Two-tower retriever gemini-3-8-flash-high-07
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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 Project Setup
I'm beginning by activating the update_topic to get a lay of the land. My immediate focus is on understanding the environment, the data files, any existing code, and the project requirements to ensure a solid foundation for the two-tower ranker.
Defining Model Architecture
I'm now deeply engaged in defining the architecture for the two towers, ensuring each independently generates user and item vectors. My current focus is on how the user tower will aggregate interaction history, avoiding any user ID lookups, to fulfill the requirement of deriving representations from behavior rather than static IDs.
{
"summary": "I am starting an initial investigation into the two-tower PyTorch neural network ranker task. My immediate goal is to inspect /task/data/, /task/evaluate_public.py, /task/ranking_evaluation.py, and examine the data schema, sizes, and evaluation metrics before designing the architecture.",
"title": "Initial Exploration and Data Inspection",
"strategic_intent": "Explore data files, requirements, and evaluation script to understand the dataset scale and task specifics."
}Current topic: "Initial Exploration and Data Inspection"
Topic summary: I am starting an initial investigation into the two-tower PyTorch neural network ranker task. My immediate goal is to inspect /task/data/, /task/evaluate_public.py, /task/ranking_evaluation.py, and examine the data schema, sizes, and evaluation metrics before designing the architecture.
Strategic Intent: Explore data files, requirements, and evaluation script to understand the dataset scale and task specifics.{
"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{
"command": "ls -la /task && ls -la /task/data && ls -la /app/solution",
"description": "List files in /task, /task/data, and /app/solution"
}<untrusted_context>
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 01:29 .
drwxr-xr-x 1 root root 4096 Sep 29 01:32 ..
-r--r--r-- 1 root root 0 Sep 29 01:16 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 data
-r--r--r-- 1 root root 5774 Sep 29 01:16 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:16 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:16 requirements.txt
total 347396
dr-xr-xr-x 1 root root 4096 Sep 29 01:17 .
dr-xr-xr-x 1 root root 4096 Sep 29 01:29 ..
-r--r--r-- 1 root root 349352 Sep 29 01:16 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:16 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:16 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:17 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:16 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:16 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:16 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:16 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:16 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:16 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:16 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:16 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:16 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:16 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:16 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:17 validation_preference_state.parquet
total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 01:29 .
drwxr-xr-x 3 agent agent 4096 Sep 29 01:29 ..
Process Group PGID: 1612
</untrusted_context>{
"command": "cat /task/evaluate_public.py",
"description": "Read /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: 1617
</untrusted_context>{
"command": "cat /task/ranking_evaluation.py",
"description": "Read /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: 1620
</untrusted_context>{
"description": "Read /task/requirements.txt",
"command": "cat /task/requirements.txt"
}<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
Process Group PGID: 1623
</untrusted_context>{
"command": "lscpu | grep -E \"Model name|CPU\\(s\\):|Thread|Core|Socket\" && free -h",
"description": "Check CPU and memory resources"
}<untrusted_context>
Output: CPU(s): 8
Model name: AMD EPYC 7R13 Processor
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
NUMA node0 CPU(s): 0-7
/usr/bin/bash: line 4: free: command not found
Exit Code: 127
Process Group PGID: 1626
</untrusted_context>{
"description": "Check available memory",
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemFree|MemAvailable\""
}<untrusted_context>
Output: MemTotal: 15988968 kB
MemFree: 1590908 kB
MemAvailable: 14678172 kB
Process Group PGID: 1631
</untrusted_context>{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path(\"/task/data\")\nfor f in sorted(data_dir.glob(\"*\")):\n if f.suffix == \".parquet\":\n df = pd.read_parquet(f)\n print(f\"{f.name:35s}: shape={df.shape}, cols={list(df.columns)}\")\n elif f.suffix == \".npz\":\n npz = np.load(f)\n print(f\"{f.name:35s}: keys={list(npz.keys())}, uids={len(npz[\\\"uids\\\"])}, items={len(npz[\\\"item_ids\\\"])}, packed_shape={npz[\\\"packed_eligible\\\"].shape}\")\n'",
"description": "Inspect dataset shapes and schemas"
}<untrusted_context>
Output: File "<string>", line 13
print(f"{f.name:35s}: keys={list(npz.keys())}, uids={len(npz[\"uids\"])}, items={len(npz[\"item_ids\"])}, packed_shape={npz[\"packed_eligible\"].shape}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 1635
</untrusted_context>{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path(\"/task/data\")\nfor f in sorted(data_dir.glob(\"*\")):\n if f.suffix == \".parquet\":\n df = pd.read_parquet(f)\n print(f\"{f.name:35s}: shape={df.shape}, cols={list(df.columns)}\")\n elif f.suffix == \".npz\":\n npz = np.load(f)\n uids_len = len(npz[\"uids\"])\n items_len = len(npz[\"item_ids\"])\n shape = npz[\"packed_eligible\"].shape\n print(f\"{f.name:35s}: uids={uids_len}, items={items_len}, packed_shape={shape}\")\n'",
"description": "Inspect dataset shapes and schemas"
}<untrusted_context>
Output: candidate_catalog.parquet : shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet : shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
test_eligible_candidates.npz : uids=8456, items=30000, packed_shape=(8456, 3750)
test_preference_state.parquet : shape=(484776, 3), cols=['uid', 'item_id', 'state']
train_dislikes.parquet : shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet : shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet : shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet : shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet : shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet : shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet : shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet : shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet : shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet : shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
validation_eligible_candidates.npz : uids=8456, items=30000, packed_shape=(8456, 3750)
validation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']
Process Group PGID: 1638
</untrusted_context>Analyzing Candidate Data
I'm currently examining the candidate_catalog.parquet file. It contains 30,000 items with key metrics like item_id, positive_listens, organic_positive_listens, and popularity_rank. I'm also noting the target_users.parquet file has 8,456 users.
{
"description": "Check overlap and sizes of target users in validation data",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\nprint(f\"Number of target uids: {len(target_uids)}\")\n\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(f\"Val listens: {len(val_listens)}, uids in val_listens: {val_listens.uid.nunique()}, target uids in val_listens: {val_listens.uid.isin(target_uids).sum()}\")\n\nval_likes = pd.read_parquet(\"/task/data/val_likes.parquet\")\nprint(f\"Val likes: {len(val_likes)}, target uids in val_likes: {val_likes.uid.isin(target_uids).sum()}\")\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(f\"Catalog item_id min: {catalog.item_id.min()}, max: {catalog.item_id.max()}, count: {len(catalog)}\")\n'"
}<untrusted_context>
Output: Number of target uids: 8456
Val listens: 1501697, uids in val_listens: 8196, target uids in val_listens: 1482553
Val likes: 37795, target uids in val_likes: 33177
Catalog item_id min: 206, max: 9390294, count: 30000
Process Group PGID: 1658
</untrusted_context>Analyzing Listen Data
I'm focusing on the val_listens.parquet file and noted an overwhelming majority of target users. My next step is to examine train_listens.parquet to understand the proportion of target users there and assess the reading performance of this larger dataset.
{
"description": "Inspect train_listens parquet structure",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport time\n\nt0 = time.time()\nparquet_file = pq.ParquetFile(\"/task/data/train_listens.parquet\")\nprint(f\"Num row groups: {parquet_file.num_row_groups}, metadata schema: {parquet_file.schema}\")\nprint(f\"Inspection took {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: Num row groups: 42, metadata schema: <pyarrow._parquet.ParquetSchema object at 0x73a54a072780>
required group field_id=-1 schema {
optional int32 field_id=-1 uid (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 timestamp (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 item_id (Int(bitWidth=32, isSigned=false));
optional int32 field_id=-1 is_organic (Int(bitWidth=8, isSigned=false));
optional int32 field_id=-1 played_ratio_pct (Int(bitWidth=16, isSigned=false));
optional int32 field_id=-1 track_length_seconds (Int(bitWidth=32, isSigned=false));
}
Inspection took 0.00s
Process Group PGID: 1678
</untrusted_context>{
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\nparquet_file = pq.ParquetFile(\"/task/data/train_listens.parquet\")\n# Read only timestamp min/max from metadata row groups\nmin_ts = []\nmax_ts = []\nfor i in range(parquet_file.num_row_groups):\n meta = parquet_file.metadata.row_group(i)\n col = meta.column(1) # timestamp\n min_ts.append(col.statistics.min)\n max_ts.append(col.statistics.max)\nprint(f\"Timestamp range: min={min(min_ts)}, max={max(max_ts)}\")\n'",
"description": "Check timestamp range in train_listens"
}<untrusted_context>
Output: Timestamp range: min=0, max=24786795
Process Group PGID: 1689
</untrusted_context>Considering Timestamp Ranges
My focus has been on precisely defining the available data, particularly the timestamp range of 0 to 24786795, and understanding how it aligns with the training data cutoff at 24786800. I'm now processing the implications of the 30-minute validation gap and the subsequent seven-day public validation period.
{
"description": "Check timestamp range in val_listens",
"command": "python3 -c '\nimport pandas as pd\nval = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(f\"Val timestamp range: min={val.timestamp.min()}, max={val.timestamp.max()}, span={val.timestamp.max() - val.timestamp.min()} seconds ({(val.timestamp.max() - val.timestamp.min())/86400:.2f} days)\")\n'"
}<untrusted_context>
Output: Val timestamp range: min=24788600, max=25393395, span=604795 seconds (7.00 days)
Process Group PGID: 1700
</untrusted_context>Calculating Test Window
I'm refining the calculation for the hidden test window. The current timestamp, after accounting for the initial 30-minute interval and a full week, stands at 25393400. I'm now focusing on the subsequent 30-minute addition to pinpoint the exact start of the hidden test period.
{
"command": "python3 -c '\nimport pandas as pd\nvp = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(\"Validation preference state:\")\nprint(vp.head())\nprint(vp.state.value_counts())\n\ntp = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(\"Test preference state:\")\nprint(tp.head())\nprint(tp.state.value_counts())\n'",
"description": "Inspect preference state parquets"
}<untrusted_context>
Output: Validation preference state:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
state
liked 418435
disliked 50689
Name: count, dtype: int64
Test preference state:
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
state
liked 432659
disliked 52117
Name: count, dtype: int64
Process Group PGID: 1720
</untrusted_context>{
"command": "python3 -c '\nimport numpy as np\n\nvec = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = vec[\"packed_eligible\"]\nuids = vec[\"uids\"]\nitem_ids = vec[\"item_ids\"]\n\n# Count bits set per user\ncounts = [np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\").sum() for r in range(100)]\nprint(f\"Sample eligible item counts per user: min={min(counts)}, max={max(counts)}, mean={np.mean(counts):.1f}\")\nprint(f\"Total catalog size: {len(item_ids)}\")\n'",
"description": "Check number of eligible items per user"
}<untrusted_context>
Output: Sample eligible item counts per user: min=25547, max=29971, mean=29098.9
Total catalog size: 30000
Process Group PGID: 1736
</untrusted_context>Refining Item Eligibility
I'm reviewing the criteria for item ineligibility, as it appears a vast majority of the 30,000 candidates, nearly 29,000, meet the requirements for each user. My focus is now on understanding the reasons behind the few hundred exclusions.
{
"description": "Check alignment of uids and item_ids across npz and parquets",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nv_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nt_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\n\nprint(\"v_uids == t_uids:\", np.array_equal(v_npz[\"uids\"], t_npz[\"uids\"]))\nprint(\"v_uids == targets.uid:\", np.array_equal(v_npz[\"uids\"], targets[\"uid\"].values))\nprint(\"v_items == t_items:\", np.array_equal(v_npz[\"item_ids\"], t_npz[\"item_ids\"]))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"v_items == catalog.item_id:\", np.array_equal(v_npz[\"item_ids\"], catalog[\"item_id\"].values))\n'"
}<untrusted_context>
Output: v_uids == t_uids: True
v_uids == targets.uid: True
v_items == t_items: True
v_items == catalog.item_id: True
Process Group PGID: 1746
</untrusted_context>Confirming Alignment
I've confirmed the user and item identifiers across the datasets are precisely matched. Now, I'm proceeding to evaluate a baseline popularity model against the public validation set.
{
"description": "Generate and evaluate popularity baseline",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport time\n\nt0 = time.time()\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nv_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = v_npz[\"packed_eligible\"]\nuids = v_npz[\"uids\"]\nitem_ids = v_npz[\"item_ids\"]\n\n# Candidate catalog is already sorted by popularity!\n# Let us verify if popularity_rank is 1 to 30000\nprint(\"Catalog head:\")\nprint(catalog.head())\n\nrecords = []\nfor r, uid in enumerate(uids):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # Take top 100 eligible item_ids (catalog is ordered by popularity)\n eligible_items = item_ids[mask == 1][:100]\n for rank, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/baseline_popularity.parquet\")\nprint(f\"Generated baseline in {time.time()-t0:.2f}s, shape={df.shape}\")\n' && python3 /task/evaluate_public.py /app/solution/baseline_popularity.parquet"
}<untrusted_context>
Output: Catalog head:
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
Generated baseline in 1.44s, shape=(845600, 3)
{
"model": "/app/solution/baseline_popularity.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1763
</untrusted_context>Validating Baseline Model
I've successfully run the popularity baseline, which completed in a swift 1.44 seconds. It achieved a quality_ndcg@10 of 0.01071 across 4,263 users and validated without any errors. I'm now proceeding to generate a baseline submission for testing.
{
"description": "Generate initial valid fallback submission.parquet",
"command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nt_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\npacked = t_npz[\"packed_eligible\"]\nuids = t_npz[\"uids\"]\nitem_ids = t_npz[\"item_ids\"]\n\nrecords = []\nfor r, uid in enumerate(uids):\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_items = item_ids[mask == 1][:100]\n for rank, item in enumerate(eligible_items, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/submission.parquet\")\nprint(f\"Generated fallback test submission, shape={df.shape}\")\n'"
}<untrusted_context>
Output: Generated fallback test submission, shape=(845600, 3)
Process Group PGID: 1798
</untrusted_context>{
"command": "python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items, RankingValidationError\n\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\nt_cand = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nnorm = validate_ranking(df, targets)\nfor uid, item in norm[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not t_cand.is_eligible(uid, item):\n raise RankingValidationError(f\"Ineligible item {item} for user {uid}\")\nprint(\"Test submission is 100% structurally valid!\")\n'",
"description": "Validate fallback test submission against ranking_evaluation contract"
}<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: 1815
</untrusted_context>{
"description": "Validate test submission with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items, RankingValidationError\n\ntargets = set(pd.read_parquet(\"/task/data/target_users.parquet\").uid.astype(int))\nt_cand = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nnorm = validate_ranking(df, targets)\nfor uid, item in norm[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not t_cand.is_eligible(uid, item):\n raise RankingValidationError(f\"Ineligible item {item} for user {uid}\")\nprint(\"Test submission is 100% structurally valid!\")\n'"
}<untrusted_context>
Output: Test submission is 100% structurally valid!
Process Group PGID: 1826
</untrusted_context>Reviewing and Validating
I've confirmed our initial submission meets all structural requirements. Now, I'm deeply analyzing the prompt's specifics, constraints, and the underlying mathematical framework.
Analyzing the Constraints
I've thoroughly parsed the prompt, focusing on the core requirements. My current thought process is centering on the critical constraint of avoiding a global user-item matrix and ensuring scalability for large production systems. The real-time latency budget and offline candidate generation are also key considerations I'm integrating.
Integrating Data Nuances
I'm now focused on the specifics of the event families, particularly differentiating between organic and recommended interactions. The temporal splits for training, validation, and testing, with their specific gaps, are crucial for my backtesting strategy. I'm also noting the different thresholds for "listen" events depending on the objective.
Refining Ranker Logic
I'm solidifying the strategy for using public windows. Specifically, I'm ensuring val_* events function solely for tuning and not as features during validation measurement, while correctly incorporating them for final ranker refitting. The discovery objective's graded relevance and NDCG@10 metric are now integrated.
Developing Candidate Generation
My thoughts are now focused on efficiently generating candidates per user, avoiding any global user-item matrix construction. I'm considering how to best leverage the provided event data for this, aiming for a scalable offline process that still respects the real-time latency requirements.
{
"description": "Inspect item IDs and target user distribution",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\n# Let us check unique item_ids across catalog and train/val sets\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(f\"Catalog items: {len(cat)}, min: {cat.item_id.min()}, max: {cat.item_id.max()}\")\n\nlikes = pd.read_parquet(\"/task/data/train_likes.parquet\")\nprint(f\"Train likes count: {len(likes)}, unique items in likes: {likes.item_id.nunique()}, items in catalog: {likes.item_id.isin(cat.item_id).sum()}/{len(likes)}\")\n\ndislikes = pd.read_parquet(\"/task/data/train_dislikes.parquet\")\nprint(f\"Train dislikes count: {len(dislikes)}, unique items: {dislikes.item_id.nunique()}\")\n\ntarget_users = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(f\"Target users: {len(target_users)}\")\nprint(target_users.head())\n'"
}<untrusted_context>
Output: Catalog items: 30000, min: 206, max: 9390294
Train likes count: 814831, unique items in likes: 172303, items in catalog: 554824/814831
Train dislikes count: 101075, unique items: 50876
Target users: 8456
uid recent_positive_listens history_positive_listens
0 100 844.0 2604
1 200 50.0 286
2 300 168.0 359
3 600 1985.0 2423
4 700 1776.0 6114
Process Group PGID: 1845
</untrusted_context>Exploring Item Features
I'm reviewing the available features for items within candidate_catalog.parquet. Currently, I've identified item_id, positive_listens, organic_positive_listens, and popularity_rank. I'm now investigating whether any additional item metadata, such as artist or genre information, is present.
Examining Listen Data
I've discovered that the train_listens.parquet file contains the track_length_seconds column. I'm now confirming if any other parquet files hold additional relevant metadata.
Verifying Candidate Set
I'm confirming that the 30,000 items in the candidate catalog represent the complete set for recommendations across training, validation, and testing. This ensures all eligible candidates are sourced from this defined pool.
Implementing User Tower
My focus is now on building the user tower as a PyTorch neural network. It will generate user representations solely from their interaction history, avoiding simple user-ID lookups. I'm incorporating features like listen counts, likes, preference states, and recency.
Developing Item Tower
I'm defining the item tower architecture within PyTorch. This network will process item features, mapping them into item vectors, which will be crucial for downstream similarity calculations.
Incorporating Temporal Backtest
I'm integrating the provided temporal backtesting functionality to ensure robust evaluation. This will allow me to assess model performance across different time periods and validate its generalizability.
Refining Interaction Pooling
I'm currently exploring various pooling strategies for user interaction history. My goal is to determine the most effective method, whether it's attentive pooling, average pooling, or a more complex approach like a GRU or Transformer, to generate robust user embeddings.
{
"description": "Check interaction counts for target users",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\nlikes = pd.read_parquet(\"/task/data/train_likes.parquet\")\ntarget_likes = likes[likes.uid.isin(target_uids)]\nprint(f\"Target likes: {len(target_likes)}, per user: {len(target_likes)/len(target_uids):.1f}\")\n\ndislikes = pd.read_parquet(\"/task/data/train_dislikes.parquet\")\ntarget_dislikes = dislikes[dislikes.uid.isin(target_uids)]\nprint(f\"Target dislikes: {len(target_dislikes)}, per user: {len(target_dislikes)/len(target_uids):.1f}\")\n\nv_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(f\"Validation preference state: {len(v_pref)}, per user: {len(v_pref)/len(target_uids):.1f}\")\nprint(\"Pref state value counts:\\n\", v_pref.state.value_counts())\n'"
}<untrusted_context>
Output: Target likes: 690968, per user: 81.7
Target dislikes: 84266, per user: 10.0
Validation preference state: 469124, per user: 55.5
Pref state value counts:
state
liked 418435
disliked 50689
Name: count, dtype: int64
Process Group PGID: 1862
</untrusted_context>Analyzing User Behavior
My focus has shifted to understanding the engagement patterns of our target users. I'm currently analyzing their recent activity, specifically counting their listens within the train_listens.parquet dataset to correlate with their established preference states.
{
"description": "Test reading train_listens columns",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"played_ratio_pct\", \"is_organic\", \"timestamp\"])\nprint(f\"Loaded columns in {time.time()-t0:.2f}s, rows={len(table)}\")\n\nuids = table.column(\"uid\")\nprint(f\"Total rows: {len(uids)}\")\n# Check unique users or sample\n'"
}<untrusted_context>
Output: Loaded columns in 1.25s, rows=43484159
Total rows: 43484159
Process Group PGID: 1879
</untrusted_context>{
"description": "Check target user listens in train_listens",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport time\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\nt0 = time.time()\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nuids = table.column(\"uid\").to_numpy()\nprint(f\"Total unique uids in train_listens: {len(np.unique(uids)) if False else 'computing...'}\")\nimport numpy as np\nunique_uids = np.unique(uids)\nprint(f\"Unique uids in train_listens: {len(unique_uids)}\")\nmask = np.isin(uids, list(target_uids))\nprint(f\"Target user listens: {mask.sum()} / {len(uids)} ({mask.sum()/len(uids)*100:.1f}%)\")\nprint(f\"Elapsed: {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: File "<string>", line 13
print(f"Total unique uids in train_listens: {len(np.unique(uids)) if False else computing...}")
^^^^^^^^^^^^
SyntaxError: invalid syntax. Perhaps you forgot a comma?
Exit Code: 1
Process Group PGID: 1903
</untrusted_context>{
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nimport time\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\nt0 = time.time()\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nuids = table.column(\"uid\").to_numpy()\nunique_uids = np.unique(uids)\nprint(\"Unique uids in train_listens:\", len(unique_uids))\ntarget_uids_arr = np.array(list(target_uids), dtype=np.int32)\nmask = np.isin(uids, target_uids_arr)\nprint(f\"Target user listens: {mask.sum()} / {len(uids)} ({mask.sum()/len(uids)*100:.1f}%)\")\nprint(f\"Elapsed: {time.time()-t0:.2f}s\")\n'",
"description": "Check target user listens in train_listens"
}<untrusted_context>
Output: Unique uids in train_listens: 9238
Target user listens: 43222297 / 43484159 (99.4%)
Elapsed: 0.62s
Process Group PGID: 1906
</untrusted_context>Refining Listener Metrics
I'm focusing on the surprisingly high concentration of listens within our target demographic; it appears an overwhelming 99.4% originate from them. This, coupled with the relatively small pool of 9,238 unique users in train_listens, suggests a deep engagement from this core group. I'm now investigating the duration of this training data to better contextualize these figures.
Investigating Validation Metrics
I'm now examining the validation metric details. It appears we're averaging NDCG@10 over users with eligible relevant items, prioritizing likes and then listens with high played_ratio_pct. I'm also checking the timestamps to understand the training data's historical context relative to the target user definition.
{
"command": "python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nfrom collections import Counter\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\nprint(f\"Num users with at least 1 relevant item: {len(graded)}\")\nitem_counts = [len(items) for items in graded.values()]\nprint(f\"Items per graded user: min={min(item_counts)}, max={max(item_counts)}, mean={sum(item_counts)/len(item_counts):.2f}, median={pd.Series(item_counts).median()}\")\n\ngrade_dist = Counter()\nfor items in graded.values():\n for g in items.values():\n grade_dist[g] += 1\nprint(f\"Grade distribution: {grade_dist}\")\n'",
"description": "Check validation graded statistics"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 5, in <module>
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 1927
</untrusted_context>{
"description": "Check validation graded statistics with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nfrom collections import Counter\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\nprint(f\"Num users with at least 1 relevant item: {len(graded)}\")\nitem_counts = [len(items) for items in graded.values()]\nprint(f\"Items per graded user: min={min(item_counts)}, max={max(item_counts)}, mean={sum(item_counts)/len(item_counts):.2f}, median={pd.Series(item_counts).median()}\")\n\ngrade_dist = Counter()\nfor items in graded.values():\n for g in items.values():\n grade_dist[g] += 1\nprint(f\"Grade distribution: {grade_dist}\")\n'"
}<untrusted_context>
Output: Num users with at least 1 relevant item: 4263
Items per graded user: min=1, max=442, mean=8.86, median=4.0
Grade distribution: Counter({1: 33352, 3: 4417})
Process Group PGID: 1938
</untrusted_context>Analyzing User Consumption Patterns
I'm currently dissecting user behavior within the validation set. Specifically, I'm identifying how many of the relevant items (out of 4,263 users) were previously encountered during training, aiming to differentiate between repeat consumption and genuine new discovery. This will help refine our understanding of engagement.
{
"description": "Check overlap between active liked preferences and validation graded items",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Let us check validation_preference_state\nv_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\npref_likes = set(zip(v_pref[v_pref.state == \"liked\"].uid, v_pref[v_pref.state == \"liked\"].item_id))\n\n# Check how many graded (uid, item) are in pref_likes\npref_hit = 0\ntotal_graded = 0\nfor u, items in graded.items():\n for it, g in items.items():\n total_graded += 1\n if (u, it) in pref_likes:\n pref_hit += 1\n\nprint(f\"Total graded items: {total_graded}, in active liked preference state: {pref_hit} ({pref_hit/total_graded*100:.1f}%)\")\n\n# Check if candidate eligibility excludes items or allows items\nprint(\"Eligible items total:\", eligibility.packed.sum())\n'"
}<untrusted_context>
Output: Total graded items: 37769, in active liked preference state: 0 (0.0%)
Eligible items total: 7835149449
Process Group PGID: 1958
</untrusted_context>Investigating Preference State
I'm currently examining why no graded items are showing in the active liked preference state. My immediate next step is to verify if candidate eligibility logic is inadvertently excluding currently liked tracks. I'll be testing this hypothesis shortly.
{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\nv_pref = pd.read_parquet(DATA / \"validation_preference_state.parquet\")\n\nliked = v_pref[v_pref.state == \"liked\"]\ndisliked = v_pref[v_pref.state == \"disliked\"]\n\nliked_eligible = [eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(1000).itertuples()]\nprint(\"Are liked items in preference state eligible?\", sum(liked_eligible), \"/\", len(liked_eligible))\n\ndisliked_eligible = [eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(1000).itertuples()]\nprint(\"Are disliked items in preference state eligible?\", sum(disliked_eligible), \"/\", len(disliked_eligible))\n'",
"description": "Check if liked or disliked items are eligible"
}<untrusted_context>
Output: Are liked items in preference state eligible? 0 / 1000
Are disliked items in preference state eligible? 0 / 1000
Process Group PGID: 1978
</untrusted_context>Exploring Discovery Logic
I'm currently investigating the eligibility criteria for our "Made for You" discovery shelf. Specifically, I'm examining whether previously listened-to tracks, beyond just recently liked/disliked ones, are being considered for the validation set.
{
"description": "Check if recently listened items are eligible and appear in validation truth",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Check recent listens in train\ntrain_listens = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"], filters=[(\"timestamp\", \">=\", 24786800 - 86400 * 30)])\ntarget_recent = train_listens[train_listens.uid.isin(targets)]\nprint(f\"Target recent listens (30d): {len(target_recent)}\")\n\n# Check eligibility of recent listens\nsample = target_recent.sample(1000, random_state=42)\nsample_eligible = [eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in sample.itertuples()]\nprint(\"Are recent listened items eligible?\", sum(sample_eligible), \"/\", len(sample_eligible))\n\n# Check how many graded validation items were listened to in the past (30d, 90d, all)\nrecent_pairs = set(zip(target_recent.uid, target_recent.item_id))\ngraded_in_recent = sum((u, it) in recent_pairs for u, items in graded.items() for it in items)\nprint(f\"Graded items that were listened to in last 30d of train: {graded_in_recent} / 37769 ({graded_in_recent/37769*100:.1f}%)\")\n'"
}<untrusted_context>
Output: Target recent listens (30d): 6113327
Are recent listened items eligible? 0 / 1000
Graded items that were listened to in last 30d of train: 0 / 37769 (0.0%)
Process Group PGID: 1994
</untrusted_context>Analyzing Eligibility Criteria
I'm currently investigating the specific criteria for item eligibility, particularly noting that recently listened items are excluded. My focus is on identifying all excluded categories and testing the exact parameters of the eligibility filter, examining 60-day, 90-day, and all-time listen counts.
{
"description": "Check overlap of graded items with different history windows",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Let us check 90 days vs 180 days vs all time\nfor days in [14, 30, 60, 90, 180, 300]:\n train_sample = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"], filters=[(\"timestamp\", \">=\", 24786800 - 86400 * days)])\n target_sample = train_sample[train_sample.uid.isin(targets)]\n pairs = set(zip(target_sample.uid, target_sample.item_id))\n graded_in = sum((u, it) in pairs for u, items in graded.items() for it in items)\n print(f\"Days {days}: pairs={len(pairs)}, graded in history={graded_in}/37769\")\n'"
}<untrusted_context>
Output: Days 14: pairs=1813796, graded in history=0/37769
Days 30: pairs=3171806, graded in history=0/37769
Days 60: pairs=5059070, graded in history=0/37769
Days 90: pairs=6447731, graded in history=0/37769
Days 180: pairs=9487886, graded in history=0/37769
Days 300: pairs=12305820, graded in history=0/37769
Process Group PGID: 2015
</untrusted_context>{
"description": "Verify if any past interacted items are eligible",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n\ntrain_sample = pd.read_parquet(DATA / \"train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntarget_sample = train_sample[train_sample.uid.isin(targets)].drop_duplicates()\nprint(f\"Total unique (user, item) pairs in train: {len(target_sample)}\")\nsample_pairs = target_sample.sample(5000, random_state=42)\neligible_count = sum(eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in sample_pairs.itertuples())\nprint(f\"Are any past listened pairs eligible? {eligible_count} / {len(sample_pairs)}\")\n'"
}<untrusted_context>
Output: Total unique (user, item) pairs in train: 12305820
Are any past listened pairs eligible? 0 / 5000
Process Group PGID: 2036
</untrusted_context>Refining Candidate Selection
I'm currently focusing on ensuring that the candidate eligibility set for each user exclusively contains items that are unconsumed, unlistened, unliked, and undisliked. This precise filtering is key to providing truly relevant suggestions.
Focusing on Discovery
My current thinking is solidifying around the core objective: this is purely a discovery task. The goal is to surface brand new items a user hasn't encountered. This means every candidate must be unconsumed, and relevance hinges on matching the user's taste profile derived from their past interactions.
Developing Model Architecture
I'm currently focused on designing a model that decomposes into independent user and item towers. This will allow for efficient offline precomputation of item embeddings. The user tower will learn representations from interaction history, avoiding large matrix operations for scalability.
Designing Item Tower Logic
My focus is on the item tower, specifically how to represent items using embeddings derived from an item ID and potentially other features. I'm considering an embedding table followed by an MLP, with offline computation of embeddings for the entire candidate catalog.
{
"description": "Benchmark matrix multiplication time on CPU",
"command": "python3 -c '\nimport torch\nimport time\n\nU = torch.randn(8456, 128)\nV = torch.randn(30000, 128)\n\nt0 = time.time()\nscores = torch.matmul(U, V.T)\ntorch.cuda.synchronize() if torch.cuda.is_available() else None\ndt = time.time() - t0\nprint(f\"Matrix multiply 8456x128 with 30000x128 took: {dt*1000:.2f} ms\")\n'"
}<untrusted_context>
Output: Matrix multiply 8456x128 with 30000x128 took: 334.49 ms
Process Group PGID: 2058
</untrusted_context>Achieving Sub-Millisecond Latency
I'm thrilled to report that the system is now handling all 8,456 users against 30,000 items in just 334 milliseconds. This translates to an incredible per-user latency of only 39 microseconds, easily meeting production requirements.
{
"description": "Benchmark unpacking and topk on CPU",
"command": "python3 -c '\nimport torch\nimport numpy as np\nimport time\n\nv_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = v_npz[\"packed_eligible\"]\nscores = torch.randn(8456, 30000)\n\nt0 = time.time()\n# Unpack mask:\n# In numpy or torch\nmasks = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\")\nmasks_torch = torch.from_numpy(masks)\nscores[~masks_torch] = -float(\"inf\")\ntopk = torch.topk(scores, 100, dim=1)\nprint(f\"Masking and top-100 for all 8456 users took: {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 15, in <module>
RuntimeError: masked_fill_ only supports boolean masks, but got mask with dtype unsigned char
Exit Code: 1
Process Group PGID: 2071
</untrusted_context>{
"description": "Benchmark boolean masking and top-100",
"command": "python3 -c '\nimport torch\nimport numpy as np\nimport time\n\nv_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = v_npz[\"packed_eligible\"]\nscores = torch.randn(8456, 30000)\n\nt0 = time.time()\nmasks = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\").astype(bool)\nmasks_torch = torch.from_numpy(masks)\nscores[~masks_torch] = -float(\"inf\")\ntopk = torch.topk(scores, 100, dim=1)\nprint(f\"Masking and top-100 for all 8456 users took: {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: Masking and top-100 for all 8456 users took: 0.35s
Process Group PGID: 2084
</untrusted_context>Processing User Data
My focus has shifted to understanding the scale of user history and item data. I'm analyzing the total number of unique items within the candidate catalog to grasp the system's overall scope.
{
"description": "Check catalog item coverage in train_listens",
"command": "python3 -c '\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nimport time\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\n\nt0 = time.time()\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"item_id\"])\nitems = table.column(\"item_id\").to_numpy()\nunique_items = np.unique(items)\nprint(f\"Total unique items in train_listens: {len(unique_items)}\")\n\ncat_items_arr = np.array(list(cat_items), dtype=np.int32)\nin_cat = np.isin(items, cat_items_arr)\nprint(f\"Listens on catalog items: {in_cat.sum()} / {len(items)} ({in_cat.sum()/len(items)*100:.1f}%)\")\nprint(f\"Elapsed: {time.time()-t0:.2f}s\")\n'"
}<untrusted_context>
Output: Total unique items in train_listens: 845395
Listens on catalog items: 31249613 / 43484159 (71.9%)
Elapsed: 0.85s
Process Group PGID: 2097
</untrusted_context>Analyzing Listen Data
I'm currently focused on understanding the significant concentration of listens on a small portion of the catalog. I'm also examining the validation process, specifically how val_listens are being scored against eligible candidates within validation_eligible_candidates.npz in evaluate_public.py.
{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ncatalog = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat_set = set(catalog.item_id)\n\ngraded = evaluate_public.validation_truth(eligibility, targets)\nall_graded_items = set(it for items in graded.values() for it in items)\nprint(\"Total distinct graded items in validation:\", len(all_graded_items))\nprint(\"Are all graded items in catalog?\", all_graded_items.issubset(cat_set))\n'",
"description": "Verify all graded items are in candidate catalog"
}<untrusted_context>
Output: Total distinct graded items in validation: 14124
Are all graded items in catalog? True
Process Group PGID: 2117
</untrusted_context>Initiating Plan Phase
All graded items are now within the candidate catalog, which totals 30,000 items. I'm now shifting focus to architecting the model, data pipeline, loss function, negative sampling, validation, and training strategy using enter_plan_mode.
{
"reason": "Design a high-quality two-tower PyTorch neural network ranker conforming to all latency, production scaling, and architectural constraints."
}Switching to Plan mode.Planning the Strategy
I'm currently in Plan Mode, focusing on the initial steps of exploring and analyzing requirements. My next action will be to consult with you informally regarding the proposed strategy before I proceed with drafting the implementation plan.
Analyzing the Requirements
I'm diving deep into the prompt's specifics, focusing on mandates for the two-tower neural network in CPU PyTorch. My current thinking is to formulate an optimal architecture and training strategy based on this detailed analysis.
Developing the Architecture
My thoughts are now focused on architecting a solution that avoids global matrices. I'm prioritizing independent user and item towers, with user representations derived from interaction history. This approach will enable efficient candidate retrieval via nearest-neighbor search.
Refining the Features
I'm now considering how to differentiate between user-initiated listens and those driven by recommendations. This distinction is crucial for accurately evaluating model performance and will inform how I incorporate is_organic flags and played ratio thresholds. I'm also noting that validation events shouldn't be used as predictive features.
{
"description": "Analyze target user history characteristics",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\nDATA = \"/task/data\"\ntargets = pd.read_parquet(f\"{DATA}/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\ntarget_likes = likes[likes.uid.isin(target_uids)]\nprint(\"Target likes per user:\")\nprint(target_likes.groupby(\"uid\").size().describe())\n\n# Check recent listens (e.g. 60 days)\nCUTOFF = 24786800\nDAYS_60 = 60 * 86400\nlistens = pd.read_parquet(f\"{DATA}/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"], filters=[(\"timestamp\", \">=\", CUTOFF - DAYS_60)])\ntarget_listens = listens[listens.uid.isin(target_uids)]\nprint(\"\\nRecent 60d listens per user:\")\nprint(target_listens.groupby(\"uid\").size().describe())\n\n# Check positive listens (played_ratio_pct >= 50)\npos_listens = target_listens[target_listens.played_ratio_pct >= 50]\nprint(\"\\nRecent 60d positive listens (>=50%) per user:\")\nprint(pos_listens.groupby(\"uid\").size().describe())\n\n# Check strong organic listens (played_ratio_pct >= 80, is_organic == 1)\nstrong_organic = target_listens[(target_listens.played_ratio_pct >= 80) & (target_listens.is_organic == 1)]\nprint(\"\\nRecent 60d strong organic listens (>=80%, organic) per user:\")\nprint(strong_organic.groupby(\"uid\").size().describe())\n'"
}Tool "run_shell_command" not found. Did you mean one of: "update_topic", "grep_search", "replace"?Discovering Tool Limitations
I've noted that run_shell_command isn't among the tools currently available in Plan Mode. My focus has been on understanding the constraints and the specific set of operations I can perform within this planning environment, which includes replace, exit_plan_mode, glob, invoke_agent, read_file, list_directory, and grep.
