yambda-discovery-ranking-two-tower-base / gemini-3-8-flash-high-base-fixed-r03-3d6fca60
Recorded model: gemini-3.8-flash
## Step 1 · user · 2026-09-29T05:48:06.478Z
This is the Gemini CLI. We are setting up the context for our chat.
Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale).
My operating system is: linux
The project's temporary directory is: /home/agent/.gemini/tmp/app
- **Workspace Directories:**
- /app
- /task
- **Directory Structure:**
Showing up to 200 items (files + folders).
/app/
└───solution/
Showing up to 200 items (files + folders).
/task/
├───.keep
├───evaluate_public.py
├───ranking_evaluation.py
├───requirements.txt
└───data/
├───candidate_catalog.parquet
├───target_users.parquet
├───test_eligible_candidates.npz
├───test_preference_state.parquet
├───train_dislikes.parquet
├───train_likes.parquet
├───train_listens.parquet
├───train_undislikes.parquet
├───train_unlikes.parquet
├───val_dislikes.parquet
├───val_likes.parquet
├───val_listens.parquet
├───val_undislikes.parquet
├───val_unlikes.parquet
├───validation_eligible_candidates.npz
└───validation_preference_state.parquet
## Step 2 · user · 2026-09-29T05:48:06.523Z
# 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 · 2026-09-29T05:48:15.637Z
### Recorded reasoning summary
Analyzing Requirements
I'm currently dissecting the core requirements for this PyTorch two-tower ranker. My focus is on understanding the nuances of CPU implementation and the specific constraints for the "Made for You" music shelf.
### Recorded reasoning summary
Building Neural Towers
I'm focusing on the distinct neural towers now. The user tower needs to learn representations solely from interaction history, eschewing simple ID lookups. Meanwhile, the item tower will generate its vectors independently, setting the stage for efficient dot-product calculation and maximum inner-product search.
### Recorded reasoning summary
Processing Data
I'm currently parsing through the provided parquet files, specifically focusing on the validation and test preference states. This data will be crucial for understanding user behavior and evaluating the ranking performance against the defined discovery objectives, including graded NDCG@10.
### update_topic · 2026-09-29T05:48:15.637Z
{
"summary": "Starting the project to build a CPU PyTorch two-tower neural network ranker. In this initial phase, I will inspect the data files, directory structure, public evaluation script, and verify python packages and CPU resources.",
"strategic_intent": "Inspect the environment, data files, evaluation scripts, and baseline properties.",
"title": "Phase 1: Environment & Data Inspection"
}
### Tool output · 2026-09-29T05:48:15.684Z
Current topic: "Phase 1: Environment & Data Inspection"
Topic summary: Starting the project to build a CPU PyTorch two-tower neural network ranker. In this initial phase, I will inspect the data files, directory structure, public evaluation script, and verify python packages and CPU resources.
Strategic Intent: Inspect the environment, data files, evaluation scripts, and baseline properties.
### run_shell_command · 2026-09-29T05:48:15.637Z
{
"description": "Inspect CPU, memory, and directories",
"command": "lscpu || nproc; free -h; ls -la /task; ls -la /app"
}
### Tool output · 2026-09-29T05:48:15.684Z
Output: Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: AuthenticAMD
Model name: AMD EPYC 7R13 Processor
CPU family: 25
Model: 1
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 1
BogoMIPS: 5300.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 2 MiB (4 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Ghostwrite: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Old microcode: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; Safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsa: Mitigation; Clear CPU buffers
Vulnerability Tsx async abort: Not affected
Vulnerability Vmscape: Not affected
/usr/bin/bash: line 4: free: command not found
total 36
dr-xr-xr-x 1 root root 4096 Sep 29 05:45 .
drwxr-xr-x 1 root root 4096 Sep 29 05:48 ..
-r--r--r-- 1 root root 0 Sep 29 05:31 .keep
dr-xr-xr-x 1 root root 4096 Sep 29 05:32 data
-r--r--r-- 1 root root 5774 Sep 29 05:31 evaluate_public.py
-r--r--r-- 1 root root 8265 Sep 29 05:31 ranking_evaluation.py
-r--r--r-- 1 root root 1295 Sep 29 05:31 requirements.txt
total 12
drwxr-xr-x 3 agent agent 4096 Sep 29 05:45 .
drwxr-xr-x 1 root root 4096 Sep 29 05:48 ..
drwxr-xr-x 2 agent agent 4096 Sep 29 05:45 solution
Process Group PGID: 1612
### read_file · 2026-09-29T05:48:15.637Z
{
"start_line": 1,
"end_line": 100,
"file_path": "/task/evaluate_public.py"
}
### Tool output · 2026-09-29T05:48:15.684Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.
--- FILE CONTENT (truncated) ---
#!/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.
### read_file · 2026-09-29T05:48:15.637Z
{
"end_line": 100,
"file_path": "/task/ranking_evaluation.py",
"start_line": 1
}
### Tool output · 2026-09-29T05:48:15.684Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 1-100 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 101.
--- FILE CONTENT (truncated) ---
"""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)
## Step 4 · agent · 2026-09-29T05:48:19.904Z
### read_file · 2026-09-29T05:48:19.904Z
{
"start_line": 101,
"file_path": "/task/evaluate_public.py",
"end_line": 160
}
### Tool output · 2026-09-29T05:48:19.912Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 101-158 of 158 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 159.
--- FILE CONTENT (truncated) ---
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()
### read_file · 2026-09-29T05:48:19.904Z
{
"file_path": "/task/ranking_evaluation.py",
"start_line": 101,
"end_line": 232
}
### Tool output · 2026-09-29T05:48:19.912Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 101-232 of 232 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 233.
--- FILE CONTENT (truncated) ---
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),
}
### read_file · 2026-09-29T05:48:19.904Z
{
"start_line": 1,
"file_path": "/task/requirements.txt",
"end_line": 100
}
### Tool output · 2026-09-29T05:48:19.912Z
# 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
## Step 5 · agent · 2026-09-29T05:48:22.760Z
### run_shell_command · 2026-09-29T05:48:22.760Z
{
"description": "Inspect datasets in /task/data",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\n\ndata_dir = Path('/task/data')\nfor p in sorted(data_dir.glob('*')):\n if p.suffix == '.parquet':\n df = pd.read_parquet(p)\n print(f'{p.name}: shape={df.shape}, cols={list(df.columns)}')\n elif p.suffix == '.npz':\n with np.load(p) as a:\n print(f'{p.name}: files={a.files}, uids={len(a[\\\"uids\\\"])}, item_ids={len(a[\\\"item_ids\\\"])}, packed_shape={a[\\\"packed_eligible\\\"].shape}')\n\""
}
### Tool output · 2026-09-29T05:48:25.343Z
Output: candidate_catalog.parquet: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
test_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)
test_preference_state.parquet: shape=(484776, 3), cols=['uid', 'item_id', 'state']
train_dislikes.parquet: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_likes.parquet: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_listens.parquet: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
train_undislikes.parquet: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
train_unlikes.parquet: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_dislikes.parquet: shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_likes.parquet: shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_listens.parquet: shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds']
val_undislikes.parquet: shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
val_unlikes.parquet: shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic']
validation_eligible_candidates.npz: files=['uids', 'item_ids', 'packed_eligible'], uids=8456, item_ids=30000, packed_shape=(8456, 3750)
validation_preference_state.parquet: shape=(469124, 3), cols=['uid', 'item_id', 'state']
Process Group PGID: 1618
## Step 6 · agent · 2026-09-29T05:48:29.391Z
### Recorded reasoning summary
Reviewing Data Sizes
I'm currently analyzing the scale of our datasets. The `train_listens` file is particularly massive, containing over 43 million entries. The `candidate_catalog` and `target_users` files are smaller, but `train_likes` and `train_dislikes` are also substantial in their own right.
### run_shell_command · 2026-09-29T05:48:29.391Z
{
"description": "Check system resources",
"command": "python3 -c \"\nimport psutil, os\nprint('CPU count:', os.cpu_count())\nprint('RAM GB:', psutil.virtual_memory().total / (1024**3), 'Available GB:', psutil.virtual_memory().available / (1024**3))\n\" && df -h"
}
### Tool output · 2026-09-29T05:48:29.424Z
Output: Traceback (most recent call last):
File "", line 2, in
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 1638
## Step 7 · agent · 2026-09-29T05:48:33.838Z
### run_shell_command · 2026-09-29T05:48:33.838Z
{
"command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable|MemFree\" && df -h",
"description": "Check memory and disk space via /proc/meminfo"
}
### Tool output · 2026-09-29T05:48:33.855Z
Output: MemTotal: 15988964 kB
MemFree: 2033312 kB
MemAvailable: 14430976 kB
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: 1641
## Step 8 · agent · 2026-09-29T05:48:36.384Z
### Recorded reasoning summary
Assessing Memory Requirements
I'm currently evaluating memory constraints. With 14.4 GB available RAM and a 43.5 million-row dataset, loading the entire `train_listens.parquet` as a single DataFrame could exceed my capacity. I'll explore alternative loading strategies to manage this.
### run_shell_command · 2026-09-29T05:48:36.384Z
{
"command": "ls -lh /task/data/",
"description": "Check file sizes in /task/data"
}
### Tool output · 2026-09-29T05:48:36.398Z
Output: total 340M
-r--r--r-- 1 root root 342K Sep 29 05:31 candidate_catalog.parquet
-r--r--r-- 1 root root 83K Sep 29 05:31 target_users.parquet
-r--r--r-- 1 root root 7.4M Sep 29 05:31 test_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 05:32 test_preference_state.parquet
-r--r--r-- 1 root root 838K Sep 29 05:31 train_dislikes.parquet
-r--r--r-- 1 root root 5.2M Sep 29 05:31 train_likes.parquet
-r--r--r-- 1 root root 305M Sep 29 05:31 train_listens.parquet
-r--r--r-- 1 root root 153K Sep 29 05:31 train_undislikes.parquet
-r--r--r-- 1 root root 1.8M Sep 29 05:31 train_unlikes.parquet
-r--r--r-- 1 root root 36K Sep 29 05:31 val_dislikes.parquet
-r--r--r-- 1 root root 292K Sep 29 05:31 val_likes.parquet
-r--r--r-- 1 root root 9.5M Sep 29 05:31 val_listens.parquet
-r--r--r-- 1 root root 7.7K Sep 29 05:31 val_undislikes.parquet
-r--r--r-- 1 root root 71K Sep 29 05:31 val_unlikes.parquet
-r--r--r-- 1 root root 7.3M Sep 29 05:31 validation_eligible_candidates.npz
-r--r--r-- 1 root root 1.1M Sep 29 05:32 validation_preference_state.parquet
Process Group PGID: 1646
## Step 9 · agent · 2026-09-29T05:48:41.979Z
### Recorded reasoning summary
Exploring Data Loading
I'm starting to investigate loading the `train_listens.parquet` file, which is 305MB. My immediate plan is to use `pyarrow` to quickly inspect its schema and row group counts. This will help me understand the data structure before diving deeper and identifying target users.
### run_shell_command · 2026-09-29T05:48:41.979Z
{
"description": "Explore datasets schema and content",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nprint('target_users:', len(target_users), 'uids min/max:', target_users.uid.min(), target_users.uid.max())\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('catalog:', len(cat), 'item_ids min/max:', cat.item_id.min(), cat.item_id.max())\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('validation_preference_state:', v_pref.head(), v_pref.state.value_counts())\n\nschema = pq.read_schema('/task/data/train_listens.parquet')\nprint('train_listens schema:', schema)\n\nt_users_set = set(target_users.uid)\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nprint('val_listens len:', len(val_listens), 'unique users:', val_listens.uid.nunique(), 'overlap with target_users:', len(set(val_listens.uid) & t_users_set))\nprint('val_listens time range:', val_listens.timestamp.min(), val_listens.timestamp.max())\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('val_likes len:', len(val_likes), 'unique users:', val_likes.uid.nunique())\n\""
}
### Tool output · 2026-09-29T05:48:43.724Z
Output: target_users: 8456 uids min/max: 100 1000000
catalog: 30000 item_ids min/max: 206 9390294
validation_preference_state: uid item_id state
0 100 410868 liked
1 100 732449 liked
2 100 982903 liked
3 100 1730273 liked
4 100 2621343 liked state
liked 418435
disliked 50689
Name: count, dtype: int64
train_listens schema: uid: uint32
timestamp: uint32
item_id: uint32
is_organic: uint8
played_ratio_pct: uint16
track_length_seconds: uint32
-- schema metadata --
pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797
val_listens len: 1501697 unique users: 8196 overlap with target_users: 7729
val_listens time range: 24788600 25393395
val_likes len: 37795 unique users: 4114
Process Group PGID: 1655
## Step 10 · agent · 2026-09-29T05:48:49.209Z
### run_shell_command · 2026-09-29T05:48:49.209Z
{
"description": "Check unique uids in train_listens",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'])\nuids = table['uid'].to_numpy()\nimport numpy as np\nunique_uids = np.unique(uids)\nprint('train_listens num unique uids:', len(unique_uids))\nimport pandas as pd\ntarget_users = pd.read_parquet('/task/data/target_users.parquet')\nt_set = set(target_users.uid)\nprint('overlap with target_users:', len(set(unique_uids) & t_set))\n\""
}
### Tool output · 2026-09-29T05:48:51.180Z
Output: train_listens num unique uids: 9238
overlap with target_users: 8456
Process Group PGID: 1675
## Step 11 · agent · 2026-09-29T05:48:55.016Z
### Recorded reasoning summary
Analyzing User Data
I'm currently examining the `train_listens` dataset. My focus is on understanding the scale of user engagement, particularly how many unique items are present and how many of those align with our `candidate_catalog` of 30,000 items.
### run_shell_command · 2026-09-29T05:48:55.016Z
{
"description": "Check unique items in train_listens and catalog overlap",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport numpy as np\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitem_ids = table['item_id'].to_numpy()\nunique_items = np.unique(item_ids)\nprint('train_listens num unique items:', len(unique_items))\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nprint('cat items in train_listens:', len(cat_set & set(unique_items)))\n\""
}
### Tool output · 2026-09-29T05:48:57.360Z
Output: train_listens num unique items: 845395
cat items in train_listens: 30000
Process Group PGID: 1697
## Step 12 · agent · 2026-09-29T05:49:00.994Z
### run_shell_command · 2026-09-29T05:49:00.994Z
{
"description": "Check validation ground truth statistics",
"command": "python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\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 = validation_truth(eligibility, targets)\nprint('Num users with gold labels in val:', len(graded))\ntotal_pos = sum(len(v) for v in graded.values())\ngrade3 = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1 = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f'Total positive labels: {total_pos}, grade 3: {grade3}, grade 1: {grade1}')\n\""
}
### Tool output · 2026-09-29T05:49:03.052Z
Output: Num users with gold labels in val: 4263
Total positive labels: 37769, grade 3: 4417, grade 1: 33352
Process Group PGID: 1718
## Step 13 · agent · 2026-09-29T05:49:07.358Z
### Recorded reasoning summary
Evaluating Validation Data
My focus has shifted to understanding the validation set. I'm reviewing the counts of gold-labeled users and total positive labels, noting the breakdown by grade. I'm also re-examining the specific definition of quality grading based on NDCG@10. My next step is to investigate the `validation_eligible_candidates`.
### run_shell_command · 2026-09-29T05:49:07.358Z
{
"description": "Check eligible candidate counts per user",
"command": "python3 -c \"\nimport numpy as np\n\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n print('validation packed shape:', packed.shape)\n # unpack bits for all users\n # packed is (8456, 3750) uint8\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n print('unpacked shape:', unpacked.shape)\n counts = unpacked.sum(axis=1)\n print('validation eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n packed = a['packed_eligible']\n unpacked = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\n counts = unpacked.sum(axis=1)\n print('test eligible counts per user: min, median, max, mean:', counts.min(), np.median(counts), counts.max(), counts.mean())\n\""
}
### Tool output · 2026-09-29T05:49:08.153Z
Output: validation packed shape: (8456, 3750)
unpacked shape: (8456, 30000)
validation eligible counts per user: min, median, max, mean: 24657 29242.0 30000 29067.306291390727
test eligible counts per user: min, median, max, mean: 24552 29220.0 30000 29041.3178807947
Process Group PGID: 1738
## Step 14 · agent · 2026-09-29T05:49:17.498Z
### Recorded reasoning summary
Refining Candidate Lists
I'm currently focusing on verifying the item IDs and their order within `candidate_catalog.parquet`. This step is crucial to ensure accuracy after filtering down the initial 30,000 catalog candidates to approximately 29,000 eligible ones per user.
### run_shell_command · 2026-09-29T05:49:17.498Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nwith np.load('/task/data/validation_eligible_candidates.npz') as a:\n val_items = a['item_ids']\n val_uids = a['uids']\n\nwith np.load('/task/data/test_eligible_candidates.npz') as a:\n test_items = a['item_ids']\n test_uids = a['uids']\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\n\nprint('cat.item_id equals val_items?', (cat.item_id.to_numpy() == val_items).all())\nprint('val_items equals test_items?', (val_items == test_items).all())\nprint('targets.uid equals val_uids?', (targets.uid.to_numpy() == val_uids).all())\nprint('val_uids equals test_uids?', (val_uids == test_uids).all())\n\"",
"description": "Check alignment of item_ids and uids across datasets"
}
### Tool output · 2026-09-29T05:49:19.048Z
Output: cat.item_id equals val_items? True
val_items equals test_items? True
targets.uid equals val_uids? True
val_uids equals test_uids? True
Process Group PGID: 1748
## Step 15 · agent · 2026-09-29T05:49:23.018Z
### Recorded reasoning summary
Confirming Data Integrity
I've confirmed that the `item_id`s, `val_items`, and `test_items` are identical across datasets, as are the user identifiers in `target_users.uid`, `val_uids`, and `test_uids`. This consistency is crucial. My next step is to evaluate the performance of a simple popularity baseline on the public validation set to establish an initial benchmark.
### run_shell_command · 2026-09-29T05:49:23.018Z
{
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\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 = validation_truth(eligibility, targets)\n\n# Generate baseline: for each user, top 100 eligible items in catalog order\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\nuids = targets_frame.uid.to_numpy()\npacked = eligibility.packed\nunpacked = np.unpackbits(packed, axis=1, count=len(cat_items), bitorder='little')\n\nranking_rows = []\nranked_dict = {}\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1]\n top100 = eligible_items[:100]\n ranked_dict[int(uid)] = top100.tolist()\n\nres = score(ranked_dict, graded)\nprint('Popularity baseline NDCG@10:', res)\n\"",
"description": "Test popularity baseline on validation set"
}
### Tool output · 2026-09-29T05:49:25.524Z
Output: Popularity baseline NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1765
## Step 16 · agent · 2026-09-29T05:49:32.070Z
### Recorded reasoning summary
Evaluating New Baselines
I'm currently verifying a new popularity baseline, which yielded an NDCG@10 of 0.010710. My next steps involve confirming the accurate saving of parquet rankings and running `evaluate_public.py` on them directly for compatibility checks. I'll also test the evaluation script on a dummy validation file to ensure its integrity.
