yambda-discovery-ranking-two-tower-base / gemini-3-8-flash-high-base-r05-b9049e57
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T02:12:14.462Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
## Step 2 · user · 2026-09-29T02:12:14.504Z
# Build a latency-efficient PyTorch two-tower neural-network ranker
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.
## Data
All inputs are under `/task/data/`. Training data ends at timestamp `24786800`.
A 30-minute gap precedes a seven-day public validation window. The hidden test
is the following seven-day window after another 30-minute gap.
For each event family there is a `train_*.parquet` and `val_*.parquet` file:
| 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:
1. Build and tune a validation ranker using only `train_*` as history and
`val_*` as subsequent outcomes.
2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test
time the validation events are observed history, not recommendable outcomes.
Do not use `val_*` events as features when measuring a validation ranking. They
may and should be used when refitting the final hidden-test ranker.
Additional files under `/task/data/`:
- `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by
training positive-listen popularity.
- `target_users.parquet`: users with at least 20 positive listens in the 90 days
before the training cutoff, selected without future labels.
- `validation_preference_state.parquet`: active explicit state at the training
cutoff, for validation ranking.
- `test_preference_state.parquet`: active explicit state after validation, for
the final hidden-test ranking. State is `liked` or `disliked`; a pair may have
both active channels.
- `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`:
cutoff-correct candidate sets for every target user. Each contains aligned
arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover
the Boolean item mask with `np.unpackbits(packed_eligible[r],
count=len(item_ids), bitorder="little")`.
The exact available Python distribution inventory is listed in
`/task/requirements.txt`. Future test interactions are hidden and used only by
the separate Harbor verifier.
## Discovery objective
Rank only the candidates returned for the relevant cutoff. Relevance is based
on organic behavior in the immediately following window:
- grade 3: the user organically likes the track;
- grade 1: the user organically listens to at least 80% of the track;
- grade 0: otherwise.
If both positive events occur, grade 3 wins. Ranking quality is measured with
graded NDCG@10. DCG uses gain `2^grade - 1` and discount
`1/log2(rank + 1)`, then averages per-user NDCG over users with at least one
relevant item.
The public evaluator reports this objective for development, but its score is
not a separate acceptance stage. Final acceptance is determined on the hidden
test using the same objective.
The Yambda logs contain interactions, not impression propensities. Treat the
organic objective as a robust offline target, not as a causal estimate of
online lift.
## Public tools
`/task/evaluate_public.py` scores a validation-stage ranking against the public
window. Pass the path to a ranking with the same schema as the final
deliverable:
```sh
python /task/evaluate_public.py solution/validation.parquet
```
Stage-independent evaluation helpers are available in
`/task/ranking_evaluation.py`. The builder installs the same checked-in module
in the public agent image and private verifier image so scoring mechanics cannot
drift. The public copy contains no hidden labels, evaluator-owned data paths, or
pass thresholds.
The public evaluator uses `validation_eligible_candidates.npz`; it is not a
validator for the final artifact. No model 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.
## Deliverable
Write `/app/solution/submission.parquet` or
`/app/solution/submission.csv`, with exactly 100 rows for every target user and
columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties
or duplicate items. Every submitted item must be present in that user's
`test_eligible_candidates.npz` candidate set.
Leave the code that 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.
## Constraints
- Write deliverables and intermediate artifacts only under `/app/solution/`.
- Treat `/task/` and the installed Python environment as read-only.
- Do not invoke nested agents or attempt to access verifier files, credentials,
other trials, or host paths.
- Do not install packages or use network access except for the model provider
connection managed by Harbor.
- The environment includes CPU PyTorch 2.8.0. No accelerator is available.
- You have up to 60 minutes. Improve the model as much as possible within that
limit. Structural validity alone is not sufficient; the final submission is
also evaluated for ranking quality.
## Step 3 · agent · 2026-09-29T02:12:24.740Z
### Recorded reasoning summary
Reviewing Instructions
I'm currently focusing on meticulously reviewing the prompt, mandates, and all associated instructions. My primary objective is to ensure complete adherence to the stated requirements, particularly regarding the "update_topic" exception.
### Recorded reasoning summary
Clarifying Task Parameters
I'm now focused on the specific parameters of the task. I need to build a latency-efficient PyTorch two-tower ranker for a music streaming service, ensuring it operates at production scale without constructing a global user-item interaction matrix. Candidates will be precomputed offline.
### Recorded reasoning summary
Designing Two-Tower Architecture
I'm actively designing the two-tower architecture. The core idea is to decompose the relevance score into user and item vectors via separate PyTorch networks. User vectors will be derived from interaction history, not just IDs, to capture richer user preferences. I'm also considering how to efficiently handle the 30,000 candidate tracks for MIPS.
### Recorded reasoning summary
Processing Data Sources
I am now examining the provided data files. My focus is on understanding the structure of `validation_preference_state.parquet`, `test_preference_state.parquet`, and the `.npz` files. I'm also detailing the column types and the specific metrics for evaluation, including graded NDCG@10.
### update_topic · 2026-09-29T02:12:24.740Z
{
"strategic_intent": "Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.",
"summary": "Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.",
"title": "Initial Research & Environment Setup"
}
### Tool output · 2026-09-29T02:12:24.783Z
Current topic: "Initial Research & Environment Setup"
Topic summary: Starting the initial research phase. We will inspect the task files, evaluator scripts, requirements, and dataset properties to understand data sizes, schemas, and performance constraints.
Strategic Intent: Explore the environment, inspect the evaluation scripts, data shapes, and directory structure.
### run_shell_command · 2026-09-29T02:12:24.740Z
{
"description": "Inspect the contents of /task and /task/data",
"command": "ls -la /task; ls -la /task/data"
}
### Tool output · 2026-09-29T02:12:24.783Z
Output: total 36
dr-xr-xr-x 1 root root 4096 Sep 29 02:09 .
drwxr-xr-x 1 root root 4096 Sep 29 02:12 ..
-r--r--r-- 1 root root 0 Sep 29 01:55 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 01:56 data
-r--r--r-- 1 root root 5774 Sep 29 01:55 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 01:55 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 01:55 requirements.txt
total 347392
dr-xr-xr-x 1 root root 4096 Sep 29 01:56 .
dr-xr-xr-x 1 root root 4096 Sep 29 02:09 ..
-r--r--r-- 1 root root 349352 Sep 29 01:55 candidate_catalog.parquet
-r--r--r-- 1 root root 84127 Sep 29 01:55 target_users.parquet
-r--r--r-- 1 root root 7736341 Sep 29 01:55 test_eligible_candidates.npz
-r--r--r-- 1 root root 1092329 Sep 29 01:56 test_preference_state.parquet
-r--r--r-- 1 root root 857213 Sep 29 01:55 train_dislikes.parquet
-r--r--r-- 1 root root 5374652 Sep 29 01:55 train_likes.parquet
-r--r--r-- 1 root root 319183255 Sep 29 01:55 train_listens.parquet
-r--r--r-- 1 root root 155988 Sep 29 01:55 train_undislikes.parquet
-r--r--r-- 1 root root 1855885 Sep 29 01:55 train_unlikes.parquet
-r--r--r-- 1 root root 36571 Sep 29 01:55 val_dislikes.parquet
-r--r--r-- 1 root root 298484 Sep 29 01:55 val_likes.parquet
-r--r--r-- 1 root root 9947609 Sep 29 01:55 val_listens.parquet
-r--r--r-- 1 root root 7845 Sep 29 01:55 val_undislikes.parquet
-r--r--r-- 1 root root 72536 Sep 29 01:55 val_unlikes.parquet
-r--r--r-- 1 root root 7573821 Sep 29 01:55 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1061761 Sep 29 01:56 validation_preference_state.parquet
Process Group PGID: 1612
### read_file · 2026-09-29T02:12:24.740Z
{
"end_line": 100,
"file_path": "/task/evaluate_public.py",
"start_line": 1
}
### Tool error · 2026-09-29T02:12:24.783Z
Path not in workspace: Attempted path "/task/evaluate_public.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
### read_file · 2026-09-29T02:12:24.740Z
{
"start_line": 1,
"file_path": "/task/ranking_evaluation.py",
"end_line": 100
}
### Tool error · 2026-09-29T02:12:24.783Z
Path not in workspace: Attempted path "/task/ranking_evaluation.py" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app
## Step 4 · agent · 2026-09-29T02:12:27.570Z
### Recorded reasoning summary
Exploring File Access
I've identified a key constraint: `read_file` is restricted to specific directories. To circumvent this, I'm now considering leveraging `run_shell_command` with utilities like `cat` and `head` to access files outside these designated areas. This approach will allow me to inspect files such as `evaluate_public.py`, `ranking_evaluation.py`, and `requirements.txt`.
### run_shell_command · 2026-09-29T02:12:27.570Z
{
"description": "View evaluate_public.py to see evaluation logic and options",
"command": "cat /task/evaluate_public.py"
}
### Tool output · 2026-09-29T02:12:27.601Z
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
"""
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 "
)
path = Path(sys.argv[1])
ranked = load_submission(path, eligibility, targets)
print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))
if __name__ == "__main__":
main()
Process Group PGID: 1616
### run_shell_command · 2026-09-29T02:12:27.570Z
{
"command": "cat /task/ranking_evaluation.py",
"description": "View ranking_evaluation.py to understand evaluation metrics and data structures"
}
### Tool output · 2026-09-29T02:12:27.601Z
Output: """Shared public primitives for Yambda discovery-ranking evaluation.
Both the solver-visible validation evaluator and the private submission judge
import this module from the generated task's ``public`` directory. It owns the
stage-independent contract: reading a ranking, validating its shape, converting
it to per-user rank order, looking up packed candidate eligibility, and
computing graded NDCG@10.
The module deliberately contains no event-derived labels, private artifact
paths, or pass thresholds. Each evaluator supplies its own relevance data and
per-cutoff eligibility checks around these common operations.
"""
from __future__ import annotations
from pathlib import Path
import numpy as np
import pandas as pd
__all__ = [
"CandidateSet",
"QUALITY_K",
"RankingValidationError",
"REQUIRED_COLUMNS",
"TOP_N",
"ndcg_at_k",
"ranked_items",
"read_ranking",
"score_rankings",
"validate_ranking",
]
TOP_N = 100
QUALITY_K = 10
REQUIRED_COLUMNS = ("uid", "item_id", "rank")
class RankingValidationError(ValueError):
"""Report a ranking-schema or structural-contract violation."""
class CandidateSet:
"""Provide membership lookups over one packed candidate-set archive.
The archive contains aligned ``uids``, ``item_ids``, and
``packed_eligible`` arrays. Candidate column ``c`` is represented by bit
``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are
copied into memory so they remain valid after the archive is closed.
"""
def __init__(self, path: Path):
"""Load an eligibility archive and build ID-to-position indexes.
Args:
path: Path to a ``.npz`` archive with the candidate-set schema.
"""
with np.load(path, allow_pickle=False) as artifact:
self.uids = artifact["uids"].astype(np.int64, copy=True)
self.item_ids = artifact["item_ids"].astype(np.int64, copy=True)
self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True)
self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}
self.item_column = {
int(item): column for column, item in enumerate(self.item_ids)
}
def is_eligible(self, uid: int, item_id: int) -> bool:
"""Return whether ``item_id`` is eligible for ``uid``.
IDs absent from the target-user or candidate-item arrays are treated as
ineligible rather than raising an exception.
Args:
uid: Target user ID to query.
item_id: Candidate item ID to query.
Returns:
``True`` only when the corresponding packed eligibility bit is set.
"""
row = self.uid_row.get(int(uid))
column = self.item_column.get(int(item_id))
if row is None or column is None:
return False
byte, bit = divmod(column, 8)
return bool((int(self.packed[row, byte]) >> bit) & 1)
def read_ranking(path: Path) -> pd.DataFrame:
"""Read a parquet or CSV ranking into a pandas frame.
Args:
path: Input path. A ``.parquet`` suffix selects parquet; every other
suffix is interpreted as CSV.
Returns:
The ranking frame as loaded by pandas.
"""
return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path)
def validate_ranking(
frame: pd.DataFrame,
targets: set[int],
) -> pd.DataFrame:
"""Normalize and validate the stage-independent ranking contract.
Required values must be finite integers. The user set must exactly match
``targets``; every user must have 100 rows, distinct items, and each rank
from 1 through 100 exactly once. Cutoff-specific candidate eligibility is
intentionally left to the calling evaluator.
Args:
frame: Raw ranking frame. Extra columns are discarded from an internal
copy; the caller's frame is not modified.
targets: Exact set of target user IDs the ranking must cover.
Returns:
A normalized frame containing only the required ``int64`` columns.
Raises:
RankingValidationError: If the ranking violates the shared contract.
"""
required = list(REQUIRED_COLUMNS)
if not all(column in frame for column in required):
raise RankingValidationError(f"submission requires columns {required}")
normalized = frame[required].copy()
for column in required:
numeric = pd.to_numeric(normalized[column], errors="coerce")
values = numeric.to_numpy(dtype=float)
if numeric.isna().any() or not np.isfinite(values).all():
raise RankingValidationError(
f"submission {column} must be finite numeric values"
)
if not np.equal(numeric, np.floor(numeric)).all():
raise RankingValidationError(
f"submission {column} must contain integers"
)
normalized[column] = numeric.astype(np.int64)
submitted_users = set(map(int, normalized.uid.unique()))
if submitted_users != targets:
raise RankingValidationError(
f"submission user set mismatch: missing={len(targets - submitted_users)} "
f"extra={len(submitted_users - targets)}"
)
counts = normalized.groupby("uid").size()
if not (counts == TOP_N).all():
raise RankingValidationError(
f"submission must contain exactly {TOP_N} rows for every target user"
)
if normalized.duplicated(["uid", "item_id"]).any():
raise RankingValidationError(
"submission contains duplicate (uid, item_id) pairs"
)
valid_ranks = set(range(1, TOP_N + 1))
ranks_are_valid = normalized.groupby("uid")["rank"].apply(
lambda values: set(map(int, values)) == valid_ranks
)
if not ranks_are_valid.all():
raise RankingValidationError(
f"each user must have every rank from 1 through {TOP_N} exactly once"
)
return normalized
def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:
"""Convert a validated ranking frame to ordered items keyed by user.
Args:
frame: Frame that has passed :func:`validate_ranking`.
Returns:
A ``uid -> item IDs`` mapping ordered by ascending rank.
"""
ordered = frame.sort_values(["uid", "rank"])
return {
int(uid): list(map(int, values))
for uid, values in ordered.groupby("uid").item_id
}
def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:
"""Compute graded normalized discounted cumulative gain at ``k``.
Relevance gain is ``2**grade - 1`` and the discount at one-based rank
``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.
Args:
ranked: Item IDs in predicted rank order.
gold: Mapping from relevant item IDs to integer relevance grades.
k: Maximum number of predicted positions to score.
Returns:
For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns
``0.0`` when the ideal gain is zero.
"""
gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)
discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))
dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))
ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)
ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))
idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))
return dcg / idcg if idcg else 0.0
def score_rankings(
ranked: dict[int, list[int]],
graded: dict[int, dict[int, int]],
) -> dict[str, float | int]:
"""Aggregate per-user NDCG@10 over users with relevance labels.
Args:
ranked: Ranked item IDs for every target user.
graded: Relevant item grades keyed by user and item.
Returns:
The six-decimal ``quality_ndcg@10`` value and number of scored users.
"""
quality = float(
np.mean(
[
ndcg_at_k(ranked[uid], gold, QUALITY_K)
for uid, gold in graded.items()
]
)
)
return {
"quality_ndcg@10": round(quality, 6),
"quality_users": len(graded),
}
Process Group PGID: 1619
### run_shell_command · 2026-09-29T02:12:27.570Z
{
"description": "View requirements.txt",
"command": "cat /task/requirements.txt"
}
### Tool output · 2026-09-29T02:12:27.601Z
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: 1622
## Step 5 · agent · 2026-09-29T02:12:30.019Z
### run_shell_command · 2026-09-29T02:12:30.019Z
{
"command": "nproc; free -h; df -h",
"description": "Check system CPU, memory, and disk space"
}
### Tool output · 2026-09-29T02:12:30.037Z
Output: 8
/usr/bin/bash: line 4: free: command not found
Filesystem Size Used Avail Use% Mounted on
overlay 96G 14G 83G 15% /
tmpfs 64M 0 64M 0% /dev
shm 64M 0 64M 0% /dev/shm
/dev/root 96G 14G 83G 15% /etc/hosts
tmpfs 7.7G 0 7.7G 0% /proc/acpi
tmpfs 7.7G 0 7.7G 0% /proc/scsi
tmpfs 7.7G 0 7.7G 0% /sys/firmware
Process Group PGID: 1625
## Step 6 · agent · 2026-09-29T02:12:34.240Z
### run_shell_command · 2026-09-29T02:12:34.240Z
{
"command": "cat /proc/meminfo | head -n 5",
"description": "Check RAM from /proc/meminfo"
}
### Tool output · 2026-09-29T02:12:34.257Z
Output: MemTotal: 15988968 kB
MemFree: 1284324 kB
MemAvailable: 14408784 kB
Buffers: 248460 kB
Cached: 12547192 kB
Process Group PGID: 1630
## Step 7 · agent · 2026-09-29T02:12:38.807Z
### Recorded reasoning summary
Analyzing Datasets
I'm currently focused on inspecting the `candidate_catalog.parquet` and `target_users.parquet` datasets. My plan is to write a Python script to determine their column structures, row counts, and the ID ranges within each, leveraging our available CPU, RAM, and disk space.
### run_shell_command · 2026-09-29T02:12:38.807Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\ndef info(name):\n df = pd.read_parquet(DATA / f'{name}.parquet')\n print(f'{name}: shape={df.shape}, cols={list(df.columns)}')\n for col in df.columns:\n if df[col].dtype in ['int64', 'float64', 'int32']:\n print(f' {col}: min={df[col].min()}, max={df[col].max()}')\n return df\n\nprint('--- TARGET USERS ---')\ntu = info('target_users')\nprint('--- CANDIDATE CATALOG ---')\ncc = info('candidate_catalog')\nprint('--- PREFERENCE STATES ---')\nvps = info('validation_preference_state')\ntps = info('test_preference_state')\nprint('--- TRAIN EVENTS ---')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'train_{fam}.parquet')\n print(f'train_{fam}: len={len(df)}')\nfor fam in ['likes', 'dislikes', 'unlikes', 'undislikes']:\n df = pd.read_parquet(DATA / f'val_{fam}.parquet')\n print(f'val_{fam}: len={len(df)}')\n\"",
"description": "Explore metadata and sizes of parquet files"
}
### Tool output · 2026-09-29T02:12:40.515Z
Output: --- TARGET USERS ---
target_users: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
recent_positive_listens: min=20.0, max=19592.0
history_positive_listens: min=20, max=26597
--- CANDIDATE CATALOG ---
candidate_catalog: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
positive_listens: min=163, max=26615
popularity_rank: min=1, max=30000
--- PREFERENCE STATES ---
validation_preference_state: shape=(469124, 3), cols=['uid', 'item_id', 'state']
test_preference_state: shape=(484776, 3), cols=['uid', 'item_id', 'state']
--- TRAIN EVENTS ---
train_likes: len=814831
train_dislikes: len=101075
train_unlikes: len=292665
train_undislikes: len=19960
val_likes: len=37795
val_dislikes: len=3563
val_unlikes: len=9346
val_undislikes: len=606
Process Group PGID: 1634
## Step 8 · agent · 2026-09-29T02:12:43.858Z
### run_shell_command · 2026-09-29T02:12:43.858Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\n\nprint('Reading val_listens...')\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens:', vl.shape, vl.columns)\nprint(vl.head(3))\n\nprint('Reading train_listens metadata...')\nimport pyarrow.parquet as pq\ntable_meta = pq.read_metadata(DATA / 'train_listens.parquet')\nprint('train_listens num_rows:', table_meta.num_rows, 'num_row_groups:', table_meta.num_row_groups)\nschema = table_meta.schema\nprint('schema:', schema)\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nprint('validation_eligible_candidates:', list(vec.keys()))\nprint('uids:', vec['uids'].shape, 'item_ids:', vec['item_ids'].shape, 'packed:', vec['packed_eligible'].shape)\n\ntec = np.load(DATA / 'test_eligible_candidates.npz')\nprint('test_eligible_candidates:', list(tec.keys()))\nprint('uids:', tec['uids'].shape, 'item_ids:', tec['item_ids'].shape, 'packed:', tec['packed_eligible'].shape)\n\"",
"description": "Inspect listens datasets and eligible candidates npz files"
}
### Tool output · 2026-09-29T02:12:45.657Z
Output: Reading val_listens...
val_listens: (1501697, 6) Index(['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct',
'track_length_seconds'],
dtype='object')
uid timestamp item_id is_organic played_ratio_pct track_length_seconds
0 100 24943430 9321269 0 100 195
1 100 24943605 5452713 0 100 175
2 100 24943765 6448777 0 100 160
Reading train_listens metadata...
train_listens num_rows: 43484159 num_row_groups: 42
schema:
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));
}
validation_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
test_eligible_candidates: ['uids', 'item_ids', 'packed_eligible']
uids: (8456,) item_ids: (30000,) packed: (8456, 3750)
Process Group PGID: 1651
## Step 9 · agent · 2026-09-29T02:12:57.533Z
### Recorded reasoning summary
Analyzing Data Dimensions
My current focus is on understanding the scale of the data. The training set is substantial at over 43 million entries, while the validation set is considerably smaller. We're dealing with a user base of 8,456 individuals and a candidate catalog of 30,000 items. The validation candidate file confirms the 8,456 user IDs.
### run_shell_command · 2026-09-29T02:12:57.533Z
{
"description": "Check candidate catalog matching and candidate count distributions",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\ntec = np.load(DATA / 'test_eligible_candidates.npz')\n\nprint('cc item_id equals vec item_ids?', np.array_equal(cc['item_id'].values, vec['item_ids']))\nprint('vec item_ids equals tec item_ids?', np.array_equal(vec['item_ids'], tec['item_ids']))\nprint('vec uids equals tec uids?', np.array_equal(vec['uids'], tec['uids']))\n\n# check eligible counts per user\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_val = unpacked.sum(axis=1)\nprint(f'Val eligible counts per user: min={counts_val.min()}, max={counts_val.max()}, mean={counts_val.mean():.1f}, median={np.median(counts_val)}')\n\nunpacked_test = np.unpackbits(tec['packed_eligible'], axis=1, count=30000, bitorder='little')\ncounts_test = unpacked_test.sum(axis=1)\nprint(f'Test eligible counts per user: min={counts_test.min()}, max={counts_test.max()}, mean={counts_test.mean():.1f}, median={np.median(counts_test)}')\n\""
}
### Tool output · 2026-09-29T02:12:59.459Z
Output: cc item_id equals vec item_ids? True
vec item_ids equals tec item_ids? True
vec uids equals tec uids? True
Val eligible counts per user: min=24657, max=30000, mean=29067.3, median=29242.0
Test eligible counts per user: min=24552, max=30000, mean=29041.3, median=29220.0
Process Group PGID: 1671
## Step 10 · agent · 2026-09-29T02:13:01.921Z
### Recorded reasoning summary
Exploring Eligibility Parameters
I'm currently examining the `validation_preference_state` and `test_preference_state` to understand the nuances of item eligibility. The vast majority of the 30,000 items are indeed accessible, with only a small fraction excluded per user.
### run_shell_command · 2026-09-29T02:13:01.921Z
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state values:')\nprint(vps['state'].value_counts())\nprint(vps.head(10))\n\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state values:')\nprint(tps['state'].value_counts())\n\"",
"description": "Inspect preference states"
}
### Tool output · 2026-09-29T02:13:03.549Z
Output: Validation preference state values:
state
liked 418435
disliked 50689
Name: count, dtype: int64
uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked
5 100 3535902 liked
6 100 3633039 liked
7 100 4099299 liked
8 100 4221787 liked
9 100 4927727 disliked
Test preference state values:
state
liked 432659
disliked 52117
Name: count, dtype: int64
Process Group PGID: 1688
## Step 11 · agent · 2026-09-29T02:13:07.105Z
### Recorded reasoning summary
Investigating data structure
I'm currently examining the timestamps within the training data, specifically focusing on the end point and the subsequent validation and test windows. This is to understand the temporal organization of the dataset.
### run_shell_command · 2026-09-29T02:13:07.105Z
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvl = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('val_listens ts min:', vl.timestamp.min(), 'max:', vl.timestamp.max(), 'delta:', vl.timestamp.max() - vl.timestamp.min())\nvlk = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('val_likes ts min:', vlk.timestamp.min(), 'max:', vlk.timestamp.max())\n\ntlk = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('train_likes ts min:', tlk.timestamp.min(), 'max:', tlk.timestamp.max())\n\n# check how many uids in train_listens vs target_users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint('target_users count:', len(tu))\n\"",
"description": "Check timestamps across sets"
}
### Tool output · 2026-09-29T02:13:08.718Z
Output: val_listens ts min: 24788600 max: 25393395 delta: 604795
val_likes ts min: 24788815 max: 25393390
train_likes ts min: 80 max: 24786790
target_users count: 8456
Process Group PGID: 1706
## Step 12 · agent · 2026-09-29T02:13:19.924Z
### Recorded reasoning summary
Confirming Interval Logic
I'm verifying the exact duration of a week and a half-hour gap. It seems the intervals are precisely 604,800 seconds and 1800 seconds, respectively, which aligns with the upcoming hidden test's timing.
### Recorded reasoning summary
Analyzing Validation Scripts
I'm now examining the `evaluate_public.py` script, specifically the `validation_truth` function. I need to understand its expectations for listens and likes data, and how it defines "strong" listens and organic likes to derive the ground truth for validation.
### run_shell_command · 2026-09-29T02:13:19.924Z
{
"description": "Analyze validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\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 = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
}
### Tool output · 2026-09-29T02:13:21.428Z
Output: Traceback (most recent call last):
File "", line 4, in
ModuleNotFoundError: No module named 'ranking_evaluation'
Exit Code: 1
Process Group PGID: 1726
## Step 13 · agent · 2026-09-29T02:13:29.069Z
### run_shell_command · 2026-09-29T02:13:29.069Z
{
"description": "Run validation ground truth analysis with PYTHONPATH=/task",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\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 = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\nprint('Number of users with at least 1 relevant item in val:', len(graded))\nitem_counts = [len(v) for v in graded.values()]\nimport numpy as np\nprint('Relevant items per user: min, median, mean, max:', np.min(item_counts), np.median(item_counts), np.mean(item_counts), np.max(item_counts))\ntotal_pos = sum(item_counts)\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positives: {total_pos}, grade 3 (likes): {grade3_count}, grade 1 (strong listens): {grade1_count}')\n\""
}
### Tool output · 2026-09-29T02:13:31.165Z
Output: Number of users with at least 1 relevant item in val: 4263
Relevant items per user: min, median, mean, max: 1 4.0 8.859723199624677 442
Total positives: 37769, grade 3 (likes): 4417, grade 1 (strong listens): 33352
Process Group PGID: 1737
## Step 14 · agent · 2026-09-29T02:13:37.345Z
### Recorded reasoning summary
Assessing Baseline Popularity
I'm now investigating a baseline strategy, specifically ranking items by their catalog popularity. This will serve as a crucial point of comparison as I evaluate the effectiveness of other approaches.
### run_shell_command · 2026-09-29T02:13:37.345Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# For each user, get top 100 eligible items by catalog popularity\ncatalog_items = cc['item_id'].values # already sorted by popularity rank 1..30000\n\n# Let's vectorize candidate filtering\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=30000, bitorder='little')\n# unpacked is shape (8456, 30000), where col c corresponds to item_ids[c]\nitem_ids = eligibility.item_ids # array of item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n # eligible item indices\n mask = unpacked[i] == 1\n # top 100 eligible items\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nimport evaluate_public\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\nvalidated = validate_ranking(df, targets_set)\nranked = ranked_items(validated)\nprint(score_rankings(ranked, graded))\n\"",
"description": "Evaluate popularity baseline on validation set"
}
### Tool output · 2026-09-29T02:13:41.458Z
Output: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1757
## Step 15 · agent · 2026-09-29T02:13:51.721Z
### Recorded reasoning summary
Refining Model Architecture
My focus is now on meticulously dissecting the prompt's requirements, specifically examining each sentence. I'm also noting that the current popularity baseline stands at `quality_ndcg@10`: 0.010710, and I am starting to build a two-tower neural network in CPU PyTorch.
### Recorded reasoning summary
Developing Two-Tower Model
I'm focusing on how to ensure my model can handle millions of users and items without building a global matrix. The architecture will necessitate separate user and item towers for efficient, offline computation and strict request-time latency. I'm implementing this in PyTorch, prioritizing interaction history for user representation.
### Recorded reasoning summary
Integrating Temporal Data
I'm now incorporating the specific data structures provided, focusing on how to represent cutoff-specific preferences and temporal dynamics within my two-tower architecture. The training data's end timestamp and validation/test windows are crucial for the temporal backtest I'm setting up in PyTorch.
### Recorded reasoning summary
Validating Approach Scenarios
I'm now planning the validation strategy, distinguishing between building a ranker with training history and validation outcomes, and then refitting it for the final submission. My focus is on ensuring validation events are not used as features when measuring performance.
### run_shell_command · 2026-09-29T02:13:51.721Z
{
"description": "Investigate preference state and eligibility",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state:')\nprint(vps.head(10))\nprint(vps.groupby('state').size())\n# Are pairs with state='disliked' in eligible candidates?\nvec = np.load = __import__('numpy').load(DATA / 'validation_eligible_candidates.npz')\nfrom ranking_evaluation import CandidateSet\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\n# Check if disliked items are eligible\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\""
}
### Tool output · 2026-09-29T02:13:53.331Z
Output: Traceback (most recent call last):
File "", line 11, in
NameError: name 'np' is not defined
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
5 100 3535902 liked
6 100 3633039 liked
7 100 4099299 liked
8 100 4221787 liked
9 100 4927727 disliked
state
disliked 50689
liked 418435
dtype: int64
Exit Code: 1
Process Group PGID: 1777
## Step 16 · agent · 2026-09-29T02:14:11.470Z
### run_shell_command · 2026-09-29T02:14:11.470Z
{
"description": "Check disliked and liked items against candidate eligibility",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\nfrom ranking_evaluation import CandidateSet\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncs = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\ndisliked = vps[vps.state == 'disliked']\ndisliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in disliked.head(100).itertuples()]\nprint('Are disliked items eligible?:', sum(disliked_el), 'out of 100')\n\nliked = vps[vps.state == 'liked']\nliked_el = [cs.is_eligible(int(r.uid), int(r.item_id)) for r in liked.head(100).itertuples()]\nprint('Are liked items eligible?:', sum(liked_el), 'out of 100')\n\n# Check whole dataset\ndisliked_uids = disliked.uid.values\ndisliked_items = disliked.item_id.values\ndisliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(disliked_uids, disliked_items))\nprint(f'Total disliked items eligible: {disliked_el_all} out of {len(disliked)}')\n\nliked_uids = liked.uid.values\nliked_items = liked.item_id.values\nliked_el_all = sum(cs.is_eligible(int(u), int(it)) for u, it in zip(liked_uids[:5000], liked_items[:5000]))\nprint(f'Liked sample (5000) eligible: {liked_el_all} out of 5000')\n\""
}
### Tool output · 2026-09-29T02:14:13.282Z
Output: Are disliked items eligible?: 0 out of 100
Are liked items eligible?: 0 out of 100
Total disliked items eligible: 0 out of 50689
Liked sample (5000) eligible: 0 out of 5000
Process Group PGID: 1794
## Step 17 · agent · 2026-09-29T02:14:16.584Z
### Recorded reasoning summary
Filtering out liked items
I'm realizing that items already present in the `validation_preference_state`, meaning they've been liked or disliked, are automatically excluded from `validation_eligible_candidates`. This clarifies the "discovery shelf" concept – the platform proactively removes previously interacted-with items.
### run_shell_command · 2026-09-29T02:14:16.584Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\ncc_items = set(cc.item_id)\nprint('VPS items in candidate catalog:', vps.item_id.isin(cc_items).sum(), 'out of', len(vps))\n\"",
"description": "Check VPS items in candidate catalog"
}
### Tool output · 2026-09-29T02:14:18.222Z
Output: VPS items in candidate catalog: 469124 out of 469124
Process Group PGID: 1811
## Step 18 · agent · 2026-09-29T02:14:22.257Z
### Recorded reasoning summary
Verifying Candidate Pool
I've confirmed that every pair within `validation_preference_state` is indeed present in `candidate_catalog`. Furthermore, I've established that each user's existing liked and disliked items are successfully excluded from their candidate pool, which precisely aligns with the observed preference state counts.
### run_shell_command · 2026-09-29T02:14:22.257Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_counts = 30000 - unpacked.sum(axis=1)\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_counts = vps.groupby('uid').size()\nuids = vec['uids']\nvps_c = [vps_counts.get(uid, 0) for uid in uids]\nprint('Ineligible count stats: min, mean, max:', ineligible_counts.min(), ineligible_counts.mean(), ineligible_counts.max())\nprint('VPS count stats: min, mean, max:', np.min(vps_c), np.mean(vps_c), np.max(vps_c))\n\"",
"description": "Compare ineligible counts and VPS counts"
}
### Tool output · 2026-09-29T02:14:24.067Z
Output: Ineligible count stats: min, mean, max: 0 932.6937086092715 5343
VPS count stats: min, mean, max: 0 55.478240302743615 1847
Process Group PGID: 1828
## Step 19 · agent · 2026-09-29T02:14:36.470Z
### Recorded reasoning summary
Refining Candidate Selection
My recent focus has been on a more precise approach to candidate selection. I've confirmed that the platform provides explicit boolean masks, eliminating any need for inference regarding candidate eligibility. This direct data, found in `validation_eligible_candidates.npz` and `test_eligible`, simplifies the process significantly.
### Recorded reasoning summary
Analyzing Production Constraints
I'm now thoroughly analyzing the architectural constraints and production setting requirements. The system must handle millions of users and items without a global user-item matrix. Candidate embeddings are precomputed offline for fast retrieval, requiring an exact model decomposition into independent user and item vectors.
### Recorded reasoning summary
Developing User Tower Logic
I'm concentrating on the user tower's functionality, specifically its reliance on interaction history over simple user-ID lookups. This dynamic approach is crucial for handling cold-start users and evolving preferences in a streaming environment, ensuring better generalization and real-time adaptability for relevance scoring.
### Recorded reasoning summary
Implementing Item Tower
I've clarified the item tower's role: taking item IDs/features to output a d-dimensional embedding. Precomputing these offline is key for fast inference. I'll then compute scores using dot products with user embeddings, mask ineligible items, and select the top candidates.
### Recorded reasoning summary
Exploring Score Function
My current thinking is focused on the scoring mechanism. I've noted the requirement for a dot product between user and item embeddings, suggesting a maximum inner product search scenario, possibly with cosine similarity if embeddings are normalized. I'm now investigating appropriate training objectives for this two-tower architecture, considering options like InfoNCE loss.
### run_shell_command · 2026-09-29T02:14:36.470Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid.values)\nprint('Target users count:', len(target_uids))\n\n# Let's see how many listens are for target users vs other users\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid'])\nall_listens_uids = table['uid'].to_numpy()\nprint('Total rows in train_listens:', len(all_listens_uids))\nimport numpy as np\nunique_uids = np.unique(all_listens_uids)\nprint('Unique uids in train_listens:', len(unique_uids))\nis_target = np.isin(unique_uids, list(target_uids))\nprint('Target uids present in train_listens:', is_target.sum())\n\"",
"description": "Check unique users in train_listens"
}
### Tool output · 2026-09-29T02:14:38.482Z
Output: Target users count: 8456
Total rows in train_listens: 43484159
Unique uids in train_listens: 9238
Target uids present in train_listens: 8456
Process Group PGID: 1845
## Step 20 · agent · 2026-09-29T02:14:50.737Z
### Recorded reasoning summary
Analyzing User Data
I'm currently investigating the overlap between the unique users in `train_listens` and our `target_users`. It appears only a fraction of the total listens are coming from these key individuals, and I'm now digging into the timestamps to understand usage patterns.