Expanding Tool Capabilities
My recent thoughts have centered on identifying and then utilizing additional tools to enhance my planning capabilities. Specifically, I've been considering the utility of search, update_topic, and write_file to better gather necessary information and refine our architectural design. This exploration aims to move us closer to a complete solution by addressing potential data needs and implementation steps.
Designing Two-Tower Model
I'm now focusing on the intricate details of a Two-Tower Model architecture. My thoughts are on how best to represent items within the item tower, considering offline embeddings for latency and distinct PyTorch networks for both user and item towers, with a keen eye on validation and hidden test stages.
Structuring Item Embeddings
My current thinking is focused on efficiently handling item IDs and features for the item tower. I'm leaning towards using the 30,000 catalog items plus an unknown embedding, incorporating optional features like popularity rank into an MLP for robust representation.
Developing User Tower Logic
I'm now considering how to construct the user tower's representation by processing interaction history. My thoughts involve integrating various signals like likes, strong listens, and recent listens, alongside explicit dislikes, to create a comprehensive user profile, distinct from a simple ID lookup.
Processing User History
My current focus is on how the user tower neural network will process interaction history. I'm exploring a multi-channel attentive pooling approach, segmenting history by interaction type and applying recency weighting before pooling, potentially using weighted averages or self-attention mechanisms to derive a rich user representation.
Refining User Vector Generation
I'm now concentrating on refining how user vectors are generated, specifically through multi-channel attentive pooling with recency weighting and exploring channel concatenation or learned combinations. This involves processing interaction history through various signals, aiming for a comprehensive user profile that feeds into an MLP to produce L2-normalized user vectors.
Optimizing Loss Functions
I've been analyzing different loss functions for training. My thoughts are now focused on refining the in-batch softmax approach with popularity and negative sampling corrections, and exploring the implications of weighted positives based on interaction grades for our discovery objective.
Improving Retrieval Effectiveness
I'm now considering how to enhance retrieval effectiveness beyond basic BPR. My thoughts are focused on implementing InfoNCE or sampled softmax with a controlled temperature, as these are known to significantly outperform BPR for top-k retrieval and NDCG@10 by incorporating multiple negatives.
Defining Training Cutoffs
I'm now focusing on how to best define our training data by establishing multiple snapshot cutoffs. This approach, mirroring our validation and testing methodology, will allow me to use history prior to each cutoff and target events within a subsequent 7-day window, effectively creating robust training examples for a large number of users.
Aligning Training Objective
I'm now focusing on aligning our training objective precisely with the evaluation. My thoughts are on using specific cutoffs, defining positive signals like organic likes and strong listens within a 7-day window, and filtering out already consumed items to directly learn predictions of future user discovery.
Calculating Model Memory
I've been calculating the memory requirements for the model. The item embedding table for 30,000 items at a 64-dimension size appears quite small, around 7.6MB, making it very lightweight. This also informs my current thoughts on the architecture of both the user and item towers, including specific layer configurations.
{
"strategic_intent": "Draft detailed implementation plan and reach agreement before proceeding to implementation.",
"summary": "I have analyzed the data, requirements, baseline popularity performance, and architectural constraints. I am now formulating the complete implementation plan for the two-tower neural ranker, including data preprocessing, PyTorch user and item towers, in-batch InfoNCE loss, offline candidate index, inference pipeline, validation tuning, refitting on train+val, and submission verification.",
"title": "Drafting Two-Tower Neural Ranker Plan"
}Current topic: "Drafting Two-Tower Neural Ranker Plan"
Topic summary: I have analyzed the data, requirements, baseline popularity performance, and architectural constraints. I am now formulating the complete implementation plan for the two-tower neural ranker, including data preprocessing, PyTorch user and item towers, in-batch InfoNCE loss, offline candidate index, inference pipeline, validation tuning, refitting on train+val, and submission verification.
Strategic Intent: Draft detailed implementation plan and reach agreement before proceeding to implementation.Drafting our ranking plan
I'm currently focused on constructing the plan file for the two-tower ranker, specifically targeting the /home/agent/.gemini/tmp/app/7be8e86f-ff77-4a57-8b94-c3b55aff938b/plans/two_tower_ranker.md location. My immediate next step involves reviewing the write_file function's limitations within Plan Mode to ensure adherence to established protocols.
{
"file_path": "/home/agent/.gemini/tmp/app/7be8e86f-ff77-4a57-8b94-c3b55aff938b/plans/two_tower_ranker.md",
"content": "# Two-Tower Neural-Network Ranker Implementation Plan\n\n## Objective\nBuild, tune, and deploy a latency-efficient PyTorch two-tower neural-network ranker for the \"Made for You\" discovery shelf of a music streaming service. The model must decompose into a user tower and an independently precomputable item tower of the same dimensionality, scoring items via dot product $s(u, i) = \\mathbf{u} \\cdot \\mathbf{v}_i$, with candidate eligibility masking and top-100 ranking. The approach scales to millions of users/items without global matrix factorization.\n\n---\n\n## Architectural Design\n\n### 1. Item Tower (`ItemTower(nn.Module)`)\n- **Inputs**: Candidate item ID $i \\in \\{0, \\dots, 29999\\}$ and item-level discovery features from `candidate_catalog.parquet` (log-transformed positive listens, organic listen count, and normalized popularity rank).\n- **Architecture**:\n - Item Embedding: $d_{\\text{emb}} = 64$ embedding layer for the 30,000 catalog items.\n - Feature Projection: Linear layer mapping item metadata features (3 dims) to $d_{\\text{feat}} = 16$.\n - MLP: Linear($64 + 16 \\to 128$), LayerNorm, GELU, Dropout(0.1), Linear($128 \\to 64$).\n - L2 Normalization: Produces unit vector $\\mathbf{v}_i \\in \\mathbb{R}^{64}$.\n- **Offline Indexing**: Precompute $\\mathbf{V} \\in \\mathbb{R}^{30000 \\times 64}$ for all catalog items once offline.\n\n### 2. User Tower (`UserTower(nn.Module)`)\n- **Constraint**: Must derive representation purely from interaction history, NOT from a user-ID lookup table.\n- **Inputs**: User interaction history across distinct event channels:\n 1. *Channel 1 (Likes & Preference State)*: Item IDs of explicitly liked tracks and active cutoff preference state.\n 2. *Channel 2 (Strong Organic Listens)*: Item IDs of organic listens with $\\ge 80\\%$ completion ratio.\n 3. *Channel 3 (Recent Positive Listens)*: Item IDs of recent listens with $\\ge 50\\%$ completion ratio.\n- **Interaction History Encoder**:\n - Item Embedding lookup (sharing or distinct table of size $30001 \\times 64$, with index 30,000 as OOV token for non-catalog items).\n - Recency-decayed exponential weighting: $w_t = \\exp(-\\lambda (T_{\\text{cutoff}} - t))$.\n - Channel Pooling: Attentive and weighted average pooling per channel, computing $\\mathbf{h}_{\\text{like}}$, $\\mathbf{h}_{\\text{strong}}$, and $\\mathbf{h}_{\\text{recent}}$.\n - Channel Fusion: Concatenation $[\\mathbf{h}_{\\text{like}}; \\mathbf{h}_{\\text{strong}}; \\mathbf{h}_{\\text{recent}}]$ (dimension $3 \\times 64 = 192$).\n - User MLP: Linear($192 \\to 128$), LayerNorm, GELU, Dropout(0.1), Linear($128 \\to 64$).\n - L2 Normalization: Produces unit vector $\\mathbf{u} \\in \\mathbb{R}^{64}$.\n\n### 3. Relevance Scoring & Retrieval\n- Dot product score: $s(u, i) = \\mathbf{u} \\cdot \\mathbf{v}_i$.\n- Offline MIPS / candidate ranking:\n - User vectors $\\mathbf{U} \\in \\mathbb{R}^{B \\times 64}$, candidate vectors $\\mathbf{V} \\in \\mathbb{R}^{30000 \\times 64}$.\n - Scores matrix $\\mathbf{S} = \\mathbf{U} \\mathbf{V}^T \\in \\mathbb{R}^{B \\times 30000}$.\n - Candidate eligibility mask applied: $\\mathbf{S}_{u, i} = -\\infty$ for ineligible items.\n - Efficient PyTorch `torch.topk(S, 100, dim=1)` extracts top 100 distinct items in descending order of score.\n - Per-user latency is $< 0.1$ ms on CPU.\n\n### 4. Training Objective & Optimization\n- **Supervision Signal**: Future organic interactions aligned with the target evaluation window:\n - Grade 3: organic like ($w = 3.0$).\n - Grade 1: organic listen $\\ge 80\\%$ ($w = 1.0$).\n- **Multi-Snapshot Temporal Slicing**:\n - Sample training snapshots within `train_*` timestamps (e.g. at $T_{\\text{cutoff}} - 7\\text{d}$, $T_{\\text{cutoff}} - 14\\text{d}$, $T_{\\text{cutoff}} - 21\\text{d}$).\n - For each snapshot, history is drawn strictly before the snapshot, and positive labels are drawn from the subsequent 7-day window.\n- **Loss Function**: Weighted in-batch InfoNCE / sampled softmax loss with temperature $\\tau = 0.07$:\n $$\\mathcal{L} = -\\sum_{b=1}^B w_b \\log \\frac{\\exp(\\mathbf{u}_b \\cdot \\mathbf{v}_{i_b} / \\tau)}{\\sum_{j=1}^B \\exp(\\mathbf{u}_b \\cdot \\mathbf{v}_{i_j} / \\tau) + \\sum_{k=1}^K \\exp(\\mathbf{u}_b \\cdot \\mathbf{v}_{\\text{neg}_k} / \\tau)}$$\n Incorporating in-batch negatives plus sampled catalog negatives.\n- **Optimizer**: AdamW with learning rate $1\\text{e-}3$, cosine annealing schedule, and weight decay $1\\text{e-}4$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Pipeline & Temporal Preprocessing\n- Implement `data_loader.py` to extract:\n - Item catalog features (normalized popularity, listen counts).\n - History extraction module for any user at timestamp $T$: builds liked items, strong organic listens, recent listens with recency decay.\n - Training dataset generator creating $(u, \\text{history}, \\text{target\\_item}, \\text{weight})$ pairs from historical snapshots.\n - Validation dataset generator for the validation cutoff $T = 24786800$ using `train_*` and `validation_preference_state.parquet`.\n\n### Phase 2: PyTorch Model Definition\n- Implement `model.py`:\n - `ItemTower(nn.Module)`: takes item indices and item features, outputs 64-dim unit vector.\n - `UserTower(nn.Module)`: takes channel item sequences and recency weights, outputs 64-dim unit vector.\n - `TwoTowerModel(nn.Module)`: orchestrates towers and forward training loss computation.\n\n### Phase 3: Training & Validation Loop\n- Implement `train.py`:\n - Training loop with mixed batches, InfoNCE loss, and AdamW optimizer.\n - Fast validation evaluation calling the offline item precomputation $\\mathbf{V}$, user tower evaluation $\\mathbf{U}$, matrix multiplication $\\mathbf{U} \\mathbf{V}^T$, eligibility masking, and scoring with `evaluate_public.py`.\n - Iterate and tune hyperparameters (dimension, learning rate, temperature $\\tau$, history length, pooling weights) to maximize `quality_ndcg@10` well above baseline.\n\n### Phase 4: Refitting on Train + Val & Final Submission\n- Refit / train the winning two-tower architecture on the full history `train_* + val_*` up to test cutoff timestamp $25393400$, with `test_preference_state.parquet`.\n- Compute final item embeddings $\\mathbf{V}$ for all 30,000 catalog items.\n- Compute user embeddings $\\mathbf{U}$ for all 8,456 target users using their full updated history.\n- Apply `test_eligible_candidates.npz` mask and retrieve top 100 items per user.\n- Export `/app/solution/submission.parquet` with columns `uid`, `item_id`, `rank`.\n- Validate against all ranking invariants using `ranking_evaluation.py`.\n- Write `/app/solution/README.md` detailing architecture, training objective, public validation score, and final submission command.\n\n---\n\n## Verification & Validation Strategy\n1. **Contract Validation**:\n - Check schema: exactly 845,600 rows (8,456 users $\\times$ 100 items).\n - Check ranks: 1 through 100 per user, integers, no ties, no duplicates.\n - Check eligibility: 100% of recommended items must pass `test_eligible_candidates.is_eligible(uid, item)`.\n2. **Evaluation Metric**:\n - Run `/task/evaluate_public.py` on the validation predictions; verify `quality_ndcg@10` improves significantly over the popularity baseline (0.01071).\n3. **Execution Latency**:\n - Verify offline item precomputation and real-time user inference / top-k retrieval latency strictly meets production budgets ($< 1$ ms per user).\n"
}Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/7be8e86f-ff77-4a57-8b94-c3b55aff938b/plans/two_tower_ranker.md. Here is the updated code:
# Two-Tower Neural-Network Ranker Implementation Plan
## Objective
Build, tune, and deploy a latency-efficient PyTorch two-tower neural-network ranker for the "Made for You" discovery shelf of a music streaming service. The model must decompose into a user tower and an independently precomputable item tower of the same dimensionality, scoring items via dot product $s(u, i) = \mathbf{u} \cdot \mathbf{v}_i$, with candidate eligibility masking and top-100 ranking. The approach scales to millions of users/items without global matrix factorization.
---
## Architectural Design
### 1. Item Tower (`ItemTower(nn.Module)`)
- **Inputs**: Candidate item ID $i \in \{0, \dots, 29999\}$ and item-level discovery features from `candidate_catalog.parquet` (log-transformed positive listens, organic listen count, and normalized popularity rank).
- **Architecture**:
- Item Embedding: $d_{\text{emb}} = 64$ embedding layer for the 30,000 catalog items.
- Feature Projection: Linear layer mapping item metadata features (3 dims) to $d_{\text{feat}} = 16$.
- MLP: Linear($64 + 16 \to 128$), LayerNorm, GELU, Dropout(0.1), Linear($128 \to 64$).
- L2 Normalization: Produces unit vector $\mathbf{v}_i \in \mathbb{R}^{64}$.
- **Offline Indexing**: Precompute $\mathbf{V} \in \mathbb{R}^{30000 \times 64}$ for all catalog items once offline.
### 2. User Tower (`UserTower(nn.Module)`)
- **Constraint**: Must derive representation purely from interaction history, NOT from a user-ID lookup table.
- **Inputs**: User interaction history across distinct event channels:
1. *Channel 1 (Likes & Preference State)*: Item IDs of explicitly liked tracks and active cutoff preference state.
2. *Channel 2 (Strong Organic Listens)*: Item IDs of organic listens with $\ge 80\%$ completion ratio.
3. *Channel 3 (Recent Positive Listens)*: Item IDs of recent listens with $\ge 50\%$ completion ratio.
- **Interaction History Encoder**:
- Item Embedding lookup (sharing or distinct table of size $30001 \times 64$, with index 30,000 as OOV token for non-catalog items).
- Recency-decayed exponential weighting: $w_t = \exp(-\lambda (T_{\text{cutoff}} - t))$.
- Channel Pooling: Attentive and weighted average pooling per channel, computing $\mathbf{h}_{\text{like}}$, $\mathbf{h}_{\text{strong}}$, and $\mathbf{h}_{\text{recent}}$.
- Channel Fusion: Concatenation $[\mathbf{h}_{\text{like}}; \mathbf{h}_{\text{strong}}; \mathbf{h}_{\text{recent}}]$ (dimension $3 \times 64 = 192$).
- User MLP: Linear($192 \to 128$), LayerNorm, GELU, Dropout(0.1), Linear($128 \to 64$).
- L2 Normalization: Produces unit vector $\mathbf{u} \in \mathbb{R}^{64}$.
### 3. Relevance Scoring & Retrieval
- Dot product score: $s(u, i) = \mathbf{u} \cdot \mathbf{v}_i$.
- Offline MIPS / candidate ranking:
- User vectors $\mathbf{U} \in \mathbb{R}^{B \times 64}$, candidate vectors $\mathbf{V} \in \mathbb{R}^{30000 \times 64}$.
- Scores matrix $\mathbf{S} = \mathbf{U} \mathbf{V}^T \in \mathbb{R}^{B \times 30000}$.
- Candidate eligibility mask applied: $\mathbf{S}_{u, i} = -\infty$ for ineligible items.
- Efficient PyTorch `torch.topk(S, 100, dim=1)` extracts top 100 distinct items in descending order of score.
- Per-user latency is $< 0.1$ ms on CPU.
### 4. Training Objective & Optimization
- **Supervision Signal**: Future organic interactions aligned with the target evaluation window:
- Grade 3: organic like ($w = 3.0$).
- Grade 1: organic listen $\ge 80\%$ ($w = 1.0$).
- **Multi-Snapshot Temporal Slicing**:
- Sample training snapshots within `train_*` timestamps (e.g. at $T_{\text{cutoff}} - 7\text{d}$, $T_{\text{cutoff}} - 14\text{d}$, $T_{\text{cutoff}} - 21\text{d}$).
- For each snapshot, history is drawn strictly before the snapshot, and positive labels are drawn from the subsequent 7-day window.
- **Loss Function**: Weighted in-batch InfoNCE / sampled softmax loss with temperature $\tau = 0.07$:
$$\mathcal{L} = -\sum_{b=1}^B w_b \log \frac{\exp(\mathbf{u}_b \cdot \mathbf{v}_{i_b} / \tau)}{\sum_{j=1}^B \exp(\mathbf{u}_b \cdot \mathbf{v}_{i_j} / \tau) + \sum_{k=1}^K \exp(\mathbf{u}_b \cdot \mathbf{v}_{\text{neg}_k} / \tau)}$$
Incorporating in-batch negatives plus sampled catalog negatives.
- **Optimizer**: AdamW with learning rate $1\text{e-}3$, cosine annealing schedule, and weight decay $1\text{e-}4$.
---
## Phased Implementation Plan
### Phase 1: Data Pipeline & Temporal Preprocessing
- Implement `data_loader.py` to extract:
- Item catalog features (normalized popularity, listen counts).
- History extraction module for any user at timestamp $T$: builds liked items, strong organic listens, recent listens with recency decay.
- Training dataset generator creating $(u, \text{history}, \text{target\_item}, \text{weight})$ pairs from historical snapshots.
- Validation dataset generator for the validation cutoff $T = 24786800$ using `train_*` and `validation_preference_state.parquet`.
### Phase 2: PyTorch Model Definition
- Implement `model.py`:
- `ItemTower(nn.Module)`: takes item indices and item features, outputs 64-dim unit vector.
- `UserTower(nn.Module)`: takes channel item sequences and recency weights, outputs 64-dim unit vector.
- `TwoTowerModel(nn.Module)`: orchestrates towers and forward training loss computation.
### Phase 3: Training & Validation Loop
- Implement `train.py`:
- Training loop with mixed batches, InfoNCE loss, and AdamW optimizer.
- Fast validation evaluation calling the offline item precomputation $\mathbf{V}$, user tower evaluation $\mathbf{U}$, matrix multiplication $\mathbf{U} \mathbf{V}^T$, eligibility masking, and scoring with `evaluate_public.py`.
- Iterate and tune hyperparameters (dimension, learning rate, temperature $\tau$, history length, pooling weights) to maximize `quality_ndcg@10` well above baseline.
### Phase 4: Refitting on Train + Val & Final Submission
- Refit / train the winning two-tower architecture on the full history `train_* + val_*` up to test cutoff timestamp $25393400$, with `test_preference_state.parquet`.
- Compute final item embeddings $\mathbf{V}$ for all 30,000 catalog items.
- Compute user embeddings $\mathbf{U}$ for all 8,456 target users using their full updated history.
- Apply `test_eligible_candidates.npz` mask and retrieve top 100 items per user.
- Export `/app/solution/submission.parquet` with columns `uid`, `item_id`, `rank`.
- Validate against all ranking invariants using `ranking_evaluation.py`.
- Write `/app/solution/README.md` detailing architecture, training objective, public validation score, and final submission command.
---
## Verification & Validation Strategy
1. **Contract Validation**:
- Check schema: exactly 845,600 rows (8,456 users $\times$ 100 items).
- Check ranks: 1 through 100 per user, integers, no ties, no duplicates.
- Check eligibility: 100% of recommended items must pass `test_eligible_candidates.is_eligible(uid, item)`.
2. **Evaluation Metric**:
- Run `/task/evaluate_public.py` on the validation predictions; verify `quality_ndcg@10` improves significantly over the popularity baseline (0.01071).
3. **Execution Latency**:
- Verify offline item precomputation and real-time user inference / top-k retrieval latency strictly meets production budgets ($< 1$ ms per user).
{
"plan_filename": "two_tower_ranker.md"
}Plan approved. Switching to YOLO mode (all tool calls auto-approved).
The approved implementation plan is stored at: /home/agent/.gemini/tmp/app/7be8e86f-ff77-4a57-8b94-c3b55aff938b/plans/two_tower_ranker.md
Read and follow the plan strictly during implementation.{
"summary": "Having finalized and approved the two-tower ranker plan, I am now transitioning to Phase 1: Data Pipeline and Feature Engineering. I will build high-efficiency data loading and history extraction utilities under /app/solution/, enabling fast sequence extraction and positive target generation aligned with the 7-day discovery window.",
"title": "Phase 1: Data Pipeline and History Extraction",
"strategic_intent": "Implement data pipeline and temporal preprocessing for training and validation datasets."
}Current topic: "Phase 1: Data Pipeline and History Extraction"
Topic summary: Having finalized and approved the two-tower ranker plan, I am now transitioning to Phase 1: Data Pipeline and Feature Engineering. I will build high-efficiency data loading and history extraction utilities under /app/solution/, enabling fast sequence extraction and positive target generation aligned with the 7-day discovery window.
Strategic Intent: Implement data pipeline and temporal preprocessing for training and validation datasets.Processing Candidate Catalog
I'm currently mapping item_id to item_idx for the 30,000 items found within the candidate_catalog.parquet file, located at /task/data. This mapping will be in the range of 0 to 299.
Extracting User History
I'm now focusing on extracting informative interactions for 8,456 target users. This involves pulling likes, strong organic listens (played $\ge$ 80%), positive listens (played $\ge$ 50%), and dislikes from various parquet files, prioritizing the most recent 100 interactions of each type.
Refining Training Targets
I'm now investigating how to generate positive training pairs, considering a training cutoff of $T_0 = 24786800$ seconds. I'm examining the train_listens.parquet and train_likes.parquet files, and considering a 1-week window and a 30-minute gap to define history and evaluation periods. Positives will include organic likes and organic listens $\ge 80\%$ within this evaluation window.
{
"description": "Check positive organic likes in training snapshots",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\nG = 1800\n\ntargets = set(pd.read_parquet(f\"{DATA}/target_users.parquet\").uid)\ncat = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\n\nt0 = time.time()\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\nprint(f\"Loaded train_likes in {time.time()-t0:.2f}s, rows={len(likes)}\")\n\n# Check likes in windows\nfor k in range(1, 5):\n snap = T0 - k * W\n win_start = snap + G\n win_end = snap + W\n sub_likes = likes[(likes.timestamp >= win_start) & (likes.timestamp <= win_end) & (likes.is_organic == 1) & (likes.uid.isin(targets)) & (likes.item_id.isin(cat_items))]\n print(f\"Window {k} likes: {len(sub_likes)}\")\n'"
}<untrusted_context>
Output: Loaded train_likes in 0.02s, rows=814831
Window 1 likes: 7730
Window 2 likes: 7120
Window 3 likes: 7432
Window 4 likes: 7611
Process Group PGID: 2137
</untrusted_context>{
"description": "Check strong organic listens in training snapshots",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\nG = 1800\n\ntargets = set(pd.read_parquet(f\"{DATA}/target_users.parquet\").uid)\ncat = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\n\nt0 = time.time()\n# Read listens for the last 4 weeks\nlistens = pd.read_parquet(f\"{DATA}/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"timestamp\", \">=\", T0 - 4 * W)])\nprint(f\"Loaded 4-week listens in {time.time()-t0:.2f}s, rows={len(listens)}\")\n\nfor k in range(1, 5):\n snap = T0 - k * W\n win_start = snap + G\n win_end = snap + W\n sub = listens[(listens.timestamp >= win_start) & (listens.timestamp <= win_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.uid.isin(targets)) & (listens.item_id.isin(cat_items))]\n print(f\"Window {k} strong organic listens: {len(sub)}\")\n'"
}<untrusted_context>
Output: Loaded 4-week listens in 0.34s, rows=5736707
Window 1 strong organic listens: 304374
Window 2 strong organic listens: 288563
Window 3 strong organic listens: 286379
Window 4 strong organic listens: 288764
Process Group PGID: 2154
</untrusted_context>Analyzing Recent Listener Activity
I'm seeing a substantial ~300,000 organic listens and ~7,500 organic likes within the last seven days. Importantly, these are all unconsumed items, as I'm actively filtering out anything already in a user's history before this window.
{
"description": "Check unconsumed positive interactions in snapshot window",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\nG = 1800\n\ntargets = set(pd.read_parquet(f\"{DATA}/target_users.parquet\").uid)\ncat = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\n\nsnap = T0 - 1 * W\nwin_start = snap + G\nwin_end = snap + W\n\n# Positives in window 1\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\nsub_likes = likes[(likes.timestamp >= win_start) & (likes.timestamp <= win_end) & (likes.is_organic == 1) & (likes.uid.isin(targets)) & (likes.item_id.isin(cat_items))]\n\nlistens = pd.read_parquet(f\"{DATA}/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"timestamp\", \">=\", T0 - 2 * W)])\nsub_listens = listens[(listens.timestamp >= win_start) & (listens.timestamp <= win_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.uid.isin(targets)) & (listens.item_id.isin(cat_items))]\n\n# Check history prior to snap\nhist_listens = listens[listens.timestamp < snap]\nhist_pairs = set(zip(hist_listens.uid, hist_listens.item_id))\n\nunconsumed_likes = [r for r in sub_likes[[\"uid\", \"item_id\"]].itertuples(index=False) if (r.uid, r.item_id) not in hist_pairs]\nunconsumed_listens = [r for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False) if (r.uid, r.item_id) not in hist_pairs]\n\nprint(f\"Unconsumed organic likes in window 1: {len(unconsumed_likes)}\")\nprint(f\"Unconsumed strong organic listens in window 1: {len(unconsumed_listens)}\")\n'"
}<untrusted_context>
Output: Unconsumed organic likes in window 1: 6985
Unconsumed strong organic listens in window 1: 107593
Process Group PGID: 2175
</untrusted_context>Observing Engagement Metrics
I'm seeing a massive influx of unconsumed organic likes and strong listens across two windows, totaling over 117,000 and an additional ~110,000 respectively. This volume presents a rich dataset, ideal for optimizing discovery, and I'm noting the difference in quality between Grade 3 likes and Grade 1 listens.
Weighting Interactions
I'm realizing the significant value difference between interaction grades. A Grade 3 interaction provides seven times the gain of a Grade 1 interaction, suggesting we should apply substantially higher weights to these higher-grade signals for better optimization.
{
"description": "Benchmark user likes history extraction",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_idx = {uid: i for i, uid in enumerate(uids)}\n\ncat_df = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\n# Map catalog item_id to [0, 29999]\nitem_to_idx = {item: i for i, item in enumerate(cat_df.item_id)}\nOOV_IDX = len(cat_df) # 30000\n\nt0 = time.time()\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\nv_pref = pd.read_parquet(f\"{DATA}/validation_preference_state.parquet\")\nprint(f\"Loaded likes in {time.time()-t0:.2f}s\")\n\n# Extract likes for target users\nt1 = time.time()\ntarget_likes = likes[likes.uid.isin(uid_to_idx.keys())]\n# Also add pref likes\npref_likes = v_pref[(v_pref.state == \"liked\") & (v_pref.uid.isin(uid_to_idx.keys()))].copy()\npref_likes[\"timestamp\"] = T0 # active at cutoff\n\ncombined_likes = pd.concat([target_likes[[\"uid\", \"item_id\", \"timestamp\"]], pref_likes[[\"uid\", \"item_id\", \"timestamp\"]]])\ncombined_likes = combined_likes.sort_values(\"timestamp\", ascending=False)\n# Group by uid\nuser_likes = {u: [] for u in uids}\nfor uid, item in combined_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if len(user_likes[uid]) < 64:\n user_likes[uid].append(item_to_idx.get(item, OOV_IDX))\n\nprint(f\"Extracted user likes in {time.time()-t1:.2f}s. Sample user 0 likes: {len(user_likes[uids[0]])}\")\n'"
}<untrusted_context>
Output: Loaded likes in 0.04s
Extracted user likes in 0.85s. Sample user 0 likes: 19
Process Group PGID: 2196
</untrusted_context>Analyzing User Data Extraction
I've successfully extracted the like history for all target users, which took a mere 0.85 seconds. My current focus is on optimizing the extraction of user listen history, specifically investigating the performance impact of loading either 60 or 90 days of data.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 90 * 86400\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\nuids = targets_df.uid.values\nuid_set = set(uids)\n\ncat_df = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\nitem_to_idx = {item: i for i, item in enumerate(cat_df.item_id)}\nOOV_IDX = len(cat_df)\n\nt0 = time.time()\nlistens = pd.read_parquet(\n f\"{DATA}/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", T0 - W)]\n)\nprint(f\"Loaded 90d listens in {time.time()-t0:.2f}s, rows={len(listens)}\")\n\nt1 = time.time()\ntarget_listens = listens[listens.uid.isin(uid_set)]\ntarget_listens = target_listens.sort_values(\"timestamp\", ascending=False)\n\n# Strong organic listens\nstrong = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\nuser_strong = {u: [] for u in uids}\nfor uid, item in strong[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if len(user_strong[uid]) < 64:\n user_strong[uid].append(item_to_idx.get(item, OOV_IDX))\n\n# Positive listens\npos = target_listens[target_listens.played_ratio_pct >= 50]\nuser_pos = {u: [] for u in uids}\nfor uid, item in pos[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if len(user_pos[uid]) < 64:\n user_pos[uid].append(item_to_idx.get(item, OOV_IDX))\n\nprint(f\"Extracted strong & pos listens in {time.time()-t1:.2f}s\")\nprint(f\"Sample user strong listens count: {len(user_strong[uids[0]])}, pos listens count: {len(user_pos[uids[0]])}\")\n'",
"description": "Benchmark user listens history extraction"
}<untrusted_context>
Output: Loaded 90d listens in 0.38s, rows=16769274
Extracted strong & pos listens in 8.91s
Sample user strong listens count: 64, pos listens count: 64
Process Group PGID: 2213
</untrusted_context>Optimizing Data Processing
I'm currently exploring avenues to accelerate our data grouping and extraction processes. Recent progress indicates we can significantly speed up loading and identifying positive listens, and I'm investigating techniques like groupby and pyarrow dictionary/numba optimizations to further enhance performance.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 90 * 86400\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\nuid_set = set(targets_df.uid)\n\nlistens = pd.read_parquet(\n f\"{DATA}/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", T0 - W)]\n)\ntarget_listens = listens[listens.uid.isin(uid_set)]\ntarget_listens = target_listens.sort_values(\"timestamp\", ascending=False)\n\nt0 = time.time()\nstrong = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\ntop_strong = strong.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(64)\nprint(f\"groupby.head(64) took: {time.time()-t0:.2f}s, rows={len(top_strong)}\")\n'",
"description": "Test pandas vectorized groupby head for fast history extraction"
}<untrusted_context>
Output: groupby.head(64) took: 0.36s, rows=439754
Process Group PGID: 2234
</untrusted_context>Optimizing Listen Grouping
I'm thrilled to report that grouping and retrieving the top 64 listens per user is incredibly fast, completing in just 0.36 seconds. This rapid processing opens up exciting possibilities for efficiently packing this data into a 2D tensor.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport torch\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 90 * 86400\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\nuids = targets_df.uid.values\nuid_to_row = {uid: i for i, uid in enumerate(uids)}\n\ncat_df = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\nitem_to_idx = {item: i for i, item in enumerate(cat_df.item_id)}\nOOV_IDX = len(cat_df) # 30000 (padding/OOV index)\n\nlistens = pd.read_parquet(\n f\"{DATA}/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", T0 - W)]\n)\ntarget_listens = listens[listens.uid.isin(uid_to_row.keys())].sort_values(\"timestamp\", ascending=False)\n\nt0 = time.time()\nstrong = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\ntop_strong = strong.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(64)\n\n# Build tensor\narr = np.full((len(uids), 64), OOV_IDX, dtype=np.int32)\n# Map uid to row, item_id to idx\nrows = top_strong.uid.map(uid_to_row).values\ncol_idx = top_strong.groupby(\"uid\").cumcount().values\nitems = top_strong.item_id.map(item_to_idx).fillna(OOV_IDX).astype(np.int32).values\n\narr[rows, col_idx] = items\ntensor = torch.from_numpy(arr)\nprint(f\"Packed tensor shape={tensor.shape}, non-oov count={(tensor != OOV_IDX).sum().item()} in {time.time()-t0:.2f}s\")\n'",
"description": "Test packing history tensor in vectorized numpy"
}<untrusted_context>
Output: Packed tensor shape=torch.Size([8456, 64]), non-oov count=304058 in 0.41s
Process Group PGID: 2255
</untrusted_context>Constructing User Channels
I'm now focusing on defining the specific data channels for each user. This involves constructing tensors for explicit likes and active liked preference states, with the likes_tensor already taking shape (8456, 64) after a rapid 0.41-second build.
Defining User Preference Vectors
I'm now defining preference vectors for users, creating strong_listens_tensor and recent_listens_tensor, both shaped (8456, 64). I'm also considering an optional dislikes_tensor. The next step is to examine how these channels integrate within the User Tower, specifically through embedding lookups and pooling mechanisms.
Integrating User & Item Embeddings
I'm now focusing on the integration phase. For users, I'm preparing to concatenate channel vectors (likes, strong listens, recent listens) before passing them through a User MLP, followed by normalization. For items, I'm looking up embeddings and combining them with features like listens and popularity rank.
Refining Item Embeddings
I'm now refining item embeddings by concatenating their core embedding with 16 dimensional features, followed by an Item MLP and normalization. This produces unit vectors for item embeddings, enabling the dot product to function as cosine similarity for scoring user-item relevance, with candidate item embeddings precomputable.
Formulating Training Objectives
I'm now focused on the training objective. I'm considering how to form batches, pairing users with positive items and their associated grades. The next step is to compute the loss, exploring how to leverage in-batch negatives and externally sampled negatives for effective model learning.
Calculating Batch Losses
I'm now calculating the batch losses, constructing negative item embeddings and logits. I'm addressing potential in-batch negative collisions by implementing a masking strategy to avoid penalizing true positives as negatives within the InfoNCE framework. The final batch loss will incorporate importance weights based on user grades.
{
"description": "Benchmark loss computation time",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nB = 256\nK = 256\nd = 64\n\nU = F.normalize(torch.randn(B, d), dim=-1)\nV_pos = F.normalize(torch.randn(B, d), dim=-1)\nV_neg = F.normalize(torch.randn(K, d), dim=-1)\n\nt0 = time.time()\ntau = 0.07\nS = torch.matmul(U, V_pos.T) / tau\nS_neg = torch.matmul(U, V_neg.T) / tau\nlogits = torch.cat([S, S_neg], dim=1)\ntargets = torch.arange(B)\nloss = F.cross_entropy(logits, targets)\nloss.backward()\nprint(f\"Forward + backward took: {(time.time()-t0)*1000:.2f} ms\")\n'"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 22, in <module>
File "/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py", line 647, in backward
torch.autograd.backward(
File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py", line 354, in backward
_engine_run_backward(
File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn
Exit Code: 1
Process Group PGID: 2279
</untrusted_context>{
"description": "Benchmark loss forward and backward pass",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\nB = 256\nK = 256\nd = 64\n\nU = torch.randn(B, d, requires_grad=True)\nV_pos = torch.randn(B, d, requires_grad=True)\nV_neg = torch.randn(K, d, requires_grad=True)\n\nt0 = time.time()\ntau = 0.07\nu_norm = F.normalize(U, dim=-1)\nv_norm = F.normalize(V_pos, dim=-1)\nv_neg_norm = F.normalize(V_neg, dim=-1)\n\nS = torch.matmul(u_norm, v_norm.T) / tau\nS_neg = torch.matmul(u_norm, v_neg_norm.T) / tau\nlogits = torch.cat([S, S_neg], dim=1)\ntargets = torch.arange(B)\nloss = F.cross_entropy(logits, targets)\nloss.backward()\nprint(f\"Forward + backward took: {(time.time()-t0)*1000:.2f} ms\")\n'"
}<untrusted_context>
Output: Forward + backward took: 3.24 ms
Process Group PGID: 2292
</untrusted_context>Achieving Incredible Speeds
I'm thrilled by the progress; forward and backward passes are completing in a mere 3.24 ms per batch. At a batch size of 256, this translates to an astounding ~80,000 samples processed per second. A 100,000-sample dataset can now be trained in just 1.25 seconds per epoch, enabling rapid iteration.
Structuring User History
I'm considering how to best structure user history for training. The current focus is on using temporal snapshots and how to handle positive interactions within defined windows. I'm exploring the implications of using multiple snapshots to capture evolving user behavior, which seems key.
Precomputing User Histories
I've confirmed that precomputing user history tensors for each snapshot is incredibly efficient. Even with four snapshots, the total memory required for 8,456 users is a mere 24 MB, making it both fast and deterministic. This approach aligns perfectly with the task structure and evaluation window requirements.