### run_shell_command · 2026-09-29T05:49:32.070Z
{
"description": "Validate baseline with evaluate_public.py",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'validation_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\nsubmission.to_parquet('/app/solution/validation_baseline.parquet', index=False)\n\" && python3 /task/evaluate_public.py /app/solution/validation_baseline.parquet"
}
### Tool output · 2026-09-29T05:49:38.602Z
Output: {
"model": "/app/solution/validation_baseline.parquet",
"scores": {
"quality_ndcg@10": 0.01071,
"quality_users": 4263
}
}
Process Group PGID: 1785
## Step 17 · agent · 2026-09-29T05:49:43.429Z
### Recorded reasoning summary
Confirming evaluation completion
I've successfully run the `evaluate_public.py` script on the validation baseline parquet file. I'm now proceeding to generate and validate a baseline test submission to ensure a robust fallback option is in place.
### run_shell_command · 2026-09-29T05:49:43.429Z
{
"description": "Generate baseline test submission to guarantee a valid submission exists",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\nDATA = Path('/task/data')\ntargets_frame = pd.read_parquet(DATA / 'target_users.parquet')\neligibility = CandidateSet(DATA / 'test_eligible_candidates.npz')\ncat = pd.read_parquet(DATA / 'candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nuids = targets_frame.uid.to_numpy()\nunpacked = np.unpackbits(eligibility.packed, axis=1, count=len(cat_items), bitorder='little')\n\ndfs = []\nfor i, uid in enumerate(uids):\n mask = unpacked[i]\n eligible_items = cat_items[mask == 1][:100]\n dfs.append(pd.DataFrame({\n 'uid': uid,\n 'item_id': eligible_items,\n 'rank': np.arange(1, 101)\n }))\nsubmission = pd.concat(dfs, ignore_index=True)\ntargets_set = set(targets_frame.uid.astype(int))\nnormalized = validate_ranking(submission, targets_set)\nfor uid, item in normalized[['uid', 'item_id']].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f'Item {item} ineligible for user {uid}')\nnormalized.to_parquet('/app/solution/submission.parquet', index=False)\nprint('Baseline submission generated and verified. Shape:', normalized.shape)\n\""
}
### Tool output · 2026-09-29T05:49:47.587Z
Output: Baseline submission generated and verified. Shape: (845600, 3)
Process Group PGID: 1820
## Step 18 · agent · 2026-09-29T05:50:11.373Z
### Recorded reasoning summary
Starting Two-Tower Architecture
I've successfully saved our initial baseline submission. Now, my focus shifts to a deep dive into the requirements and architectural design for the two-tower model, starting with a thorough re-reading of the task itself.
### Recorded reasoning summary
Designing Two-Tower Model
I've reviewed the core requirements. My current thoughts center on architecting the two-tower model, ensuring scalability for millions of users and items without a full interaction matrix. The focus is on independent user and item towers, each producing embeddings for offline candidate generation and real-time retrieval.
### Recorded reasoning summary
Analyzing Interaction Data
My current thinking is focused on parsing the details of the interaction history. I'm specifically examining the provided data fields like `is_organic` and `played_ratio_pct` to understand user engagement nuances. The temporal split of the training and validation windows is also a key consideration for the backtesting strategy.
### Recorded reasoning summary
Validating Ranking Strategies
My thoughts are now on precisely how to split and utilize the public windows for effective validation. I'm considering how to build and tune a ranker using only the `train_*` data as history, then refitting on the combined `train_*` and `val_*` datasets for the final ranker, ensuring `val_*` events aren't used as predictive features during validation itself. I'm also reviewing the preference state files for both validation and testing.
### run_shell_command · 2026-09-29T05:50:11.373Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('train_likes time min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('val_likes time min, max:', val_likes.timestamp.min(), val_likes.timestamp.max())\n\n# Cutoff stated: 24786800. 30 min gap = 1800s -> 24788600.\n# val_listens min was 24788600!\nprint('Diff between val start and train cutoff:', 24788600 - 24786800)\n# Val window length: 25393395 - 24788600 = 604795s ~ 7 days (7 * 86400 = 604800s).\n# So hidden test window: 25393400 + 1800 = 25395200 to + 7 days.\n\n# Check preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('val_pref states:', val_pref.state.value_counts())\nprint('test_pref states:', test_pref.state.value_counts())\n\n# Check candidate catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(cat.head(10))\nprint(cat.describe())\n\"",
"description": "Inspect timestamps and preference state"
}
### Tool output · 2026-09-29T05:50:13.052Z
Output: train_likes time min, max: 80 24786790
val_likes time min, max: 24788815 25393390
Diff between val start and train cutoff: 1800
val_pref states: state
liked 418435
disliked 50689
Name: count, dtype: int64
test_pref states: state
liked 432659
disliked 52117
Name: count, dtype: int64
item_id positive_listens organic_positive_listens popularity_rank
0 5862961 26615 20620 1
1 6901374 25536 17347 2
2 5635052 24691 17194 3
3 3542184 24075 17699 4
4 9378983 24036 18195 5
5 5463340 22746 17398 6
6 8213481 22505 17071 7
7 906358 22238 17183 8
8 2859641 18380 13704 9
9 3033749 16682 12267 10
item_id ... popularity_rank
count 3.000000e+04 ... 30000.000000
mean 4.698278e+06 ... 15000.500000
std 2.700274e+06 ... 8660.398374
min 2.060000e+02 ... 1.000000
25% 2.382368e+06 ... 7500.750000
50% 4.687992e+06 ... 15000.500000
75% 7.040711e+06 ... 22500.250000
max 9.390294e+06 ... 30000.000000
[8 rows x 4 columns]
Process Group PGID: 1843
## Step 19 · agent · 2026-09-29T05:50:18.440Z
### run_shell_command · 2026-09-29T05:50:18.440Z
{
"command": "python3 -c \"\nimport pandas as pd\ntu = pd.read_parquet('/task/data/target_users.parquet')\nprint(tu.head())\nprint(tu.describe())\n\"",
"description": "Inspect target_users.parquet"
}
### Tool output · 2026-09-29T05:50:20.012Z
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
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: 1860
## Step 20 · agent · 2026-09-29T05:50:39.388Z
### Recorded reasoning summary
Analyzing Organic Engagement
I'm currently dissecting the definition of "organic" within listens and likes, specifically how it relates to the user's immediate behavioral window. My focus is on the two grades provided: grade 3 for organic likes and grade 1 for organically listening to 80% of a track.
### Recorded reasoning summary
Refining Engagement Metrics
I'm currently refining how we define and grade organic engagement. Specifically, I'm examining the criteria for grade 3 (organic likes) and grade 1 (listening to 80% of a track), and considering how these interact. My attention is also drawn to the crucial architectural constraint that user representations must stem from interaction history, not just IDs.
### Recorded reasoning summary
Considering System Architecture
My thoughts are currently focused on the architectural constraints, particularly how user representations are derived from interaction history, not just IDs. I'm analyzing the implications of pre-computing item embeddings offline for efficient retrieval, avoiding a global user-item matrix.
### Recorded reasoning summary
Integrating Item Embeddings
I'm exploring how to leverage item embeddings within the user tower. Specifically, I'm considering whether these embeddings can share weights with or be derived from those generated by the item tower, as is common in two-tower architectures.
### Recorded reasoning summary
Deriving User Representation
I'm focusing on how user representations must originate from interaction history, not mere IDs. I'm investigating the specific features available for both items and user interactions to understand how to best construct these representations, considering the 30,000 candidate items versus the much larger set of items in user histories.
### run_shell_command · 2026-09-29T05:50:39.388Z
{
"description": "Check overlap of interactions with catalog items",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train_listens:', len(df))\nin_cat = df.item_id.isin(cat_set)\nprint(f'Train listens in catalog: {in_cat.sum()} ({in_cat.mean():.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_in_cat = likes.item_id.isin(cat_set)\nprint(f'Train likes in catalog: {likes_in_cat.sum()} ({likes_in_cat.mean():.2%})')\n\n# Check validation preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref_in_cat = val_pref.item_id.isin(cat_set)\nprint(f'Validation preference state in catalog: {pref_in_cat.sum()} ({pref_in_cat.mean():.2%})')\n\n# Check target users in train_listens\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ndf_targets = df[df.uid.isin(set(targets.uid))]\nprint(f'Target users listens: {len(df_targets)} ({len(df_targets)/len(df):.2%})')\nin_cat_targets = df_targets.item_id.isin(cat_set)\nprint(f'Target users listens in catalog: {in_cat_targets.sum()} ({in_cat_targets.mean():.2%})')\n\""
}
### Tool output · 2026-09-29T05:50:50.933Z
Output: Total train_listens: 43484159
Train listens in catalog: 31249613 (71.86%)
Train likes in catalog: 554824 (68.09%)
Validation preference state in catalog: 469124 (100.00%)
Target users listens: 43222297 (99.40%)
Target users listens in catalog: 31062540 (71.87%)
Process Group PGID: 1876
## Step 21 · agent · 2026-09-29T05:50:54.438Z
### Recorded reasoning summary
Analyzing Catalog Data
It seems all items in `validation_preference_state` are present in the candidate catalog, which is a promising start. I'll now pivot to examining the `test_preference_state` and the `target_users_listens_in_catalog` figures to see if a similar pattern holds.
### run_shell_command · 2026-09-29T05:50:54.438Z
{
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('test_pref in cat:', test_pref.item_id.isin(cat_set).mean())\n\"",
"description": "Check test_pref overlap with catalog"
}
### Tool output · 2026-09-29T05:50:56.121Z
Output: test_pref in cat: 1.0
Process Group PGID: 1902
## Step 22 · agent · 2026-09-29T05:51:06.329Z
### Recorded reasoning summary
Analyzing Preference States
I'm currently examining the contents of `validation_preference_state` and `test_preference_state` to understand their roles. It appears that `test_preference_state` is fully incorporated into the candidate catalog. I'm now delving into the specifics of these states, noting that `validation_preference_state.parquet` contains a substantial 469,124 rows across 8,456 distinct entries.
### run_shell_command · 2026-09-29T05:51:06.329Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\neligible_count = 0\nfor uid, item, state in v_pref.itertuples(index=False):\n if eligibility.is_eligible(uid, item):\n eligible_count += 1\nprint(f'Preference state items eligible: {eligible_count} / {len(v_pref)} ({eligible_count / len(v_pref):.2%})')\n\n# Check by state\nfor s, df in v_pref.groupby('state'):\n el = sum(eligibility.is_eligible(u, i) for u, i in df[['uid', 'item_id']].itertuples(index=False))\n print(f'State {s}: {el} / {len(df)} eligible ({el/len(df):.2%})')\n\"",
"description": "Check preference state eligibility"
}
### Tool output · 2026-09-29T05:51:09.271Z
Output: Preference state items eligible: 0 / 469124 (0.00%)
State disliked: 0 / 50689 eligible (0.00%)
State liked: 0 / 418435 eligible (0.00%)
Process Group PGID: 1926
## Step 23 · agent · 2026-09-29T05:51:12.102Z
### Recorded reasoning summary
Checking Eligibility
My focus has shifted to verifying the eligibility status of existing user preferences within the `validation_eligible_candidates` set. It appears that every single active preference is currently marked as ineligible, prompting me to extend this check to the `test_preference_state`.
### run_shell_command · 2026-09-29T05:51:12.102Z
{
"description": "Check test preference state eligibility",
"command": "python3 -c \"\nimport pandas as pd\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/test_eligible_candidates.npz')\nt_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\n\neligible_count = sum(eligibility.is_eligible(uid, item) for uid, item in t_pref[['uid', 'item_id']].itertuples(index=False))\nprint(f'Test preference state items eligible: {eligible_count} / {len(t_pref)} ({eligible_count / len(t_pref):.2%})')\n\""
}
### Tool output · 2026-09-29T05:51:14.378Z
Output: Test preference state items eligible: 0 / 484776 (0.00%)
Process Group PGID: 1943
## Step 24 · agent · 2026-09-29T05:51:18.813Z
### Recorded reasoning summary
Refining Discovery Candidates
My thinking solidified; it's now clear that actively liked or disliked tracks are excluded from the "Made for You" discovery set for each cutoff. This aligns perfectly with the user's established preferences.
### run_shell_command · 2026-09-29T05:51:18.813Z
{
"description": "Check why items are ineligible",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\n# Look at user 100\nuid = 100\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nmask = [eligibility.is_eligible(uid, item) for item in cat_items]\nineligible_items = set(cat_items[~np.array(mask)])\nprint(f'User {uid}: {len(ineligible_items)} ineligible items')\n\n# Check user 100's listens\nlistens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\nuser_listens_cat = set(listens.item_id) & set(cat_items)\nprint(f'User {uid} listened to {len(user_listens_cat)} catalog items')\nprint(f'How many of those listened items are ineligible? {len(user_listens_cat & ineligible_items)}')\n\""
}
### Tool output · 2026-09-29T05:51:20.531Z
Output: User 100: 682 ineligible items
User 100 listened to 682 catalog items
How many of those listened items are ineligible? 682
Process Group PGID: 1960
## Step 25 · agent · 2026-09-29T05:51:23.790Z
### Recorded reasoning summary
Investigating Filtering Logic
I'm currently examining user interaction data. My initial hypothesis is that our candidate eligibility filters might be overly aggressive, potentially excluding items users have already engaged with or disliked. I'm sampling across 20 users to see if this pattern holds true for a broader user base.
### run_shell_command · 2026-09-29T05:51:23.790Z
{
"description": "Check candidate eligibility for sample users",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom ranking_evaluation import CandidateSet\n\neligibility = CandidateSet('/task/data/validation_eligible_candidates.npz')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nsample_uids = targets.uid.iloc[:20].tolist()\n\nfor uid in sample_uids[:5]:\n mask = [eligibility.is_eligible(uid, item) for item in cat_items]\n ineligible_items = set(cat_items[~np.array(mask)])\n listens = pd.read_parquet('/task/data/train_listens.parquet', filters=[('uid', '=', uid)])\n user_listens_cat = set(listens.item_id) & set(cat_items)\n print(f'User {uid}: ineligible={len(ineligible_items)}, listened_in_cat={len(user_listens_cat)}, overlap={len(user_listens_cat & ineligible_items)}')\n\""
}
### Tool output · 2026-09-29T05:51:25.655Z
Output: User 100: ineligible=682, listened_in_cat=682, overlap=682
User 200: ineligible=168, listened_in_cat=168, overlap=168
User 300: ineligible=69, listened_in_cat=69, overlap=69
User 600: ineligible=680, listened_in_cat=680, overlap=680
User 700: ineligible=1653, listened_in_cat=1653, overlap=1653
Process Group PGID: 1980
## Step 26 · agent · 2026-09-29T05:51:46.353Z
### Recorded reasoning summary
Discovering Unheard Tracks
I'm focusing on identifying music the user hasn't encountered. My current task is to filter out anything previously played or engaged with, ensuring I only surface genuinely new listening experiences that align with their established preferences.
### Recorded reasoning summary
Analyzing Training Data
I'm now examining the available training datasets, specifically looking at `train_listens.parquet`, `train_likes.parquet`, and other preference files. My next step is to understand their structure and how they inform the candidate catalog for recommendation.
### Recorded reasoning summary
Designing Efficient Models
My focus has shifted to the architectural constraints for model design. I'm specifically addressing the requirement for user and item vectors that decompose via dot product, generated by independent neural network towers, with user representations derived from interaction history, not IDs. This design needs to scale to millions of users and items without relying on a global interaction matrix, and I'm keeping the 60-minute training time constraint firmly in mind.
### Recorded reasoning summary
Building Item Embeddings
I'm delving into the architecture of the item tower, considering inputs like item ID embeddings and popularity features. The goal is to process these through an MLP and project them to dimension $D$, potentially with L2 normalization for cosine similarity use.
### Recorded reasoning summary
Synthesizing User Data
I'm now focused on how to represent users based on their historical interactions. My thoughts are on effectively encoding listened items, likes, dislikes, and recency into a user vector, aiming for a representation that captures nuanced preferences without relying on simple ID lookups, and considering how to weight different interaction types.
### Recorded reasoning summary
Processing User Interaction Data
I'm currently exploring standard neural architectures to represent user history. My focus is on weighted pooling of item embeddings from listened, liked, and disliked items, potentially combining these with other behavioral statistics, and then feeding them into an MLP for user vector generation.
### Recorded reasoning summary
Refining User Representation
I'm evaluating different methods for user vector generation. My current thoughts are on the trade-offs between standard weighted pooling of item embeddings (factoring in recency and interaction type) followed by an MLP, and more complex approaches like attention mechanisms or Transformers over recent interactions. I'm also checking the average number of historical items per user to inform feasibility.
### run_shell_command · 2026-09-29T05:51:46.353Z
{
"description": "Inspect user history statistics",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ntu = pd.read_parquet('/task/data/target_users.parquet')\nuids = set(tu.uid)\n\nprint('Likes in train per target user:', likes[likes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Dislikes in train per target user:', dislikes[dislikes.uid.isin(uids)].groupby('uid').size().describe())\nprint('Pref state per target user:', pref.groupby('uid').size().describe())\n\""
}
### Tool output · 2026-09-29T05:51:48.067Z
Output: Likes in train per target user: count 7071.000000
mean 97.718569
std 176.159086
min 1.000000
25% 9.000000
50% 40.000000
75% 116.000000
max 3479.000000
dtype: float64
Dislikes in train per target user: count 4977.000000
mean 16.931083
std 89.609589
min 1.000000
25% 2.000000
50% 4.000000
75% 9.000000
max 2985.000000
dtype: float64
Pref state per target user: count 7038.000000
mean 66.655868
std 108.686351
min 1.000000
25% 7.000000
50% 30.000000
75% 81.000000
max 1847.000000
dtype: float64
Process Group PGID: 2000
## Step 27 · agent · 2026-09-29T05:51:54.428Z
### Recorded reasoning summary
Analyzing User Listen Data
I'm currently examining the `train_listens` dataset, specifically within `target_users.parquet`, to understand listen counts per user. Initial findings for `recent_positive_listens` show a wide range, with a mean of 1,251 and a max of 19,592. `history_positive_listens` also shows significant numbers, averaging 3,236.
### Recorded reasoning summary
Exploring User Engagement Patterns
I'm refining my understanding of user engagement by analyzing the timestamp cutoff and its implications for "recent positive listens." I'm now shifting focus to correlate user listening history with their validation positive items, specifically exploring how liked tracks relate to artists and co-occurring items, even without direct artist metadata.
### run_shell_command · 2026-09-29T05:51:54.428Z
{
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nprint('Total train listens:', len(df))\nprint('played_ratio_pct >= 80%:', (df.played_ratio_pct >= 80).sum(), f'({(df.played_ratio_pct >= 80).mean():.2%})')\nprint('played_ratio_pct >= 80% & is_organic:', ((df.played_ratio_pct >= 80) & (df.is_organic == 1)).sum(), f'{((df.played_ratio_pct >= 80) & (df.is_organic == 1)).mean():.2%}')\nprint('played_ratio_pct >= 50%:', (df.played_ratio_pct >= 50).sum(), f'({(df.played_ratio_pct >= 50).mean():.2%})')\n\"",
"description": "Check train listens statistics"
}
### Tool output · 2026-09-29T05:51:56.328Z
Output: Total train listens: 43484159
played_ratio_pct >= 80%: 25755197 (59.23%)
played_ratio_pct >= 80% & is_organic: 12177583 28.00%
played_ratio_pct >= 50%: 27500755 (63.24%)
Process Group PGID: 2017
## Step 28 · agent · 2026-09-29T05:52:03.019Z
### run_shell_command · 2026-09-29T05:52:03.019Z
{
"description": "Check strong organic events in catalog",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'is_organic', 'played_ratio_pct'])\ndf = table.to_pandas()\nstrong_organic = df[(df.played_ratio_pct >= 80) & (df.is_organic == 1)]\nprint('Strong organic listens in cat:', strong_organic.item_id.isin(cat_set).sum(), f'{strong_organic.item_id.isin(cat_set).mean():.2%}')\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\norganic_likes = likes[likes.is_organic == 1]\nprint('Organic likes in cat:', organic_likes.item_id.isin(cat_set).sum(), f'{organic_likes.item_id.isin(cat_set).mean():.2%}')\n\""
}
### Tool output · 2026-09-29T05:52:06.905Z
Output: Strong organic listens in cat: 8699160 71.44%
Organic likes in cat: 301527 65.29%
Process Group PGID: 2044
## Step 29 · agent · 2026-09-29T05:52:59.844Z
### Recorded reasoning summary
Analyzing Validation Data
My focus has shifted to quantifying the data within `validation_truth`. Currently, I've identified 4,263 users possessing ground truth labels. The total count of positive labels stands at 37,769, with a significant portion, 4,417, categorized as grade 3.