### run_shell_command · 2026-09-29T02:14:50.737Z
{
"description": "Check timestamps distribution in train_listens",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['timestamp'])\nts = table['timestamp'].to_numpy()\nprint('Timestamp min:', ts.min(), 'max:', ts.max())\nprint('Max ts in days:', ts.max() / 86400, 'Min ts in days:', ts.min() / 86400)\ncutoff = 24786800\nprint('Distance from max to cutoff:', cutoff - ts.max())\n# 90 days before cutoff:\nday90 = cutoff - 90 * 86400\nprint('90 days before cutoff timestamp:', day90)\nrecent_count = (ts >= day90).sum()\nprint(f'Rows in last 90 days: {recent_count} ({recent_count / len(ts) * 100:.1f}%)')\nday30 = cutoff - 30 * 86400\nrecent_30 = (ts >= day30).sum()\nprint(f'Rows in last 30 days: {recent_30} ({recent_30 / len(ts) * 100:.1f}%)')\nday14 = cutoff - 14 * 86400\nrecent_14 = (ts >= day14).sum()\nprint(f'Rows in last 14 days: {recent_14} ({recent_14 / len(ts) * 100:.1f}%)')\n\""
}
### Tool output · 2026-09-29T02:14:52.786Z
Output: Timestamp min: 0 max: 24786795
Max ts in days: 286.88420138888887 Min ts in days: 0.0
Distance from max to cutoff: 5
90 days before cutoff timestamp: 17010800
Rows in last 90 days: 16769274 (38.6%)
Rows in last 30 days: 6126103 (14.1%)
Rows in last 14 days: 2920227 (6.7%)
Process Group PGID: 1866
## Step 21 · agent · 2026-09-29T02:14:56.553Z
### Recorded reasoning summary
Analyzing Listen Data
I'm currently reviewing the temporal distribution of our training dataset, noting the significant volume of listens within the last 90, 30, and 14 days. My next step is to examine the specific items present within the `train_listens` dataset to understand their composition relative to these recent listen volumes.
### run_shell_command · 2026-09-29T02:14:56.553Z
{
"description": "Check item overlap between train_listens and candidate_catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\nprint('Catalog items count:', len(catalog_items))\n\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['item_id'])\nitems = table['item_id'].to_numpy()\nunique_items = np.unique(items)\nprint('Unique items in train_listens:', len(unique_items))\nin_catalog = np.isin(unique_items, list(catalog_items))\nprint(f'Items in catalog: {in_catalog.sum()} ({in_catalog.sum() / len(unique_items) * 100:.1f}%)')\n\n# Fraction of listens that are catalog items:\nlistens_in_catalog = np.isin(items, list(catalog_items)).mean()\nprint(f'Fraction of all listens in catalog: {listens_in_catalog * 100:.2f}%')\n\""
}
### Tool output · 2026-09-29T02:14:59.102Z
Output: Catalog items count: 30000
Unique items in train_listens: 845395
Items in catalog: 30000 (3.5%)
Fraction of all listens in catalog: 71.86%
Process Group PGID: 1886
## Step 22 · agent · 2026-09-29T02:15:06.693Z
### Recorded reasoning summary
Confirming Candidate Scope
I've confirmed that our candidate catalog of 30,000 items is fully represented within the training listens, with these candidates accounting for a substantial 71.86% of all listens. This indicates strong alignment with the overall music streaming universe.
### Recorded reasoning summary
Refining Candidate Scope
I've clarified that the 30,000 items in our candidate catalog are the critical focus, distinct from the broader log history. My current effort centers on ensuring this specific set is fully accounted for, as per the prompt's explicit definition of `candidate_catalog.parquet`.
### Recorded reasoning summary
Verifying Candidate Item Set
I've confirmed that the `validation_eligible_candidates.npz` and `test_eligible_candidates.npz` files both contain precisely the 30,000 items from the `candidate_catalog`. This means the items I need to consider for predictions are consistent and complete across these datasets.
### run_shell_command · 2026-09-29T02:15:06.693Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncatalog_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nprint('Train likes in catalog:', likes.item_id.isin(catalog_items).mean())\nval_likes = pd.read_parquet(DATA / 'val_likes.parquet')\nprint('Val likes in catalog:', val_likes.item_id.isin(catalog_items).mean())\n\nval_listens = pd.read_parquet(DATA / 'val_listens.parquet')\nprint('Val listens in catalog:', val_listens.item_id.isin(catalog_items).mean())\n\"",
"description": "Check catalog coverage on likes and val listens"
}
### Tool output · 2026-09-29T02:15:08.524Z
Output: Train likes in catalog: 0.6809068383505291
Val likes in catalog: 0.5570842704061384
Val listens in catalog: 0.6737517621730615
Process Group PGID: 1908
## Step 23 · agent · 2026-09-29T02:15:14.230Z
### Recorded reasoning summary
Refining Validation Logic
I'm currently zeroing in on the `validation_truth` function. My focus is on precisely defining the eligibility criteria within the validation evaluation, specifically by refining the `strong` condition to accurately capture organic plays against target IDs.
### Recorded reasoning summary
Sharpening Eligibility Criteria
I'm currently focused on ensuring only items within the defined candidate set are considered eligible. This means rigorously checking the `eligibility.is_eligible` function, particularly its interaction with user and item arrays, to prevent any out-of-set items from erroneously passing validation.
### run_shell_command · 2026-09-29T02:15:14.230Z
{
"description": "Verify all gold items are subset of candidate catalog",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\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 = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\n\nall_gold_items = set()\nfor items in graded.values():\n all_gold_items.update(items.keys())\nprint('Total unique gold items in val:', len(all_gold_items))\nprint('Are all gold items in candidate catalog?:', all_gold_items.issubset(cc_set))\n\""
}
### Tool output · 2026-09-29T02:15:16.315Z
Output: Total unique gold items in val: 14124
Are all gold items in candidate catalog?: True
Process Group PGID: 1928
## Step 24 · agent · 2026-09-29T02:15:32.521Z
### Recorded reasoning summary
Analyzing Production Constraints
I'm currently delving into the two-tower model's architectural constraints, particularly focusing on the strict request-time latency requirements for the production shelf. Understanding these limitations is key to ensuring our validated gold items can be efficiently cataloged.
### Recorded reasoning summary
Refining Two-Tower Model
I'm refining the two-tower architecture, ensuring user towers use interaction history, not just IDs. I'm also detailing the offline computation of item embeddings and their storage in a nearest-neighbor index for efficient retrieval at request time.
### Recorded reasoning summary
Optimizing Inference Path
My focus is now on optimizing the inference path, specifically ensuring our user and item towers can deliver embeddings within the strict production latency budgets. I'm also verifying that all submitted items are indeed from the eligible candidate sets and that the output format adheres precisely to the submission guidelines.
### Recorded reasoning summary
Exploring Tower Inputs
I am exploring the specific features each tower can accept, meticulously examining the candidate catalog data. My current focus is on how to best leverage `item_id`, `positive_listens`, `organic_positive_listens`, and `popularity_rank` for the item tower's embedding generation.
### Recorded reasoning summary
Processing User Interaction Data
I'm now analyzing the rich user interaction data, considering features like normalized popularity and listen ratios for the item tower. For the user tower, I'm investigating how to effectively process recent listens, likes, dislikes, and the significant historical preference states to generate meaningful user embeddings.
### Recorded reasoning summary
Synthesizing User Representations
I'm now synthesizing user representations by pooling embeddings from recent listens (weighted by play ratio and time decay), likes, and dislikes. I'm also incorporating summary statistics like listen counts, which will be concatenated to form the final user tower input.
### Recorded reasoning summary
Validating Production Readiness
I am now verifying that the entire architecture adheres to all production constraints, ensuring our CPU PyTorch neural network for user and item towers, with offline item embedding precomputation and request-time retrieval via nearest-neighbor search, meets all performance targets. The model correctly decomposes into independent user and item vectors.
### run_shell_command · 2026-09-29T02:15:32.521Z
{
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nDATA = '/task/data'\nt0 = time.time()\ntable = pq.read_table(f'{DATA}/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read 43M rows in {time.time() - t0:.2f} seconds')\n\"",
"description": "Benchmark parquet reading time for train_listens"
}
### Tool output · 2026-09-29T02:15:34.477Z
Output: Read 43M rows in 1.37 seconds
Process Group PGID: 1948
## Step 25 · agent · 2026-09-29T02:15:40.604Z
### Recorded reasoning summary
Exploring Data Deeply
I'm impressed by PyArrow's speed, processing 43 million rows in under two seconds. My focus now shifts to a more granular exploration, specifically defining positive events within the training window. I'm also examining how positive labels are determined in validation, based on organic behavior in the subsequent window.
### Recorded reasoning summary
Defining Positive Events
My current focus is on precisely defining what constitutes a "positive event." I'm differentiating between an organic listener liking a track (grade 3) and them listening to 80% or more (grade 1), noting grade 3 takes precedence. I'm also scrutinizing the validation window's construction and how it relates to the training cutoff, ensuring a clear separation between feature generation and outcome assessment for the ranking evaluation.
### run_shell_command · 2026-09-29T02:15:40.604Z
{
"description": "Check target users coverage in preference states",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nprint('Validation preference state count:', len(vps))\ntps = pd.read_parquet(DATA / 'test_preference_state.parquet')\nprint('Test preference state count:', len(tps))\n\nprint('Target users count in VPS:', vps.uid.nunique(), 'in TPS:', tps.uid.nunique())\n\""
}
### Tool output · 2026-09-29T02:15:42.212Z
Output: Validation preference state count: 469124
Test preference state count: 484776
Target users count in VPS: 7038 in TPS: 7092
Process Group PGID: 1970
## Step 26 · agent · 2026-09-29T02:15:44.531Z
### Recorded reasoning summary
Analyzing User Data
I'm focusing on the discrepancy between `target_users` and VPS users, noting that many users lack explicit preference data but do have listening history. I'll explore this further to understand its implications for my analysis.
### run_shell_command · 2026-09-29T02:15:44.531Z
{
"description": "Inspect target_users.parquet",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nprint(tu.head(10))\nprint(tu.describe())\n\""
}
### Tool output · 2026-09-29T02:15:46.101Z
Output: 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
5 800 1703.0 7281
6 900 42.0 136
7 1000 525.0 597
8 1100 1976.0 3653
9 1400 782.0 10713
uid recent_positive_listens history_positive_listens
count 8456.000000 8456.000000 8456.000000
mean 502289.108325 1251.076750 3236.440752
std 287953.395912 1414.340904 3697.575286
min 100.000000 20.000000 20.000000
25% 254375.000000 281.000000 630.000000
50% 503850.000000 784.500000 1920.500000
75% 751725.000000 1706.250000 4494.250000
max 1000000.000000 19592.000000 26597.000000
Process Group PGID: 1988
## Step 27 · agent · 2026-09-29T02:15:51.573Z
### Recorded reasoning summary
Analyzing User Interaction
My current focus is on understanding the richness of user interaction data. It's clear all target users possess a significant number of recent and historical positive listens, indicating a strong engagement history. I'm now examining the columns within `train_listens` to further analyze these patterns.
### run_shell_command · 2026-09-29T02:15:51.573Z
{
"description": "Check whether validation target items are repeat listens or discovery",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\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 = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\n# For each user, let's see their past listens in train_listens\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas()\ndf_train = df_train[df_train.uid.isin(targets)]\ntrain_user_items = df_train.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Check overlap of graded items with train items\ntotal_graded = 0\nin_train_count = 0\nfor uid, items in graded.items():\n past = train_user_items.get(uid, set())\n for it in items:\n total_graded += 1\n if it in past:\n in_train_count += 1\nprint(f'Total graded items in val: {total_graded}, previously listened in train: {in_train_count} ({in_train_count / total_graded * 100:.1f}%)')\nprint(f'Never listened before (true discovery): {total_graded - in_train_count} ({(total_graded - in_train_count) / total_graded * 100:.1f}%)')\n\""
}
### Tool output · 2026-09-29T02:16:04.348Z
Output: Total graded items in val: 37769, previously listened in train: 0 (0.0%)
Never listened before (true discovery): 37769 (100.0%)
Process Group PGID: 2004
## Step 28 · agent · 2026-09-29T02:16:07.611Z
### Recorded reasoning summary
Discovering true novelties
It appears my current candidate eligibility filters are too stringent, as they're excluding all tracks from consideration in the validation set. This is leading to a perceived 100% discovery rate, which is not a true reflection of the data. I need to re-evaluate these filters.
### run_shell_command · 2026-09-29T02:16:07.611Z
{
"description": "Check if past listens are eligible in validation candidate set",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\n\nimport pyarrow.parquet as pq\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id'])\ndf_train = table.to_pandas().drop_duplicates()\n# Check 10,000 pairs from train_listens\nsample = df_train.head(10000)\neligible_count = sum(eligibility.is_eligible(int(r.uid), int(r.item_id)) for r in sample.itertuples())\nprint(f'Train listens eligible: {eligible_count} out of {len(sample)}')\n\""
}
### Tool output · 2026-09-29T02:16:12.690Z
Output: Train listens eligible: 0 out of 10000
Process Group PGID: 2028
## Step 29 · agent · 2026-09-29T02:16:16.591Z
### Recorded reasoning summary
Discovering Novel Candidates
I've realized a crucial detail: candidate eligibility for the "Made for You" shelf is strictly about discovery. The system is designed to only present items the user has *never* interacted with, aiming for novel recommendations.
### enter_plan_mode · 2026-09-29T02:16:16.591Z
{
"reason": "Plan the two-tower neural network architecture, data pipeline, loss function, validation workflow, and final submission pipeline."
}
### Tool output · 2026-09-29T02:16:16.599Z
Switching to Plan mode.
## Step 30 · agent · 2026-09-29T02:16:57.900Z
### Recorded reasoning summary
Exploring Plan Mode
I've entered Plan Mode and am exploring its limitations. I understand I can only utilize read-only tools and write to a specific directory for plans. I've noted that the `replace` tool is available.
### Recorded reasoning summary
Confirming Plan Constraints
I've confirmed that `run_shell_command` is unavailable in Plan Mode, which aligns with the exploration phase's focus on read-only tools and specific plan writing. My prior comprehensive data analysis should suffice for this stage.
### Recorded reasoning summary
Evaluating Task Protocols
I've been reviewing the protocols for handling different task complexities. It appears I must strictly adhere to presenting strategies for simple tasks, outlining multiple approaches for standard tasks, and detailing trade-offs for complex tasks, all without direct user interaction.
### Recorded reasoning summary
Confirming Non-Interactive Protocols
I've thoroughly reviewed the system prompt and available tools. It's clear that direct user interaction is impossible in this headless environment, and attempting to use `ask_user` or similar functions would terminate the session. My strategy will focus on generating the plan file at `/home/agent/.gem` using the available `write_file` tool.
### Recorded reasoning summary
Reviewing Architecture Details
I am meticulously reviewing the system's architecture and technical specifications. My focus is on understanding the constraints for user and item towers, precomputed embeddings, and the request-time latency budget to ensure the plan accurately reflects these requirements.
### Recorded reasoning summary
Analyzing Task Flow
I am currently processing the detailed task steps, focusing on the training, evaluation, and submission phases. I've noted the specific scripts and data artifacts involved, and I'm preparing to outline the required code for tower definition, training, and application within the given constraints.
### Recorded reasoning summary
Processing Item/User Tower Structures
I'm now deeply analyzing the defined structures for both the item and user towers. Specifically, I'm focusing on how item embeddings will be integrated with feature vectors in the item tower and how the user tower will leverage interaction history through listen sequences and weighted engagement.
### Recorded reasoning summary
Synthesizing Tower Inputs
I'm now integrating the weighted pooled listens, liked items, and disliked items into a unified representation. This is being concatenated with aggregated summary features before being passed through the User Tower MLP for final processing.
### Recorded reasoning summary
Refining Score Calculations
I am now focusing on the mechanics of the scoring function, specifically how the user and item tower outputs, $u$ and $v$ respectively, are combined. I've noted that the dot product $u \cdot v$ is used, and that the resulting output is $L_2$ normalized to the unit sphere, ensuring the dot product remains within $[-1, 1]$.
### Recorded reasoning summary
Calculating Loss Weights
I am now calculating the loss weights based on positive pair importance, applying higher values for organic likes and strong listens, and lower values for regular listens. I'm also incorporating explicit hard negative sampling from disliked items to prevent recommending undesirable genres or artists.
### Recorded reasoning summary
Defining Training Strategies
I am now defining the training strategies, specifically exploring temporal splits for backtesting and creating training pairs from recent history using rolling window masking. This data augmentation aims to generate diverse training pairs while preventing leakage and overfitting.
### Recorded reasoning summary
Developing Training Approach
I am now focused on the computational aspects of training. I've noted the plan to utilize PyTorch on CPU with fast batching, employing `Dataset` and `DataLoader` for efficient data handling, and incorporating vectorized operations for rapid iteration.
### write_file · 2026-09-29T02:16:57.900Z
{
"file_path": "/home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md",
"content": "# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker\n\n## Objective\nDesign, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the \"Made for You\" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).\n\n## Key Architectural Invariants & Constraints\n1. **Strict Two-Tower Decomposition**:\n - `ItemTower(item_id, item_features) -> v \\in R^d`\n - `UserTower(user_interaction_history) -> u \\in R^d`\n - Relevance score: `s(u, v) = u^T v` (dot product)\n - Item embeddings are precomputed offline into a candidate matrix $V \\in R^{30000 \\times d}$.\n - User embedding $u \\in R^d$ is computed online from interaction history.\n - User tower does NOT use user ID lookup (must derive representation from interaction history).\n - No construction or decomposition of a global user-item interaction matrix.\n2. **Platform Candidate Boundary**:\n - Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).\n - Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.\n3. **Temporal Split & Retraining**:\n - Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.\n - Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.\n\n---\n\n## Technical Design\n\n### 1. Item Tower Architecture\n- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).\n- **Inputs**:\n - `item_id`: mapped to item index in `[0, 30000)`.\n - Item metadata features from `candidate_catalog.parquet`:\n - $\\log(1 + \\text{positive\\_listens})$ (standardized)\n - $\\log(1 + \\text{organic\\_positive\\_listens})$ (standardized)\n - Organic ratio: $\\frac{\\text{organic\\_positive\\_listens}}{\\text{positive\\_listens} + 1}$\n - Normalized popularity rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Layers**:\n - Item Embedding table: `nn.Embedding(30001, d_item_embed)`\n - Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`\n - Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`\n - $L_2$ Normalization to ensure stable dot product space.\n\n### 2. User Tower Architecture (History-Derived)\n- **Inputs**:\n - Sequence of recent positive listens (item IDs, time decay weights $e^{-\\lambda \\Delta t}$, played ratio, organic flag).\n - Set of explicit liked items (from likes & preference state).\n - Set of explicit disliked items (from dislikes & preference state).\n - User activity features (listen count, organic listen fraction, like count, dislike count).\n- **Pooling & Layers**:\n - Listens pooling: Weighted average of item embeddings using time-decayed attention weights.\n - Likes pooling: Mean pooling of liked item embeddings.\n - Dislikes pooling: Mean pooling of disliked item embeddings.\n - User MLP:\n - Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`\n - `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`\n - $L_2$ Normalization to unit sphere.\n\n### 3. Training Scheme & Loss Function\n- **Dataset Construction**:\n - Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\\ge 80\\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).\n - For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).\n- **Loss Function**:\n - In-batch sampled softmax (InfoNCE) with popularity logit debiasing:\n $$S_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log P(v_j)$$\n $$\\mathcal{L}_{InfoNCE} = - \\sum_i w_i \\log \\frac{\\exp(S_{i, i})}{\\sum_j \\exp(S_{i, j})}$$\n - Dislike penalty / Margin Ranking loss on explicitly disliked items:\n $$\\mathcal{L}_{dislike} = \\sum_i \\max(0, \\gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$\n - Combined loss: $\\mathcal{L} = \\mathcal{L}_{InfoNCE} + \\alpha \\mathcal{L}_{dislike}$.\n\n---\n\n## Phased Implementation Plan\n\n### Phase 1: Data Preprocessing & Feature Extraction\n- Build fast extraction scripts in `/app/solution/`:\n - `data_prep.py`:\n - Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.\n - Build user history representations (recent listens with decay, likes, dislikes, summary stats).\n - Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.\n - Extract item metadata features for the 30,000 catalog tracks.\n\n### Phase 2: PyTorch Model Definition\n- `model.py`:\n - `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.\n - `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.\n - `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.\n\n### Phase 3: Training & Validation Loop\n- `train_val.py`:\n - DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.\n - Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).\n - Precompute all candidate item embeddings $V \\in R^{30000 \\times d}$.\n - Compute user embeddings for all 8,456 target users at the validation cutoff.\n - Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.\n - Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.\n\n### Phase 4: Tuning & Optimization\n- Experiment with:\n - Embedding dimensions ($d=64, 128$)\n - InfoNCE temperature $\\tau \\in \\{0.05, 0.07, 0.1\\}$\n - Recency half-life (14 days vs 30 days vs 60 days)\n - Incorporating dislike embeddings into user representation\n - Validate score progression against the popularity baseline (`0.01071`).\n\n### Phase 5: Refitting on Full Data (Train + Val) & Test Submission\n- `refit_submission.py`:\n - Combine `train_*` and `val_*` data (listens, likes, dislikes).\n - Update user histories up to validation cutoff with all observed events.\n - Use `test_preference_state.parquet` for active explicit preferences.\n - Train final model on full combined dataset.\n - Precompute item embeddings for all 30,000 catalog items.\n - Compute user embeddings for all 8,456 target users.\n - Apply `test_eligible_candidates.npz` mask.\n - Retrieve top 100 items per user, write `/app/solution/submission.parquet`.\n - Validate submission structure with `ranking_evaluation.validate_ranking`.\n\n### Phase 6: Documentation & Deliverables Verification\n- Ensure `/app/solution/README.md` documents:\n - Model architecture (User Tower, Item Tower, History aggregation)\n - Training objective (InfoNCE with popularity correction and dislike penalty)\n - Public validation NDCG@10 score\n - Final submission command\n- Verify `/app/solution/submission.parquet` meets all schema requirements:\n - Columns: `uid`, `item_id`, `rank` (1..100)\n - Exactly 8,456 * 100 = 845,600 rows\n - No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.\n"
}
### Tool output · 2026-09-29T02:16:57.914Z
Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md. Here is the updated code:
# Implementation Plan: Latency-Efficient PyTorch Two-Tower Ranker
## Objective
Design, train, validate, tune, and deploy a latency-efficient two-tower neural network ranker in CPU PyTorch for the "Made for You" discovery shelf. The ranker predicts top-100 items for each of the 8,456 target users, optimizing graded NDCG@10 on organic discovery events (grade 3 for organic like, grade 1 for organic listen >= 80%).
## Key Architectural Invariants & Constraints
1. **Strict Two-Tower Decomposition**:
- `ItemTower(item_id, item_features) -> v \in R^d`
- `UserTower(user_interaction_history) -> u \in R^d`
- Relevance score: `s(u, v) = u^T v` (dot product)
- Item embeddings are precomputed offline into a candidate matrix $V \in R^{30000 \times d}$.
- User embedding $u \in R^d$ is computed online from interaction history.
- User tower does NOT use user ID lookup (must derive representation from interaction history).
- No construction or decomposition of a global user-item interaction matrix.
2. **Platform Candidate Boundary**:
- Candidates are masked by `validation_eligible_candidates.npz` (validation stage) and `test_eligible_candidates.npz` (test submission).
- Top 100 eligible items retrieved via Maximum Inner Product Search (MIPS) with eligibility bitmask.
3. **Temporal Split & Retraining**:
- Validation stage: Model trained on historical data before training cutoff (`24786800`), evaluated against `val_*` outcomes using `/task/evaluate_public.py`.
- Hidden-test stage: Model refitted using all available history (`train_* + val_*` and `test_preference_state`), generating `/app/solution/submission.parquet`.
---
## Technical Design
### 1. Item Tower Architecture
- **Catalog Size**: 30,000 items (plus index 30,000 for unknown/out-of-catalog items).
- **Inputs**:
- `item_id`: mapped to item index in `[0, 30000)`.
- Item metadata features from `candidate_catalog.parquet`:
- $\log(1 + \text{positive\_listens})$ (standardized)
- $\log(1 + \text{organic\_positive\_listens})$ (standardized)
- Organic ratio: $\frac{\text{organic\_positive\_listens}}{\text{positive\_listens} + 1}$
- Normalized popularity rank: $\frac{\text{popularity\_rank}}{30000}$
- **Layers**:
- Item Embedding table: `nn.Embedding(30001, d_item_embed)`
- Item Feature Projection: `nn.Linear(4, d_item_embed // 2)`
- Combine & MLP: `Concat([embed, feat_proj]) -> Linear -> LayerNorm -> GELU -> Linear -> LayerNorm -> Linear -> d_out`
- $L_2$ Normalization to ensure stable dot product space.
### 2. User Tower Architecture (History-Derived)
- **Inputs**:
- Sequence of recent positive listens (item IDs, time decay weights $e^{-\lambda \Delta t}$, played ratio, organic flag).
- Set of explicit liked items (from likes & preference state).
- Set of explicit disliked items (from dislikes & preference state).
- User activity features (listen count, organic listen fraction, like count, dislike count).
- **Pooling & Layers**:
- Listens pooling: Weighted average of item embeddings using time-decayed attention weights.
- Likes pooling: Mean pooling of liked item embeddings.
- Dislikes pooling: Mean pooling of disliked item embeddings.
- User MLP:
- Input: `Concat([pooled_listens, pooled_likes, pooled_dislikes, user_activity_feats])`
- `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> GELU -> Linear -> d_out`
- $L_2$ Normalization to unit sphere.
### 3. Training Scheme & Loss Function
- **Dataset Construction**:
- Extract training pairs $(u, v^+)$ from historical organic likes (grade 3, weight 3.0), strong organic listens $\ge 80\%$ (grade 1, weight 1.0), and general positive listens (weight 0.5).
- For each user history, use events prior to a temporal split cutoff (with history windowing to prevent leakage).
- **Loss Function**:
- In-batch sampled softmax (InfoNCE) with popularity logit debiasing:
$$S_{i, j} = \frac{u_i^T v_j}{\tau} - \log P(v_j)$$
$$\mathcal{L}_{InfoNCE} = - \sum_i w_i \log \frac{\exp(S_{i, i})}{\sum_j \exp(S_{i, j})}$$
- Dislike penalty / Margin Ranking loss on explicitly disliked items:
$$\mathcal{L}_{dislike} = \sum_i \max(0, \gamma + u_i^T v_{disliked} - u_i^T v_i^+)$$
- Combined loss: $\mathcal{L} = \mathcal{L}_{InfoNCE} + \alpha \mathcal{L}_{dislike}$.
---
## Phased Implementation Plan
### Phase 1: Data Preprocessing & Feature Extraction
- Build fast extraction scripts in `/app/solution/`:
- `data_prep.py`:
- Process `train_listens`, `train_likes`, `train_dislikes`, `validation_preference_state`.
- Build user history representations (recent listens with decay, likes, dislikes, summary stats).
- Extract training interaction pairs $(u, i, grade, weight)$ using temporal splits.
- Extract item metadata features for the 30,000 catalog tracks.
### Phase 2: PyTorch Model Definition
- `model.py`:
- `ItemTower(nn.Module)`: embeds item ID + metadata features -> $L_2$ normalized vector.
- `UserTower(nn.Module)`: pools user history (listens, likes, dislikes) + MLP -> $L_2$ normalized vector.
- `TwoTowerRanker(nn.Module)`: combines both towers, provides training loss and offline candidate index computation.
### Phase 3: Training & Validation Loop
- `train_val.py`:
- DataLoader with batch size 256/512, AdamW optimizer, cosine annealing learning rate schedule.
- Train for 5-10 epochs on CPU (leveraging PyTorch multithreading on 8 cores).
- Precompute all candidate item embeddings $V \in R^{30000 \times d}$.
- Compute user embeddings for all 8,456 target users at the validation cutoff.
- Score $U V^T$, apply `validation_eligible_candidates.npz` mask, retrieve top 100 ranks per user.
- Save `/app/solution/validation.parquet` and run `/task/evaluate_public.py` to record `quality_ndcg@10`.
### Phase 4: Tuning & Optimization
- Experiment with:
- Embedding dimensions ($d=64, 128$)
- InfoNCE temperature $\tau \in \{0.05, 0.07, 0.1\}$
- Recency half-life (14 days vs 30 days vs 60 days)
- Incorporating dislike embeddings into user representation
- Validate score progression against the popularity baseline (`0.01071`).
### Phase 5: Refitting on Full Data (Train + Val) & Test Submission
- `refit_submission.py`:
- Combine `train_*` and `val_*` data (listens, likes, dislikes).
- Update user histories up to validation cutoff with all observed events.
- Use `test_preference_state.parquet` for active explicit preferences.
- Train final model on full combined dataset.
- Precompute item embeddings for all 30,000 catalog items.
- Compute user embeddings for all 8,456 target users.
- Apply `test_eligible_candidates.npz` mask.
- Retrieve top 100 items per user, write `/app/solution/submission.parquet`.
- Validate submission structure with `ranking_evaluation.validate_ranking`.
### Phase 6: Documentation & Deliverables Verification
- Ensure `/app/solution/README.md` documents:
- Model architecture (User Tower, Item Tower, History aggregation)
- Training objective (InfoNCE with popularity correction and dislike penalty)
- Public validation NDCG@10 score
- Final submission command
- Verify `/app/solution/submission.parquet` meets all schema requirements:
- Columns: `uid`, `item_id`, `rank` (1..100)
- Exactly 8,456 * 100 = 845,600 rows
- No duplicates, all items strictly eligible in `test_eligible_candidates.npz`.
## Step 31 · agent · 2026-09-29T02:17:05.982Z
### exit_plan_mode · 2026-09-29T02:17:05.982Z
{
"plan_filename": "plan.md"
}
### Tool output · 2026-09-29T02:17:07.686Z
Plan approved. Switching to YOLO mode (all tool calls auto-approved).
The approved implementation plan is stored at: /home/agent/.gemini/tmp/app/a666d72d-5c39-4e8f-8a1d-19dfce207292/plans/plan.md
Read and follow the plan strictly during implementation.
## Step 32 · agent · 2026-09-29T02:17:12.428Z
### update_topic · 2026-09-29T02:17:12.428Z
{
"strategic_intent": "Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.",
"title": "Data Preprocessing & Feature Extraction",
"summary": "Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages."
}
### Tool output · 2026-09-29T02:17:12.432Z
Current topic: "Data Preprocessing & Feature Extraction"
Topic summary: Transitioning to Phase 1: Data Preprocessing & Feature Extraction. We will build preprocessing utilities to parse interaction histories (listens, likes, dislikes, preference states), compute time-decay weights, and prepare training pairs and target user histories for both validation and hidden-test stages.
Strategic Intent: Build efficient data preprocessing and feature extraction pipeline for target users, interaction histories, and catalog candidates.
## Step 33 · agent · 2026-09-29T02:17:19.815Z
### Recorded reasoning summary
Exploring Data Structures
I'm currently evaluating the most efficient data structures for both training and inference, focusing on optimizing the mapping of up to 30,000 candidate items to a compact index range. This initial step is crucial for performance.
### Recorded reasoning summary
Confirming Catalog Consistency
I've confirmed that the `item_id` order in `validation_eligible_candidates.npz` perfectly matches `candidate_catalog.parquet`. This means indices directly correspond across `item_id`, catalog rows, and the `packed_eligible` bits, which is excellent for platform consistency.
### run_shell_command · 2026-09-29T02:17:19.815Z
{
"description": "Check target users catalog listens",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_set = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\n# Let's read train_listens for target users\ntable = pq.read_table(DATA / 'train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\ndf_tu = df[df.uid.isin(target_uids)]\nprint('Total listens for target users in train:', len(df_tu))\nin_cc = df_tu.item_id.isin(cc_set)\nprint(f'Catalog listens for target users: {in_cc.sum()} ({in_cc.mean() * 100:.1f}%)')\npos = df_tu[df_tu.played_ratio_pct >= 50]\nprint(f'Positive listens (>=50%): {len(pos)}, in catalog: {pos.item_id.isin(cc_set).sum()} ({pos.item_id.isin(cc_set).mean() * 100:.1f}%)')\n\""
}
### Tool output · 2026-09-29T02:17:32.743Z
Output: Total listens for target users in train: 43222297
Catalog listens for target users: 31062540 (71.9%)
Positive listens (>=50%): 27367343, in catalog: 19959853 (72.9%)
Process Group PGID: 2051
## Step 34 · agent · 2026-09-29T02:17:38.933Z
### Recorded reasoning summary
Analyzing Target User Engagement
I've confirmed that nearly all of the 43.48 million listens in `train_listens` belong to our target users. Within this group, I've identified an impressive 19.96 million positive catalog listens. I'm now proceeding to the next analytical step.
### run_shell_command · 2026-09-29T02:17:38.933Z
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nvps_liked = set(zip(vps[vps.state == 'liked'].uid, vps[vps.state == 'liked'].item_id))\nvps_disliked = set(zip(vps[vps.state == 'disliked'].uid, vps[vps.state == 'disliked'].item_id))\n\ntl = pd.read_parquet(DATA / 'train_likes.parquet')\ntl_set = set(zip(tl.uid, tl.item_id))\ntu = pd.read_parquet(DATA / 'train_unlikes.parquet')\ntu_set = set(zip(tu.uid, tu.item_id))\n\nprint('VPS liked in train_likes:', len(vps_liked.intersection(tl_set)), 'out of', len(vps_liked))\nprint('VPS liked in train_unlikes:', len(vps_liked.intersection(tu_set)), 'out of', len(vps_liked))\n\"",
"description": "Check consistency of VPS liked with train_likes and train_unlikes"
}
### Tool output · 2026-09-29T02:17:42.004Z
Output: VPS liked in train_likes: 418435 out of 418435
VPS liked in train_unlikes: 5417 out of 418435
Process Group PGID: 2073
## Step 35 · agent · 2026-09-29T02:17:53.594Z
### Recorded reasoning summary
Validating Preference States
My current focus is on understanding how the explicit preference state (`validation_preference_state`) aligns with the state at the test cutoff (`test_preference_state`), accounting for all interactions. This comparison is crucial for the next steps.
### Recorded reasoning summary
Developing Training Targets
I am now focusing on how to construct effective training targets for the two-tower model. Specifically, I'm exploring how to leverage historical user behavior within defined windows before a validation cutoff to identify positive interactions, such as organic likes and high listen percentages.
### run_shell_command · 2026-09-29T02:17:53.594Z
{
"description": "Check event counts across 7-day windows in training data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\n# Let's inspect organic likes in train\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\norg_likes = likes[likes.is_organic == 1]\nprint('Total organic likes in train:', len(org_likes))\n\n# Read listens with is_organic == 1 and played_ratio_pct >= 80\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nstrong_listens = table.to_pandas()\nprint('Strong organic listens in last 35 days of train:', len(strong_listens))\n\nfor w in range(4):\n w_end = cutoff - w * (week + gap)\n w_start = w_end - week\n w_likes = org_likes[(org_likes.timestamp >= w_start) & (org_likes.timestamp <= w_end)]\n w_strong = strong_listens[(strong_listens.timestamp >= w_start) & (strong_listens.timestamp <= w_end)]\n print(f'Window {w} [{w_start}..{w_end}]: organic likes={len(w_likes)}, strong listens={len(w_strong)}, unique users with targets={len(set(w_likes.uid).union(set(w_strong.uid)))}')\n\""
}
### Tool output · 2026-09-29T02:17:55.738Z
Output: Total organic likes in train: 461839
Strong organic listens in last 35 days of train: 2024728
Window 0 [24182000..24786800]: organic likes=17201, strong listens=428612, unique users with targets=6853
Window 1 [23575400..24180200]: organic likes=14981, strong listens=406037, unique users with targets=6657
Window 2 [22968800..23573600]: organic likes=14715, strong listens=400103, unique users with targets=6545
Window 3 [22362200..22967000]: organic likes=16990, strong listens=399933, unique users with targets=6412
Process Group PGID: 2091
## Step 36 · agent · 2026-09-29T02:18:02.557Z
### Recorded reasoning summary
Observing Performance Metrics
I'm seeing a remarkably consistent pattern across all 7-day intervals. Each week delivers approximately 15,000 to 17,000 organic likes and around 400,000 strong organic listens. Crucially, I'm also consistently reaching about 6,500 target users who have set specific targets. This stability is quite promising.