{
"description": "Inspect snapshot 1 unconsumed positives distribution",
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport time\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\nG = 1800\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\ntarget_uids = set(targets_df.uid)\nuid_to_row = {uid: i for i, uid in enumerate(targets_df.uid)}\n\ncat_df = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\nitem_to_idx = {item: i for i, item in enumerate(cat_df.item_id)}\nOOV_IDX = len(cat_df)\n\nsnap = T0 - 1 * W\nwin_start = snap + G\nwin_end = snap + W\n\n# Positives in [win_start, win_end]\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\nsnap_likes = likes[(likes.timestamp >= win_start) & (likes.timestamp <= win_end) & (likes.is_organic == 1) & (likes.uid.isin(target_uids)) & (likes.item_id.isin(item_to_idx))]\n\nlistens = pd.read_parquet(f\"{DATA}/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"timestamp\", \">=\", snap - 60*86400), (\"timestamp\", \"<=\", win_end)])\nsnap_listens = listens[(listens.timestamp >= win_start) & (listens.timestamp <= win_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.uid.isin(target_uids)) & (listens.item_id.isin(item_to_idx))]\n\n# History items before snap (to exclude from positives)\nhist_listens = listens[listens.timestamp < snap]\nhist_pairs = set(zip(hist_listens.uid, hist_listens.item_id))\n\nhist_likes = likes[likes.timestamp < snap]\nhist_like_pairs = set(zip(hist_likes.uid, hist_likes.item_id))\nhist_pairs.update(hist_like_pairs)\n\npos_dict = {} # (uid, item) -> grade\nfor r in snap_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n pos_dict[(r.uid, r.item_id)] = 1\n\nfor r in snap_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n pos_dict[(r.uid, r.item_id)] = 3 # grade 3 overwrites grade 1\n\nprint(f\"Snapshot 1 total unconsumed positives: {len(pos_dict)}\")\ngrades = pd.Series(list(pos_dict.values())).value_counts()\nprint(\"Grades:\\n\", grades)\n'"
}<untrusted_context>
Output: Snapshot 1 total unconsumed positives: 53513
Grades:
1 47738
3 5775
Name: count, dtype: int64
Process Group PGID: 2305
</untrusted_context>{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\nDATA = \"/task/data\"\nT0 = 24786800\nW = 7 * 86400\nG = 1800\n\ntargets_df = pd.read_parquet(f\"{DATA}/target_users.parquet\")\ntarget_uids = set(targets_df.uid)\n\ncat_df = pd.read_parquet(f\"{DATA}/candidate_catalog.parquet\")\nitem_to_idx = {item: i for i, item in enumerate(cat_df.item_id)}\n\nsnap = T0 - 1 * W\nwin_start = snap + G\nwin_end = snap + W\n\nlikes = pd.read_parquet(f\"{DATA}/train_likes.parquet\")\nsnap_likes = likes[(likes.timestamp >= win_start) & (likes.timestamp <= win_end) & (likes.is_organic == 1) & (likes.uid.isin(target_uids)) & (likes.item_id.isin(item_to_idx))]\n\nlistens = pd.read_parquet(f\"{DATA}/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"], filters=[(\"timestamp\", \">=\", snap - 60*86400), (\"timestamp\", \"<=\", win_end)])\nsnap_listens = listens[(listens.timestamp >= win_start) & (listens.timestamp <= win_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80) & (listens.uid.isin(target_uids)) & (listens.item_id.isin(item_to_idx))]\n\nhist_listens = listens[listens.timestamp < snap]\nhist_pairs = set(zip(hist_listens.uid, hist_listens.item_id))\nhist_likes = likes[likes.timestamp < snap]\nhist_pairs.update(zip(hist_likes.uid, hist_likes.item_id))\n\nuids_with_pos = set()\nfor r in snap_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n uids_with_pos.add(r.uid)\nfor r in snap_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n uids_with_pos.add(r.uid)\n\nprint(f\"Users with unconsumed positive: {len(uids_with_pos)} / {len(target_uids)}\")\n'",
"description": "Check user coverage in snapshot 1 unconsumed positives"
}<untrusted_context>
Output: Users with unconsumed positive: 4684 / 8456
Process Group PGID: 2326
</untrusted_context>Analyzing User Engagement Trends
My current focus is on observing user engagement across multiple snapshots. It's interesting to see the consistency between unconsumed positive users in snapshot 1 and scored users in validation, suggesting a stable core group. I'm also examining how these snapshots collectively capture a broad spectrum of our target users over time.
Investigating Preference States
I've pinpointed the precise active preference states for both validation and testing cutoffs. Now, I'm delving into the specifics of our Two-Tower Model, focusing on designing the model.py and dataset.py files. A key consideration is the item embedding architecture, specifically whether to share the embedding table between the user and item towers, which seems to be a standard and beneficial practice.
Refining Embedding Strategy
I'm now solidifying the embedding architecture. I've confirmed that sharing item embeddings is compliant with the separate tower requirement and will implement this as a configurable option (share_embeddings=True/False). My focus is now on the item tower's specific input processing, including its embedding layer and subsequent MLP transformations.
Processing User Channel Data
I'm now actively processing user history from multiple channels, including likes, strong listens, and recent listens. My plan is to embed these items, mask out any padding, and then apply an attentive pooling mechanism using learnable query vectors to derive a representation for each channel.
Combining Channel Embeddings
I'm now merging the representations from different user interaction channels like likes and listens. This fused vector will then pass through a user-specific MLP to generate a final user embedding, which will be L2-normalized. The retrieval process will utilize this user embedding for scoring against precomputed item embeddings.
Implementing Core Model Components
I'm now moving to implementation. My focus is on creating the dataset.py and model.py files within /app/solution/. The dataset script will handle parquets, item features, temporal training pairs, and user history tensors for validation and testing cutoffs. The model script will define the neural network architectures for the user and item towers.
{
"content": "\"\"\"Dataset extraction and preprocessing for Two-Tower Ranker.\n\nHandles:\n- Catalog item features mapping and normalization.\n- History extraction per user (likes, strong listens, recent listens, dislikes).\n- Multi-snapshot temporal training set construction aligned with the 7-day discovery objective.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Set\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nTRAIN_CUTOFF = 24786800\nVAL_CUTOFF = 25393400\nWINDOW_SECS = 7 * 86400 # 7 days\nGAP_SECS = 1800 # 30 minutes\nOOV_IDX = 30000 # 30,000 catalog items, index 30000 is padding / OOV\n\nMAX_LEN_LIKES = 64\nMAX_LEN_STRONG = 64\nMAX_LEN_RECENT = 64\nMAX_LEN_DISLIKES = 32\n\n\ndef load_catalog() -> Tuple[pd.DataFrame, Dict[int, int], np.ndarray, torch.Tensor]:\n \"\"\"Load candidate catalog and prepare item metadata features.\n\n Returns:\n catalog_df: DataFrame of 30,000 items.\n item_to_idx: dict mapping raw item_id to catalog index [0, 29999].\n idx_to_item: array mapping catalog index to raw item_id.\n item_features_tensor: FloatTensor of shape (30000, 3) with normalized features.\n \"\"\"\n catalog_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}\n idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)\n\n # Compute normalized item features\n pos_listens = catalog_df.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)\n pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize\n feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)\n feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)\n feat_rank = norm_rank - 0.5\n\n features = np.column_stack([feat_pos, feat_org, feat_rank]).astype(np.float32)\n return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)\n\n\ndef extract_user_histories(\n cutoff_ts: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n history_days: int = 90,\n pref_state_path: Path | None = None,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract multi-channel history tensors for given target users up to cutoff_ts.\n\n Channels:\n - likes: explicit likes up to cutoff + active liked preference state (up to 64 items).\n - strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).\n - recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).\n - dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).\n\n Returns:\n Dict with keys \"likes\", \"strong\", \"recent\", \"dislikes\", each a Tensor of shape (N, max_len).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n num_users = len(target_uids)\n min_ts = cutoff_ts - history_days * 86400\n\n # 1. Load likes\n likes_dfs = [pd.read_parquet(DATA_DIR / \"train_likes.parquet\")]\n if include_val and (DATA_DIR / \"val_likes.parquet\").exists():\n likes_dfs.append(pd.read_parquet(DATA_DIR / \"val_likes.parquet\"))\n likes_df = pd.concat(likes_dfs, ignore_index=True)\n likes_df = likes_df[\n (likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n # Preference state (likes & dislikes)\n pref_likes = []\n pref_dislikes = []\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]\n pref_likes = pref_df[pref_df.state == \"liked\"][[\"uid\", \"item_id\"]]\n pref_dislikes = pref_df[pref_df.state == \"disliked\"][[\"uid\", \"item_id\"]]\n\n # Combine likes with preference likes\n all_likes = likes_df[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_likes) > 0:\n all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n\n top_likes = all_likes.groupby(\"uid\").head(MAX_LEN_LIKES)\n\n # 2. Load dislikes\n dislikes_dfs = [pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")]\n if include_val and (DATA_DIR / \"val_dislikes.parquet\").exists():\n dislikes_dfs.append(pd.read_parquet(DATA_DIR / \"val_dislikes.parquet\"))\n dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)\n dislikes_df = dislikes_df[\n (dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n all_dislikes = dislikes_df[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_dislikes) > 0:\n all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_dislikes = all_dislikes.groupby(\"uid\").head(MAX_LEN_DISLIKES)\n\n # 3. Load listens (filtered to history_days)\n listens_dfs = [\n pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n ]\n if include_val and (DATA_DIR / \"val_listens.parquet\").exists():\n val_l = pd.read_parquet(\n DATA_DIR / \"val_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n listens_dfs.append(val_l)\n listens_df = pd.concat(listens_dfs, ignore_index=True)\n target_listens = listens_df[listens_df.uid.isin(uid_to_row.keys())].sort_values(\"timestamp\", ascending=False)\n\n # Strong organic listens\n strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\n top_strong = strong_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_STRONG)\n\n # Recent positive listens\n recent_df = target_listens[target_listens.played_ratio_pct >= 50]\n top_recent = recent_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_RECENT)\n\n # Vectorized packing into torch tensors\n def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:\n arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)\n if len(df) > 0:\n rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)\n cols = df.groupby(\"uid\").cumcount().to_numpy(dtype=np.int64)\n mapped_items = df.item_id.map(item_to_idx).fillna(OOV_IDX).to_numpy(dtype=np.int64)\n arr[rows, cols] = mapped_items\n return torch.from_numpy(arr)\n\n return {\n \"likes\": pack_tensor(top_likes, MAX_LEN_LIKES),\n \"strong\": pack_tensor(top_strong, MAX_LEN_STRONG),\n \"recent\": pack_tensor(top_recent, MAX_LEN_RECENT),\n \"dislikes\": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),\n }\n\n\nclass TwoTowerDataset(Dataset):\n \"\"\"Dataset of (user_history, target_item_idx, sample_weight) triplets.\"\"\"\n\n def __init__(\n self,\n user_rows: np.ndarray,\n target_item_idxs: np.ndarray,\n weights: np.ndarray,\n user_histories: Dict[str, torch.Tensor],\n ):\n self.user_rows = torch.from_numpy(user_rows.astype(np.int64))\n self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))\n self.weights = torch.from_numpy(weights.astype(np.float32))\n self.user_histories = user_histories\n\n def __len__(self) -> int:\n return len(self.user_rows)\n\n def __getitem__(self, idx: int) -> Tuple[int, int, float]:\n return (\n self.user_rows[idx].item(),\n self.target_item_idxs[idx].item(),\n self.weights[idx].item(),\n )\n\n\ndef build_training_snapshots(\n num_snapshots: int = 3,\n like_weight: float = 3.0,\n strong_weight: float = 1.0,\n) -> Tuple[TwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:\n \"\"\"Build multi-snapshot training dataset strictly before TRAIN_CUTOFF.\n\n Each snapshot simulates the 7-day discovery task:\n - Cutoff at T_k.\n - Positive outcomes in [T_k + GAP_SECS, T_k + WINDOW_SECS].\n - Items observed prior to T_k are excluded from positives.\n \"\"\"\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n\n _, item_to_idx, _, _ = load_catalog()\n\n # Pre-load likes and listens\n likes_all = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n likes_target = likes_all[likes_all.uid.isin(target_set)]\n\n # Load listens for the past snapshots\n min_snapshot_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 30 * 86400\n listens_all = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_snapshot_ts)],\n )\n listens_target = listens_all[listens_all.uid.isin(target_set)]\n\n all_user_rows = []\n all_target_items = []\n all_weights = []\n\n # Build snapshots\n for s in range(1, num_snapshots + 1):\n snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS\n win_start = snap_ts + GAP_SECS\n win_end = snap_ts + WINDOW_SECS\n\n # Find history items before snap_ts to exclude\n hist_l = listens_target[listens_target.timestamp < snap_ts]\n hist_likes = likes_target[likes_target.timestamp < snap_ts]\n hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))\n\n # Positives in [win_start, win_end]\n sub_listens = listens_target[\n (listens_target.timestamp >= win_start)\n & (listens_target.timestamp <= win_end)\n & (listens_target.is_organic == 1)\n & (listens_target.played_ratio_pct >= 80)\n & (listens_target.item_id.isin(item_to_idx))\n ]\n sub_likes = likes_target[\n (likes_target.timestamp >= win_start)\n & (likes_target.timestamp <= win_end)\n & (likes_target.is_organic == 1)\n & (likes_target.item_id.isin(item_to_idx))\n ]\n\n # Combine, grade 3 wins\n snap_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = strong_weight\n\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = like_weight\n\n for (uid, item_id), w in snap_pos.items():\n all_user_rows.append(uid_to_row[uid])\n all_target_items.append(item_to_idx[item_id])\n all_weights.append(w)\n\n print(f\"Total training pairs across {num_snapshots} snapshots: {len(all_user_rows)}\")\n\n # For user histories, extract at TRAIN_CUTOFF (most complete representation for target users)\n user_histories = extract_user_histories(\n cutoff_ts=TRAIN_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\",\n )\n\n dataset = TwoTowerDataset(\n user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n user_histories=user_histories,\n )\n return dataset, user_histories, target_uids\n",
"file_path": "/app/solution/dataset.py"
}Successfully created and wrote to new file: /app/solution/dataset.py. Here is the updated code:
"""Dataset extraction and preprocessing for Two-Tower Ranker.
Handles:
- Catalog item features mapping and normalization.
- History extraction per user (likes, strong listens, recent listens, dislikes).
- Multi-snapshot temporal training set construction aligned with the 7-day discovery objective.
"""
from __future__ import annotations
import math
from pathlib import Path
from typing import Dict, List, Tuple, Set
import numpy as np
import pandas as pd
import torch
from torch.utils.data import Dataset
DATA_DIR = Path("/task/data")
TRAIN_CUTOFF = 24786800
VAL_CUTOFF = 25393400
WINDOW_SECS = 7 * 86400 # 7 days
GAP_SECS = 1800 # 30 minutes
OOV_IDX = 30000 # 30,000 catalog items, index 30000 is padding / OOV
MAX_LEN_LIKES = 64
MAX_LEN_STRONG = 64
MAX_LEN_RECENT = 64
MAX_LEN_DISLIKES = 32
def load_catalog() -> Tuple[pd.DataFrame, Dict[int, int], np.ndarray, torch.Tensor]:
"""Load candidate catalog and prepare item metadata features.
Returns:
catalog_df: DataFrame of 30,000 items.
item_to_idx: dict mapping raw item_id to catalog index [0, 29999].
idx_to_item: array mapping catalog index to raw item_id.
item_features_tensor: FloatTensor of shape (30000, 3) with normalized features.
"""
catalog_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}
idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)
# Compute normalized item features
pos_listens = catalog_df.positive_listens.to_numpy(dtype=np.float32)
org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)
pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)
log_pos = np.log1p(pos_listens)
log_org = np.log1p(org_listens)
norm_rank = pop_rank / float(len(catalog_df))
# Standardize
feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)
feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)
feat_rank = norm_rank - 0.5
features = np.column_stack([feat_pos, feat_org, feat_rank]).astype(np.float32)
return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)
def extract_user_histories(
cutoff_ts: int,
target_uids: np.ndarray,
item_to_idx: Dict[int, int],
history_days: int = 90,
pref_state_path: Path | None = None,
include_val: bool = False,
) -> Dict[str, torch.Tensor]:
"""Extract multi-channel history tensors for given target users up to cutoff_ts.
Channels:
- likes: explicit likes up to cutoff + active liked preference state (up to 64 items).
- strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).
- recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).
- dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).
Returns:
Dict with keys "likes", "strong", "recent", "dislikes", each a Tensor of shape (N, max_len).
"""
uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
num_users = len(target_uids)
min_ts = cutoff_ts - history_days * 86400
# 1. Load likes
likes_dfs = [pd.read_parquet(DATA_DIR / "train_likes.parquet")]
if include_val and (DATA_DIR / "val_likes.parquet").exists():
likes_dfs.append(pd.read_parquet(DATA_DIR / "val_likes.parquet"))
likes_df = pd.concat(likes_dfs, ignore_index=True)
likes_df = likes_df[
(likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())
].sort_values("timestamp", ascending=False)
# Preference state (likes & dislikes)
pref_likes = []
pref_dislikes = []
if pref_state_path and pref_state_path.exists():
pref_df = pd.read_parquet(pref_state_path)
pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]
pref_likes = pref_df[pref_df.state == "liked"][["uid", "item_id"]]
pref_dislikes = pref_df[pref_df.state == "disliked"][["uid", "item_id"]]
# Combine likes with preference likes
all_likes = likes_df[["uid", "item_id"]].drop_duplicates()
if len(pref_likes) > 0:
all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=["uid", "item_id"])
top_likes = all_likes.groupby("uid").head(MAX_LEN_LIKES)
# 2. Load dislikes
dislikes_dfs = [pd.read_parquet(DATA_DIR / "train_dislikes.parquet")]
if include_val and (DATA_DIR / "val_dislikes.parquet").exists():
dislikes_dfs.append(pd.read_parquet(DATA_DIR / "val_dislikes.parquet"))
dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)
dislikes_df = dislikes_df[
(dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())
].sort_values("timestamp", ascending=False)
all_dislikes = dislikes_df[["uid", "item_id"]].drop_duplicates()
if len(pref_dislikes) > 0:
all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=["uid", "item_id"])
top_dislikes = all_dislikes.groupby("uid").head(MAX_LEN_DISLIKES)
# 3. Load listens (filtered to history_days)
listens_dfs = [
pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_ts), ("timestamp", "<", cutoff_ts)],
)
]
if include_val and (DATA_DIR / "val_listens.parquet").exists():
val_l = pd.read_parquet(
DATA_DIR / "val_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_ts), ("timestamp", "<", cutoff_ts)],
)
listens_dfs.append(val_l)
listens_df = pd.concat(listens_dfs, ignore_index=True)
target_listens = listens_df[listens_df.uid.isin(uid_to_row.keys())].sort_values("timestamp", ascending=False)
# Strong organic listens
strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]
top_strong = strong_df.drop_duplicates(subset=["uid", "item_id"]).groupby("uid").head(MAX_LEN_STRONG)
# Recent positive listens
recent_df = target_listens[target_listens.played_ratio_pct >= 50]
top_recent = recent_df.drop_duplicates(subset=["uid", "item_id"]).groupby("uid").head(MAX_LEN_RECENT)
# Vectorized packing into torch tensors
def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:
arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)
if len(df) > 0:
rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)
cols = df.groupby("uid").cumcount().to_numpy(dtype=np.int64)
mapped_items = df.item_id.map(item_to_idx).fillna(OOV_IDX).to_numpy(dtype=np.int64)
arr[rows, cols] = mapped_items
return torch.from_numpy(arr)
return {
"likes": pack_tensor(top_likes, MAX_LEN_LIKES),
"strong": pack_tensor(top_strong, MAX_LEN_STRONG),
"recent": pack_tensor(top_recent, MAX_LEN_RECENT),
"dislikes": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),
}
class TwoTowerDataset(Dataset):
"""Dataset of (user_history, target_item_idx, sample_weight) triplets."""
def __init__(
self,
user_rows: np.ndarray,
target_item_idxs: np.ndarray,
weights: np.ndarray,
user_histories: Dict[str, torch.Tensor],
):
self.user_rows = torch.from_numpy(user_rows.astype(np.int64))
self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))
self.weights = torch.from_numpy(weights.astype(np.float32))
self.user_histories = user_histories
def __len__(self) -> int:
return len(self.user_rows)
def __getitem__(self, idx: int) -> Tuple[int, int, float]:
return (
self.user_rows[idx].item(),
self.target_item_idxs[idx].item(),
self.weights[idx].item(),
)
def build_training_snapshots(
num_snapshots: int = 3,
like_weight: float = 3.0,
strong_weight: float = 1.0,
) -> Tuple[TwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:
"""Build multi-snapshot training dataset strictly before TRAIN_CUTOFF.
Each snapshot simulates the 7-day discovery task:
- Cutoff at T_k.
- Positive outcomes in [T_k + GAP_SECS, T_k + WINDOW_SECS].
- Items observed prior to T_k are excluded from positives.
"""
targets_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = targets_df.uid.to_numpy(dtype=np.int64)
target_set = set(target_uids)
uid_to_row = {int(u): r for r, u in enumerate(target_uids)}
_, item_to_idx, _, _ = load_catalog()
# Pre-load likes and listens
likes_all = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_target = likes_all[likes_all.uid.isin(target_set)]
# Load listens for the past snapshots
min_snapshot_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 30 * 86400
listens_all = pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_snapshot_ts)],
)
listens_target = listens_all[listens_all.uid.isin(target_set)]
all_user_rows = []
all_target_items = []
all_weights = []
# Build snapshots
for s in range(1, num_snapshots + 1):
snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS
win_start = snap_ts + GAP_SECS
win_end = snap_ts + WINDOW_SECS
# Find history items before snap_ts to exclude
hist_l = listens_target[listens_target.timestamp < snap_ts]
hist_likes = likes_target[likes_target.timestamp < snap_ts]
hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))
# Positives in [win_start, win_end]
sub_listens = listens_target[
(listens_target.timestamp >= win_start)
& (listens_target.timestamp <= win_end)
& (listens_target.is_organic == 1)
& (listens_target.played_ratio_pct >= 80)
& (listens_target.item_id.isin(item_to_idx))
]
sub_likes = likes_target[
(likes_target.timestamp >= win_start)
& (likes_target.timestamp <= win_end)
& (likes_target.is_organic == 1)
& (likes_target.item_id.isin(item_to_idx))
]
# Combine, grade 3 wins
snap_pos: Dict[Tuple[int, int], float] = {}
for r in sub_listens[["uid", "item_id"]].drop_duplicates().itertuples(index=False):
if (r.uid, r.item_id) not in hist_pairs:
snap_pos[(r.uid, r.item_id)] = strong_weight
for r in sub_likes[["uid", "item_id"]].drop_duplicates().itertuples(index=False):
if (r.uid, r.item_id) not in hist_pairs:
snap_pos[(r.uid, r.item_id)] = like_weight
for (uid, item_id), w in snap_pos.items():
all_user_rows.append(uid_to_row[uid])
all_target_items.append(item_to_idx[item_id])
all_weights.append(w)
print(f"Total training pairs across {num_snapshots} snapshots: {len(all_user_rows)}")
# For user histories, extract at TRAIN_CUTOFF (most complete representation for target users)
user_histories = extract_user_histories(
cutoff_ts=TRAIN_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "validation_preference_state.parquet",
)
dataset = TwoTowerDataset(
user_rows=np.array(all_user_rows, dtype=np.int64),
target_item_idxs=np.array(all_target_items, dtype=np.int64),
weights=np.array(all_weights, dtype=np.float32),
user_histories=user_histories,
)
return dataset, user_histories, target_uids
{
"command": "python3 -c '\nimport time\nfrom solution.dataset import build_training_snapshots, load_catalog\n\nt0 = time.time()\ncatalog_df, item_to_idx, idx_to_item, item_features = load_catalog()\nprint(f\"Catalog loaded: {len(catalog_df)} items, features shape {item_features.shape} in {time.time()-t0:.2f}s\")\n\nt1 = time.time()\ndataset, user_histories, target_uids = build_training_snapshots(num_snapshots=2)\nprint(f\"Dataset built: {len(dataset)} pairs in {time.time()-t1:.2f}s\")\nfor k, v in user_histories.items():\n print(f\" {k}: shape {v.shape}, non-oov {(v != 30000).sum().item()}\")\n'",
"description": "Test dataset.py loading and snapshot extraction"
}<untrusted_context>
Output: Catalog loaded: 30000 items, features shape torch.Size([30000, 3]) in 0.04s
Total training pairs across 2 snapshots: 114564
Dataset built: 114564 pairs in 18.71s
likes: shape torch.Size([8456, 64]), non-oov 227333
strong: shape torch.Size([8456, 64]), non-oov 304058
recent: shape torch.Size([8456, 64]), non-oov 372972
dislikes: shape torch.Size([8456, 32]), non-oov 29870
Process Group PGID: 2347
</untrusted_context>Processing Data Further
I'm continuing to analyze the output, noting impressive numbers of liked and organic listens. My focus is now shifting to the next stage, specifically designing the PyTorch implementation.
{
"title": "Phase 2: Model Architecture Implementation",
"summary": "Completed Phase 1 with verified fast catalog loading, history extraction, and multi-snapshot dataset construction. Moving to Phase 2: implementing model.py containing ItemTower, UserTower with multi-channel attentive pooling, and TwoTowerModel with weighted in-batch InfoNCE loss and offline indexing interface.",
"strategic_intent": "Implement two-tower PyTorch neural network model with separate ItemTower and UserTower modules."
}Current topic: "Phase 2: Model Architecture Implementation"
Topic summary: Completed Phase 1 with verified fast catalog loading, history extraction, and multi-snapshot dataset construction. Moving to Phase 2: implementing model.py containing ItemTower, UserTower with multi-channel attentive pooling, and TwoTowerModel with weighted in-batch InfoNCE loss and offline indexing interface.
Strategic Intent: Implement two-tower PyTorch neural network model with separate ItemTower and UserTower modules.Designing Model Architecture
I am focusing on the architectural design for model.py. My current emphasis is on implementing distinct, trainable user and item towers using PyTorch. The user tower's representation will be derived specifically from interaction history, ensuring a tailored approach.
Developing Model Components
My focus has shifted to the concrete implementation of ItemTower within model.py. I'm incorporating an embedding layer for item indices and a linear layer for metadata features, followed by an MLP to generate the item representation, ensuring it's L2-normalized for efficient dot product calculation.
Processing Input Sequences
I am refining the processing of multi-channel sequences, specifically likes, strong, recent, and dislikes. Each sequence will be passed through embedding lookups, followed by an attentive pooling mechanism and an MLP to generate an L2-normalized vector representing user preferences. I am carefully implementing the masking and handling of padding for attentive pooling.
Validating Output Scores
I've confirmed that the L2 normalization of user and item embeddings correctly leads to dot products representing relevance scores. This means the score $s(u, i) = u \cdot v_i$ will directly map to a value in $[-1, 1]$, simplifying training and inference.
Implementing Loss Calculation
I am now focusing on the loss calculation logic. This involves computing similarities between user and positive item embeddings, as well as negative sampled items. I'm carefully masking out false negatives and implementing the cross-entropy loss with appropriate weighting for each sample. This will be crucial for training the model effectively.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to item vector v_i.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass ChannelAttentionPooling(nn.Module):\n \"\"\"Attentive pooling with masking for a sequence of item embeddings.\"\"\"\n\n def __init__(self, emb_dim: int):\n super().__init__()\n self.query = nn.Parameter(torch.randn(emb_dim))\n nn.init.normal_(self.query, std=0.02)\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n # Score: (B, L)\n scores = torch.einsum(\"bld,d->bl\", embeddings, self.query) / math.sqrt(embeddings.shape[-1])\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1) # (B, L)\n # In case all items are masked, weights will be 1/L or 0\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n\n pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings) # (B, D)\n return pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 64,\n out_dim: int = 64,\n num_features: int = 3,\n feat_dim: int = 16,\n hidden_dim: int = 128,\n dropout: float = 0.1,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) L2-normalized item vectors\n \"\"\"\n # Lookup embeddings\n item_emb = self.item_embedding(item_indices) # (B, emb_dim)\n\n # Lookup features (if index is OOV_IDX, use zeros)\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats) # (B, feat_dim)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n v = self.mlp(combined)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 64,\n out_dim: int = 64,\n hidden_dim: int = 128,\n dropout: float = 0.1,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = ChannelAttentionPooling(emb_dim)\n self.pool_strong = ChannelAttentionPooling(emb_dim)\n self.pool_recent = ChannelAttentionPooling(emb_dim)\n self.pool_dislikes = ChannelAttentionPooling(emb_dim)\n\n # 4 channels: likes, strong listens, recent listens, dislikes\n in_mlp = emb_dim * 4\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) L2-normalized user vectors\n \"\"\"\n # Pool likes\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n # Pool strong listens\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n # Pool recent listens\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n # Pool dislikes\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n u = self.mlp(fused)\n return F.normalize(u, p=2, dim=-1)\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 64,\n out_dim: int = 64,\n hidden_dim: int = 128,\n temperature: float = 0.07,\n num_negatives: int = 256,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. In-batch logits: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Neural Ranker Architecture.
Decomposes strictly into:
- ItemTower: maps candidate item ID and discovery catalog features to item vector v_i.
- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u.
- Relevance score: exact dot product u . v_i.
"""
from __future__ import annotations
import math
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
OOV_IDX = 30000
class ChannelAttentionPooling(nn.Module):
"""Attentive pooling with masking for a sequence of item embeddings."""
def __init__(self, emb_dim: int):
super().__init__()
self.query = nn.Parameter(torch.randn(emb_dim))
nn.init.normal_(self.query, std=0.02)
def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Args:
embeddings: (B, L, D)
mask: (B, L) bool tensor where True indicates valid item.
"""
# Score: (B, L)
scores = torch.einsum("bld,d->bl", embeddings, self.query) / math.sqrt(embeddings.shape[-1])
scores = scores.masked_fill(~mask, -1e9)
weights = F.softmax(scores, dim=-1) # (B, L)
# In case all items are masked, weights will be 1/L or 0
weights = weights.masked_fill(~mask, 0.0)
weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6
weights = weights / weights_sum
pooled = torch.einsum("bl,bld->bd", weights, embeddings) # (B, D)
return pooled
class ItemTower(nn.Module):
"""Candidate Item Tower producing precomputable item vector v_i in R^D."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 64,
out_dim: int = 64,
num_features: int = 3,
feat_dim: int = 16,
hidden_dim: int = 128,
dropout: float = 0.1,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
self.feat_encoder = nn.Sequential(
nn.Linear(num_features, feat_dim),
nn.GELU(),
nn.LayerNorm(feat_dim),
)
in_mlp = emb_dim + feat_dim
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, out_dim),
)
def forward(
self,
item_indices: torch.Tensor,
catalog_features: torch.Tensor,
) -> torch.Tensor:
"""Args:
item_indices: (B,) int64 tensor in [0, 29999]
catalog_features: (30000, F) float32 tensor
Returns:
v: (B, out_dim) L2-normalized item vectors
"""
# Lookup embeddings
item_emb = self.item_embedding(item_indices) # (B, emb_dim)
# Lookup features (if index is OOV_IDX, use zeros)
valid_mask = (item_indices < len(catalog_features))
safe_indices = torch.where(valid_mask, item_indices, 0)
feats = catalog_features[safe_indices]
feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))
feat_emb = self.feat_encoder(feats) # (B, feat_dim)
combined = torch.cat([item_emb, feat_emb], dim=-1)
v = self.mlp(combined)
return F.normalize(v, p=2, dim=-1)
class UserTower(nn.Module):
"""User Tower deriving representation strictly from interaction history."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 64,
out_dim: int = 64,
hidden_dim: int = 128,
dropout: float = 0.1,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
self.pool_likes = ChannelAttentionPooling(emb_dim)
self.pool_strong = ChannelAttentionPooling(emb_dim)
self.pool_recent = ChannelAttentionPooling(emb_dim)
self.pool_dislikes = ChannelAttentionPooling(emb_dim)
# 4 channels: likes, strong listens, recent listens, dislikes
in_mlp = emb_dim * 4
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, out_dim),
)
def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Args:
histories: dict of channel tensors:
- "likes": (B, L1)
- "strong": (B, L2)
- "recent": (B, L3)
- "dislikes": (B, L4)
Returns:
u: (B, out_dim) L2-normalized user vectors
"""
# Pool likes
likes = histories["likes"]
h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)
# Pool strong listens
strong = histories["strong"]
h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)
# Pool recent listens
recent = histories["recent"]
h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)
# Pool dislikes
dislikes = histories["dislikes"]
h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)
fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)
u = self.mlp(fused)
return F.normalize(u, p=2, dim=-1)
class TwoTowerModel(nn.Module):
"""Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval."""
def __init__(
self,
emb_dim: int = 64,
out_dim: int = 64,
hidden_dim: int = 128,
temperature: float = 0.07,
num_negatives: int = 256,
share_embeddings: bool = True,
):
super().__init__()
self.temperature = temperature
self.num_negatives = num_negatives
if share_embeddings:
shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(shared_emb.weight, std=0.05)
else:
shared_emb = None
self.item_tower = ItemTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
shared_embeddings=shared_emb,
)
self.user_tower = UserTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
shared_embeddings=shared_emb,
)
def compute_loss(
self,
user_histories_batch: Dict[str, torch.Tensor],
target_item_idxs: torch.Tensor,
weights: torch.Tensor,
catalog_features: torch.Tensor,
) -> torch.Tensor:
"""Compute weighted InfoNCE loss with in-batch + sampled catalog negatives."""
B = target_item_idxs.shape[0]
# 1. Compute user vectors: (B, D)
user_vecs = self.user_tower(user_histories_batch)
# 2. Compute positive item vectors: (B, D)
pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)
# 3. Sample catalog negatives: (K, D)
K = min(self.num_negatives, 30000)
neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)
neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)
# 4. In-batch logits: (B, B)
in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature
# Mask collision in positive items across different users in batch
same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))
off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))
in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)
# Sampled negative logits: (B, K)
neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature
# Combine logits: (B, B + K)
logits = torch.cat([in_batch_sim, neg_sim], dim=1)
targets = torch.arange(B, device=target_item_idxs.device)
# Weighted cross-entropy
losses = F.cross_entropy(logits, targets, reduction="none")
weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)
return weighted_loss
@torch.no_grad()
def precompute_item_embeddings(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute candidate item embeddings for all 30,000 catalog items.
Returns:
V: (30000, out_dim) float32 tensor of normalized vectors.
"""
self.eval()
num_catalog = len(catalog_features)
item_vecs = []
for start in range(0, num_catalog, batch_size):
end = min(start + batch_size, num_catalog)
idxs = torch.arange(start, end)
v = self.item_tower(idxs, catalog_features)
item_vecs.append(v)
return torch.cat(item_vecs, dim=0)
@torch.no_grad()
def compute_user_embeddings(
self,
user_histories: Dict[str, torch.Tensor],
batch_size: int = 1024,
) -> torch.Tensor:
"""Compute user embeddings for target users strictly from their history.
Returns:
U: (N, out_dim) float32 tensor of normalized vectors.
"""
self.eval()
num_users = user_histories["likes"].shape[0]
user_vecs = []
for start in range(0, num_users, batch_size):
end = min(start + batch_size, num_users)
batch_hist = {k: v[start:end] for k, v in user_histories.items()}
u = self.user_tower(batch_hist)
user_vecs.append(u)
return torch.cat(user_vecs, dim=0)
{
"command": "python3 -c '\nimport torch\nfrom solution.model import TwoTowerModel\n\nmodel = TwoTowerModel(emb_dim=64, out_dim=64, hidden_dim=128)\nB = 16\nhist = {\n \"likes\": torch.randint(0, 30001, (B, 64)),\n \"strong\": torch.randint(0, 30001, (B, 64)),\n \"recent\": torch.randint(0, 30001, (B, 64)),\n \"dislikes\": torch.randint(0, 30001, (B, 32)),\n}\ntargets = torch.randint(0, 30000, (B,))\nweights = torch.ones(B)\ncat_feats = torch.randn(30000, 3)\n\nloss = model.compute_loss(hist, targets, weights, cat_feats)\nloss.backward()\nprint(\"Forward and backward successful! Loss:\", loss.item())\n\n# Test precompute item vectors\nV = model.precompute_item_embeddings(cat_feats)\nprint(\"Item embeddings shape:\", V.shape)\n\n# Test user vectors\nU = model.compute_user_embeddings(hist)\nprint(\"User embeddings shape:\", U.shape)\n\n# Dot product scores\nscores = torch.matmul(U, V.T)\nprint(\"Scores shape:\", scores.shape)\n'",
"description": "Unit test model forward, backward, precompute and scoring"
}<untrusted_context>
Output: Forward and backward successful! Loss: 6.549716949462891
Item embeddings shape: torch.Size([30000, 64])
User embeddings shape: torch.Size([16, 64])
Scores shape: torch.Size([16, 30000])
Process Group PGID: 2371
</untrusted_context>Implementing Inference & Evaluation
I've successfully refined the TwoTowerModel's core functionalities and am now shifting focus to Phase 3. My immediate task is to develop the inference.py and evaluate.py modules, which will be crucial for generating predictions and assessing model performance.
Processing User & Item Data
I'm currently focused on preparing the user histories and catalog features for model consumption. Specifically, I'm working on generating item embeddings V and user embeddings U, and integrating candidate eligibility masks for efficient inference.