### Recorded reasoning summary
Investigating Model Formulation
I'm now delving into the specifics of the two-tower model. My current task involves understanding its training objective and reconfirming the core requirements, particularly how to avoid a global interaction matrix and ensure offline item embedding computation.
### Recorded reasoning summary
Refining Candidate Selection
I'm currently focused on how candidate eligibility is managed, as it's platform-owned. My understanding is that all modeling within this boundary is fair game for exploration. My next step involves solidifying the approach for building and tuning the validation ranker using the designated train and val datasets.
### Recorded reasoning summary
Exploring Candidate Data
I am examining the candidate catalog size and how it relates to user listening history, particularly focusing on how to handle items not present in the main catalog. My next step involves determining the best strategy for mapping these out-of-catalog items.
### Recorded reasoning summary
Estimating Embedding Sizes
My analysis reveals that item embeddings are remarkably memory-efficient, even with tens of thousands of items. I'm now calculating the size for both 64 and 128 dimensions, considering out-of-catalog tracks and the potential inclusion of top-performing OOC items.
### Recorded reasoning summary
Processing User Input Signals
I am currently analyzing the signals that inform the user tower. My focus is on how to represent a user's listening history, prioritizing recently heard tracks with high completion rates and organic listens. Explicitly liked tracks are also being considered as key inputs.
### Recorded reasoning summary
Formulating User History
I am now focusing on representing user listening history. My approach involves a weighted average of item embeddings, prioritizing recent, high-completion, organic listens while also accounting for dislikes. This will be implemented using a temporal decay factor and completion weights.
### Recorded reasoning summary
Synthesizing User History Representations
I'm now consolidating various user history signals, including listening patterns, explicit likes and dislikes, and recent sequential interactions. These are being combined into a single user representation for the model's user tower, with plans for further refinement using MLPs.
### Recorded reasoning summary
Calculating Latency Impact
I'm now assessing the computational impact of calculating relevance scores. Specifically, I'm examining the latency of matrix multiplication for generating user scores across all catalog items. My goal is to confirm how quickly this operation completes given our embedding dimensions and user/item counts.
### Recorded reasoning summary
Validating Latency Performance
I've confirmed that the latency for calculating relevance scores is well within production requirements. Matrix multiplication operations for generating user scores across all catalog items are completing in under 20 milliseconds on an 8-core AMD EPYC system, easily satisfying the request-time budget.
### Recorded reasoning summary
Optimizing Model Objectives
I am now focusing on tailoring the model's training objective. My current thinking is to implement weighted positive sampling, leveraging graded cross-entropy to directly prioritize items with higher discovery relevance, such as organic likes and strong listens. Additionally, I am incorporating popularity debiasing through logQ correction to refine the model's understanding of true user affinity over mere popularity.
### Recorded reasoning summary
Exploring Negative Sampling
I am investigating the inclusion of hard negatives in the model, considering disliked tracks and those skipped early by users as potential negative examples. My current thinking favors InfoNCE over BPR for its superior performance in retrieval and top-K metrics.
### run_shell_command · 2026-09-29T05:52:59.844Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\n# Check train timestamps\ntrain_likes = pd.read_parquet('/task/data/train_likes.parquet')\ntrain_dislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\n\nprint('train_likes timestamp min, max:', train_likes.timestamp.min(), train_likes.timestamp.max())\nprint('Days in train:', (train_likes.timestamp.max() - train_likes.timestamp.min()) / 86400)\n\n# Check cutoff\ncutoff = 24786800\nprint('Cutoff:', cutoff)\n# Check how many likes in the last 7 days before cutoff (from cutoff - 7*86400 to cutoff)\nw7 = train_likes[train_likes.timestamp >= cutoff - 7 * 86400]\nprint('Likes in last 7 days of train:', len(w7), 'unique users:', w7.uid.nunique())\n\nw30 = train_likes[train_likes.timestamp >= cutoff - 30 * 86400]\nprint('Likes in last 30 days of train:', len(w30), 'unique users:', w30.uid.nunique())\n\"",
"description": "Analyze temporal distribution of training data"
}
### Tool output · 2026-09-29T05:53:01.424Z
Output: train_likes timestamp min, max: 80 24786790
Days in train: 286.8832175925926
Cutoff: 24786800
Likes in last 7 days of train: 30156 unique users: 3969
Likes in last 30 days of train: 121098 unique users: 6151
Process Group PGID: 2066
## Step 30 · agent · 2026-09-29T05:53:14.941Z
### Recorded reasoning summary
Analyzing Training Data
I'm reviewing the training data, noting it spans nearly 10 months. Recent analysis of the last week shows around 30,000 likes from nearly 4,000 users, with similar volume observed in the validation set. This consistency is encouraging.
### Recorded reasoning summary
Formulating Training Windows
I'm now focusing on how to best construct training examples. My current thought is to leverage multiple temporal windows, using a user's interaction history up to a specific timestamp $T$ to predict their subsequent engagement within a defined future interval. This approach appears quite robust for capturing evolving user behavior.
### Recorded reasoning summary
Refining Training Sample Generation
I'm exploring different ways to create training samples. My latest idea is to use a user's interaction history *before* a specific split time to predict their engagement *after* that time. This seems more efficient for capturing longer-term trends. I'm also considering next-item prediction.
### Recorded reasoning summary
Expanding Training Slices
I'm considering multiple cutoff points for training, not just one. This will allow me to predict engagement over various preceding periods and explore next-event prediction strategies. I'm also re-evaluating the user tower's dynamic representation calculation from interaction history, as per the documentation.
### Recorded reasoning summary
Defining Model Architecture
I'm now focusing on the precise structure of the user and item towers, aiming for PyTorch implementations. My current thinking is that the user tower will process historical interaction features, while the item tower will generate item embeddings. The final relevance score will be their dot product.
### run_shell_command · 2026-09-29T05:53:14.941Z
{
"description": "Search for tower references in /task",
"command": "grep -rn \"tower\" /task/"
}
### Tool output · 2026-09-29T05:53:15.136Z
Output: /task/requirements.txt:1:# Exact distribution inventory for the Yambda two-tower task family.
Process Group PGID: 2091
## Step 31 · agent · 2026-09-29T05:53:25.525Z
### Recorded reasoning summary
Designing PyTorch Model
I'm focusing on designing the core PyTorch neural two-tower model, as no prior implementations or constraints exist in the `/task` directory. My current effort involves architecting the network's structure and defining its initial components.
### Recorded reasoning summary
Analyzing User-Item Interactions
I'm now delving into user history and item data, considering what truly drives effective music recommendations. This involves understanding user preferences for genres, artists, and moods, and recognizing patterns like track co-occurrence and recency of listening, alongside explicit signals like play ratios and dislikes.
### run_shell_command · 2026-09-29T05:53:25.525Z
{
"description": "Inspect sample users and their validation gold items",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport sys\nsys.path.append('/task')\nfrom evaluate_public import validation_truth\nfrom ranking_evaluation import CandidateSet\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 = validation_truth(eligibility, targets)\n\n# Let's inspect 5 users who have gold labels\ncat = pd.read_parquet(DATA + '/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nsample_users = list(graded.keys())[:5]\nfor uid in sample_users:\n gold_items = graded[uid]\n likes = pd.read_parquet(DATA + '/train_likes.parquet', filters=[('uid', '=', uid)])\n listens = pd.read_parquet(DATA + '/train_listens.parquet', filters=[('uid', '=', uid)])\n print(f'UID {uid}: gold_count={len(gold_items)}, train_likes={len(likes)}, train_listens={len(listens)}')\n print(' Gold items:', gold_items)\n\""
}
### Tool output · 2026-09-29T05:53:29.091Z
Output: UID 200: gold_count=13, train_likes=0, train_listens=312
Gold items: {7406680: 1, 694058: 1, 4082738: 1, 6079576: 1, 6006390: 1, 2644025: 1, 4897148: 1, 7930756: 1, 3744896: 1, 3763080: 1, 7481745: 1, 9233959: 1, 9166040: 1}
UID 800: gold_count=25, train_likes=132, train_listens=11793
Gold items: {7179437: 1, 1607923: 1, 1816101: 1, 804416: 1, 2650514: 1, 4678741: 1, 2565336: 1, 6803177: 1, 6952637: 1, 4537506: 1, 2766576: 1, 9342289: 1, 9186305: 1, 308692: 1, 5559790: 1, 776584: 1, 388527: 1, 7400303: 1, 6551500: 1, 704566: 1, 843730: 1, 7326035: 1, 4702202: 1, 3531147: 1, 5433005: 1}
UID 900: gold_count=1, train_likes=0, train_listens=851
Gold items: {3673625: 1}
UID 1100: gold_count=20, train_likes=46, train_listens=4217
Gold items: {4588549: 1, 635564: 1, 1613989: 1, 8681149: 1, 3910656: 1, 1030400: 1, 7387845: 1, 7340717: 1, 6146700: 1, 647839: 1, 7570645: 1, 3891369: 1, 3857830: 1, 5335567: 1, 8971417: 1, 368089: 1, 8653336: 1, 2051915: 1, 6318072: 1, 8078607: 1}
UID 1400: gold_count=38, train_likes=42, train_listens=11345
Gold items: {6945311: 1, 2158715: 1, 2194514: 1, 3530629: 1, 2453912: 1, 7767752: 1, 4859530: 1, 3585411: 1, 4500834: 1, 4770069: 1, 3706360: 1, 6083906: 1, 1118882: 1, 8675053: 1, 5551069: 1, 6473581: 1, 2285672: 1, 5933848: 1, 7008219: 1, 5265819: 1, 975128: 1, 3970945: 1, 4345553: 1, 6901050: 1, 6901374: 1, 1851456: 1, 4678741: 1, 4897148: 1, 8420846: 3, 1327266: 1, 9233959: 1, 3203533: 1, 1407989: 1, 6417502: 1, 9016646: 1, 3763080: 1, 9378983: 1, 8669062: 1}
Process Group PGID: 2094
## Step 32 · agent · 2026-09-29T05:53:33.166Z
### Recorded reasoning summary
Analyzing Gold Items
I'm focusing on our gold items now. Specifically, I'm checking if any of the 38 gold items, like the organically liked item 8420846 with grade 3, are present in `cat_items`. It appears `validation_truth` currently only includes items deemed eligible.
### run_shell_command · 2026-09-29T05:53:33.166Z
{
"command": "python3 -c \"\nimport time\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nprint(f'Read table in {time.time() - t0:.2f}s, rows: {len(table)}')\n\"",
"description": "Benchmark reading train_listens columns"
}
### Tool output · 2026-09-29T05:53:35.106Z
Output: Read table in 1.36s, rows: 43484159
Process Group PGID: 2114
## Step 33 · agent · 2026-09-29T05:53:56.054Z
### run_shell_command · 2026-09-29T05:53:56.054Z
{
"description": "Measure memory and numpy conversion speed",
"command": "python3 -c \"\nimport time, pyarrow.parquet as pq, numpy as np\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nuids = table['uid'].to_numpy()\nitems = table['item_id'].to_numpy()\ntimestamps = table['timestamp'].to_numpy()\nratios = table['played_ratio_pct'].to_numpy()\norganics = table['is_organic'].to_numpy()\nprint('Converted to numpy in', round(time.time() - t0, 2), 's')\nprint('Shapes:', uids.shape, 'Memory MB:', (uids.nbytes + items.nbytes + timestamps.nbytes + ratios.nbytes + organics.nbytes) / 1024**2)\n\""
}
### Tool output · 2026-09-29T05:53:58.356Z
Output: Converted to numpy in 1.67 s
Shapes: (43484159,) Memory MB: 622.0458841323853
Process Group PGID: 2136
## Step 34 · agent · 2026-09-29T05:54:19.419Z
### Recorded reasoning summary
Confirming Memory Availability
I've confirmed the full dataset is a mere 622 MB, loading swiftly in under two seconds. Given my 14.4 GB of RAM, this represents a negligible ~4% usage, assuring ample memory and speed for processing all interaction logs without constraint.
### Recorded reasoning summary
Analyzing Model Requirements
My focus is now on dissecting the core architectural mandates. I'm specifically examining the requirement for independent user and item vector decomposition, ensuring each originates from distinct, trainable PyTorch towers. The emphasis on deriving user representations from interaction history, not ID lookups, is a key constraint being processed.
### Recorded reasoning summary
Designing Two-Tower Architecture
I'm now actively designing the two-tower model architecture. My current thoughts center on how to independently decompose user and item vectors using distinct PyTorch towers. I'm also considering how to derive user representations solely from their interaction history, as opposed to relying on ID lookups.
### Recorded reasoning summary
Refining Item Tower Inputs
I'm now integrating item catalog data into the item tower design. My thoughts are on how to best leverage item ID alongside tabular features like `positive_listens`, `popularity_rank`, and derived metrics such as like counts, all feeding into the embedding and dense layers to produce the final item vector.
### Recorded reasoning summary
Processing User Signals
I'm now consolidating the diverse user interaction signals. My focus is on how to effectively encode sequences of recent listens, incorporating timestamps, track IDs, played ratios, and organic flags. I'm also considering how to integrate explicit likes and dislikes to enrich the user representation.
### Recorded reasoning summary
Integrating Multi-View User Representations
I'm now merging the distinct user interaction signals into a unified representation. My approach involves combining embeddings from liked and disliked tracks, recent listens with recency decay, strong organic listens, and the last few interactions. This multi-view strategy aims to create a rich user profile.
### Recorded reasoning summary
Synthesizing User Representation
I'm now refining the user tower's final representation. My current thinking involves concatenating various signal embeddings, including likes, dislikes, recent and strong listens, and interaction statistics. This combined vector will then be processed through a multi-layer perceptron, potentially with L2 normalization, to produce the final user embedding. I've also considered and confirmed that sharing item embeddings between towers is standard and beneficial for synchronous updates.
### Recorded reasoning summary
Validating Vector Dot Product
I've confirmed my current approach precisely meets the requirement for the final relevance score being the dot product of independently computable user and item vectors. The user vector is derived from interaction history, as planned, and the item vectors can indeed be pre-computed offline for efficient retrieval.
### Recorded reasoning summary
Formulating Training Strategy
My current thoughts are on how to effectively train the two-tower model. I'm investigating how to derive target positives from the validation set's criteria, specifically organic likes and significant organic listens within a defined future window. The potential for utilizing multiple temporal slices from the training data is also under consideration.
### Recorded reasoning summary
Refining Training Temporal Slices
I'm now focusing on the temporal slicing for training, recognizing how each slice directly mirrors the evaluation task: predicting future organic likes and strong listens based on past history. This setup efficiently utilizes all available data to train the model for real-world prediction.
### run_shell_command · 2026-09-29T05:54:19.419Z
{
"description": "Check positive events count per 7-day window in train",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf_listens = table.to_pandas()\ndf_listens = df_listens[(df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80) & (df_listens.item_id.isin(cat_set))]\n\ndf_likes = pd.read_parquet('/task/data/train_likes.parquet')\ndf_likes = df_likes[(df_likes.is_organic == 1) & (df_likes.item_id.isin(cat_set))]\n\ncutoff = 24786800\nweek = 7 * 86400\n\nfor w in range(1, 9):\n t_end = cutoff - (w - 1) * week\n t_start = cutoff - w * week\n n_likes = ((df_likes.timestamp >= t_start) & (df_likes.timestamp < t_end)).sum()\n n_listens = ((df_listens.timestamp >= t_start) & (df_listens.timestamp < t_end)).sum()\n print(f'Week -{w}: likes={n_likes}, listens_80={n_listens}, total={n_likes + n_listens}')\n\""
}
### Tool output · 2026-09-29T05:54:25.126Z
Output: Week -1: likes=11210, listens_80=305188, total=316398
Week -2: likes=9918, listens_80=289174, total=299092
Week -3: likes=9774, listens_80=287149, total=296923
Week -4: likes=10721, listens_80=289244, total=299965
Week -5: likes=9641, listens_80=281100, total=290741
Week -6: likes=9639, listens_80=270971, total=280610
Week -7: likes=12160, listens_80=278744, total=290904
Week -8: likes=8709, listens_80=272184, total=280893
Process Group PGID: 2164
## Step 35 · agent · 2026-09-29T05:54:31.553Z
### Recorded reasoning summary
Analyzing Engagement Metrics
I'm currently reviewing the organic engagement data from the candidate catalog. It appears there are around 10,000 organic likes and 300,000 strong organic listens weekly. My next step is to cross-reference this with the eligibility conditions established during the validation window.
### run_shell_command · 2026-09-29T05:54:31.553Z
{
"description": "Check new discovery items in Week -1",
"command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\ndf = table.to_pandas()\ndf_cat = df[df.item_id.isin(cat_set)]\n\ncutoff = 24786800\nweek = 7 * 86400\nt_split = cutoff - week\n\n# Prior listens before t_split\nprior = df_cat[df_cat.timestamp < t_split]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Events in Week -1\nw1 = df_cat[(df_cat.timestamp >= t_split) & (df_cat.timestamp < cutoff)]\nw1_strong = w1[(w1.is_organic == 1) & (w1.played_ratio_pct >= 80)]\nw1_pairs = set(zip(w1_strong.uid, w1_strong.item_id))\n\nnew_pairs = w1_pairs - prior_user_items\nprint(f'Week -1 strong pairs: {len(w1_pairs)}, new unlistened pairs: {len(new_pairs)} ({len(new_pairs)/len(w1_pairs):.2%})')\n\n# Check likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nlikes_w1 = likes_cat[(likes_cat.timestamp >= t_split) & (likes_cat.timestamp < cutoff) & (likes_cat.is_organic == 1)]\nlikes_w1_pairs = set(zip(likes_w1.uid, likes_w1.item_id))\nnew_likes_pairs = likes_w1_pairs - prior_user_items\nprint(f'Week -1 likes pairs: {len(likes_w1_pairs)}, new unlistened likes pairs: {len(new_likes_pairs)} ({len(new_likes_pairs)/len(likes_w1_pairs):.2%})')\n\""
}
### Tool output · 2026-09-29T05:54:49.874Z
Output: Week -1 strong pairs: 173200, new unlistened pairs: 39606 (22.87%)
Week -1 likes pairs: 10959, new unlistened likes pairs: 8639 (78.83%)
Process Group PGID: 2186
## Step 36 · agent · 2026-09-29T05:55:29.505Z
### Recorded reasoning summary
Analyzing New Discovery Data
I'm currently processing approximately 48,000 new positive discovery pairs from last week. This includes both unlistened likes and strong listens, which I'll be correlating against the existing validation ground truth of 37,700 positive labels.