### run_shell_command · 2026-09-29T02:18:02.557Z
{
"description": "Check discovery fraction in training window",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nw_end = cutoff\nw_start = w_end - week\nc_cut = w_start - gap # history cutoff\n\n# Load candidate catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nw_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp <= w_end) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', w_start), ('timestamp', '<=', w_end), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nw_listens = table.to_pandas()\nw_listens = w_listens[w_listens.item_id.isin(cc_items)]\n\n# Check history before c_cut\nhist_table = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', c_cut)]\n)\nhist_df = hist_table.to_pandas()\nhist_pairs = set(zip(hist_df.uid, hist_df.item_id))\n\ndiscovery_likes = [pair not in hist_pairs for pair in zip(w_likes.uid, w_likes.item_id)]\ndiscovery_listens = [pair not in hist_pairs for pair in zip(w_listens.uid, w_listens.item_id)]\n\nprint('Catalog organic likes in window:', len(w_likes), 'discovery fraction:', np.mean(discovery_likes))\nprint('Catalog strong listens in window:', len(w_listens), 'discovery fraction:', np.mean(discovery_listens))\nprint('Discovery organic likes count:', sum(discovery_likes))\nprint('Discovery strong listens count:', sum(discovery_listens))\n\""
}
### Tool output · 2026-09-29T02:18:24.524Z
Output: Catalog organic likes in window: 11210 discovery fraction: 0.7895628902765388
Catalog strong listens in window: 305188 discovery fraction: 0.2222695518827739
Discovery organic likes count: 8851
Discovery strong listens count: 67834
Process Group PGID: 2113
## Step 37 · agent · 2026-09-29T02:18:35.715Z
### Recorded reasoning summary
Analyzing Discovery Performance
My current focus is on the substantial volume of organic likes and strong listens within a 7-day period, revealing hundreds of thousands of valuable discovery training examples. I'm also noting how filtering prior listening behavior precisely mirrors the validation process.
### Recorded reasoning summary
Refining Input Features
I'm now considering how to best represent user taste, exploring the impact of recent listens versus longer-term preferences, and how to weight them. I'm also investigating the utility of item embeddings, with a focus on differentiable pooling of these embeddings based on recent activity.
### Recorded reasoning summary
Incorporating Explicit Feedback
I'm now integrating explicit user feedback like track likes and dislikes. For likes, I'm calculating a mean embedding of liked tracks, and similarly for dislikes. These, along with aggregated user features, will form the input to the user tower network.
### Recorded reasoning summary
Precomputing Item Embeddings
I've finalized the item tower architecture, incorporating catalog features and projecting them with a linear layer, layer normalization, and GELU activation. The item embedding and projected features are concatenated, then passed through an MLP for normalization. Crucially, these item embeddings can be precomputed offline. This allows for efficient scoring of user embeddings against all candidate items at request time, effectively performing Maximum Inner Product Search.
### run_shell_command · 2026-09-29T02:18:35.715Z
{
"command": "python3 -c \"\nimport torch\nimport time\n\nB = 8456\nD = 128\nN = 30000\n\nu = torch.randn(B, D)\nu = u / u.norm(dim=1, keepdim=True)\nv = torch.randn(N, D)\nv = v / v.norm(dim=1, keepdim=True)\n\nt0 = time.time()\nscores = torch.matmul(u, v.t())\ntop100 = torch.topk(scores, 100, dim=1)\nt1 = time.time()\nprint(f'MIPS for all {B} target users over {N} candidate items took: {t1 - t0:.3f} seconds!')\n\"",
"description": "Benchmark MIPS retrieval latency for all 8456 target users"
}
### Tool output · 2026-09-29T02:18:39.299Z
Output: MIPS for all 8456 target users over 30000 candidate items took: 0.495 seconds!
Process Group PGID: 2135
## Step 38 · agent · 2026-09-29T02:18:47.235Z
### Recorded reasoning summary
Optimizing Candidate Generation
I've just confirmed a significant speedup in candidate generation, processing all users and items in under a second. The next critical question is whether the item embedding table can be shared to further streamline this process.
### Recorded reasoning summary
Evaluating Embedding Sharing
I'm currently exploring the implications of sharing the item embedding table between the user and item towers. My current focus is on how gradients would flow and impact item representations if the user tower directly pools item embeddings.
### Recorded reasoning summary
Analyzing Embedding Sharing Implications
My current thoughts are focused on the practicalities of sharing the item embedding table. I'm examining how user interaction history is extracted and stored for both training and inference, ensuring it aligns with the model's architecture requirements. This involves checking available data files for the `candidate_catalog.parquet`.
### run_shell_command · 2026-09-29T02:18:47.235Z
{
"description": "Check contents of /app/solution",
"command": "ls -la /app/solution"
}
### Tool output · 2026-09-29T02:18:47.252Z
Output: total 8
drwxr-xr-x 2 agent agent 4096 Sep 29 02:09 .
drwxr-xr-x 3 agent agent 4096 Sep 29 02:09 ..
Process Group PGID: 2148
## Step 39 · agent · 2026-09-29T02:18:52.434Z
### Recorded reasoning summary
Establishing a Baseline
I'm focusing on creating an initial, valid submission right away. This serves as a crucial starting point before diving into complex model development, ensuring I have a functional baseline that can be refined.
### run_shell_command · 2026-09-29T02:18:52.434Z
{
"description": "Create and verify initial valid baseline submission",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ntest_eligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\n# Unpack eligible bits\nunpacked = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder='little')\nitem_ids = test_eligibility.item_ids\n\nrows = []\nfor i, uid in enumerate(targets):\n mask = unpacked[i] == 1\n el_items = item_ids[mask][:100]\n for r, itm in enumerate(el_items, 1):\n rows.append((uid, itm, r))\n\nsub = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\nval_sub = validate_ranking(sub, targets_set)\n# Check all items eligible\nall_el = all(test_eligibility.is_eligible(uid, itm) for uid, itm in zip(val_sub.uid, val_sub.item_id))\nprint('All items eligible in test candidate set?:', all_el)\nprint('Shape:', val_sub.shape)\nval_sub.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Successfully saved initial baseline submission to /app/solution/submission.parquet!')\n\""
}
### Tool output · 2026-09-29T02:18:56.302Z
Output: All items eligible in test candidate set?: True
Shape: (845600, 3)
Successfully saved initial baseline submission to /app/solution/submission.parquet!
Process Group PGID: 2151
## Step 40 · agent · 2026-09-29T02:18:58.595Z
### run_shell_command · 2026-09-29T02:18:58.595Z
{
"command": "ls -lh /app/solution/submission.parquet",
"description": "Check size of baseline submission"
}
### Tool output · 2026-09-29T02:18:58.613Z
Output: -rw-r--r-- 1 agent agent 719K Sep 29 02:18 /app/solution/submission.parquet
Process Group PGID: 2168
## Step 41 · agent · 2026-09-29T02:19:10.877Z
### Recorded reasoning summary
Constructing Two-Tower Model
I'm now focused on setting up the two-tower model pipeline. My immediate steps involve creating the `/app/solution/` directory and populating it with essential modules: `data_prep.py` for all data handling and `model.py` for defining the PyTorch UserTower, ItemTower, and the overarching TwoTowerModel.
### Recorded reasoning summary
Developing Evaluation & Refitting Scripts
I'm now outlining the training and validation loop, ensuring it accurately evaluates against the public benchmark. Following that, I'll refine the model on combined training and validation data for final submission, ensuring predictions are generated for the hidden test set and saved as per the specified format. Documentation will also be finalized.
### Recorded reasoning summary
Generating Training Samples
I am currently exploring methods for generating training samples, specifically focusing on creating $(u, v^+, \text{grade})$ tuples. My current thinking involves leveraging historical data prior to $T_{train\_cut}$ and considering both multiple temporal backtest slices and continuous history slices as potential approaches.
### run_shell_command · 2026-09-29T02:19:10.877Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntargets = set(tu.uid)\n\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(targets) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 35 * 86400), ('is_organic', '==', 1), ('played_ratio_pct', '>=', 80)]\n)\nlistens = table.to_pandas()\nlistens = listens[listens.uid.isin(targets) & (listens.item_id.isin(cc_items))]\n\ntotal_pairs = 0\nfor w in range(4):\n c_cut = cutoff - (w + 1) * (week + gap)\n target_start = c_cut + gap\n target_end = target_start + week\n \n w_likes = likes[(likes.timestamp >= target_start) & (likes.timestamp <= target_end)]\n w_listens = listens[(listens.timestamp >= target_start) & (listens.timestamp <= target_end)]\n \n # User history items before c_cut\n # Let's see how many pairs\n u_likes = len(w_likes)\n u_listens = len(w_listens)\n print(f'Window {w}: c_cut={c_cut}, target_likes={u_likes}, target_listens={u_listens}')\n total_pairs += u_likes + u_listens\n\nprint('Total potential target pairs across 4 windows:', total_pairs)\n\"",
"description": "Count target pairs across 4 windows"
}
### Tool output · 2026-09-29T02:19:13.318Z
Output: Window 0: c_cut=24180200, target_likes=7774, target_listens=304885
Window 1: c_cut=23573600, target_likes=7063, target_listens=289032
Window 2: c_cut=22967000, target_likes=7518, target_listens=287032
Window 3: c_cut=22360400, target_likes=7572, target_listens=289103
Total potential target pairs across 4 windows: 1199979
Process Group PGID: 2171
## Step 42 · agent · 2026-09-29T02:19:31.743Z
### Recorded reasoning summary
Analyzing User History
I'm currently processing a massive dataset of 1.2 million pairs across four windows, with a special focus on the week preceding the cutoff in Window 0. This period saw a significant volume of engagement, with 7,774 likes and 304,885 strong listens. My immediate task is to determine the optimal representation for user history at the cutoff point.
### Recorded reasoning summary
Defining User History
I'm refining how I represent recent user interactions. My focus is on incorporating weighted recent listens, with a 14-day half-life and adjusted for play ratio and organic engagement. I'm also considering explicit likes for a more comprehensive user profile.
### Recorded reasoning summary
Incorporating Explicit Interactions
I'm now integrating explicit user feedback. This involves creating embeddings for both liked and disliked tracks, weighting them by recent activity and play ratio. I'm also developing a feature vector to capture overall user engagement, which will be fed into a user tower MLP for representation learning.
### Recorded reasoning summary
Refining Item Representation
I'm now solidifying item feature embeddings. This involves projecting catalog features like normalized listens and organic ratios, then combining them with the base item embedding. A subsequent MLP transforms this into a dense, L2-normalized representation for similarity calculations.
### Recorded reasoning summary
Correcting for Popularity Bias
I'm now exploring techniques to mitigate popularity bias. Specifically, I'm implementing LogQ correction to prevent over-recommending hyper-popular items and am investigating its impact on discovery. I'm also incorporating sample weighting based on engagement grade to refine the training process.
### Recorded reasoning summary
Implementing Dislike Penalties
I'm now incorporating explicit negative feedback by penalizing user embeddings when they are too close to disliked items. This aims to push my learned user representations away from content they actively dislike, thereby improving recommendation accuracy. I'll be testing this approach in Python to assess its efficiency.
### run_shell_command · 2026-09-29T02:19:31.743Z
{
"description": "Test catalog and user mapping initialization",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\n\n# 1. Catalog items\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\nprint(f'Catalog items: {len(item_to_idx)}')\n\n# 2. Target users\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = list(tu.uid.values)\nuid_to_idx = {int(u): i for i, u in enumerate(target_uids)}\nprint(f'Target users: {len(uid_to_idx)}')\n\nprint(f'Init maps in {time.time() - t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T02:19:33.313Z
Output: Catalog items: 30000
Target users: 8456
Init maps in 0.04s
Process Group PGID: 2193
## Step 43 · agent · 2026-09-29T02:19:45.569Z
### Recorded reasoning summary
Processing Train Listens
I'm currently focusing on optimizing the extraction of the last 100 listens for 8,456 users before a specified cutoff. I'll be testing this approach in Python to assess its efficiency.
### run_shell_command · 2026-09-29T02:19:45.569Z
{
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 45 * 86400), ('played_ratio_pct', '>=', 50)]\n)\nprint(f'Read filtered pyarrow table in {time.time() - t0:.2f}s, rows={table.num_rows}')\ndf = table.to_pandas()\nprint(f'Converted to pandas in {time.time() - t0:.2f}s')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ndf = df[df.uid.isin(target_uids)]\nprint(f'Filtered to target users in {time.time() - t0:.2f}s, rows={len(df)}')\nuser_counts = df.groupby('uid').size()\nprint('Target users covered in last 45d:', len(user_counts), 'out of 8456')\nprint('Min listens in last 45d:', user_counts.min(), 'median:', user_counts.median(), 'max:', user_counts.max())\n\"",
"description": "Benchmark reading filtered train_listens for target users"
}
### Tool output · 2026-09-29T02:19:48.014Z
Output: Read filtered pyarrow table in 0.35s, rows=5683546
Converted to pandas in 0.36s
Filtered to target users in 0.81s, rows=5681249
Target users covered in last 45d: 8330 out of 8456
Min listens in last 45d: 1 median: 425.0 max: 11435
Process Group PGID: 2210
## Step 44 · agent · 2026-09-29T02:19:51.457Z
### Recorded reasoning summary
Confirming Coverage Metrics
I'm reviewing the recent coverage data. It appears that over the last 45 days, 8,330 users have been covered. Looking back 90 days, our entire target user base of 8,456 has been accounted for, aligning with our definition of "target_users."
### run_shell_command · 2026-09-29T02:19:51.457Z
{
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\nuids_in_90d = set(table['uid'].to_numpy())\nprint('Target users in last 90d:', len(target_uids.intersection(uids_in_90d)), 'out of 8456')\n\"",
"description": "Verify 90d coverage of all target users"
}
### Tool output · 2026-09-29T02:19:53.861Z
Output: Target users in last 90d: 8456 out of 8456
Process Group PGID: 2232
## Step 45 · agent · 2026-09-29T02:19:58.675Z
### Recorded reasoning summary
Covering User Needs
I've confirmed that all target users are accounted for within the 90-day window prior to the cutoff. My current focus is on efficiently retrieving the most recent K listens for each of these users. I'm exploring pandas and numpy for optimized sorting and extraction.
### run_shell_command · 2026-09-29T02:19:58.675Z
{
"description": "Benchmark sorting and taking tail(100) listens per user",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nt0 = time.time()\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ndf = df[df.uid.isin(set(tu.uid))]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, len={len(df)}')\n\nt1 = time.time()\ndf = df.sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\nprint(f'Sorted and took tail(100) in {time.time() - t1:.2f}s, len={len(recent)}')\nprint(f'Users in recent: {recent.uid.nunique()}')\n\""
}
### Tool output · 2026-09-29T02:20:03.010Z
Output: Read and filtered in 1.22s, len=10579105
Sorted and took tail(100) in 1.54s, len=808180
Users in recent: 8456
Process Group PGID: 2256
## Step 46 · agent · 2026-09-29T02:20:11.163Z
### Recorded reasoning summary
Processing Recent Listens
I've successfully retrieved the most recent 100 positive listens for all target users, noting the scale of this data. My current focus is on efficiently converting this `recent` list into a more performant lookup structure or a NumPy array for faster processing.
### run_shell_command · 2026-09-29T02:20:11.163Z
{
"description": "Benchmark vectorized listen tensor population",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nuid_to_idx = {int(u): i for i, u in enumerate(uids)}\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_to_idx = {int(it): i for i, it in enumerate(cc.item_id)}\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400), ('played_ratio_pct', '>=', 50)]\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(set(uids))].sort_values('timestamp')\nrecent = df.groupby('uid').tail(100)\n\nt0 = time.time()\nN = len(uids)\nK = 100\nlisten_items = np.full((N, K), 30000, dtype=np.int64)\nlisten_weights = np.zeros((N, K), dtype=np.float32)\n\n# Vectorized weight calculation\ndecay_lambda = np.log(2) / (14 * 86400)\ntime_diff = (cutoff - recent['timestamp'].values).astype(np.float32)\nrec_weights = np.exp(-decay_lambda * time_diff) * (recent['played_ratio_pct'].values / 100.0) * (1.0 + 0.5 * recent['is_organic'].values)\n\n# Map items and uids\nitem_col = [item_to_idx.get(int(x), 30000) for x in recent['item_id'].values]\nuid_rows = [uid_to_idx[int(x)] for x in recent['uid'].values]\n\n# Position within user\nrecent['pos'] = recent.groupby('uid').cumcount()\npositions = recent['pos'].values\n\nlisten_items[uid_rows, positions] = item_col\nlisten_weights[uid_rows, positions] = rec_weights\n\n# Normalize weights\nsum_w = listen_weights.sum(axis=1, keepdims=True) + 1e-6\nlisten_weights /= sum_w\n\nprint(f'Populated listen arrays in {time.time() - t0:.2f}s!')\nprint('Shape items:', listen_items.shape, 'Shape weights:', listen_weights.shape)\nprint('Non-zero weights per user min:', (listen_weights > 0).sum(axis=1).min())\n\""
}
### Tool output · 2026-09-29T02:20:18.264Z
Output: :43: SettingWithCopyWarning:
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead
See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
Populated listen arrays in 0.45s!
Shape items: (8456, 100) Shape weights: (8456, 100)
Non-zero weights per user min: 20
Process Group PGID: 2278
## Step 47 · agent · 2026-09-29T02:20:52.895Z
### Recorded reasoning summary
Populating Tensors Swiftly
I've successfully populated the complete listen tensor for all 8,456 users, with each user having between 20 and 100 items. My next immediate task is to replicate this efficient tensor population process for both explicit likes and dislikes.
### run_shell_command · 2026-09-29T02:20:52.895Z
{
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nliked = vps[vps.state == 'liked'].groupby('uid').size()\ndisliked = vps[vps.state == 'disliked'].groupby('uid').size()\n\nprint('Liked counts: min, median, 90th percentile, max:', liked.min(), liked.median(), liked.quantile(0.9), liked.max())\nprint('Disliked counts: min, median, 90th percentile, max:', disliked.min(), disliked.median(), disliked.quantile(0.9), disliked.max())\n\"",
"description": "Check liked and disliked counts distribution"
}
### Tool output · 2026-09-29T02:20:54.544Z
Output: Liked counts: min, median, 90th percentile, max: 1 28.0 158.0 1327
Disliked counts: min, median, 90th percentile, max: 1 3.0 20.0 1703
Process Group PGID: 2300
## Step 48 · agent · 2026-09-29T02:21:07.459Z
### Recorded reasoning summary
Analyzing Engagement Metrics
My current focus is on interpreting engagement data. I've observed that the median 'like' count is 28, with the 90th percentile at 158. Conversely, 'dislikes' are significantly lower, with a median of 3 and a 90th percentile of 20. This suggests a robust strategy for retaining a substantial number of positively engaged items, potentially up to 50 or 100, prioritizing recency or sampling for those exceeding this threshold.
### Recorded reasoning summary
Refining User Features
I'm now incorporating new user summary features, including log-transformed total and recent listens, organic listen ratio, and average played ratio. These, alongside existing like and dislike counts, form a 6-dimensional, standardized feature vector for each user. I've also finalized the logic for handling users with no likes or dislikes, ensuring their masks are correctly set to zero.
### Recorded reasoning summary
Defining Training Windows
I'm exploring the creation of multiple training windows prior to validation. My current thinking involves setting three distinct cutoffs, each separated by a week plus a buffer. I'm also considering an additional window that includes recent positive interactions, focusing on organic discovery events within specific timeframes.
### Recorded reasoning summary
Evaluating Discovery Pairs
I've confirmed approximately 220,000 discovery pairs across the three training windows, each mirroring the evaluation task. This dataset size, with a batch size of 512, allows for rapid training iterations, potentially completing multiple epochs in under a minute on CPU. I'm also exploring incorporating general positive listens with reduced weighting.
### run_shell_command · 2026-09-29T02:21:07.459Z
{
"description": "Check active likes at historical cutoffs",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nunlikes = pd.read_parquet(DATA / 'train_unlikes.parquet')\ndislikes = pd.read_parquet(DATA / 'train_dislikes.parquet')\nundislikes = pd.read_parquet(DATA / 'train_undislikes.parquet')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\n\nfor w in range(1, 4):\n c_cut = cutoff - w * (week + gap)\n w_likes = likes[(likes.timestamp < c_cut) & (likes.uid.isin(target_uids))]\n w_unlikes = unlikes[(unlikes.timestamp < c_cut) & (unlikes.uid.isin(target_uids))]\n \n # Active likes\n active_likes = set(zip(w_likes.uid, w_likes.item_id)) - set(zip(w_unlikes.uid, w_unlikes.item_id))\n print(f'Cutoff w={w} ({c_cut}): active likes={len(active_likes)}')\n\""
}
### Tool output · 2026-09-29T02:21:11.585Z
Output: Cutoff w=1 (24180200): active likes=583825
Cutoff w=2 (23573600): active likes=563988
Cutoff w=3 (22967000): active likes=543365
Process Group PGID: 2318
## Step 49 · agent · 2026-09-29T02:22:33.834Z
### Recorded reasoning summary
Calculating Preferences
I've realized that active likes and dislikes can be determined at any specific point in time, $C$, by applying simple filters to past data. I can isolate likes before $C$ and subtract unlikes before $C$, and similarly for dislikes and undislikes.
### Recorded reasoning summary
Structuring Data Loading
I'm now focusing on organizing the data loading process. I will implement functions within `data_prep.py` to handle catalog features and construct detailed user histories. This includes parsing listen and preference data up to specific cutoffs.
### write_file · 2026-09-29T02:22:33.834Z
{
"file_path": "/app/solution/data_prep.py",
"content": "\"\"\"Data preparation and feature extraction for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport time\nfrom pathlib import Path\nfrom typing import Dict, List, Optional, Set, Tuple\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\n\n\ndef load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:\n \"\"\"Load candidate catalog items, normalized features, and popularity.\n\n Returns:\n catalog_features: FloatTensor of shape (30000, 4).\n item_to_idx: mapping from catalog item_id to index [0, 29999].\n catalog_item_ids: int64 ndarray of shape (30000,).\n item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.\n \"\"\"\n catalog_df = pd.read_parquet(data_dir / \"candidate_catalog.parquet\")\n catalog_item_ids = catalog_df[\"item_id\"].to_numpy(dtype=np.int64)\n item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}\n\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 org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)\n norm_rank = pop_rank / float(len(catalog_df))\n\n # Standardize features\n features = np.column_stack([\n (log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),\n (log_org - log_org.mean()) / (log_org.std() + 1e-6),\n org_ratio,\n norm_rank,\n ]).astype(np.float32)\n\n # LogQ for popularity debiasing\n prob = pos_listens / pos_listens.sum()\n item_logq = np.log(prob + 1e-12).astype(np.float32)\n\n return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq\n\n\ndef extract_user_histories(\n data_dir: Path,\n cutoff: int,\n target_uids: np.ndarray,\n item_to_idx: Dict[int, int],\n pref_state_path: Optional[Path] = None,\n lookback_days: int = 90,\n max_listens: int = 100,\n max_likes: int = 60,\n max_dislikes: int = 30,\n decay_half_life_days: float = 14.0,\n include_val: bool = False,\n) -> Dict[str, torch.Tensor]:\n \"\"\"Extract user history tensors at a given cutoff timestamp.\n\n Args:\n data_dir: Path to data directory.\n cutoff: Timestamp cutoff. Events must have timestamp < cutoff.\n target_uids: Ordered array of target user IDs.\n item_to_idx: Catalog mapping.\n pref_state_path: Optional path to preference state parquet.\n lookback_days: Max days to look back for listens.\n max_listens: Max recent listens per user.\n max_likes: Max liked items per user.\n max_dislikes: Max disliked items per user.\n decay_half_life_days: Exponential decay half life.\n include_val: Whether to include val_* files in history (for final test refit).\n\n Returns:\n Dictionary of tensors:\n - listen_items: LongTensor (N, max_listens)\n - listen_weights: FloatTensor (N, max_listens)\n - like_items: LongTensor (N, max_likes)\n - like_mask: FloatTensor (N, max_likes)\n - dislike_items: LongTensor (N, max_dislikes)\n - dislike_mask: FloatTensor (N, max_dislikes)\n - user_features: FloatTensor (N, 6)\n \"\"\"\n n_users = len(target_uids)\n uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # --- 1. Process Listens ---\n lookback_ts = cutoff - int(lookback_days * 86400)\n filters = [(\"timestamp\", \">=\", lookback_ts), (\"timestamp\", \"<\", cutoff), (\"played_ratio_pct\", \">=\", 50)]\n \n train_tbl = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n listens_df = train_tbl.to_pandas()\n\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=filters,\n )\n val_df = val_tbl.to_pandas()\n listens_df = pd.concat([listens_df, val_df], ignore_index=True)\n\n listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values(\"timestamp\")\n recent_listens = listens_df.groupby(\"uid\").tail(max_listens).copy()\n\n listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)\n listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)\n\n decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)\n time_diff = (cutoff - recent_listens[\"timestamp\"].to_numpy()).astype(np.float32)\n raw_weights = (\n np.exp(-decay_lambda * time_diff)\n * (recent_listens[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * recent_listens[\"is_organic\"].to_numpy())\n ).astype(np.float32)\n\n recent_uids = recent_listens[\"uid\"].to_numpy(dtype=np.int64)\n recent_items = recent_listens[\"item_id\"].to_numpy(dtype=np.int64)\n recent_listens[\"pos\"] = recent_listens.groupby(\"uid\").cumcount()\n positions = recent_listens[\"pos\"].to_numpy()\n\n row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)\n mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)\n\n listen_items[row_indices, positions] = mapped_item_indices\n listen_weights[row_indices, positions] = raw_weights\n\n # Normalize weights row-wise\n w_sum = listen_weights.sum(axis=1, keepdims=True)\n mask = w_sum > 0\n listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)\n\n # --- 2. Process Preference State (Likes & Dislikes) ---\n like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)\n like_mask = np.zeros((n_users, max_likes), dtype=np.float32)\n dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)\n dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)\n\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(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:\n # Reconstruct active preferences from events\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n unlikes_df = pd.read_parquet(data_dir / \"train_unlikes.parquet\")\n dislikes_df = pd.read_parquet(data_dir / \"train_dislikes.parquet\")\n undislikes_df = pd.read_parquet(data_dir / \"train_undislikes.parquet\")\n\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / \"val_unlikes.parquet\")], ignore_index=True)\n dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / \"val_dislikes.parquet\")], ignore_index=True)\n undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / \"val_undislikes.parquet\")], ignore_index=True)\n\n likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]\n dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]\n undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]\n\n # Set difference for active likes\n active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))\n liked_df = pd.DataFrame(list(active_likes), columns=[\"uid\", \"item_id\"])\n\n active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))\n disliked_df = pd.DataFrame(list(active_dislikes), columns=[\"uid\", \"item_id\"])\n\n # Populate likes\n if len(liked_df) > 0:\n liked_tail = liked_df.groupby(\"uid\").tail(max_likes).copy()\n liked_tail[\"pos\"] = liked_tail.groupby(\"uid\").cumcount()\n l_rows = np.array([uid_to_row[int(u)] for u in liked_tail[\"uid\"].values], dtype=np.int64)\n l_pos = liked_tail[\"pos\"].values\n l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail[\"item_id\"].values], dtype=np.int64)\n like_items[l_rows, l_pos] = l_items\n like_mask[l_rows, l_pos] = 1.0\n\n # Populate dislikes\n if len(disliked_df) > 0:\n disliked_tail = disliked_df.groupby(\"uid\").tail(max_dislikes).copy()\n disliked_tail[\"pos\"] = disliked_tail.groupby(\"uid\").cumcount()\n d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail[\"uid\"].values], dtype=np.int64)\n d_pos = disliked_tail[\"pos\"].values\n d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail[\"item_id\"].values], dtype=np.int64)\n dislike_items[d_rows, d_pos] = d_items\n dislike_mask[d_rows, d_pos] = 1.0\n\n # --- 3. User Summary Features ---\n tu_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}\n\n user_feats = np.zeros((n_users, 6), dtype=np.float32)\n listen_counts = recent_listens.groupby(\"uid\").size().to_dict()\n organic_counts = recent_listens[recent_listens.is_organic == 1].groupby(\"uid\").size().to_dict()\n like_counts = liked_df.groupby(\"uid\").size().to_dict() if len(liked_df) > 0 else {}\n dislike_counts = disliked_df.groupby(\"uid\").size().to_dict() if len(disliked_df) > 0 else {}\n\n for uid, r in uid_to_row.items():\n rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))\n n_rec = float(listen_counts.get(uid, 0))\n n_org = float(organic_counts.get(uid, 0))\n n_like = float(like_counts.get(uid, 0))\n n_dis = float(dislike_counts.get(uid, 0))\n\n user_feats[r, 0] = np.log1p(hist_listens)\n user_feats[r, 1] = np.log1p(rec_listens)\n user_feats[r, 2] = np.log1p(n_rec)\n user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)\n user_feats[r, 4] = np.log1p(n_like)\n user_feats[r, 5] = np.log1p(n_dis)\n\n # Standardize user features\n feat_mean = user_feats.mean(axis=0, keepdims=True)\n feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6\n user_feats = (user_feats - feat_mean) / feat_std\n\n return {\n \"listen_items\": torch.from_numpy(listen_items),\n \"listen_weights\": torch.from_numpy(listen_weights),\n \"like_items\": torch.from_numpy(like_items),\n \"like_mask\": torch.from_numpy(like_mask),\n \"dislike_items\": torch.from_numpy(dislike_items),\n \"dislike_mask\": torch.from_numpy(dislike_mask),\n \"user_features\": torch.from_numpy(user_feats),\n }\n\n\ndef build_training_pairs(\n data_dir: Path,\n cutoffs: List[int],\n item_to_idx: Dict[int, int],\n target_uids: np.ndarray,\n week_seconds: int = 7 * 86400,\n gap_seconds: int = 1800,\n listen_strong_threshold: int = 80,\n sample_listens_per_user: int = 30,\n include_val: bool = False,\n) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:\n \"\"\"Build multi-window training samples for the two-tower model.\n\n For each cutoff C:\n - History extracted up to C\n - Targets extracted from [C + gap, C + gap + week]\n - Only target items in candidate catalog that user had NOT interacted with before C (discovery).\n\n Returns:\n histories_list: list of user history dicts (one per cutoff).\n user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.\n target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].\n pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).\n \"\"\"\n uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}\n target_uids_set = set(uid_to_row.keys())\n\n # Load likes\n likes_df = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]\n\n # Load strong organic listens\n min_cutoff = min(cutoffs)\n table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n filters=[(\"timestamp\", \">=\", min_cutoff), (\"is_organic\", \"==\", 1), (\"played_ratio_pct\", \">=\", listen_strong_threshold)],\n )\n listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)\n strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)\n\n np_pairs = np.array(pairs_list, dtype=np.float32)\n np.random.shuffle(np_pairs)\n\n user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))\n target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))\n weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))\n\n print(f\"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.\")\n return histories_list, user_win, target_items, weights\n"
}
### Tool output · 2026-09-29T02:22:33.847Z
Successfully created and wrote to new file: /app/solution/data_prep.py. Here is the updated code:
"""Data preparation and feature extraction for the two-tower music ranker."""
from __future__ import annotations
import time
from pathlib import Path
from typing import Dict, List, Optional, Set, Tuple
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
def load_catalog_features(data_dir: Path) -> Tuple[torch.Tensor, Dict[int, int], np.ndarray, np.ndarray]:
"""Load candidate catalog items, normalized features, and popularity.
Returns:
catalog_features: FloatTensor of shape (30000, 4).
item_to_idx: mapping from catalog item_id to index [0, 29999].
catalog_item_ids: int64 ndarray of shape (30000,).
item_logq: float32 ndarray of shape (30000,) for popularity logit adjustment.
"""
catalog_df = pd.read_parquet(data_dir / "candidate_catalog.parquet")
catalog_item_ids = catalog_df["item_id"].to_numpy(dtype=np.int64)
item_to_idx = {int(item): idx for idx, item in enumerate(catalog_item_ids)}
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)
org_ratio = (org_listens + 1e-4) / (pos_listens + 1.0)
norm_rank = pop_rank / float(len(catalog_df))
# Standardize features
features = np.column_stack([
(log_pos - log_pos.mean()) / (log_pos.std() + 1e-6),
(log_org - log_org.mean()) / (log_org.std() + 1e-6),
org_ratio,
norm_rank,
]).astype(np.float32)
# LogQ for popularity debiasing
prob = pos_listens / pos_listens.sum()
item_logq = np.log(prob + 1e-12).astype(np.float32)
return torch.from_numpy(features), item_to_idx, catalog_item_ids, item_logq
def extract_user_histories(
data_dir: Path,
cutoff: int,
target_uids: np.ndarray,
item_to_idx: Dict[int, int],
pref_state_path: Optional[Path] = None,
lookback_days: int = 90,
max_listens: int = 100,
max_likes: int = 60,
max_dislikes: int = 30,
decay_half_life_days: float = 14.0,
include_val: bool = False,
) -> Dict[str, torch.Tensor]:
"""Extract user history tensors at a given cutoff timestamp.
Args:
data_dir: Path to data directory.
cutoff: Timestamp cutoff. Events must have timestamp < cutoff.
target_uids: Ordered array of target user IDs.
item_to_idx: Catalog mapping.
pref_state_path: Optional path to preference state parquet.
lookback_days: Max days to look back for listens.
max_listens: Max recent listens per user.
max_likes: Max liked items per user.
max_dislikes: Max disliked items per user.
decay_half_life_days: Exponential decay half life.
include_val: Whether to include val_* files in history (for final test refit).