{
"content": "\"\"\"Inference and Top-K retrieval service for Two-Tower Ranker.\n\nDecomposes into:\n1. Offline precomputation of all candidate item vectors.\n2. Request-time computation of user vectors from history.\n3. Inner product search against cutoff-eligible items.\n4. Top-100 ranking generation.\n\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, Optional\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom solution.model import TwoTowerModel\n\n\ndef generate_ranking(\n model: TwoTowerModel,\n catalog_features: torch.Tensor,\n user_histories: Dict[str, torch.Tensor],\n target_uids: np.ndarray,\n eligible_candidates_path: Path,\n output_path: Optional[Path] = None,\n batch_size: int = 1024,\n) -> pd.DataFrame:\n \"\"\"Generate top-100 ranking for all target users subject to cutoff eligibility.\n\n Args:\n model: Trained TwoTowerModel.\n catalog_features: (30000, F) tensor.\n user_histories: Dict of channel tensors for target_uids.\n target_uids: Array of target user IDs (must match user_histories row order).\n eligible_candidates_path: Path to .npz archive containing packed_eligible.\n output_path: Optional path to save parquet ranking.\n batch_size: User batch size for scoring.\n\n Returns:\n DataFrame with columns ['uid', 'item_id', 'rank'].\n \"\"\"\n t0 = time.time()\n device = torch.device(\"cpu\")\n model.eval()\n\n # Step 1: Precompute offline candidate item vectors (30000, D)\n item_vectors = model.precompute_item_embeddings(catalog_features).to(device)\n\n # Step 2: Compute request-time user vectors (N, D)\n user_vectors = model.compute_user_embeddings(user_histories, batch_size=batch_size).to(device)\n\n # Step 3: Load packed candidate eligibility\n with np.load(eligible_candidates_path, allow_pickle=False) as artifact:\n cand_uids = artifact[\"uids\"].astype(np.int64, copy=True)\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n packed_eligible = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n\n # Ensure alignment with target_uids\n uid_to_cand_row = {int(uid): r for r, uid in enumerate(cand_uids)}\n target_rows = np.array([uid_to_cand_row[int(uid)] for uid in target_uids], dtype=np.int64)\n packed_for_targets = packed_eligible[target_rows]\n\n # Unpack masks: (N, 30000) boolean array\n masks = np.unpackbits(packed_for_targets, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\n num_users = len(target_uids)\n all_uids = []\n all_items = []\n all_ranks = []\n ranks_pattern = np.arange(1, 101, dtype=np.int64)\n\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n u_batch = user_vectors[start:end]\n mask_batch = torch.from_numpy(masks[start:end])\n\n # Dot product relevance scores: (B, 30000)\n scores = torch.matmul(u_batch, item_vectors.T)\n scores[~mask_batch] = -float(\"inf\")\n\n # Top 100 items per user\n topk = torch.topk(scores, 100, dim=1)\n topk_indices = topk.indices.cpu().numpy() # (B, 100)\n\n batch_uids = target_uids[start:end]\n for b in range(end - start):\n uid = batch_uids[b]\n rec_items = cand_item_ids[topk_indices[b]]\n all_uids.append(np.full(100, uid, dtype=np.int64))\n all_items.append(rec_items)\n all_ranks.append(ranks_pattern)\n\n ranking_df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n\n if output_path is not None:\n ranking_df.to_parquet(output_path, index=False)\n\n dt = time.time() - t0\n print(f\"Generated ranking for {num_users} users in {dt:.2f}s ({dt / num_users * 1000:.3f} ms/user)\")\n return ranking_df\n",
"file_path": "/app/solution/inference.py"
}Successfully created and wrote to new file: /app/solution/inference.py. Here is the updated code:
"""Inference and Top-K retrieval service for Two-Tower Ranker.
Decomposes into:
1. Offline precomputation of all candidate item vectors.
2. Request-time computation of user vectors from history.
3. Inner product search against cutoff-eligible items.
4. Top-100 ranking generation.
"""
from __future__ import annotations
import time
from pathlib import Path
from typing import Dict, Optional
import numpy as np
import pandas as pd
import torch
from solution.model import TwoTowerModel
def generate_ranking(
model: TwoTowerModel,
catalog_features: torch.Tensor,
user_histories: Dict[str, torch.Tensor],
target_uids: np.ndarray,
eligible_candidates_path: Path,
output_path: Optional[Path] = None,
batch_size: int = 1024,
) -> pd.DataFrame:
"""Generate top-100 ranking for all target users subject to cutoff eligibility.
Args:
model: Trained TwoTowerModel.
catalog_features: (30000, F) tensor.
user_histories: Dict of channel tensors for target_uids.
target_uids: Array of target user IDs (must match user_histories row order).
eligible_candidates_path: Path to .npz archive containing packed_eligible.
output_path: Optional path to save parquet ranking.
batch_size: User batch size for scoring.
Returns:
DataFrame with columns ['uid', 'item_id', 'rank'].
"""
t0 = time.time()
device = torch.device("cpu")
model.eval()
# Step 1: Precompute offline candidate item vectors (30000, D)
item_vectors = model.precompute_item_embeddings(catalog_features).to(device)
# Step 2: Compute request-time user vectors (N, D)
user_vectors = model.compute_user_embeddings(user_histories, batch_size=batch_size).to(device)
# Step 3: Load packed candidate eligibility
with np.load(eligible_candidates_path, allow_pickle=False) as artifact:
cand_uids = artifact["uids"].astype(np.int64, copy=True)
cand_item_ids = artifact["item_ids"].astype(np.int64, copy=True)
packed_eligible = artifact["packed_eligible"].astype(np.uint8, copy=True)
# Ensure alignment with target_uids
uid_to_cand_row = {int(uid): r for r, uid in enumerate(cand_uids)}
target_rows = np.array([uid_to_cand_row[int(uid)] for uid in target_uids], dtype=np.int64)
packed_for_targets = packed_eligible[target_rows]
# Unpack masks: (N, 30000) boolean array
masks = np.unpackbits(packed_for_targets, axis=1, count=len(cand_item_ids), bitorder="little").astype(bool)
num_users = len(target_uids)
all_uids = []
all_items = []
all_ranks = []
ranks_pattern = np.arange(1, 101, dtype=np.int64)
for start in range(0, num_users, batch_size):
end = min(start + batch_size, num_users)
u_batch = user_vectors[start:end]
mask_batch = torch.from_numpy(masks[start:end])
# Dot product relevance scores: (B, 30000)
scores = torch.matmul(u_batch, item_vectors.T)
scores[~mask_batch] = -float("inf")
# Top 100 items per user
topk = torch.topk(scores, 100, dim=1)
topk_indices = topk.indices.cpu().numpy() # (B, 100)
batch_uids = target_uids[start:end]
for b in range(end - start):
uid = batch_uids[b]
rec_items = cand_item_ids[topk_indices[b]]
all_uids.append(np.full(100, uid, dtype=np.int64))
all_items.append(rec_items)
all_ranks.append(ranks_pattern)
ranking_df = pd.DataFrame({
"uid": np.concatenate(all_uids),
"item_id": np.concatenate(all_items),
"rank": np.concatenate(all_ranks),
})
if output_path is not None:
ranking_df.to_parquet(output_path, index=False)
dt = time.time() - t0
print(f"Generated ranking for {num_users} users in {dt:.2f}s ({dt / num_users * 1000:.3f} ms/user)")
return ranking_df
{
"description": "Test end-to-end inference and evaluation with evaluate_public.py",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport torch\nimport pandas as pd\nfrom solution.dataset import load_catalog, extract_user_histories\nfrom solution.model import TwoTowerModel\nfrom solution.inference import generate_ranking\n\nDATA_DIR = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = targets_df.uid.to_numpy()\n\ncatalog_df, item_to_idx, idx_to_item, item_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=64, out_dim=64, hidden_dim=128)\ndf = generate_ranking(\n model=model,\n catalog_features=item_features,\n user_histories=histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"validation_eligible_candidates.npz\",\n output_path=Path(\"/app/solution/val_test.parquet\")\n)\nprint(\"Ranking generated successfully! Shape:\", df.shape)\n' && python3 /task/evaluate_public.py /app/solution/val_test.parquet"
}<untrusted_context>
Output: Generated ranking for 8456 users in 0.87s (0.103 ms/user)
Ranking generated successfully! Shape: (845600, 3)
{
"model": "/app/solution/val_test.parquet",
"scores": {
"quality_ndcg@10": 0.001291,
"quality_users": 4263
}
}
Process Group PGID: 2384
</untrusted_context>Achieving Full Pipeline Success
The entire inference pipeline has been successfully implemented and tested. It efficiently handles ranking over 8,000 users against 30,000 items in under a second, producing a valid, large-scale output. This marks a significant milestone before transitioning to the training and validation phase.
{
"summary": "Completed Phase 2 and validated the end-to-end inference pipeline against evaluate_public.py. Now transitioning to Phase 3: implementing train.py, training the two-tower model on the multi-snapshot dataset, evaluating against the public validation discovery window, and tuning hyperparameters to maximize quality_ndcg@10.",
"title": "Phase 3: Training and Validation Tuning",
"strategic_intent": "Implement training loop and optimize hyperparameters on the validation set."
}Current topic: "Phase 3: Training and Validation Tuning"
Topic summary: Completed Phase 2 and validated the end-to-end inference pipeline against evaluate_public.py. Now transitioning to Phase 3: implementing train.py, training the two-tower model on the multi-snapshot dataset, evaluating against the public validation discovery window, and tuning hyperparameters to maximize quality_ndcg@10.
Strategic Intent: Implement training loop and optimize hyperparameters on the validation set.Designing Training Loop
I'm focusing on refining the training loop within train.py. My current concentration is on optimizing batch size and the strategy for negative sampling, recognizing the significant impact these have on achieving high NDCG@10, especially with in-batch negatives.
Exploring Sampling Strategies
I'm exploring different sampling strategies for negative items, considering both catalog negatives and the impact of weighting positive interactions. The current focus is on how to best leverage multiple historical snapshots to create a more robust and diverse training dataset.
Detecting Target Leakage
I've identified a critical potential issue in build_training_snapshots: the user history extraction might be including target items themselves. This could lead to target leakage, as a positive interaction might appear in the history used to predict that same interaction. I need to rigorously verify the timestamp logic to ensure history predates the target.
Ensuring Leakage Prevention
I've confirmed that history extraction prior to a snapshot's timestamp ($T_s$) inherently prevents target leakage. Each snapshot will now have its own distinct history, ensuring data integrity. The computational overhead for extracting these histories is minimal, under a second per snapshot. This approach allows for efficient batch construction by stacking user history tensors.
{
"command": "python3 -c '\nimport numpy as np\nimport torch\n\nS = 3\nN = 8456\nL = 64\nhist = torch.randint(0, 30000, (S * N, L))\n\nbatch_user_indices = torch.randint(0, S * N, (256,))\nbatch_hist = hist[batch_user_indices]\nprint(\"batch_hist shape:\", batch_hist.shape)\n'",
"description": "Test stacked snapshot history indexing"
}<untrusted_context>
Output: batch_hist shape: torch.Size([256, 64])
Process Group PGID: 2426
</untrusted_context>Exploring Preference States
I'm currently investigating how to determine the most appropriate preference state for each generated snapshot. Specifically, I'm examining if the active preference at time $T_s$ can be inferred from existing train_likes and train_un data, leveraging pure vectorized slicing in PyTorch for efficiency.
{
"file_path": "/app/solution/dataset.py",
"content": "\"\"\"Dataset extraction and preprocessing for Two-Tower Ranker.\n\nHandles:\n- Catalog item features mapping and normalization.\n- History extraction per user (likes, strong listens, recent listens, dislikes).\n- Multi-snapshot temporal training set construction with ZERO target leakage.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Set\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nTRAIN_CUTOFF = 24786800\nVAL_CUTOFF = 25393400\nWINDOW_SECS = 7 * 86400 # 7 days\nGAP_SECS = 1800 # 30 minutes\nOOV_IDX = 30000 # 30,000 catalog items, index 30000 is padding / OOV\n\nMAX_LEN_LIKES = 64\nMAX_LEN_STRONG = 64\nMAX_LEN_RECENT = 64\nMAX_LEN_DISLIKES = 32\n\n\ndef load_catalog() -> Tuple[pd.DataFrame, Dict[int, int], np.ndarray, torch.Tensor]:\n \"\"\"Load candidate catalog and prepare item metadata features.\n\n Returns:\n catalog_df: DataFrame of 30,000 items.\n item_to_idx: dict mapping raw item_id to catalog index [0, 29999].\n idx_to_item: array mapping catalog index to raw item_id.\n item_features_tensor: FloatTensor of shape (30000, 3) with normalized features.\n \"\"\"\n catalog_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}\n idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)\n\n # Compute normalized item features\n pos_listens = catalog_df.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)\n pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize\n feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)\n feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)\n feat_rank = norm_rank - 0.5\n\n features = np.column_stack([feat_pos, feat_org, feat_rank]).astype(np.float32)\n return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)\n\n\ndef extract_user_histories(\n cutoff_ts: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n history_days: int = 90,\n pref_state_path: Path | None = None,\n include_val: bool = False,\n likes_df: Optional[pd.DataFrame] = None,\n dislikes_df: Optional[pd.DataFrame] = None,\n listens_df: Optional[pd.DataFrame] = None,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract multi-channel history tensors for target users strictly up to cutoff_ts.\n\n Channels:\n - likes: explicit likes up to cutoff + active liked preference state (up to 64 items).\n - strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).\n - recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).\n - dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n num_users = len(target_uids)\n min_ts = cutoff_ts - history_days * 86400\n\n # 1. Likes\n if likes_df is None:\n likes_dfs = [pd.read_parquet(DATA_DIR / \"train_likes.parquet\")]\n if include_val and (DATA_DIR / \"val_likes.parquet\").exists():\n likes_dfs.append(pd.read_parquet(DATA_DIR / \"val_likes.parquet\"))\n likes_df = pd.concat(likes_dfs, ignore_index=True)\n\n target_likes = likes_df[\n (likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n pref_likes = []\n pref_dislikes = []\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]\n pref_likes = pref_df[pref_df.state == \"liked\"][[\"uid\", \"item_id\"]]\n pref_dislikes = pref_df[pref_df.state == \"disliked\"][[\"uid\", \"item_id\"]]\n\n all_likes = target_likes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_likes) > 0:\n all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_likes = all_likes.groupby(\"uid\").head(MAX_LEN_LIKES)\n\n # 2. Dislikes\n if dislikes_df is None:\n dislikes_dfs = [pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")]\n if include_val and (DATA_DIR / \"val_dislikes.parquet\").exists():\n dislikes_dfs.append(pd.read_parquet(DATA_DIR / \"val_dislikes.parquet\"))\n dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)\n\n target_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n all_dislikes = target_dislikes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_dislikes) > 0:\n all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_dislikes = all_dislikes.groupby(\"uid\").head(MAX_LEN_DISLIKES)\n\n # 3. Listens\n if listens_df is None:\n listens_dfs = [\n pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n ]\n if include_val and (DATA_DIR / \"val_listens.parquet\").exists():\n val_l = pd.read_parquet(\n DATA_DIR / \"val_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n listens_dfs.append(val_l)\n listens_df = pd.concat(listens_dfs, ignore_index=True)\n\n target_listens = listens_df[\n (listens_df.timestamp >= min_ts)\n & (listens_df.timestamp < cutoff_ts)\n & listens_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\n top_strong = strong_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_STRONG)\n\n recent_df = target_listens[target_listens.played_ratio_pct >= 50]\n top_recent = recent_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_RECENT)\n\n def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:\n arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)\n if len(df) > 0:\n rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)\n cols = df.groupby(\"uid\").cumcount().to_numpy(dtype=np.int64)\n mapped_items = df.item_id.map(item_to_idx).fillna(OOV_IDX).to_numpy(dtype=np.int64)\n arr[rows, cols] = mapped_items\n return torch.from_numpy(arr)\n\n return {\n \"likes\": pack_tensor(top_likes, MAX_LEN_LIKES),\n \"strong\": pack_tensor(top_strong, MAX_LEN_STRONG),\n \"recent\": pack_tensor(top_recent, MAX_LEN_RECENT),\n \"dislikes\": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),\n }\n\n\nclass SnapshotTwoTowerDataset(Dataset):\n \"\"\"Dataset with stacked multi-snapshot user histories (ZERO target leakage).\"\"\"\n\n def __init__(\n self,\n global_user_rows: np.ndarray,\n target_item_idxs: np.ndarray,\n weights: np.ndarray,\n stacked_histories: Dict[str, torch.Tensor],\n ):\n self.global_user_rows = torch.from_numpy(global_user_rows.astype(np.int64))\n self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))\n self.weights = torch.from_numpy(weights.astype(np.float32))\n self.stacked_histories = stacked_histories\n\n def __len__(self) -> int:\n return len(self.global_user_rows)\n\n def __getitem__(self, idx: int) -> Tuple[int, int, float]:\n return (\n self.global_user_rows[idx].item(),\n self.target_item_idxs[idx].item(),\n self.weights[idx].item(),\n )\n\n\ndef build_training_snapshots(\n num_snapshots: int = 3,\n like_weight: float = 5.0,\n strong_weight: float = 1.0,\n) -> Tuple[SnapshotTwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:\n \"\"\"Build multi-snapshot training dataset strictly before TRAIN_CUTOFF with NO leakage.\"\"\"\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n N = len(target_uids)\n\n _, item_to_idx, _, _ = load_catalog()\n\n # Pre-load all likes & dislikes\n likes_all = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n likes_target = likes_all[likes_all.uid.isin(target_set)]\n\n dislikes_all = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n dislikes_target = dislikes_all[dislikes_all.uid.isin(target_set)]\n\n # Load listens for all snapshots + 90 days history\n min_listen_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 90 * 86400\n listens_all = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_listen_ts)],\n )\n listens_target = listens_all[listens_all.uid.isin(target_set)]\n\n all_user_rows = []\n all_target_items = []\n all_weights = []\n snapshot_histories_list: List[Dict[str, torch.Tensor]] = []\n\n for s in range(1, num_snapshots + 1):\n snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS\n win_start = snap_ts + GAP_SECS\n win_end = snap_ts + WINDOW_SECS\n\n # Extract clean history for this snapshot (strictly before snap_ts)\n snap_hist = extract_user_histories(\n cutoff_ts=snap_ts,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n likes_df=likes_target,\n dislikes_df=dislikes_target,\n listens_df=listens_target,\n )\n snapshot_histories_list.append(snap_hist)\n\n # History items before snap_ts to exclude from candidate positives\n hist_l = listens_target[listens_target.timestamp < snap_ts]\n hist_likes = likes_target[likes_target.timestamp < snap_ts]\n hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))\n\n # Positives in [win_start, win_end]\n sub_listens = listens_target[\n (listens_target.timestamp >= win_start)\n & (listens_target.timestamp <= win_end)\n & (listens_target.is_organic == 1)\n & (listens_target.played_ratio_pct >= 80)\n & (listens_target.item_id.isin(item_to_idx))\n ]\n sub_likes = likes_target[\n (likes_target.timestamp >= win_start)\n & (likes_target.timestamp <= win_end)\n & (likes_target.is_organic == 1)\n & (likes_target.item_id.isin(item_to_idx))\n ]\n\n # Combine, grade 3 wins\n snap_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = strong_weight\n\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = like_weight\n\n # Global user index for this snapshot = (s - 1) * N + uid_to_row[uid]\n s_offset = (s - 1) * N\n for (uid, item_id), w in snap_pos.items():\n all_user_rows.append(s_offset + uid_to_row[uid])\n all_target_items.append(item_to_idx[item_id])\n all_weights.append(w)\n\n print(f\"Total zero-leakage training pairs across {num_snapshots} snapshots: {len(all_user_rows)}\")\n\n # Stack histories along dimension 0: shape (num_snapshots * N, max_len)\n stacked_histories = {\n channel: torch.cat([h[channel] for h in snapshot_histories_list], dim=0)\n for channel in [\"likes\", \"strong\", \"recent\", \"dislikes\"]\n }\n\n dataset = SnapshotTwoTowerDataset(\n global_user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n stacked_histories=stacked_histories,\n )\n return dataset, stacked_histories, target_uids\n"
}Successfully overwrote file: /app/solution/dataset.py. Here is the updated code:
"""Dataset extraction and preprocessing for Two-Tower Ranker.
Handles:
- Catalog item features mapping and normalization.
- History extraction per user (likes, strong listens, recent listens, dislikes).
- Multi-snapshot temporal training set construction with ZERO target leakage.
"""
from __future__ import annotations
import math
from pathlib import Path
...
target_uids: np.ndarray,
item_to_idx: Dict[int, int],
history_days: int = 90,
pref_state_path: Path | None = None,
include_val: bool = False,
likes_df: Optional[pd.DataFrame] = None,
dislikes_df: Optional[pd.DataFrame] = None,
listens_df: Optional[pd.DataFrame] = None,
) -> Dict[str, torch.Tensor]:
"""Extract multi-channel history tensors for target users strictly up to cutoff_ts.
Channels:
- likes: explicit likes up to cutoff + active liked preference state (up to 64 items).
- strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).
- recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).
- dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).
"""
uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
num_users = len(target_uids)
min_ts = cutoff_ts - history_days * 86400
# 1. Likes
if likes_df is None:
likes_dfs = [pd.read_parquet(DATA_DIR / "train_likes.parquet")]
if include_val and (DATA_DIR / "val_likes.parquet").exists():
likes_dfs.append(pd.read_parquet(DATA_DIR / "val_likes.parquet"))
likes_df = pd.concat(likes_dfs, ignore_index=True)
target_likes = likes_df[
(likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())
].sort_values("timestamp", ascending=False)
pref_likes = []
pref_dislikes = []
if pref_state_path and pref_state_path.exists():
pref_df = pd.read_parquet(pref_state_path)
pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]
pref_likes = pref_df[pref_df.state == "liked"][["uid", "item_id"]]
pref_dislikes = pref_df[pref_df.state == "disliked"][["uid", "item_id"]]
all_likes = target_likes[["uid", "item_id"]].drop_duplicates()
if len(pref_likes) > 0:
all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=["uid", "item_id"])
top_likes = all_likes.groupby("uid").head(MAX_LEN_LIKES)
# 2. Dislikes
if dislikes_df is None:
dislikes_dfs = [pd.read_parquet(DATA_DIR / "train_dislikes.parquet")]
if include_val and (DATA_DIR / "val_dislikes.parquet").exists():
dislikes_dfs.append(pd.read_parquet(DATA_DIR / "val_dislikes.parquet"))
dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)
target_dislikes = dislikes_df[
(dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())
].sort_values("timestamp", ascending=False)
all_dislikes = target_dislikes[["uid", "item_id"]].drop_duplicates()
if len(pref_dislikes) > 0:
all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=["uid", "item_id"])
top_dislikes = all_dislikes.groupby("uid").head(MAX_LEN_DISLIKES)
# 3. Listens
if listens_df is None:
listens_dfs = [
pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_ts), ("timestamp", "<", cutoff_ts)],
)
]
if include_val and (DATA_DIR / "val_listens.parquet").exists():
val_l = pd.read_parquet(
DATA_DIR / "val_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_ts), ("timestamp", "<", cutoff_ts)],
)
listens_dfs.append(val_l)
listens_df = pd.concat(listens_dfs, ignore_index=True)
target_listens = listens_df[
(listens_df.timestamp >= min_ts)
& (listens_df.timestamp < cutoff_ts)
& listens_df.uid.isin(uid_to_row.keys())
].sort_values("timestamp", ascending=False)
strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]
top_strong = strong_df.drop_duplicates(subset=["uid", "item_id"]).groupby("uid").head(MAX_LEN_STRONG)
recent_df = target_listens[target_listens.played_ratio_pct >= 50]
top_recent = recent_df.drop_duplicates(subset=["uid", "item_id"]).groupby("uid").head(MAX_LEN_RECENT)
def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:
arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)
if len(df) > 0:
rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)
cols = df.groupby("uid").cumcount().to_numpy(dtype=np.int64)
...
"recent": pack_tensor(top_recent, MAX_LEN_RECENT),
"dislikes": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),
}
class SnapshotTwoTowerDataset(Dataset):
"""Dataset with stacked multi-snapshot user histories (ZERO target leakage)."""
def __init__(
self,
global_user_rows: np.ndarray,
target_item_idxs: np.ndarray,
weights: np.ndarray,
stacked_histories: Dict[str, torch.Tensor],
):
self.global_user_rows = torch.from_numpy(global_user_rows.astype(np.int64))
self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))
self.weights = torch.from_numpy(weights.astype(np.float32))
self.stacked_histories = stacked_histories
def __len__(self) -> int:
return len(self.global_user_rows)
def __getitem__(self, idx: int) -> Tuple[int, int, float]:
return (
self.global_user_rows[idx].item(),
self.target_item_idxs[idx].item(),
self.weights[idx].item(),
)
def build_training_snapshots(
num_snapshots: int = 3,
like_weight: float = 5.0,
strong_weight: float = 1.0,
) -> Tuple[SnapshotTwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:
"""Build multi-snapshot training dataset strictly before TRAIN_CUTOFF with NO leakage."""
targets_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = targets_df.uid.to_numpy(dtype=np.int64)
target_set = set(target_uids)
uid_to_row = {int(u): r for r, u in enumerate(target_uids)}
N = len(target_uids)
_, item_to_idx, _, _ = load_catalog()
# Pre-load all likes & dislikes
likes_all = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_target = likes_all[likes_all.uid.isin(target_set)]
dislikes_all = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_target = dislikes_all[dislikes_all.uid.isin(target_set)]
# Load listens for all snapshots + 90 days history
min_listen_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 90 * 86400
listens_all = pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_listen_ts)],
)
listens_target = listens_all[listens_all.uid.isin(target_set)]
all_user_rows = []
all_target_items = []
all_weights = []
snapshot_histories_list: List[Dict[str, torch.Tensor]] = []
for s in range(1, num_snapshots + 1):
snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS
win_start = snap_ts + GAP_SECS
win_end = snap_ts + WINDOW_SECS
# Extract clean history for this snapshot (strictly before snap_ts)
snap_hist = extract_user_histories(
cutoff_ts=snap_ts,
target_uids=target_uids,
item_to_idx=item_to_idx,
likes_df=likes_target,
dislikes_df=dislikes_target,
listens_df=listens_target,
)
snapshot_histories_list.append(snap_hist)
# History items before snap_ts to exclude from candidate positives
hist_l = listens_target[listens_target.timestamp < snap_ts]
hist_likes = likes_target[likes_target.timestamp < snap_ts]
hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))
# Positives in [win_start, win_end]
...
for r in sub_likes[["uid", "item_id"]].drop_duplicates().itertuples(index=False):
if (r.uid, r.item_id) not in hist_pairs:
snap_pos[(r.uid, r.item_id)] = like_weight
# Global user index for this snapshot = (s - 1) * N + uid_to_row[uid]
s_offset = (s - 1) * N
for (uid, item_id), w in snap_pos.items():
all_user_rows.append(s_offset + uid_to_row[uid])
all_target_items.append(item_to_idx[item_id])
all_weights.append(w)
print(f"Total zero-leakage training pairs across {num_snapshots} snapshots: {len(all_user_rows)}")
# Stack histories along dimension 0: shape (num_snapshots * N, max_len)
stacked_histories = {
channel: torch.cat([h[channel] for h in snapshot_histories_list], dim=0)
for channel in ["likes", "strong", "recent", "dislikes"]
}
dataset = SnapshotTwoTowerDataset(
global_user_rows=np.array(all_user_rows, dtype=np.int64),
target_item_idxs=np.array(all_target_items, dtype=np.int64),
weights=np.array(all_weights, dtype=np.float32),
stacked_histories=stacked_histories,
)
return dataset, stacked_histories, target_uids
{
"command": "python3 -c '\nimport time\nfrom solution.dataset import build_training_snapshots\n\nt0 = time.time()\ndataset, stacked_histories, target_uids = build_training_snapshots(num_snapshots=2)\nprint(f\"Loaded {len(dataset)} pairs in {time.time()-t0:.2f}s\")\nfor k, v in stacked_histories.items():\n print(f\" {k}: shape {v.shape}\")\n'",
"description": "Test zero-leakage snapshot building"
}<untrusted_context>
Output: Total zero-leakage training pairs across 2 snapshots: 94927
Loaded 94927 pairs in 34.81s
likes: shape torch.Size([16912, 64])
strong: shape torch.Size([16912, 64])
recent: shape torch.Size([16912, 64])
dislikes: shape torch.Size([16912, 32])
Process Group PGID: 2439
</untrusted_context>Implementing Training Logic
My focus is now on train.py. I've successfully generated a substantial dataset of 94,927 zero-leakage training pairs. Next, I'll be implementing the collate_fn to handle batching, stacking the target_item_idxs and weights into tensors.
Refining Training Loop
I'm now detailing the training loop within train.py. This involves setting up the AdamW optimizer and a CosineAnnealingLR scheduler. I'll be iterating through batches, calculating loss, and performing gradient clipping before an optimizer step. Validation will occur periodically, with checkpointing of the best model.
{
"command": "PYTHONPATH=/task python3 -c '\nimport time\nfrom pathlib import Path\nimport pandas as pd\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\nt0 = time.time()\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint(f\"Preloaded validation truth for {len(graded)} users in {time.time()-t0:.2f}s\")\n\n# Test scoring baseline\ndf = pd.read_parquet(\"/app/solution/baseline_popularity.parquet\")\nt1 = time.time()\nranked = ranked_items(df)\nres = score_rankings(ranked, graded)\nprint(f\"Scored in {time.time()-t1:.2f}s: {res}\")\n'",
"description": "Benchmark in-memory validation scoring"
}<untrusted_context>
Output: Preloaded validation truth for 4263 users in 0.55s
Scored in 0.33s: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 2463
</untrusted_context>Accelerating Training Cycles
I'm thrilled to report that validation now completes in a mere 0.33 seconds, bringing the total evaluation time, including ranking generation, to just 1.2 seconds. This speed allows for epoch-end evaluations without impacting training performance. My focus is now on refining train.py, ensuring all arguments and flags are meticulously addressed.
{
"content": "\"\"\"Training, Validation, and Refitting Pipeline for Two-Tower Ranker.\n\nFeatures:\n- Zero-leakage multi-snapshot temporal dataset.\n- In-batch InfoNCE loss + sampled catalog negatives.\n- Sub-second in-memory validation scoring against public discovery objective.\n- Automatic best checkpoint saving.\n- Full refit mode on train + val data producing final submission.parquet.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport copy\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\n# Add /task to path for evaluator modules\nsys.path.insert(0, \"/task\")\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nfrom solution.dataset import (\n DATA_DIR,\n TRAIN_CUTOFF,\n VAL_CUTOFF,\n build_training_snapshots,\n extract_user_histories,\n load_catalog,\n)\nfrom solution.inference import generate_ranking\nfrom solution.model import TwoTowerModel\n\n\ndef collate_fn_builder(stacked_histories: Dict[str, torch.Tensor]):\n \"\"\"Creates a fast collation function using pre-stacked history tensors.\"\"\"\n def collate_fn(batch):\n global_user_rows = torch.tensor([item[0] for item in batch], dtype=torch.int64)\n target_item_idxs = torch.tensor([item[1] for item in batch], dtype=torch.int64)\n weights = torch.tensor([item[2] for item in batch], dtype=torch.float32)\n\n batch_hist = {\n channel: stacked_histories[channel][global_user_rows]\n for channel in stacked_histories\n }\n return batch_hist, target_item_idxs, weights\n\n return collate_fn\n\n\ndef run_validation(\n model: TwoTowerModel,\n catalog_features: torch.Tensor,\n val_histories: Dict[str, torch.Tensor],\n target_uids: np.ndarray,\n graded: Dict[int, Dict[int, int]],\n output_path: Path | None = None,\n) -> float:\n \"\"\"Fast in-memory validation scoring.\"\"\"\n ranking_df = generate_ranking(\n model=model,\n catalog_features=catalog_features,\n user_histories=val_histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"validation_eligible_candidates.npz\",\n output_path=output_path,\n batch_size=1024,\n )\n ranked = ranked_items(ranking_df)\n res = score_rankings(ranked, graded)\n return float(res[\"quality_ndcg@10\"])\n\n\ndef train_model(\n epochs: int = 10,\n batch_size: int = 256,\n lr: float = 1e-3,\n emb_dim: int = 64,\n hidden_dim: int = 128,\n temperature: float = 0.07,\n num_negatives: int = 256,\n num_snapshots: int = 2,\n like_weight: float = 5.0,\n strong_weight: float = 1.0,\n) -> Tuple[TwoTowerModel, float]:\n \"\"\"Train two-tower model on multi-snapshot data and tune against validation set.\"\"\"\n print(\"=\" * 60)\n print(\"PHASE 3: TRAINING & VALIDATION TUNING\")\n print(f\"Params: epochs={epochs}, batch_size={batch_size}, lr={lr}, emb_dim={emb_dim}, temp={temperature}\")\n print(\"=\" * 60)\n\n # 1. Load catalog\n catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\n\n # 2. Build multi-snapshot training dataset\n t0 = time.time()\n train_dataset, stacked_histories, target_uids = build_training_snapshots(\n num_snapshots=num_snapshots,\n like_weight=like_weight,\n strong_weight=strong_weight,\n )\n print(f\"Built training dataset in {time.time()-t0:.2f}s with {len(train_dataset)} pairs.\")\n\n # 3. Prepare validation user histories at TRAIN_CUTOFF\n t1 = time.time()\n val_histories = extract_user_histories(\n cutoff_ts=TRAIN_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\",\n )\n print(f\"Extracted validation histories in {time.time()-t1:.2f}s.\")\n\n # 4. Preload validation truth for fast in-memory scoring\n eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(eligibility, set(target_uids))\n print(f\"Preloaded validation labels for {len(graded)} users.\")\n\n # 5. Initialize model & optimizer\n model = TwoTowerModel(\n emb_dim=emb_dim,\n out_dim=emb_dim,\n hidden_dim=hidden_dim,\n temperature=temperature,\n num_negatives=num_negatives,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)\n\n loader = DataLoader(\n train_dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn_builder(stacked_histories),\n drop_last=True,\n )\n\n best_score = -1.0\n best_weights = None\n val_out_path = Path(\"/app/solution/validation.parquet\")\n\n # Train loop\n for epoch in range(1, epochs + 1):\n t_epoch = time.time()\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n for batch_hist, target_items, weights in loader:\n optimizer.zero_grad()\n loss = model.compute_loss(batch_hist, target_items, weights, catalog_features)\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(num_batches, 1)\n\n # Evaluate validation score\n val_score = run_validation(\n model=model,\n catalog_features=catalog_features,\n val_histories=val_histories,\n target_uids=target_uids,\n graded=graded,\n output_path=val_out_path,\n )\n\n dt = time.time() - t_epoch\n print(f\"Epoch {epoch:02d}/{epochs:02d} | Train Loss: {avg_loss:.4f} | Val NDCG@10: {val_score:.6f} | Time: {dt:.1f}s\")\n\n if val_score > best_score:\n best_score = val_score\n best_weights = copy.deepcopy(model.state_dict())\n torch.save(best_weights, \"/app/solution/best_model.pt\")\n print(f\" >>> New Best Val NDCG@10: {best_score:.6f}! Model checkpoint saved.\")\n\n # Load best model\n if best_weights is not None:\n model.load_state_dict(best_weights)\n print(f\"\\nFinal Best Validation Score: {best_score:.6f}\")\n\n return model, best_score\n\n\ndef refit_and_submit(\n best_model: TwoTowerModel,\n epochs: int = 4,\n batch_size: int = 256,\n lr: float = 5e-4,\n) -> None:\n \"\"\"Refit two-tower model on train + val observed history and generate final submission.\"\"\"\n print(\"=\" * 60)\n print(\"PHASE 4: REFITTING ON TRAIN + VAL & GENERATING FINAL SUBMISSION\")\n print(\"=\" * 60)\n\n t0 = time.time()\n catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\n\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n N = len(target_uids)\n\n # In refit mode: validation window [TRAIN_CUTOFF + GAP, VAL_CUTOFF] is the latest observed positive window!\n # History for this window is TRAIN_CUTOFF.\n # Positives are in val_likes and val_listens!\n val_likes = pd.read_parquet(DATA_DIR / \"val_likes.parquet\")\n val_listens = pd.read_parquet(DATA_DIR / \"val_listens.parquet\")\n\n # Exclude items in train history\n train_l = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\"],\n filters=[(\"timestamp\", \">=\", TRAIN_CUTOFF - 90 * 86400)],\n )\n train_likes = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n train_pairs = set(zip(train_l.uid, train_l.item_id)).union(zip(train_likes.uid, train_likes.item_id))\n\n sub_likes = val_likes[(val_likes.is_organic == 1) & val_likes.uid.isin(target_set) & val_likes.item_id.isin(item_to_idx)]\n sub_listens = val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80) & val_listens.uid.isin(target_set) & val_listens.item_id.isin(item_to_idx)]\n\n refit_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in train_pairs:\n refit_pos[(r.uid, r.item_id)] = 1.0\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in train_pairs:\n refit_pos[(r.uid, r.item_id)] = 5.0\n\n print(f\"Extracted {len(refit_pos)} unconsumed positives from validation window for refitting.\")\n\n # Validation snapshot history at TRAIN_CUTOFF\n val_snap_hist = extract_user_histories(\n cutoff_ts=TRAIN_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\",\n )\n\n all_user_rows = [uid_to_row[uid] for (uid, item_id) in refit_pos.keys()]\n all_target_items = [item_to_idx[item_id] for (uid, item_id) in refit_pos.keys()]\n all_weights = list(refit_pos.values())\n\n from solution.dataset import SnapshotTwoTowerDataset\n refit_dataset = SnapshotTwoTowerDataset(\n global_user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n stacked_histories=val_snap_hist,\n )\n\n refit_loader = DataLoader(\n refit_dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn_builder(val_snap_hist),\n drop_last=False,\n )\n\n # Fine-tune the best model on validation outcomes\n optimizer = torch.optim.AdamW(best_model.parameters(), lr=lr, weight_decay=1e-4)\n best_model.train()\n for ep in range(1, epochs + 1):\n total_loss = 0.0\n n_b = 0\n for batch_hist, target_items, weights in refit_loader:\n optimizer.zero_grad()\n loss = best_model.compute_loss(batch_hist, target_items, weights, catalog_features)\n loss.backward()\n nn.utils.clip_grad_norm_(best_model.parameters(), max_norm=1.0)\n optimizer.step()\n total_loss += loss.item()\n n_b += 1\n print(f\"Refit Epoch {ep}/{epochs} | Loss: {total_loss / max(n_b, 1):.4f}\")\n\n # Now extract full test histories at VAL_CUTOFF (including val_listens, val_likes, and test_preference_state!)\n print(\"\\nExtracting test histories up to VAL_CUTOFF (including validation events)...\")\n test_histories = extract_user_histories(\n cutoff_ts=VAL_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"test_preference_state.parquet\",\n include_val=True,\n )\n\n # Generate final submission\n sub_path = Path(\"/app/solution/submission.parquet\")\n print(f\"Generating final submission to {sub_path}...\")\n sub_df = generate_ranking(\n model=best_model,\n catalog_features=catalog_features,\n user_histories=test_histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"test_eligible_candidates.npz\",\n output_path=sub_path,\n batch_size=1024,\n )\n\n # Verify submission contract\n print(\"Verifying final submission contract...\")\n targets_set = set(target_uids)\n test_cand = CandidateSet(DATA_DIR / \"test_eligible_candidates.npz\")\n norm = validate_ranking(sub_df, targets_set)\n\n # Verify candidate eligibility\n ineligible_count = 0\n for uid, item in norm[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not test_cand.is_eligible(uid, item):\n ineligible_count += 1\n if ineligible_count > 0:\n raise ValueError(f\"CRITICAL ERROR: {ineligible_count} items fail test eligibility!\")\n\n print(f\"SUCCESS: Submission verified! Rows={len(norm)}, Target users={norm.uid.nunique()}, All items eligible.\")\n print(f\"Total refit and submission time: {time.time()-t0:.2f}s\")\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Train and evaluate Two-Tower Ranker\")\n parser.add_argument(\"--epochs\", type=int, default=8)\n parser.add_argument(\"--batch_size\", type=int, default=256)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--emb_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--snapshots\", type=int, default=2)\n parser.add_argument(\"--refit\", action=\"store_true\", help=\"Refit on train+val and create final submission\")\n args = parser.parse_args()\n\n model, best_score = train_model(\n epochs=args.epochs,\n batch_size=args.batch_size,\n lr=args.lr,\n emb_dim=args.emb_dim,\n temperature=args.temperature,\n num_snapshots=args.snapshots,\n )\n\n if args.refit:\n refit_and_submit(model)\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/train.py"
}Successfully created and wrote to new file: /app/solution/train.py. Here is the updated code:
"""Training, Validation, and Refitting Pipeline for Two-Tower Ranker.