### Recorded reasoning summary
Refining User Preference Models
I'm observing a striking numerical correlation between the new positive discoveries and existing validated data, suggesting the discovery engine is effectively predicting novel, well-received tracks. My next steps involve a deeper dive into the candidate eligibility rules to understand how this prediction accuracy is maintained during inference. I'm also beginning to explore the underlying factors driving these user preferences, focusing on acoustic and collaborative filtering insights.
### Recorded reasoning summary
Investigating Two-Tower Architecture
I'm analyzing how user history embeddings in the user tower are formed, specifically how they aggregate listened tracks like X, Y, and Z to become proximate to discovered track W's embedding. I'm also considering how explicit likes and dislikes directly influence the scoring of similar items.
### Recorded reasoning summary
Verifying Architecture Design
I'm confirming the two-tower architecture adheres to the requirement of producing user and item vectors whose dot product yields the relevance score. I'm also investigating whether unit norm or bias terms are compatible with this core constraint, specifically how interaction history dictates the user tower's output.
### Recorded reasoning summary
Incorporating Item Features
I'm actively exploring how to integrate rich item features into the model. My focus is on leveraging readily available data like total plays, listen completion ratios, and like-to-dislike metrics from training logs. This integration aims to enhance the item tower's ability to represent both inherent item characteristics and their associated popularity.
### Recorded reasoning summary
Aggregating Historical Data
I'm now focusing on how to aggregate historical item embeddings. Given a user's interaction history, I'm considering different methods to represent this data effectively for the user tower, exploring options for handling out-of-catalog items.
### Recorded reasoning summary
Modeling User Embeddings
I'm currently focused on how to aggregate various user interaction signals, like listens and dislikes, into a unified user embedding. My objective is to explore weighted averages, incorporating decay functions and interaction weights, to capture nuanced user preferences and explicitly move away from disliked item embeddings.
### Recorded reasoning summary
Exploring Attention Mechanisms
I'm currently investigating the potential of using Attention Pooling, specifically self-attention or multi-head attention, over user history. The computational feasibility appears promising, with self-attention over 100 items projected to take less than a millisecond. I'm also considering learned query attention pooling as an alternative approach.
### Recorded reasoning summary
Benchmarking CPU Training Speed
I'm now rigorously evaluating the CPU training speed for my model. I'm prototyping a PyTorch script to benchmark forward and backward passes for both item and user towers, alongside the InfoNCE loss computation. This is to understand computational feasibility with varying batch sizes, embedding dimensions, and catalog sizes.
### run_shell_command · 2026-09-29T05:55:29.505Z
{
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\n\ntorch.set_num_threads(8)\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n \n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n d_proj = self.dense_proj(dense_features)\n x = torch.cat([emb, d_proj], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, num_views, stats_dim, out_dim):\n super().__init__()\n in_dim = embed_dim * num_views + stats_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Linear(embed_dim * 2, out_dim),\n )\n \n def forward(self, view_vectors, stats):\n # view_vectors: (batch_size, num_views * embed_dim)\n x = torch.cat([view_vectors, stats], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\nnum_items = 30000\nembed_dim = 64\nout_dim = 64\nbatch_size = 512\n\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 4, 6, out_dim)\n\n# Dummy inputs\nitem_ids = torch.randint(0, num_items, (batch_size,))\ndense_feat = torch.randn(batch_size, 4)\nuser_views = torch.randn(batch_size, 4 * embed_dim)\nuser_stats = torch.randn(batch_size, 6)\n\n# Test timing\nt0 = time.time()\nfor _ in range(100):\n u_vec = user_tower(user_views, user_stats)\n v_vec = item_tower(item_ids, dense_feat)\n \n # In-batch logits: (batch_size, batch_size)\n logits = torch.matmul(u_vec, v_vec.T) / 0.07\n labels = torch.arange(batch_size)\n loss = F.cross_entropy(logits, labels)\n loss.backward()\n\nprint(f'100 batches of size {batch_size} took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark basic two-tower forward/backward speed"
}
### Tool output · 2026-09-29T05:55:32.903Z
Output: 100 batches of size 512 took: 0.33s
Process Group PGID: 2214
## Step 37 · agent · 2026-09-29T05:57:33.392Z
### Recorded reasoning summary
Processing Data Quickly
My recent analysis indicates an impressive processing speed, handling 51,200 training examples in just 0.33 seconds on the CPU. I'm now focused on understanding the specific mapping of user history to input features within the training data.
### run_shell_command · 2026-09-29T05:57:33.392Z
{
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\ntorch.set_num_threads(8)\n\nemb_bag = nn.EmbeddingBag(30001, 64, mode='sum')\nbatch_size = 512\nitems_per_user = 50\n\nindices = torch.randint(0, 30000, (batch_size * items_per_user,))\noffsets = torch.arange(0, batch_size * items_per_user, items_per_user)\nweights = torch.rand(batch_size * items_per_user)\n\nt0 = time.time()\nfor _ in range(100):\n out = emb_bag(indices, offsets, per_sample_weights=weights)\n loss = out.sum()\n loss.backward()\n\nprint(f'100 batches of EmbeddingBag took: {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark EmbeddingBag speed on CPU"
}
### Tool output · 2026-09-29T05:57:36.691Z
Output: 100 batches of EmbeddingBag took: 0.36s
Process Group PGID: 2250
## Step 38 · agent · 2026-09-29T05:57:53.520Z
### Recorded reasoning summary
Optimizing Embedding Computation
My recent focus has been on the performance of `nn.EmbeddingBag`, which has shown excellent results, processing 100 batches in just 0.36 seconds. This sub-second performance for end-to-end item embedding training and user history pooling on the CPU is a significant breakthrough. My current thoughts are now turning towards architecting the best representation and analyzing the available information.
### Recorded reasoning summary
Analyzing Ranking Objectives
My current focus is on understanding the core ranking task: for each user, predicting the top 100 eligible tracks out of 29,000. I'm evaluating the impact of different interaction signals like organic likes and listens on the graded NDCG@10 metric. Next, I'll explore item-to-item co-occurrence representations.
### Recorded reasoning summary
Designing Scalable Towers
I'm currently focused on ensuring our model design avoids constructing a global user-item matrix, which is crucial for scaling to millions of users and items. The architecture must decompose into independent user and item towers, allowing for efficient, real-time retrieval via inner-product search with precomputed item embeddings.
### Recorded reasoning summary
Refining User Representation
I'm confirming the feasibility of a dot product score between independently computed user and item embeddings. My current thoughts are on how the user tower will exclusively leverage interaction history, avoiding direct user ID lookups, and how to structure this input for effective PyTorch neural network processing.
### Recorded reasoning summary
Structuring Interaction Windows
My current thoughts are exploring how to structure user interaction history across different time windows to define positive target items. I'm considering using within-session transitions and training with multiple sequential time windows, like the last 12 weeks, for richer representation.
### Recorded reasoning summary
Evaluating Training Window Efficiency
I'm currently assessing the computational cost of generating user histories and positive target pairs across multiple weekly windows. My focus is on determining the feasibility of using fixed cutoffs for these windows, comparing strategies like using specific historical periods and targeting subsequent days to optimize this process for efficiency.
### run_shell_command · 2026-09-29T05:57:53.520Z
{
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_item_to_idx = {item: idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read likes and dislikes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Read listens\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens_df = table.to_pandas()\n\nprint(f'Loaded all data in {time.time() - t0:.2f}s')\n\"",
"description": "Test loading all interaction data into memory"
}
### Tool output · 2026-09-29T05:57:55.646Z
Output: Loaded all data in 0.48s
Process Group PGID: 2274
## Step 39 · agent · 2026-09-29T05:58:00.411Z
### Recorded reasoning summary
Loading and Analyzing Data
I've successfully loaded all the data. My current focus is on the rapid aggregation of user histories, specifically examining their interactions up to a designated cutoff point. I'm also beginning to dissect the components of effective user representation, starting with "liked items."
### run_shell_command · 2026-09-29T05:58:00.411Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Tag actions\nlikes['action'] = 'like'\nunlikes['action'] = 'unlike'\ndislikes['action'] = 'dislike'\nundislikes['action'] = 'undislike'\n\nall_actions = pd.concat([likes, unlikes, dislikes, undislikes], ignore_index=True)\nall_actions = all_actions.sort_values('timestamp')\n\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('v_pref shape:', v_pref.shape)\nprint('v_pref unique uids:', v_pref.uid.nunique())\n\"",
"description": "Compare reconstructed preference state with validation_preference_state"
}
### Tool output · 2026-09-29T05:58:02.117Z
Output: v_pref shape: (469124, 3)
v_pref unique uids: 7038
Process Group PGID: 2296
## Step 40 · agent · 2026-09-29T05:58:15.923Z
### Recorded reasoning summary
Utilizing Pre-computed Data
I've confirmed the `validation_preference_state` and `test_preference_state` datasets are pre-computed and available, with substantial row counts. This means I can directly leverage `validation_preference_state.parquet` for validation inference, streamlining the process.
### Recorded reasoning summary
Leveraging Train Data
I can now directly use `test_preference_state.parquet` for test inference. For earlier training slices, I'll filter `train_likes` and `train_dislikes` by timestamp. I'm currently considering what features are most impactful for the User Tower, starting with average embeddings of liked and disliked items, and recent high-completion listens.
### Recorded reasoning summary
Refining User Tower Features
I'm now focusing on refining the User Tower features. I'm exploring incorporating strongly completed listens with exponential recency decay and recent general listens, also weighted by recency and played ratio. Additionally, I'm considering the last 20 listens and several scalar user features like listen counts and organic listen ratios.
### Recorded reasoning summary
Investigating Item Tower Inputs
I'm now examining the Item Tower's inputs. I've identified item embeddings, tabular features like `positive_listens` and `popularity_rank`, and a subsequent MLP. A key question is whether the item embeddings used here can be shared with those in the User Tower for pooling.
### Recorded reasoning summary
Confirming Model Architecture
I've confirmed the proposed model architecture perfectly aligns with the system's requirements. The user and item vectors are of identical dimensionality, with the score being their dot product, and both vectors are generated by separate, trainable PyTorch towers. This design also supports efficient offline item embedding computation and online retrieval.
### Recorded reasoning summary
Training Item Embeddings
I'm currently contemplating the best approach for training item embeddings to maximize NDCG@10. I'm weighing pre-training using session co-occurrences with Word2Vec or similar methods against end-to-end training via PyTorch with AdamW and cosine annealing, or a combination of both.
### update_topic · 2026-09-29T05:58:15.923Z
{
"strategic_intent": "Build and benchmark a prototype two-tower ranker and evaluate on public validation set.",
"title": "Phase 2: Prototype Two-Tower Architecture & Training",
"summary": "Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py."
}
### Tool output · 2026-09-29T05:58:15.926Z
Current topic: "Phase 2: Prototype Two-Tower Architecture & Training"
Topic summary: Completed Phase 1 exploration of the dataset, candidate eligibility, and architectural requirements. Verified that baseline popularity achieves 0.0107 NDCG@10 and generated a safe test submission baseline. Now moving to Phase 2: building and benchmarking a prototype PyTorch two-tower ranker evaluated via evaluate_public.py.
Strategic Intent: Build and benchmark a prototype two-tower ranker and evaluate on public validation set.
## Step 41 · agent · 2026-09-29T05:58:28.931Z
### Recorded reasoning summary
Structuring Training Pipeline
I'm currently mapping out the essential components for our training pipeline. My focus is on defining the data preprocessor, specifically outlining its inputs, which will include the `candidate_catalog.parquet` with its 30,000 items and associated popularity and listen data, alongside data for the validation model.
### Recorded reasoning summary
Extracting User Features
I'm now focusing on the detailed extraction of user features for the validation set. This involves calculating lists of liked and disliked items, incorporating recent and strong listens with their respective weighting schemes, and compiling key user statistics such as listen counts and mean play ratios. I plan to develop a performant Python script to expedite this process for all target users.
### run_shell_command · 2026-09-29T05:58:28.931Z
{
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ntargets = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = set(targets.uid)\n\n# Read preference state\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuser_pref_likes = {u: [] for u in targets.uid}\nuser_pref_dislikes = {u: [] for u in targets.uid}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == 'liked':\n user_pref_likes[int(uid)].append(idx)\n elif state == 'disliked':\n user_pref_dislikes[int(uid)].append(idx)\n\n# Read listens\ncutoff = 24786800\nt_start = cutoff - 60 * 86400 # last 60 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.uid.isin(target_uids)]\n\nprint(f'Filtered listens in last 60 days: {len(listens_df)} in {time.time() - t0:.2f}s')\n\"",
"description": "Benchmark feature extraction from listens for last 60 days"
}
### Tool output · 2026-09-29T05:58:32.326Z
Output: Filtered listens in last 60 days: 11699019 in 1.80s
Process Group PGID: 2319
## Step 42 · agent · 2026-09-29T05:59:08.050Z
### Recorded reasoning summary
Aggregating User Listens
I'm focusing on the aggregation of user listens now, specifically grouping by user and item. This is important because users can listen to the same track multiple times, and I need to account for that.
### run_shell_command · 2026-09-29T05:59:08.050Z
{
"description": "Benchmark aggregation speed of user-item weights",
"command": "python3 -c \"\nimport time\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nt_start = cutoff - 60 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', t_start)])\ndf = table.to_pandas()\n\n# Filter only catalog items\ncat_items = set(cat.item_id)\ndf = df[df.item_id.isin(cat_items)]\nprint('Filtered to catalog items:', len(df), f'in {time.time() - t0:.2f}s')\n\n# Map to item idx\ndf['item_idx'] = df.item_id.map(item_to_idx)\n# Compute weights\ndays_ago = (cutoff - df.timestamp) / 86400.0\ndf['weight'] = np.exp(-0.05 * days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# Groupby\nagg = df.groupby(['uid', 'item_idx'])['weight'].sum().reset_index()\nprint('Aggregated rows:', len(agg), f'in {time.time() - t0:.2f}s')\n\""
}
### Tool output · 2026-09-29T05:59:12.250Z
Output: Filtered to catalog items: 8417391 in 1.37s
Aggregated rows: 3406169 in 2.61s
Process Group PGID: 2347
## Step 43 · agent · 2026-09-29T05:59:18.710Z
### Recorded reasoning summary
Aggregating Listen Data
I've successfully aggregated over 8 million listens into user, item, and weight pairs, revealing an average of 400 distinct items per user. My next step is to precisely calculate the item count for each individual user.
### run_shell_command · 2026-09-29T05:59:18.710Z
{
"description": "Benchmark EmbeddingBag for all 8456 users",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb_bag = nn.EmbeddingBag(30000, 64, mode='sum')\nindices = torch.randint(0, 30000, (3400000,))\noffsets = torch.linspace(0, 3400000, 8456, dtype=torch.long)[:-1]\nweights = torch.rand(3400000)\n\nt0 = time.time()\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('EmbeddingBag forward for all 8456 users took:', round(time.time() - t0, 4), 's, output shape:', out.shape)\n\""
}
### Tool output · 2026-09-29T05:59:21.718Z
Output: EmbeddingBag forward for all 8456 users took: 0.0072 s, output shape: torch.Size([8455, 64])
Process Group PGID: 2369
## Step 44 · agent · 2026-09-29T05:59:45.838Z
### Recorded reasoning summary
Optimizing User History Processing
I'm thrilled to report that user history representation for all 8,456 users is now computed in a mere 7.2 milliseconds. My current focus is on assessing performance with multiple user views, specifically `bag_likes` and `bag_dislikes` embeddings.
### run_shell_command · 2026-09-29T05:59:45.838Z
{
"description": "Check environment setup for prototype",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Load catalog\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Targets\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\n\nprint('Catalog items:', num_items, 'Target users:', len(target_uids))\n\""
}
### Tool output · 2026-09-29T05:59:49.852Z
Output: Catalog items: 30000 Target users: 8456
Process Group PGID: 2393
## Step 45 · agent · 2026-09-29T06:00:24.305Z
### Recorded reasoning summary
Developing Feature Extraction
I am now focusing on building out the feature extraction components for the experiment. My current effort is dedicated to designing modular and clean extractors that can efficiently capture user features at a given cutoff time $T$.
### Recorded reasoning summary
Refining Preference Extraction
I've realized that for the validation cutoff, the active liked and disliked items are already available in `validation_preference_state.parquet`. For earlier cutoffs, I can efficiently reconstruct this active preference state by processing likes/unlikes and dislikes/undislikes, which takes less than a second. I'll filter listens to a 90-day window preceding the cutoff $T$.
### run_shell_command · 2026-09-29T06:00:24.305Z
{
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Load likes and listens\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nprint(f'Data loaded in {time.time() - t0:.2f}s')\n\ncutoff = 24786800\nweek = 7 * 86400\n\n# Let's inspect target extraction for week -1: [cutoff - week, cutoff]\nw_end = cutoff\nw_start = cutoff - week\n\n# Prior listens before w_start\nprior = listens[listens.timestamp < w_start]\nprior_user_items = set(zip(prior.uid, prior.item_id))\n\n# Likes in target window\ntarget_likes = likes[(likes.timestamp >= w_start) & (likes.timestamp < w_end) & (likes.is_organic == 1)]\ntarget_listens = listens[(listens.timestamp >= w_start) & (listens.timestamp < w_end) & (listens.is_organic == 1) & (listens.played_ratio_pct >= 80)]\n\n# Combine into gold targets\ntargets_dict = {}\nfor uid, item in zip(target_listens.uid, target_listens.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 1\n\nfor uid, item in zip(target_likes.uid, target_likes.item_id):\n if (uid, item) not in prior_user_items:\n targets_dict[(uid, item)] = 3\n\nprint('Target pairs for week -1:', len(targets_dict))\ngrades = list(targets_dict.values())\nprint('Grade 3 count:', grades.count(3), 'Grade 1 count:', grades.count(1))\n\"",
"description": "Verify target pair extraction for week -1"
}
### Tool output · 2026-09-29T06:00:42.580Z
Output: Data loaded in 4.08s
Target pairs for week -1: 45238
Grade 3 count: 8639 Grade 1 count: 36599
Process Group PGID: 2423
## Step 46 · agent · 2026-09-29T06:00:53.777Z
### Recorded reasoning summary
Analyzing Prior Interactions
I'm currently dissecting user interactions preceding `w_start`. My focus is on extracting `user_listens` (considering the last 60-90 days before `w_start`), `user_likes`, and `user_dislikes` to build a comprehensive profile of their engagement.
### Recorded reasoning summary
Incorporating Time-Sensitive Data
I'm refining the input by considering `bag_recent_listens` to capture immediate preferences and `bag_strong_listens` for highly engaged listening sessions. This allows for a more nuanced representation of current user interests beyond historical data.
### Recorded reasoning summary
Integrating Item and User Embeddings
I'm now focusing on the Item Tower's architecture, detailing its item embeddings and dense features. A key consideration is whether the item embeddings used in my User Tower history pooling can be the same embedding table. This integration would streamline processing and ensure consistency across both towers.
### Recorded reasoning summary
Sharing Embedding Tables
I'm exploring the impact of sharing the base item embedding table $E$ between the Item Tower and User Tower. This approach could streamline model architecture and ensure feature consistency, though I'm carefully considering the implications for each tower's distinct trainable components and the User Tower's reliance on history features rather than user IDs.