Returns:
Dictionary of tensors:
- listen_items: LongTensor (N, max_listens)
- listen_weights: FloatTensor (N, max_listens)
- like_items: LongTensor (N, max_likes)
- like_mask: FloatTensor (N, max_likes)
- dislike_items: LongTensor (N, max_dislikes)
- dislike_mask: FloatTensor (N, max_dislikes)
- user_features: FloatTensor (N, 6)
"""
n_users = len(target_uids)
uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
target_uids_set = set(uid_to_row.keys())
# --- 1. Process Listens ---
lookback_ts = cutoff - int(lookback_days * 86400)
filters = [("timestamp", ">=", lookback_ts), ("timestamp", "<", cutoff), ("played_ratio_pct", ">=", 50)]
train_tbl = pq.read_table(
data_dir / "train_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=filters,
)
listens_df = train_tbl.to_pandas()
if include_val and (data_dir / "val_listens.parquet").exists():
val_tbl = pq.read_table(
data_dir / "val_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=filters,
)
val_df = val_tbl.to_pandas()
listens_df = pd.concat([listens_df, val_df], ignore_index=True)
listens_df = listens_df[listens_df.uid.isin(target_uids_set)].sort_values("timestamp")
recent_listens = listens_df.groupby("uid").tail(max_listens).copy()
listen_items = np.full((n_users, max_listens), 30000, dtype=np.int64)
listen_weights = np.zeros((n_users, max_listens), dtype=np.float32)
decay_lambda = np.log(2.0) / (decay_half_life_days * 86400.0)
time_diff = (cutoff - recent_listens["timestamp"].to_numpy()).astype(np.float32)
raw_weights = (
np.exp(-decay_lambda * time_diff)
* (recent_listens["played_ratio_pct"].to_numpy() / 100.0)
* (1.0 + 0.5 * recent_listens["is_organic"].to_numpy())
).astype(np.float32)
recent_uids = recent_listens["uid"].to_numpy(dtype=np.int64)
recent_items = recent_listens["item_id"].to_numpy(dtype=np.int64)
recent_listens["pos"] = recent_listens.groupby("uid").cumcount()
positions = recent_listens["pos"].to_numpy()
row_indices = np.array([uid_to_row[int(u)] for u in recent_uids], dtype=np.int64)
mapped_item_indices = np.array([item_to_idx.get(int(it), 30000) for it in recent_items], dtype=np.int64)
listen_items[row_indices, positions] = mapped_item_indices
listen_weights[row_indices, positions] = raw_weights
# Normalize weights row-wise
w_sum = listen_weights.sum(axis=1, keepdims=True)
mask = w_sum > 0
listen_weights = np.where(mask, listen_weights / (w_sum + 1e-7), 0.0)
# --- 2. Process Preference State (Likes & Dislikes) ---
like_items = np.full((n_users, max_likes), 30000, dtype=np.int64)
like_mask = np.zeros((n_users, max_likes), dtype=np.float32)
dislike_items = np.full((n_users, max_dislikes), 30000, dtype=np.int64)
dislike_mask = np.zeros((n_users, max_dislikes), dtype=np.float32)
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(target_uids_set)]
liked_df = pref_df[pref_df.state == "liked"]
disliked_df = pref_df[pref_df.state == "disliked"]
else:
# Reconstruct active preferences from events
likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
unlikes_df = pd.read_parquet(data_dir / "train_unlikes.parquet")
dislikes_df = pd.read_parquet(data_dir / "train_dislikes.parquet")
undislikes_df = pd.read_parquet(data_dir / "train_undislikes.parquet")
if include_val and (data_dir / "val_likes.parquet").exists():
likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
unlikes_df = pd.concat([unlikes_df, pd.read_parquet(data_dir / "val_unlikes.parquet")], ignore_index=True)
dislikes_df = pd.concat([dislikes_df, pd.read_parquet(data_dir / "val_dislikes.parquet")], ignore_index=True)
undislikes_df = pd.concat([undislikes_df, pd.read_parquet(data_dir / "val_undislikes.parquet")], ignore_index=True)
likes_df = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]
unlikes_df = unlikes_df[(unlikes_df.timestamp < cutoff) & (unlikes_df.uid.isin(target_uids_set))]
dislikes_df = dislikes_df[(dislikes_df.timestamp < cutoff) & (dislikes_df.uid.isin(target_uids_set))]
undislikes_df = undislikes_df[(undislikes_df.timestamp < cutoff) & (undislikes_df.uid.isin(target_uids_set))]
# Set difference for active likes
active_likes = set(zip(likes_df.uid, likes_df.item_id)) - set(zip(unlikes_df.uid, unlikes_df.item_id))
liked_df = pd.DataFrame(list(active_likes), columns=["uid", "item_id"])
active_dislikes = set(zip(dislikes_df.uid, dislikes_df.item_id)) - set(zip(undislikes_df.uid, undislikes_df.item_id))
disliked_df = pd.DataFrame(list(active_dislikes), columns=["uid", "item_id"])
# Populate likes
if len(liked_df) > 0:
liked_tail = liked_df.groupby("uid").tail(max_likes).copy()
liked_tail["pos"] = liked_tail.groupby("uid").cumcount()
l_rows = np.array([uid_to_row[int(u)] for u in liked_tail["uid"].values], dtype=np.int64)
l_pos = liked_tail["pos"].values
l_items = np.array([item_to_idx.get(int(it), 30000) for it in liked_tail["item_id"].values], dtype=np.int64)
like_items[l_rows, l_pos] = l_items
like_mask[l_rows, l_pos] = 1.0
# Populate dislikes
if len(disliked_df) > 0:
disliked_tail = disliked_df.groupby("uid").tail(max_dislikes).copy()
disliked_tail["pos"] = disliked_tail.groupby("uid").cumcount()
d_rows = np.array([uid_to_row[int(u)] for u in disliked_tail["uid"].values], dtype=np.int64)
d_pos = disliked_tail["pos"].values
d_items = np.array([item_to_idx.get(int(it), 30000) for it in disliked_tail["item_id"].values], dtype=np.int64)
dislike_items[d_rows, d_pos] = d_items
dislike_mask[d_rows, d_pos] = 1.0
# --- 3. User Summary Features ---
tu_df = pd.read_parquet(data_dir / "target_users.parquet")
tu_row_map = {int(u): (float(rec), float(hist)) for u, rec, hist in zip(tu_df.uid, tu_df.recent_positive_listens, tu_df.history_positive_listens)}
user_feats = np.zeros((n_users, 6), dtype=np.float32)
listen_counts = recent_listens.groupby("uid").size().to_dict()
organic_counts = recent_listens[recent_listens.is_organic == 1].groupby("uid").size().to_dict()
like_counts = liked_df.groupby("uid").size().to_dict() if len(liked_df) > 0 else {}
dislike_counts = disliked_df.groupby("uid").size().to_dict() if len(disliked_df) > 0 else {}
for uid, r in uid_to_row.items():
rec_listens, hist_listens = tu_row_map.get(uid, (0.0, 0.0))
n_rec = float(listen_counts.get(uid, 0))
n_org = float(organic_counts.get(uid, 0))
n_like = float(like_counts.get(uid, 0))
n_dis = float(dislike_counts.get(uid, 0))
user_feats[r, 0] = np.log1p(hist_listens)
user_feats[r, 1] = np.log1p(rec_listens)
user_feats[r, 2] = np.log1p(n_rec)
user_feats[r, 3] = (n_org + 1e-4) / (n_rec + 1.0)
user_feats[r, 4] = np.log1p(n_like)
user_feats[r, 5] = np.log1p(n_dis)
# Standardize user features
feat_mean = user_feats.mean(axis=0, keepdims=True)
feat_std = user_feats.std(axis=0, keepdims=True) + 1e-6
user_feats = (user_feats - feat_mean) / feat_std
return {
"listen_items": torch.from_numpy(listen_items),
"listen_weights": torch.from_numpy(listen_weights),
"like_items": torch.from_numpy(like_items),
"like_mask": torch.from_numpy(like_mask),
"dislike_items": torch.from_numpy(dislike_items),
"dislike_mask": torch.from_numpy(dislike_mask),
"user_features": torch.from_numpy(user_feats),
}
def build_training_pairs(
data_dir: Path,
cutoffs: List[int],
item_to_idx: Dict[int, int],
target_uids: np.ndarray,
week_seconds: int = 7 * 86400,
gap_seconds: int = 1800,
listen_strong_threshold: int = 80,
sample_listens_per_user: int = 30,
include_val: bool = False,
) -> Tuple[List[Dict[str, torch.Tensor]], torch.Tensor, torch.Tensor, torch.Tensor]:
"""Build multi-window training samples for the two-tower model.
For each cutoff C:
- History extracted up to C
- Targets extracted from [C + gap, C + gap + week]
- Only target items in candidate catalog that user had NOT interacted with before C (discovery).
Returns:
histories_list: list of user history dicts (one per cutoff).
user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.
target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].
pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).
"""
uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}
target_uids_set = set(uid_to_row.keys())
# Load likes
likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
if include_val and (data_dir / "val_likes.parquet").exists():
likes_df = pd.concat([likes_df, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
org_likes = likes_df[(likes_df.uid.isin(target_uids_set)) & (likes_df.is_organic == 1)]
# Load strong organic listens
min_cutoff = min(cutoffs)
table = pq.read_table(
data_dir / "train_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
)
listens_df = table.to_pandas()
if include_val and (data_dir / "val_listens.parquet").exists():
val_tbl = pq.read_table(
data_dir / "val_listens.parquet",
columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"],
filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
)
listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)
strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]
histories_list: List[Dict[str, torch.Tensor]] = []
pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)
for w_idx, cutoff in enumerate(cutoffs):
t_start = cutoff + gap_seconds
t_end = t_start + week_seconds
# 1. Extract history up to cutoff
print(f"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...")
hist = extract_user_histories(
data_dir=data_dir,
cutoff=cutoff,
target_uids=target_uids,
item_to_idx=item_to_idx,
include_val=include_val,
)
histories_list.append(hist)
# 2. Get past listens before cutoff to guarantee DISCOVERY filter
# Read past items from the history listen_items and likes
past_items_per_user: Dict[int, Set[int]] = {}
for r, uid in enumerate(target_uids):
# Items in listens and likes
l_itms = set(hist["listen_items"][r].numpy())
lk_itms = set(hist["like_items"][r][hist["like_mask"][r] > 0].numpy())
past = l_itms.union(lk_itms)
past.discard(30000)
past_items_per_user[r] = past
# 3. Targets in [t_start, t_end]
w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]
w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]
# Map and filter discovery likes (grade 3, weight 3.0)
user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}
for uid, item in zip(w_likes.uid, w_likes.item_id):
r = uid_to_row[int(uid)]
item_idx = item_to_idx.get(int(item))
if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:
pairs_list.append((w_idx, r, item_idx, 3.0))
user_seen_targets[r].add(item_idx)
# Map and filter discovery listens (grade 1, weight 1.0)
# To balance, subsample if a user has too many listens
grouped_listens = w_listens.groupby("uid")
for uid, group in grouped_listens:
r = uid_to_row[int(uid)]
items = group["item_id"].to_numpy()
if len(items) > sample_listens_per_user:
items = np.random.choice(items, size=sample_listens_per_user, replace=False)
for itm in items:
item_idx = item_to_idx.get(int(itm))
if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:
pairs_list.append((w_idx, r, item_idx, 1.0))
user_seen_targets[r].add(item_idx)
np_pairs = np.array(pairs_list, dtype=np.float32)
np.random.shuffle(np_pairs)
user_win = torch.from_numpy(np_pairs[:, :2].astype(np.int64))
target_items = torch.from_numpy(np_pairs[:, 2].astype(np.int64))
weights = torch.from_numpy(np_pairs[:, 3].astype(np.float32))
print(f"Generated {len(target_items)} discovery training pairs across {len(cutoffs)} windows.")
return histories_list, user_win, target_items, weights
## Step 50 · agent · 2026-09-29T02:23:21.439Z
### run_shell_command · 2026-09-29T02:23:21.439Z
{
"description": "Test load_catalog_features and extract_user_histories",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, extract_user_histories\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\nprint(f'Catalog loaded in {time.time() - t0:.2f}s: feats shape={cat_feats.shape}, logq shape={item_logq.shape}')\n\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nt1 = time.time()\nhist = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\nprint(f'Extracted validation user histories in {time.time() - t1:.2f}s:')\nfor k, v in hist.items():\n print(f' {k}: shape={v.shape}, dtype={v.dtype}')\n\""
}
### Tool output · 2026-09-29T02:23:28.924Z
Output: Catalog loaded in 0.03s: feats shape=torch.Size([30000, 4]), logq shape=(30000,)
Extracted validation user histories in 3.42s:
listen_items: shape=torch.Size([8456, 100]), dtype=torch.int64
listen_weights: shape=torch.Size([8456, 100]), dtype=torch.float32
like_items: shape=torch.Size([8456, 60]), dtype=torch.int64
like_mask: shape=torch.Size([8456, 60]), dtype=torch.float32
dislike_items: shape=torch.Size([8456, 30]), dtype=torch.int64
dislike_mask: shape=torch.Size([8456, 30]), dtype=torch.float32
user_features: shape=torch.Size([8456, 6]), dtype=torch.float32
Process Group PGID: 2337
## Step 51 · agent · 2026-09-29T02:23:45.552Z
### Recorded reasoning summary
Refining training pair construction
My current focus is on optimizing `build_training_pairs`. I've successfully extracted user histories in 3.42 seconds and am now calculating the necessary training window boundaries, denoted as $C_1$ and $C_2$, with values 24180200 and 23573600 respectively, to ensure robust training data.
### run_shell_command · 2026-09-29T02:23:45.552Z
{
"description": "Test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
}
### Tool output · 2026-09-29T02:23:49.550Z
Output: Traceback (most recent call last):
File "", line 17, in
File "/app/solution/data_prep.py", line 263, in build_training_pairs
uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}
^
NameError: name 'u' is not defined
Exit Code: 1
Process Group PGID: 2359
## Step 52 · agent · 2026-09-29T02:23:52.137Z
### replace · 2026-09-29T02:23:52.137Z
{
"old_string": " uid_to_row = {int(u): r for r, uid in enumerate(target_uids)}",
"instruction": "Fix variable name in uid_to_row comprehension",
"file_path": "/app/solution/data_prep.py",
"allow_multiple": false,
"new_string": " uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}"
}
### Tool output · 2026-09-29T02:23:52.150Z
Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
histories_list: list of user history dicts (one per cutoff).
user_window_indices: LongTensor of shape (N_pairs, 2) where col 0 is window_idx, col 1 is user_row.
target_items: LongTensor of shape (N_pairs,) with item index [0, 29999].
pair_weights: FloatTensor of shape (N_pairs,) with training weights (e.g. 3.0 for like, 1.0 for listen).
"""
uid_to_row = {int(uid): r for r, uid in enumerate(target_uids)}
target_uids_set = set(uid_to_row.keys())
# Load likes
likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
if include_val and (data_dir / "val_likes.parquet").exists():
...
## Step 53 · agent · 2026-09-29T02:23:55.581Z
### run_shell_command · 2026-09-29T02:23:55.581Z
{
"description": "Re-run test build_training_pairs",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport time\nfrom pathlib import Path\nimport pandas as pd\nfrom data_prep import load_catalog_features, build_training_pairs\n\nDATA = Path('/task/data')\nt0 = time.time()\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nc1 = 24786800 - 7 * 86400 - 1800\nc2 = c1 - 7 * 86400 - 1800\ncutoffs = [c1, c2]\n\nhists, user_win, target_items, weights = build_training_pairs(\n data_dir=DATA,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=20,\n)\nprint(f'Total time: {time.time() - t0:.2f}s')\nprint(f'Pairs: user_win shape={user_win.shape}, target_items shape={target_items.shape}, weights={weights.shape}')\n\""
}
### Tool output · 2026-09-29T02:24:11.006Z
Output: Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Generated 76517 discovery training pairs across 2 windows.
Total time: 11.34s
Pairs: user_win shape=torch.Size([76517, 2]), target_items shape=torch.Size([76517]), weights=torch.Size([76517])
Process Group PGID: 2376
## Step 54 · agent · 2026-09-29T02:24:19.727Z
### Recorded reasoning summary
Generating Discovery Pairs
I've successfully generated over 76,000 discovery training pairs within just 11 seconds using two windows. I'm now exploring the impact of increasing the number of windows to three or raising `sample_listens_per_user` to 40-50, aiming for over 150,000 pairs in approximately 16 seconds. The cleanliness of the `user_win` output is also a positive observation.
### Recorded reasoning summary
Optimizing History Indexing
I'm considering how to best index user history features for training efficiency. Instead of storing duplicate tensors, I can pre-index them by stacking user histories across windows. This would allow for a single tensor indexing operation, potentially streamlining the batching process and reducing memory overhead.
### update_topic · 2026-09-29T02:24:19.727Z
{
"title": "PyTorch Two-Tower Architecture",
"strategic_intent": "Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.",
"summary": "Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval."
}
### Tool output · 2026-09-29T02:24:19.730Z
Current topic: "PyTorch Two-Tower Architecture"
Topic summary: Completed Phase 1 (Data Preprocessing & Feature Extraction). We verified feature extraction and pair generation. Now transitioning to Phase 2: Model Architecture. We will build UserTower, ItemTower, and TwoTowerRanker in model.py, ensuring strictly decoupled towers, offline candidate precomputation, and MIPS retrieval.
Strategic Intent: Design and implement the PyTorch two-tower neural network architecture with history-based user tower and candidate item tower.
## Step 55 · agent · 2026-09-29T02:24:51.379Z
### Recorded reasoning summary
Designing Model Architecture
I am now focusing on the precise design of the model architecture within `solution/model.py`. My current consideration revolves around fulfilling the requirement for an exact decomposition into user vectors and independently precomputable components.
### Recorded reasoning summary
Developing Item Tower
I'm actively building the `ItemTower` in PyTorch. The design focuses on embedding items and projecting catalog features using an MLP. This is a key step towards generating the item vectors needed for our model.
### Recorded reasoning summary
Refining Item Projection
I'm refining the `ItemTower`'s output projection. The logic now concatenates item embeddings with projected catalog features before passing them through a final MLP. This ensures normalized item vectors suitable for downstream computations.
**Integrating User Tower Design**
I am now considering the `UserTower` design. I am debating whether to share the `item_embed` layer from the `ItemTower` or create a separate one. Sharing is standard and beneficial for two-tower models, maintaining a consistent representation space.
### Recorded reasoning summary
Considering Embedding Sharing
I am weighing the benefits of sharing the item embedding table between the `UserTower` and `ItemTower`. While a separate table is technically feasible, sharing it could reduce parameter count and ensure a more consistent representation space for items across both towers.
### Recorded reasoning summary
Processing User Embeddings
I'm finalizing the `UserTower`. It now processes listen, like, and dislike histories, pooling embeddings and incorporating projected user features. The combined representation is then passed through a multi-layer perceptron, finally normalizing to produce the user vector.
### Recorded reasoning summary
Validating Decomposition
I have confirmed that the model architecture precisely decomposes into a user vector and independently precomputable item vectors. Candidate item embeddings can indeed be computed offline, and user vectors will be generated at request time, allowing for efficient scoring via dot product.
### Recorded reasoning summary
Implementing Loss Function
I am now implementing the InfoNCE loss with sampled softmax and popularity debiasing. This involves calculating dot products between user and item embeddings, applying temperature scaling and logit debiasing, and computing the loss for positive items. Additionally, I am incorporating a hard negative dislike penalty to further refine the model.
### write_file · 2026-09-29T02:24:51.379Z
{
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 64,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n in_dim = embed_dim * 3 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Args:\n listen_items: LongTensor (B, max_listens)\n listen_weights: FloatTensor (B, max_listens)\n like_items: LongTensor (B, max_likes)\n like_mask: FloatTensor (B, max_likes)\n dislike_items: LongTensor (B, max_dislikes)\n dislike_mask: FloatTensor (B, max_dislikes)\n user_features: FloatTensor (B, user_feat_dim)\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n # 1. Weighted listens pooling\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 3. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 4. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 5. Combined MLP\n combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\n\n Args:\n catalog_features: FloatTensor of shape (30000, 4).\n\n Returns:\n FloatTensor of shape (30000, out_dim).\n \"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\n\n Args:\n user_history: Dictionary containing history tensors.\n\n Returns:\n FloatTensor of shape (N_users, out_dim).\n \"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n target_item_logq: FloatTensor of positive item logq (B,).\n sample_weights: FloatTensor of positive sample weights (B,).\n dislike_item_indices: Optional LongTensor of hard negative items (B,).\n dislike_item_features: Optional FloatTensor (B, 4).\n dislike_mask: Optional FloatTensor (B,).\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized\n\n # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 3.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n # Dislike penalty\n if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:\n v_neg = self.item_tower(dislike_item_indices, dislike_item_features)\n dislike_sim = (u * v_neg).sum(dim=-1)\n # Penalize similarity to disliked tracks\n dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)\n return infonce_loss + 0.2 * dislike_loss\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
}
### Tool output · 2026-09-29T02:24:51.389Z
Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code:
"""PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval."""
from __future__ import annotations
from typing import Dict, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
class ItemTower(nn.Module):
"""Item Tower computing independently precomputable item embeddings."""
def __init__(
self,
item_embed: nn.Embedding,
feat_dim: int = 4,
embed_dim: int = 64,
hidden_dim: int = 64,
out_dim: int = 64,
):
super().__init__()
self.item_embed = item_embed
self.feat_mlp = nn.Sequential(
nn.Linear(feat_dim, 32),
nn.LayerNorm(32),
nn.GELU(),
)
self.out_mlp = nn.Sequential(
nn.Linear(embed_dim + 32, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, out_dim),
)
def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:
"""Compute normalized item embeddings.
Args:
item_indices: LongTensor of shape (B,) with indices in [0, 30000].
item_features: FloatTensor of shape (B, 4).
Returns:
L2-normalized FloatTensor of shape (B, out_dim).
"""
emb = self.item_embed(item_indices)
feat = self.feat_mlp(item_features)
combined = torch.cat([emb, feat], dim=-1)
out = self.out_mlp(combined)
return F.normalize(out, p=2, dim=-1)
class UserTower(nn.Module):
"""User Tower deriving user embedding strictly from interaction history."""
def __init__(
self,
item_embed: nn.Embedding,
embed_dim: int = 64,
user_feat_dim: int = 6,
hidden_dim: int = 128,
out_dim: int = 64,
dropout: float = 0.1,
):
super().__init__()
self.item_embed = item_embed
self.user_feat_mlp = nn.Sequential(
nn.Linear(user_feat_dim, 32),
nn.LayerNorm(32),
nn.GELU(),
)
in_dim = embed_dim * 3 + 32
self.mlp = nn.Sequential(
nn.Linear(in_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
nn.Linear(hidden_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Linear(hidden_dim, out_dim),
)
def forward(
self,
listen_items: torch.Tensor,
listen_weights: torch.Tensor,
like_items: torch.Tensor,
like_mask: torch.Tensor,
dislike_items: torch.Tensor,
dislike_mask: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
"""Compute normalized user embeddings from interaction history.
Args:
listen_items: LongTensor (B, max_listens)
listen_weights: FloatTensor (B, max_listens)
like_items: LongTensor (B, max_likes)
like_mask: FloatTensor (B, max_likes)
dislike_items: LongTensor (B, max_dislikes)
dislike_mask: FloatTensor (B, max_dislikes)
user_features: FloatTensor (B, user_feat_dim)
Returns:
L2-normalized FloatTensor of shape (B, out_dim).
"""
# 1. Weighted listens pooling
l_emb = self.item_embed(listen_items) # (B, K, D)
pooled_listens = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)
# 2. Likes pooling
lk_emb = self.item_embed(like_items) # (B, L, D)
lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6
pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom
has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()
pooled_likes = pooled_likes * has_likes
# 3. Dislikes pooling
dk_emb = self.item_embed(dislike_items) # (B, M, D)
dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6
pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom
has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()
pooled_dislikes = pooled_dislikes * has_dislikes
# 4. User features projection
user_feat_proj = self.user_feat_mlp(user_features)
# 5. Combined MLP
combined = torch.cat([pooled_listens, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)
out = self.mlp(combined)
return F.normalize(out, p=2, dim=-1)
class TwoTowerRanker(nn.Module):
"""Complete Two-Tower Neural Network Ranker."""
def __init__(
self,
num_catalog_items: int = 30000,
embed_dim: int = 64,
hidden_dim: int = 128,
out_dim: int = 64,
dropout: float = 0.1,
temperature: float = 0.07,
):
super().__init__()
# Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
nn.init.normal_(self.item_embed.weight, std=0.05)
with torch.no_grad():
self.item_embed.weight[num_catalog_items].fill_(0.0)
self.item_tower = ItemTower(
item_embed=self.item_embed,
feat_dim=4,
embed_dim=embed_dim,
hidden_dim=hidden_dim,
out_dim=out_dim,
)
self.user_tower = UserTower(
item_embed=self.item_embed,
embed_dim=embed_dim,
user_feat_dim=6,
hidden_dim=hidden_dim,
out_dim=out_dim,
dropout=dropout,
)
self.temperature = temperature
def precompute_candidate_embeddings(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute offline item embeddings for all 30,000 catalog items.
Args:
catalog_features: FloatTensor of shape (30000, 4).
Returns:
FloatTensor of shape (30000, out_dim).
"""
self.eval()
n_items = len(catalog_features)
embeddings = []
with torch.no_grad():
for i in range(0, n_items, batch_size):
end = min(i + batch_size, n_items)
idx = torch.arange(i, end, dtype=torch.int64)
feat = catalog_features[i:end]
emb = self.item_tower(idx, feat)
embeddings.append(emb)
return torch.cat(embeddings, dim=0)
def compute_user_embeddings(
self,
user_history: Dict[str, torch.Tensor],
batch_size: int = 1024,
) -> torch.Tensor:
"""Compute user embeddings from interaction history.
Args:
user_history: Dictionary containing history tensors.
Returns:
FloatTensor of shape (N_users, out_dim).
"""
self.eval()
n_users = len(user_history["listen_items"])
user_embeddings = []
with torch.no_grad():
for i in range(0, n_users, batch_size):
end = min(i + batch_size, n_users)
u_emb = self.user_tower(
listen_items=user_history["listen_items"][i:end],
listen_weights=user_history["listen_weights"][i:end],
like_items=user_history["like_items"][i:end],
like_mask=user_history["like_mask"][i:end],
dislike_items=user_history["dislike_items"][i:end],
dislike_mask=user_history["dislike_mask"][i:end],
user_features=user_history["user_features"][i:end],
)
user_embeddings.append(u_emb)
return torch.cat(user_embeddings, dim=0)
def forward_loss(
self,
user_history_batch: Dict[str, torch.Tensor],
target_item_indices: torch.Tensor,
target_item_features: torch.Tensor,
target_item_logq: torch.Tensor,
sample_weights: torch.Tensor,
dislike_item_indices: Optional[torch.Tensor] = None,
dislike_item_features: Optional[torch.Tensor] = None,
dislike_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Compute InfoNCE sampled softmax loss with popularity debiasing and optional dislike penalty.
Args:
user_history_batch: Dict of user history tensors of batch size B.
target_item_indices: LongTensor of positive item indices (B,).
target_item_features: FloatTensor of positive item features (B, 4).
target_item_logq: FloatTensor of positive item logq (B,).
sample_weights: FloatTensor of positive sample weights (B,).
dislike_item_indices: Optional LongTensor of hard negative items (B,).
dislike_item_features: Optional FloatTensor (B, 4).
dislike_mask: Optional FloatTensor (B,).
Returns:
Scalar loss tensor.
"""
# User vectors (B, D)
u = self.user_tower(
listen_items=user_history_batch["listen_items"],
listen_weights=user_history_batch["listen_weights"],
like_items=user_history_batch["like_items"],
like_mask=user_history_batch["like_mask"],
dislike_items=user_history_batch["dislike_items"],
dislike_mask=user_history_batch["dislike_mask"],
user_features=user_history_batch["user_features"],
)
# Positive candidate vectors (B, D)
v_pos = self.item_tower(target_item_indices, target_item_features)
# Dot product matrix (B, B)
sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized
# Popularity correction and temperature scaling
# sim / tau - logq
logits = sim / self.temperature - target_item_logq.unsqueeze(0)
# Cross entropy with in-batch negatives
batch_size = len(target_item_indices)
targets = torch.arange(batch_size, device=target_item_indices.device)
per_sample_loss = F.cross_entropy(logits, targets, reduction="none")
# Weight by grade importance (likes 3.0, listens 1.0)
norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
infonce_loss = (per_sample_loss * norm_weights).mean()
# Dislike penalty
if dislike_item_indices is not None and dislike_mask is not None and dislike_mask.sum() > 0:
v_neg = self.item_tower(dislike_item_indices, dislike_item_features)
dislike_sim = (u * v_neg).sum(dim=-1)
# Penalize similarity to disliked tracks
dislike_loss = (F.relu(dislike_sim + 0.1) * dislike_mask).sum() / (dislike_mask.sum() + 1e-6)
return infonce_loss + 0.2 * dislike_loss
return infonce_loss
## Step 56 · agent · 2026-09-29T02:25:08.070Z
### run_shell_command · 2026-09-29T02:25:08.070Z
{
"description": "Test model forward, backward, precompute and retrieval",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nfrom model import TwoTowerRanker\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\ncat_feats = torch.randn(30000, 4)\nv = model.precompute_candidate_embeddings(cat_feats)\nprint('Precomputed candidate embeddings shape:', v.shape, 'norms:', v.norm(dim=1)[:5])\n\nB = 16\nu_hist = {\n 'listen_items': torch.randint(0, 30000, (B, 100)),\n 'listen_weights': torch.ones(B, 100) / 100.0,\n 'like_items': torch.randint(0, 30000, (B, 60)),\n 'like_mask': torch.ones(B, 60),\n 'dislike_items': torch.randint(0, 30000, (B, 30)),\n 'dislike_mask': torch.ones(B, 30),\n 'user_features': torch.randn(B, 6),\n}\nu = model.compute_user_embeddings(u_hist)\nprint('User embeddings shape:', u.shape, 'norms:', u.norm(dim=1)[:5])\n\nscores = torch.matmul(u, v.t())\nprint('Scores shape:', scores.shape, 'min:', scores.min().item(), 'max:', scores.max().item())\n\n# Test loss\nloss = model.forward_loss(\n user_history_batch=u_hist,\n target_item_indices=torch.randint(0, 30000, (B,)),\n target_item_features=torch.randn(B, 4),\n target_item_logq=torch.zeros(B),\n sample_weights=torch.ones(B),\n)\nprint('Loss:', loss.item())\nloss.backward()\nprint('Backward succeeded!')\n\""
}
### Tool output · 2026-09-29T02:25:11.108Z
Output: Precomputed candidate embeddings shape: torch.Size([30000, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
User embeddings shape: torch.Size([16, 64]) norms: tensor([1.0000, 1.0000, 1.0000, 1.0000, 1.0000])
Scores shape: torch.Size([16, 30000]) min: -0.33630895614624023 max: 0.42073172330856323
Loss: 3.4881701469421387
Backward succeeded!
Process Group PGID: 2398
## Step 57 · agent · 2026-09-29T02:25:29.805Z
### Recorded reasoning summary
Implementing Training Loop
My focus has shifted to the training and validation phase. I'm now working on integrating the `update_topic` function to manage progression through training epochs and ensure smooth validation.
### update_topic · 2026-09-29T02:25:29.805Z
{
"title": "Training & Public Validation",
"strategic_intent": "Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.",
"summary": "Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py."
}
### Tool output · 2026-09-29T02:25:29.809Z
Current topic: "Training & Public Validation"
Topic summary: Completed Phase 2 (Model Definition). Moving to Phase 3: Training & Validation Loop. We will implement train_val.py to train the TwoTowerRanker on the historical backtest windows, generate validation rankings, and score with /task/evaluate_public.py.
Strategic Intent: Implement training and validation script, evaluate against public evaluator, and measure NDCG@10.
## Step 58 · agent · 2026-09-29T02:25:53.212Z
### Recorded reasoning summary
Designing `train_val.py`
I'm focusing on the initial steps for `solution/train_val.py`. My immediate concern is implementing the functionality to load catalog features, including extracting `catalog_features`, `item_to_idx`, `catalog_item_ids`, and `item_logq` from the specified data directory.
### Recorded reasoning summary
Implementing Data Loading
I've begun implementing the data loading logic for `solution/train_val.py`. My current focus is on accurately extracting catalog features and preparing the user histories based on historical cutoffs for PyTorch Dataset construction.
### Recorded reasoning summary
Calculating Validation Scores
I'm now focused on the validation phase. This involves extracting user histories at the validation cutoff, precomputing candidate and user embeddings, and then efficiently calculating MIPS scores using a matrix multiplication. I'll also apply the validation candidate eligibility mask before retrieving the top 100 items per user.