Features:
- Zero-leakage multi-snapshot temporal dataset.
- In-batch InfoNCE loss + sampled catalog negatives.
- Sub-second in-memory validation scoring against public discovery objective.
- Automatic best checkpoint saving.
- Full refit mode on train + val data producing final submission.parquet.
"""
from __future__ import annotations
import argparse
import copy
import sys
import time
from pathlib import Path
from typing import Dict, Tuple
import numpy as np
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
# Add /task to path for evaluator modules
sys.path.insert(0, "/task")
import evaluate_public
from ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking
from solution.dataset import (
DATA_DIR,
TRAIN_CUTOFF,
VAL_CUTOFF,
build_training_snapshots,
extract_user_histories,
load_catalog,
)
from solution.inference import generate_ranking
from solution.model import TwoTowerModel
def collate_fn_builder(stacked_histories: Dict[str, torch.Tensor]):
"""Creates a fast collation function using pre-stacked history tensors."""
def collate_fn(batch):
global_user_rows = torch.tensor([item[0] for item in batch], dtype=torch.int64)
target_item_idxs = torch.tensor([item[1] for item in batch], dtype=torch.int64)
weights = torch.tensor([item[2] for item in batch], dtype=torch.float32)
batch_hist = {
channel: stacked_histories[channel][global_user_rows]
for channel in stacked_histories
}
return batch_hist, target_item_idxs, weights
return collate_fn
def run_validation(
model: TwoTowerModel,
catalog_features: torch.Tensor,
val_histories: Dict[str, torch.Tensor],
target_uids: np.ndarray,
graded: Dict[int, Dict[int, int]],
output_path: Path | None = None,
) -> float:
"""Fast in-memory validation scoring."""
ranking_df = generate_ranking(
model=model,
catalog_features=catalog_features,
user_histories=val_histories,
target_uids=target_uids,
eligible_candidates_path=DATA_DIR / "validation_eligible_candidates.npz",
output_path=output_path,
batch_size=1024,
)
ranked = ranked_items(ranking_df)
res = score_rankings(ranked, graded)
return float(res["quality_ndcg@10"])
def train_model(
epochs: int = 10,
batch_size: int = 256,
lr: float = 1e-3,
emb_dim: int = 64,
hidden_dim: int = 128,
temperature: float = 0.07,
num_negatives: int = 256,
num_snapshots: int = 2,
like_weight: float = 5.0,
strong_weight: float = 1.0,
) -> Tuple[TwoTowerModel, float]:
"""Train two-tower model on multi-snapshot data and tune against validation set."""
print("=" * 60)
print("PHASE 3: TRAINING & VALIDATION TUNING")
print(f"Params: epochs={epochs}, batch_size={batch_size}, lr={lr}, emb_dim={emb_dim}, temp={temperature}")
print("=" * 60)
# 1. Load catalog
catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()
# 2. Build multi-snapshot training dataset
t0 = time.time()
train_dataset, stacked_histories, target_uids = build_training_snapshots(
num_snapshots=num_snapshots,
like_weight=like_weight,
strong_weight=strong_weight,
)
print(f"Built training dataset in {time.time()-t0:.2f}s with {len(train_dataset)} pairs.")
# 3. Prepare validation user histories at TRAIN_CUTOFF
t1 = time.time()
val_histories = extract_user_histories(
cutoff_ts=TRAIN_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "validation_preference_state.parquet",
)
print(f"Extracted validation histories in {time.time()-t1:.2f}s.")
# 4. Preload validation truth for fast in-memory scoring
eligibility = CandidateSet(DATA_DIR / "validation_eligible_candidates.npz")
graded = evaluate_public.validation_truth(eligibility, set(target_uids))
print(f"Preloaded validation labels for {len(graded)} users.")
# 5. Initialize model & optimizer
model = TwoTowerModel(
emb_dim=emb_dim,
out_dim=emb_dim,
hidden_dim=hidden_dim,
temperature=temperature,
num_negatives=num_negatives,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
loader = DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn_builder(stacked_histories),
drop_last=True,
)
best_score = -1.0
best_weights = None
val_out_path = Path("/app/solution/validation.parquet")
# Train loop
for epoch in range(1, epochs + 1):
t_epoch = time.time()
model.train()
total_loss = 0.0
num_batches = 0
for batch_hist, target_items, weights in loader:
optimizer.zero_grad()
loss = model.compute_loss(batch_hist, target_items, weights, catalog_features)
loss.backward()
nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
total_loss += loss.item()
num_batches += 1
scheduler.step()
avg_loss = total_loss / max(num_batches, 1)
# Evaluate validation score
val_score = run_validation(
model=model,
catalog_features=catalog_features,
val_histories=val_histories,
target_uids=target_uids,
graded=graded,
output_path=val_out_path,
)
dt = time.time() - t_epoch
print(f"Epoch {epoch:02d}/{epochs:02d} | Train Loss: {avg_loss:.4f} | Val NDCG@10: {val_score:.6f} | Time: {dt:.1f}s")
if val_score > best_score:
best_score = val_score
best_weights = copy.deepcopy(model.state_dict())
torch.save(best_weights, "/app/solution/best_model.pt")
print(f" >>> New Best Val NDCG@10: {best_score:.6f}! Model checkpoint saved.")
# Load best model
if best_weights is not None:
model.load_state_dict(best_weights)
print(f"\nFinal Best Validation Score: {best_score:.6f}")
return model, best_score
def refit_and_submit(
best_model: TwoTowerModel,
epochs: int = 4,
batch_size: int = 256,
lr: float = 5e-4,
) -> None:
"""Refit two-tower model on train + val observed history and generate final submission."""
print("=" * 60)
print("PHASE 4: REFITTING ON TRAIN + VAL & GENERATING FINAL SUBMISSION")
print("=" * 60)
t0 = time.time()
catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()
targets_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = targets_df.uid.to_numpy(dtype=np.int64)
target_set = set(target_uids)
uid_to_row = {int(u): r for r, u in enumerate(target_uids)}
N = len(target_uids)
# In refit mode: validation window [TRAIN_CUTOFF + GAP, VAL_CUTOFF] is the latest observed positive window!
# History for this window is TRAIN_CUTOFF.
# Positives are in val_likes and val_listens!
val_likes = pd.read_parquet(DATA_DIR / "val_likes.parquet")
val_listens = pd.read_parquet(DATA_DIR / "val_listens.parquet")
# Exclude items in train history
train_l = pd.read_parquet(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id"],
filters=[("timestamp", ">=", TRAIN_CUTOFF - 90 * 86400)],
)
train_likes = pd.read_parquet(DATA_DIR / "train_likes.parquet")
train_pairs = set(zip(train_l.uid, train_l.item_id)).union(zip(train_likes.uid, train_likes.item_id))
sub_likes = val_likes[(val_likes.is_organic == 1) & val_likes.uid.isin(target_set) & val_likes.item_id.isin(item_to_idx)]
sub_listens = val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80) & val_listens.uid.isin(target_set) & val_listens.item_id.isin(item_to_idx)]
refit_pos: Dict[Tuple[int, int], float] = {}
for r in sub_listens[["uid", "item_id"]].drop_duplicates().itertuples(index=False):
if (r.uid, r.item_id) not in train_pairs:
refit_pos[(r.uid, r.item_id)] = 1.0
for r in sub_likes[["uid", "item_id"]].drop_duplicates().itertuples(index=False):
if (r.uid, r.item_id) not in train_pairs:
refit_pos[(r.uid, r.item_id)] = 5.0
print(f"Extracted {len(refit_pos)} unconsumed positives from validation window for refitting.")
# Validation snapshot history at TRAIN_CUTOFF
val_snap_hist = extract_user_histories(
cutoff_ts=TRAIN_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "validation_preference_state.parquet",
)
all_user_rows = [uid_to_row[uid] for (uid, item_id) in refit_pos.keys()]
all_target_items = [item_to_idx[item_id] for (uid, item_id) in refit_pos.keys()]
all_weights = list(refit_pos.values())
from solution.dataset import SnapshotTwoTowerDataset
refit_dataset = SnapshotTwoTowerDataset(
global_user_rows=np.array(all_user_rows, dtype=np.int64),
target_item_idxs=np.array(all_target_items, dtype=np.int64),
weights=np.array(all_weights, dtype=np.float32),
stacked_histories=val_snap_hist,
)
refit_loader = DataLoader(
refit_dataset,
batch_size=batch_size,
shuffle=True,
collate_fn=collate_fn_builder(val_snap_hist),
drop_last=False,
)
# Fine-tune the best model on validation outcomes
optimizer = torch.optim.AdamW(best_model.parameters(), lr=lr, weight_decay=1e-4)
best_model.train()
for ep in range(1, epochs + 1):
total_loss = 0.0
n_b = 0
for batch_hist, target_items, weights in refit_loader:
optimizer.zero_grad()
loss = best_model.compute_loss(batch_hist, target_items, weights, catalog_features)
loss.backward()
nn.utils.clip_grad_norm_(best_model.parameters(), max_norm=1.0)
optimizer.step()
total_loss += loss.item()
n_b += 1
print(f"Refit Epoch {ep}/{epochs} | Loss: {total_loss / max(n_b, 1):.4f}")
# Now extract full test histories at VAL_CUTOFF (including val_listens, val_likes, and test_preference_state!)
print("\nExtracting test histories up to VAL_CUTOFF (including validation events)...")
test_histories = extract_user_histories(
cutoff_ts=VAL_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "test_preference_state.parquet",
include_val=True,
)
# Generate final submission
sub_path = Path("/app/solution/submission.parquet")
print(f"Generating final submission to {sub_path}...")
sub_df = generate_ranking(
model=best_model,
catalog_features=catalog_features,
user_histories=test_histories,
target_uids=target_uids,
eligible_candidates_path=DATA_DIR / "test_eligible_candidates.npz",
output_path=sub_path,
batch_size=1024,
)
# Verify submission contract
print("Verifying final submission contract...")
targets_set = set(target_uids)
test_cand = CandidateSet(DATA_DIR / "test_eligible_candidates.npz")
norm = validate_ranking(sub_df, targets_set)
# Verify candidate eligibility
ineligible_count = 0
for uid, item in norm[["uid", "item_id"]].itertuples(index=False, name=None):
if not test_cand.is_eligible(uid, item):
ineligible_count += 1
if ineligible_count > 0:
raise ValueError(f"CRITICAL ERROR: {ineligible_count} items fail test eligibility!")
print(f"SUCCESS: Submission verified! Rows={len(norm)}, Target users={norm.uid.nunique()}, All items eligible.")
print(f"Total refit and submission time: {time.time()-t0:.2f}s")
def main():
parser = argparse.ArgumentParser(description="Train and evaluate Two-Tower Ranker")
parser.add_argument("--epochs", type=int, default=8)
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--emb_dim", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.07)
parser.add_argument("--snapshots", type=int, default=2)
parser.add_argument("--refit", action="store_true", help="Refit on train+val and create final submission")
args = parser.parse_args()
model, best_score = train_model(
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
emb_dim=args.emb_dim,
temperature=args.temperature,
num_snapshots=args.snapshots,
)
if args.refit:
refit_and_submit(model)
if __name__ == "__main__":
main()
{
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 4 --snapshots 1",
"description": "Run initial 4-epoch training to test training loop and validation score"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.001, emb_dim=64, temp=0.07
============================================================
Total zero-leakage training pairs across 1 snapshots: 48743
Built training dataset in 18.69s with 48743 pairs.
Extracted validation histories in 4.84s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.87s (0.103 ms/user)
Epoch 01/04 | Train Loss: 6.0196 | Val NDCG@10: 0.010309 | Time: 5.1s
>>> New Best Val NDCG@10: 0.010309! Model checkpoint saved.
Generated ranking for 8456 users in 0.87s (0.103 ms/user)
Epoch 02/04 | Train Loss: 5.6940 | Val NDCG@10: 0.012071 | Time: 5.1s
>>> New Best Val NDCG@10: 0.012071! Model checkpoint saved.
Generated ranking for 8456 users in 0.87s (0.103 ms/user)
Epoch 03/04 | Train Loss: 5.3289 | Val NDCG@10: 0.008396 | Time: 5.2s
Generated ranking for 8456 users in 0.86s (0.102 ms/user)
Epoch 04/04 | Train Loss: 4.9853 | Val NDCG@10: 0.007953 | Time: 5.1s
Final Best Validation Score: 0.012071
Process Group PGID: 2483
</untrusted_context>Observing Rapid Progress
My validation NDCG@10 hit 0.012071 in epoch 2, surpassing the popularity baseline. Each epoch is processing swiftly in about 5.1 seconds. I've also seen significant reductions in training loss, dropping from 5.69 to 4 across epochs 3 and 4.
Investigating Overfitting Behavior
It appears my validation NDCG@10 has started overfitting rapidly after achieving 0.98. I'm now analyzing potential causes, focusing on embedding dimension, learning rate, insufficient weight decay, and the benefits of increased dropout and more training snapshots. The impact of temperature and catalog popularity bias is also under review.
Incorporating Popularity Bias
I've discovered a method to integrate item popularity directly into the item vector for two-tower models. By appending a learned bias term to the item tower's output, the dot product naturally includes this popularity component, satisfying the exact decomposition requirement. This should help mitigate catalog popularity bias.
Refining Popularity Integration
I'm now precisely embedding the learned item bias into the item vector and a corresponding learned scalar into the user vector. This architectural adjustment ensures the dot product naturally decomposes into personalized similarity plus an item-specific bias, directly addressing the constraint while refining popularity modeling.
Testing Bias Integration
I'm now experimenting with integrating a learned item bias directly into the Item Tower and a user affinity scalar in the User Tower. This approach, which decomposes relevance into personalized similarity and item bias, appears to be standard practice. I will be testing several configurations.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing with lr=5e-4, 2 snapshots, epochs=6:\")\ntrain_model(epochs=6, batch_size=256, lr=5e-4, emb_dim=64, temperature=0.07, num_snapshots=2)\n'",
"description": "Test training with lr=5e-4, 2 snapshots, 6 epochs"
}<untrusted_context>
Output: Testing with lr=5e-4, 2 snapshots, epochs=6:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=6, batch_size=256, lr=0.0005, emb_dim=64, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.33s with 94927 pairs.
Extracted validation histories in 4.86s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.86s (0.101 ms/user)
Epoch 01/06 | Train Loss: 6.0338 | Val NDCG@10: 0.012513 | Time: 8.8s
>>> New Best Val NDCG@10: 0.012513! Model checkpoint saved.
Generated ranking for 8456 users in 0.88s (0.105 ms/user)
Epoch 02/06 | Train Loss: 5.7600 | Val NDCG@10: 0.010613 | Time: 8.7s
Generated ranking for 8456 users in 0.86s (0.102 ms/user)
Epoch 03/06 | Train Loss: 5.5068 | Val NDCG@10: 0.007562 | Time: 8.7s
Generated ranking for 8456 users in 0.87s (0.103 ms/user)
Epoch 04/06 | Train Loss: 5.2430 | Val NDCG@10: 0.008995 | Time: 8.8s
Generated ranking for 8456 users in 0.88s (0.103 ms/user)
Epoch 05/06 | Train Loss: 5.0252 | Val NDCG@10: 0.007204 | Time: 8.7s
Generated ranking for 8456 users in 0.86s (0.101 ms/user)
Epoch 06/06 | Train Loss: 4.8967 | Val NDCG@10: 0.006529 | Time: 8.6s
Final Best Validation Score: 0.012513
Process Group PGID: 2507
</untrusted_context>Investigating Early Overfitting
My validation score dropped significantly after Epoch 1. I suspect the model quickly learned broad user-item preference alignments, then began overfitting by Epoch 2, potentially memorizing specific patterns rather than generalizing.
Analyzing Overfitting Drivers
I'm realizing the InfoNCE loss, particularly with sampled negatives, allows the model to overly focus on specific training positives. This might be causing it to essentially memorize those pairs, leading to limited downstream recommendation diversity and coverage beyond the initial training set.
Exploring Negative Sampling Strategies
I'm re-examining how popular items are handled in negative sampling. The paper highlights that even with in-batch or sampled negatives, frequent items can be disproportionately favored or penalized depending on the strategy, and SVD initialization is out due to scaling constraints.
{
"command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nitem_pop = dict(zip(cat.item_id, cat.positive_listens))\n\n# Check baseline popularity items\nbase = pd.read_parquet(\"/app/solution/baseline_popularity.parquet\")\nprint(\"Baseline top items avg listens:\", base.item_id.map(item_pop).mean())\n\n# Check val_test (untrained model)\nvtest = pd.read_parquet(\"/app/solution/val_test.parquet\")\nprint(\"Untrained model avg listens:\", vtest.item_id.map(item_pop).mean())\n\n# Check validation.parquet (best model)\nval = pd.read_parquet(\"/app/solution/validation.parquet\")\nprint(\"Trained model avg listens:\", val.item_id.map(item_pop).mean())\n'",
"description": "Check average positive listens of recommended items"
}<untrusted_context>
Output: Baseline top items avg listens: 9789.132909176917
Untrained model avg listens: 1406.5648320719017
Trained model avg listens: 1484.9026312677388
Process Group PGID: 2531
</untrusted_context>Investigating Model Performance
I'm currently scrutinizing the performance of the trained model against the baseline. It seems the baseline is significantly outperforming my trained model in terms of average positive listens. My next step is to determine the average positive listens of truly relevant items within the validation set.
{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nitem_pop = dict(zip(cat.item_id, cat.positive_listens))\n\nall_graded_items = [it for items in graded.values() for it in items]\ngraded_pops = [item_pop[it] for it in all_graded_items if it in item_pop]\nprint(f\"Validation truth items avg listens: {np.mean(graded_pops):.1f}, median: {np.median(graded_pops):.1f}\")\n'",
"description": "Check popularity distribution of validation truth items"
}<untrusted_context>
Output: Validation truth items avg listens: 1761.1, median: 855.0
Process Group PGID: 2550
</untrusted_context>Investigating Discovery Item Performance
I'm analyzing why discovery items appear to be mid-tail, noting the significant difference between average and median listens. My current focus is on understanding the model's low score of 0.0125 and identifying strategies to substantially improve it.
{
"description": "Analyze hit rates at 10 and 100 on validation",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, ndcg_at_k\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\nval_df = pd.read_parquet(\"/app/solution/validation.parquet\")\nval_ranked = ranked_items(val_df)\n\nhits_10 = 0\nhits_100 = 0\ntotal_users = len(graded)\nndcgs = []\n\nfor uid, gold in graded.items():\n preds = val_ranked[uid]\n n = ndcg_at_k(preds, gold, 10)\n ndcgs.append(n)\n if any(p in gold for p in preds[:10]):\n hits_10 += 1\n if any(p in gold for p in preds[:100]):\n hits_100 += 1\n\nprint(f\"Mean NDCG@10: {np.mean(ndcgs):.6f}\")\nprint(f\"Hit@10: {hits_10} / {total_users} ({hits_10/total_users*100:.2f}%)\")\nprint(f\"Hit@100: {hits_100} / {total_users} ({hits_100/total_users*100:.2f}%)\")\n'"
}<untrusted_context>
Output: Mean NDCG@10: 0.006529
Hit@10: 158 / 4263 (3.71%)
Hit@100: 634 / 4263 (14.87%)
Process Group PGID: 2570
</untrusted_context>Analyzing Epoch Scores
I've identified a critical issue: the validation script mistakenly overwrote the validation.parquet file at epoch 6, causing the sharp drop in NDCG. The actual score at epoch 1 was significantly higher at 0.012513. Current metrics show Hit@100 at 14.87% and Hit@10 at 3.71%, suggesting relevant items are indeed present within the top results.
Prioritizing Top 10 Ranking
I'm now focused on understanding why relevant items aren't reaching the top 10. It seems a balance between taste match and discovery prior is crucial. I'm investigating the organic_positive_listens feature in candidate_catalog.parquet to see how organic discovery influences ranking.
{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport evaluate_public\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = evaluate_public.CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\ncat[\"organic_ratio\"] = cat[\"organic_positive_listens\"] / (cat[\"positive_listens\"] + 1e-6)\n\n# Check all 30000 items vs graded items\ngraded_counts = {}\nfor gold in graded.values():\n for item in gold:\n graded_counts[item] = graded_counts.get(item, 0) + 1\n\ncat[\"graded_count\"] = cat.item_id.map(graded_counts).fillna(0)\n\nprint(\"Correlation with graded count:\")\nprint(cat[[\"positive_listens\", \"organic_positive_listens\", \"organic_ratio\", \"popularity_rank\", \"graded_count\"]].corr()[\"graded_count\"])\n'",
"description": "Check correlation between catalog features and validation graded counts"
}<untrusted_context>
Output: Correlation with graded count:
positive_listens 0.434977
organic_positive_listens 0.457331
organic_ratio 0.165159
popularity_rank -0.316570
graded_count 1.000000
Name: graded_count, dtype: float64
Process Group PGID: 2590
</untrusted_context>Analyzing Model Correlations
I'm exploring the correlations between my current metrics. Specifically, I've observed strong positive correlations for organic_positive_listens with graded_count (0.457) and positive_listens (0.435). My next step is to determine the correlation of our two-tower model's dot product with L2-normalized model vectors.
Investigating Vector Properties
I've realized the item vectors don't need to be L2-normalized as initially assumed. This opens up the possibility for the norm of the item vector, or specific dimensions within it, to directly encode item popularity or discovery strength, which could be quite informative.
Refining Scoring Mechanisms
I'm investigating how learned item biases ($b_i$) or weighted organic listens interact with user tastes for a richer score. This implies the item tower might not need strict L2-normalization, allowing vector norms to signal popularity, potentially optimizing recommendation relevance. I will test both approaches.
Exploring Model Capacity
I'm realizing the dot product could inherently capture user taste and item popularity if the final dimension learns this interaction. This is a powerful insight for improving recommendation relevance without explicit normalization. I'm now examining the loss function and weight decay to understand potential overfitting issues observed earlier.
Updating Embeddings
My recent focus is on how item embeddings are updated. With only a fraction of items receiving gradients per batch, frequent positive items are heavily updated while rare ones remain near their initial random noise. I am now considering constructing initial item representations from interaction patterns to address this.
Analyzing Overfitting Causes
I'm investigating overfitting, considering embedding dimensionality, weight decay, and learning rates for AdamW. I'm exploring smaller emb_dim values like 32 or 48, higher weight decay (1e-2/1e-3), and adjusted learning rates (3e-4/2e-4). The possibility of the item tower directly using catalog features with increased MLP capacity is also under consideration.
{
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nfrom solution.train import train_model\n\nprint(\"Testing with emb_dim=32, lr=3e-4, 2 snapshots:\")\ntrain_model(epochs=6, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_snapshots=2)\n'",
"description": "Test training with emb_dim=32, lr=3e-4, hidden_dim=64"
}<untrusted_context>
Output: Testing with emb_dim=32, lr=3e-4, 2 snapshots:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=6, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.67s with 94927 pairs.
Extracted validation histories in 4.87s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.79s (0.094 ms/user)
Epoch 01/06 | Train Loss: 6.1545 | Val NDCG@10: 0.007681 | Time: 6.4s
>>> New Best Val NDCG@10: 0.007681! Model checkpoint saved.
Generated ranking for 8456 users in 0.79s (0.094 ms/user)
Epoch 02/06 | Train Loss: 5.9330 | Val NDCG@10: 0.014372 | Time: 6.5s
>>> New Best Val NDCG@10: 0.014372! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 03/06 | Train Loss: 5.7925 | Val NDCG@10: 0.012411 | Time: 6.6s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 04/06 | Train Loss: 5.6967 | Val NDCG@10: 0.011017 | Time: 6.5s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 05/06 | Train Loss: 5.6279 | Val NDCG@10: 0.010255 | Time: 6.5s
Generated ranking for 8456 users in 0.81s (0.095 ms/user)
Epoch 06/06 | Train Loss: 5.5927 | Val NDCG@10: 0.010208 | Time: 6.5s
Final Best Validation Score: 0.014372
Process Group PGID: 2610
</untrusted_context>Observing Metric Improvements
My NDCG@10 validation score has significantly improved, now standing at 0.014372, a 34% leap from the previous popularity baseline. This milestone was achieved in Epoch 2. Interestingly, while the loss continued to decrease through subsequent epochs, the NDCG@10 metric plateaued.
Investigating Metric Degradation
I've noticed my validation NDCG@10 score has begun to decline after reaching a peak. I'm exploring potential causes, including overfitting to the limited training data and considering regularization techniques like increased weight decay, dropout, or label smoothing. I'm also evaluating the impact of using more training snapshots or increasing the number of negative samples per batch.
{
"description": "Test higher negatives and temperature",
"command": "python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing with num_negatives=512, temp=0.10, emb_dim=32:\")\ntrain_model(epochs=6, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.10, num_negatives=512, num_snapshots=2)\n'"
}<untrusted_context>
Output: Testing with num_negatives=512, temp=0.10, emb_dim=32:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=6, batch_size=256, lr=0.0003, emb_dim=32, temp=0.1
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.46s with 94927 pairs.
Extracted validation histories in 4.87s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.76s (0.090 ms/user)
Epoch 01/06 | Train Loss: 6.4533 | Val NDCG@10: 0.012033 | Time: 6.8s
>>> New Best Val NDCG@10: 0.012033! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 02/06 | Train Loss: 6.2215 | Val NDCG@10: 0.014250 | Time: 6.7s
>>> New Best Val NDCG@10: 0.014250! Model checkpoint saved.
Generated ranking for 8456 users in 0.80s (0.095 ms/user)
Epoch 03/06 | Train Loss: 6.0702 | Val NDCG@10: 0.011886 | Time: 6.8s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 04/06 | Train Loss: 5.9637 | Val NDCG@10: 0.012292 | Time: 6.9s
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 05/06 | Train Loss: 5.8905 | Val NDCG@10: 0.011343 | Time: 6.7s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 06/06 | Train Loss: 5.8543 | Val NDCG@10: 0.011111 | Time: 6.8s
Final Best Validation Score: 0.014250
Process Group PGID: 2634
</untrusted_context>Improving NDCG Scores
I'm observing a positive trend in my NDCG, with a notable increase from 0.012033 in Epoch 1 to 0.014250 in Epoch 2. My current focus is on identifying further avenues for performance enhancement by examining the data inputs of the User Tower and comparing them against the full spectrum of available information.
Investigating Feature Weighting
I'm now focusing on the internal workings of the User Tower. It appears the current pooling mechanism treats all input channels equally. I'm considering how introducing learned weights for each channel might better capture user preferences, especially in cases where certain interaction types are absent for a user.
{
"description": "Evaluate combining semantic dot product with discovery prior",
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Load candidate catalog\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\n# Organic listens log normalized\nlog_org = np.log1p(cat.organic_positive_listens.values)\np_items = (log_org - log_org.min()) / (log_org.max() - log_org.min()) # in [0, 1]\n\n# Load model vectors from best_model.pt\nfrom solution.model import TwoTowerModel\nfrom solution.dataset import load_catalog, extract_user_histories\n\ncatalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\ntarget_uids = targets_frame.uid.to_numpy()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\nitem_vecs = model.precompute_item_embeddings(catalog_features).numpy() # (30000, 32)\nuser_vecs = model.compute_user_embeddings(histories).numpy() # (8456, 32)\n\nsemantic_scores = np.matmul(user_vecs, item_vecs.T) # (8456, 30000)\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as artifact:\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"].astype(np.uint8)\nmasks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\nranks_pattern = np.arange(1, 101, dtype=np.int64)\n\nfor beta in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]:\n # Combine scores\n combined_scores = (1 - beta) * semantic_scores + beta * p_items\n combined_scores[~masks] = -1e9\n \n # Top 100\n top100_idx = np.argpartition(-combined_scores, 100, axis=1)[:, :100]\n # Sort top 100\n row_indices = np.arange(len(target_uids))[:, None]\n top100_scores = combined_scores[row_indices, top100_idx]\n sorted_order = np.argsort(-top100_scores, axis=1)\n sorted_top100 = top100_idx[row_indices, sorted_order]\n \n # Evaluate\n all_uids = []\n all_items = []\n all_ranks = []\n for b in range(len(target_uids)):\n uid = target_uids[b]\n rec = cand_item_ids[sorted_top100[b]]\n all_uids.append(np.full(100, uid, dtype=np.int64))\n all_items.append(rec)\n all_ranks.append(ranks_pattern)\n \n df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n res = score_rankings(ranked_items(df), graded)\n print(f\"Beta {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}\")\n'"
}<untrusted_context>
Output: File "<string>", line 80
print(f"Beta {beta:.2f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 2658
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nlog_org = np.log1p(cat.organic_positive_listens.values)\np_items = (log_org - log_org.min()) / (log_org.max() - log_org.min())\n\nfrom solution.model import TwoTowerModel\nfrom solution.dataset import load_catalog, extract_user_histories\n\ncatalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\ntarget_uids = targets_frame.uid.to_numpy()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\nitem_vecs = model.precompute_item_embeddings(catalog_features).numpy()\nuser_vecs = model.compute_user_embeddings(histories).numpy()\n\nsemantic_scores = np.matmul(user_vecs, item_vecs.T)\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as artifact:\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"].astype(np.uint8)\nmasks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\nranks_pattern = np.arange(1, 101, dtype=np.int64)\n\nfor beta in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]:\n combined_scores = (1 - beta) * semantic_scores + beta * p_items\n combined_scores[~masks] = -1e9\n \n top100_idx = np.argpartition(-combined_scores, 100, axis=1)[:, :100]\n row_indices = np.arange(len(target_uids))[:, None]\n top100_scores = combined_scores[row_indices, top100_idx]\n sorted_order = np.argsort(-top100_scores, axis=1)\n sorted_top100 = top100_idx[row_indices, sorted_order]\n \n all_uids = []\n all_items = []\n all_ranks = []\n for b in range(len(target_uids)):\n uid = target_uids[b]\n rec = cand_item_ids[sorted_top100[b]]\n all_uids.append(np.full(100, uid, dtype=np.int64))\n all_items.append(rec)\n all_ranks.append(ranks_pattern)\n \n df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n res = score_rankings(ranked_items(df), graded)\n score_val = res[\"quality_ndcg@10\"]\n print(f\"Beta {beta:.2f}: NDCG@10 = {score_val:.6f}\")\n'",
"description": "Evaluate combining semantic dot product with discovery prior"
}<untrusted_context>
Output: /usr/bin/bash: line 79: 2663 Killed PYTHONPATH=/task python3 -c '
from pathlib import Path
import pandas as pd
import numpy as np
import torch
import evaluate_public
from ranking_evaluation import CandidateSet, ranked_items, score_rankings
DATA = Path("/task/data")
targets_frame = pd.read_parquet(DATA / "target_users.parquet")
targets = set(targets_frame.uid.astype(int))
eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz")
graded = evaluate_public.validation_truth(eligibility, targets)
cat = pd.read_parquet(DATA / "candidate_catalog.parquet")
log_org = np.log1p(cat.organic_positive_listens.values)
p_items = (log_org - log_org.min()) / (log_org.max() - log_org.min())
from solution.model import TwoTowerModel
from solution.dataset import load_catalog, extract_user_histories
catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()
target_uids = targets_frame.uid.to_numpy()
histories = extract_user_histories(
cutoff_ts=24786800,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA / "validation_preference_state.parquet"
)
model = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)
model.load_state_dict(torch.load("/app/solution/best_model.pt"))
model.eval()
item_vecs = model.precompute_item_embeddings(catalog_features).numpy()
user_vecs = model.compute_user_embeddings(histories).numpy()
semantic_scores = np.matmul(user_vecs, item_vecs.T)
with np.load(DATA / "validation_eligible_candidates.npz") as artifact:
cand_item_ids = artifact["item_ids"].astype(np.int64)
packed = artifact["packed_eligible"].astype(np.uint8)
masks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder="little").astype(bool)
ranks_pattern = np.arange(1, 101, dtype=np.int64)
for beta in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6]:
combined_scores = (1 - beta) * semantic_scores + beta * p_items
combined_scores[~masks] = -1e9
top100_idx = np.argpartition(-combined_scores, 100, axis=1)[:, :100]
row_indices = np.arange(len(target_uids))[:, None]
top100_scores = combined_scores[row_indices, top100_idx]
sorted_order = np.argsort(-top100_scores, axis=1)
sorted_top100 = top100_idx[row_indices, sorted_order]
all_uids = []
all_items = []
all_ranks = []
for b in range(len(target_uids)):
uid = target_uids[b]
rec = cand_item_ids[sorted_top100[b]]
all_uids.append(np.full(100, uid, dtype=np.int64))
all_items.append(rec)
all_ranks.append(ranks_pattern)
df = pd.DataFrame({
"uid": np.concatenate(all_uids),
"item_id": np.concatenate(all_items),
"rank": np.concatenate(all_ranks),
})
res = score_rankings(ranked_items(df), graded)
score_val = res["quality_ndcg@10"]
print(f"Beta {beta:.2f}: NDCG@10 = {score_val:.6f}")
'
Exit Code: 137
Process Group PGID: 2661
</untrusted_context>Investigating Memory Leaks
It seems my memory consumption spiked unexpectedly. I've identified that creating multiple large DataFrames within a loop, specifically from loading extensive listening histories, is the likely culprit.
Refining Matrix Multiplication
My focus has shifted to optimizing the matrix multiplication in inference.py. Batching with torch.matmul and torch.topk is proving highly efficient, consuming minimal RAM by processing users in manageable chunks and only retaining the top results. The model architecture in model.py is being reviewed to ensure user and item towers output vectors that directly compute scores via dot product.
Revising Item Representation
I've discovered an issue in model.py where F.normalize nullifies the impact of catalog features on an item's vector magnitude. I'm now exploring a revised output structure, separating semantic direction and magnitude, to allow item features to influence popularity representation.
Adapting Vector Architecture
I'm refining the model architecture by splitting user and item representations into semantic and magnitude components. This allows separate learning of taste similarity and discovery influence, with the score still being a dot product in $\mathbb{R}^D$, enabling flexible trade-offs. I am also reviewing user history pooling.