### run_shell_command · 2026-09-29T06:00:53.777Z
{
"description": "Check PyTorch environment",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\nimport torch, torch.nn as nn, torch.nn.functional as F\nfrom pathlib import Path\n\ntorch.set_num_threads(8)\n\n# Check imports\nprint('PyTorch version:', torch.__version__)\nprint('CUDA available:', torch.cuda.is_available())\n\""
}
### Tool output · 2026-09-29T06:00:59.616Z
Output: PyTorch version: 2.8.0+cpu
CUDA available: False
Process Group PGID: 2445
## Step 47 · agent · 2026-09-29T06:01:07.721Z
### Recorded reasoning summary
Developing Prototype Script
I'm focusing on constructing the prototype script, specifically defining the necessary components for `test_prototype.py`. The immediate priority is establishing the feature extraction function, which will take a dataframe of listener data as input.
### run_shell_command · 2026-09-29T06:01:07.721Z
{
"description": "Test preparing listens with vectorized mapping",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\n# Let's test vectorized extraction for validation cutoff\ncutoff = 24786800\nt0 = time.time()\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)]\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\nlistens['days_ago'] = (cutoff - listens.timestamp) / 86400.0\n\nprint(f'Listens prepared in {time.time() - t0:.2f}s, rows: {len(listens)}')\n\""
}
### Tool output · 2026-09-29T06:01:11.223Z
Output: Listens prepared in 1.88s, rows: 12117965
Process Group PGID: 2463
## Step 48 · agent · 2026-09-29T06:01:19.183Z
### Recorded reasoning summary
Processing Data Speedily
The recent performance is astonishing; 12.1 million rows processed in under two seconds. My next focus is on optimizing the aggregation of `listens` per user, specifically by grouping `(uid, item_idx)` to efficiently sum their associated weights.
### run_shell_command · 2026-09-29T06:01:19.183Z
{
"description": "Benchmark multi-weight aggregation in pandas",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\nt0 = time.time()\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 90 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf['days_ago'] = (cutoff - df.timestamp) / 86400.0\n\n# 1. Recent weight (last 14 days)\ndf['w_recent'] = np.where(df.days_ago <= 14, np.exp(-0.1 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0), 0.0)\n\n# 2. General history weight (90 days)\ndf['w_history'] = np.exp(-0.02 * df.days_ago) * (1.0 + df.is_organic) * (df.played_ratio_pct / 100.0)\n\n# 3. Strong organic (played_ratio >= 80 & is_organic == 1)\ndf['w_strong'] = np.where((df.played_ratio_pct >= 80) & (df.is_organic == 1), np.exp(-0.05 * df.days_ago), 0.0)\n\nprint(f'Computed weights in {time.time() - t0:.2f}s')\n\n# Groupby uid, item_idx\nt1 = time.time()\nagg = df.groupby(['uid', 'item_idx'])[['w_recent', 'w_history', 'w_strong']].sum().reset_index()\nprint(f'Aggregated in {time.time() - t1:.2f}s, unique pairs: {len(agg)}')\n\""
}
### Tool output · 2026-09-29T06:01:24.607Z
Output: Computed weights in 2.29s
Aggregated in 1.51s, unique pairs: 4321941
Process Group PGID: 2491
## Step 49 · agent · 2026-09-29T06:01:34.873Z
### Recorded reasoning summary
Processing Data Rapidly
I'm pleased to report significant progress. Feature extraction for 12 million listens is now consistently under 4 seconds. Even processing the likes and dislikes datasets, with their considerably smaller row counts, is remarkably swift, taking less than a tenth of a second. This efficiency is exceeding expectations.
### Recorded reasoning summary
Structuring User Embeddings
I'm now focusing on how to effectively feed user interaction data into PyTorch's `nn.EmbeddingBag`. Specifically, I'm examining the structure of recent, history, likes, and dislikes, along with their associated weights. A key consideration is how `EmbeddingBag` handles users with no interactions in a particular category, like dislikes.
### run_shell_command · 2026-09-29T06:01:34.873Z
{
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2, 3], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify PyTorch EmbeddingBag empty offset handling"
}
### Tool output · 2026-09-29T06:01:37.821Z
Output: Output shape: torch.Size([4, 4])
Row 0: tensor([-4.0461, -2.2352, -2.8407, -1.9140], grad_fn=)
Row 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)
Row 2: tensor([ 1.7677, -1.5779, 0.4143, 2.0241], grad_fn=)
Process Group PGID: 2513
## Step 50 · agent · 2026-09-29T06:07:31.933Z
### Recorded reasoning summary
Exploring EmbeddingBag Logic
I'm currently analyzing `EmbeddingBag`'s behavior with empty slices, noting its all-zero output. I've observed how `offsets` with four elements generate four rows, even when the last row is empty, and I'm verifying this.
### run_shell_command · 2026-09-29T06:07:31.933Z
{
"command": "python3 -c \"\nimport torch, torch.nn as nn\nemb_bag = nn.EmbeddingBag(10, 4, mode='sum')\nindices = torch.tensor([0, 1, 2], dtype=torch.long)\noffsets = torch.tensor([0, 2, 2], dtype=torch.long) # User 0: [0, 1], User 1: empty, User 2: [2]\nweights = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('Output shape:', out.shape)\nprint('Row 0:', out[0])\nprint('Row 1 (empty):', out[1])\nprint('Row 2:', out[2])\n\"",
"description": "Verify EmbeddingBag with 3 offsets"
}
### Tool output · 2026-09-29T06:07:34.861Z
Output: Output shape: torch.Size([3, 4])
Row 0: tensor([ 1.6807, -2.1469, 1.1582, 1.8539], grad_fn=)
Row 1 (empty): tensor([0., 0., 0., 0.], grad_fn=)
Row 2: tensor([-1.9523, -0.4724, 1.8892, 0.2620], grad_fn=)
Process Group PGID: 2556
## Step 51 · agent · 2026-09-29T06:08:01.250Z
### Recorded reasoning summary
Exploring EmbeddingBag Sum Mode
I'm currently analyzing the `EmbeddingBag(mode='sum')` functionality. My initial tests with `offsets` confirm the row generation, but I'm now considering the implications of normalization. The inherent sum aggregation will likely lead to significant value disparities between users with vastly different item counts, and I'm exploring strategies to address this bias.
### Recorded reasoning summary
Normalizing Pooled Embeddings
I'm now focusing on normalization strategies for the aggregated embeddings. I'm leaning towards applying L2 normalization to each user's pooled view vector to ensure invariance to the number of items. This approach, combined with passing normalized activity counts as dense features for users and items, should effectively mitigate bias and enrich the representations. I'll proceed with Python implementation to test these ideas.
### Recorded reasoning summary
Designing User Embeddings
I'm now detailing the architecture for both item and user towers. For items, I'm combining their IDs with dense features and an MLP, ensuring normalized output vectors. The user tower will concatenate normalized pooled embeddings from various listening contexts with user statistics, preparing them for further processing.
### Recorded reasoning summary
Validating Training Setup
I'm now designing a self-contained training script to validate the model's trainability. I'm structuring the training data into four distinct slices, each focusing on recent engagement within specific timeframes and incorporating organic likes and strong listens. This approach aims to generate approximately 180,000 training pairs, ensuring each pair includes a target user ID and a positive item index.
### run_shell_command · 2026-09-29T06:08:01.250Z
{
"description": "Benchmark slice target extraction",
"command": "python3 -c \"\nimport time, os, sys\nimport pandas as pd, numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\n\ncutoff = 24786800\nweek = 7 * 86400\n\nt0 = time.time()\n# Load all likes and listens once\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)].copy()\nlikes['item_idx'] = likes.item_id.map(item_to_idx)\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)].copy()\ndislikes['item_idx'] = dislikes.item_id.map(item_to_idx)\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp', 'played_ratio_pct', 'is_organic'],\n filters=[('timestamp', '>=', cutoff - 120 * 86400)])\nlistens = table.to_pandas()\nlistens = listens[listens.item_id.isin(item_to_idx)].copy()\nlistens['item_idx'] = listens.item_id.map(item_to_idx)\n\nprint(f'Initial load in {time.time() - t0:.2f}s')\n\nfor w in range(1, 3):\n t_w = cutoff - w * week\n t1 = time.time()\n \n # 1. Targets in [t_w, t_w + week]\n # Prior listens before t_w\n prior_listens = listens[listens.timestamp < t_w]\n prior_user_items = set(zip(prior_listens.uid, prior_listens.item_idx))\n \n # Target window\n win_listens = listens[(listens.timestamp >= t_w) & (listens.timestamp < t_w + week)]\n win_likes = likes[(likes.timestamp >= t_w) & (likes.timestamp < t_w + week)]\n \n targets = {}\n # Grade 1\n w_strong = win_listens[(win_listens.is_organic == 1) & (win_listens.played_ratio_pct >= 80)]\n for u, i in zip(w_strong.uid, w_strong.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n # Grade 3\n w_likes = win_likes[win_likes.is_organic == 1]\n for u, i in zip(w_likes.uid, w_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n \n print(f'Slice {w} (cutoff={t_w}): targets={len(targets)} (extracted in {time.time() - t1:.2f}s)')\n\""
}
### Tool output · 2026-09-29T06:08:17.985Z
Output: Initial load in 2.45s
Slice 1 (cutoff=24182000): targets=51814 (extracted in 4.97s)
Slice 2 (cutoff=23577200): targets=49902 (extracted in 6.05s)
Process Group PGID: 2575
## Step 52 · agent · 2026-09-29T06:10:37.101Z
### Recorded reasoning summary
Analyzing Extraction Speeds
The initial analysis of target extraction shows promising speeds, with over 51,000 targets processed in under 5 seconds for the first slice. I'm now moving to examine user history representation to understand its impact.
### Recorded reasoning summary
Exploring User History Efficiency
I'm considering precomputing user representations at a given cutoff. The initial estimate shows that processing ~4,000 active users' history features into pooled tensors or EmbeddingBag inputs is feasible, even with large numbers of historical items per user. This seems efficient.
### run_shell_command · 2026-09-29T06:10:37.101Z
{
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\ntable = pq.read_table('/task/data/train_listens.parquet',\n columns=['uid', 'item_id', 'timestamp'],\n filters=[('timestamp', '>=', cutoff - 14 * 86400)])\ndf14 = table.to_pandas()\ndf14 = df14[df14.item_id.isin(item_to_idx)]\nprint('Distinct items per user in last 14 days:')\nprint(df14.groupby('uid')['item_id'].nunique().describe())\n\"",
"description": "Check distinct items per user in last 14 days"
}
### Tool output · 2026-09-29T06:10:39.312Z
Output: Distinct items per user in last 14 days:
count 8053.000000
mean 154.685459
std 156.381635
min 1.000000
25% 38.000000
50% 105.000000
75% 222.000000
max 1626.000000
Name: item_id, dtype: float64
Process Group PGID: 2609
## Step 53 · agent · 2026-09-29T06:10:54.069Z
### Recorded reasoning summary
Exploring Item Representation
I'm currently considering how best to represent individual items for recommendation, specifically within the music domain. My focus is on whether co-occurrence patterns between tracks can offer valuable insights beyond simple listening frequency metrics observed over the past fortnight.
### Recorded reasoning summary
Building Item Embeddings
My recent thoughts confirm that items listened to together often share musical affinity. I'm exploring computing an item-item co-occurrence matrix, which, despite its potential sparsity, seems manageable. I'm now evaluating how to derive embeddings from this matrix, specifically considering truncated SVD on PPMI and ensuring it aligns with scaling constraints by avoiding a global user-item matrix.
### Recorded reasoning summary
Training Neural Networks Directly
I'm now focusing on training PyTorch neural networks directly for both user and item towers, rather than solely relying on matrix factorization. The plan is to explore contrastive learning, specifically InfoNCE, to learn embeddings from interaction history, avoiding any global user-item matrix construction.
### Recorded reasoning summary
Training Embeddings End-to-End
My latest thinking centers on directly training neural network towers with contrastive learning using InfoNCE, inspired by SimCLR and DSSM. I'm initiating embeddings randomly and plan to empirically validate this approach by developing a Python prototype script to evaluate performance on the validation set.
### Recorded reasoning summary
Designing User Embeddings
I'm now refining the user embedding architecture, incorporating historical, liked, and disliked item averages, alongside specific user statistics. This combined representation will then pass through an MLP and be normalized, ready for end-to-end training with contrastive loss.
### run_shell_command · 2026-09-29T06:10:54.069Z
{
"description": "Verify data loading for prototype",
"command": "python3 -c \"\nimport sys, os, time\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\ntorch.set_num_threads(8)\n\n# 1. Load catalog & target users\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {uid: idx for idx, uid in enumerate(target_uids)}\n\nprint(f'Catalog: {num_items} items, Targets: {len(target_uids)} users')\n\""
}
### Tool output · 2026-09-29T06:10:58.063Z
Output: Catalog: 30000 items, Targets: 8456 users
Process Group PGID: 2637
## Step 54 · agent · 2026-09-29T06:11:29.956Z
### Recorded reasoning summary
Analyzing User Engagement Metrics
I'm currently digging into user engagement, specifically focusing on organic positive listens and how they correlate with popularity rank. My computations for total plays, likes, and dislikes from the training data are finalized. I'm also in the process of extracting key user features, such as recent and historical listening habits, along with like data, for validation at cutoff T.
### write_file · 2026-09-29T06:11:29.956Z
{
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\nprint(\"Computing item features...\")\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\n# Standardize\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\n# Load listens in the last 120 days before validation cutoff\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n \"\"\"Extract multi-view history features for a set of users at cutoff_time.\"\"\"\n u_set = set(user_ids)\n\n # Filter listens before cutoff_time in last 90 days\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n # Recent (last 14 days)\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n # General history (90 days)\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n # Strong organic\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n # Group by uid, item_idx\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n # Likes and dislikes\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n # User stats\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n # Build bag structures\n recent_indices, recent_offsets, recent_weights = [], [0], []\n history_indices, history_offsets, history_weights = [], [0], []\n strong_indices, strong_offsets, strong_weights = [], [0], []\n like_indices, like_offsets = [], [0]\n dislike_indices, dislike_offsets = [], [0]\n stats_list = []\n\n agg_by_user = {}\n for uid, g in agg.groupby(\"uid\"):\n agg_by_user[uid] = g\n\n for uid in user_ids:\n # Stats\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_list.append([\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ])\n\n if uid in agg_by_user:\n g = agg_by_user[uid]\n # Recent\n mask_r = g.w_recent > 0\n if mask_r.any():\n recent_indices.extend(g.item_idx[mask_r].tolist())\n recent_weights.extend(g.w_recent[mask_r].tolist())\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n # Strong\n mask_s = g.w_strong > 0\n if mask_s.any():\n strong_indices.extend(g.item_idx[mask_s].tolist())\n strong_weights.extend(g.w_strong[mask_s].tolist())\n\n recent_offsets.append(len(recent_indices))\n history_offsets.append(len(history_indices))\n strong_offsets.append(len(strong_indices))\n\n # Likes\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_offsets.append(len(like_indices))\n\n # Dislikes\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_offsets.append(len(dislike_indices))\n\n features = {\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_offsets\": torch.tensor(recent_offsets[:-1], dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_offsets\": torch.tensor(history_offsets[:-1], dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_offsets\": torch.tensor(strong_offsets[:-1], dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_offsets\": torch.tensor(like_offsets[:-1], dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_offsets\": torch.tensor(dislike_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n }\n return features\n\n\nprint(\"Extracting validation user features...\")\nval_user_features = build_user_features(CUTOFF_VAL, target_uids)\nprint(\"Validation features ready.\")\n\n\ndef extract_slice_training_data(cutoff_time):\n \"\"\"Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS].\"\"\"\n w_end = cutoff_time + WEEK_SECONDS\n # Prior listens\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n # Target window\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n # Filter to active users who have at least one target\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = list(user_positives.keys())\n print(f\"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets\")\n\n # Extract user features for these users at this cutoff\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slice 1 (Week -1)...\")\nslice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)\nprint(\"Extracting training slice 2 (Week -2)...\")\nslice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.xavier_normal_(self.item_emb.weight)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n emb = self.item_emb(item_ids)\n dense_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([emb, dense_p], dim=-1)\n v = self.mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool_bag(self, indices, offsets, weights=None):\n if len(indices) == 0:\n return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n # Sum within slices\n # Vectorized sum using torch.scatter_add_\n batch_size = len(offsets)\n counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))\n user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)\n out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)\n out.index_add_(0, user_ids, embs)\n # Normalize non-zero rows\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def forward_batch(self, user_indices, feats):\n # Slice features for user_indices\n # For efficiency, user_indices indexes into precomputed view matrices\n pass\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n h_rec = self._pool_bag(feats[\"recent_indices\"], feats[\"recent_offsets\"], feats[\"recent_weights\"])\n h_his = self._pool_bag(feats[\"history_indices\"], feats[\"history_offsets\"], feats[\"history_weights\"])\n h_str = self._pool_bag(feats[\"strong_indices\"], feats[\"strong_offsets\"], feats[\"strong_weights\"])\n h_lik = self._pool_bag(feats[\"like_indices\"], feats[\"like_offsets\"])\n h_dis = self._pool_bag(feats[\"dislike_indices\"], feats[\"dislike_offsets\"])\n s_proj = self.stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)\n u = self.mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n\nembed_dim = 64\nout_dim = 64\nitem_tower = ItemTower(num_items, embed_dim, 4, out_dim)\nuser_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)\n\nprint(\"Model architecture initialized.\")\n",
"instruction": "Write train_prototype.py with feature extraction, data loading, and model definition",
"file_path": "/app/solution/train_prototype.py"
}
### Tool output · 2026-09-29T06:11:29.972Z
Successfully created and wrote to new file: /app/solution/train_prototype.py. Here is the updated code:
import os
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append("/task")
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet
torch.set_num_threads(8)
DATA_DIR = Path("/task/data")
OUT_DIR = Path("/app/solution")
OUT_DIR.mkdir(parents=True, exist_ok=True)
CUTOFF_VAL = 24786800
WEEK_SECONDS = 7 * 86400
print("Loading metadata...")
cat_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
cat_items = cat_df.item_id.to_numpy()
item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}
num_items = len(cat_items)
target_users_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = target_users_df.uid.to_numpy()
target_uid_set = set(target_uids)
uid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}
num_targets = len(target_uids)
# Item features
print("Computing item features...")
cat_features = np.zeros((num_items, 4), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))
cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))
cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)
cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)
# Standardize
for c in range(4):
mean = cat_features[:, c].mean()
std = cat_features[:, c].std() + 1e-6
cat_features[:, c] = (cat_features[:, c] - mean) / std
item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)
print("Loading raw events...")
t0 = time.time()
likes_df = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()
likes_df["item_idx"] = likes_df.item_id.map(item_to_idx)
dislikes_df = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()
dislikes_df["item_idx"] = dislikes_df.item_id.map(item_to_idx)
# Load listens in the last 120 days before validation cutoff
min_listen_time = CUTOFF_VAL - 120 * 86400
table = pq.read_table(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"],
filters=[("timestamp", ">=", min_listen_time)],
)
listens_df = table.to_pandas()
listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()
listens_df["item_idx"] = listens_df.item_id.map(item_to_idx)
print(f"Events loaded in {time.time() - t0:.2f}s (listens: {len(listens_df)})")
def build_user_features(cutoff_time, user_ids):
"""Extract multi-view history features for a set of users at cutoff_time."""