### write_file · 2026-09-29T02:25:53.212Z
{
"content": "\"\"\"Training and validation script for the two-tower music ranker.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport subprocess\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom torch.utils.data import DataLoader, TensorDataset\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n\ndef train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)\n\n\ndef evaluate_on_validation(\n model: TwoTowerRanker,\n val_user_history: dict[str, torch.Tensor],\n catalog_features: torch.Tensor,\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n) -> float:\n print(\"Precomputing candidate item embeddings offline...\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(catalog_features)\n print(f\"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Computing user embeddings for validation targets...\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(val_user_history)\n print(f\"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s\")\n\n print(\"Running MIPS candidate retrieval and applying eligibility masks...\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load validation eligibility bitmask\n vec = np.load(data_dir / \"validation_eligible_candidates.npz\")\n unpacked_eligible = np.unpackbits(vec[\"packed_eligible\"], axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_eligible == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n # Build validation dataframe\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = catalog_item_ids[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n val_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n output_path.parent.mkdir(parents=True, exist_ok=True)\n val_df.to_parquet(output_path, index=False)\n print(f\"Saved validation ranking to {output_path}\")\n\n # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)\n print(\"Evaluator Output:\\n\", result.stdout)\n if result.returncode != 0:\n print(\"Evaluator Error:\\n\", result.stderr)\n return 0.0\n\n try:\n report = json.loads(result.stdout)\n score = report.get(\"scores\", {}).get(\"quality_ndcg@10\", 0.0)\n return score\n except Exception as e:\n print(f\"Failed to parse evaluator JSON: {e}\")\n return 0.0\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--output_path\", type=str, default=\"/app/solution/validation.parquet\")\n parser.add_argument(\"--epochs\", type=int, default=5)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=1e-3)\n parser.add_argument(\"--embed_dim\", type=int, default=64)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=64)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)\n parser.add_argument(\"--sample_listens\", type=int, default=30)\n args = parser.parse_args()\n\n data_dir = Path(args.data_dir)\n torch.manual_seed(42)\n np.random.seed(42)\n\n # Set intraop parallelism\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]\n print(f\"Training cutoffs: {cutoffs}\")\n\n print(\"=== Building Training Pairs ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=args.sample_listens_per_user,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Extracting Validation Cutoff History ===\")\n t0 = time.time()\n val_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=val_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\"Validation history ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)\n total_steps = (len(target_items) // args.batch_size + 1) * args.epochs\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)\n\n print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")\n\n\nif __name__ == \"__main__\":\n main()\n",
"file_path": "/app/solution/train_val.py"
}
### Tool output · 2026-09-29T02:25:53.223Z
Successfully created and wrote to new file: /app/solution/train_val.py. Here is the updated code:
"""Training and validation script for the two-tower music ranker."""
from __future__ import annotations
import argparse
import json
import subprocess
import time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from torch.utils.data import DataLoader, TensorDataset
from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker
def train_epoch(
model: TwoTowerRanker,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
histories_stacked: dict[str, torch.Tensor],
global_uids: torch.Tensor,
target_items: torch.Tensor,
sample_weights: torch.Tensor,
catalog_features: torch.Tensor,
item_logq: torch.Tensor,
batch_size: int = 512,
) -> float:
model.train()
total_loss = 0.0
num_batches = 0
n_samples = len(target_items)
perm = torch.randperm(n_samples)
for i in range(0, n_samples, batch_size):
idx = perm[i : min(i + batch_size, n_samples)]
b_uids = global_uids[idx]
b_items = target_items[idx]
b_weights = sample_weights[idx]
b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
b_feats = catalog_features[b_items]
b_logq = item_logq[b_items]
optimizer.zero_grad()
loss = model.forward_loss(
user_history_batch=b_hist,
target_item_indices=b_items,
target_item_features=b_feats,
target_item_logq=b_logq,
sample_weights=b_weights,
)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
total_loss += loss.item()
num_batches += 1
return total_loss / max(num_batches, 1)
def evaluate_on_validation(
model: TwoTowerRanker,
val_user_history: dict[str, torch.Tensor],
catalog_features: torch.Tensor,
catalog_item_ids: np.ndarray,
target_uids: np.ndarray,
data_dir: Path,
output_path: Path,
) -> float:
print("Precomputing candidate item embeddings offline...")
t0 = time.time()
cand_embeddings = model.precompute_candidate_embeddings(catalog_features)
print(f"Precomputed {len(cand_embeddings)} item embeddings in {time.time() - t0:.2f}s")
print("Computing user embeddings for validation targets...")
t0 = time.time()
user_embeddings = model.compute_user_embeddings(val_user_history)
print(f"Computed {len(user_embeddings)} user embeddings in {time.time() - t0:.2f}s")
print("Running MIPS candidate retrieval and applying eligibility masks...")
t0 = time.time()
scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)
# Load validation eligibility bitmask
vec = np.load(data_dir / "validation_eligible_candidates.npz")
unpacked_eligible = np.unpackbits(vec["packed_eligible"], axis=1, count=30000, bitorder="little")
ineligible_mask = unpacked_eligible == 0
scores[torch.from_numpy(ineligible_mask)] = -1e9
# Top 100 retrieval
top100 = torch.topk(scores, 100, dim=1).indices.numpy()
print(f"MIPS retrieval completed in {time.time() - t0:.2f}s")
# Build validation dataframe
rows = []
for u_idx, uid in enumerate(target_uids):
top_items = catalog_item_ids[top100[u_idx]]
for r, item_id in enumerate(top_items, 1):
rows.append((int(uid), int(item_id), r))
val_df = pd.DataFrame(rows, columns=["uid", "item_id", "rank"])
output_path.parent.mkdir(parents=True, exist_ok=True)
val_df.to_parquet(output_path, index=False)
print(f"Saved validation ranking to {output_path}")
# Run public evaluator
cmd = ["python3", "/task/evaluate_public.py", str(output_path)]
env = {"PYTHONPATH": "/task"}
result = subprocess.run(cmd, capture_output=True, text=True, env=env)
print("Evaluator Output:\n", result.stdout)
if result.returncode != 0:
print("Evaluator Error:\n", result.stderr)
return 0.0
try:
report = json.loads(result.stdout)
score = report.get("scores", {}).get("quality_ndcg@10", 0.0)
return score
except Exception as e:
print(f"Failed to parse evaluator JSON: {e}")
return 0.0
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default="/task/data")
parser.add_argument("--output_path", type=str, default="/app/solution/validation.parquet")
parser.add_argument("--epochs", type=int, default=5)
parser.add_argument("--batch_size", type=int, default=512)
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--embed_dim", type=int, default=64)
parser.add_argument("--hidden_dim", type=int, default=128)
parser.add_argument("--out_dim", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.07)
parser.add_argument("--num_windows", type=int, default=3)
parser.add_argument("--sample_listens", type=int, default=30)
args = parser.parse_args()
data_dir = Path(args.data_dir)
torch.manual_seed(42)
np.random.seed(42)
# Set intraop parallelism
torch.set_num_threads(8)
print("=== Loading Catalog and Users ===")
cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)
target_users_df = pd.read_parquet(data_dir / "target_users.parquet")
target_uids = target_users_df["uid"].to_numpy(dtype=np.int64)
val_cutoff = 24786800
week_sec = 7 * 86400
gap_sec = 1800
cutoffs = [val_cutoff - (w + 1) * (week_sec + gap_sec) for w in range(args.num_windows)]
print(f"Training cutoffs: {cutoffs}")
print("=== Building Training Pairs ===")
t0 = time.time()
histories_list, user_win, target_items, sample_weights = build_training_pairs(
data_dir=data_dir,
cutoffs=cutoffs,
item_to_idx=item_to_idx,
target_uids=target_uids,
sample_listens_per_user=args.sample_listens_per_user,
)
print(f"Data preparation completed in {time.time() - t0:.2f}s")
# Stack user histories across windows
keys = list(histories_list[0].keys())
histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}
n_users = len(target_uids)
global_uids = user_win[:, 0] * n_users + user_win[:, 1]
item_logq_t = torch.from_numpy(item_logq)
print("=== Extracting Validation Cutoff History ===")
t0 = time.time()
val_history = extract_user_histories(
data_dir=data_dir,
cutoff=val_cutoff,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=data_dir / "validation_preference_state.parquet",
)
print(f"Validation history ready in {time.time() - t0:.2f}s")
print("=== Initializing Two-Tower Ranker ===")
model = TwoTowerRanker(
num_catalog_items=30000,
embed_dim=args.embed_dim,
hidden_dim=args.hidden_dim,
out_dim=args.out_dim,
temperature=args.temperature,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)
print(f"=== Starting Training for {args.epochs} Epochs ===")
best_score = 0.0
for epoch in range(1, args.epochs + 1):
t_ep = time.time()
avg_loss = train_epoch(
model=model,
optimizer=optimizer,
scheduler=scheduler,
histories_stacked=histories_stacked,
global_uids=global_uids,
target_items=target_items,
sample_weights=sample_weights,
catalog_features=cat_feats,
item_logq=item_logq_t,
batch_size=args.batch_size,
)
ep_duration = time.time() - t_ep
print(f"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)")
# Validate
score = evaluate_on_validation(
model=model,
val_user_history=val_history,
catalog_features=cat_feats,
catalog_item_ids=cat_items,
target_uids=target_uids,
data_dir=data_dir,
output_path=Path(args.output_path),
)
print(f"Epoch {epoch} Public NDCG@10: {score:.6f}")
if score > best_score:
best_score = score
torch.save(model.state_dict(), "/app/solution/best_val_model.pt")
print(f"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===")
if __name__ == "__main__":
main()
## Step 59 · agent · 2026-09-29T02:26:07.771Z
### run_shell_command · 2026-09-29T02:26:07.771Z
{
"description": "Run initial train_val test with 2 windows and 3 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
}
### Tool output · 2026-09-29T02:26:11.786Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600]
=== Building Training Pairs ===
Traceback (most recent call last):
File "/app/solution/train_val.py", line 244, in
main()
File "/app/solution/train_val.py", line 171, in main
sample_listens_per_user=args.sample_listens_per_user,
^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Namespace' object has no attribute 'sample_listens_per_user'
Exit Code: 1
Process Group PGID: 2411
## Step 60 · agent · 2026-09-29T02:26:17.307Z
### replace · 2026-09-29T02:26:17.307Z
{
"old_string": " sample_listens_per_user=args.sample_listens_per_user,",
"new_string": " sample_listens_per_user=args.sample_listens,",
"instruction": "Fix argument name for sample_listens",
"file_path": "/app/solution/train_val.py",
"allow_multiple": false
}
### Tool output · 2026-09-29T02:26:17.322Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
histories_list, user_win, target_items, sample_weights = build_training_pairs(
data_dir=data_dir,
cutoffs=cutoffs,
item_to_idx=item_to_idx,
target_uids=target_uids,
sample_listens_per_user=args.sample_listens,
)
print(f"Data preparation completed in {time.time() - t0:.2f}s")
# Stack user histories across windows
keys = list(histories_list[0].keys())
...
## Step 61 · agent · 2026-09-29T02:26:19.480Z
### run_shell_command · 2026-09-29T02:26:19.480Z
{
"description": "Run train_val with fixed sample_listens argument",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 2 --epochs 3 --sample_listens 20"
}
### Tool output · 2026-09-29T02:26:47.417Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Generated 76229 discovery training pairs across 2 windows.
Data preparation completed in 11.35s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.32s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 3 Epochs ===
Epoch 1/3: Loss=6.2061 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Traceback (most recent call last):
File "/app/solution/train_val.py", line 244, in
main()
File "/app/solution/train_val.py", line 226, in main
score = evaluate_on_validation(
^^^^^^^^^^^^^^^^^^^^^^^
File "/app/solution/train_val.py", line 115, in evaluate_on_validation
result = subprocess.run(cmd, capture_output=True, text=True, env=env)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/subprocess.py", line 548, in run
with Popen(*popenargs, **kwargs) as process:
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/subprocess.py", line 1026, in __init__
self._execute_child(args, executable, preexec_fn, close_fds,
File "/usr/local/lib/python3.12/subprocess.py", line 1955, in _execute_child
raise child_exception_type(errno_num, err_msg, err_filename)
FileNotFoundError: [Errno 2] No such file or directory: 'python3'
Exit Code: 1
Process Group PGID: 2435
## Step 62 · agent · 2026-09-29T02:26:52.042Z
### Recorded reasoning summary
Investigating Runtime Errors
I'm currently pinpointing the cause of a `FileNotFoundError` related to 'python3'. It appears the issue stems from how the `PATH` environment variable is being handled when `PYTHONPATH` is explicitly set.
### run_shell_command · 2026-09-29T02:26:52.042Z
{
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Evaluate saved validation ranking with evaluate_public.py"
}
### Tool output · 2026-09-29T02:26:55.368Z
Output: {
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.009969,
"quality_users": 4263
}
}
Process Group PGID: 2472
## Step 63 · agent · 2026-09-29T02:26:59.706Z
### Recorded reasoning summary
Evaluating Model Performance
I've successfully saved the validation ranking and initiated the public evaluation script. Initial results show a `quality_ndcg@10` of 0.009969 with 4263 users, which I'm now analyzing further.
### replace · 2026-09-29T02:26:59.706Z
{
"new_string": " # Run public evaluator\n import os\n import sys\n cmd = [sys.executable, \"/task/evaluate_public.py\", str(output_path)]\n env = os.environ.copy()\n env[\"PYTHONPATH\"] = f\"/task:{env.get('PYTHONPATH', '')}\"\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)",
"file_path": "/app/solution/train_val.py",
"instruction": "Fix subprocess call to use sys.executable and preserved environment",
"allow_multiple": false,
"old_string": " # Run public evaluator\n cmd = [\"python3\", \"/task/evaluate_public.py\", str(output_path)]\n env = {\"PYTHONPATH\": \"/task\"}\n result = subprocess.run(cmd, capture_output=True, text=True, env=env)"
}
### Tool output · 2026-09-29T02:26:59.717Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
output_path.parent.mkdir(parents=True, exist_ok=True)
val_df.to_parquet(output_path, index=False)
print(f"Saved validation ranking to {output_path}")
# Run public evaluator
import os
import sys
cmd = [sys.executable, "/task/evaluate_public.py", str(output_path)]
env = os.environ.copy()
env["PYTHONPATH"] = f"/task:{env.get('PYTHONPATH', '')}"
result = subprocess.run(cmd, capture_output=True, text=True, env=env)
print("Evaluator Output:\n", result.stdout)
if result.returncode != 0:
print("Evaluator Error:\n", result.stderr)
return 0.0
...
## Step 64 · agent · 2026-09-29T02:27:02.565Z
### Recorded reasoning summary
Exploring Window Configurations
I'm currently running `train_val.py` with an experimental setup: 5 epochs and 4 windows, each defined by specific cutoffs. I'm also evaluating the impact of `sample_listens = 40` within this configuration.
### run_shell_command · 2026-09-29T02:27:02.565Z
{
"description": "Run train_val with 4 windows and 5 epochs",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30"
}
### Tool output · 2026-09-29T02:28:37.365Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 192471 discovery training pairs across 4 windows.
Data preparation completed in 21.84s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.35s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.0946 (7.8s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011541,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.011541
Epoch 2/5: Loss=5.7236 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.06s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011515,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.011515
Epoch 3/5: Loss=5.4151 (7.8s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.14s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011372,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.011372
Epoch 4/5: Loss=5.1981 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.06s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010071,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.010071
Epoch 5/5: Loss=5.0703 (7.7s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.009783,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.009783
=== Training Complete! Best Validation NDCG@10: 0.011541 ===
Process Group PGID: 2492
## Step 65 · agent · 2026-09-29T02:28:43.234Z
### Recorded reasoning summary
Improving Recommendation Accuracy
I've achieved a `quality_ndcg@10` of `0.011541` in Epoch 1, surpassing the popularity baseline. Subsequent epochs show a slight dip, indicating potential overfitting or need for further parameter tuning.
### run_shell_command · 2026-09-29T02:28:43.234Z
{
"description": "Check logq range",
"command": "PYTHONPATH=/app/solution python3 -c \"\nfrom pathlib import Path\nfrom data_prep import load_catalog_features\nDATA = Path('/task/data')\n_, _, _, logq = load_catalog_features(DATA)\nprint('logq min, mean, max:', logq.min(), logq.mean(), logq.max())\nprint('Range of logq:', logq.max() - logq.min())\n\""
}
### Tool output · 2026-09-29T02:28:47.280Z
Output: logq min, mean, max: -11.720305 -10.745149 -6.624825
Range of logq: 5.0954804
Process Group PGID: 2618
## Step 66 · agent · 2026-09-29T02:28:53.165Z
### Recorded reasoning summary
Analyzing LogQ Influence
I'm seeing that the `logq` values, ranging from -11.7 to -6.6, significantly impact the logits. Subtracting a `target_item_logq` of around -10 effectively shifts all logits upwards by approximately 10. This is particularly noticeable for high `logq` items, like one at -6.
### Recorded reasoning summary
Testing LogQ Debiasing
I'm evaluating the impact of scaling `logq` with a debiasing power $\alpha$. Initial results suggest that $\alpha=1.0$ over-penalizes popular items, leading to an extreme bias towards obscure content. I'll be testing $\alpha$ values of $0.0$, $0.2$, and $0.5$ to find a more balanced approach.
### run_shell_command · 2026-09-29T02:28:53.165Z
{
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport sys\nsys.path.append('/app/solution')\nfrom train_val import main\n# Let's test with alpha = 0.0\n\"",
"description": "Check import"
}
### Tool output · 2026-09-29T02:28:57.174Z
Output: (empty)
Process Group PGID: 2635
## Step 67 · agent · 2026-09-29T02:29:06.363Z
### Recorded reasoning summary
Refining Scoring and Training
I'm currently integrating `logq_weight` and temperature into the `TwoTowerRanker` and `train_val.py` scripts. Simultaneously, I'm analyzing the inference-time candidate item score computation, specifically examining the `torch.matmul(user_embeddings, cand_embeddings.t)` operation.
### replace · 2026-09-29T02:29:06.363Z
{
"old_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n\n def precompute_candidate_embeddings(",
"file_path": "/app/solution/model.py",
"instruction": "Add logq_weight parameter to TwoTowerRanker forward_loss",
"new_string": " def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.1,\n temperature: float = 0.07,\n logq_weight: float = 0.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(",
"allow_multiple": false
}
### Tool output · 2026-09-29T02:29:06.374Z
Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:
...
embed_dim: int = 64,
hidden_dim: int = 128,
out_dim: int = 64,
dropout: float = 0.1,
temperature: float = 0.07,
logq_weight: float = 0.0,
):
super().__init__()
# Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
nn.init.normal_(self.item_embed.weight, std=0.05)
...
hidden_dim=hidden_dim,
out_dim=out_dim,
dropout=dropout,
)
self.temperature = temperature
self.logq_weight = logq_weight
def precompute_candidate_embeddings(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
...
## Step 68 · agent · 2026-09-29T02:29:12.324Z
### replace · 2026-09-29T02:29:12.324Z
{
"old_string": " # Popularity correction and temperature scaling\n # sim / tau - logq\n logits = sim / self.temperature - target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives",
"instruction": "Scale target_item_logq by self.logq_weight in forward_loss",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"new_string": " # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives"
}
### Tool output · 2026-09-29T02:29:12.335Z
Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:
...
# Dot product matrix (B, B)
sim = torch.matmul(u, v_pos.t()) # Cosine similarities since both L2 normalized
# Popularity correction and temperature scaling
logits = sim / self.temperature
if self.logq_weight > 0.0:
logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)
# Cross entropy with in-batch negatives
batch_size = len(target_item_indices)
targets = torch.arange(batch_size, device=target_item_indices.device)
per_sample_loss = F.cross_entropy(logits, targets, reduction="none")
...
## Step 69 · agent · 2026-09-29T02:29:15.225Z
### replace · 2026-09-29T02:29:15.225Z
{
"instruction": "Add logq_weight argument and pass to model in train_val.py",
"new_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=0.0)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"old_string": " parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--num_windows\", type=int, default=3)",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
}
### Tool output · 2026-09-29T02:29:15.235Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
parser.add_argument("--lr", type=float, default=1e-3)
parser.add_argument("--embed_dim", type=int, default=64)
parser.add_argument("--hidden_dim", type=int, default=128)
parser.add_argument("--out_dim", type=int, default=64)
parser.add_argument("--temperature", type=float, default=0.07)
parser.add_argument("--logq_weight", type=float, default=0.0)
parser.add_argument("--num_windows", type=int, default=3)
parser.add_argument("--sample_listens", type=int, default=30)
args = parser.parse_args()
data_dir = Path(args.data_dir)
...
## Step 70 · agent · 2026-09-29T02:29:18.443Z
### replace · 2026-09-29T02:29:18.443Z
{
"old_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n )",
"new_string": " model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n )",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py",
"instruction": "Pass logq_weight to TwoTowerRanker instance"
}
### Tool output · 2026-09-29T02:29:18.452Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
num_catalog_items=30000,
embed_dim=args.embed_dim,
hidden_dim=args.hidden_dim,
out_dim=args.out_dim,
temperature=args.temperature,
logq_weight=args.logq_weight,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)
...
## Step 71 · agent · 2026-09-29T02:29:21.110Z
### run_shell_command · 2026-09-29T02:29:21.110Z
{
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 30 --logq_weight 0.0",
"description": "Run train_val with logq_weight 0.0"
}
### Tool output · 2026-09-29T02:30:58.762Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 192471 discovery training pairs across 4 windows.
Data preparation completed in 21.74s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.33s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.0840 (8.4s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.00425,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.004250
Epoch 2/5: Loss=5.7201 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.53s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.005138,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.005138
Epoch 3/5: Loss=5.4037 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.13s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.004426,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.004426
Epoch 4/5: Loss=5.1761 (8.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.13s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.004201,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.004201
Epoch 5/5: Loss=5.0458 (8.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.002985,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.002985
=== Training Complete! Best Validation NDCG@10: 0.005138 ===
Process Group PGID: 2646
## Step 72 · agent · 2026-09-29T02:31:21.500Z
### Recorded reasoning summary
Analyzing Logq Impact
I'm observing a significant score improvement when `logq_weight` is adjusted to `0.0`, dropping from `0.011541` to `0.005138`. This contrasts with an earlier `1.0` setting that yielded the same initial score. I'm now delving into the reasons behind this substantial benefit.
### Recorded reasoning summary
Investigating Batch Bias
My focus has shifted to understanding how popular items skew recommendations. Without LogQ, popular items dominate user embeddings, causing collapses. I'm now exploring how different `logq_weight` and `temperature` values impact this bias, and inspecting the UserTower pooling mechanism.
### Recorded reasoning summary
Refining User Embeddings
I'm investigating why the Item Tower incorporates richer item features into its MLP while the User Tower directly uses item embeddings. I'm considering how attention mechanisms, applied to recent listens within the User Tower, could potentially enhance its representation by capturing temporal dynamics, similar to how the Item Tower utilizes features.
### Recorded reasoning summary
Examining Data Ordering
I'm realizing that the chronological order in my data, indicated by `groupby("uid").cumcount()`, might be critical. Position 99 represents the most recent listen, and I'm questioning the proportion of users with active likes compared to listens and how this affects the discovery objective's relevance grading.
### Recorded reasoning summary
Evaluating Like-Listen Ratio
I'm now scrutinizing the generated `pairs_list`, specifically observing the proportion of likes versus listens. This distinction seems critical, and I'm investigating its impact on the discovery objective's relevance grading, especially considering the differing weights assigned.
### Recorded reasoning summary
Adjusting Weight Priorities
I'm realizing that likes are vastly undervalued in the current metric. A single organic like is seven times more valuable than a strong listen. I'm now exploring significantly increasing the weight for likes and rebalancing the ratio of likes to listens in sampling.
### run_shell_command · 2026-09-29T02:31:21.500Z
{
"description": "Check user full past listens vs tail(100)",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport pyarrow.parquet as pq\nfrom pathlib import Path\nimport pandas as pd\nimport numpy as np\n\nDATA = Path('/task/data')\ncutoff = 24180200\n# User's full listen history before cutoff\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id'],\n filters=[('timestamp', '<', cutoff)]\n)\nall_hist = table.to_pandas().drop_duplicates()\nprint('Total past listen pairs before cutoff:', len(all_hist))\nall_hist_set = set(zip(all_hist.uid, all_hist.item_id))\n\n# Now let's see for 1000 pairs from our previous training pairs\nfrom data_prep import load_catalog_features, extract_user_histories\ncat_feats, item_to_idx, cat_items, _ = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\nuids = tu.uid.values\nhist = extract_user_histories(DATA, cutoff, uids, item_to_idx)\nl_itms = set(hist['listen_items'][0].numpy())\nprint('User 0 tail listens count:', len(l_itms))\nuser0_full_hist = all_hist[all_hist.uid == uids[0]]\nprint('User 0 FULL past listens count:', len(user0_full_hist))\n\""
}
### Tool output · 2026-09-29T02:31:46.991Z
Output: Total past listen pairs before cutoff: 12039859
User 0 tail listens count: 85
User 0 FULL past listens count: 781
Process Group PGID: 2772
## Step 73 · agent · 2026-09-29T02:32:00.437Z
### Recorded reasoning summary
Analyzing Listen Item Discrepancy
I'm delving into why a significant portion of User 0's listening history wasn't reflected in the initial count. It appears a substantial number of items were filtered out, impacting the accuracy of `past_items_per_user` and consequently, my understanding of their target items.
### run_shell_command · 2026-09-29T02:32:00.437Z
{
"description": "Benchmark full past items extraction for all target users",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\nt0 = time.time()\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\nprint(f'Read and filtered in {time.time() - t0:.2f}s, rows={len(df)}')\n\ncutoff = 24180200\nt1 = time.time()\ndf_past = df[df.timestamp < cutoff]\n# Group by uid to get set of past items\npast_sets = df_past.groupby('uid')['item_id'].apply(set).to_dict()\nprint(f'Built full past sets for all {len(past_sets)} users in {time.time() - t1:.2f}s')\n\""
}
### Tool output · 2026-09-29T02:32:12.521Z
Output: Read and filtered in 3.53s, rows=43222297
Built full past sets for all 8359 users in 6.11s
Process Group PGID: 2794
## Step 74 · agent · 2026-09-29T02:32:28.220Z
### Recorded reasoning summary
Processing All Past Items
I've successfully compiled a complete set of all historical listening data across all users in under 7 seconds. This allows me to definitively identify discovery items by excluding anything present in a user's past listening history. Every remaining event is now confirmed as a true discovery.
### run_shell_command · 2026-09-29T02:32:28.220Z
{
"description": "Check true discovery counts in window 0",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = set(tu.uid)\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncc_items = set(cc.item_id)\n\ntable = pq.read_table(\n DATA / 'train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'is_organic', 'played_ratio_pct']\n)\ndf = table.to_pandas()\ndf = df[df.uid.isin(target_uids)]\n\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nlikes = likes[likes.uid.isin(target_uids) & (likes.is_organic == 1) & (likes.item_id.isin(cc_items))]\n\nval_cutoff = 24786800\nweek = 7 * 86400\ngap = 1800\n\nc0 = val_cutoff - (week + gap)\ndf_past0 = df[df.timestamp < c0]\npast0 = df_past0.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Target window 0: [c0 + gap, c0 + gap + week]\nw0_likes = likes[(likes.timestamp >= c0 + gap) & (likes.timestamp <= c0 + gap + week)]\nw0_listens = df[(df.timestamp >= c0 + gap) & (df.timestamp <= c0 + gap + week) & (df.is_organic == 1) & (df.played_ratio_pct >= 80) & (df.item_id.isin(cc_items))]\n\ndisc_likes = [itm for uid, itm in zip(w0_likes.uid, w0_likes.item_id) if itm not in past0.get(uid, set())]\ndisc_listens = [itm for uid, itm in zip(w0_listens.uid, w0_listens.item_id) if itm not in past0.get(uid, set())]\n\nprint(f'Window 0 True Discovery Likes: {len(disc_likes)} out of {len(w0_likes)}')\nprint(f'Window 0 True Discovery Strong Listens: {len(disc_listens)} out of {len(w0_listens)}')\n\""
}
### Tool output · 2026-09-29T02:32:44.136Z
Output: Window 0 True Discovery Likes: 5420 out of 7774
Window 0 True Discovery Strong Listens: 67576 out of 304885
Process Group PGID: 2816
## Step 75 · agent · 2026-09-29T02:33:00.029Z
### Recorded reasoning summary
Analyzing Listen Distribution
My current focus is on dissecting the listen data, particularly the significant proportion of repeat listens. It seems the model's training might have been overly influenced by existing user history, potentially hindering its exploration of new content.
### replace · 2026-09-29T02:33:00.029Z
{
"new_string": " # Load full target user listens to guarantee 100% true discovery filtering\n full_table = pq.read_table(\n data_dir / \"train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = full_table.to_pandas()\n if include_val and (data_dir / \"val_listens.parquet\").exists():\n val_tbl_full = pq.read_table(\n data_dir / \"val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\"],\n )\n all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)\n all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]\n\n histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter\n df_prior = all_listens_df[all_listens_df.timestamp < cutoff]\n past_items_per_user = df_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n # Add liked items before cutoff as well\n likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]\n for uid, itm in zip(likes_prior.uid, likes_prior.item_id):\n past_items_per_user.setdefault(int(uid), set()).add(int(itm))\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 5.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_int = int(item)\n if item_int not in past_items_per_user.get(int(uid), set()):\n item_idx = item_to_idx.get(item_int)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 5.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n user_past = past_items_per_user.get(int(uid), set())\n items = [int(it) for it in group[\"item_id\"].to_numpy() if int(it) not in user_past]\n if len(items) > sample_listens_per_user:\n items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))\n for itm in items:\n item_idx = item_to_idx.get(itm)\n if item_idx is not None and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"instruction": "Build full history set for each user to strictly filter out non-discovery targets",
"file_path": "/app/solution/data_prep.py",
"old_string": " histories_list: List[Dict[str, torch.Tensor]] = []\n pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)\n\n for w_idx, cutoff in enumerate(cutoffs):\n t_start = cutoff + gap_seconds\n t_end = t_start + week_seconds\n\n # 1. Extract history up to cutoff\n print(f\"Extracting user histories for training cutoff {w_idx}: timestamp={cutoff}...\")\n hist = extract_user_histories(\n data_dir=data_dir,\n cutoff=cutoff,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n include_val=include_val,\n )\n histories_list.append(hist)\n\n # 2. Get past listens before cutoff to guarantee DISCOVERY filter\n # Read past items from the history listen_items and likes\n past_items_per_user: Dict[int, Set[int]] = {}\n for r, uid in enumerate(target_uids):\n # Items in listens and likes\n l_itms = set(hist[\"listen_items\"][r].numpy())\n lk_itms = set(hist[\"like_items\"][r][hist[\"like_mask\"][r] > 0].numpy())\n past = l_itms.union(lk_itms)\n past.discard(30000)\n past_items_per_user[r] = past\n\n # 3. Targets in [t_start, t_end]\n w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]\n w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]\n\n # Map and filter discovery likes (grade 3, weight 3.0)\n user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}\n\n for uid, item in zip(w_likes.uid, w_likes.item_id):\n r = uid_to_row[int(uid)]\n item_idx = item_to_idx.get(int(item))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 3.0))\n user_seen_targets[r].add(item_idx)\n\n # Map and filter discovery listens (grade 1, weight 1.0)\n # To balance, subsample if a user has too many listens\n grouped_listens = w_listens.groupby(\"uid\")\n for uid, group in grouped_listens:\n r = uid_to_row[int(uid)]\n items = group[\"item_id\"].to_numpy()\n if len(items) > sample_listens_per_user:\n items = np.random.choice(items, size=sample_listens_per_user, replace=False)\n for itm in items:\n item_idx = item_to_idx.get(int(itm))\n if item_idx is not None and item_idx not in past_items_per_user[r] and item_idx not in user_seen_targets[r]:\n pairs_list.append((w_idx, r, item_idx, 1.0))\n user_seen_targets[r].add(item_idx)",
"allow_multiple": false
}
### Tool output · 2026-09-29T02:33:00.044Z
Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
filters=[("timestamp", ">=", min_cutoff), ("is_organic", "==", 1), ("played_ratio_pct", ">=", listen_strong_threshold)],
)
listens_df = pd.concat([listens_df, val_tbl.to_pandas()], ignore_index=True)
strong_listens = listens_df[listens_df.uid.isin(target_uids_set)]
# Load full target user listens to guarantee 100% true discovery filtering
full_table = pq.read_table(
data_dir / "train_listens.parquet",
columns=["uid", "timestamp", "item_id"],
)
all_listens_df = full_table.to_pandas()
if include_val and (data_dir / "val_listens.parquet").exists():
val_tbl_full = pq.read_table(
data_dir / "val_listens.parquet",
columns=["uid", "timestamp", "item_id"],
)
all_listens_df = pd.concat([all_listens_df, val_tbl_full.to_pandas()], ignore_index=True)
all_listens_df = all_listens_df[all_listens_df.uid.isin(target_uids_set)]
histories_list: List[Dict[str, torch.Tensor]] = []
pairs_list: List[Tuple[int, int, int, float]] = [] # (window_idx, user_row, item_idx, weight)
for w_idx, cutoff in enumerate(cutoffs):
t_start = cutoff + gap_seconds
...
item_to_idx=item_to_idx,
include_val=include_val,
)
histories_list.append(hist)
# 2. Get 100% COMPLETE past items before cutoff to guarantee STRICT DISCOVERY filter
df_prior = all_listens_df[all_listens_df.timestamp < cutoff]
past_items_per_user = df_prior.groupby("uid")["item_id"].apply(set).to_dict()
# Add liked items before cutoff as well
likes_prior = likes_df[(likes_df.timestamp < cutoff) & (likes_df.uid.isin(target_uids_set))]
for uid, itm in zip(likes_prior.uid, likes_prior.item_id):
past_items_per_user.setdefault(int(uid), set()).add(int(itm))
# 3. Targets in [t_start, t_end]
w_likes = org_likes[(org_likes.timestamp >= t_start) & (org_likes.timestamp <= t_end)]
w_listens = strong_listens[(strong_listens.timestamp >= t_start) & (strong_listens.timestamp <= t_end)]
# Map and filter discovery likes (grade 3, weight 5.0)
user_seen_targets: Dict[int, Set[int]] = {r: set() for r in range(len(target_uids))}
for uid, item in zip(w_likes.uid, w_likes.item_id):
r = uid_to_row[int(uid)]
item_int = int(item)
if item_int not in past_items_per_user.get(int(uid), set()):
item_idx = item_to_idx.get(item_int)
if item_idx is not None and item_idx not in user_seen_targets[r]:
pairs_list.append((w_idx, r, item_idx, 5.0))
user_seen_targets[r].add(item_idx)
# Map and filter discovery listens (grade 1, weight 1.0)
# To balance, subsample if a user has too many listens
grouped_listens = w_listens.groupby("uid")
for uid, group in grouped_listens:
r = uid_to_row[int(uid)]
user_past = past_items_per_user.get(int(uid), set())
items = [int(it) for it in group["item_id"].to_numpy() if int(it) not in user_past]
if len(items) > sample_listens_per_user:
items = list(np.random.choice(items, size=sample_listens_per_user, replace=False))
for itm in items:
item_idx = item_to_idx.get(itm)
if item_idx is not None and item_idx not in user_seen_targets[r]:
pairs_list.append((w_idx, r, item_idx, 1.0))
user_seen_targets[r].add(item_idx)
np_pairs = np.array(pairs_list, dtype=np.float32)
np.random.shuffle(np_pairs)
...
## Step 76 · agent · 2026-09-29T02:33:02.922Z
### run_shell_command · 2026-09-29T02:33:02.922Z
{
"description": "Run train_val with strict discovery filtering and logq_weight 0.5",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 0.5"
}
### Tool output · 2026-09-29T02:34:34.666Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Generated 76663 discovery training pairs across 3 windows.
Data preparation completed in 39.79s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.39s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.1895 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007081,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.007081
Epoch 2/5: Loss=5.9163 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007211,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.007211
Epoch 3/5: Loss=5.5643 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.005033,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.005033
Epoch 4/5: Loss=5.2741 (3.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.003885,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.003885
Epoch 5/5: Loss=5.1058 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.004044,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.004044
=== Training Complete! Best Validation NDCG@10: 0.007211 ===
Process Group PGID: 2838
## Step 77 · agent · 2026-09-29T02:34:48.165Z
### Recorded reasoning summary
Analyzing LogQ Weights
I'm examining the impact of different `logq_weight` values. It's clear LogQ plays a significant role, as seen with the distinct outcomes for `1.0`, `0.5`, and `0.0`. I'm continuing to investigate the `logq_weight = 1.0` scenario to fully understand its effect.
### run_shell_command · 2026-09-29T02:34:48.165Z
{
"description": "Run train_val with logq_weight 1.0",
"command": "PYTHONPATH=/app/solution python3 /app/solution/train_val.py --num_windows 3 --epochs 5 --sample_listens 20 --logq_weight 1.0"
}
### Tool output · 2026-09-29T02:36:17.425Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Generated 76663 discovery training pairs across 3 windows.
Data preparation completed in 39.44s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.41s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.1998 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.009107,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.009107
Epoch 2/5: Loss=5.9321 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.00915,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.009150
Epoch 3/5: Loss=5.5913 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.10s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007496,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.007496
Epoch 4/5: Loss=5.3021 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.005305,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.005305
Epoch 5/5: Loss=5.1320 (3.2s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.00526,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.005260
=== Training Complete! Best Validation NDCG@10: 0.009150 ===
Process Group PGID: 2964
## Step 78 · agent · 2026-09-29T02:36:32.287Z
### Recorded reasoning summary
Improving Model Performance
My current focus is on refining the model's predictive accuracy. I've observed a notable decrease in the error score, from `0.011541` with a larger dataset (192,471 pairs) to `0.009150` with a reduced set of 76,663 pairs. This suggests that more comprehensive user-item interaction data significantly enhances recommender system performance by ensuring denser coverage.
### Recorded reasoning summary
Exploring Data Augmentation
I'm considering ways to increase our training pairs for better model performance. The current interaction density per item is quite low. I plan to investigate strategies like increasing weekly cutoffs and sampling more listens per user, and I'll also re-evaluate how `item_logq` is computed and its impact on both training and retrieval, especially regarding the influence of item popularity.
### Recorded reasoning summary
Validating Score Formulation
I've realized my current approach might be ignoring global item popularity, which is a strong baseline. My new hypothesis is that the true ranking score should combine personalized affinity (the dot product $u^T v$) with a scaled global popularity term ($\beta \log P(v)$). I'm now investigating if the two-tower architecture can actually represent this combined score as a direct vector product.
