.zero_grad()\n u_vecs, i_vecs = ranker(\n liked_items=liked,\n disliked_items=disliked,\n recent_items=recent,\n recent_weights=recent_w,\n recent_mask=recent_m,\n organic_items=organic,\n target_items=target_items,\n features=catalog_features,\n )\n\n loss = criterion(\n user_vecs=u_vecs,\n item_vecs=i_vecs,\n target_items=target_items,\n weights=weights,\n log_p=log_p,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n avg_loss = total_loss / max(num_batches, 1)\n epoch_dur = time.time() - t_epoch_start\n\n # Evaluate after epoch\n print(f\"Epoch {epoch}/{epochs} finished in {epoch_dur:.1f} s | Avg Loss: {avg_loss:.4f} | Evaluating validation...\")\n val_eval_path = Path(f\"/app/solution/val_epoch_{epoch}.parquet\")\n scores = evaluate_on_validation(\n ranker=ranker,\n catalog_data=catalog_data,\n val_user_tensors=val_tensors,\n target_uids=target_uids,\n output_parquet_path=val_eval_path,\n )\n ndcg = scores[\"quality_ndcg@10\"]\n print(f\"Epoch {epoch} Public Validation NDCG@10: {ndcg:.6f} (Users: {scores['quality_users']})\")\n\n if ndcg > best_score:\n best_score = ndcg\n torch.save(ranker.state_dict(), \"/app/solution/best_ranker.pt\")\n shutil.copyfile(val_eval_path, best_parquet_path)\n print(f\"*** New best validation NDCG@10: {best_score:.6f}! Saved model and {best_parquet_path} ***\")\n\n print(f\"\\nTraining complete! Best Public Validation NDCG@10: {best_score:.6f}\")\n\n\nif __name__ == \"__main__\":\n main()\n"
}Successfully overwrote file: /app/solution/train.py. Here is the updated code:
"""Training script for Two-Tower Neural Ranker with public validation evaluation."""
from __future__ import annotations
import shutil
import sys
import time
from pathlib import Path
from typing import List, Tuple
...
def build_window_datasets(
catalog_data,
target_uids: List[int],
num_windows: int = 4,
lookback_days: int = 60,
) -> Tuple[ConcatDataset, int]:
"""Build multi-window discovery training datasets from training event logs."""
t0 = time.time()
cutoff = 24786800
day = 86400
uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}
item2idx = catalog_data.item2idx
# Load all needed events
t_min_load = cutoff - (num_windows * 7 + lookback_days) * day
print(f"Loading event logs from t >= {t_min_load}...")
...
(likes.timestamp >= tw_start)
& (likes.timestamp < tw_end)
& (likes.is_organic == 1)
]
pairs = {} # (uid, item_id) -> weight
for u, item in zip(w_listens.uid, w_listens.item_id):
if item in item2idx and item not in hist_items.get(u, set()):
pairs[(u, item)] = 1.0
...
item2idx=catalog_data.item2idx,
preference_state_df=vps,
listens_df=val_listens,
lookback_days=60,
)
del val_listens
del vps
print("Validation tensors ready.")
# 3. Build multi-window training dataset
train_dataset, total_pairs = build_window_datasets(
catalog_data=catalog_data,
target_uids=target_uids,
num_windows=4,
lookback_days=60,
)
print(f"Total training pairs across windows: {total_pairs}")
batch_size = 512
...
)
# 4. Initialize model, optimizer, loss
embed_dim = 128
out_dim = 128
alpha = 0.04
ranker = TwoTowerRanker(
num_items=NUM_CATALOG_ITEMS,
embed_dim=embed_dim,
out_dim=out_dim,
feature_dim=4,
dropout=0.1,
alpha=alpha,
)
criterion = InBatchNegativesLoss(temperature=0.07, taste_dim=out_dim - 1)
optimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)
epochs = 4
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs * len(train_loader), eta_min=1e-5)
best_score = 0.0
best_parquet_path = Path("/app/solution/validation.parquet")
catalog_features = catalog_data.features
log_p = catalog_data.log_p
print("\n--- Starting Training ---")
for epoch in range(1, epochs + 1):
...
print(f"Epoch {epoch} Public Validation NDCG@10: {ndcg:.6f} (Users: {scores['quality_users']})")
if ndcg > best_score:
best_score = ndcg
torch.save(ranker.state_dict(), "/app/solution/best_ranker.pt")
shutil.copyfile(val_eval_path, best_parquet_path)
print(f"*** New best validation NDCG@10: {best_score:.6f}! Saved model and {best_parquet_path} ***")
print(f"\nTraining complete! Best Public Validation NDCG@10: {best_score:.6f}")
...{
"command": "python3 /app/solution/train.py && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Run complete train.py and evaluate with evaluate_public.py"
}<untrusted_context>
Output: PyTorch threads: 8
Loading catalog and target users...
Target users: 8456, Catalog items: 30000
Extracting validation user history at cutoff 24786800...
Validation tensors ready.
Loading event logs from t >= 17183600...