u_set = set(user_ids)
# Filter listens before cutoff_time in last 90 days
t_min = cutoff_time - 90 * 86400
sub_listens = listens_df[
(listens_df.timestamp >= t_min)
& (listens_df.timestamp < cutoff_time)
& (listens_df.uid.isin(u_set))
].copy()
sub_listens["days_ago"] = (cutoff_time - sub_listens.timestamp) / 86400.0
# Recent (last 14 days)
sub_listens["w_recent"] = np.where(
sub_listens.days_ago <= 14.0,
np.exp(-0.1 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0),
0.0,
)
# General history (90 days)
sub_listens["w_history"] = (
np.exp(-0.02 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0)
)
# Strong organic
sub_listens["w_strong"] = np.where(
(sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),
np.exp(-0.05 * sub_listens.days_ago),
0.0,
)
# Group by uid, item_idx
agg = (
sub_listens.groupby(["uid", "item_idx"])[["w_recent", "w_history", "w_strong"]]
.sum()
.reset_index()
)
# Likes and dislikes
sub_likes = likes_df[
(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))
]
likes_grouped = sub_likes.groupby("uid")["item_idx"].apply(list).to_dict()
sub_dislikes = dislikes_df[
(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))
]
dislikes_grouped = sub_dislikes.groupby("uid")["item_idx"].apply(list).to_dict()
# User stats
uid_counts = sub_listens.groupby("uid").agg(
n_listens=("item_idx", "count"),
mean_ratio=("played_ratio_pct", "mean"),
organic_ratio=("is_organic", "mean"),
).to_dict(orient="index")
# Build bag structures
recent_indices, recent_offsets, recent_weights = [], [0], []
history_indices, history_offsets, history_weights = [], [0], []
strong_indices, strong_offsets, strong_weights = [], [0], []
like_indices, like_offsets = [], [0]
dislike_indices, dislike_offsets = [], [0]
stats_list = []
agg_by_user = {}
for uid, g in agg.groupby("uid"):
agg_by_user[uid] = g
for uid in user_ids:
# Stats
st = uid_counts.get(uid, {"n_listens": 0, "mean_ratio": 50.0, "organic_ratio": 0.5})
n_likes = len(likes_grouped.get(uid, []))
n_dislikes = len(dislikes_grouped.get(uid, []))
stats_list.append([
np.log1p(st["n_listens"]),
st["mean_ratio"] / 100.0,
st["organic_ratio"],
np.log1p(n_likes),
np.log1p(n_dislikes),
])
if uid in agg_by_user:
g = agg_by_user[uid]
# Recent
mask_r = g.w_recent > 0
if mask_r.any():
recent_indices.extend(g.item_idx[mask_r].tolist())
recent_weights.extend(g.w_recent[mask_r].tolist())
# History
history_indices.extend(g.item_idx.tolist())
history_weights.extend(g.w_history.tolist())
# Strong
mask_s = g.w_strong > 0
if mask_s.any():
strong_indices.extend(g.item_idx[mask_s].tolist())
strong_weights.extend(g.w_strong[mask_s].tolist())
recent_offsets.append(len(recent_indices))
history_offsets.append(len(history_indices))
strong_offsets.append(len(strong_indices))
# Likes
l_items = likes_grouped.get(uid, [])
like_indices.extend(l_items)
like_offsets.append(len(like_indices))
# Dislikes
d_items = dislikes_grouped.get(uid, [])
dislike_indices.extend(d_items)
dislike_offsets.append(len(dislike_indices))
features = {
"recent_indices": torch.tensor(recent_indices, dtype=torch.long),
"recent_offsets": torch.tensor(recent_offsets[:-1], dtype=torch.long),
"recent_weights": torch.tensor(recent_weights, dtype=torch.float32),
"history_indices": torch.tensor(history_indices, dtype=torch.long),
"history_offsets": torch.tensor(history_offsets[:-1], dtype=torch.long),
"history_weights": torch.tensor(history_weights, dtype=torch.float32),
"strong_indices": torch.tensor(strong_indices, dtype=torch.long),
"strong_offsets": torch.tensor(strong_offsets[:-1], dtype=torch.long),
"strong_weights": torch.tensor(strong_weights, dtype=torch.float32),
"like_indices": torch.tensor(like_indices, dtype=torch.long),
"like_offsets": torch.tensor(like_offsets[:-1], dtype=torch.long),
"dislike_indices": torch.tensor(dislike_indices, dtype=torch.long),
"dislike_offsets": torch.tensor(dislike_offsets[:-1], dtype=torch.long),
"user_stats": torch.tensor(stats_list, dtype=torch.float32),
}
return features
print("Extracting validation user features...")
val_user_features = build_user_features(CUTOFF_VAL, target_uids)
print("Validation features ready.")
def extract_slice_training_data(cutoff_time):
"""Extract target positive items for users in [cutoff_time, cutoff_time + WEEK_SECONDS]."""
w_end = cutoff_time + WEEK_SECONDS
# Prior listens
prior = listens_df[listens_df.timestamp < cutoff_time]
prior_user_items = set(zip(prior.uid, prior.item_idx))
# Target window
win_listens = listens_df[
(listens_df.timestamp >= cutoff_time)
& (listens_df.timestamp < w_end)
& (listens_df.is_organic == 1)
& (listens_df.played_ratio_pct >= 80)
]
win_likes = likes_df[
(likes_df.timestamp >= cutoff_time)
& (likes_df.timestamp < w_end)
& (likes_df.is_organic == 1)
]
targets = {}
for u, i in zip(win_listens.uid, win_listens.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 1
for u, i in zip(win_likes.uid, win_likes.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 3
# Filter to active users who have at least one target
user_positives = {}
for (u, i), grade in targets.items():
user_positives.setdefault(u, []).append((i, grade))
unique_users = list(user_positives.keys())
print(f"Slice cutoff {cutoff_time}: {len(unique_users)} users with {len(targets)} targets")
# Extract user features for these users at this cutoff
user_feats = build_user_features(cutoff_time, unique_users)
user_to_idx = {u: idx for idx, u in enumerate(unique_users)}
pairs = []
for u, items in user_positives.items():
u_idx = user_to_idx[u]
for item_idx, grade in items:
pairs.append((u_idx, item_idx, float(grade)))
return user_feats, pairs
print("Extracting training slice 1 (Week -1)...")
slice1_feats, slice1_pairs = extract_slice_training_data(CUTOFF_VAL - WEEK_SECONDS)
print("Extracting training slice 2 (Week -2)...")
slice2_feats, slice2_pairs = extract_slice_training_data(CUTOFF_VAL - 2 * WEEK_SECONDS)
class ItemTower(nn.Module):
def __init__(self, num_items, embed_dim, dense_dim, out_dim):
super().__init__()
self.item_emb = nn.Embedding(num_items, embed_dim)
nn.init.xavier_normal_(self.item_emb.weight)
self.dense_proj = nn.Linear(dense_dim, embed_dim)
self.mlp = nn.Sequential(
nn.Linear(embed_dim * 2, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
def forward(self, item_ids, dense_features):
emb = self.item_emb(item_ids)
dense_p = self.dense_proj(dense_features[item_ids])
x = torch.cat([emb, dense_p], dim=-1)
v = self.mlp(x)
return F.normalize(v, p=2, dim=-1)
class UserTower(nn.Module):
def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):
super().__init__()
self.shared_emb = shared_item_emb
self.stats_proj = nn.Linear(stats_dim, embed_dim)
in_dim = embed_dim * 5 + embed_dim # 4 views + dislikes + stats_proj
self.mlp = nn.Sequential(
nn.Linear(in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool_bag(self, indices, offsets, weights=None):
if len(indices) == 0:
return torch.zeros((len(offsets), self.shared_emb.embedding_dim), dtype=torch.float32)
embs = self.shared_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
# Sum within slices
# Vectorized sum using torch.scatter_add_
batch_size = len(offsets)
counts = torch.diff(torch.cat([offsets, torch.tensor([len(indices)])]))
user_ids = torch.repeat_interleave(torch.arange(batch_size), counts)
out = torch.zeros((batch_size, self.shared_emb.embedding_dim), dtype=torch.float32)
out.index_add_(0, user_ids, embs)
# Normalize non-zero rows
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def forward_batch(self, user_indices, feats):
# Slice features for user_indices
# For efficiency, user_indices indexes into precomputed view matrices
pass
def compute_all_user_vectors(self, feats):
with torch.no_grad():
h_rec = self._pool_bag(feats["recent_indices"], feats["recent_offsets"], feats["recent_weights"])
h_his = self._pool_bag(feats["history_indices"], feats["history_offsets"], feats["history_weights"])
h_str = self._pool_bag(feats["strong_indices"], feats["strong_offsets"], feats["strong_weights"])
h_lik = self._pool_bag(feats["like_indices"], feats["like_offsets"])
h_dis = self._pool_bag(feats["dislike_indices"], feats["dislike_offsets"])
s_proj = self.stats_proj(feats["user_stats"])
x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_proj], dim=-1)
u = self.mlp(x)
return F.normalize(u, p=2, dim=-1)
embed_dim = 64
out_dim = 64
item_tower = ItemTower(num_items, embed_dim, 4, out_dim)
user_tower = UserTower(embed_dim, 5, out_dim, item_tower.item_emb)
print("Model architecture initialized.")
## Step 55 · agent · 2026-09-29T06:12:42.462Z
### Recorded reasoning summary
Exploring UserTower Precomputation
I'm currently investigating the feasibility of precomputing pooled view vectors within `UserTower` during training, specifically considering the scenario with $N$ users in a slice. This aims to optimize computational efficiency by calculating $\mathbf{h}_{\text{rec}}, \mathbf{h}_{\text{his}}, \mathbf{h}_{\text{str}}, \mathbf{h}_{\text{lik}}$ beforehand.
### run_shell_command · 2026-09-29T06:12:42.462Z
{
"description": "Test index_add autograd gradient flow",
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nemb = nn.Embedding(10, 4)\nindices = torch.tensor([0, 1, 2, 3], dtype=torch.long)\ne = emb(indices)\nuser_ids = torch.tensor([0, 0, 1, 1], dtype=torch.long)\nout = torch.zeros((2, 4), requires_grad=False)\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('emb grad:', emb.weight.grad)\n\""
}
### Tool output · 2026-09-29T06:12:45.458Z
Output: emb grad: tensor([[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[1., 1., 1., 1.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.],
[0., 0., 0., 0.]])
Process Group PGID: 2673
## Step 56 · agent · 2026-09-29T06:15:24.541Z
### run_shell_command · 2026-09-29T06:15:24.541Z
{
"description": "Benchmark full index_add forward and backward",
"command": "python3 -c \"\nimport torch, torch.nn as nn, time\n\ntorch.set_num_threads(8)\nemb = nn.Embedding(30000, 64)\nindices = torch.randint(0, 30000, (4000000,))\nuser_ids = torch.randint(0, 8456, (4000000,))\nweights = torch.rand(4000000)\n\nt0 = time.time()\ne = emb(indices) * weights.unsqueeze(-1)\nout = torch.zeros((8456, 64))\nout = torch.index_add(out, 0, user_ids, e)\nloss = out.sum()\nloss.backward()\nprint('Full index_add forward + backward took:', round(time.time() - t0, 3), 's')\n\""
}
### Tool output · 2026-09-29T06:15:28.517Z
Output: Full index_add forward + backward took: 0.915 s
Process Group PGID: 2695
## Step 57 · agent · 2026-09-29T06:16:05.471Z
### Recorded reasoning summary
Optimizing Memory Usage
I'm exploring ways to optimize memory. We're seeing incredible speed, under a second for processing all history, and I'm realizing we don't need this on every mini-batch. I'm now considering computing user pooled representations per epoch or sub-batch to further enhance efficiency.
### Recorded reasoning summary
Refining User Representations
I'm considering how to best integrate user pooled representations. Computing them per epoch seems efficient, allowing a fast $O(1)$ lookup during training. This approach will update user and item tower parameters, with item embeddings refreshing user representations for subsequent epochs.
### Recorded reasoning summary
Testing Hyperparameters
My focus has shifted to hyperparameter tuning. I'm now investigating the impact of temperature scaling ($\tau$) and the inclusion of popularity bias within the InfoNCE loss function to refine gradient sharpness and overall ranking performance. I'm also experimenting with using both in-batch and random catalog items as negative samples.
### write_file · 2026-09-29T06:16:05.471Z
{
"instruction": "Create prototype_eval.py to run full train and eval loop",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet, validate_ranking\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nuid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}\nnum_targets = len(target_uids)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\nprint(\"Loading raw events...\")\nt0 = time.time()\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Events loaded in {time.time() - t0:.2f}s\")\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_data = []\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n \n # Pack for torch\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n # Recent\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n # History\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n # Strong\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n features = {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n return features\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n return user_feats, pairs\n\n\nprint(\"Extracting training slices...\")\nslice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)\nslice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)\nslice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\n\n\nclass TwoTowerModel(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):\n super().__init__()\n self.embed_dim = embed_dim\n self.out_dim = out_dim\n # Shared item embeddings\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n\n # Item Tower\n self.item_dense_proj = nn.Linear(dense_dim, embed_dim)\n self.item_mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n # User Tower\n self.user_stats_proj = nn.Linear(stats_dim, embed_dim)\n user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.user_mlp = nn.Sequential(\n nn.Linear(user_in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.item_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Evaluate validation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Dot product scores: (8456, 30000)\n # In chunks of 1000 users to keep memory small\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n # (chunk_size, 30000)\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n # Mask out ineligibles\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n # Top 100 per user\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n # Sort within top 100\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n\n print(\n f\"Epoch {epoch}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={val_score['quality_ndcg@10']:.6f} \"\n f\"({time.time() - t_ep:.1f}s)\"\n )\n",
"file_path": "/app/solution/prototype_eval.py"
}
### Tool output · 2026-09-29T06:16:05.482Z
Successfully created and wrote to new file: /app/solution/prototype_eval.py. Here is the updated code:
import os
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append("/task")
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet, validate_ranking
torch.set_num_threads(8)
DATA_DIR = Path("/task/data")
OUT_DIR = Path("/app/solution")
OUT_DIR.mkdir(parents=True, exist_ok=True)
CUTOFF_VAL = 24786800
WEEK_SECONDS = 7 * 86400
print("Loading metadata...")
cat_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
cat_items = cat_df.item_id.to_numpy()
item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}
num_items = len(cat_items)
target_users_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = target_users_df.uid.to_numpy()
target_uid_set = set(target_uids)
uid_to_target_idx = {int(uid): idx for idx, uid in enumerate(target_uids)}
num_targets = len(target_uids)
# Item features
cat_features = np.zeros((num_items, 4), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))
cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))
cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)
cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)
for c in range(4):
mean = cat_features[:, c].mean()
std = cat_features[:, c].std() + 1e-6
cat_features[:, c] = (cat_features[:, c] - mean) / std
item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)
print("Loading raw events...")
t0 = time.time()
likes_df = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()
likes_df["item_idx"] = likes_df.item_id.map(item_to_idx)
dislikes_df = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()
dislikes_df["item_idx"] = dislikes_df.item_id.map(item_to_idx)
min_listen_time = CUTOFF_VAL - 120 * 86400
table = pq.read_table(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"],
filters=[("timestamp", ">=", min_listen_time)],
)
listens_df = table.to_pandas()
listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()
listens_df["item_idx"] = listens_df.item_id.map(item_to_idx)
print(f"Events loaded in {time.time() - t0:.2f}s")
def build_user_features(cutoff_time, user_ids):
u_set = set(user_ids)
t_min = cutoff_time - 90 * 86400
sub_listens = listens_df[
(listens_df.timestamp >= t_min)
& (listens_df.timestamp < cutoff_time)
& (listens_df.uid.isin(u_set))
].copy()
sub_listens["days_ago"] = (cutoff_time - sub_listens.timestamp) / 86400.0
sub_listens["w_recent"] = np.where(
sub_listens.days_ago <= 14.0,
np.exp(-0.1 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0),
0.0,
)
sub_listens["w_history"] = (
np.exp(-0.02 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0)
)
sub_listens["w_strong"] = np.where(
(sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),
np.exp(-0.05 * sub_listens.days_ago),
0.0,
)
agg = (
sub_listens.groupby(["uid", "item_idx"])[["w_recent", "w_history", "w_strong"]]
.sum()
.reset_index()
)
sub_likes = likes_df[
(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))
]
likes_grouped = sub_likes.groupby("uid")["item_idx"].apply(list).to_dict()
sub_dislikes = dislikes_df[
(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))
]
dislikes_grouped = sub_dislikes.groupby("uid")["item_idx"].apply(list).to_dict()
uid_counts = sub_listens.groupby("uid").agg(
n_listens=("item_idx", "count"),
mean_ratio=("played_ratio_pct", "mean"),
organic_ratio=("is_organic", "mean"),
).to_dict(orient="index")
user_data = []
user_to_pos = {u: i for i, u in enumerate(user_ids)}
# Pack for torch
recent_indices, recent_uids, recent_weights = [], [], []
history_indices, history_uids, history_weights = [], [], []
strong_indices, strong_uids, strong_weights = [], [], []
like_indices, like_uids = [], []
dislike_indices, dislike_uids = [], []
stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)
for i, uid in enumerate(user_ids):
st = uid_counts.get(uid, {"n_listens": 0, "mean_ratio": 50.0, "organic_ratio": 0.5})
n_likes = len(likes_grouped.get(uid, []))
n_dislikes = len(dislikes_grouped.get(uid, []))
stats_arr[i] = [
np.log1p(st["n_listens"]),
st["mean_ratio"] / 100.0,
st["organic_ratio"],
np.log1p(n_likes),
np.log1p(n_dislikes),
]
l_items = likes_grouped.get(uid, [])
like_indices.extend(l_items)
like_uids.extend([i] * len(l_items))
d_items = dislikes_grouped.get(uid, [])
dislike_indices.extend(d_items)
dislike_uids.extend([i] * len(d_items))
for uid, g in agg.groupby("uid"):
if uid not in user_to_pos:
continue
u_idx = user_to_pos[uid]
# Recent
m_r = g.w_recent > 0
if m_r.any():
recent_indices.extend(g.item_idx[m_r].tolist())
recent_weights.extend(g.w_recent[m_r].tolist())
recent_uids.extend([u_idx] * int(m_r.sum()))
# History
history_indices.extend(g.item_idx.tolist())
history_weights.extend(g.w_history.tolist())
history_uids.extend([u_idx] * len(g))
# Strong
m_s = g.w_strong > 0
if m_s.any():
strong_indices.extend(g.item_idx[m_s].tolist())
strong_weights.extend(g.w_strong[m_s].tolist())
strong_uids.extend([u_idx] * int(m_s.sum()))
N = len(user_ids)
features = {
"num_users": N,
"recent_indices": torch.tensor(recent_indices, dtype=torch.long),
"recent_uids": torch.tensor(recent_uids, dtype=torch.long),
"recent_weights": torch.tensor(recent_weights, dtype=torch.float32),
"history_indices": torch.tensor(history_indices, dtype=torch.long),
"history_uids": torch.tensor(history_uids, dtype=torch.long),
"history_weights": torch.tensor(history_weights, dtype=torch.float32),
"strong_indices": torch.tensor(strong_indices, dtype=torch.long),
"strong_uids": torch.tensor(strong_uids, dtype=torch.long),
"strong_weights": torch.tensor(strong_weights, dtype=torch.float32),
"like_indices": torch.tensor(like_indices, dtype=torch.long),
"like_uids": torch.tensor(like_uids, dtype=torch.long),
"dislike_indices": torch.tensor(dislike_indices, dtype=torch.long),
"dislike_uids": torch.tensor(dislike_uids, dtype=torch.long),
"user_stats": torch.tensor(stats_arr, dtype=torch.float32),
}
return features
def extract_slice_data(cutoff_time):
w_end = cutoff_time + WEEK_SECONDS
prior = listens_df[listens_df.timestamp < cutoff_time]
prior_user_items = set(zip(prior.uid, prior.item_idx))
win_listens = listens_df[
(listens_df.timestamp >= cutoff_time)
& (listens_df.timestamp < w_end)
& (listens_df.is_organic == 1)
& (listens_df.played_ratio_pct >= 80)
]
win_likes = likes_df[
(likes_df.timestamp >= cutoff_time)
& (likes_df.timestamp < w_end)
& (likes_df.is_organic == 1)
]
targets = {}
for u, i in zip(win_listens.uid, win_listens.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 1
for u, i in zip(win_likes.uid, win_likes.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 3
user_positives = {}
for (u, i), grade in targets.items():
user_positives.setdefault(u, []).append((i, grade))
unique_users = sorted(list(user_positives.keys()))
user_feats = build_user_features(cutoff_time, unique_users)
user_to_idx = {u: idx for idx, u in enumerate(unique_users)}
pairs = []
for u, items in user_positives.items():
u_idx = user_to_idx[u]
for item_idx, grade in items:
pairs.append((u_idx, item_idx, float(grade)))
return user_feats, pairs
print("Extracting training slices...")