### run_shell_command · 2026-09-29T02:36:32.287Z
{
"description": "Test combining personalized two-tower score with popularity",
"command": "PYTHONPATH=/app/solution python3 -c \"\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\n# Catalog popularity feature: column 3 is norm_rank (0=most popular, 1=least), or log_pos (col 0)\n# Let's inspect cat_feats\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\""
}
### Tool output · 2026-09-29T02:36:36.248Z
Output: Traceback (most recent call last):
File "", line 8, in
ModuleNotFoundError: No module named 'evaluate_public'
Exit Code: 1
Process Group PGID: 3090
## Step 79 · agent · 2026-09-29T02:36:55.195Z
### run_shell_command · 2026-09-29T02:36:55.195Z
{
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\n# Precompute items\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\n# User histories\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\n# Dot product scores\nbase_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = np.log1p(cc.positive_listens.values.astype(np.float32))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\n# Validation eligibility\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:\n scores = base_scores + beta * log_pop_norm\n scores[unpacked_eligible == 0] = -1e9\n \n top100 = np.argsort(-scores, axis=1)[:, :100]\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Evaluate beta popularity weighting with two-tower dot product"
}
### Tool output · 2026-09-29T02:37:16.805Z
Output: /usr/bin/bash: line 67: 3103 Killed PYTHONPATH=/app/solution:/task python3 -c "
import sys
sys.path.extend(['/app/solution', '/task'])
import torch
import numpy as np
import pandas as pd
from pathlib import Path
from model import TwoTowerRanker
from data_prep import load_catalog_features, extract_user_histories
import evaluate_public
from ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking
DATA = Path('/task/data')
cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)
tu = pd.read_parquet(DATA / 'target_users.parquet')
target_uids = tu.uid.values
targets_set = set(target_uids)
model = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)
model.load_state_dict(torch.load('/app/solution/best_val_model.pt'))
model.eval()
# Precompute items
cand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)
# User histories
val_hist = extract_user_histories(
DATA, 24786800, target_uids, item_to_idx,
pref_state_path=DATA / 'validation_preference_state.parquet'
)
user_emb = model.compute_user_embeddings(val_hist) # (8456, 64)
# Dot product scores
base_scores = torch.matmul(user_emb, cand_emb.t()).numpy() # (8456, 30000)
cc = pd.read_parquet(DATA / 'candidate_catalog.parquet')
log_pop = np.log1p(cc.positive_listens.values.astype(np.float32))
log_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())
# Validation eligibility
vec = np.load(DATA / 'validation_eligible_candidates.npz')
unpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')
eligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')
graded = evaluate_public.validation_truth(eligibility, targets_set)
for beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.25, 0.3, 0.4, 0.5]:
scores = base_scores + beta * log_pop_norm
scores[unpacked_eligible == 0] = -1e9
top100 = np.argsort(-scores, axis=1)[:, :100]
rows = []
for u_idx, uid in enumerate(target_uids):
top_items = cat_items[top100[u_idx]]
for r, item_id in enumerate(top_items, 1):
rows.append((int(uid), int(item_id), r))
val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])
validated = validate_ranking(val_df, targets_set)
ranked = ranked_items(validated)
res = score_rankings(ranked, graded)
print(f'Beta = {beta:.2f}: NDCG@10 = {res[\"quality_ndcg@10\"]:.6f}')
"
Exit Code: 137
Process Group PGID: 3101
## Step 80 · agent · 2026-09-29T02:37:24.535Z
### Recorded reasoning summary
Investigating Memory Allocation
I'm currently probing the memory footprint of the `np.argsort` operation. It appears the internal buffer allocation within the loop is responsible for the OOM error. I'm exploring PyTorch's `torch.topk` as a more memory-efficient alternative.
### run_shell_command · 2026-09-29T02:37:24.535Z
{
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nimport evaluate_public\nfrom ranking_evaluation import CandidateSet, ranked_items, score_rankings, validate_ranking\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\ntargets_set = set(target_uids)\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\ncand_emb = model.precompute_candidate_embeddings(cat_feats) # (30000, 64)\n\nval_hist = extract_user_histories(\n DATA, 24786800, target_uids, item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet'\n)\nuser_emb = model.compute_user_embeddings(val_hist) # (8456, 64)\n\nbase_scores = torch.matmul(user_emb, cand_emb.t()) # (8456, 30000)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nlog_pop = torch.from_numpy(np.log1p(cc.positive_listens.values.astype(np.float32)))\nlog_pop_norm = (log_pop - log_pop.min()) / (log_pop.max() - log_pop.min())\n\nvec = np.load(DATA / 'validation_eligible_candidates.npz')\nunpacked_eligible = np.unpackbits(vec['packed_eligible'], axis=1, count=30000, bitorder='little')\nineligible_mask = torch.from_numpy(unpacked_eligible == 0)\n\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets_set)\n\nfor beta in [0.0, 0.05, 0.1, 0.15, 0.2, 0.3]:\n scores = base_scores + beta * log_pop_norm\n scores[ineligible_mask] = -1e9\n \n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n \n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n \n val_df = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\n validated = validate_ranking(val_df, targets_set)\n ranked = ranked_items(validated)\n res = score_rankings(ranked, graded)\n print(f'Beta = {beta:.2f}: NDCG@10 = {res[\\\"quality_ndcg@10\\\"]:.6f}')\n\"",
"description": "Test popularity addition with torch.topk"
}
### Tool output · 2026-09-29T02:37:44.174Z
Output: Beta = 0.00: NDCG@10 = 0.009150
Beta = 0.05: NDCG@10 = 0.009835
Beta = 0.10: NDCG@10 = 0.010169
Beta = 0.15: NDCG@10 = 0.010700
Beta = 0.20: NDCG@10 = 0.010400
Beta = 0.30: NDCG@10 = 0.010395
Process Group PGID: 3126
## Step 81 · agent · 2026-09-29T02:37:50.966Z
### Recorded reasoning summary
Investigating Model Performance
I'm currently digging into the discrepancy in our model's performance. It seems a recent run with 76k samples achieved a better score (0.009150) than a previous run with 192k samples, which scored 0.011541. My focus is on understanding the root cause of this difference, especially the early epoch performance from the 192k sample run.
### Recorded reasoning summary
Refining User Representation
I've identified a critical flaw in how liked items are being processed for validation. It appears the current method prioritizes `item_id` order over temporal recency, leading to a skewed representation of user preferences. I am investigating if timestamps exist and how to leverage them for more accurate historical data.
### run_shell_command · 2026-09-29T02:37:50.966Z
{
"description": "Check merging VPS liked with train_likes timestamps",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nDATA = Path('/task/data')\n\nvps = pd.read_parquet(DATA / 'validation_preference_state.parquet')\nlikes = pd.read_parquet(DATA / 'train_likes.parquet')\nmerged = pd.merge(vps[vps.state == 'liked'], likes[['uid', 'item_id', 'timestamp']], on=['uid', 'item_id'], how='inner')\nprint('VPS liked merged with train_likes:', len(merged), 'out of', len(vps[vps.state == 'liked']))\n\""
}
### Tool output · 2026-09-29T02:37:52.774Z
Output: VPS liked merged with train_likes: 428358 out of 418435
Process Group PGID: 3151
## Step 82 · agent · 2026-09-29T02:38:30.473Z
### Recorded reasoning summary
Prioritizing Timestamps
I've confirmed nearly all active likes in `train_likes` have timestamps. Prioritizing these recent interactions over arbitrary `item_id` ordering seems to significantly improve performance. Next, I'm focusing on sequential and multi-interest attention within the user tower.
### Recorded reasoning summary
Developing User Interest Representations
I'm exploring ways to capture diverse user interests beyond a single average vector. My current thinking involves splitting listens into short-term and long-term pools, incorporating explicit likes and dislikes, and concatenating these with user activity features into the User MLP.
### Recorded reasoning summary
Increasing Item Embeddings
I'm considering increasing the item embedding dimension from 64 to 128. This offers significantly more expressive power for distinguishing item nuances, with a manageable parameter size increase of around 15MB, which remains computationally efficient.
**Refining Training Cutoffs**
I'm also evaluating several potential training data cutoffs, structured around weekly intervals and gaps, to optimize the learning process. My current thinking explores five distinct cutoff points for data segmentation.
### Recorded reasoning summary
Adjusting Loss and Popularity
I'm now focusing on the temperature parameter $\tau$ in the sampled softmax, exploring values between 0.05 and 0.07. LogQ correction dramatically improved scores by mitigating sampling bias towards popular items, and I'm now investigating integrating popularity directly into the two-tower dot product, rather than as a separate inference term.
### Recorded reasoning summary
Incorporating Popularity
I'm ensuring that all popularity-related signals are intrinsically part of the dot product. The item tower already has access to features like `norm_rank` and `log_pos` within `catalog_features`, allowing it to naturally incorporate popularity.
### Recorded reasoning summary
Adjusting Learning Rates
I'm investigating the learning rate and regularization parameters to combat early overfitting. My current thinking suggests that a lower learning rate, increased weight decay, and higher dropout in the User Tower MLP, coupled with early stopping based on validation NDCG@10, will resolve the issue.
### Recorded reasoning summary
Experimenting with Architectures
My validation evaluation is now incredibly fast, taking under a second. This allows me to evaluate every epoch and save the best performing model. I'm refining the User Tower with specific pooling strategies for short-term and long-term listens, likes, and dislikes, alongside activity features, feeding into an MLP with dropout. The Item Tower will now include catalog feature projection before combining with the item embedding.
### Recorded reasoning summary
Structuring User Histories
I've confirmed that the last 20 listens in `listen_items` already represent the most recent activity. This simplifies structuring short-term listens in the `UserTower` to directly slice these recent items and their corresponding weights. I am now proceeding to implement this.
### write_file · 2026-09-29T02:38:30.473Z
{
"content": "\"\"\"PyTorch Two-Tower Ranker architecture for latency-efficient candidate retrieval.\"\"\"\nfrom __future__ import annotations\n\nfrom typing import Dict, Optional, Tuple\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item Tower computing independently precomputable item embeddings.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n feat_dim: int = 4,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_mlp = nn.Sequential(\n nn.Linear(feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n self.out_mlp = nn.Sequential(\n nn.Linear(embed_dim + 32, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(self, item_indices: torch.Tensor, item_features: torch.Tensor) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: LongTensor of shape (B,) with indices in [0, 30000].\n item_features: FloatTensor of shape (B, 4).\n\n Returns:\n L2-normalized FloatTensor of shape (B, out_dim).\n \"\"\"\n emb = self.item_embed(item_indices)\n feat = self.feat_mlp(item_features)\n combined = torch.cat([emb, feat], dim=-1)\n out = self.out_mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User Tower deriving user embedding strictly from interaction history.\"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n user_feat_dim: int = 6,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n short_window: int = 20,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.short_window = short_window\n self.user_feat_mlp = nn.Sequential(\n nn.Linear(user_feat_dim, 32),\n nn.LayerNorm(32),\n nn.GELU(),\n )\n # short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)\n in_dim = embed_dim * 4 + 32\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n )\n\n def forward(\n self,\n listen_items: torch.Tensor,\n listen_weights: torch.Tensor,\n like_items: torch.Tensor,\n like_mask: torch.Tensor,\n dislike_items: torch.Tensor,\n dislike_mask: torch.Tensor,\n user_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\"\"\"\n # 1. Full listen history pooling (long-term)\n l_emb = self.item_embed(listen_items) # (B, K, D)\n pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 2. Short-term listens pooling (most recent short_window items)\n s_items = listen_items[:, -self.short_window :]\n s_weights = listen_weights[:, -self.short_window :]\n s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7\n s_norm_weights = s_weights / s_sum\n s_emb = self.item_embed(s_items)\n pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)\n\n # 3. Likes pooling\n lk_emb = self.item_embed(like_items) # (B, L, D)\n lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom\n has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_likes = pooled_likes * has_likes\n\n # 4. Dislikes pooling\n dk_emb = self.item_embed(dislike_items) # (B, M, D)\n dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6\n pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom\n has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()\n pooled_dislikes = pooled_dislikes * has_dislikes\n\n # 5. User features projection\n user_feat_proj = self.user_feat_mlp(user_features)\n\n # 6. Combined MLP\n combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)\n out = self.mlp(combined)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n \"\"\"Complete Two-Tower Neural Network Ranker.\"\"\"\n\n def __init__(\n self,\n num_catalog_items: int = 30000,\n embed_dim: int = 64,\n hidden_dim: int = 128,\n out_dim: int = 64,\n dropout: float = 0.2,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n ):\n super().__init__()\n # Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding\n self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)\n nn.init.normal_(self.item_embed.weight, std=0.05)\n with torch.no_grad():\n self.item_embed.weight[num_catalog_items].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n feat_dim=4,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n user_feat_dim=6,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n dropout=dropout,\n )\n self.temperature = temperature\n self.logq_weight = logq_weight\n\n def precompute_candidate_embeddings(\n self,\n catalog_features: torch.Tensor,\n batch_size: int = 4096,\n ) -> torch.Tensor:\n \"\"\"Precompute offline item embeddings for all 30,000 catalog items.\"\"\"\n self.eval()\n n_items = len(catalog_features)\n embeddings = []\n with torch.no_grad():\n for i in range(0, n_items, batch_size):\n end = min(i + batch_size, n_items)\n idx = torch.arange(i, end, dtype=torch.int64)\n feat = catalog_features[i:end]\n emb = self.item_tower(idx, feat)\n embeddings.append(emb)\n return torch.cat(embeddings, dim=0)\n\n def compute_user_embeddings(\n self,\n user_history: Dict[str, torch.Tensor],\n batch_size: int = 1024,\n ) -> torch.Tensor:\n \"\"\"Compute user embeddings from interaction history.\"\"\"\n self.eval()\n n_users = len(user_history[\"listen_items\"])\n user_embeddings = []\n with torch.no_grad():\n for i in range(0, n_users, batch_size):\n end = min(i + batch_size, n_users)\n u_emb = self.user_tower(\n listen_items=user_history[\"listen_items\"][i:end],\n listen_weights=user_history[\"listen_weights\"][i:end],\n like_items=user_history[\"like_items\"][i:end],\n like_mask=user_history[\"like_mask\"][i:end],\n dislike_items=user_history[\"dislike_items\"][i:end],\n dislike_mask=user_history[\"dislike_mask\"][i:end],\n user_features=user_history[\"user_features\"][i:end],\n )\n user_embeddings.append(u_emb)\n return torch.cat(user_embeddings, dim=0)\n\n def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss\n",
"file_path": "/app/solution/model.py"
}
### Tool output · 2026-09-29T02:38:30.490Z
Successfully overwrote file: /app/solution/model.py. Here is the updated code:
...
def __init__(
self,
item_embed: nn.Embedding,
feat_dim: int = 4,
embed_dim: int = 64,
hidden_dim: int = 128,
out_dim: int = 64,
):
super().__init__()
self.item_embed = item_embed
self.feat_mlp = nn.Sequential(
...
item_embed: nn.Embedding,
embed_dim: int = 64,
user_feat_dim: int = 6,
hidden_dim: int = 128,
out_dim: int = 64,
dropout: float = 0.2,
short_window: int = 20,
):
super().__init__()
self.item_embed = item_embed
self.short_window = short_window
self.user_feat_mlp = nn.Sequential(
nn.Linear(user_feat_dim, 32),
nn.LayerNorm(32),
nn.GELU(),
)
# short_listens (D) + long_listens (D) + likes (D) + dislikes (D) + user_feat (32)
in_dim = embed_dim * 4 + 32
self.mlp = nn.Sequential(
nn.Linear(in_dim, hidden_dim),
nn.LayerNorm(hidden_dim),
nn.GELU(),
nn.Dropout(dropout),
...
like_mask: torch.Tensor,
dislike_items: torch.Tensor,
dislike_mask: torch.Tensor,
user_features: torch.Tensor,
) -> torch.Tensor:
"""Compute normalized user embeddings from interaction history."""
# 1. Full listen history pooling (long-term)
l_emb = self.item_embed(listen_items) # (B, K, D)
pooled_long = (l_emb * listen_weights.unsqueeze(-1)).sum(dim=1) # (B, D)
# 2. Short-term listens pooling (most recent short_window items)
s_items = listen_items[:, -self.short_window :]
s_weights = listen_weights[:, -self.short_window :]
s_sum = s_weights.sum(dim=1, keepdim=True) + 1e-7
s_norm_weights = s_weights / s_sum
s_emb = self.item_embed(s_items)
pooled_short = (s_emb * s_norm_weights.unsqueeze(-1)).sum(dim=1) # (B, D)
# 3. Likes pooling
lk_emb = self.item_embed(like_items) # (B, L, D)
lk_denom = like_mask.sum(dim=1, keepdim=True) + 1e-6
pooled_likes = (lk_emb * like_mask.unsqueeze(-1)).sum(dim=1) / lk_denom
has_likes = (like_mask.sum(dim=1, keepdim=True) > 0).float()
pooled_likes = pooled_likes * has_likes
# 4. Dislikes pooling
dk_emb = self.item_embed(dislike_items) # (B, M, D)
dk_denom = dislike_mask.sum(dim=1, keepdim=True) + 1e-6
pooled_dislikes = (dk_emb * dislike_mask.unsqueeze(-1)).sum(dim=1) / dk_denom
has_dislikes = (dislike_mask.sum(dim=1, keepdim=True) > 0).float()
pooled_dislikes = pooled_dislikes * has_dislikes
# 5. User features projection
user_feat_proj = self.user_feat_mlp(user_features)
# 6. Combined MLP
combined = torch.cat([pooled_short, pooled_long, pooled_likes, pooled_dislikes, user_feat_proj], dim=-1)
out = self.mlp(combined)
return F.normalize(out, p=2, dim=-1)
class TwoTowerRanker(nn.Module):
...
self,
num_catalog_items: int = 30000,
embed_dim: int = 64,
hidden_dim: int = 128,
out_dim: int = 64,
dropout: float = 0.2,
temperature: float = 0.07,
logq_weight: float = 1.0,
):
super().__init__()
# Item embedding table: 0..num_catalog_items-1 are catalog tracks, index num_catalog_items is UNK/padding
self.item_embed = nn.Embedding(num_catalog_items + 1, embed_dim, padding_idx=num_catalog_items)
nn.init.normal_(self.item_embed.weight, std=0.05)
...
def precompute_candidate_embeddings(
self,
catalog_features: torch.Tensor,
batch_size: int = 4096,
) -> torch.Tensor:
"""Precompute offline item embeddings for all 30,000 catalog items."""
self.eval()
n_items = len(catalog_features)
embeddings = []
with torch.no_grad():
for i in range(0, n_items, batch_size):
...
def compute_user_embeddings(
self,
user_history: Dict[str, torch.Tensor],
batch_size: int = 1024,
) -> torch.Tensor:
"""Compute user embeddings from interaction history."""
self.eval()
n_users = len(user_history["listen_items"])
user_embeddings = []
with torch.no_grad():
for i in range(0, n_users, batch_size):
...
sample_weights: torch.Tensor,
dislike_item_indices: Optional[torch.Tensor] = None,
dislike_item_features: Optional[torch.Tensor] = None,
dislike_mask: Optional[torch.Tensor] = None,
) -> torch.Tensor:
"""Compute InfoNCE sampled softmax loss with popularity debiasing."""
# User vectors (B, D)
u = self.user_tower(
listen_items=user_history_batch["listen_items"],
listen_weights=user_history_batch["listen_weights"],
like_items=user_history_batch["like_items"],
...
# Positive candidate vectors (B, D)
v_pos = self.item_tower(target_item_indices, target_item_features)
# Dot product matrix (B, B)
sim = torch.matmul(u, v_pos.t())
# Popularity correction and temperature scaling
logits = sim / self.temperature
if self.logq_weight > 0.0:
logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)
...
# Cross entropy with in-batch negatives
batch_size = len(target_item_indices)
targets = torch.arange(batch_size, device=target_item_indices.device)
per_sample_loss = F.cross_entropy(logits, targets, reduction="none")
# Weight by grade importance (likes 5.0, listens 1.0)
norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
infonce_loss = (per_sample_loss * norm_weights).mean()
return infonce_loss
## Step 83 · agent · 2026-09-29T02:38:38.377Z
### Recorded reasoning summary
Refining Preference Extraction
I'm updating `data_prep.py` to sort user likes by timestamp within `extract_user_histories`. This ensures that only the most recent likes are consistently selected when building preference states.
### replace · 2026-09-29T02:38:38.377Z
{
"new_string": " 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(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n\n # Merge with likes to get timestamps for chronological ordering\n likes_ts = pd.read_parquet(data_dir / \"train_likes.parquet\")\n if include_val and (data_dir / \"val_likes.parquet\").exists():\n likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / \"val_likes.parquet\")], ignore_index=True)\n likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][[\"uid\", \"item_id\", \"timestamp\"]].drop_duplicates([\"uid\", \"item_id\"])\n liked_df = pd.merge(liked_df, likes_ts, on=[\"uid\", \"item_id\"], how=\"left\").sort_values(\"timestamp\")\n else:",
"old_string": " 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(target_uids_set)]\n liked_df = pref_df[pref_df.state == \"liked\"]\n disliked_df = pref_df[pref_df.state == \"disliked\"]\n else:",
"allow_multiple": false,
"instruction": "Order preference state likes by timestamp for recent preference prioritization",
"file_path": "/app/solution/data_prep.py"
}
### Tool output · 2026-09-29T02:38:38.388Z
Successfully modified file: /app/solution/data_prep.py (1 replacements). Here is the updated code:
...
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(target_uids_set)]
liked_df = pref_df[pref_df.state == "liked"]
disliked_df = pref_df[pref_df.state == "disliked"]
# Merge with likes to get timestamps for chronological ordering
likes_ts = pd.read_parquet(data_dir / "train_likes.parquet")
if include_val and (data_dir / "val_likes.parquet").exists():
likes_ts = pd.concat([likes_ts, pd.read_parquet(data_dir / "val_likes.parquet")], ignore_index=True)
likes_ts = likes_ts[likes_ts.uid.isin(target_uids_set)][["uid", "item_id", "timestamp"]].drop_duplicates(["uid", "item_id"])
liked_df = pd.merge(liked_df, likes_ts, on=["uid", "item_id"], how="left").sort_values("timestamp")
else:
# Reconstruct active preferences from events
likes_df = pd.read_parquet(data_dir / "train_likes.parquet")
unlikes_df = pd.read_parquet(data_dir / "train_unlikes.parquet")
dislikes_df = pd.read_parquet(data_dir / "train_dislikes.parquet")
...
## Step 84 · agent · 2026-09-29T02:38:41.063Z
### run_shell_command · 2026-09-29T02:38:41.063Z
{
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 5e-4",
"description": "Run train_val with updated architecture and 4 windows"
}
### Tool output · 2026-09-29T02:40:33.356Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.82s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.60s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.2144 (5.6s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010692,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.010692
Epoch 2/5: Loss=6.0222 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.008403,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.008403
Epoch 3/5: Loss=5.7904 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007303,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.007303
Epoch 4/5: Loss=5.6115 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007151,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.007151
Epoch 5/5: Loss=5.5162 (5.5s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.007181,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.007181
=== Training Complete! Best Validation NDCG@10: 0.010692 ===
Process Group PGID: 3169
## Step 85 · agent · 2026-09-29T02:40:42.515Z
### Recorded reasoning summary
Analyzing Training Dynamics
I'm observing a peculiar trend during training. Specifically, at Epoch 1, my NDCG is around 0.011. Interestingly, as the loss on in-batch negatives decreases from 6.2 to 5.5, the validation NDCG actually drops. I'm trying to understand why minimizing this in-batch sampled softmax loss seems to be negatively impacting validation discovery.
### Recorded reasoning summary
Investigating Embedding Drift
I'm realizing that the in-batch negatives, which are predominantly popular items, are driving my user embeddings away from the most common tracks. This seems to be inadvertently pushing the model towards retrieving obscure items during validation, as my embeddings are no longer strongly associated with the popular items that might otherwise be recommended.
### run_shell_command · 2026-09-29T02:40:42.515Z
{
"description": "Check popularity rank distribution of gold validation items",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet\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 = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ngraded = evaluate_public.validation_truth(eligibility, targets)\n\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\ngold_ranks = []\nfor uid, items in graded.items():\n for it in items:\n gold_ranks.append(item_pop_rank[it])\n\ngold_ranks = np.array(gold_ranks)\nprint(f'Gold items popularity ranks: min={gold_ranks.min()}, 25%={np.percentile(gold_ranks, 25):.0f}, median={np.median(gold_ranks):.0f}, 75%={np.percentile(gold_ranks, 75):.0f}, max={gold_ranks.max()}')\n\""
}
### Tool output · 2026-09-29T02:40:44.627Z
Output: Gold items popularity ranks: min=1, 25%=1733, median=5622, 75%=13374, max=30000
Process Group PGID: 3295
## Step 86 · agent · 2026-09-29T02:40:49.059Z
### Recorded reasoning summary
Analyzing Gold Item Popularity
I've been examining the popularity distribution of our gold validation items. It's striking that the median popularity rank sits at 5,622, with the 75th percentile extending to 13,374, indicating a good spread of high-value items.
### run_shell_command · 2026-09-29T02:40:49.059Z
{
"description": "Check popularity rank of predicted items at Epoch 5",
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\nDATA = Path('/task/data')\ncc = pd.read_parquet(DATA / 'candidate_catalog.parquet')\nitem_pop_rank = {int(it): int(r) for it, r in zip(cc.item_id, cc.popularity_rank)}\n\nval = pd.read_parquet('/app/solution/validation.parquet')\npred_ranks = [item_pop_rank[it] for it in val.item_id.values]\npred_ranks = np.array(pred_ranks)\nprint(f'Predicted items popularity ranks: min={pred_ranks.min()}, 25%={np.percentile(pred_ranks, 25):.0f}, median={np.median(pred_ranks):.0f}, 75%={np.percentile(pred_ranks, 75):.0f}, max={pred_ranks.max()}')\n\""
}
### Tool output · 2026-09-29T02:40:50.836Z
Output: Predicted items popularity ranks: min=1, 25%=162, median=651, 75%=4160, max=29987
Process Group PGID: 3315
## Step 87 · agent · 2026-09-29T02:41:02.799Z
### Recorded reasoning summary
Analyzing Prediction Bias
I'm investigating why my model is overpredicting highly popular items. The median popularity for predicted items is surprisingly low, but the top 25% are extremely concentrated at rank 162. This contrasts sharply with the gold items' much higher median popularity of 5,622. I'm examining `ItemTower` to pinpoint the source of this discrepancy.
### Recorded reasoning summary
Refining Embeddings & Bias
I've identified that `feat_mlp` is feeding the same features to all items, potentially creating a dominant, shared component in item vectors aligned with `popularity_rank`. This, combined with normalization, could be pushing popular items higher. I'm now exploring how bypassing this shortcut or using explicit negative sampling might mitigate this.
### Recorded reasoning summary
Addressing Catalog Imbalance
My focus has shifted to the profound bias introduced by in-batch negatives. These only represent interacted items, heavily skewed towards the top 2,000. This means the vast majority of the catalog, 28,000 items, rarely gets updated, while popular items are disproportionately penalized. I'm now implementing mixed negative sampling to include uniform catalog negatives, ensuring all items receive appropriate penalty and the model learns a more accurate global ranking distribution.
### Recorded reasoning summary
Implementing Mixed Negatives
I'm now actively incorporating mixed negative sampling into the model. This involves generating random catalog negatives alongside in-batch negatives for each user. I'm testing the implementation to ensure it's efficient and correctly addresses the catalog coverage issues and popularity distortions I've observed.
### run_shell_command · 2026-09-29T02:41:02.799Z
{
"description": "Test mixed negative sampling logits and loss",
"command": "python3 -c \"\nimport torch\nimport torch.nn.functional as F\n\nB = 8\nM = 16\nD = 64\n\nu = torch.randn(B, D)\nu = F.normalize(u, p=2, dim=-1)\n\nv_pos = torch.randn(B, D)\nv_pos = F.normalize(v_pos, p=2, dim=-1)\n\nv_neg = torch.randn(M, D)\nv_neg = F.normalize(v_neg, p=2, dim=-1)\n\nv_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\nsim = torch.matmul(u, v_all.t()) # (B, B + M)\nprint('sim shape:', sim.shape)\nlogits = sim / 0.07\ntargets = torch.arange(B)\nloss = F.cross_entropy(logits, targets)\nprint('loss:', loss.item())\n\""
}
### Tool output · 2026-09-29T02:41:05.766Z
Output: sim shape: torch.Size([8, 24])
loss: 3.8542702198028564
Process Group PGID: 3332
## Step 88 · agent · 2026-09-29T02:41:19.931Z
### Recorded reasoning summary
Analyzing Sampling Probabilities
I'm currently dissecting the sampling probabilities within the Mixed Negative Sampling framework. My focus is on understanding the influence of popularity on in-batch items and the uniform sampling distribution for the negative items.
### Recorded reasoning summary
Implementing Mixed Sampling
I've successfully derived the exact Mixed Negative Sampling probability, aligning perfectly with established formulations. This means popular, mid-tier, and tail items are now handled with consistent mathematical rigor. I'm proceeding to implement this update in `model.py`.
### replace · 2026-09-29T02:41:19.931Z
{
"new_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n num_uniform_negatives: int = 256,\n ) -> torch.Tensor:\n \"\"\"Compute Mixed Negative Sampling InfoNCE loss.\n\n Args:\n user_history_batch: Dict of user history tensors of batch size B.\n target_item_indices: LongTensor of positive item indices (B,).\n target_item_features: FloatTensor of positive item features (B, 4).\n catalog_features: All catalog features (30000, 4).\n item_logq: Precomputed log probabilities of catalog items (30000,).\n sample_weights: FloatTensor of sample weights (B,).\n num_uniform_negatives: Number of uniform random catalog negatives.\n\n Returns:\n Scalar loss tensor.\n \"\"\"\n batch_size = len(target_item_indices)\n\n # 1. User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # 2. Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # 3. Uniform catalog negatives (M, D)\n if num_uniform_negatives > 0:\n neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)\n neg_features = catalog_features[neg_indices]\n v_neg = self.item_tower(neg_indices, neg_features)\n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)\n all_indices = torch.cat([target_item_indices, neg_indices], dim=0)\n else:\n v_all = v_pos\n all_indices = target_item_indices\n\n # 4. Dot product matrix (B, B + M)\n sim = torch.matmul(u, v_all.t())\n\n # 5. Temperature scaling\n logits = sim / self.temperature\n\n # 6. Mixed sampling logq correction\n if self.logq_weight > 0.0:\n # For in-batch items: logq from popularity\n # For uniform items: log(1 / N)\n total_items = float(len(catalog_features))\n uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))\n pos_logq = item_logq[target_item_indices]\n if num_uniform_negatives > 0:\n neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)\n cand_logq = torch.cat([pos_logq, neg_logq], dim=0)\n else:\n cand_logq = pos_logq\n logits = logits - self.logq_weight * cand_logq.unsqueeze(0)\n\n # 7. Cross entropy with targets = 0..B-1\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by sample importance\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n return (per_sample_loss * norm_weights).mean()",
"allow_multiple": false,
"file_path": "/app/solution/model.py",
"old_string": " def forward_loss(\n self,\n user_history_batch: Dict[str, torch.Tensor],\n target_item_indices: torch.Tensor,\n target_item_features: torch.Tensor,\n target_item_logq: torch.Tensor,\n sample_weights: torch.Tensor,\n dislike_item_indices: Optional[torch.Tensor] = None,\n dislike_item_features: Optional[torch.Tensor] = None,\n dislike_mask: Optional[torch.Tensor] = None,\n ) -> torch.Tensor:\n \"\"\"Compute InfoNCE sampled softmax loss with popularity debiasing.\"\"\"\n # User vectors (B, D)\n u = self.user_tower(\n listen_items=user_history_batch[\"listen_items\"],\n listen_weights=user_history_batch[\"listen_weights\"],\n like_items=user_history_batch[\"like_items\"],\n like_mask=user_history_batch[\"like_mask\"],\n dislike_items=user_history_batch[\"dislike_items\"],\n dislike_mask=user_history_batch[\"dislike_mask\"],\n user_features=user_history_batch[\"user_features\"],\n )\n\n # Positive candidate vectors (B, D)\n v_pos = self.item_tower(target_item_indices, target_item_features)\n\n # Dot product matrix (B, B)\n sim = torch.matmul(u, v_pos.t())\n\n # Popularity correction and temperature scaling\n logits = sim / self.temperature\n if self.logq_weight > 0.0:\n logits = logits - self.logq_weight * target_item_logq.unsqueeze(0)\n\n # Cross entropy with in-batch negatives\n batch_size = len(target_item_indices)\n targets = torch.arange(batch_size, device=target_item_indices.device)\n per_sample_loss = F.cross_entropy(logits, targets, reduction=\"none\")\n\n # Weight by grade importance (likes 5.0, listens 1.0)\n norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size\n infonce_loss = (per_sample_loss * norm_weights).mean()\n\n return infonce_loss",
"instruction": "Implement mixed negative sampling in TwoTowerRanker forward_loss"
}
### Tool output · 2026-09-29T02:41:19.946Z
Successfully modified file: /app/solution/model.py (1 replacements). Here is the updated code:
...
def forward_loss(
self,
user_history_batch: Dict[str, torch.Tensor],
target_item_indices: torch.Tensor,
target_item_features: torch.Tensor,
catalog_features: torch.Tensor,
item_logq: torch.Tensor,
sample_weights: torch.Tensor,
num_uniform_negatives: int = 256,
) -> torch.Tensor:
"""Compute Mixed Negative Sampling InfoNCE loss.
Args:
user_history_batch: Dict of user history tensors of batch size B.
target_item_indices: LongTensor of positive item indices (B,).
target_item_features: FloatTensor of positive item features (B, 4).
catalog_features: All catalog features (30000, 4).
item_logq: Precomputed log probabilities of catalog items (30000,).
sample_weights: FloatTensor of sample weights (B,).
num_uniform_negatives: Number of uniform random catalog negatives.
Returns:
Scalar loss tensor.
"""
batch_size = len(target_item_indices)
# 1. User vectors (B, D)
u = self.user_tower(
listen_items=user_history_batch["listen_items"],
listen_weights=user_history_batch["listen_weights"],
like_items=user_history_batch["like_items"],
like_mask=user_history_batch["like_mask"],
dislike_items=user_history_batch["dislike_items"],
dislike_mask=user_history_batch["dislike_mask"],
user_features=user_history_batch["user_features"],
)
# 2. Positive candidate vectors (B, D)
v_pos = self.item_tower(target_item_indices, target_item_features)
# 3. Uniform catalog negatives (M, D)
if num_uniform_negatives > 0:
neg_indices = torch.randint(0, len(catalog_features), (num_uniform_negatives,), device=target_item_indices.device)
neg_features = catalog_features[neg_indices]
v_neg = self.item_tower(neg_indices, neg_features)
v_all = torch.cat([v_pos, v_neg], dim=0) # (B + M, D)
all_indices = torch.cat([target_item_indices, neg_indices], dim=0)
else:
v_all = v_pos
all_indices = target_item_indices
# 4. Dot product matrix (B, B + M)
sim = torch.matmul(u, v_all.t())
# 5. Temperature scaling
logits = sim / self.temperature
# 6. Mixed sampling logq correction
if self.logq_weight > 0.0:
# For in-batch items: logq from popularity
# For uniform items: log(1 / N)
total_items = float(len(catalog_features))
uniform_logq = -torch.log(torch.tensor(total_items, dtype=torch.float32, device=target_item_indices.device))
pos_logq = item_logq[target_item_indices]
if num_uniform_negatives > 0:
neg_logq = torch.full((num_uniform_negatives,), uniform_logq.item(), device=target_item_indices.device)
cand_logq = torch.cat([pos_logq, neg_logq], dim=0)
else:
cand_logq = pos_logq
logits = logits - self.logq_weight * cand_logq.unsqueeze(0)
# 7. Cross entropy with targets = 0..B-1
targets = torch.arange(batch_size, device=target_item_indices.device)
per_sample_loss = F.cross_entropy(logits, targets, reduction="none")
# Weight by sample importance
norm_weights = sample_weights / (sample_weights.sum() + 1e-6) * batch_size
return (per_sample_loss * norm_weights).mean()
## Step 89 · agent · 2026-09-29T02:41:23.267Z
### replace · 2026-09-29T02:41:23.267Z
{
"instruction": "Update forward_loss call in train_val.py to support mixed negative sampling",
"new_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()",
"old_string": " optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n target_item_logq=b_logq,\n sample_weights=b_weights,\n )\n loss.backward()",
"allow_multiple": false,
"file_path": "/app/solution/train_val.py"
}
### Tool output · 2026-09-29T02:41:23.277Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
optimizer.zero_grad()
loss = model.forward_loss(
user_history_batch=b_hist,
target_item_indices=b_items,
target_item_features=b_feats,
catalog_features=catalog_features,
item_logq=item_logq,
sample_weights=b_weights,
num_uniform_negatives=256,
)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
...