Incorporating Recency
I'm now focusing on recency within user history. By leveraging the timestamp sorting of user interactions, I can introduce either a recency decay weight or learnable positional embeddings to give more significance to recent listens and likes, thereby better reflecting current user taste. This will be tested in model.py.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to item vector v_i in R^D.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u in R^D.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass RecencyAttentivePooling(nn.Module):\n \"\"\"Attentive pooling with learnable recency positional bias for interaction history.\"\"\"\n\n def __init__(self, emb_dim: int, max_len: int = 64):\n super().__init__()\n self.query = nn.Parameter(torch.randn(emb_dim))\n nn.init.normal_(self.query, std=0.02)\n self.pos_bias = nn.Parameter(torch.zeros(max_len))\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n L = embeddings.shape[1]\n scores = torch.einsum(\"bld,d->bl\", embeddings, self.query) / math.sqrt(embeddings.shape[-1])\n scores = scores + self.pos_bias[:L].unsqueeze(0)\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings)\n return pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n num_features: int = 3,\n feat_dim: int = 16,\n hidden_dim: int = 64,\n dropout: float = 0.2,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n self.semantic_dim = out_dim - 1\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.semantic_mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, self.semantic_dim),\n )\n\n # Discovery prior scalar head\n self.prior_head = nn.Sequential(\n nn.Linear(feat_dim, 16),\n nn.GELU(),\n nn.Linear(16, 1),\n nn.Softplus(),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) item vector\n \"\"\"\n item_emb = self.item_embedding(item_indices)\n\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n # Semantic direction (L2-normalized)\n v_sem = self.semantic_mlp(combined)\n v_sem = F.normalize(v_sem, p=2, dim=-1)\n\n # Discovery prior scalar\n v_prior = self.prior_head(feat_emb) # (B, 1)\n\n # Full vector of dimension out_dim\n v = torch.cat([v_sem, v_prior], dim=-1)\n return v\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n dropout: float = 0.2,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n self.semantic_dim = out_dim - 1\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_strong = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_recent = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_dislikes = RecencyAttentivePooling(emb_dim, max_len=32)\n\n in_mlp = emb_dim * 4\n self.semantic_mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, self.semantic_dim),\n )\n\n self.prior_head = nn.Sequential(\n nn.Linear(in_mlp, 16),\n nn.GELU(),\n nn.Linear(16, 1),\n nn.Softplus(),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) user vector\n \"\"\"\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n\n # Semantic direction (L2-normalized)\n u_sem = self.semantic_mlp(fused)\n u_sem = F.normalize(u_sem, p=2, dim=-1)\n\n # User discovery prior scale\n u_prior = self.prior_head(fused) # (B, 1)\n\n u = torch.cat([u_sem, u_prior], dim=-1)\n return u\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n temperature: float = 0.08,\n num_negatives: int = 512,\n dropout: float = 0.2,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. Exact dot product relevance: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Neural Ranker Architecture.
Decomposes strictly into:
- ItemTower: maps candidate item ID and discovery catalog features to item vector v_i in R^D.
- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u in R^D.
- Relevance score: exact dot product u . v_i.
"""
from __future__ import annotations
import math
...
import torch.nn.functional as F
OOV_IDX = 30000
class RecencyAttentivePooling(nn.Module):
"""Attentive pooling with learnable recency positional bias for interaction history."""
def __init__(self, emb_dim: int, max_len: int = 64):
super().__init__()
self.query = nn.Parameter(torch.randn(emb_dim))
nn.init.normal_(self.query, std=0.02)
self.pos_bias = nn.Parameter(torch.zeros(max_len))
def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Args:
embeddings: (B, L, D)
mask: (B, L) bool tensor where True indicates valid item.
"""
L = embeddings.shape[1]
scores = torch.einsum("bld,d->bl", embeddings, self.query) / math.sqrt(embeddings.shape[-1])
scores = scores + self.pos_bias[:L].unsqueeze(0)
scores = scores.masked_fill(~mask, -1e9)
weights = F.softmax(scores, dim=-1)
weights = weights.masked_fill(~mask, 0.0)
weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6
weights = weights / weights_sum
pooled = torch.einsum("bl,bld->bd", weights, embeddings)
return pooled
class ItemTower(nn.Module):
"""Candidate Item Tower producing precomputable item vector v_i in R^D."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 32,
num_features: int = 3,
feat_dim: int = 16,
hidden_dim: int = 64,
dropout: float = 0.2,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
self.semantic_dim = out_dim - 1
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
...
nn.GELU(),
nn.LayerNorm(feat_dim),
)
in_mlp = emb_dim + feat_dim
self.semantic_mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, self.semantic_dim),
)
# Discovery prior scalar head
self.prior_head = nn.Sequential(
nn.Linear(feat_dim, 16),
nn.GELU(),
nn.Linear(16, 1),
nn.Softplus(),
)
def forward(
self,
item_indices: torch.Tensor,
catalog_features: torch.Tensor,
) -> torch.Tensor:
"""Args:
item_indices: (B,) int64 tensor in [0, 29999]
catalog_features: (30000, F) float32 tensor
Returns:
v: (B, out_dim) item vector
"""
item_emb = self.item_embedding(item_indices)
valid_mask = (item_indices < len(catalog_features))
safe_indices = torch.where(valid_mask, item_indices, 0)
feats = catalog_features[safe_indices]
feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))
feat_emb = self.feat_encoder(feats)
combined = torch.cat([item_emb, feat_emb], dim=-1)
# Semantic direction (L2-normalized)
v_sem = self.semantic_mlp(combined)
v_sem = F.normalize(v_sem, p=2, dim=-1)
# Discovery prior scalar
v_prior = self.prior_head(feat_emb) # (B, 1)
# Full vector of dimension out_dim
v = torch.cat([v_sem, v_prior], dim=-1)
return v
class UserTower(nn.Module):
"""User Tower deriving representation strictly from interaction history."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 32,
hidden_dim: int = 64,
dropout: float = 0.2,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
self.semantic_dim = out_dim - 1
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
self.pool_likes = RecencyAttentivePooling(emb_dim, max_len=64)
self.pool_strong = RecencyAttentivePooling(emb_dim, max_len=64)
self.pool_recent = RecencyAttentivePooling(emb_dim, max_len=64)
self.pool_dislikes = RecencyAttentivePooling(emb_dim, max_len=32)
in_mlp = emb_dim * 4
self.semantic_mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, self.semantic_dim),
)
self.prior_head = nn.Sequential(
nn.Linear(in_mlp, 16),
nn.GELU(),
nn.Linear(16, 1),
nn.Softplus(),
)
def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Args:
histories: dict of channel tensors:
- "likes": (B, L1)
- "strong": (B, L2)
- "recent": (B, L3)
- "dislikes": (B, L4)
Returns:
u: (B, out_dim) user vector
"""
likes = histories["likes"]
h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)
strong = histories["strong"]
h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)
recent = histories["recent"]
h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)
dislikes = histories["dislikes"]
h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)
fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)
# Semantic direction (L2-normalized)
u_sem = self.semantic_mlp(fused)
u_sem = F.normalize(u_sem, p=2, dim=-1)
# User discovery prior scale
u_prior = self.prior_head(fused) # (B, 1)
u = torch.cat([u_sem, u_prior], dim=-1)
return u
class TwoTowerModel(nn.Module):
"""Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval."""
def __init__(
self,
emb_dim: int = 32,
out_dim: int = 32,
hidden_dim: int = 64,
temperature: float = 0.08,
num_negatives: int = 512,
dropout: float = 0.2,
share_embeddings: bool = True,
):
super().__init__()
self.temperature = temperature
self.num_negatives = num_negatives
...
self.item_tower = ItemTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
dropout=dropout,
shared_embeddings=shared_emb,
)
self.user_tower = UserTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
dropout=dropout,
shared_embeddings=shared_emb,
)
def compute_loss(
self,
...
# 3. Sample catalog negatives: (K, D)
K = min(self.num_negatives, 30000)
neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)
neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)
# 4. Exact dot product relevance: (B, B)
in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature
# Mask collision in positive items across different users in batch
same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))
off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))
...
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute candidate item embeddings for all 30,000 catalog items.
Returns:
V: (30000, out_dim) float32 tensor.
"""
self.eval()
num_catalog = len(catalog_features)
item_vecs = []
for start in range(0, num_catalog, batch_size):
...
batch_size: int = 1024,
) -> torch.Tensor:
"""Compute user embeddings for target users strictly from their history.
Returns:
U: (N, out_dim) float32 tensor.
"""
self.eval()
num_users = user_histories["likes"].shape[0]
user_vecs = []
for start in range(0, num_users, batch_size):
...{
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 6 --snapshots 2 --emb_dim 32",
"description": "Train updated model with discovery prior and recency attention"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=6, batch_size=256, lr=0.001, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.83s with 94927 pairs.
Extracted validation histories in 4.84s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 01/06 | Train Loss: 6.0581 | Val NDCG@10: 0.012051 | Time: 7.5s
>>> New Best Val NDCG@10: 0.012051! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/06 | Train Loss: 5.7551 | Val NDCG@10: 0.013872 | Time: 7.4s
>>> New Best Val NDCG@10: 0.013872! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 03/06 | Train Loss: 5.4992 | Val NDCG@10: 0.009725 | Time: 7.4s
Generated ranking for 8456 users in 0.81s (0.095 ms/user)
Epoch 04/06 | Train Loss: 5.2272 | Val NDCG@10: 0.008979 | Time: 7.5s
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 05/06 | Train Loss: 5.0091 | Val NDCG@10: 0.008049 | Time: 7.4s
Generated ranking for 8456 users in 0.80s (0.095 ms/user)
Epoch 06/06 | Train Loss: 4.8791 | Val NDCG@10: 0.008047 | Time: 7.4s
Final Best Validation Score: 0.013872
Process Group PGID: 2685
</untrusted_context>{
"description": "Train with tuned lr and temperature",
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 4 --snapshots 2 --emb_dim 32 --lr 3e-4 --temperature 0.10"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.0003, emb_dim=32, temp=0.1
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.83s with 94927 pairs.
Extracted validation histories in 4.85s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.79s (0.093 ms/user)
Epoch 01/04 | Train Loss: 6.1402 | Val NDCG@10: 0.005924 | Time: 7.4s
>>> New Best Val NDCG@10: 0.005924! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.095 ms/user)
Epoch 02/04 | Train Loss: 5.9555 | Val NDCG@10: 0.010243 | Time: 7.5s
>>> New Best Val NDCG@10: 0.010243! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 03/04 | Train Loss: 5.8221 | Val NDCG@10: 0.011104 | Time: 7.4s
>>> New Best Val NDCG@10: 0.011104! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 04/04 | Train Loss: 5.7510 | Val NDCG@10: 0.010728 | Time: 7.6s
Final Best Validation Score: 0.011104
Process Group PGID: 2709
</untrusted_context>Exploring Vector Normality
I'm currently analyzing why a normalized vector achieved 0.014372, specifically with emb_dim=32, lr=3e-4, and temp=0.08. This outcome is intriguing compared to an unconstrained prior head’s performance. I’m digging into the implications of strict normalization versus an unconstrained approach.
Investigating Parameter Space
I'm exploring how increasing emb_dim to 48 or 64, alongside adding weight decay, might enhance performance. I'm also considering utilizing three snapshots instead of two, which would significantly increase the training data. Additionally, fine-tuning positive weights and directly embedding candidate catalog features into item embeddings are on the agenda.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to unit vector v_i in R^D.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to unit vector u in R^D.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass RecencyAttentivePooling(nn.Module):\n \"\"\"Attentive pooling with learnable recency positional bias for interaction history.\"\"\"\n\n def __init__(self, emb_dim: int, max_len: int = 64):\n super().__init__()\n self.query = nn.Parameter(torch.randn(emb_dim))\n nn.init.normal_(self.query, std=0.02)\n self.pos_bias = nn.Parameter(torch.zeros(max_len))\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n L = embeddings.shape[1]\n scores = torch.einsum(\"bld,d->bl\", embeddings, self.query) / math.sqrt(embeddings.shape[-1])\n scores = scores + self.pos_bias[:L].unsqueeze(0)\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings)\n return pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n num_features: int = 3,\n feat_dim: int = 16,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) L2-normalized item vector\n \"\"\"\n item_emb = self.item_embedding(item_indices)\n\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n v = self.mlp(combined)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_strong = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_recent = RecencyAttentivePooling(emb_dim, max_len=64)\n self.pool_dislikes = RecencyAttentivePooling(emb_dim, max_len=32)\n\n in_mlp = emb_dim * 4\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) L2-normalized user vector\n \"\"\"\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n u = self.mlp(fused)\n return F.normalize(u, p=2, dim=-1)\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n temperature: float = 0.08,\n num_negatives: int = 512,\n dropout: float = 0.15,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. Exact dot product relevance: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Neural Ranker Architecture.
Decomposes strictly into:
- ItemTower: maps candidate item ID and discovery catalog features to unit vector v_i in R^D.
- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to unit vector u in R^D.
- Relevance score: exact dot product u . v_i.
"""
from __future__ import annotations
import math
...
emb_dim: int = 32,
out_dim: int = 32,
num_features: int = 3,
feat_dim: int = 16,
hidden_dim: int = 64,
dropout: float = 0.15,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
...
nn.GELU(),
nn.LayerNorm(feat_dim),
)
in_mlp = emb_dim + feat_dim
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, out_dim),
)
def forward(
self,
item_indices: torch.Tensor,
catalog_features: torch.Tensor,
) -> torch.Tensor:
"""Args:
item_indices: (B,) int64 tensor in [0, 29999]
catalog_features: (30000, F) float32 tensor
Returns:
v: (B, out_dim) L2-normalized item vector
"""
item_emb = self.item_embedding(item_indices)
valid_mask = (item_indices < len(catalog_features))
safe_indices = torch.where(valid_mask, item_indices, 0)
...
feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))
feat_emb = self.feat_encoder(feats)
combined = torch.cat([item_emb, feat_emb], dim=-1)
v = self.mlp(combined)
return F.normalize(v, p=2, dim=-1)
class UserTower(nn.Module):
"""User Tower deriving representation strictly from interaction history."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 32,
hidden_dim: int = 64,
dropout: float = 0.15,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
...
self.pool_strong = RecencyAttentivePooling(emb_dim, max_len=64)
self.pool_recent = RecencyAttentivePooling(emb_dim, max_len=64)
self.pool_dislikes = RecencyAttentivePooling(emb_dim, max_len=32)
in_mlp = emb_dim * 4
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, out_dim),
)
def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Args:
histories: dict of channel tensors:
- "likes": (B, L1)
- "strong": (B, L2)
- "recent": (B, L3)
- "dislikes": (B, L4)
Returns:
u: (B, out_dim) L2-normalized user vector
"""
likes = histories["likes"]
h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)
strong = histories["strong"]
...
dislikes = histories["dislikes"]
h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)
fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)
u = self.mlp(fused)
return F.normalize(u, p=2, dim=-1)
class TwoTowerModel(nn.Module):
"""Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval."""
def __init__(
self,
emb_dim: int = 32,
out_dim: int = 32,
hidden_dim: int = 64,
temperature: float = 0.08,
num_negatives: int = 512,
dropout: float = 0.15,
share_embeddings: bool = True,
):
super().__init__()
self.temperature = temperature
self.num_negatives = num_negatives
...
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute candidate item embeddings for all 30,000 catalog items.
Returns:
V: (30000, out_dim) float32 tensor of normalized vectors.
"""
self.eval()
num_catalog = len(catalog_features)
item_vecs = []
for start in range(0, num_catalog, batch_size):
...
batch_size: int = 1024,
) -> torch.Tensor:
"""Compute user embeddings for target users strictly from their history.
Returns:
U: (N, out_dim) float32 tensor of normalized vectors.
"""
self.eval()
num_users = user_histories["likes"].shape[0]
user_vecs = []
for start in range(0, num_users, batch_size):
...{
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 5 --snapshots 2 --emb_dim 32 --lr 4e-4 --temperature 0.08",
"description": "Train clean normalized model with recency attention and tuned hyperparameters"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=5, batch_size=256, lr=0.0004, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 35.06s with 94927 pairs.
Extracted validation histories in 4.88s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 01/05 | Train Loss: 6.0915 | Val NDCG@10: 0.011992 | Time: 7.0s
>>> New Best Val NDCG@10: 0.011992! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 02/05 | Train Loss: 5.8583 | Val NDCG@10: 0.012354 | Time: 6.9s
>>> New Best Val NDCG@10: 0.012354! Model checkpoint saved.
Generated ranking for 8456 users in 0.84s (0.099 ms/user)
Epoch 03/05 | Train Loss: 5.7203 | Val NDCG@10: 0.011541 | Time: 7.1s
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 04/05 | Train Loss: 5.6087 | Val NDCG@10: 0.009885 | Time: 7.2s
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 05/05 | Train Loss: 5.5392 | Val NDCG@10: 0.009260 | Time: 7.0s
Final Best Validation Score: 0.012354
Process Group PGID: 2733
</untrusted_context>Refining ChannelAttentionPooling
I'm exploring why ChannelAttentionPooling yielded 0.014372 and how its performance compares when positional bias is introduced. I'm focused on understanding this difference.
{
"command": "python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing emb_dim=32, lr=3e-4, epochs=4:\")\ntrain_model(epochs=4, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_snapshots=2)\n'",
"description": "Test emb_dim=32, lr=3e-4 without pos_bias"
}<untrusted_context>
Output: Testing emb_dim=32, lr=3e-4, epochs=4:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.0003, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.44s with 94927 pairs.
Extracted validation histories in 4.84s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.79s (0.093 ms/user)
Epoch 01/04 | Train Loss: 6.1387 | Val NDCG@10: 0.008246 | Time: 6.6s
>>> New Best Val NDCG@10: 0.008246! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/04 | Train Loss: 5.9410 | Val NDCG@10: 0.014185 | Time: 6.6s
>>> New Best Val NDCG@10: 0.014185! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 03/04 | Train Loss: 5.8360 | Val NDCG@10: 0.012109 | Time: 6.7s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 04/04 | Train Loss: 5.7695 | Val NDCG@10: 0.011486 | Time: 6.8s
Final Best Validation Score: 0.014185
Process Group PGID: 2757
</untrusted_context>Observing Epoch Trends
I'm noticing a consistent performance window around 0.0142 to 0.0144 in Epoch 2. My current focus is understanding why this peak occurs there and then shifts in subsequent epochs, given the volume of training data seen so far.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing batch_size=512, lr=5e-4, epochs=4:\")\ntrain_model(epochs=4, batch_size=512, lr=5e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_snapshots=2)\n'",
"description": "Test batch_size=512 with lr=5e-4"
}<untrusted_context>
Output: Testing batch_size=512, lr=5e-4, epochs=4:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=512, lr=0.0005, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.72s with 94927 pairs.
Extracted validation histories in 4.87s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 01/04 | Train Loss: 6.6543 | Val NDCG@10: 0.005734 | Time: 5.5s
>>> New Best Val NDCG@10: 0.005734! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 02/04 | Train Loss: 6.4348 | Val NDCG@10: 0.008464 | Time: 5.4s
>>> New Best Val NDCG@10: 0.008464! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.099 ms/user)
Epoch 03/04 | Train Loss: 6.3022 | Val NDCG@10: 0.010330 | Time: 5.4s
>>> New Best Val NDCG@10: 0.010330! Model checkpoint saved.
Generated ranking for 8456 users in 0.84s (0.099 ms/user)
Epoch 04/04 | Train Loss: 6.2335 | Val NDCG@10: 0.009768 | Time: 5.6s
Final Best Validation Score: 0.010330
Process Group PGID: 2781
</untrusted_context>{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing batch_size=256, lr=4e-4, epochs=3:\")\ntrain_model(epochs=3, batch_size=256, lr=4e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_snapshots=2)\n'",
"description": "Test batch_size=256, lr=4e-4, 3 epochs"
}<untrusted_context>
Output: Testing batch_size=256, lr=4e-4, epochs=3:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0004, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.60s with 94927 pairs.
Extracted validation histories in 4.87s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.77s (0.091 ms/user)
Epoch 01/03 | Train Loss: 6.1577 | Val NDCG@10: 0.012825 | Time: 6.6s
>>> New Best Val NDCG@10: 0.012825! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.096 ms/user)
Epoch 02/03 | Train Loss: 5.8985 | Val NDCG@10: 0.012188 | Time: 6.7s
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 03/03 | Train Loss: 5.7747 | Val NDCG@10: 0.011604 | Time: 6.7s
Final Best Validation Score: 0.012825
Process Group PGID: 2805
</untrusted_context>Testing Snapshot Count
I'm exploring the impact of increasing snapshot history to three, aiming to capture broader item diversity. My current focus is on evaluating num_snapshots = 3 with epochs = 3 and lr = 3e-4.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing num_snapshots=3, lr=3e-4, epochs=3:\")\ntrain_model(epochs=3, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_snapshots=3)\n'",
"description": "Test 3 snapshots with lr=3e-4"
}<untrusted_context>
Output: Testing num_snapshots=3, lr=3e-4, epochs=3:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 3 snapshots: 140160
Built training dataset in 51.66s with 140160 pairs.
Extracted validation histories in 4.90s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.79s (0.094 ms/user)
Epoch 01/03 | Train Loss: 6.1366 | Val NDCG@10: 0.011441 | Time: 9.2s
>>> New Best Val NDCG@10: 0.011441! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/03 | Train Loss: 5.9252 | Val NDCG@10: 0.012002 | Time: 9.3s
>>> New Best Val NDCG@10: 0.012002! Model checkpoint saved.
Generated ranking for 8456 users in 0.80s (0.095 ms/user)
Epoch 03/03 | Train Loss: 5.8239 | Val NDCG@10: 0.012739 | Time: 9.2s
>>> New Best Val NDCG@10: 0.012739! Model checkpoint saved.
Final Best Validation Score: 0.012739
Process Group PGID: 2829
</untrusted_context>{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing num_snapshots=3, lr=3e-4, epochs=5:\")\ntrain_model(epochs=5, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_snapshots=3)\n'",
"description": "Test 3 snapshots with 5 epochs"
}<untrusted_context>
Output: Testing num_snapshots=3, lr=3e-4, epochs=5:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=5, batch_size=256, lr=0.0003, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 3 snapshots: 140160
Built training dataset in 51.65s with 140160 pairs.
Extracted validation histories in 4.90s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.82s (0.096 ms/user)
Epoch 01/05 | Train Loss: 6.1169 | Val NDCG@10: 0.011836 | Time: 9.2s
>>> New Best Val NDCG@10: 0.011836! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/05 | Train Loss: 5.9126 | Val NDCG@10: 0.012482 | Time: 9.2s
>>> New Best Val NDCG@10: 0.012482! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 03/05 | Train Loss: 5.7917 | Val NDCG@10: 0.011538 | Time: 9.3s
Generated ranking for 8456 users in 0.83s (0.099 ms/user)
Epoch 04/05 | Train Loss: 5.7067 | Val NDCG@10: 0.010646 | Time: 9.4s
Generated ranking for 8456 users in 0.83s (0.099 ms/user)
Epoch 05/05 | Train Loss: 5.6533 | Val NDCG@10: 0.009732 | Time: 9.3s
Final Best Validation Score: 0.012482
Process Group PGID: 2853
</untrusted_context>Improving Model Architecture
My recent focus has been on enhancing the model's performance. After achieving a score of 0.014372 with two snapshots and two epochs, I'm now exploring architectural modifications and feature engineering within model.py, specifically examining ItemTower, to push for even better results.
{
"description": "Test AdditiveAttentionPooling layer",
"command": "python3 -c '\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass AdditiveAttentionPooling(nn.Module):\n def __init__(self, emb_dim: int, hidden_dim: int = 32):\n super().__init__()\n self.proj = nn.Sequential(\n nn.Linear(emb_dim, hidden_dim),\n nn.Tanh(),\n nn.Linear(hidden_dim, 1, bias=False),\n )\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n scores = self.proj(embeddings).squeeze(-1)\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n return torch.einsum(\"bl,bld->bd\", weights, embeddings)\n\npool = AdditiveAttentionPooling(32, 16)\nemb = torch.randn(8, 64, 32)\nmask = torch.ones(8, 64, dtype=torch.bool)\nout = pool(emb, mask)\nprint(\"Output shape:\", out.shape)\n'"
}<untrusted_context>
Output: Output shape: torch.Size([8, 32])
Process Group PGID: 2877
</untrusted_context>Exploring Pooling Techniques
I'm considering how UserTower can incorporate residual and mean pooling alongside attention pooling. Mean pooling could represent an average taste, while additive attention might highlight peak preferences. The combination of these approaches is an interesting avenue for exploration.
{
"content": "\"\"\"Dataset extraction and preprocessing for Two-Tower Ranker.\n\nHandles:\n- Catalog item features mapping and normalization.\n- History extraction per user (likes, strong listens, recent listens, dislikes).\n- Multi-snapshot temporal training set construction with ZERO target leakage.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Set\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nTRAIN_CUTOFF = 24786800\nVAL_CUTOFF = 25393400\nWINDOW_SECS = 7 * 86400 # 7 days\nGAP_SECS = 1800 # 30 minutes\nOOV_IDX = 30000 # 30,000 catalog items, index 30000 is padding / OOV\n\nMAX_LEN_LIKES = 64\nMAX_LEN_STRONG = 64\nMAX_LEN_RECENT = 64\nMAX_LEN_DISLIKES = 32\n\n\ndef load_catalog() -> Tuple[pd.DataFrame, Dict[int, int], np.ndarray, torch.Tensor]:\n \"\"\"Load candidate catalog and prepare item metadata features.\n\n Returns:\n catalog_df: DataFrame of 30,000 items.\n item_to_idx: dict mapping raw item_id to catalog index [0, 29999].\n idx_to_item: array mapping catalog index to raw item_id.\n item_features_tensor: FloatTensor of shape (30000, 5) with normalized features.\n \"\"\"\n catalog_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}\n idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)\n\n # Compute normalized item features\n pos_listens = catalog_df.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)\n pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n norm_rank = pop_rank / float(len(catalog_df)) - 0.5\n\n org_ratio = org_listens / (pos_listens + 1.0)\n org_rank = pd.Series(org_listens).rank(ascending=False).to_numpy(dtype=np.float32) / float(len(catalog_df)) - 0.5\n\n # Standardize\n feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)\n feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)\n feat_ratio = (org_ratio - org_ratio.mean()) / (org_ratio.std() + 1e-6)\n\n features = np.column_stack([feat_pos, feat_org, feat_ratio, norm_rank, org_rank]).astype(np.float32)\n return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)\n\n\ndef extract_user_histories(\n cutoff_ts: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n history_days: int = 90,\n pref_state_path: Path | None = None,\n include_val: bool = False,\n likes_df: Optional[pd.DataFrame] = None,\n dislikes_df: Optional[pd.DataFrame] = None,\n listens_df: Optional[pd.DataFrame] = None,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract multi-channel history tensors for target users strictly up to cutoff_ts.\n\n Channels:\n - likes: explicit likes up to cutoff + active liked preference state (up to 64 items).\n - strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).\n - recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).\n - dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n num_users = len(target_uids)\n min_ts = cutoff_ts - history_days * 86400\n\n # 1. Likes\n if likes_df is None:\n likes_dfs = [pd.read_parquet(DATA_DIR / \"train_likes.parquet\")]\n if include_val and (DATA_DIR / \"val_likes.parquet\").exists():\n likes_dfs.append(pd.read_parquet(DATA_DIR / \"val_likes.parquet\"))\n likes_df = pd.concat(likes_dfs, ignore_index=True)\n\n target_likes = likes_df[\n (likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n pref_likes = []\n pref_dislikes = []\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]\n pref_likes = pref_df[pref_df.state == \"liked\"][[\"uid\", \"item_id\"]]\n pref_dislikes = pref_df[pref_df.state == \"disliked\"][[\"uid\", \"item_id\"]]\n\n all_likes = target_likes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_likes) > 0:\n all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_likes = all_likes.groupby(\"uid\").head(MAX_LEN_LIKES)\n\n # 2. Dislikes\n if dislikes_df is None:\n dislikes_dfs = [pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")]\n if include_val and (DATA_DIR / \"val_dislikes.parquet\").exists():\n dislikes_dfs.append(pd.read_parquet(DATA_DIR / \"val_dislikes.parquet\"))\n dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)\n\n target_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n all_dislikes = target_dislikes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_dislikes) > 0:\n all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_dislikes = all_dislikes.groupby(\"uid\").head(MAX_LEN_DISLIKES)\n\n # 3. Listens\n if listens_df is None:\n listens_dfs = [\n pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n ]\n if include_val and (DATA_DIR / \"val_listens.parquet\").exists():\n val_l = pd.read_parquet(\n DATA_DIR / \"val_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n listens_dfs.append(val_l)\n listens_df = pd.concat(listens_dfs, ignore_index=True)\n\n target_listens = listens_df[\n (listens_df.timestamp >= min_ts)\n & (listens_df.timestamp < cutoff_ts)\n & listens_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\n top_strong = strong_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_STRONG)\n\n recent_df = target_listens[target_listens.played_ratio_pct >= 50]\n top_recent = recent_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_RECENT)\n\n def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:\n arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)\n if len(df) > 0:\n rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)\n cols = df.groupby(\"uid\").cumcount().to_numpy(dtype=np.int64)\n mapped_items = df.item_id.map(item_to_idx).fillna(OOV_IDX).to_numpy(dtype=np.int64)\n arr[rows, cols] = mapped_items\n return torch.from_numpy(arr)\n\n return {\n \"likes\": pack_tensor(top_likes, MAX_LEN_LIKES),\n \"strong\": pack_tensor(top_strong, MAX_LEN_STRONG),\n \"recent\": pack_tensor(top_recent, MAX_LEN_RECENT),\n \"dislikes\": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),\n }\n\n\nclass SnapshotTwoTowerDataset(Dataset):\n \"\"\"Dataset with stacked multi-snapshot user histories (ZERO target leakage).\"\"\"\n\n def __init__(\n self,\n global_user_rows: np.ndarray,\n target_item_idxs: np.ndarray,\n weights: np.ndarray,\n stacked_histories: Dict[str, torch.Tensor],\n ):\n self.global_user_rows = torch.from_numpy(global_user_rows.astype(np.int64))\n self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))\n self.weights = torch.from_numpy(weights.astype(np.float32))\n self.stacked_histories = stacked_histories\n\n def __len__(self) -> int:\n return len(self.global_user_rows)\n\n def __getitem__(self, idx: int) -> Tuple[int, int, float]:\n return (\n self.global_user_rows[idx].item(),\n self.target_item_idxs[idx].item(),\n self.weights[idx].item(),\n )\n\n\ndef build_training_snapshots(\n num_snapshots: int = 2,\n like_weight: float = 7.0,\n strong_weight: float = 1.0,\n) -> Tuple[SnapshotTwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:\n \"\"\"Build multi-snapshot training dataset strictly before TRAIN_CUTOFF with NO leakage.\"\"\"\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n N = len(target_uids)\n\n _, item_to_idx, _, _ = load_catalog()\n\n # Pre-load all likes & dislikes\n likes_all = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n likes_target = likes_all[likes_all.uid.isin(target_set)]\n\n dislikes_all = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n dislikes_target = dislikes_all[dislikes_all.uid.isin(target_set)]\n\n # Load listens for all snapshots + 90 days history\n min_listen_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 90 * 86400\n listens_all = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_listen_ts)],\n )\n listens_target = listens_all[listens_all.uid.isin(target_set)]\n\n all_user_rows = []\n all_target_items = []\n all_weights = []\n snapshot_histories_list: List[Dict[str, torch.Tensor]] = []\n\n for s in range(1, num_snapshots + 1):\n snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS\n win_start = snap_ts + GAP_SECS\n win_end = snap_ts + WINDOW_SECS\n\n # Extract clean history for this snapshot (strictly before snap_ts)\n snap_hist = extract_user_histories(\n cutoff_ts=snap_ts,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n likes_df=likes_target,\n dislikes_df=dislikes_target,\n listens_df=listens_target,\n )\n snapshot_histories_list.append(snap_hist)\n\n # History items before snap_ts to exclude from candidate positives\n hist_l = listens_target[listens_target.timestamp < snap_ts]\n hist_likes = likes_target[likes_target.timestamp < snap_ts]\n hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))\n\n # Positives in [win_start, win_end]\n sub_listens = listens_target[\n (listens_target.timestamp >= win_start)\n & (listens_target.timestamp <= win_end)\n & (listens_target.is_organic == 1)\n & (listens_target.played_ratio_pct >= 80)\n & (listens_target.item_id.isin(item_to_idx))\n ]\n sub_likes = likes_target[\n (likes_target.timestamp >= win_start)\n & (likes_target.timestamp <= win_end)\n & (likes_target.is_organic == 1)\n & (likes_target.item_id.isin(item_to_idx))\n ]\n\n # Combine, grade 3 wins\n snap_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = strong_weight\n\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = like_weight\n\n # Global user index for this snapshot = (s - 1) * N + uid_to_row[uid]\n s_offset = (s - 1) * N\n for (uid, item_id), w in snap_pos.items():\n all_user_rows.append(s_offset + uid_to_row[uid])\n all_target_items.append(item_to_idx[item_id])\n all_weights.append(w)\n\n print(f\"Total zero-leakage training pairs across {num_snapshots} snapshots: {len(all_user_rows)}\")\n\n # Stack histories along dimension 0: shape (num_snapshots * N, max_len)\n stacked_histories = {\n channel: torch.cat([h[channel] for h in snapshot_histories_list], dim=0)\n for channel in [\"likes\", \"strong\", \"recent\", \"dislikes\"]\n }\n\n dataset = SnapshotTwoTowerDataset(\n global_user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n stacked_histories=stacked_histories,\n )\n return dataset, stacked_histories, target_uids\n",
"file_path": "/app/solution/dataset.py"
}Successfully overwrote file: /app/solution/dataset.py. Here is the updated code:
...
Returns:
catalog_df: DataFrame of 30,000 items.
item_to_idx: dict mapping raw item_id to catalog index [0, 29999].
idx_to_item: array mapping catalog index to raw item_id.
item_features_tensor: FloatTensor of shape (30000, 5) with normalized features.
"""
catalog_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}
idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)
...
org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)
pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)
log_pos = np.log1p(pos_listens)
log_org = np.log1p(org_listens)
norm_rank = pop_rank / float(len(catalog_df)) - 0.5
org_ratio = org_listens / (pos_listens + 1.0)
org_rank = pd.Series(org_listens).rank(ascending=False).to_numpy(dtype=np.float32) / float(len(catalog_df)) - 0.5
# Standardize
feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)
feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)
feat_ratio = (org_ratio - org_ratio.mean()) / (org_ratio.std() + 1e-6)
features = np.column_stack([feat_pos, feat_org, feat_ratio, norm_rank, org_rank]).astype(np.float32)
return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)
def extract_user_histories(
cutoff_ts: int,
...
self.weights[idx].item(),
)
def build_training_snapshots(
num_snapshots: int = 2,
like_weight: float = 7.0,
strong_weight: float = 1.0,
) -> Tuple[SnapshotTwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:
"""Build multi-snapshot training dataset strictly before TRAIN_CUTOFF with NO leakage."""
targets_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = targets_df.uid.to_numpy(dtype=np.int64)
...{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to unit vector v_i in R^D.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to unit vector u in R^D.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass HybridAttentionPooling(nn.Module):\n \"\"\"Combines additive attention pooling with average pooling for robust taste representation.\"\"\"\n\n def __init__(self, emb_dim: int, hidden_dim: int = 32):\n super().__init__()\n self.attn = nn.Sequential(\n nn.Linear(emb_dim, hidden_dim),\n nn.Tanh(),\n nn.Linear(hidden_dim, 1, bias=False),\n )\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n mask_f = mask.unsqueeze(-1).float()\n mean_pooled = (embeddings * mask_f).sum(dim=1) / (mask_f.sum(dim=1) + 1e-6)\n\n scores = self.attn(embeddings).squeeze(-1) # (B, L)\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n attn_pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings)\n\n return mean_pooled + attn_pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable unit item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n num_features: int = 5,\n feat_dim: int = 16,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) L2-normalized item vector\n \"\"\"\n item_emb = self.item_embedding(item_indices)\n\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n v = self.mlp(combined)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = HybridAttentionPooling(emb_dim)\n self.pool_strong = HybridAttentionPooling(emb_dim)\n self.pool_recent = HybridAttentionPooling(emb_dim)\n self.pool_dislikes = HybridAttentionPooling(emb_dim)\n\n in_mlp = emb_dim * 4\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) L2-normalized user vector\n \"\"\"\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n u = self.mlp(fused)\n return F.normalize(u, p=2, dim=-1)\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n temperature: float = 0.08,\n num_negatives: int = 512,\n dropout: float = 0.15,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. Exact dot product relevance: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
import torch.nn.functional as F
OOV_IDX = 30000
class HybridAttentionPooling(nn.Module):
"""Combines additive attention pooling with average pooling for robust taste representation."""
def __init__(self, emb_dim: int, hidden_dim: int = 32):
super().__init__()
self.attn = nn.Sequential(
nn.Linear(emb_dim, hidden_dim),
nn.Tanh(),
nn.Linear(hidden_dim, 1, bias=False),
)
def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Args:
embeddings: (B, L, D)
mask: (B, L) bool tensor where True indicates valid item.
"""
mask_f = mask.unsqueeze(-1).float()
mean_pooled = (embeddings * mask_f).sum(dim=1) / (mask_f.sum(dim=1) + 1e-6)
scores = self.attn(embeddings).squeeze(-1) # (B, L)
scores = scores.masked_fill(~mask, -1e9)
weights = F.softmax(scores, dim=-1)
weights = weights.masked_fill(~mask, 0.0)
weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6
weights = weights / weights_sum
attn_pooled = torch.einsum("bl,bld->bd", weights, embeddings)
return mean_pooled + attn_pooled
class ItemTower(nn.Module):
"""Candidate Item Tower producing precomputable unit item vector v_i in R^D."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 32,
num_features: int = 5,
feat_dim: int = 16,
hidden_dim: int = 64,
dropout: float = 0.15,
shared_embeddings: Optional[nn.Embedding] = None,
):
...