slice1_feats, slice1_pairs = extract_slice_data(CUTOFF_VAL - WEEK_SECONDS)
slice2_feats, slice2_pairs = extract_slice_data(CUTOFF_VAL - 2 * WEEK_SECONDS)
slice3_feats, slice3_pairs = extract_slice_data(CUTOFF_VAL - 3 * WEEK_SECONDS)
val_user_feats = build_user_features(CUTOFF_VAL, target_uids)
class TwoTowerModel(nn.Module):
def __init__(self, num_items, embed_dim, dense_dim, stats_dim, out_dim):
super().__init__()
self.embed_dim = embed_dim
self.out_dim = out_dim
# Shared item embeddings
self.item_emb = nn.Embedding(num_items, embed_dim)
nn.init.normal_(self.item_emb.weight, std=0.05)
# Item Tower
self.item_dense_proj = nn.Linear(dense_dim, embed_dim)
self.item_mlp = nn.Sequential(
nn.Linear(embed_dim * 2, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
# User Tower
self.user_stats_proj = nn.Linear(stats_dim, embed_dim)
user_in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj
self.user_mlp = nn.Sequential(
nn.Linear(user_in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool(self, num_users, indices, uids, weights=None):
out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)
if len(indices) == 0:
return out
embs = self.item_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_all_user_vectors(self, feats):
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
s_p = self.user_stats_proj(feats["user_stats"])
x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)
u = self.user_mlp(x)
return F.normalize(u, p=2, dim=-1)
def compute_all_item_vectors(self, dense_features):
all_ids = torch.arange(len(dense_features), dtype=torch.long)
embs = self.item_emb(all_ids)
d_p = self.item_dense_proj(dense_features)
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
return F.normalize(v, p=2, dim=-1)
def forward_items(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.item_dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
return F.normalize(v, p=2, dim=-1)
model = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)
temperature = 0.07
all_slices = [
(slice1_feats, slice1_pairs),
(slice2_feats, slice2_pairs),
(slice3_feats, slice3_pairs),
]
# Validation ground truth
val_eligibility = CandidateSet(DATA_DIR / "validation_eligible_candidates.npz")
targets_set = set(target_users_df.uid.astype(int))
val_gold = validation_truth(val_eligibility, targets_set)
# Validation candidate mask unpacked
val_packed = val_eligibility.packed
val_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder="little")
print("Starting training...")
batch_size = 512
num_epochs = 6
for epoch in range(1, num_epochs + 1):
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs) in enumerate(all_slices):
# Refresh user representations for this slice
all_user_vecs = model.compute_all_user_vectors(feats)
# Shuffle pairs
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
for b_start in range(0, len(pairs_arr), batch_size):
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
u_vecs = all_user_vecs[b_u]
i_vecs = model.forward_items(b_i, item_dense_tensor)
# Sample random negatives
neg_ids = torch.randint(0, num_items, (len(b_pairs),))
neg_vecs = model.forward_items(neg_ids, item_dense_tensor)
# In-batch candidates: pos + neg
all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)
# Similarity logits
logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature
# Mask out false negatives in-batch (same user)
is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)
is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))
logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)
targets = torch.arange(len(b_pairs), dtype=torch.long)
loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)
loss = (F.cross_entropy(logits, targets, reduction="none") * loss_weights).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
num_batches += 1
# Evaluate validation
with torch.no_grad():
val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()
all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()
# Dot product scores: (8456, 30000)
# In chunks of 1000 users to keep memory small
ranked_dict = {}
for start_u in range(0, num_targets, 1000):
end_u = min(start_u + 1000, num_targets)
sub_u = val_u_vecs[start_u:end_u]
# (chunk_size, 30000)
sub_scores = np.matmul(sub_u, all_v_vecs.T)
# Mask out ineligibles
sub_mask = val_unpacked[start_u:end_u]
sub_scores[sub_mask == 0] = -1e9
# Top 100 per user
top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]
# Sort within top 100
for row in range(len(sub_u)):
u_row = start_u + row
uid = int(target_uids[u_row])
cand_idx = top100_idx[row]
sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]
ranked_dict[uid] = cat_items[sorted_idx].tolist()
val_score = score(ranked_dict, val_gold)
print(
f"Epoch {epoch}: loss={total_loss / num_batches:.4f}, "
f"val_ndcg@10={val_score['quality_ndcg@10']:.6f} "
f"({time.time() - t_ep:.1f}s)"
)
## Step 58 · agent · 2026-09-29T06:16:09.999Z
### run_shell_command · 2026-09-29T06:16:09.999Z
{
"description": "Run prototype_eval.py",
"command": "python3 /app/solution/prototype_eval.py"
}
### Tool output · 2026-09-29T06:17:09.120Z
Output: Loading metadata...
Loading raw events...
Events loaded in 2.45s
Extracting training slices...
Starting training...
Traceback (most recent call last):
File "/app/solution/prototype_eval.py", line 382, in
loss.backward()
File "/opt/task-venv/lib/python3.12/site-packages/torch/_tensor.py", line 647, in backward
torch.autograd.backward(
File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/__init__.py", line 354, in backward
_engine_run_backward(
File "/opt/task-venv/lib/python3.12/site-packages/torch/autograd/graph.py", line 829, in _engine_run_backward
return Variable._execution_engine.run_backward( # Calls into the C++ engine to run the backward pass
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
RuntimeError: Trying to backward through the graph a second time (or directly access saved tensors after they have already been freed). Saved intermediate values of the graph are freed when you call .backward() or autograd.grad(). Specify retain_graph=True if you need to backward through the graph a second time or if you need to access saved tensors after calling backward.
Exit Code: 1
Process Group PGID: 2725
## Step 59 · agent · 2026-09-29T06:17:31.953Z
### Recorded reasoning summary
Analyzing Backpropagation Error
I'm currently dissecting a `RuntimeError` that surfaced. It appears `model.compute_all_user_vectors(feats)` was executed prior to the mini-batch loop. This has resulted in a shared computation graph for `all_user_vecs` that's persisting across batches. When `loss.backward()` was triggered on batch 2, PyTorch encountered an issue trying to backpropagate through this shared structure.
### Recorded reasoning summary
Refining User Vector Computation
I'm exploring ways to detach `all_user_vecs` to prevent shared computation graphs, considering options like `torch.no_grad()` or `.detach()`. I need to ensure the user MLP still receives gradients, so I'm examining if pre-computing and detaching pooled views, then recomputing user vectors within each mini-batch, is the most effective approach.
### replace · 2026-09-29T06:17:31.953Z
{
"allow_multiple": false,
"new_string": " def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"old_string": " def compute_all_user_vectors(self, feats):\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([h_rec, h_his, h_str, h_lik, h_dis, s_p], dim=-1)\n u = self.user_mlp(x)\n return F.normalize(u, p=2, dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user representations for this slice\n all_user_vecs = model.compute_all_user_vectors(feats)\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = all_user_vecs[b_u]\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"instruction": "Compute pooled views with no_grad and forward user MLP per batch so graph is fresh per batch",
"file_path": "/app/solution/prototype_eval.py"
}
### Tool output · 2026-09-29T06:17:31.973Z
Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:
...
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_user_pooled_views(self, feats):
with torch.no_grad():
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)
def forward_user(self, pooled_views_batch, stats_batch):
s_p = self.user_stats_proj(stats_batch)
x = torch.cat([pooled_views_batch, s_p], dim=-1)
u = self.user_mlp(x)
return F.normalize(u, p=2, dim=-1)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.compute_user_pooled_views(feats)
s_p = self.user_stats_proj(feats["user_stats"])
x = torch.cat([pooled, s_p], dim=-1)
u = self.user_mlp(x)
return F.normalize(u, p=2, dim=-1)
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
embs = self.item_emb(all_ids)
d_p = self.item_dense_proj(dense_features)
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
return F.normalize(v, p=2, dim=-1)
def forward_items(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.item_dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
...
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs) in enumerate(all_slices):
# Refresh user pooled views for this slice using current item embeddings
slice_pooled_views = model.compute_user_pooled_views(feats)
slice_stats = feats["user_stats"]
# Shuffle pairs
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
...
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
# Fresh forward pass for user batch
u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])
i_vecs = model.forward_items(b_i, item_dense_tensor)
# Sample random negatives
neg_ids = torch.randint(0, num_items, (len(b_pairs),))
neg_vecs = model.forward_items(neg_ids, item_dense_tensor)
...
## Step 60 · agent · 2026-09-29T06:17:34.586Z
### run_shell_command · 2026-09-29T06:17:34.586Z
{
"command": "python3 /app/solution/prototype_eval.py",
"description": "Run prototype_eval.py training and validation"
}
### Tool output · 2026-09-29T06:19:17.969Z
Output: Loading metadata...
Loading raw events...
Events loaded in 2.46s
Extracting training slices...
Starting training...
Epoch 1: loss=8.9639, val_ndcg@10=0.008040 (7.5s)
Epoch 2: loss=8.6391, val_ndcg@10=0.010863 (7.7s)
Epoch 3: loss=8.2330, val_ndcg@10=0.008554 (7.4s)
Epoch 4: loss=7.9092, val_ndcg@10=0.006901 (7.4s)
Epoch 5: loss=7.6701, val_ndcg@10=0.004623 (7.4s)
Epoch 6: loss=7.4679, val_ndcg@10=0.006293 (7.4s)
Process Group PGID: 2767
## Step 61 · agent · 2026-09-29T06:19:49.226Z
### Recorded reasoning summary
Observing Rapid Progress
I'm seeing impressive speed gains; training three slices took only 7.4 seconds per epoch. Val_ndcg surpassed the baseline early, but then dipped. I'm now focused on diagnosing this decrease, suspecting in-batch negative sampling bias as a primary culprit.
### Recorded reasoning summary
Investigating Negative Sampling
My attention is now fixed on the in-batch negative sampling. I'm examining how frequently occurring positive items are disproportionately affecting negative evaluations and how uniform sampling might be introducing bias without proper popularity debiasing, particularly concerning how other users' positives are treated.
### Recorded reasoning summary
Diagnosing Over-Penalization
I've confirmed my suspicion: popular items appearing multiple times within a batch are being incorrectly penalized as negatives for different users. This explains the performance dip as the model was actively driven away from items liked by more than one user.
### Recorded reasoning summary
Refining Batch Negatives
I'm now exploring how to correctly handle duplicate positive items within a batch to avoid penalizing them as negatives. My current hypothesis is to mask these out for accurate loss calculation. I'm also considering alternative negative sampling strategies, such as using sampled catalog negatives or a debiased in-batch approach, with a focus on ensuring candidate items in the batch are unique to prevent false negative conflicts.
### Recorded reasoning summary
Testing New Strategies
I'm now implementing a refined approach where unique items are paired with random catalog negatives. This setup ensures each item is evaluated only once by the item tower, and critically, eliminates false negative collisions by handling duplicate positives within a batch through masking or multi-label loss functions.
### Recorded reasoning summary
Analyzing Feature Influence
I'm now investigating which specific features contribute most to NDCG@10 and delving into the item tower's categorical features, like listen counts and popularity rank, considering how to best incorporate item popularity, perhaps through learned bias or by adjusting the scoring mechanism itself, potentially moving beyond simple dot products of normalized vectors.
### Recorded reasoning summary
Incorporating Bias Terms
I'm now exploring how to integrate bias terms directly into the dot product. This allows the model to account for both user affinity and inherent item quality or popularity, ensuring a more nuanced ranking beyond simple embedding similarity.
### Recorded reasoning summary
Adding Bias Terms
I'm now formulating a strategy to integrate learned bias terms into the scoring function. The plan is to have the item tower output a vector including a popularity bias, and the user tower output a temperature/bias weight. This will allow scores to balance personalization with item quality, and I'm eager to test this mathematically and then implement it.
### run_shell_command · 2026-09-29T06:19:49.226Z
{
"command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nb_i = torch.tensor([1, 5, 1, 8, 5, 10])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\nprint('Reconstruction:', unique_items[item_inverse])\n\"",
"description": "Verify unique_items and item_inverse in PyTorch"
}
### Tool output · 2026-09-29T06:19:53.611Z
Output: unique_items: tensor([ 1, 5, 8, 10])
item_inverse: tensor([0, 1, 0, 2, 1, 3])
Reconstruction: tensor([ 1, 5, 1, 8, 5, 10])
Process Group PGID: 2820
## Step 62 · agent · 2026-09-29T06:20:22.596Z
### Recorded reasoning summary
Analyzing User Similarities
I'm noticing some interesting patterns. Specifically, users 0 and 2 have identified the exact same target item, which is item 1. This shared preference suggests a potential avenue for collaborative filtering or identifying similar user profiles. It's a good lead to follow.
### replace · 2026-09-29T06:20:22.596Z
{
"old_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n return F.normalize(v, p=2, dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 6\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n # Refresh user pooled views for this slice using current item embeddings\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # Shuffle pairs\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n # Fresh forward pass for user batch\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n i_vecs = model.forward_items(b_i, item_dense_tensor)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(b_pairs),))\n neg_vecs = model.forward_items(neg_ids, item_dense_tensor)\n\n # In-batch candidates: pos + neg\n all_cand_vecs = torch.cat([i_vecs, neg_vecs], dim=0)\n\n # Similarity logits\n logits = torch.matmul(u_vecs, all_cand_vecs.T) / temperature\n\n # Mask out false negatives in-batch (same user)\n is_same_u = b_u.unsqueeze(1) == b_u.unsqueeze(0)\n is_false_neg = is_same_u & (~torch.eye(len(b_pairs), dtype=torch.bool))\n logits[:, :len(b_pairs)].masked_fill_(is_false_neg, -1e9)\n\n targets = torch.arange(len(b_pairs), dtype=torch.long)\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1",
"allow_multiple": false,
"file_path": "/app/solution/prototype_eval.py",
"instruction": "Use unique candidate items in batch and incorporate bias dimension in dot product",
"new_string": " def forward_items(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.item_dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n def forward_user(self, pooled_views_batch, stats_batch):\n s_p = self.user_stats_proj(stats_batch)\n x = torch.cat([pooled_views_batch, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.compute_user_pooled_views(feats)\n s_p = self.user_stats_proj(feats[\"user_stats\"])\n x = torch.cat([pooled, s_p], dim=-1)\n u = self.user_mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n embs = self.item_emb(all_ids)\n d_p = self.item_dense_proj(dense_features)\n x = torch.cat([embs, d_p], dim=-1)\n v = self.item_mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nmodel = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\n\nall_slices = [\n (slice1_feats, slice1_pairs),\n (slice2_feats, slice2_pairs),\n (slice3_feats, slice3_pairs),\n]\n\n# Validation ground truth\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\n\n# Validation candidate mask unpacked\nval_packed = val_eligibility.packed\nval_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder=\"little\")\n\nprint(\"Starting training...\")\nbatch_size = 512\nnum_epochs = 8\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(all_slices):\n slice_pooled_views = model.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n # User positive sets for false negative masking\n u_to_pos = {}\n for u_idx, i_idx, _ in pairs:\n u_to_pos.setdefault(u_idx, set()).add(i_idx)\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)\n\n # Dot product logits: (B, num_candidates)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]\n cand_items_np = cand_item_ids.numpy()\n b_u_np = b_u.numpy()\n inv_np = item_inverse.numpy()\n mask_matrix = torch.zeros_like(logits, dtype=torch.bool)\n for row in range(len(b_u)):\n u_row = b_u_np[row]\n pos_set = u_to_pos.get(u_row, set())\n tgt = inv_np[row]\n for c in range(len(unique_pos_items)):\n if c != tgt and cand_items_np[c] in pos_set:\n mask_matrix[row, c] = True\n\n logits.masked_fill_(mask_matrix, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1"
}
### Tool output · 2026-09-29T06:20:22.613Z
Successfully modified file: /app/solution/prototype_eval.py (1 replacements). Here is the updated code:
...
def forward_items(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.item_dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
def forward_user(self, pooled_views_batch, stats_batch):
s_p = self.user_stats_proj(stats_batch)
x = torch.cat([pooled_views_batch, s_p], dim=-1)
u = self.user_mlp(x)
u_norm = F.normalize(u[:, :-1], p=2, dim=-1)
# Learnable scaling for item bias
alpha = torch.sigmoid(u[:, -1:])
return torch.cat([u_norm, alpha], dim=-1)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.compute_user_pooled_views(feats)
s_p = self.user_stats_proj(feats["user_stats"])
x = torch.cat([pooled, s_p], dim=-1)
u = self.user_mlp(x)
u_norm = F.normalize(u[:, :-1], p=2, dim=-1)
alpha = torch.sigmoid(u[:, -1:])
return torch.cat([u_norm, alpha], dim=-1)
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
embs = self.item_emb(all_ids)
d_p = self.item_dense_proj(dense_features)
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
model = TwoTowerModel(num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)
temperature = 0.07
all_slices = [
...
val_packed = val_eligibility.packed
val_unpacked = np.unpackbits(val_packed, axis=1, count=num_items, bitorder="little")
print("Starting training...")
batch_size = 512
num_epochs = 8
for epoch in range(1, num_epochs + 1):
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs) in enumerate(all_slices):
slice_pooled_views = model.compute_user_pooled_views(feats)
slice_stats = feats["user_stats"]
# User positive sets for false negative masking
u_to_pos = {}
for u_idx, i_idx, _ in pairs:
u_to_pos.setdefault(u_idx, set()).add(i_idx)
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
for b_start in range(0, len(pairs_arr), batch_size):
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
u_vecs = model.forward_user(slice_pooled_views[b_u], slice_stats[b_u])
# Deduplicate target items in batch
unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)
# Sample random negatives
neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))
cand_item_ids = torch.cat([unique_pos_items, neg_ids])
cand_vecs = model.forward_items(cand_item_ids, item_dense_tensor)
# Dot product logits: (B, num_candidates)
logits = torch.matmul(u_vecs, cand_vecs.T) / temperature
# Mask out false negatives: if cand_item_ids[c] in u_to_pos[u], and c != item_inverse[row]
cand_items_np = cand_item_ids.numpy()
b_u_np = b_u.numpy()
inv_np = item_inverse.numpy()
mask_matrix = torch.zeros_like(logits, dtype=torch.bool)
for row in range(len(b_u)):
u_row = b_u_np[row]
pos_set = u_to_pos.get(u_row, set())
tgt = inv_np[row]
for c in range(len(unique_pos_items)):
if c != tgt and cand_items_np[c] in pos_set:
mask_matrix[row, c] = True
logits.masked_fill_(mask_matrix, -1e9)
targets = item_inverse
loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)
loss = (F.cross_entropy(logits, targets, reduction="none") * loss_weights).mean()
optimizer.zero_grad()
loss.backward()
...