## Step 90 · agent · 2026-09-29T02:41:28.947Z
### run_shell_command · 2026-09-29T02:41:28.947Z
{
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 5 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with mixed negative sampling"
}
### Tool output · 2026-09-29T02:43:23.791Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.52s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.56s
=== Initializing Two-Tower Ranker ===
=== Starting Training for 5 Epochs ===
Epoch 1/5: Loss=6.5575 (6.0s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014781,
"quality_users": 4263
}
}
Epoch 1 Public NDCG@10: 0.014781
Epoch 2/5: Loss=6.2701 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011245,
"quality_users": 4263
}
}
Epoch 2 Public NDCG@10: 0.011245
Epoch 3/5: Loss=5.9364 (6.0s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010538,
"quality_users": 4263
}
}
Epoch 3 Public NDCG@10: 0.010538
Epoch 4/5: Loss=5.6442 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.00954,
"quality_users": 4263
}
}
Epoch 4 Public NDCG@10: 0.009540
Epoch 5/5: Loss=5.4715 (6.1s)
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.00938,
"quality_users": 4263
}
}
Epoch 5 Public NDCG@10: 0.009380
=== Training Complete! Best Validation NDCG@10: 0.014781 ===
Process Group PGID: 3345
## Step 91 · agent · 2026-09-29T02:43:40.774Z
### Recorded reasoning summary
Achieving Breakthrough NDCG
My validation NDCG@10 has seen a significant leap to **0.014781**. This represents a substantial enhancement compared to both the popularity baseline and our prior model, with the best performing state now saved for further analysis.
### Recorded reasoning summary
Investigating NDCG Peaks
I'm focusing on the observed peak at Epoch 1 and subsequent decline in NDCG. I'm considering factors like learning rate, weight decay, and the number of uniform negatives. My hypothesis is that the model might be overfitting to specific negative samples or training data after the initial epoch, prompting exploration of more frequent evaluations and adjusted hyperparameters.
### replace · 2026-09-29T02:43:40.774Z
{
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"instruction": "Add intra-epoch evaluation every eval_steps steps",
"new_string": "def train_and_evaluate(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n val_user_history: dict[str, torch.Tensor],\n catalog_item_ids: np.ndarray,\n target_uids: np.ndarray,\n data_dir: Path,\n output_path: Path,\n epochs: int = 3,\n batch_size: int = 512,\n num_uniform_negatives: int = 512,\n eval_every_steps: int = 60,\n) -> float:\n best_score = 0.0\n global_step = 0\n n_samples = len(target_items)\n item_logq_t = torch.from_numpy(item_logq)\n\n for epoch in range(1, epochs + 1):\n model.train()\n perm = torch.randperm(n_samples)\n ep_loss = 0.0\n n_batches = 0\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n ep_loss += loss.item()\n n_batches += 1\n global_step += 1\n\n if global_step % eval_every_steps == 0:\n avg_l = ep_loss / n_batches\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n model.train()\n\n # End of epoch evaluation\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_user_history,\n catalog_features=catalog_features,\n catalog_item_ids=catalog_item_ids,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=output_path,\n )\n print(f\"[End of Epoch {epoch}] Val NDCG@10={score:.6f}\")\n if score > best_score:\n best_score = score\n print(f\"*** New best score: {best_score:.6f}! Saving model checkpoint...\")\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n return best_score",
"old_string": "def train_epoch(\n model: TwoTowerRanker,\n optimizer: torch.optim.Optimizer,\n scheduler: torch.optim.lr_scheduler.LRScheduler,\n histories_stacked: dict[str, torch.Tensor],\n global_uids: torch.Tensor,\n target_items: torch.Tensor,\n sample_weights: torch.Tensor,\n catalog_features: torch.Tensor,\n item_logq: torch.Tensor,\n batch_size: int = 512,\n) -> float:\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n\n for i in range(0, n_samples, batch_size):\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = catalog_features[b_items]\n b_logq = item_logq[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=catalog_features,\n item_logq=item_logq,\n sample_weights=b_weights,\n num_uniform_negatives=256,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n return total_loss / max(num_batches, 1)"
}
### Tool output · 2026-09-29T02:43:43.257Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker
def train_and_evaluate(
model: TwoTowerRanker,
optimizer: torch.optim.Optimizer,
scheduler: torch.optim.lr_scheduler.LRScheduler,
histories_stacked: dict[str, torch.Tensor],
global_uids: torch.Tensor,
target_items: torch.Tensor,
sample_weights: torch.Tensor,
catalog_features: torch.Tensor,
item_logq: torch.Tensor,
val_user_history: dict[str, torch.Tensor],
catalog_item_ids: np.ndarray,
target_uids: np.ndarray,
data_dir: Path,
output_path: Path,
epochs: int = 3,
batch_size: int = 512,
num_uniform_negatives: int = 512,
eval_every_steps: int = 60,
) -> float:
best_score = 0.0
global_step = 0
n_samples = len(target_items)
item_logq_t = torch.from_numpy(item_logq)
for epoch in range(1, epochs + 1):
model.train()
perm = torch.randperm(n_samples)
ep_loss = 0.0
n_batches = 0
for i in range(0, n_samples, batch_size):
idx = perm[i : min(i + batch_size, n_samples)]
b_uids = global_uids[idx]
b_items = target_items[idx]
b_weights = sample_weights[idx]
b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
b_feats = catalog_features[b_items]
optimizer.zero_grad()
loss = model.forward_loss(
user_history_batch=b_hist,
target_item_indices=b_items,
target_item_features=b_feats,
catalog_features=catalog_features,
item_logq=item_logq_t,
sample_weights=b_weights,
num_uniform_negatives=num_uniform_negatives,
)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
ep_loss += loss.item()
n_batches += 1
global_step += 1
if global_step % eval_every_steps == 0:
avg_l = ep_loss / n_batches
score = evaluate_on_validation(
model=model,
val_user_history=val_user_history,
catalog_features=catalog_features,
catalog_item_ids=catalog_item_ids,
target_uids=target_uids,
data_dir=data_dir,
output_path=output_path,
)
print(f"[Step {global_step} | Ep {epoch}] Loss={avg_l:.4f} | Val NDCG@10={score:.6f}")
if score > best_score:
best_score = score
print(f"*** New best score: {best_score:.6f}! Saving model checkpoint...")
torch.save(model.state_dict(), "/app/solution/best_val_model.pt")
model.train()
# End of epoch evaluation
score = evaluate_on_validation(
model=model,
val_user_history=val_user_history,
catalog_features=catalog_features,
catalog_item_ids=catalog_item_ids,
target_uids=target_uids,
data_dir=data_dir,
output_path=output_path,
)
print(f"[End of Epoch {epoch}] Val NDCG@10={score:.6f}")
if score > best_score:
best_score = score
print(f"*** New best score: {best_score:.6f}! Saving model checkpoint...")
torch.save(model.state_dict(), "/app/solution/best_val_model.pt")
return best_score
def evaluate_on_validation(
model: TwoTowerRanker,
val_user_history: dict[str, torch.Tensor],
catalog_features: torch.Tensor,
catalog_item_ids: np.ndarray,
...
## Step 92 · agent · 2026-09-29T02:43:48.786Z
### replace · 2026-09-29T02:43:48.786Z
{
"file_path": "/app/solution/train_val.py",
"allow_multiple": false,
"new_string": " print(f\"=== Starting Training with Intra-Epoch Validation ===\")\n best_score = train_and_evaluate(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq,\n val_user_history=val_history,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n epochs=args.epochs,\n batch_size=args.batch_size,\n num_uniform_negatives=512,\n eval_every_steps=40,\n )\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")",
"instruction": "Call train_and_evaluate from main",
"old_string": " print(f\"=== Starting Training for {args.epochs} Epochs ===\")\n best_score = 0.0\n for epoch in range(1, args.epochs + 1):\n t_ep = time.time()\n avg_loss = train_epoch(\n model=model,\n optimizer=optimizer,\n scheduler=scheduler,\n histories_stacked=histories_stacked,\n global_uids=global_uids,\n target_items=target_items,\n sample_weights=sample_weights,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n batch_size=args.batch_size,\n )\n ep_duration = time.time() - t_ep\n print(f\"Epoch {epoch}/{args.epochs}: Loss={avg_loss:.4f} ({ep_duration:.1f}s)\")\n\n # Validate\n score = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=data_dir,\n output_path=Path(args.output_path),\n )\n print(f\"Epoch {epoch} Public NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_val_model.pt\")\n\n print(f\"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===\")"
}
### Tool output · 2026-09-29T02:43:48.800Z
Successfully modified file: /app/solution/train_val.py (1 replacements). Here is the updated code:
...
optimizer = torch.optim.AdamW(model.parameters(), lr=args.lr, weight_decay=1e-4)
total_steps = (len(target_items) // args.batch_size + 1) * args.epochs
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=args.lr * 0.05)
print(f"=== Starting Training with Intra-Epoch Validation ===")
best_score = train_and_evaluate(
model=model,
optimizer=optimizer,
scheduler=scheduler,
histories_stacked=histories_stacked,
global_uids=global_uids,
target_items=target_items,
sample_weights=sample_weights,
catalog_features=cat_feats,
item_logq=item_logq,
val_user_history=val_history,
catalog_item_ids=cat_items,
target_uids=target_uids,
data_dir=data_dir,
output_path=Path(args.output_path),
epochs=args.epochs,
batch_size=args.batch_size,
num_uniform_negatives=512,
eval_every_steps=40,
)
print(f"=== Training Complete! Best Validation NDCG@10: {best_score:.6f} ===")
if __name__ == "__main__":
main()
...
## Step 93 · agent · 2026-09-29T02:43:51.297Z
### run_shell_command · 2026-09-29T02:43:51.297Z
{
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 4 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 1e-3",
"description": "Run train_val with intra-epoch checkpoints and 512 uniform negatives"
}
### Tool output · 2026-09-29T02:46:05.361Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Generated 122113 discovery training pairs across 4 windows.
Data preparation completed in 50.54s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.57s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.010606,
"quality_users": 4263
}
}
[Step 40 | Ep 1] Loss=6.9659 | Val NDCG@10=0.010606
*** New best score: 0.010606! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011484,
"quality_users": 4263
}
}
[Step 80 | Ep 1] Loss=6.9158 | Val NDCG@10=0.011484
*** New best score: 0.011484! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012202,
"quality_users": 4263
}
}
[Step 120 | Ep 1] Loss=6.8938 | Val NDCG@10=0.012202
*** New best score: 0.012202! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014921,
"quality_users": 4263
}
}
[Step 160 | Ep 1] Loss=6.8727 | Val NDCG@10=0.014921
*** New best score: 0.014921! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01712,
"quality_users": 4263
}
}
[Step 200 | Ep 1] Loss=6.8518 | Val NDCG@10=0.017120
*** New best score: 0.017120! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017548,
"quality_users": 4263
}
}
[End of Epoch 1] Val NDCG@10=0.017548
*** New best score: 0.017548! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.47s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017774,
"quality_users": 4263
}
}
[Step 240 | Ep 2] Loss=6.7210 | Val NDCG@10=0.017774
*** New best score: 0.017774! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.49s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016264,
"quality_users": 4263
}
}
[Step 280 | Ep 2] Loss=6.5972 | Val NDCG@10=0.016264
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.11s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015541,
"quality_users": 4263
}
}
[Step 320 | Ep 2] Loss=6.5819 | Val NDCG@10=0.015541
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014918,
"quality_users": 4263
}
}
[Step 360 | Ep 2] Loss=6.5692 | Val NDCG@10=0.014918
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.50s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014588,
"quality_users": 4263
}
}
[Step 400 | Ep 2] Loss=6.5617 | Val NDCG@10=0.014588
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014989,
"quality_users": 4263
}
}
[Step 440 | Ep 2] Loss=6.5526 | Val NDCG@10=0.014989
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.08s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.48s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014672,
"quality_users": 4263
}
}
[End of Epoch 2] Val NDCG@10=0.014672
=== Training Complete! Best Validation NDCG@10: 0.017774 ===
Process Group PGID: 3471
## Step 94 · agent · 2026-09-29T02:46:33.762Z
### Recorded reasoning summary
Achieving Improved NDCG
I've achieved a new best score of 0.017774 on validation NDCG@10 at step 240. This represents a significant gain of over 66% compared to the baseline, and the intra-epoch evaluation clearly shows this peak.
### run_shell_command · 2026-09-29T02:46:33.762Z
{
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=64, hidden_dim=128, out_dim=64)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Verify saved best_val_model.pt score"
}
### Tool output · 2026-09-29T02:46:46.319Z
Output: Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.07s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017774,
"quality_users": 4263
}
}
Verified saved best model score: 0.017774
Process Group PGID: 3741
## Step 95 · agent · 2026-09-29T02:46:53.658Z
### Recorded reasoning summary
Refining Performance Metrics
My verification of the 0.017774 score is complete. I'm now analyzing the `embed_dim` and `out_dim` parameters, specifically looking to see if increasing them from their current setting of 64 can further improve our results.
### run_shell_command · 2026-09-29T02:46:53.658Z
{
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96",
"description": "Experiment with embed_dim=96 and 5 windows"
}
### Tool output · 2026-09-29T02:49:43.776Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 61.25s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.59s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01299,
"quality_users": 4263
}
}
[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990
*** New best score: 0.012990! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012391,
"quality_users": 4263
}
}
[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01336,
"quality_users": 4263
}
}
[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360
*** New best score: 0.013360! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014308,
"quality_users": 4263
}
}
[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308
*** New best score: 0.014308! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016339,
"quality_users": 4263
}
}
[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339
*** New best score: 0.016339! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018645,
"quality_users": 4263
}
}
[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645
*** New best score: 0.018645! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018652,
"quality_users": 4263
}
}
[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652
*** New best score: 0.018652! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.019376,
"quality_users": 4263
}
}
[End of Epoch 1] Val NDCG@10=0.019376
*** New best score: 0.019376! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018836,
"quality_users": 4263
}
}
[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.21s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018001,
"quality_users": 4263
}
}
[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017624,
"quality_users": 4263
}
}
[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017974,
"quality_users": 4263
}
}
[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017418,
"quality_users": 4263
}
}
[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017469,
"quality_users": 4263
}
}
[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017564,
"quality_users": 4263
}
}
[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017218,
"quality_users": 4263
}
}
[End of Epoch 2] Val NDCG@10=0.017218
=== Training Complete! Best Validation NDCG@10: 0.019376 ===
Process Group PGID: 3784
## Step 96 · agent · 2026-09-29T02:49:55.950Z
### Recorded reasoning summary
Achieving Significant Improvement
I'm thrilled to report a substantial leap in performance! My validation NDCG@10 has reached an impressive 0.019376, representing an 81% relative improvement over the baseline of 0.010710. The progress has been remarkably stable, climbing incrementally through stages.
### run_shell_command · 2026-09-29T02:49:55.950Z
{
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 6e-4 --embed_dim 128 --out_dim 128",
"description": "Experiment with embed_dim=128 and out_dim=128"
}
### Tool output · 2026-09-29T02:52:57.524Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 65.83s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.62s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011977,
"quality_users": 4263
}
}
[Step 40 | Ep 1] Loss=6.9523 | Val NDCG@10=0.011977
*** New best score: 0.011977! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.28s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012458,
"quality_users": 4263
}
}
[Step 80 | Ep 1] Loss=6.9107 | Val NDCG@10=0.012458
*** New best score: 0.012458! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012316,
"quality_users": 4263
}
}
[Step 120 | Ep 1] Loss=6.8925 | Val NDCG@10=0.012316
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012337,
"quality_users": 4263
}
}
[Step 160 | Ep 1] Loss=6.8767 | Val NDCG@10=0.012337
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.25s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015118,
"quality_users": 4263
}
}
[Step 200 | Ep 1] Loss=6.8610 | Val NDCG@10=0.015118
*** New best score: 0.015118! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.54s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016676,
"quality_users": 4263
}
}
[Step 240 | Ep 1] Loss=6.8429 | Val NDCG@10=0.016676
*** New best score: 0.016676! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.30s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018768,
"quality_users": 4263
}
}
[Step 280 | Ep 1] Loss=6.8254 | Val NDCG@10=0.018768
*** New best score: 0.018768! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018617,
"quality_users": 4263
}
}
[End of Epoch 1] Val NDCG@10=0.018617
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017452,
"quality_users": 4263
}
}
[Step 320 | Ep 2] Loss=6.5896 | Val NDCG@10=0.017452
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017485,
"quality_users": 4263
}
}
[Step 360 | Ep 2] Loss=6.5827 | Val NDCG@10=0.017485
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.29s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016044,
"quality_users": 4263
}
}
[Step 400 | Ep 2] Loss=6.5715 | Val NDCG@10=0.016044
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015823,
"quality_users": 4263
}
}
[Step 440 | Ep 2] Loss=6.5632 | Val NDCG@10=0.015823
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.31s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.56s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016519,
"quality_users": 4263
}
}
[Step 480 | Ep 2] Loss=6.5536 | Val NDCG@10=0.016519
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.04s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.0163,
"quality_users": 4263
}
}
[Step 520 | Ep 2] Loss=6.5480 | Val NDCG@10=0.016300
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016186,
"quality_users": 4263
}
}
[Step 560 | Ep 2] Loss=6.5458 | Val NDCG@10=0.016186
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.26s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.55s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016426,
"quality_users": 4263
}
}
[End of Epoch 2] Val NDCG@10=0.016426
=== Training Complete! Best Validation NDCG@10: 0.018768 ===
Process Group PGID: 4108
## Step 97 · agent · 2026-09-29T02:53:06.373Z
### Recorded reasoning summary
Testing Embedding Dimensions
I'm exploring the impact of `embed_dim`, finding 96 performs slightly better than 128, offering a superior metric with improved speed. My next step is to assess the effect of increasing the training window size to six.
### run_shell_command · 2026-09-29T02:53:06.373Z
{
"description": "Run train_val with 6 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 6 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
}
### Tool output · 2026-09-29T02:56:25.447Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800, 21147200]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Extracting user histories for training cutoff 5: timestamp=21147200...
Generated 178230 discovery training pairs across 6 windows.
Data preparation completed in 71.28s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.62s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.011399,
"quality_users": 4263
}
}
[Step 40 | Ep 1] Loss=6.9946 | Val NDCG@10=0.011399
*** New best score: 0.011399! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012145,
"quality_users": 4263
}
}
[Step 80 | Ep 1] Loss=6.9303 | Val NDCG@10=0.012145
*** New best score: 0.012145! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.013402,
"quality_users": 4263
}
}
[Step 120 | Ep 1] Loss=6.9087 | Val NDCG@10=0.013402
*** New best score: 0.013402! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.013911,
"quality_users": 4263
}
}
[Step 160 | Ep 1] Loss=6.8934 | Val NDCG@10=0.013911
*** New best score: 0.013911! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014808,
"quality_users": 4263
}
}
[Step 200 | Ep 1] Loss=6.8799 | Val NDCG@10=0.014808
*** New best score: 0.014808! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016187,
"quality_users": 4263
}
}
[Step 240 | Ep 1] Loss=6.8634 | Val NDCG@10=0.016187
*** New best score: 0.016187! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017724,
"quality_users": 4263
}
}
[Step 280 | Ep 1] Loss=6.8476 | Val NDCG@10=0.017724
*** New best score: 0.017724! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018013,
"quality_users": 4263
}
}
[Step 320 | Ep 1] Loss=6.8328 | Val NDCG@10=0.018013
*** New best score: 0.018013! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017491,
"quality_users": 4263
}
}
[End of Epoch 1] Val NDCG@10=0.017491
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018002,
"quality_users": 4263
}
}
[Step 360 | Ep 2] Loss=6.6126 | Val NDCG@10=0.018002
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016227,
"quality_users": 4263
}
}
[Step 400 | Ep 2] Loss=6.6077 | Val NDCG@10=0.016227
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016475,
"quality_users": 4263
}
}
[Step 440 | Ep 2] Loss=6.5923 | Val NDCG@10=0.016475
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015871,
"quality_users": 4263
}
}
[Step 480 | Ep 2] Loss=6.5801 | Val NDCG@10=0.015871
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015568,
"quality_users": 4263
}
}
[Step 520 | Ep 2] Loss=6.5679 | Val NDCG@10=0.015568
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015686,
"quality_users": 4263
}
}
[Step 560 | Ep 2] Loss=6.5607 | Val NDCG@10=0.015686
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015682,
"quality_users": 4263
}
}
[Step 600 | Ep 2] Loss=6.5577 | Val NDCG@10=0.015682
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015152,
"quality_users": 4263
}
}
[Step 640 | Ep 2] Loss=6.5543 | Val NDCG@10=0.015152
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.015548,
"quality_users": 4263
}
}
[Step 680 | Ep 2] Loss=6.5490 | Val NDCG@10=0.015548
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016087,
"quality_users": 4263
}
}
[End of Epoch 2] Val NDCG@10=0.016087
=== Training Complete! Best Validation NDCG@10: 0.018013 ===
Process Group PGID: 4432
## Step 98 · agent · 2026-09-29T02:56:52.482Z
### Recorded reasoning summary
Achieving Higher Scores
My current focus is on pushing performance further. I've observed a promising NDCG@10 score of 0.019376 with a 5-window configuration. This suggests that exploring window sizes might be a fruitful avenue for improvement, and I'm keen to investigate this further to see if we can build upon this result.
### run_shell_command · 2026-09-29T02:56:52.482Z
{
"description": "Reproduce best model with 5 windows and embed_dim 96",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/train_val.py --num_windows 5 --epochs 2 --sample_listens 35 --logq_weight 1.0 --lr 7e-4 --embed_dim 96 --out_dim 96"
}
### Tool output · 2026-09-29T02:59:42.418Z
Output: === Loading Catalog and Users ===
Training cutoffs: [24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Training Pairs ===
Extracting user histories for training cutoff 0: timestamp=24180200...
Extracting user histories for training cutoff 1: timestamp=23573600...
Extracting user histories for training cutoff 2: timestamp=22967000...
Extracting user histories for training cutoff 3: timestamp=22360400...
Extracting user histories for training cutoff 4: timestamp=21753800...
Generated 151372 discovery training pairs across 5 windows.
Data preparation completed in 61.24s
=== Extracting Validation Cutoff History ===
Validation history ready in 3.64s
=== Initializing Two-Tower Ranker ===
=== Starting Training with Intra-Epoch Validation ===
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.21s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.53s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01299,
"quality_users": 4263
}
}
[Step 40 | Ep 1] Loss=6.9748 | Val NDCG@10=0.012990
*** New best score: 0.012990! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.012391,
"quality_users": 4263
}
}
[Step 80 | Ep 1] Loss=6.9262 | Val NDCG@10=0.012391
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.01336,
"quality_users": 4263
}
}
[Step 120 | Ep 1] Loss=6.9027 | Val NDCG@10=0.013360
*** New best score: 0.013360! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.014308,
"quality_users": 4263
}
}
[Step 160 | Ep 1] Loss=6.8854 | Val NDCG@10=0.014308
*** New best score: 0.014308! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.016339,
"quality_users": 4263
}
}
[Step 200 | Ep 1] Loss=6.8727 | Val NDCG@10=0.016339
*** New best score: 0.016339! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018645,
"quality_users": 4263
}
}
[Step 240 | Ep 1] Loss=6.8561 | Val NDCG@10=0.018645
*** New best score: 0.018645! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018652,
"quality_users": 4263
}
}
[Step 280 | Ep 1] Loss=6.8407 | Val NDCG@10=0.018652
*** New best score: 0.018652! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.52s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.019376,
"quality_users": 4263
}
}
[End of Epoch 1] Val NDCG@10=0.019376
*** New best score: 0.019376! Saving model checkpoint...
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018836,
"quality_users": 4263
}
}
[Step 320 | Ep 2] Loss=6.6198 | Val NDCG@10=0.018836
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.018001,
"quality_users": 4263
}
}
[Step 360 | Ep 2] Loss=6.6096 | Val NDCG@10=0.018001
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017624,
"quality_users": 4263
}
}
[Step 400 | Ep 2] Loss=6.5994 | Val NDCG@10=0.017624
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.20s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017974,
"quality_users": 4263
}
}
[Step 440 | Ep 2] Loss=6.5907 | Val NDCG@10=0.017974
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.22s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017418,
"quality_users": 4263
}
}
[Step 480 | Ep 2] Loss=6.5828 | Val NDCG@10=0.017418
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.23s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017469,
"quality_users": 4263
}
}
[Step 520 | Ep 2] Loss=6.5765 | Val NDCG@10=0.017469
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.24s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017564,
"quality_users": 4263
}
}
[Step 560 | Ep 2] Loss=6.5718 | Val NDCG@10=0.017564
Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.01s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.19s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.51s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017218,
"quality_users": 4263
}
}
[End of Epoch 2] Val NDCG@10=0.017218
=== Training Complete! Best Validation NDCG@10: 0.019376 ===
Process Group PGID: 4810
## Step 99 · agent · 2026-09-29T02:59:47.495Z
### run_shell_command · 2026-09-29T02:59:47.495Z
{
"description": "Evaluate /app/solution/validation.parquet with public evaluator",
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet"
}
### Tool output · 2026-09-29T02:59:50.845Z
Output: {
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.017218,
"quality_users": 4263
}
}
Process Group PGID: 5134
## Step 100 · agent · 2026-09-29T02:59:55.944Z
### Recorded reasoning summary
Refining Validation Output
I'm focusing on ensuring the final `validation.parquet` precisely reflects the optimal performance point. My current effort involves regenerating this file using the best saved checkpoint to guarantee the accuracy of the reported metric, specifically targeting a value of 0.0.
### run_shell_command · 2026-09-29T02:59:55.944Z
{
"command": "PYTHONPATH=/app/solution:/task python3 -c \"\nimport sys\nsys.path.extend(['/app/solution', '/task'])\nimport torch\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom model import TwoTowerRanker\nfrom data_prep import load_catalog_features, extract_user_histories\nfrom train_val import evaluate_on_validation\n\nDATA = Path('/task/data')\ncat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(DATA)\ntu = pd.read_parquet(DATA / 'target_users.parquet')\ntarget_uids = tu.uid.values\n\nmodel = TwoTowerRanker(num_catalog_items=30000, embed_dim=96, hidden_dim=128, out_dim=96)\nmodel.load_state_dict(torch.load('/app/solution/best_val_model.pt'))\nmodel.eval()\n\nval_history = extract_user_histories(\n data_dir=DATA,\n cutoff=24786800,\n target_uids=target_uids,\n item_to_idx=item_to_idx,\n pref_state_path=DATA / 'validation_preference_state.parquet',\n)\n\nscore = evaluate_on_validation(\n model=model,\n val_user_history=val_history,\n catalog_features=cat_feats,\n catalog_item_ids=cat_items,\n target_uids=target_uids,\n data_dir=DATA,\n output_path=Path('/app/solution/validation.parquet'),\n)\nprint('Verified saved best model score:', score)\n\"",
"description": "Generate and verify validation.parquet from best_val_model.pt"
}
### Tool output · 2026-09-29T03:00:08.588Z
Output: Precomputing candidate item embeddings offline...
Precomputed 30000 item embeddings in 0.02s
Computing user embeddings for validation targets...
Computed 8456 user embeddings in 0.12s
Running MIPS candidate retrieval and applying eligibility masks...
MIPS retrieval completed in 0.60s
Saved validation ranking to /app/solution/validation.parquet
Evaluator Output:
{
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.019376,
"quality_users": 4263
}
}
Verified saved best model score: 0.019376
Process Group PGID: 5154
## Step 101 · agent · 2026-09-29T03:00:16.322Z
### Recorded reasoning summary
Saving Best Score
I've successfully saved `validation.parquet` with the optimal score of `0.019376`. My focus now shifts to the next stage: constructing and refining a validation ranker, utilizing `train_*` data for history and `val_*` for its performance evaluation.
### update_topic · 2026-09-29T03:00:16.322Z
{
"strategic_intent": "Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.",
"summary": "Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.",
"title": "Full Data Refit & Submission Generation"
}
### Tool output · 2026-09-29T03:00:16.328Z
Current topic: "Full Data Refit & Submission Generation"
Topic summary: Successfully tuned the two-tower model architecture, achieving a public validation NDCG@10 of 0.019376 (an 81% relative improvement over the popularity baseline of 0.010710). Now transitioning to Phase 5: Refitting on Full Data & Test Submission. We will train the selected architecture using train_* + val_* events up to the test cutoff, compute offline candidate embeddings, generate user embeddings using test_preference_state.parquet, mask with test_eligible_candidates.npz, and write the final submission.parquet.
Strategic Intent: Refit the selected two-tower ranker on combined train and validation history, then generate the final hidden-test submission.
## Step 102 · agent · 2026-09-29T03:00:46.991Z
### Recorded reasoning summary
Evaluating Hidden Test Logic
I'm currently dissecting the intricacies of the final hidden-test phase. My focus is on meticulously understanding the distinct functionalities of the two public windows. I need to ensure a robust validation ranker is built and tuned solely on `train_*` history, with `val_*` serving as the subsequent outcomes. Following this, the selected approach must be refitted.
### Recorded reasoning summary
Refining Feature Usage
I'm now focusing on clarifying how validation data is used. Specifically, validation events should be treated as observed history for feature generation during training, but not as direct outcomes for measuring validation ranking performance. The final ranker, however, will be refitted using this combined history.
### Recorded reasoning summary
Determining Refit Cutoffs
I'm now establishing the precise training cutoff timestamps for refitting. With validation data now integrated into the training set, I'm shifting the original training cutoffs forward by seven days to encompass the validation window. This ensures the refitted model leverages the full historical context available up to the hidden test period.
### Recorded reasoning summary
Adjusting Training Boundaries
I've shifted my training cutoffs forward by a week. This aligns the validation window perfectly as the target, allowing the model to learn organic discovery behaviors while still incorporating historical patterns from earlier weeks. Inference will now incorporate recent interaction histories and platform-provided preferences.
### write_file · 2026-09-29T03:00:46.991Z
{
"file_path": "/app/solution/refit_submission.py",
"content": "\"\"\"Refit the two-tower ranker on train + val data and generate the final hidden-test submission.\"\"\"\nfrom __future__ import annotations\n\nimport argparse\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport torch\n\nfrom data_prep import build_training_pairs, extract_user_histories, load_catalog_features\nfrom model import TwoTowerRanker\n\n# Add /task to path for evaluation validators\nsys.path.append(\"/task\")\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\n\ndef refit_and_submit(\n data_dir: Path,\n submission_path: Path,\n epochs: int = 1,\n steps_to_train: int = 280,\n batch_size: int = 512,\n lr: float = 7e-4,\n embed_dim: int = 96,\n hidden_dim: int = 128,\n out_dim: int = 96,\n temperature: float = 0.07,\n logq_weight: float = 1.0,\n num_windows: int = 6,\n sample_listens: int = 35,\n num_uniform_negatives: int = 512,\n) -> None:\n torch.manual_seed(42)\n np.random.seed(42)\n torch.set_num_threads(8)\n\n print(\"=== Loading Catalog and Users for Full Refit ===\")\n cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)\n target_users_df = pd.read_parquet(data_dir / \"target_users.parquet\")\n target_uids = target_users_df[\"uid\"].to_numpy(dtype=np.int64)\n targets_set = set(target_uids)\n\n test_cutoff = 25393400\n val_cutoff = 24786800\n week_sec = 7 * 86400\n gap_sec = 1800\n\n # Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!\n cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]\n print(f\"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}\")\n\n print(\"=== Building Full Training Pairs (including Val Window) ===\")\n t0 = time.time()\n histories_list, user_win, target_items, sample_weights = build_training_pairs(\n data_dir=data_dir,\n cutoffs=cutoffs,\n item_to_idx=item_to_idx,\n target_uids=target_uids,\n sample_listens_per_user=sample_listens,\n include_val=True,\n )\n print(f\"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}\")\n\n # Stack user histories across windows\n keys = list(histories_list[0].keys())\n histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}\n n_users = len(target_uids)\n global_uids = user_win[:, 0] * n_users + user_win[:, 1]\n item_logq_t = torch.from_numpy(item_logq)\n\n print(\"=== Initializing Two-Tower Ranker ===\")\n model = TwoTowerRanker(\n num_catalog_items=30000,\n embed_dim=embed_dim,\n hidden_dim=hidden_dim,\n out_dim=out_dim,\n temperature=temperature,\n logq_weight=logq_weight,\n )\n\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)\n total_steps = steps_to_train\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)\n\n print(f\"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===\")\n model.train()\n n_samples = len(target_items)\n perm = torch.randperm(n_samples)\n global_step = 0\n t_train = time.time()\n\n while global_step < steps_to_train:\n for i in range(0, n_samples, batch_size):\n if global_step >= steps_to_train:\n break\n idx = perm[i : min(i + batch_size, n_samples)]\n b_uids = global_uids[idx]\n b_items = target_items[idx]\n b_weights = sample_weights[idx]\n\n b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}\n b_feats = cat_feats[b_items]\n\n optimizer.zero_grad()\n loss = model.forward_loss(\n user_history_batch=b_hist,\n target_item_indices=b_items,\n target_item_features=b_feats,\n catalog_features=cat_feats,\n item_logq=item_logq_t,\n sample_weights=b_weights,\n num_uniform_negatives=num_uniform_negatives,\n )\n loss.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)\n optimizer.step()\n scheduler.step()\n global_step += 1\n\n if global_step % 40 == 0:\n print(f\"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}\")\n\n print(f\"Refit training completed in {time.time() - t_train:.2f}s\")\n torch.save(model.state_dict(), \"/app/solution/final_refitted_model.pt\")\n\n # --- Generate Hidden Test Predictions ---\n print(\"=== Extracting Hidden-Test User Histories ===\")\n t0 = time.time()\n test_history = extract_user_histories(\n data_dir=data_dir,\n cutoff=test_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 print(f\"Test user histories ready in {time.time() - t0:.2f}s\")\n\n print(\"=== Precomputing Candidate Item Embeddings Offline ===\")\n t0 = time.time()\n cand_embeddings = model.precompute_candidate_embeddings(cat_feats)\n print(f\"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing Target User Embeddings ===\")\n t0 = time.time()\n user_embeddings = model.compute_user_embeddings(test_history)\n print(f\"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s\")\n\n print(\"=== Computing MIPS Retrieval with Test Candidate Mask ===\")\n t0 = time.time()\n scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)\n\n # Load test eligibility bitmask\n test_eligibility = CandidateSet(data_dir / \"test_eligible_candidates.npz\")\n unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder=\"little\")\n ineligible_mask = unpacked_test == 0\n scores[torch.from_numpy(ineligible_mask)] = -1e9\n\n # Top 100 retrieval\n top100 = torch.topk(scores, 100, dim=1).indices.numpy()\n print(f\"MIPS retrieval completed in {time.time() - t0:.2f}s\")\n\n print(\"=== Building Final Submission DataFrame ===\")\n rows = []\n for u_idx, uid in enumerate(target_uids):\n top_items = cat_items[top100[u_idx]]\n for r, item_id in enumerate(top_items, 1):\n rows.append((int(uid), int(item_id), r))\n\n sub_df = pd.DataFrame(rows, columns=[\"uid\", \"item_id\", \"rank\"])\n\n # Strict contract validation\n print(\"=== Validating Submission Contract ===\")\n validated = validate_ranking(sub_df, targets_set)\n print(\"Contract structure validation passed!\")\n\n # Check candidate eligibility for all rows\n print(\"Checking candidate eligibility against test_eligible_candidates.npz...\")\n is_valid_candidate = [\n test_eligibility.is_eligible(int(u), int(it))\n for u, it in zip(validated[\"uid\"].values, validated[\"item_id\"].values)\n ]\n if not all(is_valid_candidate):\n num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)\n raise ValueError(f\"Found {num_invalid} ineligible items in final submission!\")\n print(\"100% of submitted items are verified eligible in test_eligible_candidates.npz!\")\n\n submission_path.parent.mkdir(parents=True, exist_ok=True)\n validated.to_parquet(submission_path, index=False)\n print(f\"Successfully saved final submission to {submission_path}\")\n print(f\"Rows: {len(validated)}, Users: {validated['uid'].nunique()}\")\n\n\ndef main():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--data_dir\", type=str, default=\"/task/data\")\n parser.add_argument(\"--submission_path\", type=str, default=\"/app/solution/submission.parquet\")\n parser.add_argument(\"--steps\", type=int, default=280)\n parser.add_argument(\"--batch_size\", type=int, default=512)\n parser.add_argument(\"--lr\", type=float, default=7e-4)\n parser.add_argument(\"--embed_dim\", type=int, default=96)\n parser.add_argument(\"--hidden_dim\", type=int, default=128)\n parser.add_argument(\"--out_dim\", type=int, default=96)\n parser.add_argument(\"--temperature\", type=float, default=0.07)\n parser.add_argument(\"--logq_weight\", type=float, default=1.0)\n parser.add_argument(\"--num_windows\", type=int, default=6)\n parser.add_argument(\"--sample_listens\", type=int, default=35)\n args = parser.parse_args()\n\n refit_and_submit(\n data_dir=Path(args.data_dir),\n submission_path=Path(args.submission_path),\n steps_to_train=args.steps,\n batch_size=args.batch_size,\n lr=args.lr,\n embed_dim=args.embed_dim,\n hidden_dim=args.hidden_dim,\n out_dim=args.out_dim,\n temperature=args.temperature,\n logq_weight=args.logq_weight,\n num_windows=args.num_windows,\n sample_listens=args.sample_listens,\n )\n\n\nif __name__ == \"__main__\":\n main()\n"
}
### Tool output · 2026-09-29T03:00:47.004Z
Successfully created and wrote to new file: /app/solution/refit_submission.py. Here is the updated code:
"""Refit the two-tower ranker on train + val data and generate the final hidden-test submission."""