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
self.pool_likes = HybridAttentionPooling(emb_dim)
self.pool_strong = HybridAttentionPooling(emb_dim)
self.pool_recent = HybridAttentionPooling(emb_dim)
self.pool_dislikes = HybridAttentionPooling(emb_dim)
in_mlp = emb_dim * 4
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
...{
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 4 --snapshots 2 --emb_dim 32 --lr 3.5e-4 --temperature 0.08",
"description": "Train with HybridAttentionPooling and 5 catalog features"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.00035, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.99s with 94927 pairs.
Extracted validation histories in 4.84s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.91s (0.107 ms/user)
Epoch 01/04 | Train Loss: 6.0897 | Val NDCG@10: 0.009593 | Time: 9.7s
>>> New Best Val NDCG@10: 0.009593! Model checkpoint saved.
Generated ranking for 8456 users in 0.91s (0.108 ms/user)
Epoch 02/04 | Train Loss: 5.8864 | Val NDCG@10: 0.009877 | Time: 9.8s
>>> New Best Val NDCG@10: 0.009877! Model checkpoint saved.
Generated ranking for 8456 users in 0.93s (0.110 ms/user)
Epoch 03/04 | Train Loss: 5.7792 | Val NDCG@10: 0.009988 | Time: 9.8s
>>> New Best Val NDCG@10: 0.009988! Model checkpoint saved.
Generated ranking for 8456 users in 0.92s (0.109 ms/user)
Epoch 04/04 | Train Loss: 5.7064 | Val NDCG@10: 0.009661 | Time: 9.8s
Final Best Validation Score: 0.009988
Process Group PGID: 2890
</untrusted_context>Exploring Attention Mechanism
I'm investigating why the ChannelAttentionPooling method, which utilizes dot-product attention, yielded a significantly better score of 0.014372 compared to the 0.0099 from previous approaches. My focus is on understanding the specific computational advantages of this scores calculation.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to unit vector v_i in R^D.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to unit vector u in R^D.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass ChannelAttentionPooling(nn.Module):\n \"\"\"Attentive pooling with masking for a sequence of item embeddings.\"\"\"\n\n def __init__(self, emb_dim: int):\n super().__init__()\n self.query = nn.Parameter(torch.randn(emb_dim))\n nn.init.normal_(self.query, std=0.02)\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n scores = torch.einsum(\"bld,d->bl\", embeddings, self.query) / math.sqrt(embeddings.shape[-1])\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n\n pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings)\n return pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable unit item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n num_features: int = 3,\n feat_dim: int = 16,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) L2-normalized item vector\n \"\"\"\n item_emb = self.item_embedding(item_indices)\n\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n v = self.mlp(combined)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = ChannelAttentionPooling(emb_dim)\n self.pool_strong = ChannelAttentionPooling(emb_dim)\n self.pool_recent = ChannelAttentionPooling(emb_dim)\n self.pool_dislikes = ChannelAttentionPooling(emb_dim)\n\n in_mlp = emb_dim * 4\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) L2-normalized user vector\n \"\"\"\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n u = self.mlp(fused)\n return F.normalize(u, p=2, dim=-1)\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 32,\n out_dim: int = 32,\n hidden_dim: int = 64,\n temperature: float = 0.08,\n num_negatives: int = 512,\n dropout: float = 0.15,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n num_features=3,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. Exact dot product relevance: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
import torch.nn.functional as F
OOV_IDX = 30000
class ChannelAttentionPooling(nn.Module):
"""Attentive pooling with masking for a sequence of item embeddings."""
def __init__(self, emb_dim: int):
super().__init__()
self.query = nn.Parameter(torch.randn(emb_dim))
nn.init.normal_(self.query, std=0.02)
def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:
"""Args:
embeddings: (B, L, D)
mask: (B, L) bool tensor where True indicates valid item.
"""
scores = torch.einsum("bld,d->bl", embeddings, self.query) / math.sqrt(embeddings.shape[-1])
scores = scores.masked_fill(~mask, -1e9)
weights = F.softmax(scores, dim=-1)
weights = weights.masked_fill(~mask, 0.0)
weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6
weights = weights / weights_sum
pooled = torch.einsum("bl,bld->bd", weights, embeddings)
return pooled
class ItemTower(nn.Module):
"""Candidate Item Tower producing precomputable unit item vector v_i in R^D."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 32,
num_features: int = 3,
feat_dim: int = 16,
hidden_dim: int = 64,
dropout: float = 0.15,
shared_embeddings: Optional[nn.Embedding] = None,
):
...
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(self.item_embedding.weight, std=0.05)
self.pool_likes = ChannelAttentionPooling(emb_dim)
self.pool_strong = ChannelAttentionPooling(emb_dim)
self.pool_recent = ChannelAttentionPooling(emb_dim)
self.pool_dislikes = ChannelAttentionPooling(emb_dim)
in_mlp = emb_dim * 4
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
...
self.item_tower = ItemTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
num_features=3,
dropout=dropout,
shared_embeddings=shared_emb,
)
self.user_tower = UserTower(
emb_dim=emb_dim,
...{
"content": "\"\"\"Dataset extraction and preprocessing for Two-Tower Ranker.\n\nHandles:\n- Catalog item features mapping and normalization.\n- History extraction per user (likes, strong listens, recent listens, dislikes).\n- Multi-snapshot temporal training set construction with ZERO target leakage.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom pathlib import Path\nfrom typing import Dict, List, Tuple, Set\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import Dataset\n\nDATA_DIR = Path(\"/task/data\")\nTRAIN_CUTOFF = 24786800\nVAL_CUTOFF = 25393400\nWINDOW_SECS = 7 * 86400 # 7 days\nGAP_SECS = 1800 # 30 minutes\nOOV_IDX = 30000 # 30,000 catalog items, index 30000 is padding / OOV\n\nMAX_LEN_LIKES = 64\nMAX_LEN_STRONG = 64\nMAX_LEN_RECENT = 64\nMAX_LEN_DISLIKES = 32\n\n\ndef load_catalog() -> Tuple[pd.DataFrame, Dict[int, int], np.ndarray, torch.Tensor]:\n \"\"\"Load candidate catalog and prepare item metadata features.\n\n Returns:\n catalog_df: DataFrame of 30,000 items.\n item_to_idx: dict mapping raw item_id to catalog index [0, 29999].\n idx_to_item: array mapping catalog index to raw item_id.\n item_features_tensor: FloatTensor of shape (30000, 3) with normalized features.\n \"\"\"\n catalog_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}\n idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)\n\n # Compute normalized item features\n pos_listens = catalog_df.positive_listens.to_numpy(dtype=np.float32)\n org_listens = catalog_df.organic_positive_listens.to_numpy(dtype=np.float32)\n pop_rank = catalog_df.popularity_rank.to_numpy(dtype=np.float32)\n\n log_pos = np.log1p(pos_listens)\n log_org = np.log1p(org_listens)\n norm_rank = pop_rank / float(len(catalog_df)) - 0.5\n\n # Standardize\n feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)\n feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)\n\n features = np.column_stack([feat_pos, feat_org, norm_rank]).astype(np.float32)\n return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)\n\n\ndef extract_user_histories(\n cutoff_ts: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n history_days: int = 90,\n pref_state_path: Path | None = None,\n include_val: bool = False,\n likes_df: Optional[pd.DataFrame] = None,\n dislikes_df: Optional[pd.DataFrame] = None,\n listens_df: Optional[pd.DataFrame] = None,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract multi-channel history tensors for target users strictly up to cutoff_ts.\n\n Channels:\n - likes: explicit likes up to cutoff + active liked preference state (up to 64 items).\n - strong: organic listens with played_ratio_pct >= 80 up to cutoff (up to 64 items).\n - recent: positive listens with played_ratio_pct >= 50 up to cutoff (up to 64 items).\n - dislikes: explicit dislikes up to cutoff + active disliked preference state (up to 32 items).\n \"\"\"\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n num_users = len(target_uids)\n min_ts = cutoff_ts - history_days * 86400\n\n # 1. Likes\n if likes_df is None:\n likes_dfs = [pd.read_parquet(DATA_DIR / \"train_likes.parquet\")]\n if include_val and (DATA_DIR / \"val_likes.parquet\").exists():\n likes_dfs.append(pd.read_parquet(DATA_DIR / \"val_likes.parquet\"))\n likes_df = pd.concat(likes_dfs, ignore_index=True)\n\n target_likes = likes_df[\n (likes_df.timestamp < cutoff_ts) & likes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n pref_likes = []\n pref_dislikes = []\n if pref_state_path and pref_state_path.exists():\n pref_df = pd.read_parquet(pref_state_path)\n pref_df = pref_df[pref_df.uid.isin(uid_to_row.keys())]\n pref_likes = pref_df[pref_df.state == \"liked\"][[\"uid\", \"item_id\"]]\n pref_dislikes = pref_df[pref_df.state == \"disliked\"][[\"uid\", \"item_id\"]]\n\n all_likes = target_likes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_likes) > 0:\n all_likes = pd.concat([pref_likes, all_likes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_likes = all_likes.groupby(\"uid\").head(MAX_LEN_LIKES)\n\n # 2. Dislikes\n if dislikes_df is None:\n dislikes_dfs = [pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")]\n if include_val and (DATA_DIR / \"val_dislikes.parquet\").exists():\n dislikes_dfs.append(pd.read_parquet(DATA_DIR / \"val_dislikes.parquet\"))\n dislikes_df = pd.concat(dislikes_dfs, ignore_index=True)\n\n target_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_ts) & dislikes_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n all_dislikes = target_dislikes[[\"uid\", \"item_id\"]].drop_duplicates()\n if len(pref_dislikes) > 0:\n all_dislikes = pd.concat([pref_dislikes, all_dislikes], ignore_index=True).drop_duplicates(subset=[\"uid\", \"item_id\"])\n top_dislikes = all_dislikes.groupby(\"uid\").head(MAX_LEN_DISLIKES)\n\n # 3. Listens\n if listens_df is None:\n listens_dfs = [\n pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n ]\n if include_val and (DATA_DIR / \"val_listens.parquet\").exists():\n val_l = pd.read_parquet(\n DATA_DIR / \"val_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_ts), (\"timestamp\", \"<\", cutoff_ts)],\n )\n listens_dfs.append(val_l)\n listens_df = pd.concat(listens_dfs, ignore_index=True)\n\n target_listens = listens_df[\n (listens_df.timestamp >= min_ts)\n & (listens_df.timestamp < cutoff_ts)\n & listens_df.uid.isin(uid_to_row.keys())\n ].sort_values(\"timestamp\", ascending=False)\n\n strong_df = target_listens[(target_listens.is_organic == 1) & (target_listens.played_ratio_pct >= 80)]\n top_strong = strong_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_STRONG)\n\n recent_df = target_listens[target_listens.played_ratio_pct >= 50]\n top_recent = recent_df.drop_duplicates(subset=[\"uid\", \"item_id\"]).groupby(\"uid\").head(MAX_LEN_RECENT)\n\n def pack_tensor(df: pd.DataFrame, max_len: int) -> torch.Tensor:\n arr = np.full((num_users, max_len), OOV_IDX, dtype=np.int64)\n if len(df) > 0:\n rows = df.uid.map(uid_to_row).to_numpy(dtype=np.int64)\n cols = df.groupby(\"uid\").cumcount().to_numpy(dtype=np.int64)\n mapped_items = df.item_id.map(item_to_idx).fillna(OOV_IDX).to_numpy(dtype=np.int64)\n arr[rows, cols] = mapped_items\n return torch.from_numpy(arr)\n\n return {\n \"likes\": pack_tensor(top_likes, MAX_LEN_LIKES),\n \"strong\": pack_tensor(top_strong, MAX_LEN_STRONG),\n \"recent\": pack_tensor(top_recent, MAX_LEN_RECENT),\n \"dislikes\": pack_tensor(top_dislikes, MAX_LEN_DISLIKES),\n }\n\n\nclass SnapshotTwoTowerDataset(Dataset):\n \"\"\"Dataset with stacked multi-snapshot user histories (ZERO target leakage).\"\"\"\n\n def __init__(\n self,\n global_user_rows: np.ndarray,\n target_item_idxs: np.ndarray,\n weights: np.ndarray,\n stacked_histories: Dict[str, torch.Tensor],\n ):\n self.global_user_rows = torch.from_numpy(global_user_rows.astype(np.int64))\n self.target_item_idxs = torch.from_numpy(target_item_idxs.astype(np.int64))\n self.weights = torch.from_numpy(weights.astype(np.float32))\n self.stacked_histories = stacked_histories\n\n def __len__(self) -> int:\n return len(self.global_user_rows)\n\n def __getitem__(self, idx: int) -> Tuple[int, int, float]:\n return (\n self.global_user_rows[idx].item(),\n self.target_item_idxs[idx].item(),\n self.weights[idx].item(),\n )\n\n\ndef build_training_snapshots(\n num_snapshots: int = 2,\n like_weight: float = 7.0,\n strong_weight: float = 1.0,\n) -> Tuple[SnapshotTwoTowerDataset, Dict[str, torch.Tensor], np.ndarray]:\n \"\"\"Build multi-snapshot training dataset strictly before TRAIN_CUTOFF with NO leakage.\"\"\"\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n N = len(target_uids)\n\n _, item_to_idx, _, _ = load_catalog()\n\n # Pre-load all likes & dislikes\n likes_all = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n likes_target = likes_all[likes_all.uid.isin(target_set)]\n\n dislikes_all = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\n dislikes_target = dislikes_all[dislikes_all.uid.isin(target_set)]\n\n # Load listens for all snapshots + 90 days history\n min_listen_ts = TRAIN_CUTOFF - (num_snapshots + 1) * WINDOW_SECS - 90 * 86400\n listens_all = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_listen_ts)],\n )\n listens_target = listens_all[listens_all.uid.isin(target_set)]\n\n all_user_rows = []\n all_target_items = []\n all_weights = []\n snapshot_histories_list: List[Dict[str, torch.Tensor]] = []\n\n for s in range(1, num_snapshots + 1):\n snap_ts = TRAIN_CUTOFF - s * WINDOW_SECS\n win_start = snap_ts + GAP_SECS\n win_end = snap_ts + WINDOW_SECS\n\n # Extract clean history for this snapshot (strictly before snap_ts)\n snap_hist = extract_user_histories(\n cutoff_ts=snap_ts,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n likes_df=likes_target,\n dislikes_df=dislikes_target,\n listens_df=listens_target,\n )\n snapshot_histories_list.append(snap_hist)\n\n # History items before snap_ts to exclude from candidate positives\n hist_l = listens_target[listens_target.timestamp < snap_ts]\n hist_likes = likes_target[likes_target.timestamp < snap_ts]\n hist_pairs = set(zip(hist_l.uid, hist_l.item_id)).union(zip(hist_likes.uid, hist_likes.item_id))\n\n # Positives in [win_start, win_end]\n sub_listens = listens_target[\n (listens_target.timestamp >= win_start)\n & (listens_target.timestamp <= win_end)\n & (listens_target.is_organic == 1)\n & (listens_target.played_ratio_pct >= 80)\n & (listens_target.item_id.isin(item_to_idx))\n ]\n sub_likes = likes_target[\n (likes_target.timestamp >= win_start)\n & (likes_target.timestamp <= win_end)\n & (likes_target.is_organic == 1)\n & (likes_target.item_id.isin(item_to_idx))\n ]\n\n # Combine, grade 3 wins\n snap_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = strong_weight\n\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in hist_pairs:\n snap_pos[(r.uid, r.item_id)] = like_weight\n\n # Global user index for this snapshot = (s - 1) * N + uid_to_row[uid]\n s_offset = (s - 1) * N\n for (uid, item_id), w in snap_pos.items():\n all_user_rows.append(s_offset + uid_to_row[uid])\n all_target_items.append(item_to_idx[item_id])\n all_weights.append(w)\n\n print(f\"Total zero-leakage training pairs across {num_snapshots} snapshots: {len(all_user_rows)}\")\n\n # Stack histories along dimension 0: shape (num_snapshots * N, max_len)\n stacked_histories = {\n channel: torch.cat([h[channel] for h in snapshot_histories_list], dim=0)\n for channel in [\"likes\", \"strong\", \"recent\", \"dislikes\"]\n }\n\n dataset = SnapshotTwoTowerDataset(\n global_user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n stacked_histories=stacked_histories,\n )\n return dataset, stacked_histories, target_uids\n",
"file_path": "/app/solution/dataset.py"
}Successfully overwrote file: /app/solution/dataset.py. Here is the updated code:
...
Returns:
catalog_df: DataFrame of 30,000 items.
item_to_idx: dict mapping raw item_id to catalog index [0, 29999].
idx_to_item: array mapping catalog index to raw item_id.
item_features_tensor: FloatTensor of shape (30000, 3) with normalized features.
"""
catalog_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
item_to_idx = {int(item): idx for idx, item in enumerate(catalog_df.item_id)}
idx_to_item = catalog_df.item_id.to_numpy(dtype=np.int64)
...
log_pos = np.log1p(pos_listens)
log_org = np.log1p(org_listens)
norm_rank = pop_rank / float(len(catalog_df)) - 0.5
# Standardize
feat_pos = (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6)
feat_org = (log_org - log_org.mean()) / (log_org.std() + 1e-6)
features = np.column_stack([feat_pos, feat_org, norm_rank]).astype(np.float32)
return catalog_df, item_to_idx, idx_to_item, torch.from_numpy(features)
def extract_user_histories(
cutoff_ts: int,
...{
"description": "Train with like_weight=7.0 and ChannelAttentionPooling",
"command": "PYTHONPATH=/app python3 -m solution.train --epochs 5 --snapshots 2 --emb_dim 32 --lr 3.5e-4 --temperature 0.08"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=5, batch_size=256, lr=0.00035, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.88s with 94927 pairs.
Extracted validation histories in 4.91s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 01/05 | Train Loss: 6.1255 | Val NDCG@10: 0.007820 | Time: 6.9s
>>> New Best Val NDCG@10: 0.007820! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 02/05 | Train Loss: 5.8851 | Val NDCG@10: 0.012269 | Time: 6.8s
>>> New Best Val NDCG@10: 0.012269! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 03/05 | Train Loss: 5.7507 | Val NDCG@10: 0.009018 | Time: 6.8s
Generated ranking for 8456 users in 0.85s (0.100 ms/user)
Epoch 04/05 | Train Loss: 5.6592 | Val NDCG@10: 0.007696 | Time: 6.9s
Generated ranking for 8456 users in 0.85s (0.101 ms/user)
Epoch 05/05 | Train Loss: 5.6050 | Val NDCG@10: 0.008396 | Time: 6.8s
Final Best Validation Score: 0.012269
Process Group PGID: 2914
</untrusted_context>Investigating like weight
I'm puzzled by the results; a lower like_weight seemed to perform better. It appears the higher like_weight might be overly prioritizing likes, causing the stronger influence of listens to be diluted, which is counterintuitive.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing lr=2.5e-4:\")\ntrain_model(epochs=4, batch_size=256, lr=2.5e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_snapshots=2, like_weight=5.0)\n'",
"description": "Test lr=2.5e-4 with like_weight=5.0"
}<untrusted_context>
Output: Testing lr=2.5e-4:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.00025, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.63s with 94927 pairs.
Extracted validation histories in 4.86s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.78s (0.092 ms/user)
Epoch 01/04 | Train Loss: 6.1506 | Val NDCG@10: 0.008199 | Time: 6.5s
>>> New Best Val NDCG@10: 0.008199! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 02/04 | Train Loss: 5.9689 | Val NDCG@10: 0.010961 | Time: 6.5s
>>> New Best Val NDCG@10: 0.010961! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 03/04 | Train Loss: 5.8608 | Val NDCG@10: 0.010562 | Time: 6.5s
Generated ranking for 8456 users in 0.82s (0.096 ms/user)
Epoch 04/04 | Train Loss: 5.8034 | Val NDCG@10: 0.010618 | Time: 6.6s
Final Best Validation Score: 0.010961
Process Group PGID: 2938
</untrusted_context>Exploring Learning Rate
I'm re-running the experiment with a learning rate of 3e-4 to confirm the previous results, as a significant jump in loss was observed. This iteration aims to validate the impact of this specific lr setting.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing lr=3.0e-4:\")\ntrain_model(epochs=3, batch_size=256, lr=3.0e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_snapshots=2, like_weight=5.0)\n'",
"description": "Re-verify lr=3e-4 with like_weight=5.0"
}<untrusted_context>
Output: Testing lr=3.0e-4:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 35.03s with 94927 pairs.
Extracted validation histories in 4.94s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 01/03 | Train Loss: 6.1396 | Val NDCG@10: 0.009382 | Time: 6.5s
>>> New Best Val NDCG@10: 0.009382! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/03 | Train Loss: 5.9470 | Val NDCG@10: 0.010614 | Time: 6.6s
>>> New Best Val NDCG@10: 0.010614! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 03/03 | Train Loss: 5.8463 | Val NDCG@10: 0.009317 | Time: 6.6s
Final Best Validation Score: 0.010614
Process Group PGID: 2962
</untrusted_context>Investigating Value Fluctuations
I'm currently digging into the anomaly where the value hit 0.014372 previously. My focus is on dissecting the specific configuration and parameters from that earlier run, particularly the epochs, batch_size, lr, emb_dim, hidden_dim, temperature, and num_snapshots to understand what led to that outcome.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing num_negatives=256, lr=3e-4:\")\ntrain_model(epochs=3, batch_size=256, lr=3.0e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_negatives=256, num_snapshots=2, like_weight=5.0)\n'",
"description": "Test num_negatives=256"
}<untrusted_context>
Output: Testing num_negatives=256, lr=3e-4:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.43s with 94927 pairs.
Extracted validation histories in 4.90s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.77s (0.091 ms/user)
Epoch 01/03 | Train Loss: 6.1594 | Val NDCG@10: 0.010068 | Time: 6.5s
>>> New Best Val NDCG@10: 0.010068! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 02/03 | Train Loss: 5.9562 | Val NDCG@10: 0.012759 | Time: 6.6s
>>> New Best Val NDCG@10: 0.012759! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 03/03 | Train Loss: 5.8512 | Val NDCG@10: 0.013647 | Time: 6.6s
>>> New Best Val NDCG@10: 0.013647! Model checkpoint saved.
Final Best Validation Score: 0.013647
Process Group PGID: 2986
</untrusted_context>{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing num_negatives=256, temp=0.07, lr=3e-4, epochs=5:\")\ntrain_model(epochs=5, batch_size=256, lr=3.0e-4, emb_dim=32, hidden_dim=64, temperature=0.07, num_negatives=256, num_snapshots=2, like_weight=5.0)\n'",
"description": "Test 5 epochs with num_negatives=256, temp=0.07"
}<untrusted_context>
Output: Testing num_negatives=256, temp=0.07, lr=3e-4, epochs=5:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=5, batch_size=256, lr=0.0003, emb_dim=32, temp=0.07
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.72s with 94927 pairs.
Extracted validation histories in 4.93s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.76s (0.090 ms/user)
Epoch 01/05 | Train Loss: 6.1624 | Val NDCG@10: 0.008394 | Time: 6.4s
>>> New Best Val NDCG@10: 0.008394! Model checkpoint saved.
Generated ranking for 8456 users in 0.77s (0.092 ms/user)
Epoch 02/05 | Train Loss: 6.0145 | Val NDCG@10: 0.011435 | Time: 6.4s
>>> New Best Val NDCG@10: 0.011435! Model checkpoint saved.
Generated ranking for 8456 users in 0.80s (0.095 ms/user)
Epoch 03/05 | Train Loss: 5.8569 | Val NDCG@10: 0.010345 | Time: 6.5s
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 04/05 | Train Loss: 5.7602 | Val NDCG@10: 0.010117 | Time: 6.6s
Generated ranking for 8456 users in 0.82s (0.098 ms/user)
Epoch 05/05 | Train Loss: 5.7122 | Val NDCG@10: 0.009231 | Time: 6.5s
Final Best Validation Score: 0.011435
Process Group PGID: 3010
</untrusted_context>{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nprint(\"Testing temp=0.09:\")\ntrain_model(epochs=4, batch_size=256, lr=3.0e-4, emb_dim=32, hidden_dim=64, temperature=0.09, num_negatives=256, num_snapshots=2, like_weight=5.0)\n'",
"description": "Test temp=0.09"
}<untrusted_context>
Output: Testing temp=0.09:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.0003, emb_dim=32, temp=0.09
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.46s with 94927 pairs.
Extracted validation histories in 4.92s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 01/04 | Train Loss: 6.1308 | Val NDCG@10: 0.007788 | Time: 6.6s
>>> New Best Val NDCG@10: 0.007788! Model checkpoint saved.
Generated ranking for 8456 users in 0.82s (0.097 ms/user)
Epoch 02/04 | Train Loss: 5.9364 | Val NDCG@10: 0.012130 | Time: 6.6s
>>> New Best Val NDCG@10: 0.012130! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.099 ms/user)
Epoch 03/04 | Train Loss: 5.8302 | Val NDCG@10: 0.010878 | Time: 6.5s
Generated ranking for 8456 users in 0.83s (0.099 ms/user)
Epoch 04/04 | Train Loss: 5.7699 | Val NDCG@10: 0.010700 | Time: 6.7s
Final Best Validation Score: 0.012130
Process Group PGID: 3034
</untrusted_context>Exploring Weight Decay
I'm currently investigating the impact of applying weight decay specifically to embeddings within the TwoTowerModel. My current implementation uniformly applies a 1e-4 weight decay to all parameters via the AdamW optimizer. I need to assess if a targeted approach yields better performance.
{
"description": "Test weight_decay=1e-2",
"command": "python3 -c '\nimport torch\nfrom solution.train import train_model\n\nprint(\"Testing weight_decay=1e-2, temp=0.08, num_negatives=256:\")\ntrain_model(epochs=4, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_negatives=256, num_snapshots=2, like_weight=5.0)\n'"
}<untrusted_context>
Output: Testing weight_decay=1e-2, temp=0.08, num_negatives=256:
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=4, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.88s with 94927 pairs.
Extracted validation histories in 4.83s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.79s (0.093 ms/user)
Epoch 01/04 | Train Loss: 6.1945 | Val NDCG@10: 0.007994 | Time: 6.5s
>>> New Best Val NDCG@10: 0.007994! Model checkpoint saved.
Generated ranking for 8456 users in 0.79s (0.094 ms/user)
Epoch 02/04 | Train Loss: 5.9472 | Val NDCG@10: 0.012737 | Time: 6.5s
>>> New Best Val NDCG@10: 0.012737! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 03/04 | Train Loss: 5.8329 | Val NDCG@10: 0.012959 | Time: 6.7s
>>> New Best Val NDCG@10: 0.012959! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 04/04 | Train Loss: 5.7661 | Val NDCG@10: 0.012438 | Time: 6.6s
Final Best Validation Score: 0.012959
Process Group PGID: 3058
</untrusted_context>{
"command": "python3 -c '\nfrom pathlib import Path\nimport torch\nimport pandas as pd\nfrom solution.dataset import load_catalog, extract_user_histories\nfrom solution.model import TwoTowerModel\nfrom solution.inference import generate_ranking\n\nDATA_DIR = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = targets_df.uid.to_numpy()\n\ncatalog_df, item_to_idx, idx_to_item, item_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64, num_features=3)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\ndf = generate_ranking(\n model=model,\n catalog_features=item_features,\n user_histories=histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"validation_eligible_candidates.npz\",\n output_path=Path(\"/app/solution/validation.parquet\")\n)\n' && python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Evaluate best_model.pt on public validation set"
}<untrusted_context>
Output: Traceback (most recent call last):
File "<string>", line 21, in <module>
TypeError: TwoTowerModel.__init__() got an unexpected keyword argument 'num_features'
Exit Code: 1
Process Group PGID: 3082
</untrusted_context>{
"description": "Evaluate best_model.pt with evaluate_public.py",
"command": "python3 -c '\nfrom pathlib import Path\nimport torch\nimport pandas as pd\nfrom solution.dataset import load_catalog, extract_user_histories\nfrom solution.model import TwoTowerModel\nfrom solution.inference import generate_ranking\n\nDATA_DIR = Path(\"/task/data\")\ntargets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = targets_df.uid.to_numpy()\n\ncatalog_df, item_to_idx, idx_to_item, item_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\ndf = generate_ranking(\n model=model,\n catalog_features=item_features,\n user_histories=histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"validation_eligible_candidates.npz\",\n output_path=Path(\"/app/solution/validation.parquet\")\n)\n' && python3 /task/evaluate_public.py /app/solution/validation.parquet"
}<untrusted_context>
Output: Generated ranking for 8456 users in 0.83s (0.098 ms/user)
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012959,
"quality_users": 4263
}
}
Process Group PGID: 3103
</untrusted_context>Improving NDCG Score
My current focus is on pushing our NDCG@10 beyond its current promising 0.012959. I'm investigating a previous run that achieved a higher 0.014372 to meticulously re-examine its specific parameter configurations, aiming to replicate and surpass this performance.
{
"command": "PYTHONPATH=/app python3 -c '\nfrom solution.train import train_model\n\nmodel, score = train_model(epochs=2, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_snapshots=2)\nprint(\"Score achieved:\", score)\n'",
"description": "Train model with epochs=2, emb_dim=32, lr=3e-4, temp=0.08"
}<untrusted_context>
Output: ============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=2, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.61s with 94927 pairs.
Extracted validation histories in 4.95s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.78s (0.093 ms/user)
Epoch 01/02 | Train Loss: 6.1355 | Val NDCG@10: 0.009322 | Time: 6.5s
>>> New Best Val NDCG@10: 0.009322! Model checkpoint saved.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 02/02 | Train Loss: 5.9451 | Val NDCG@10: 0.010758 | Time: 6.6s
>>> New Best Val NDCG@10: 0.010758! Model checkpoint saved.
Final Best Validation Score: 0.010758
Score achieved: 0.010758
Process Group PGID: 3145
</untrusted_context>Investigating Score Fluctuations
I'm currently examining why the recent score dipped to 0.010758 from the earlier 0.014372. My focus is on identifying the specific parameters or configurations in the train_model function during the earlier run that might explain this difference, referencing the bash output for details on emb_dim and lr.
{
"description": "Test multiple seeds to find best convergence",
"command": "PYTHONPATH=/app python3 -c '\nimport torch\nfrom solution.train import train_model\n\nfor seed in [42, 123, 777]:\n torch.manual_seed(seed)\n print(f\"\\n--- Testing seed {seed} ---\")\n model, score = train_model(epochs=3, batch_size=256, lr=3e-4, emb_dim=32, hidden_dim=64, temperature=0.08, num_snapshots=2, like_weight=3.0)\n print(f\"Seed {seed} best score: {score:.6f}\")\n'"
}<untrusted_context>
Output: --- Testing seed 42 ---
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.85s with 94927 pairs.
Extracted validation histories in 4.94s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.77s (0.091 ms/user)
Epoch 01/03 | Train Loss: 6.1375 | Val NDCG@10: 0.005619 | Time: 6.5s
>>> New Best Val NDCG@10: 0.005619! Model checkpoint saved.
Generated ranking for 8456 users in 0.80s (0.094 ms/user)
Epoch 02/03 | Train Loss: 5.9430 | Val NDCG@10: 0.009828 | Time: 6.5s
>>> New Best Val NDCG@10: 0.009828! Model checkpoint saved.
Generated ranking for 8456 users in 0.80s (0.094 ms/user)
Epoch 03/03 | Train Loss: 5.8376 | Val NDCG@10: 0.009330 | Time: 6.6s
Final Best Validation Score: 0.009828
Seed 42 best score: 0.009828
--- Testing seed 123 ---
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.54s with 94927 pairs.
Extracted validation histories in 4.86s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.82s (0.098 ms/user)
Epoch 01/03 | Train Loss: 6.1367 | Val NDCG@10: 0.008855 | Time: 6.6s
>>> New Best Val NDCG@10: 0.008855! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 02/03 | Train Loss: 5.9217 | Val NDCG@10: 0.011456 | Time: 6.6s
>>> New Best Val NDCG@10: 0.011456! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 03/03 | Train Loss: 5.8387 | Val NDCG@10: 0.011933 | Time: 6.6s
>>> New Best Val NDCG@10: 0.011933! Model checkpoint saved.
Final Best Validation Score: 0.011933
Seed 123 best score: 0.011933
--- Testing seed 777 ---
============================================================
PHASE 3: TRAINING & VALIDATION TUNING
Params: epochs=3, batch_size=256, lr=0.0003, emb_dim=32, temp=0.08
============================================================
Total zero-leakage training pairs across 2 snapshots: 94927
Built training dataset in 34.40s with 94927 pairs.
Extracted validation histories in 4.80s.
Preloaded validation labels for 4263 users.
Generated ranking for 8456 users in 0.81s (0.096 ms/user)
Epoch 01/03 | Train Loss: 6.1347 | Val NDCG@10: 0.010343 | Time: 6.6s
>>> New Best Val NDCG@10: 0.010343! Model checkpoint saved.
Generated ranking for 8456 users in 0.83s (0.098 ms/user)
Epoch 02/03 | Train Loss: 5.9264 | Val NDCG@10: 0.012928 | Time: 6.6s
>>> New Best Val NDCG@10: 0.012928! Model checkpoint saved.
Generated ranking for 8456 users in 0.84s (0.099 ms/user)
Epoch 03/03 | Train Loss: 5.8371 | Val NDCG@10: 0.011079 | Time: 6.6s
Final Best Validation Score: 0.012928
Seed 777 best score: 0.012928
Process Group PGID: 3169
</untrusted_context>Exploring Ranking Combinations
I'm currently investigating how to enhance ranking by integrating learned user preferences with existing discovery priors. Specifically, I'm considering how to leverage the dot product relevance score ($\mathbf{u} \cdot \mathbf{v}_i$) within a two-tower neural network framework to achieve this fusion.
Integrating Discovery Prior
I've confirmed a method to directly incorporate discovery priors into the user and item vectors within the two-tower model. This approach aims to directly yield the desired combination of learned relevance and prior weight via the dot product, simplifying offline computation of item vectors.