Loaded raw data in 0.39 s
Building window 1: history < 24182000, outcome in [24182000, 24786800]...
Window 1 pairs: 64740
Building window 2: history < 23577200, outcome in [23577200, 24182000]...
Window 2 pairs: 62736
Building window 3: history < 22972400, outcome in [22972400, 23577200]...
Window 3 pairs: 61962
Building window 4: history < 22367600, outcome in [22367600, 22972400]...
Window 4 pairs: 60166
Total training pairs across windows: 249604
--- Starting Training ---
Epoch 1/4 finished in 27.8 s | Avg Loss: 7.1789 | Evaluating validation...
Epoch 1 Public Validation NDCG@10: 0.010825 (Users: 4263)
*** New best validation NDCG@10: 0.010825! Saved model and /app/solution/validation.parquet ***
Epoch 2/4 finished in 27.3 s | Avg Loss: 6.7522 | Evaluating validation...
Epoch 2 Public Validation NDCG@10: 0.011565 (Users: 4263)
*** New best validation NDCG@10: 0.011565! Saved model and /app/solution/validation.parquet ***
Epoch 3/4 finished in 27.3 s | Avg Loss: 6.4012 | Evaluating validation...
Epoch 3 Public Validation NDCG@10: 0.012140 (Users: 4263)
*** New best validation NDCG@10: 0.012140! Saved model and /app/solution/validation.parquet ***
Epoch 4/4 finished in 27.4 s | Avg Loss: 6.1798 | Evaluating validation...
Epoch 4 Public Validation NDCG@10: 0.010937 (Users: 4263)
Training complete! Best Public Validation NDCG@10: 0.012140
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01214,
"quality_users": 4263
}
}
Process Group PGID: 3366
</untrusted_context>Testing Optimal Alpha
My current focus is on pinpointing the ideal alpha value for the four-window model. Early indicators suggest it might surpass the performance of the three-window model, which achieved 0.013077 with alphas around 0.035-0.04. I'm now exploring this further in Python.
{
"description": "Grid search alpha on 4-window model",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker()\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n all_v = ranker.item_tower.precompute_all_items(catalog_data.features)\n all_u = ranker.user_tower(val_tensors.liked_items, val_tensors.disliked_items, val_tensors.recent_items, val_tensors.recent_weights, val_tensors.recent_mask, val_tensors.organic_items)\n taste_scores = torch.mm(all_u[:, :127], all_v[:, :127].t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\norg_feat = catalog_data.features[:, 1].unsqueeze(0)\n\nfor a in [0.01, 0.02, 0.03, 0.035, 0.04, 0.045, 0.05, 0.06]:\n s = taste_scores + a * org_feat\n s.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(s, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"Alpha:\", a, \"NDCG@10:\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: Alpha: 0.01 NDCG@10: 0.010868
Alpha: 0.02 NDCG@10: 0.011253
Alpha: 0.03 NDCG@10: 0.011679
Alpha: 0.035 NDCG@10: 0.011786
Alpha: 0.04 NDCG@10: 0.01214
Alpha: 0.045 NDCG@10: 0.012003
Alpha: 0.05 NDCG@10: 0.01208
Alpha: 0.06 NDCG@10: 0.012461
Process Group PGID: 3421
</untrusted_context>Testing Item Tower Features
I'm currently exploring the integration of pos_listens and org_listens within the item tower architecture. Initial tests in catalog_data.features are focused on incorporating pos as feature 0 to evaluate its impact on model performance.
{
"description": "Test combination of organic and positive listens",
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom data import load_catalog, extract_cutoff_history\nfrom model import TwoTowerRanker\nfrom ranking_evaluation import CandidateSet, score_rankings\nfrom evaluate_public import validation_truth\n\nDATA = Path(\"/task/data\")\ntu_df = pd.read_parquet(DATA / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\ncatalog_data = load_catalog(DATA)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nranker = TwoTowerRanker()\nranker.load_state_dict(torch.load(\"/app/solution/best_ranker.pt\", map_location=\"cpu\"))\nranker.eval()\n\nwith torch.no_grad():\n all_v = ranker.item_tower.precompute_all_items(catalog_data.features)\n all_u = ranker.user_tower(val_tensors.liked_items, val_tensors.disliked_items, val_tensors.recent_items, val_tensors.recent_weights, val_tensors.recent_mask, val_tensors.organic_items)\n taste_scores = torch.mm(all_u[:, :127], all_v[:, :127].t())\n\nval_eligibility_path = DATA / \"validation_eligible_candidates.npz\"\ntargets_set = set(target_uids)\neligibility = CandidateSet(val_eligibility_path)\ngraded = validation_truth(eligibility, targets_set)\n\nwith np.load(val_eligibility_path) as a:\n packed = a[\"packed_eligible\"]\n item_ids = a[\"item_ids\"]\nmask = torch.from_numpy(np.unpackbits(packed, count=len(item_ids), bitorder=\"little\", axis=1).astype(bool))\n\nf0 = catalog_data.features[:, 0].unsqueeze(0) # pos_listens\nf1 = catalog_data.features[:, 1].unsqueeze(0) # org_listens\n\nfor w_org in [0.7, 0.8, 0.9, 1.0]:\n for alpha in [0.04, 0.05, 0.06, 0.07]:\n feat_comb = w_org * f1 + (1.0 - w_org) * f0\n s = taste_scores + alpha * feat_comb\n s.masked_fill_(~mask, -1e9)\n _, topk_idx = torch.topk(s, 100, dim=1)\n topk_np = topk_idx.numpy()\n ranked_dict = {\n uid: [int(catalog_data.idx2item[idx]) for idx in topk_np[i]]\n for i, uid in enumerate(target_uids)\n }\n res = score_rankings(ranked_dict, graded)\n print(\"w_org:\", w_org, \"alpha:\", alpha, \"NDCG@10:\", res[\"quality_ndcg@10\"])\n'"
}<untrusted_context>
Output: w_org: 0.7 alpha: 0.04 NDCG@10: 0.01162
w_org: 0.7 alpha: 0.05 NDCG@10: 0.012061
w_org: 0.7 alpha: 0.06 NDCG@10: 0.012318
w_org: 0.7 alpha: 0.07 NDCG@10: 0.012248
w_org: 0.8 alpha: 0.04 NDCG@10: 0.011758
w_org: 0.8 alpha: 0.05 NDCG@10: 0.011886
w_org: 0.8 alpha: 0.06 NDCG@10: 0.012271
w_org: 0.8 alpha: 0.07 NDCG@10: 0.012221
w_org: 0.9 alpha: 0.04 NDCG@10: 0.011985
w_org: 0.9 alpha: 0.05 NDCG@10: 0.01198
w_org: 0.9 alpha: 0.06 NDCG@10: 0.012376
w_org: 0.9 alpha: 0.07 NDCG@10: 0.012167
w_org: 1.0 alpha: 0.04 NDCG@10: 0.01214
w_org: 1.0 alpha: 0.05 NDCG@10: 0.01208
w_org: 1.0 alpha: 0.06 NDCG@10: 0.012461
w_org: 1.0 alpha: 0.07 NDCG@10: 0.012202
Process Group PGID: 3447
</untrusted_context>Testing Window Variations
I'm evaluating how training on two windows, with alpha values of 0.04 and 0.05, impacts performance. My initial findings with organic behavior alone show promise, so this next step is crucial to explore further optimizations.
{
"command": "PYTHONPATH=/task python3 -c '\nimport sys\nsys.path.append(\"/task\")\nsys.path.append(\"/app/solution\")\nimport time\nimport pandas as pd\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.utils.data import DataLoader\nfrom data import DATA_DIR, load_catalog, extract_cutoff_history\nfrom train import build_window_datasets\nfrom model import TwoTowerRanker, InBatchNegativesLoss\nfrom evaluate import evaluate_on_validation\n\ntorch.set_num_threads(8)\n\ncatalog_data = load_catalog(DATA_DIR)\ntu_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = list(tu_df.uid)\n\ncutoff = 24786800\nvps = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_listens = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"uid\", \"in\", target_uids), (\"timestamp\", \">=\", cutoff - 60 * 86400)],\n)\nval_tensors, _ = extract_cutoff_history(\n cutoff_ts=cutoff,\n target_uids=target_uids,\n item2idx=catalog_data.item2idx,\n preference_state_df=vps,\n listens_df=val_listens,\n lookback_days=60,\n)\ndel val_listens\ndel vps\n\nfor num_w in [2, 3]:\n print(f\"\\n--- Testing num_windows={num_w} ---\")\n train_dataset, total_pairs = build_window_datasets(\n catalog_data=catalog_data,\n target_uids=target_uids,\n num_windows=num_w,\n lookback_days=60,\n )\n train_loader = DataLoader(train_dataset, batch_size=512, shuffle=True, drop_last=True)\n ranker = TwoTowerRanker(alpha=0.04)\n criterion = InBatchNegativesLoss(temperature=0.07, taste_dim=127)\n optimizer = torch.optim.AdamW(ranker.parameters(), lr=5e-4, weight_decay=1e-4)\n \n for epoch in range(1, 4):\n ranker.train()\n for batch in train_loader:\n liked, disliked, recent, recent_w, recent_m, organic, targets, weights = batch\n optimizer.zero_grad()\n u, v = ranker(liked, disliked, recent, recent_w, recent_m, organic, targets, catalog_data.features)\n loss = criterion(u, v, targets, weights, catalog_data.log_p)\n loss.backward()\n torch.nn.utils.clip_grad_norm_(ranker.parameters(), 1.0)\n optimizer.step()\n \n scores = evaluate_on_validation(ranker, catalog_data, val_tensors, target_uids)\n print(f\"Num_W: {num_w} Epoch {epoch} NDCG@10: {scores[\\\"quality_ndcg@10\\\"]:.6f}\")\n'",
"description": "Compare 2 windows vs 3 windows"
}<untrusted_context>
Output: File "<string>", line 66
print(f"Num_W: {num_w} Epoch {epoch} NDCG@10: {scores[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 3473
</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.