from __future__ import annotations
import argparse
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import torch
from data_prep import build_training_pairs, extract_user_histories, load_catalog_features
from model import TwoTowerRanker
# Add /task to path for evaluation validators
sys.path.append("/task")
from ranking_evaluation import CandidateSet, validate_ranking
def refit_and_submit(
data_dir: Path,
submission_path: Path,
epochs: int = 1,
steps_to_train: int = 280,
batch_size: int = 512,
lr: float = 7e-4,
embed_dim: int = 96,
hidden_dim: int = 128,
out_dim: int = 96,
temperature: float = 0.07,
logq_weight: float = 1.0,
num_windows: int = 6,
sample_listens: int = 35,
num_uniform_negatives: int = 512,
) -> None:
torch.manual_seed(42)
np.random.seed(42)
torch.set_num_threads(8)
print("=== Loading Catalog and Users for Full Refit ===")
cat_feats, item_to_idx, cat_items, item_logq = load_catalog_features(data_dir)
target_users_df = pd.read_parquet(data_dir / "target_users.parquet")
target_uids = target_users_df["uid"].to_numpy(dtype=np.int64)
targets_set = set(target_uids)
test_cutoff = 25393400
val_cutoff = 24786800
week_sec = 7 * 86400
gap_sec = 1800
# Cutoffs shifted forward so Window 0 uses train as history and val as outcomes!
cutoffs = [val_cutoff - w * (week_sec + gap_sec) for w in range(num_windows)]
print(f"Refit training cutoffs ({len(cutoffs)} windows): {cutoffs}")
print("=== Building Full Training Pairs (including Val Window) ===")
t0 = time.time()
histories_list, user_win, target_items, sample_weights = build_training_pairs(
data_dir=data_dir,
cutoffs=cutoffs,
item_to_idx=item_to_idx,
target_uids=target_uids,
sample_listens_per_user=sample_listens,
include_val=True,
)
print(f"Data preparation completed in {time.time() - t0:.2f}s, pairs={len(target_items)}")
# Stack user histories across windows
keys = list(histories_list[0].keys())
histories_stacked = {k: torch.cat([h[k] for h in histories_list], dim=0) for k in keys}
n_users = len(target_uids)
global_uids = user_win[:, 0] * n_users + user_win[:, 1]
item_logq_t = torch.from_numpy(item_logq)
print("=== Initializing Two-Tower Ranker ===")
model = TwoTowerRanker(
num_catalog_items=30000,
embed_dim=embed_dim,
hidden_dim=hidden_dim,
out_dim=out_dim,
temperature=temperature,
logq_weight=logq_weight,
)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=1e-4)
total_steps = steps_to_train
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=total_steps, eta_min=lr * 0.05)
print(f"=== Training Refitted Two-Tower Model for {steps_to_train} Steps ===")
model.train()
n_samples = len(target_items)
perm = torch.randperm(n_samples)
global_step = 0
t_train = time.time()
while global_step < steps_to_train:
for i in range(0, n_samples, batch_size):
if global_step >= steps_to_train:
break
idx = perm[i : min(i + batch_size, n_samples)]
b_uids = global_uids[idx]
b_items = target_items[idx]
b_weights = sample_weights[idx]
b_hist = {k: v[b_uids] for k, v in histories_stacked.items()}
b_feats = cat_feats[b_items]
optimizer.zero_grad()
loss = model.forward_loss(
user_history_batch=b_hist,
target_item_indices=b_items,
target_item_features=b_feats,
catalog_features=cat_feats,
item_logq=item_logq_t,
sample_weights=b_weights,
num_uniform_negatives=num_uniform_negatives,
)
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
optimizer.step()
scheduler.step()
global_step += 1
if global_step % 40 == 0:
print(f"[Step {global_step}/{steps_to_train}] Loss: {loss.item():.4f}")
print(f"Refit training completed in {time.time() - t_train:.2f}s")
torch.save(model.state_dict(), "/app/solution/final_refitted_model.pt")
# --- Generate Hidden Test Predictions ---
print("=== Extracting Hidden-Test User Histories ===")
t0 = time.time()
test_history = extract_user_histories(
data_dir=data_dir,
cutoff=test_cutoff,
target_uids=target_uids,
item_to_idx=item_to_idx,
pref_state_path=data_dir / "test_preference_state.parquet",
include_val=True,
)
print(f"Test user histories ready in {time.time() - t0:.2f}s")
print("=== Precomputing Candidate Item Embeddings Offline ===")
t0 = time.time()
cand_embeddings = model.precompute_candidate_embeddings(cat_feats)
print(f"Candidate embeddings shape={cand_embeddings.shape} computed in {time.time() - t0:.2f}s")
print("=== Computing Target User Embeddings ===")
t0 = time.time()
user_embeddings = model.compute_user_embeddings(test_history)
print(f"User embeddings shape={user_embeddings.shape} computed in {time.time() - t0:.2f}s")
print("=== Computing MIPS Retrieval with Test Candidate Mask ===")
t0 = time.time()
scores = torch.matmul(user_embeddings, cand_embeddings.t()) # (8456, 30000)
# Load test eligibility bitmask
test_eligibility = CandidateSet(data_dir / "test_eligible_candidates.npz")
unpacked_test = np.unpackbits(test_eligibility.packed, axis=1, count=30000, bitorder="little")
ineligible_mask = unpacked_test == 0
scores[torch.from_numpy(ineligible_mask)] = -1e9
# Top 100 retrieval
top100 = torch.topk(scores, 100, dim=1).indices.numpy()
print(f"MIPS retrieval completed in {time.time() - t0:.2f}s")
print("=== Building Final Submission DataFrame ===")
rows = []
for u_idx, uid in enumerate(target_uids):
top_items = cat_items[top100[u_idx]]
for r, item_id in enumerate(top_items, 1):
rows.append((int(uid), int(item_id), r))
sub_df = pd.DataFrame(rows, columns=["uid", "item_id", "rank"])
# Strict contract validation
print("=== Validating Submission Contract ===")
validated = validate_ranking(sub_df, targets_set)
print("Contract structure validation passed!")
# Check candidate eligibility for all rows
print("Checking candidate eligibility against test_eligible_candidates.npz...")
is_valid_candidate = [
test_eligibility.is_eligible(int(u), int(it))
for u, it in zip(validated["uid"].values, validated["item_id"].values)
]
if not all(is_valid_candidate):
num_invalid = len(is_valid_candidate) - sum(is_valid_candidate)
raise ValueError(f"Found {num_invalid} ineligible items in final submission!")
print("100% of submitted items are verified eligible in test_eligible_candidates.npz!")
submission_path.parent.mkdir(parents=True, exist_ok=True)
validated.to_parquet(submission_path, index=False)
print(f"Successfully saved final submission to {submission_path}")
print(f"Rows: {len(validated)}, Users: {validated['uid'].nunique()}")
def main():
parser = argparse.ArgumentParser()
parser.add_argument("--data_dir", type=str, default="/task/data")
parser.add_argument("--submission_path", type=str, default="/app/solution/submission.parquet")
parser.add_argument("--steps", type=int, default=280)
parser.add_argument("--batch_size", type=int, default=512)
parser.add_argument("--lr", type=float, default=7e-4)
parser.add_argument("--embed_dim", type=int, default=96)
parser.add_argument("--hidden_dim", type=int, default=128)
parser.add_argument("--out_dim", type=int, default=96)
parser.add_argument("--temperature", type=float, default=0.07)
parser.add_argument("--logq_weight", type=float, default=1.0)
parser.add_argument("--num_windows", type=int, default=6)
parser.add_argument("--sample_listens", type=int, default=35)
args = parser.parse_args()
refit_and_submit(
data_dir=Path(args.data_dir),
submission_path=Path(args.submission_path),
steps_to_train=args.steps,
batch_size=args.batch_size,
lr=args.lr,
embed_dim=args.embed_dim,
hidden_dim=args.hidden_dim,
out_dim=args.out_dim,
temperature=args.temperature,
logq_weight=args.logq_weight,
num_windows=args.num_windows,
sample_listens=args.sample_listens,
)
if __name__ == "__main__":
main()
## Step 103 · agent · 2026-09-29T03:00:56.000Z
### run_shell_command · 2026-09-29T03:00:56.000Z
{
"description": "Run refit_submission.py to generate final submission",
"command": "PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py --steps 280 --lr 7e-4 --embed_dim 96 --out_dim 96 --num_windows 6 --sample_listens 35"
}
### Tool output · 2026-09-29T03:02:33.544Z
Output: === Loading Catalog and Users for Full Refit ===
Refit training cutoffs (6 windows): [24786800, 24180200, 23573600, 22967000, 22360400, 21753800]
=== Building Full Training Pairs (including Val Window) ===
Extracting user histories for training cutoff 0: timestamp=24786800...
Extracting user histories for training cutoff 1: timestamp=24180200...
Extracting user histories for training cutoff 2: timestamp=23573600...
Extracting user histories for training cutoff 3: timestamp=22967000...
Extracting user histories for training cutoff 4: timestamp=22360400...
Extracting user histories for training cutoff 5: timestamp=21753800...
Generated 180980 discovery training pairs across 6 windows.
Data preparation completed in 74.25s, pairs=180980
=== Initializing Two-Tower Ranker ===
=== Training Refitted Two-Tower Model for 280 Steps ===
[Step 40/280] Loss: 6.8969
[Step 80/280] Loss: 6.8447
[Step 120/280] Loss: 6.8488
[Step 160/280] Loss: 6.7927
[Step 200/280] Loss: 6.8334
[Step 240/280] Loss: 6.8618
[Step 280/280] Loss: 6.8466
Refit training completed in 10.20s
=== Extracting Hidden-Test User Histories ===
Test user histories ready in 3.69s
=== Precomputing Candidate Item Embeddings Offline ===
Candidate embeddings shape=torch.Size([30000, 96]) computed in 0.02s
=== Computing Target User Embeddings ===
User embeddings shape=torch.Size([8456, 96]) computed in 0.17s
=== Computing MIPS Retrieval with Test Candidate Mask ===
MIPS retrieval completed in 0.52s
=== Building Final Submission DataFrame ===
=== Validating Submission Contract ===
Contract structure validation passed!
Checking candidate eligibility against test_eligible_candidates.npz...
100% of submitted items are verified eligible in test_eligible_candidates.npz!
Successfully saved final submission to /app/solution/submission.parquet
Rows: 845600, Users: 8456
Process Group PGID: 5197
## Step 104 · agent · 2026-09-29T03:03:18.864Z
### Recorded reasoning summary
Confirming Submission Validity
I've successfully validated the submission contract and confirmed that all submitted items meet the eligibility criteria defined in `test_eligible_candidates.npz`. The final submission has been saved.
### run_shell_command · 2026-09-29T03:03:18.864Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking, ranked_items\n\nDATA = Path('/task/data')\ntargets = pd.read_parquet(DATA / 'target_users.parquet')['uid'].values\ntargets_set = set(targets)\n\nsub_path = Path('/app/solution/submission.parquet')\nprint('Checking submission file:', sub_path)\ndf = pd.read_parquet(sub_path)\nprint('Shape:', df.shape)\nprint('Columns:', df.columns.tolist())\nprint('Data types:\\n', df.dtypes)\n\nvalidated = validate_ranking(df, targets_set)\nprint('validate_ranking passed!')\n\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\nprint('Validating every single item against test_eligible_candidates.npz...')\ninvalids = []\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n invalids.append((uid, item))\n\nprint(f'Total invalid candidates: {len(invalids)}')\nassert len(invalids) == 0, 'Found invalid candidates!'\n\nprint('All 845,600 user-item pairs are 100% verified eligible!')\n\"",
"description": "Exhaustively verify final submission against all constraints and eligibility"
}
### Tool output · 2026-09-29T03:03:21.657Z
Output: Checking submission file: /app/solution/submission.parquet
Shape: (845600, 3)
Columns: ['uid', 'item_id', 'rank']
Data types:
uid int64
item_id int64
rank int64
dtype: object
validate_ranking passed!
Validating every single item against test_eligible_candidates.npz...
Total invalid candidates: 0
All 845,600 user-item pairs are 100% verified eligible!
Process Group PGID: 5233
## Step 105 · agent · 2026-09-29T03:03:39.003Z
### Recorded reasoning summary
Reviewing Deliverables
I'm currently in Phase 6, focusing on verifying that all required deliverables, specifically the code for defining, training, and applying both towers, are present and correctly formatted as per the prompt's instructions.
### Recorded reasoning summary
Documenting Model Details
I'm now focused on creating a comprehensive `README.md` file for the `/app/solution/` directory. This document will clearly outline the two-tower neural network architecture, the training objective, the public validation score, and the final submission command. I'll ensure it reflects the PyTorch implementation and the specific feature projections used for item metadata.
### Recorded reasoning summary
Refining User Tower
My focus is now on the user tower. I'm implementing multi-scale pooling for listening taste, incorporating both short-term weighted pooling and long-term attention-based pooling with time decay. I'm also handling explicit likes and dislikes via mean pooling and incorporating user activity summaries before the final MLP projection.
### Recorded reasoning summary
Integrating Search and Objective
I'm now integrating the Inner-Product Search mechanism, specifically the $U V^T$ calculation with platform eligibility bitmask filtering. My focus is also on refining the training objective, employing a mixed Negative Sampling InfoNCE approach with LogQ popularity debiasing, incorporating multi-window temporal backtesting, in-batch negatives, uniform catalog negatives, and relevance weighting for an optimized model.
### update_topic · 2026-09-29T03:03:39.003Z
{
"strategic_intent": "Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.",
"title": "Documentation & Verification",
"summary": "Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands."
}
### Tool output · 2026-09-29T03:03:39.007Z
Current topic: "Documentation & Verification"
Topic summary: Completed Phase 5 (Full Data Refit & Submission Generation). Generated and verified /app/solution/submission.parquet. Transitioning to Phase 6: Documentation and Final Verification. We will author /app/solution/README.md with detailed model architecture descriptions, training objective rationale, validation benchmarks, and reproduction commands.
Strategic Intent: Document the two-tower architecture, training objective, public validation score, and submission commands in /app/solution/README.md.
## Step 106 · agent · 2026-09-29T03:03:56.562Z
### write_file · 2026-09-29T03:03:56.562Z
{
"file_path": "/app/solution/README.md",
"content": "# Latency-Efficient PyTorch Two-Tower Ranker for \"Made for You\" Discovery Shelf\n\n## Overview\nThis solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the \"Made for You\" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.\n\n---\n\n## 1. Model Architecture\n\nThe ranker strictly decomposes into two independent PyTorch neural towers:\n\n### Item Tower (`ItemTower` in `model.py`)\n- **Inputs**:\n - `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).\n - Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.\n - Catalog Metadata Features (4 dimensions):\n - Standardized $\\log(1 + \\text{positive\\_listens})$\n - Standardized $\\log(1 + \\text{organic\\_positive\\_listens})$\n - Organic Listen Ratio: $\\frac{\\text{organic\\_listens} + 1e-4}{\\text{positive\\_listens} + 1}$\n - Normalized Popularity Rank: $\\frac{\\text{popularity\\_rank}}{30000}$\n- **Architecture**:\n - Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).\n - Concatenation of item embedding (96d) and feature projection (32d).\n - Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization to the unit sphere: $\\|v\\|_2 = 1$.\n- **Offline Precomputation**:\n - All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \\in \\mathbb{R}^{30000 \\times 96}$.\n\n### User Tower (`UserTower` in `model.py`)\n- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):\n 1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\\ge 50\\%$ played ratio), capturing the user's immediate session context and mood.\n 2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\\lambda = \\frac{\\ln 2}{14 \\text{ days}}$), played ratio completion weighting, and organic discovery bonus.\n 3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.\n 4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.\n 5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.\n- **Architecture**:\n - Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.\n - Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).\n - Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.\n - $L_2$ Normalization: $\\|u\\|_2 = 1$.\n\n### Relevance Scoring & Latency-Efficient Retrieval\n- **Exact Decomposition**:\n $$\\text{RelevanceScore}(u, v) = u^T v$$\n The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.\n- **MIPS Latency**:\n At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\\mu$s per user).\n\n---\n\n## 2. Training Objective\n\nThe model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:\n\n1. **Temporal Slicing & True Discovery Filtering**:\n - To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).\n - In each target window $[C + 1800, C + 1800 + 7 \\text{ days}]$, ground-truth organic likes and strong organic listens ($\\ge 80\\%$) are matched with histories strictly before $C$.\n - Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.\n2. **Grade Importance Weighting**:\n - Relevant items are weighted by their contribution to graded NDCG@10:\n - Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)\n - Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)\n3. **Mixed Negative Sampling**:\n - In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.\n - Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.\n4. **LogQ Debiasing**:\n - Logits are corrected by item sampling frequency:\n $$\\text{Logit}_{i, j} = \\frac{u_i^T v_j}{\\tau} - \\log q(v_j)$$\n where $\\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.\n\n---\n\n## 3. Public Validation Score\n\nThe validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:\n\n- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)\n- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`\n- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)\n- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.\n\nEvaluation command:\n```sh\npython /task/evaluate_public.py /app/solution/validation.parquet\n```\n\n---\n\n## 4. Final Submission Command\n\nTo reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):\n\n```sh\nPYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \\\n --steps 280 \\\n --lr 7e-4 \\\n --embed_dim 96 \\\n --out_dim 96 \\\n --num_windows 6 \\\n --sample_listens 35 \\\n --submission_path /app/solution/submission.parquet\n```\n\n### Artifacts in `/app/solution/`\n- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).\n- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.\n- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.\n- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.\n- `train_val.py`: Validation training loop and public evaluator integration.\n- `refit_submission.py`: Full data refitting and final test submission generation.\n- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.\n- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.\n"
}
### Tool output · 2026-09-29T03:03:58.793Z
Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code:
# Latency-Efficient PyTorch Two-Tower Ranker for "Made for You" Discovery Shelf
## Overview
This solution implements a production-grade, latency-efficient PyTorch two-tower neural network ranker for the "Made for You" discovery shelf of a music streaming service. The system is designed for high-throughput, low-latency CPU inference where candidate item embeddings are precomputed offline into a Maximum Inner-Product Search (MIPS) nearest-neighbor index, and user embeddings are computed dynamically at request time from interaction histories without user-ID lookups or global interaction matrices.
---
## 1. Model Architecture
The ranker strictly decomposes into two independent PyTorch neural towers:
### Item Tower (`ItemTower` in `model.py`)
- **Inputs**:
- `item_id`: mapped to index in `[0, 30000]` (`30000` reserved for UNK/out-of-catalog items).
- Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.
- Catalog Metadata Features (4 dimensions):
- Standardized $\log(1 + \text{positive\_listens})$
- Standardized $\log(1 + \text{organic\_positive\_listens})$
- Organic Listen Ratio: $\frac{\text{organic\_listens} + 1e-4}{\text{positive\_listens} + 1}$
- Normalized Popularity Rank: $\frac{\text{popularity\_rank}}{30000}$
- **Architecture**:
- Metadata features projected via a 2-layer MLP (`Linear(4, 32) -> LayerNorm -> GELU`).
- Concatenation of item embedding (96d) and feature projection (32d).
- Output MLP: `Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.
- $L_2$ Normalization to the unit sphere: $\|v\|_2 = 1$.
- **Offline Precomputation**:
- All 30,000 candidate items are passed through `ItemTower` in one vectorized offline pass (<0.02s on CPU) to produce the candidate matrix $V \in \mathbb{R}^{30000 \times 96}$.
### User Tower (`UserTower` in `model.py`)
- **Inputs & Derivation** (Derived strictly from interaction history, **no user-ID lookup**):
1. **Short-Term Listens Pooling**: Weighted average of item embeddings over the most recent 20 positive listens ($\ge 50\%$ played ratio), capturing the user's immediate session context and mood.
2. **Long-Term Listens Pooling**: Weighted average of item embeddings over the past 100 positive listens with exponential temporal decay ($\lambda = \frac{\ln 2}{14 \text{ days}}$), played ratio completion weighting, and organic discovery bonus.
3. **Explicit Likes Pooling**: Mean-pooled item embeddings over active liked tracks from cutoff preference state and explicit likes, ordered chronologically by interaction timestamp.
4. **Explicit Dislikes Pooling**: Mean-pooled item embeddings over active disliked tracks from cutoff preference state and explicit dislikes.
5. **User Activity Features** (6 dimensions): Standardized logs of historical and recent positive listens, organic listen ratio, average played ratio, and log-transformed like and dislike counts.
- **Architecture**:
- Activity features projected via `Linear(6, 32) -> LayerNorm -> GELU`.
- Combined representation: `[pooled_short (96d), pooled_long (96d), pooled_likes (96d), pooled_dislikes (96d), user_feats_proj (32d)]` (total 416d).
- Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, out_dim=96)`.
- $L_2$ Normalization: $\|u\|_2 = 1$.
### Relevance Scoring & Latency-Efficient Retrieval
- **Exact Decomposition**:
$$\text{RelevanceScore}(u, v) = u^T v$$
The final relevance score strictly equals the inner product (cosine similarity) of the user vector and candidate item vector.
- **MIPS Latency**:
At inference time, user embeddings are retrieved in ~0.17s across all 8,456 target users (~20 $\mu$s per user). Candidate scoring $U V^T$ and top-100 selection via `torch.topk` over 30,000 candidates with platform eligibility bitmasks takes ~0.52s (<60 $\mu$s per user).
---
## 2. Training Objective
The model optimizes discovery relevance using **Mixed Negative Sampling InfoNCE** with **LogQ popularity debiasing**:
1. **Temporal Slicing & True Discovery Filtering**:
- To mimic the production shelf (where users are never recommended tracks they have previously listened to or liked), training pairs are generated using multiple historical temporal cutoffs (6 sliding weekly windows).
- In each target window $[C + 1800, C + 1800 + 7 \text{ days}]$, ground-truth organic likes and strong organic listens ($\ge 80\%$) are matched with histories strictly before $C$.
- Any track previously listened to or liked before $C$ is strictly filtered out, ensuring 100% true discovery training targets.
2. **Grade Importance Weighting**:
- Relevant items are weighted by their contribution to graded NDCG@10:
- Organic Likes (Grade 3): weight $5.0$ (gain $2^3 - 1 = 7$)
- Strong Organic Listens (Grade 1): weight $1.0$ (gain $2^1 - 1 = 1$)
3. **Mixed Negative Sampling**:
- In addition to $B-1$ in-batch negatives, each batch samples $M=512$ uniform random candidate items from the retrieval catalog.
- Uniform negative sampling ensures complete coverage across all 30,000 catalog candidates, preventing the popularity collapse associated with pure in-batch negative sampling.
4. **LogQ Debiasing**:
- Logits are corrected by item sampling frequency:
$$\text{Logit}_{i, j} = \frac{u_i^T v_j}{\tau} - \log q(v_j)$$
where $\tau = 0.07$ and $q(v_j)$ reflects the mixed in-batch and uniform sampling probabilities. This removes empirical frequency bias and prevents over-recommending tail items while maintaining personalized affinity.
---
## 3. Public Validation Score
The validation ranker was trained on historical data before cutoff timestamp `24786800` and evaluated with `/task/evaluate_public.py /app/solution/validation.parquet`:
- **Popularity Baseline**: `NDCG@10 = 0.010710` (4,263 scored users)
- **Initial Two-Tower (Pure In-Batch)**: `NDCG@10 = 0.011541`
- **Optimized Two-Tower (Mixed Negatives + Multi-Scale History)**: `NDCG@10 = 0.019376` (4,263 scored users)
- **Relative Improvement**: **+80.9%** gain over the catalog popularity baseline.
Evaluation command:
```sh
python /task/evaluate_public.py /app/solution/validation.parquet
```
---
## 4. Final Submission Command
To reproduce the full pipeline (refitting the model on `train_* + val_*` and generating the hidden-test submission):
```sh
PYTHONPATH=/app/solution:/task python3 /app/solution/refit_submission.py \
--steps 280 \
--lr 7e-4 \
--embed_dim 96 \
--out_dim 96 \
--num_windows 6 \
--sample_listens 35 \
--submission_path /app/solution/submission.parquet
```
### Artifacts in `/app/solution/`
- `submission.parquet`: The verified final deliverable containing exactly 845,600 rows (100 distinct items per target user, ranks 1-100, 100% eligible against `test_eligible_candidates.npz`).
- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.
- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` implementations.
- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.
- `train_val.py`: Validation training loop and public evaluator integration.
- `refit_submission.py`: Full data refitting and final test submission generation.
- `best_val_model.pt`: Saved PyTorch model checkpoint corresponding to the best validation score.
- `final_refitted_model.pt`: Final model checkpoint trained on combined train + val data.
## Step 107 · agent · 2026-09-29T03:04:01.165Z
### run_shell_command · 2026-09-29T03:04:01.165Z
{
"command": "ls -lh /app/solution",
"description": "List files in /app/solution"
}
### Tool output · 2026-09-29T03:04:01.189Z
Output: total 26M
-rw-r--r-- 1 agent agent 7.3K Sep 29 03:03 README.md
-rw-r--r-- 1 agent agent 12M Sep 29 02:58 best_val_model.pt
-rw-r--r-- 1 agent agent 18K Sep 29 02:38 data_prep.py
-rw-r--r-- 1 agent agent 12M Sep 29 03:02 final_refitted_model.pt
-rw-r--r-- 1 agent agent 11K Sep 29 02:41 model.py
-rw-r--r-- 1 agent agent 8.7K Sep 29 03:00 refit_submission.py
-rw-r--r-- 1 agent agent 1.2M Sep 29 03:02 submission.parquet
-rw-r--r-- 1 agent agent 11K Sep 29 02:43 train_val.py
-rw-r--r-- 1 agent agent 1.3M Sep 29 03:00 validation.parquet
Process Group PGID: 5253
## Step 108 · agent · 2026-09-29T03:04:04.206Z
### Recorded reasoning summary
Validating Final Datasets
I'm currently performing a thorough check of both `validation.parquet` and `submission.parquet`. This involves running `evaluate_public.py` against `validation.parquet` to ensure it outputs valid JSON with the `quality_ndcg@10` metric, and also confirming the integrity of candidate eligibility through `ranking_evaluation.validate_ranking`.
### run_shell_command · 2026-09-29T03:04:04.206Z
{
"command": "python3 /task/evaluate_public.py /app/solution/validation.parquet",
"description": "Final check on validation.parquet using evaluate_public.py"
}
### Tool output · 2026-09-29T03:04:07.537Z
Output: {
"model": "/app/solution/validation.parquet",
"scores": {
"quality_ndcg@10": 0.019376,
"quality_users": 4263
}
}
Process Group PGID: 5256
## Step 109 · agent · 2026-09-29T03:04:15.068Z
### run_shell_command · 2026-09-29T03:04:15.068Z
{
"command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets = set(pd.read_parquet(DATA / 'target_users.parquet')['uid'].values)\nsub = pd.read_parquet('/app/solution/submission.parquet')\nvalidated = validate_ranking(sub, targets)\ntec = CandidateSet(DATA / 'test_eligible_candidates.npz')\n\nfor uid, item in zip(validated.uid, validated.item_id):\n if not tec.is_eligible(int(uid), int(item)):\n raise ValueError(f'Ineligible pair ({uid}, {item})')\n\nprint('ALL SUBMISSION CHECKS PASSED PERFECTLY!')\nprint(f'Rows: {len(validated)}, Unique UIDs: {validated.uid.nunique()}, Items per UID: exactly {len(validated) // validated.uid.nunique()}')\n\"",
"description": "Final sanity check of submission.parquet"
}
### Tool output · 2026-09-29T03:04:17.882Z
Output: ALL SUBMISSION CHECKS PASSED PERFECTLY!
Rows: 845600, Unique UIDs: 8456, Items per UID: exactly 100
Process Group PGID: 5276
## Step 110 · agent · 2026-09-29T03:04:22.105Z
### Recorded reasoning summary
Confirming All Preparations
I've double-checked all components; everything is verified and primed for the final operational sequence. I'll initiate the `update_topic` function to provide a comprehensive project recap as per the model's requirements for multi-turn tasks.
### update_topic · 2026-09-29T03:04:22.105Z
{
"title": "Project Completion & Verification",
"summary": "Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.",
"strategic_intent": "Final synthesis and deliverable verification of the two-tower neural ranker."
}
### Tool output · 2026-09-29T03:04:22.111Z
Current topic: "Project Completion & Verification"
Topic summary: Successfully designed, implemented, validated, tuned, and refitted the latency-efficient PyTorch two-tower neural network ranker for the Made for You discovery shelf. The Item Tower maps 30,000 candidate items into 96-dimensional normalized embeddings computed offline. The User Tower computes 96-dimensional normalized embeddings strictly from interaction histories (multi-scale listens, active likes, dislikes, and activity features without user-ID lookups). Using mixed negative sampling InfoNCE with LogQ debiasing and strict discovery target filtering, the model achieved a public validation NDCG@10 of 0.019376 (+80.9% relative gain over the 0.010710 popularity baseline). The model was refitted on the combined train and validation history, generating /app/solution/submission.parquet containing 845,600 verified cutoff-eligible rows. Full source code and documentation are committed in /app/solution/.
Strategic Intent: Final synthesis and deliverable verification of the two-tower neural ranker.
## Step 111 · agent · 2026-09-29T03:04:33.721Z
# Latency-Efficient PyTorch Two-Tower Ranker for "Made for You" Discovery Shelf
A latency-efficient CPU PyTorch two-tower neural network ranker has been designed, implemented, tuned, and deployed for the "Made for You" discovery shelf under `/app/solution/`.
---
## 1. Architecture & Design
The model strictly decomposes into two independent PyTorch neural towers (`/app/solution/model.py`), satisfying all latency, decomposition, and production-scale constraints:
### Item Tower (`ItemTower`)
- **Inputs**:
- `item_id` index in $[0, 30000]$ (index 30,000 reserved for UNK/out-of-catalog items).
- Trainable Item Embedding: `nn.Embedding(30001, embed_dim=96)`.
- Catalog Metadata Features (4d): standardized $\log(1 + \text{positive\_listens})$, standardized $\log(1 + \text{organic\_listens})$, organic listen ratio, and normalized popularity rank.
- **Layers**:
- Feature projection MLP (`Linear(4, 32) -> LayerNorm -> GELU`).
- Combination and output MLP (`Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, 96)`).
- $L_2$ Normalization to unit sphere: $\|v\|_2 = 1$.
- **Offline Indexing**: Precomputes all 30,000 candidate item embeddings into an offline matrix $V \in \mathbb{R}^{30000 \times 96}$ in <0.02s.
### User Tower (`UserTower`)
- **Strict History-Derived Representation** (**No user-ID lookup table**, eliminating global user-item interaction matrices and supporting cold/streaming users):
1. **Short-Term Listens Pooling**: Weighted pooling over the most recent 20 positive listens ($\ge 50\%$ completion) to capture immediate session context.
2. **Long-Term Listens Pooling**: Exponential time-decayed attention pooling ($\lambda = \frac{\ln 2}{14 \text{ days}}$) over the past 100 positive listens with completion ratio and organic discovery weighting.
3. **Explicit Likes Pooling**: Mean-pooled embeddings over active liked tracks from preference states and explicit likes, ordered chronologically by interaction timestamp.
4. **Explicit Dislikes Pooling**: Mean-pooled embeddings over active disliked tracks from preference states and explicit dislikes.
5. **User Volume & Engagement Features (6d)**: Standardized logs of historical and recent listens, organic ratio, played ratio average, and like/dislike counts.
- **Layers**:
- Activity projection MLP (`Linear(6, 32) -> LayerNorm -> GELU`).
- Deep User MLP: `Linear(416, 128) -> LayerNorm -> GELU -> Dropout(0.2) -> Linear(128, 128) -> LayerNorm -> GELU -> Linear(128, 96)`.
- $L_2$ Normalization: $\|u\|_2 = 1$.
### Relevance Scoring & Latency Budget
- **Exact Decomposition**:
$$\text{Score}(u, v) = u^T v$$
- **Request-Time Latency**: Generating user embeddings for all 8,456 target users takes ~0.17s (~20 $\mu$s per user). Scoring all 30,000 catalog items via MIPS matrix multiplication $U V^T$ and retrieving top-100 items with platform eligibility masks takes ~0.52s (<60 $\mu$s per user).
---
## 2. Training Scheme & Objective
- **Temporal Sliding Backtests & Strict Discovery Filtering**:
To replicate the platform's candidate eligibility boundary (where items already heard or liked are filtered out), training pairs were constructed using 6 historical weekly cutoffs. Only items the user had never interacted with prior to the cutoff were retained as positive targets.
- **Relevance Weighting**:
Grade 3 organic discovery likes were weighted 5.0 (reflecting the $2^3 - 1 = 7$ metric gain), while grade 1 strong organic listens were weighted 1.0.
- **Mixed Negative Sampling & LogQ Debiasing**:
In-batch negative sampling was augmented with 512 uniform random negatives per batch sampled across all 30,000 catalog candidates. Logits were debiased by empirical sampling probabilities ($\log q(v)$), preventing head collapse while preserving personal taste alignment.
---
## 3. Public Validation Benchmark
Validation was evaluated with `/task/evaluate_public.py` on the public validation window:
| Model / Approach | Public NDCG@10 | Evaluated Users | Relative Improvement |
| :--- | :---: | :---: | :---: |
| Catalog Popularity Baseline | `0.010710` | 4,263 | Baseline |
| Two-Tower (Pure In-Batch Softmax) | `0.011541` | 4,263 | +7.8% |
| **Two-Tower (Mixed Negatives + Multi-Scale History)** | **`0.019376`** | **4,263** | **+80.9%** |
---
## 4. Deliverables & Verification
The final deliverable files under `/app/solution/` include:
- `submission.parquet`: The final deliverable containing 845,600 rows (exactly 100 rows per user for all 8,456 target users, ranks 1–100, no duplicates, with 100% of items verified against `test_eligible_candidates.npz`).
- `validation.parquet`: The public validation deliverable scoring `0.019376` NDCG@10.
- `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerRanker` module definitions.
- `data_prep.py`: Preprocessing, feature extraction, temporal slicing, and discovery pair construction.
- `train_val.py`: Validation training loop with intra-epoch checkpoints and evaluator integration.
- `refit_submission.py`: Pipeline for refitting on `train_* + val_*` and generating `submission.parquet`.
- `README.md`: System documentation detailing architecture, loss function, validation score, and reproduction commands.
## Recorded session usage
[
{
"messageId": "56cbabdc-b1d2-4d6b-8de9-8e3f99dc4a9e",
"timestamp": "2026-09-29T02:12:24.740Z",
"model": "gemini-3.8-flash",
"tokens": {
"input": 9963,
"output": 204,
"cached": 0,
"thoughts": 1150,
"tool": 0,
"total": 11317
}
},
{
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