{
"command": "PYTHONPATH=/task python3 -c '\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ntarget_uids = targets_frame.uid.to_numpy()\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# Candidate catalog features\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nlog_org = np.log1p(cat.organic_positive_listens.values.astype(np.float32))\nprior = (log_org - log_org.min()) / (log_org.max() - log_org.min() + 1e-6)\n\nfrom solution.model import TwoTowerModel\nfrom solution.dataset import load_catalog, extract_user_histories\n\ncatalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\nwith torch.no_grad():\n item_vecs = model.precompute_item_embeddings(catalog_features) # (30000, 32)\n user_vecs = model.compute_user_embeddings(histories) # (8456, 32)\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as artifact:\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"].astype(np.uint8)\nmasks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\nprior_t = torch.from_numpy(prior)\nmasks_t = torch.from_numpy(masks)\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:\n w_sem = math.sqrt(1.0 - alpha**2) if alpha < 1.0 else 0.0\n w_pri = alpha\n \n # Augmented vectors\n u_aug = torch.cat([user_vecs * w_sem, torch.full((len(user_vecs), 1), w_pri)], dim=1) # (N, 33)\n v_aug = torch.cat([item_vecs * w_sem, prior_t.unsqueeze(1) * w_pri], dim=1) # (30000, 33)\n \n # Exact dot product scores in batches\n scores = torch.matmul(u_aug, v_aug.T)\n scores[~masks_t] = -float(\"inf\")\n topk = torch.topk(scores, 100, dim=1).indices.numpy()\n \n all_uids = []\n all_items = []\n all_ranks = []\n ranks_pattern = np.arange(1, 101, dtype=np.int64)\n for b in range(len(target_uids)):\n all_uids.append(np.full(100, target_uids[b], dtype=np.int64))\n all_items.append(cand_item_ids[topk[b]])\n all_ranks.append(ranks_pattern)\n \n df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n res = score_rankings(ranked_items(df), graded)\n print(f\"Alpha {alpha:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}\")\n'",
"description": "Test alpha sweep for augmented two-tower vectors"
}<untrusted_context>
Output: File "<string>", line 76
print(f"Alpha {alpha:.2f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}")
^
SyntaxError: unexpected character after line continuation character
Exit Code: 1
Process Group PGID: 3193
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport math\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ntarget_uids = targets_frame.uid.to_numpy()\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nlog_org = np.log1p(cat.organic_positive_listens.values.astype(np.float32))\nprior = (log_org - log_org.min()) / (log_org.max() - log_org.min() + 1e-6)\n\nfrom solution.model import TwoTowerModel\nfrom solution.dataset import load_catalog, extract_user_histories\n\ncatalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\nwith torch.no_grad():\n item_vecs = model.precompute_item_embeddings(catalog_features)\n user_vecs = model.compute_user_embeddings(histories)\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as artifact:\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"].astype(np.uint8)\nmasks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\nprior_t = torch.from_numpy(prior)\nmasks_t = torch.from_numpy(masks)\n\nfor alpha in [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8]:\n w_sem = math.sqrt(1.0 - alpha**2) if alpha < 1.0 else 0.0\n w_pri = alpha\n \n u_aug = torch.cat([user_vecs * w_sem, torch.full((len(user_vecs), 1), w_pri)], dim=1)\n v_aug = torch.cat([item_vecs * w_sem, prior_t.unsqueeze(1) * w_pri], dim=1)\n \n scores = torch.matmul(u_aug, v_aug.T)\n scores[~masks_t] = -float(\"inf\")\n topk = torch.topk(scores, 100, dim=1).indices.numpy()\n \n all_uids = []\n all_items = []\n all_ranks = []\n ranks_pattern = np.arange(1, 101, dtype=np.int64)\n for b in range(len(target_uids)):\n all_uids.append(np.full(100, target_uids[b], dtype=np.int64))\n all_items.append(cand_item_ids[topk[b]])\n all_ranks.append(ranks_pattern)\n \n df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n res = score_rankings(ranked_items(df), graded)\n val_res = res[\"quality_ndcg@10\"]\n print(f\"Alpha {alpha:.2f}: NDCG@10 = {val_res:.6f}\")\n'",
"description": "Test alpha sweep with fixed print"
}<untrusted_context>
Output: Alpha 0.00: NDCG@10 = 0.012928
Alpha 0.10: NDCG@10 = 0.013162
Alpha 0.20: NDCG@10 = 0.013443
Alpha 0.30: NDCG@10 = 0.015091
Alpha 0.40: NDCG@10 = 0.017450
Alpha 0.50: NDCG@10 = 0.019562
Alpha 0.60: NDCG@10 = 0.018508
Alpha 0.70: NDCG@10 = 0.014972
Alpha 0.80: NDCG@10 = 0.010693
Process Group PGID: 3196
</untrusted_context>{
"command": "PYTHONPATH=/task python3 -c '\nimport math\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\nimport torch\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings\n\nDATA = Path(\"/task/data\")\ntargets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ntarget_uids = targets_frame.uid.to_numpy()\neligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncat = pd.read_parquet(DATA / \"candidate_catalog.parquet\")\nlog_org = np.log1p(cat.organic_positive_listens.values.astype(np.float32))\nprior = (log_org - log_org.min()) / (log_org.max() - log_org.min() + 1e-6)\n\nfrom solution.model import TwoTowerModel\nfrom solution.dataset import load_catalog, extract_user_histories\n\ncatalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\nhistories = extract_user_histories(\n cutoff_ts=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / \"validation_preference_state.parquet\"\n)\n\nmodel = TwoTowerModel(emb_dim=32, out_dim=32, hidden_dim=64)\nmodel.load_state_dict(torch.load(\"/app/solution/best_model.pt\"))\nmodel.eval()\n\nwith torch.no_grad():\n item_vecs = model.precompute_item_embeddings(catalog_features)\n user_vecs = model.compute_user_embeddings(histories)\n\nwith np.load(DATA / \"validation_eligible_candidates.npz\") as artifact:\n cand_item_ids = artifact[\"item_ids\"].astype(np.int64)\n packed = artifact[\"packed_eligible\"].astype(np.uint8)\nmasks = np.unpackbits(packed, axis=1, count=len(cand_item_ids), bitorder=\"little\").astype(bool)\n\nprior_t = torch.from_numpy(prior)\nmasks_t = torch.from_numpy(masks)\n\nfor alpha in [0.46, 0.48, 0.50, 0.52, 0.54, 0.56]:\n w_sem = math.sqrt(1.0 - alpha**2) if alpha < 1.0 else 0.0\n w_pri = alpha\n \n u_aug = torch.cat([user_vecs * w_sem, torch.full((len(user_vecs), 1), w_pri)], dim=1)\n v_aug = torch.cat([item_vecs * w_sem, prior_t.unsqueeze(1) * w_pri], dim=1)\n \n scores = torch.matmul(u_aug, v_aug.T)\n scores[~masks_t] = -float(\"inf\")\n topk = torch.topk(scores, 100, dim=1).indices.numpy()\n \n all_uids = []\n all_items = []\n all_ranks = []\n ranks_pattern = np.arange(1, 101, dtype=np.int64)\n for b in range(len(target_uids)):\n all_uids.append(np.full(100, target_uids[b], dtype=np.int64))\n all_items.append(cand_item_ids[topk[b]])\n all_ranks.append(ranks_pattern)\n \n df = pd.DataFrame({\n \"uid\": np.concatenate(all_uids),\n \"item_id\": np.concatenate(all_items),\n \"rank\": np.concatenate(all_ranks),\n })\n res = score_rankings(ranked_items(df), graded)\n val_res = res[\"quality_ndcg@10\"]\n print(f\"Alpha {alpha:.2f}: NDCG@10 = {val_res:.6f}\")\n'",
"description": "Fine-grained alpha sweep around 0.50"
}<untrusted_context>
Output: Alpha 0.46: NDCG@10 = 0.018604
Alpha 0.48: NDCG@10 = 0.019109
Alpha 0.50: NDCG@10 = 0.019562
Alpha 0.52: NDCG@10 = 0.019594
Alpha 0.54: NDCG@10 = 0.019454
Alpha 0.56: NDCG@10 = 0.019273
Process Group PGID: 3220
</untrusted_context>Improving NDCG@10
I've achieved an 83% improvement on NDCG@10, reaching 0.019594 from a baseline of 0.010710. My current focus is on seamlessly integrating this enhancement into our Two-Tower PyTorch neural network, considering its architectural demands.
Refining User Representation
My thoughts are now zeroed in on ensuring the user tower's output accurately reflects interaction history, bypassing simple ID lookups. I'm meticulously structuring its architecture to align with the required decomposition for the dot product relevance score.
Integrating User Representation
My current focus is on the precise integration of the user and item towers. I'm ensuring the dot product score is derived from a 33-dimensional user vector and a corresponding item vector, where the user vector incorporates both semantic and affinity components from interaction history, aligning with the retrieval mechanism.
Finalizing Model Architecture
My focus is now on the direct integration into model.py. I've confirmed the item tower generates an independently precomputable 33-dimensional vector, enabling efficient dot product relevance scoring and scalability to millions of items without needing a global interaction matrix, achieving under 0.1ms latency per user on CPU.
{
"file_path": "/app/solution/model.py",
"content": "\"\"\"PyTorch Two-Tower Neural Ranker Architecture.\n\nDecomposes strictly into:\n- ItemTower: maps candidate item ID and discovery catalog features to precomputable vector v_i in R^D.\n- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u in R^D.\n- Relevance score: exact dot product u . v_i.\n\"\"\"\nfrom __future__ import annotations\n\nimport math\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nOOV_IDX = 30000\n\n\nclass ChannelAttentionPooling(nn.Module):\n \"\"\"Attentive pooling with masking for a sequence of item embeddings.\"\"\"\n\n def __init__(self, emb_dim: int):\n super().__init__()\n self.query = nn.Parameter(torch.randn(emb_dim))\n nn.init.normal_(self.query, std=0.02)\n\n def forward(self, embeddings: torch.Tensor, mask: torch.Tensor) -> torch.Tensor:\n \"\"\"Args:\n\n embeddings: (B, L, D)\n mask: (B, L) bool tensor where True indicates valid item.\n \"\"\"\n scores = torch.einsum(\"bld,d->bl\", embeddings, self.query) / math.sqrt(embeddings.shape[-1])\n scores = scores.masked_fill(~mask, -1e9)\n weights = F.softmax(scores, dim=-1)\n weights = weights.masked_fill(~mask, 0.0)\n weights_sum = weights.sum(dim=-1, keepdim=True) + 1e-6\n weights = weights / weights_sum\n\n pooled = torch.einsum(\"bl,bld->bd\", weights, embeddings)\n return pooled\n\n\nclass ItemTower(nn.Module):\n \"\"\"Candidate Item Tower producing precomputable item vector v_i in R^D.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 33,\n num_features: int = 3,\n feat_dim: int = 16,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n alpha: float = 0.52,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n self.semantic_dim = out_dim - 1 # 32\n self.alpha = alpha\n self.w_sem = math.sqrt(max(1.0 - alpha**2, 0.0))\n self.w_pri = alpha\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.feat_encoder = nn.Sequential(\n nn.Linear(num_features, feat_dim),\n nn.GELU(),\n nn.LayerNorm(feat_dim),\n )\n\n in_mlp = emb_dim + feat_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, self.semantic_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Args:\n\n item_indices: (B,) int64 tensor in [0, 29999]\n catalog_features: (30000, F) float32 tensor\n Returns:\n v: (B, out_dim) item vector\n \"\"\"\n item_emb = self.item_embedding(item_indices)\n\n valid_mask = (item_indices < len(catalog_features))\n safe_indices = torch.where(valid_mask, item_indices, 0)\n feats = catalog_features[safe_indices]\n feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))\n\n feat_emb = self.feat_encoder(feats)\n combined = torch.cat([item_emb, feat_emb], dim=-1)\n\n v_sem = self.mlp(combined)\n v_sem = F.normalize(v_sem, p=2, dim=-1)\n\n # Discovery prior feature (standardized log organic positive listens mapped to [0, 1])\n # Column 1 of catalog_features is feat_org\n feat_org = feats[:, 1:2]\n # Map standardized feature via sigmoid to [0, 1]\n prior_val = torch.sigmoid(feat_org)\n\n v = torch.cat([v_sem * self.w_sem, prior_val * self.w_pri], dim=-1)\n return v\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving representation strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n num_items: int = 30001,\n emb_dim: int = 32,\n out_dim: int = 33,\n hidden_dim: int = 64,\n dropout: float = 0.15,\n alpha: float = 0.52,\n shared_embeddings: Optional[nn.Embedding] = None,\n ):\n super().__init__()\n self.out_dim = out_dim\n self.semantic_dim = out_dim - 1 # 32\n self.alpha = alpha\n self.w_sem = math.sqrt(max(1.0 - alpha**2, 0.0))\n self.w_pri = alpha\n\n if shared_embeddings is not None:\n self.item_embedding = shared_embeddings\n else:\n self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(self.item_embedding.weight, std=0.05)\n\n self.pool_likes = ChannelAttentionPooling(emb_dim)\n self.pool_strong = ChannelAttentionPooling(emb_dim)\n self.pool_recent = ChannelAttentionPooling(emb_dim)\n self.pool_dislikes = ChannelAttentionPooling(emb_dim)\n\n in_mlp = emb_dim * 4\n self.mlp = nn.Sequential(\n nn.Linear(in_mlp, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, self.semantic_dim),\n )\n\n def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:\n \"\"\"Args:\n\n histories: dict of channel tensors:\n - \"likes\": (B, L1)\n - \"strong\": (B, L2)\n - \"recent\": (B, L3)\n - \"dislikes\": (B, L4)\n Returns:\n u: (B, out_dim) user vector\n \"\"\"\n likes = histories[\"likes\"]\n h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)\n\n strong = histories[\"strong\"]\n h_strong = self.pool_strong(self.item_embedding(strong), strong != OOV_IDX)\n\n recent = histories[\"recent\"]\n h_recent = self.pool_recent(self.item_embedding(recent), recent != OOV_IDX)\n\n dislikes = histories[\"dislikes\"]\n h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)\n\n fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)\n u_sem = self.mlp(fused)\n u_sem = F.normalize(u_sem, p=2, dim=-1)\n\n B = u_sem.shape[0]\n u_prior = torch.full((B, 1), self.w_pri, device=u_sem.device, dtype=u_sem.dtype)\n\n u = torch.cat([u_sem * self.w_sem, u_prior], dim=-1)\n return u\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval.\"\"\"\n\n def __init__(\n self,\n emb_dim: int = 32,\n out_dim: int = 33,\n hidden_dim: int = 64,\n temperature: float = 0.08,\n num_negatives: int = 256,\n alpha: float = 0.52,\n dropout: float = 0.15,\n share_embeddings: bool = True,\n ):\n super().__init__()\n self.temperature = temperature\n self.num_negatives = num_negatives\n self.out_dim = out_dim\n\n if share_embeddings:\n shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)\n nn.init.normal_(shared_emb.weight, std=0.05)\n else:\n shared_emb = None\n\n self.item_tower = ItemTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n num_features=3,\n dropout=dropout,\n alpha=alpha,\n shared_embeddings=shared_emb,\n )\n self.user_tower = UserTower(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n dropout=dropout,\n alpha=alpha,\n shared_embeddings=shared_emb,\n )\n\n def compute_loss(\n self,\n user_histories_batch: Dict[str, torch.Tensor],\n target_item_idxs: torch.Tensor,\n weights: torch.Tensor,\n catalog_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute weighted InfoNCE loss with in-batch + sampled catalog negatives.\"\"\"\n B = target_item_idxs.shape[0]\n\n # 1. Compute user vectors: (B, D)\n user_vecs = self.user_tower(user_histories_batch)\n\n # 2. Compute positive item vectors: (B, D)\n pos_item_vecs = self.item_tower(target_item_idxs, catalog_features)\n\n # 3. Sample catalog negatives: (K, D)\n K = min(self.num_negatives, 30000)\n neg_item_idxs = torch.randint(0, 30000, (K,), device=target_item_idxs.device)\n neg_item_vecs = self.item_tower(neg_item_idxs, catalog_features)\n\n # 4. Exact dot product relevance: (B, B)\n in_batch_sim = torch.matmul(user_vecs, pos_item_vecs.T) / self.temperature\n\n # Mask collision in positive items across different users in batch\n same_item_mask = (target_item_idxs.unsqueeze(0) == target_item_idxs.unsqueeze(1))\n off_diagonal_mask = same_item_mask & (~torch.eye(B, dtype=torch.bool, device=target_item_idxs.device))\n in_batch_sim = in_batch_sim.masked_fill(off_diagonal_mask, -1e9)\n\n # Sampled negative logits: (B, K)\n neg_sim = torch.matmul(user_vecs, neg_item_vecs.T) / self.temperature\n\n # Combine logits: (B, B + K)\n logits = torch.cat([in_batch_sim, neg_sim], dim=1)\n targets = torch.arange(B, device=target_item_idxs.device)\n\n # Weighted cross-entropy\n losses = F.cross_entropy(logits, targets, reduction=\"none\")\n weighted_loss = (losses * weights).sum() / (weights.sum() + 1e-6)\n return weighted_loss\n\n @torch.no_grad()\n def precompute_item_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute candidate item embeddings for all 30,000 catalog items.\n\n Returns:\n V: (30000, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_catalog = len(catalog_features)\n item_vecs = []\n for start in range(0, num_catalog, batch_size):\n end = min(start + batch_size, num_catalog)\n idxs = torch.arange(start, end)\n v = self.item_tower(idxs, catalog_features)\n item_vecs.append(v)\n return torch.cat(item_vecs, dim=0)\n\n @torch.no_grad()\n def compute_user_embeddings(\n self,\n user_histories: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings for target users strictly from their history.\n\n Returns:\n U: (N, out_dim) float32 tensor of normalized vectors.\n \"\"\"\n self.eval()\n num_users = user_histories[\"likes\"].shape[0]\n user_vecs = []\n for start in range(0, num_users, batch_size):\n end = min(start + batch_size, num_users)\n batch_hist = {k: v[start:end] for k, v in user_histories.items()}\n u = self.user_tower(batch_hist)\n user_vecs.append(u)\n return torch.cat(user_vecs, dim=0)\n"
}Successfully overwrote file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Neural Ranker Architecture.
Decomposes strictly into:
- ItemTower: maps candidate item ID and discovery catalog features to precomputable vector v_i in R^D.
- UserTower: maps user interaction history (likes, strong listens, recent listens, dislikes) to user vector u in R^D.
- Relevance score: exact dot product u . v_i.
"""
from __future__ import annotations
import math
...
pooled = torch.einsum("bl,bld->bd", weights, embeddings)
return pooled
class ItemTower(nn.Module):
"""Candidate Item Tower producing precomputable item vector v_i in R^D."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 33,
num_features: int = 3,
feat_dim: int = 16,
hidden_dim: int = 64,
dropout: float = 0.15,
alpha: float = 0.52,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
self.semantic_dim = out_dim - 1 # 32
self.alpha = alpha
self.w_sem = math.sqrt(max(1.0 - alpha**2, 0.0))
self.w_pri = alpha
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
...
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, self.semantic_dim),
)
def forward(
self,
item_indices: torch.Tensor,
...
"""Args:
item_indices: (B,) int64 tensor in [0, 29999]
catalog_features: (30000, F) float32 tensor
Returns:
v: (B, out_dim) item vector
"""
item_emb = self.item_embedding(item_indices)
valid_mask = (item_indices < len(catalog_features))
safe_indices = torch.where(valid_mask, item_indices, 0)
...
feats = torch.where(valid_mask.unsqueeze(-1), feats, torch.zeros_like(feats))
feat_emb = self.feat_encoder(feats)
combined = torch.cat([item_emb, feat_emb], dim=-1)
v_sem = self.mlp(combined)
v_sem = F.normalize(v_sem, p=2, dim=-1)
# Discovery prior feature (standardized log organic positive listens mapped to [0, 1])
# Column 1 of catalog_features is feat_org
feat_org = feats[:, 1:2]
# Map standardized feature via sigmoid to [0, 1]
prior_val = torch.sigmoid(feat_org)
v = torch.cat([v_sem * self.w_sem, prior_val * self.w_pri], dim=-1)
return v
class UserTower(nn.Module):
"""User Tower deriving representation strictly from interaction history."""
def __init__(
self,
num_items: int = 30001,
emb_dim: int = 32,
out_dim: int = 33,
hidden_dim: int = 64,
dropout: float = 0.15,
alpha: float = 0.52,
shared_embeddings: Optional[nn.Embedding] = None,
):
super().__init__()
self.out_dim = out_dim
self.semantic_dim = out_dim - 1 # 32
self.alpha = alpha
self.w_sem = math.sqrt(max(1.0 - alpha**2, 0.0))
self.w_pri = alpha
if shared_embeddings is not None:
self.item_embedding = shared_embeddings
else:
self.item_embedding = nn.Embedding(num_items, emb_dim, padding_idx=OOV_IDX)
...
self.mlp = nn.Sequential(
nn.Linear(in_mlp, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, self.semantic_dim),
)
def forward(self, histories: Dict[str, torch.Tensor]) -> torch.Tensor:
"""Args:
...
- "likes": (B, L1)
- "strong": (B, L2)
- "recent": (B, L3)
- "dislikes": (B, L4)
Returns:
u: (B, out_dim) user vector
"""
likes = histories["likes"]
h_likes = self.pool_likes(self.item_embedding(likes), likes != OOV_IDX)
strong = histories["strong"]
...
dislikes = histories["dislikes"]
h_dislikes = self.pool_dislikes(self.item_embedding(dislikes), dislikes != OOV_IDX)
fused = torch.cat([h_likes, h_strong, h_recent, h_dislikes], dim=-1)
u_sem = self.mlp(fused)
u_sem = F.normalize(u_sem, p=2, dim=-1)
B = u_sem.shape[0]
u_prior = torch.full((B, 1), self.w_pri, device=u_sem.device, dtype=u_sem.dtype)
u = torch.cat([u_sem * self.w_sem, u_prior], dim=-1)
return u
class TwoTowerModel(nn.Module):
"""Full Two-Tower Neural Ranker with In-Batch InfoNCE Training and Retrieval."""
def __init__(
self,
emb_dim: int = 32,
out_dim: int = 33,
hidden_dim: int = 64,
temperature: float = 0.08,
num_negatives: int = 256,
alpha: float = 0.52,
dropout: float = 0.15,
share_embeddings: bool = True,
):
super().__init__()
self.temperature = temperature
self.num_negatives = num_negatives
self.out_dim = out_dim
if share_embeddings:
shared_emb = nn.Embedding(30001, emb_dim, padding_idx=OOV_IDX)
nn.init.normal_(shared_emb.weight, std=0.05)
else:
...
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
num_features=3,
dropout=dropout,
alpha=alpha,
shared_embeddings=shared_emb,
)
self.user_tower = UserTower(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
dropout=dropout,
alpha=alpha,
shared_embeddings=shared_emb,
)
def compute_loss(
self,
...Adjusting train.py Defaults
I'm updating train.py with specific hyperparameter values. This includes setting out_dim to 33, alpha to 0.52, emb_dim to 32, hidden_dim to 64, temperature to 0.08, num_negatives to 256, like_weight to 5.0, and strong_weight to 1.0 to align with recent adjustments.
{
"content": "\"\"\"Training, Validation, and Refitting Pipeline for Two-Tower Ranker.\n\nFeatures:\n- Zero-leakage multi-snapshot temporal dataset.\n- In-batch InfoNCE loss + sampled catalog negatives.\n- Sub-second in-memory validation scoring against public discovery objective.\n- Automatic best checkpoint saving.\n- Full refit mode on train + val data producing final submission.parquet.\n\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport copy\nimport sys\nimport time\nfrom pathlib import Path\nfrom typing import Dict, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader\n\nsys.path.insert(0, \"/task\")\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nfrom solution.dataset import (\n DATA_DIR,\n TRAIN_CUTOFF,\n VAL_CUTOFF,\n build_training_snapshots,\n extract_user_histories,\n load_catalog,\n)\nfrom solution.inference import generate_ranking\nfrom solution.model import TwoTowerModel\n\n\ndef collate_fn_builder(stacked_histories: Dict[str, torch.Tensor]):\n \"\"\"Creates a fast collation function using pre-stacked history tensors.\"\"\"\n def collate_fn(batch):\n global_user_rows = torch.tensor([item[0] for item in batch], dtype=torch.int64)\n target_item_idxs = torch.tensor([item[1] for item in batch], dtype=torch.int64)\n weights = torch.tensor([item[2] for item in batch], dtype=torch.float32)\n\n batch_hist = {\n channel: stacked_histories[channel][global_user_rows]\n for channel in stacked_histories\n }\n return batch_hist, target_item_idxs, weights\n\n return collate_fn\n\n\ndef run_validation(\n model: TwoTowerModel,\n catalog_features: torch.Tensor,\n val_histories: Dict[str, torch.Tensor],\n target_uids: np.ndarray,\n graded: Dict[int, Dict[int, int]],\n output_path: Path | None = None,\n) -> float:\n \"\"\"Fast in-memory validation scoring.\"\"\"\n ranking_df = generate_ranking(\n model=model,\n catalog_features=catalog_features,\n user_histories=val_histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"validation_eligible_candidates.npz\",\n output_path=output_path,\n batch_size=1024,\n )\n ranked = ranked_items(ranking_df)\n res = score_rankings(ranked, graded)\n return float(res[\"quality_ndcg@10\"])\n\n\ndef train_model(\n epochs: int = 4,\n batch_size: int = 256,\n lr: float = 3e-4,\n emb_dim: int = 32,\n out_dim: int = 33,\n hidden_dim: int = 64,\n alpha: float = 0.52,\n temperature: float = 0.08,\n num_negatives: int = 256,\n num_snapshots: int = 2,\n like_weight: float = 5.0,\n strong_weight: float = 1.0,\n) -> Tuple[TwoTowerModel, float]:\n \"\"\"Train two-tower model on multi-snapshot data and tune against validation set.\"\"\"\n print(\"=\" * 60)\n print(\"PHASE 3: TRAINING & VALIDATION TUNING\")\n print(f\"Params: epochs={epochs}, batch_size={batch_size}, lr={lr}, emb_dim={emb_dim}, out_dim={out_dim}, temp={temperature}, alpha={alpha}\")\n print(\"=\" * 60)\n\n # 1. Load catalog\n catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\n\n # 2. Build multi-snapshot training dataset\n t0 = time.time()\n train_dataset, stacked_histories, target_uids = build_training_snapshots(\n num_snapshots=num_snapshots,\n like_weight=like_weight,\n strong_weight=strong_weight,\n )\n print(f\"Built training dataset in {time.time()-t0:.2f}s with {len(train_dataset)} pairs.\")\n\n # 3. Prepare validation user histories at TRAIN_CUTOFF\n t1 = time.time()\n val_histories = extract_user_histories(\n cutoff_ts=TRAIN_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\",\n )\n print(f\"Extracted validation histories in {time.time()-t1:.2f}s.\")\n\n # 4. Preload validation truth for fast in-memory scoring\n eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\n graded = evaluate_public.validation_truth(eligibility, set(target_uids))\n print(f\"Preloaded validation labels for {len(graded)} users.\")\n\n # 5. Initialize model & optimizer\n model = TwoTowerModel(\n emb_dim=emb_dim,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n temperature=temperature,\n num_negatives=num_negatives,\n alpha=alpha,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-3)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)\n\n loader = DataLoader(\n train_dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn_builder(stacked_histories),\n drop_last=True,\n )\n\n best_score = -1.0\n best_weights = None\n val_out_path = Path(\"/app/solution/validation.parquet\")\n\n # Initial zero-epoch validation\n initial_score = run_validation(\n model=model,\n catalog_features=catalog_features,\n val_histories=val_histories,\n target_uids=target_uids,\n graded=graded,\n output_path=val_out_path,\n )\n print(f\"Initial Unfitted Model | Val NDCG@10: {initial_score:.6f}\")\n if initial_score > best_score:\n best_score = initial_score\n best_weights = copy.deepcopy(model.state_dict())\n torch.save(best_weights, \"/app/solution/best_model.pt\")\n\n # Train loop\n for epoch in range(1, epochs + 1):\n t_epoch = time.time()\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n for batch_hist, target_items, weights in loader:\n optimizer.zero_grad()\n loss = model.compute_loss(batch_hist, target_items, weights, catalog_features)\n loss.backward()\n nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(num_batches, 1)\n\n # Evaluate validation score\n val_score = run_validation(\n model=model,\n catalog_features=catalog_features,\n val_histories=val_histories,\n target_uids=target_uids,\n graded=graded,\n output_path=val_out_path,\n )\n\n dt = time.time() - t_epoch\n print(f\"Epoch {epoch:02d}/{epochs:02d} | Train Loss: {avg_loss:.4f} | Val NDCG@10: {val_score:.6f} | Time: {dt:.1f}s\")\n\n if val_score > best_score:\n best_score = val_score\n best_weights = copy.deepcopy(model.state_dict())\n torch.save(best_weights, \"/app/solution/best_model.pt\")\n print(f\" >>> New Best Val NDCG@10: {best_score:.6f}! Model checkpoint saved.\")\n\n # Load best model\n if best_weights is not None:\n model.load_state_dict(best_weights)\n print(f\"\\nFinal Best Validation Score: {best_score:.6f}\")\n\n return model, best_score\n\n\ndef refit_and_submit(\n best_model: TwoTowerModel,\n epochs: int = 3,\n batch_size: int = 256,\n lr: float = 3e-4,\n) -> None:\n \"\"\"Refit two-tower model on train + val observed history and generate final submission.\"\"\"\n print(\"=\" * 60)\n print(\"PHASE 4: REFITTING ON TRAIN + VAL & GENERATING FINAL SUBMISSION\")\n print(\"=\" * 60)\n\n t0 = time.time()\n catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()\n\n targets_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\n target_uids = targets_df.uid.to_numpy(dtype=np.int64)\n target_set = set(target_uids)\n uid_to_row = {int(u): r for r, u in enumerate(target_uids)}\n N = len(target_uids)\n\n # In refit mode: validation window [TRAIN_CUTOFF + GAP, VAL_CUTOFF] is the latest observed positive window!\n val_likes = pd.read_parquet(DATA_DIR / \"val_likes.parquet\")\n val_listens = pd.read_parquet(DATA_DIR / \"val_listens.parquet\")\n\n # Exclude items in train history\n train_l = pd.read_parquet(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\"],\n filters=[(\"timestamp\", \">=\", TRAIN_CUTOFF - 90 * 86400)],\n )\n train_likes = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\n train_pairs = set(zip(train_l.uid, train_l.item_id)).union(zip(train_likes.uid, train_likes.item_id))\n\n sub_likes = val_likes[(val_likes.is_organic == 1) & val_likes.uid.isin(target_set) & val_likes.item_id.isin(item_to_idx)]\n sub_listens = val_listens[(val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80) & val_listens.uid.isin(target_set) & val_listens.item_id.isin(item_to_idx)]\n\n refit_pos: Dict[Tuple[int, int], float] = {}\n for r in sub_listens[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in train_pairs:\n refit_pos[(r.uid, r.item_id)] = 1.0\n for r in sub_likes[[\"uid\", \"item_id\"]].drop_duplicates().itertuples(index=False):\n if (r.uid, r.item_id) not in train_pairs:\n refit_pos[(r.uid, r.item_id)] = 5.0\n\n print(f\"Extracted {len(refit_pos)} unconsumed positives from validation window for refitting.\")\n\n val_snap_hist = extract_user_histories(\n cutoff_ts=TRAIN_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"validation_preference_state.parquet\",\n )\n\n all_user_rows = [uid_to_row[uid] for (uid, item_id) in refit_pos.keys()]\n all_target_items = [item_to_idx[item_id] for (uid, item_id) in refit_pos.keys()]\n all_weights = list(refit_pos.values())\n\n from solution.dataset import SnapshotTwoTowerDataset\n refit_dataset = SnapshotTwoTowerDataset(\n global_user_rows=np.array(all_user_rows, dtype=np.int64),\n target_item_idxs=np.array(all_target_items, dtype=np.int64),\n weights=np.array(all_weights, dtype=np.float32),\n stacked_histories=val_snap_hist,\n )\n\n refit_loader = DataLoader(\n refit_dataset,\n batch_size=batch_size,\n shuffle=True,\n collate_fn=collate_fn_builder(val_snap_hist),\n drop_last=False,\n )\n\n optimizer = torch.optim.AdamW(best_model.parameters(), lr=lr, weight_decay=1e-3)\n best_model.train()\n for ep in range(1, epochs + 1):\n total_loss = 0.0\n n_b = 0\n for batch_hist, target_items, weights in refit_loader:\n optimizer.zero_grad()\n loss = best_model.compute_loss(batch_hist, target_items, weights, catalog_features)\n loss.backward()\n nn.utils.clip_grad_norm_(best_model.parameters(), max_norm=1.0)\n optimizer.step()\n total_loss += loss.item()\n n_b += 1\n print(f\"Refit Epoch {ep}/{epochs} | Loss: {total_loss / max(n_b, 1):.4f}\")\n\n print(\"\\nExtracting test histories up to VAL_CUTOFF (including validation events)...\")\n test_histories = extract_user_histories(\n cutoff_ts=VAL_CUTOFF,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA_DIR / \"test_preference_state.parquet\",\n include_val=True,\n )\n\n sub_path = Path(\"/app/solution/submission.parquet\")\n print(f\"Generating final submission to {sub_path}...\")\n sub_df = generate_ranking(\n model=best_model,\n catalog_features=catalog_features,\n user_histories=test_histories,\n target_uids=target_uids,\n eligible_candidates_path=DATA_DIR / \"test_eligible_candidates.npz\",\n output_path=sub_path,\n batch_size=1024,\n )\n\n print(\"Verifying final submission contract...\")\n targets_set = set(target_uids)\n test_cand = CandidateSet(DATA_DIR / \"test_eligible_candidates.npz\")\n norm = validate_ranking(sub_df, targets_set)\n\n ineligible_count = 0\n for uid, item in norm[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not test_cand.is_eligible(uid, item):\n ineligible_count += 1\n if ineligible_count > 0:\n raise ValueError(f\"CRITICAL ERROR: {ineligible_count} items fail test eligibility!\")\n\n print(f\"SUCCESS: Submission verified! Rows={len(norm)}, Target users={norm.uid.nunique()}, All items eligible.\")\n print(f\"Total refit and submission time: {time.time()-t0:.2f}s\")\n\n\ndef main():\n parser = argparse.ArgumentParser(description=\"Train and evaluate Two-Tower Ranker\")\n parser.add_argument(\"--epochs\", type=int, default=3)\n parser.add_argument(\"--batch_size\", type=int, default=256)\n parser.add_argument(\"--lr\", type=float, default=3e-4)\n parser.add_argument(\"--emb_dim\", type=int, default=32)\n parser.add_argument(\"--out_dim\", type=int, default=33)\n parser.add_argument(\"--temperature\", type=float, default=0.08)\n parser.add_argument(\"--alpha\", type=float, default=0.52)\n parser.add_argument(\"--snapshots\", type=int, default=2)\n parser.add_argument(\"--refit\", action=\"store_true\", help=\"Refit on train+val and create final submission\")\n args = parser.parse_args()\n\n model, best_score = train_model(\n epochs=args.epochs,\n batch_size=args.batch_size,\n lr=args.lr,\n emb_dim=args.emb_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n alpha=args.alpha,\n num_snapshots=args.snapshots,\n )\n\n if args.refit:\n refit_and_submit(model)\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/train.py"
}Successfully overwrote file: /app/solution/train.py. Here is the updated code:
...
import pandas as pd
import torch
import torch.nn as nn
from torch.utils.data import DataLoader
sys.path.insert(0, "/task")
import evaluate_public
from ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking
from solution.dataset import (
...
res = score_rankings(ranked, graded)
return float(res["quality_ndcg@10"])
def train_model(
epochs: int = 4,
batch_size: int = 256,
lr: float = 3e-4,
emb_dim: int = 32,
out_dim: int = 33,
hidden_dim: int = 64,
alpha: float = 0.52,
temperature: float = 0.08,
num_negatives: int = 256,
num_snapshots: int = 2,
like_weight: float = 5.0,
strong_weight: float = 1.0,
) -> Tuple[TwoTowerModel, float]:
"""Train two-tower model on multi-snapshot data and tune against validation set."""
print("=" * 60)
print("PHASE 3: TRAINING & VALIDATION TUNING")
print(f"Params: epochs={epochs}, batch_size={batch_size}, lr={lr}, emb_dim={emb_dim}, out_dim={out_dim}, temp={temperature}, alpha={alpha}")
print("=" * 60)
# 1. Load catalog
catalog_df, item_to_idx, idx_to_item, catalog_features = load_catalog()
...
print(f"Preloaded validation labels for {len(graded)} users.")
# 5. Initialize model & optimizer
model = TwoTowerModel(
emb_dim=emb_dim,
out_dim=out_dim,
hidden_dim=hidden_dim,
temperature=temperature,
num_negatives=num_negatives,
alpha=alpha,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-3)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs)
loader = DataLoader(
train_dataset,
batch_size=batch_size,
...
best_score = -1.0
best_weights = None
val_out_path = Path("/app/solution/validation.parquet")
# Initial zero-epoch validation
initial_score = run_validation(
model=model,
catalog_features=catalog_features,
val_histories=val_histories,
target_uids=target_uids,
graded=graded,
output_path=val_out_path,
)
print(f"Initial Unfitted Model | Val NDCG@10: {initial_score:.6f}")
if initial_score > best_score:
best_score = initial_score
best_weights = copy.deepcopy(model.state_dict())
torch.save(best_weights, "/app/solution/best_model.pt")
# Train loop
for epoch in range(1, epochs + 1):
t_epoch = time.time()
model.train()
total_loss = 0.0
...
return model, best_score
def refit_and_submit(
best_model: TwoTowerModel,
epochs: int = 3,
batch_size: int = 256,
lr: float = 3e-4,
) -> None:
"""Refit two-tower model on train + val observed history and generate final submission."""
print("=" * 60)
print("PHASE 4: REFITTING ON TRAIN + VAL & GENERATING FINAL SUBMISSION")
print("=" * 60)
...
target_set = set(target_uids)
uid_to_row = {int(u): r for r, u in enumerate(target_uids)}
N = len(target_uids)
# In refit mode: validation window [TRAIN_CUTOFF + GAP, VAL_CUTOFF] is the latest observed positive window!
val_likes = pd.read_parquet(DATA_DIR / "val_likes.parquet")
val_listens = pd.read_parquet(DATA_DIR / "val_listens.parquet")
# Exclude items in train history
train_l = pd.read_parquet(
...
if (r.uid, r.item_id) not in train_pairs:
refit_pos[(r.uid, r.item_id)] = 5.0
print(f"Extracted {len(refit_pos)} unconsumed positives from validation window for refitting.")
val_snap_hist = extract_user_histories(
cutoff_ts=TRAIN_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "validation_preference_state.parquet",
...
shuffle=True,
collate_fn=collate_fn_builder(val_snap_hist),
drop_last=False,
)
optimizer = torch.optim.AdamW(best_model.parameters(), lr=lr, weight_decay=1e-3)
best_model.train()
for ep in range(1, epochs + 1):
total_loss = 0.0
n_b = 0
for batch_hist, target_items, weights in refit_loader:
...
optimizer.step()
total_loss += loss.item()
n_b += 1
print(f"Refit Epoch {ep}/{epochs} | Loss: {total_loss / max(n_b, 1):.4f}")
print("\nExtracting test histories up to VAL_CUTOFF (including validation events)...")
test_histories = extract_user_histories(
cutoff_ts=VAL_CUTOFF,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=DATA_DIR / "test_preference_state.parquet",
include_val=True,
)
sub_path = Path("/app/solution/submission.parquet")
print(f"Generating final submission to {sub_path}...")
sub_df = generate_ranking(
model=best_model,
catalog_features=catalog_features,
...
eligible_candidates_path=DATA_DIR / "test_eligible_candidates.npz",
output_path=sub_path,
batch_size=1024,
)
print("Verifying final submission contract...")
targets_set = set(target_uids)
test_cand = CandidateSet(DATA_DIR / "test_eligible_candidates.npz")
norm = validate_ranking(sub_df, targets_set)
ineligible_count = 0
for uid, item in norm[["uid", "item_id"]].itertuples(index=False, name=None):
if not test_cand.is_eligible(uid, item):
ineligible_count += 1
if ineligible_count > 0:
...
print(f"Total refit and submission time: {time.time()-t0:.2f}s")
def main():
parser = argparse.ArgumentParser(description="Train and evaluate Two-Tower Ranker")
parser.add_argument("--epochs", type=int, default=3)
parser.add_argument("--batch_size", type=int, default=256)
parser.add_argument("--lr", type=float, default=3e-4)
parser.add_argument("--emb_dim", type=int, default=32)
parser.add_argument("--out_dim", type=int, default=33)
parser.add_argument("--temperature", type=float, default=0.08)
parser.add_argument("--alpha", type=float, default=0.52)
parser.add_argument("--snapshots", type=int, default=2)
parser.add_argument("--refit", action="store_true", help="Refit on train+val and create final submission")
args = parser.parse_args()
model, best_score = train_model(
epochs=args.epochs,
batch_size=args.batch_size,
lr=args.lr,
emb_dim=args.emb_dim,
out_dim=args.out_dim,
temperature=args.temperature,
alpha=args.alpha,
num_snapshots=args.snapshots,
)
if args.refit:
refit_and_submit(model)
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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.