## Step 63 · agent · 2026-09-29T06:20:34.311Z
### run_shell_command · 2026-09-29T06:20:34.311Z
{
"description": "Run prototype_eval.py with deduplicated items and bias dimension",
"command": "python3 /app/solution/prototype_eval.py"
}
### Tool output · 2026-09-29T06:25:06.905Z
Output: Loading metadata...
Loading raw events...
Events loaded in 2.46s
Extracting training slices...
Starting training...
Epoch 1: loss=8.8755, val_ndcg@10=0.013806 (26.6s)
Epoch 2: loss=8.5592, val_ndcg@10=0.022638 (26.9s)
Epoch 3: loss=8.1530, val_ndcg@10=0.020206 (26.4s)
Epoch 4: loss=7.8067, val_ndcg@10=0.010743 (27.1s)
Epoch 5: loss=7.5487, val_ndcg@10=0.008173 (27.0s)
Epoch 6: loss=7.3324, val_ndcg@10=0.007602 (26.8s)
Epoch 7: loss=7.1409, val_ndcg@10=0.006552 (27.0s)
Epoch 8: loss=6.9729, val_ndcg@10=0.005241 (26.8s)
Process Group PGID: 2830
## Step 64 · agent · 2026-09-29T06:25:39.837Z
### Recorded reasoning summary
Observing the Trend
My recent analysis shows a concerning overfitting trend. The model's performance spiked initially, exceeding the baseline, but then deteriorated. I've identified the likely culprit: a fixed learning rate of 1e-3 without scheduling or regularization, which appears to be causing it to over-specialize on the training data.
### Recorded reasoning summary
Implementing Regularization
I'm now exploring stronger regularization techniques. This involves increasing `weight_decay` and experimenting with a cosine learning rate schedule. I'm also investigating ways to optimize the masking operation, which is currently a bottleneck. Additionally, I plan to increase the number of training slices to provide more data and combat overfitting.
### Recorded reasoning summary
Investigating Likes
My current focus is on boosting NDCG@10 by analyzing top-ranked recommendations. I've discovered a critical oversight: the validation user features did not include likes from `validation_preference_state.parquet`, only `train_likes.parquet`. This omission of over 400,000 active liked tracks is likely impacting performance.
### Recorded reasoning summary
Validating Preferences
I'm now verifying the impact of incorporating user active likes and dislikes from `validation_preference_state.parquet`. My investigation into item features is also underway, aiming to enrich recommendations with metrics like total likes, dislike ratios, and listen completion rates, which I believe will significantly improve NDCG@10.
### Recorded reasoning summary
Tuning the Model
I'm now exploring the impact of weighting Grade 3 likes more heavily (5.0 or 7.0) to prioritize highly appreciated items. Additionally, I'm experimenting with different temperature values ($\tau$) and increasing training slices to six for greater temporal diversity and to combat overfitting. I'm also investigating item embedding initialization strategies.
### update_topic · 2026-09-29T06:25:39.837Z
{
"summary": "In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.",
"strategic_intent": "Systematically improve the two-tower model architecture, features, and training recipe.",
"title": "Phase 3: Architecture & Feature Engineering Optimization"
}
### Tool output · 2026-09-29T06:25:39.842Z
Current topic: "Phase 3: Architecture & Feature Engineering Optimization"
Topic summary: In Phase 2, built and validated the first PyTorch Two-Tower ranker. Discovered that deduplicating batch items and adding an item bias dimension achieved 0.022638 NDCG@10 at epoch 2 (more than 2x baseline 0.01071). Moving to Phase 3: incorporating preference state, expanding training slices to 6 weeks, vectorizing batch negative masking, tuning learning rate and regularization, and engineering richer item and user features.
Strategic Intent: Systematically improve the two-tower model architecture, features, and training recipe.
## Step 65 · agent · 2026-09-29T06:25:53.437Z
### Recorded reasoning summary
Investigating Batch Negative Masking Speed
I'm currently dissecting the performance of batch negative masking within `prototype_eval.py`. My focus is on understanding why each epoch is averaging 27 seconds, by examining the operations within each batch, specifically the conversion to numpy arrays and the subsequent row iteration.
### Recorded reasoning summary
Optimizing Masking Logic
I've identified a significant performance bottleneck: a Python set lookup occurring within a nested loop structure. This is leading to millions of unnecessary iterations per epoch. My current focus is on exploring vectorized alternatives to eliminate these Python loops and drastically improve processing speed.
### run_shell_command · 2026-09-29T06:25:53.437Z
{
"command": "python3 -c \"\nimport torch\n\nb_u = torch.tensor([10, 20, 10, 30, 20])\nb_i = torch.tensor([100, 200, 105, 300, 200])\nunique_items, item_inverse = torch.unique(b_i, return_inverse=True)\nprint('unique_items:', unique_items)\nprint('item_inverse:', item_inverse)\n\n# For row r, the target column is item_inverse[r].\n# If another row r2 has the same user b_u[r] == b_u[r2], then column item_inverse[r2] is also a positive for that user!\n# We want mask[r, item_inverse[r2]] = True whenever b_u[r] == b_u[r2] and item_inverse[r] != item_inverse[r2].\n\nnum_cand = len(unique_items) + 2 # plus 2 random negs\nB = len(b_u)\nmask = torch.zeros((B, num_cand), dtype=torch.bool)\n\n# Vectorized:\nsame_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0)) # (B, B)\ndiff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0)) # (B, B)\nfalse_neg_pairs = same_u & diff_cand # (B, B)\n\n# Scatter into mask: for each row r, set column item_inverse[c] to True if false_neg_pairs[r, c]\nrow_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\nmask[row_idx, item_inverse[col_idx]] = True\n\nprint('Mask:\\n', mask[:, :len(unique_items)])\nfor r in range(B):\n print(f'Row {r} (user {b_u[r].item()}, target {item_inverse[r].item()}): false neg columns: {torch.nonzero(mask[r]).flatten().tolist()}')\n\"",
"description": "Test vectorized false negative masking"
}
### Tool output · 2026-09-29T06:25:56.373Z
Output: unique_items: tensor([100, 105, 200, 300])
item_inverse: tensor([0, 2, 1, 3, 2])
Mask:
tensor([[False, True, False, False],
[False, False, False, False],
[ True, False, False, False],
[False, False, False, False],
[False, False, False, False]])
Row 0 (user 10, target 0): false neg columns: [1]
Row 1 (user 20, target 2): false neg columns: []
Row 2 (user 10, target 1): false neg columns: [0]
Row 3 (user 30, target 3): false neg columns: []
Row 4 (user 20, target 2): false neg columns: []
Process Group PGID: 2896
## Step 66 · agent · 2026-09-29T06:26:06.144Z
### Recorded reasoning summary
Optimizing for speed
I've successfully vectorized a crucial segment of the processing, replacing Python loops with efficient PyTorch operations. This optimization is showing significant speedups, reducing an epoch's runtime from 27 seconds.
### Recorded reasoning summary
Integrating preference data
I'm now focusing on incorporating the explicit preference states from the validation and test datasets. I've observed the substantial volume of liked and disliked items and am developing a method to reconstruct the active preference state at various timestamps to ensure accurate feature generation for training slices.
### run_shell_command · 2026-09-29T06:26:06.144Z
{
"description": "Verify reconstruction of active preference state",
"command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\nundislikes = pd.read_parquet('/task/data/train_undislikes.parquet')\n\n# Check user 100\nuid = 100\nu_l = likes[likes.uid == uid]\nu_ul = unlikes[unlikes.uid == uid]\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('User 100 pref state liked count:', len(v_pref_100_liked))\n\n# Last action per (uid, item_id)\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nprint('Reconstructed liked count:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\""
}
### Tool output · 2026-09-29T06:26:07.771Z
Output: User 100 pref state liked count: 13
Reconstructed liked count: 19
Match exactly? False
Process Group PGID: 2906
## Step 67 · agent · 2026-09-29T06:26:11.506Z
### run_shell_command · 2026-09-29T06:26:11.506Z
{
"command": "python3 -c \"\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nunlikes = pd.read_parquet('/task/data/train_unlikes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nuid = 100\nu_l = likes[(likes.uid == uid) & (likes.item_id.isin(cat_set))]\nu_ul = unlikes[(unlikes.uid == uid) & (unlikes.item_id.isin(cat_set))]\nl_action = pd.concat([\n u_l[['item_id', 'timestamp']].assign(action=1),\n u_ul[['item_id', 'timestamp']].assign(action=0)\n]).sort_values('timestamp').groupby('item_id').last()\n\nrec_liked = set(l_action[l_action.action == 1].index)\nv_pref_100_liked = set(v_pref[(v_pref.uid == uid) & (v_pref.state == 'liked')].item_id)\nprint('Catalog filtered reconstructed:', len(rec_liked))\nprint('Match exactly?', rec_liked == v_pref_100_liked)\n\"",
"description": "Check catalog filtering on preference state"
}
### Tool output · 2026-09-29T06:26:13.241Z
Output: Catalog filtered reconstructed: 13
Match exactly? True
Process Group PGID: 2923
## Step 68 · agent · 2026-09-29T06:26:19.275Z
### Recorded reasoning summary
Confirming Exact Matches
I've confirmed that `validation_preference_state.parquet` precisely represents the active likes and dislikes. This means for any training cutoff $T_w$, I can accurately determine the active likes and dislikes by examining the most recent action prior to $T_w$.
### run_shell_command · 2026-09-29T06:26:19.275Z
{
"command": "python3 -c \"\nimport time, pandas as pd, numpy as np, pyarrow.parquet as pq\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_items = cat.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Listens stats\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['item_id', 'played_ratio_pct', 'is_organic', 'track_length_seconds'])\ndf_l = table.to_pandas()\ndf_l = df_l[df_l.item_id.isin(item_to_idx)]\n\nl_stats = df_l.groupby('item_id').agg(\n total_plays=('played_ratio_pct', 'count'),\n mean_ratio=('played_ratio_pct', 'mean'),\n strong_ratio=('played_ratio_pct', lambda x: (x >= 80).mean()),\n organic_ratio=('is_organic', 'mean'),\n track_len=('track_length_seconds', 'median'),\n).reset_index()\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nlikes = likes[likes.item_id.isin(item_to_idx)]\nlike_counts = likes.groupby('item_id').size().to_dict()\n\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ndislikes = dislikes[dislikes.item_id.isin(item_to_idx)]\ndislike_counts = dislikes.groupby('item_id').size().to_dict()\n\nprint(f'Item features computed in {time.time() - t0:.2f}s')\n\"",
"description": "Test computing rich item features"
}
### Tool output · 2026-09-29T06:26:29.119Z
Output: Item features computed in 8.26s
Process Group PGID: 2940
## Step 69 · agent · 2026-09-29T06:27:13.593Z
### Recorded reasoning summary
Achieving Rich Features
I've successfully generated rich features for all 30,000 items within 8.26 seconds. This now provides each candidate track with the `log1p(positive_listens)`, `log1p(organic_positive_listens)`, and `popularity` features.
### Recorded reasoning summary
Evaluating Feature Contributions
I'm now assessing the predictive power of the newly generated features. My focus is on understanding how metrics like `strong_ratio`, `likes_per_play`, and `like_ratio` correlate with listener engagement, aiming to quantify intrinsic track quality alongside semantic content from embeddings.
### Recorded reasoning summary
Refining Training Strategy
I'm now exploring an improved training setup, investigating a lower learning rate of 3e-4 with CosineAnnealingLR and weight decay of 1e-4. I also plan to utilize six training slices to significantly increase the training data and optimize negative masking for faster epoch completion.
### write_file · 2026-09-29T06:27:13.593Z
{
"file_path": "/app/solution/train_improved.py",
"instruction": "Write train_improved.py with 6 slices, vectorized mask, and validation tracking",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\nt0 = time.time()\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\n# Load listens for last 150 days\nmin_listen_time = CUTOFF_VAL - 150 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\nprint(f\"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})\")\n\n# 3. Compute rich item features\nprint(\"3. Computing item features...\")\nt1 = time.time()\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\n# Normalize dense features\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\nprint(f\"Item features ready in {time.time() - t1:.2f}s\")\n\n\n# 4. Preference state helper\ndef get_active_preference_state(cutoff_time, user_set):\n \"\"\"Reconstruct active catalog likes and dislikes before cutoff_time.\"\"\"\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\n# 5. Extract training slices\ndef extract_slice(cutoff_time):\n t_start = time.time()\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n\n print(f\"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s\")\n return user_feats, pairs\n\n\nprint(\"4. Extracting training slices (6 weeks)...\")\ntrain_slices = []\nfor w in range(1, 7):\n s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs))\n\n# 6. Extract validation features\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\n\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\n# 7. Model definition\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.03)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nmodel = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)\noptimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 10\n\nprint(\"\\n6. Training Two-Tower Model...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n B = len(b_pairs)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n\n # Deduplicate target items in batch\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n # Sample random negatives\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Logits: (B, num_cand)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Vectorized false negative mask:\n # Mask out other columns where user is the same but target column differs\n same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))\n diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))\n false_neg_pairs = same_u & diff_cand\n row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)\n if len(row_idx) > 0:\n mask = torch.zeros_like(logits, dtype=torch.bool)\n mask[row_idx, item_inverse[col_idx]] = True\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n # Save checkpoint\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n"
}
### Tool output · 2026-09-29T06:27:13.606Z
Successfully created and wrote to new file: /app/solution/train_improved.py. Here is the updated code:
import os
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append("/task")
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet
torch.set_num_threads(8)
DATA_DIR = Path("/task/data")
OUT_DIR = Path("/app/solution")
OUT_DIR.mkdir(parents=True, exist_ok=True)
CUTOFF_VAL = 24786800
WEEK_SECONDS = 7 * 86400
print("1. Loading metadata...")
t0 = time.time()
cat_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
cat_items = cat_df.item_id.to_numpy()
item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}
num_items = len(cat_items)
target_users_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = target_users_df.uid.to_numpy()
target_uid_set = set(target_uids)
num_targets = len(target_uids)
print("2. Loading interaction logs...")
likes_df = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()
likes_df["item_idx"] = likes_df.item_id.map(item_to_idx)
unlikes_df = pd.read_parquet(DATA_DIR / "train_unlikes.parquet")
unlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()
unlikes_df["item_idx"] = unlikes_df.item_id.map(item_to_idx)
dislikes_df = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()
dislikes_df["item_idx"] = dislikes_df.item_id.map(item_to_idx)
undislikes_df = pd.read_parquet(DATA_DIR / "train_undislikes.parquet")
undislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()
undislikes_df["item_idx"] = undislikes_df.item_id.map(item_to_idx)
# Load listens for last 150 days
min_listen_time = CUTOFF_VAL - 150 * 86400
table = pq.read_table(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic", "track_length_seconds"],
filters=[("timestamp", ">=", min_listen_time)],
)
listens_df = table.to_pandas()
listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()
listens_df["item_idx"] = listens_df.item_id.map(item_to_idx)
print(f"Loaded logs in {time.time() - t0:.2f}s (listens: {len(listens_df)})")
# 3. Compute rich item features
print("3. Computing item features...")
t1 = time.time()
l_grouped = listens_df.groupby("item_idx").agg(
total_plays=("played_ratio_pct", "count"),
mean_ratio=("played_ratio_pct", "mean"),
strong_ratio=("played_ratio_pct", lambda x: (x >= 80).mean()),
organic_ratio=("is_organic", "mean"),
med_length=("track_length_seconds", "median"),
).reset_index()
item_stats_dict = l_grouped.set_index("item_idx").to_dict(orient="index")
like_counts = likes_df.groupby("item_idx").size().to_dict()
dislike_counts = dislikes_df.groupby("item_idx").size().to_dict()
NUM_ITEM_FEATS = 10
item_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)
for idx in range(num_items):
pos_l = cat_df.positive_listens.iloc[idx]
org_l = cat_df.organic_positive_listens.iloc[idx]
pop_r = cat_df.popularity_rank.iloc[idx]
st = item_stats_dict.get(idx, {"total_plays": 0, "mean_ratio": 50.0, "strong_ratio": 0.5, "organic_ratio": 0.5, "med_length": 180.0})
n_lik = like_counts.get(idx, 0)
n_dis = dislike_counts.get(idx, 0)
item_dense[idx, 0] = np.log1p(pos_l)
item_dense[idx, 1] = np.log1p(org_l)
item_dense[idx, 2] = pop_r / float(num_items)
item_dense[idx, 3] = np.log1p(st["total_plays"])
item_dense[idx, 4] = st["mean_ratio"] / 100.0
item_dense[idx, 5] = st["strong_ratio"]
item_dense[idx, 6] = st["organic_ratio"]
item_dense[idx, 7] = min(st["med_length"] / 300.0, 3.0)
item_dense[idx, 8] = np.log1p(n_lik)
item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)
# Normalize dense features
for c in range(NUM_ITEM_FEATS):
m = item_dense[:, c].mean()
s = item_dense[:, c].std() + 1e-6
item_dense[:, c] = (item_dense[:, c] - m) / s
item_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)
print(f"Item features ready in {time.time() - t1:.2f}s")
# 4. Preference state helper
def get_active_preference_state(cutoff_time, user_set):
"""Reconstruct active catalog likes and dislikes before cutoff_time."""
l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]
ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]
d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]
ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]
l_act = pd.concat([
l[["uid", "item_idx", "timestamp"]].assign(act=1),
ul[["uid", "item_idx", "timestamp"]].assign(act=0)
]).sort_values("timestamp").groupby(["uid", "item_idx"]).last().reset_index()
active_likes = l_act[l_act.act == 1].groupby("uid")["item_idx"].apply(list).to_dict()
d_act = pd.concat([
d[["uid", "item_idx", "timestamp"]].assign(act=1),
ud[["uid", "item_idx", "timestamp"]].assign(act=0)
]).sort_values("timestamp").groupby(["uid", "item_idx"]).last().reset_index()
active_dislikes = d_act[d_act.act == 1].groupby("uid")["item_idx"].apply(list).to_dict()
return active_likes, active_dislikes
def build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):
u_set = set(user_ids)
t_min = cutoff_time - 90 * 86400
sub_listens = listens_df[
(listens_df.timestamp >= t_min)
& (listens_df.timestamp < cutoff_time)
& (listens_df.uid.isin(u_set))
].copy()
sub_listens["days_ago"] = (cutoff_time - sub_listens.timestamp) / 86400.0
sub_listens["w_recent"] = np.where(
sub_listens.days_ago <= 14.0,
np.exp(-0.1 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0),
0.0,
)
sub_listens["w_history"] = (
np.exp(-0.02 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0)
)
sub_listens["w_strong"] = np.where(
(sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),
np.exp(-0.05 * sub_listens.days_ago),
0.0,
)
agg = (
sub_listens.groupby(["uid", "item_idx"])[["w_recent", "w_history", "w_strong"]]
.sum()
.reset_index()
)
if pref_likes is None or pref_dislikes is None:
pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)
uid_counts = sub_listens.groupby("uid").agg(
n_listens=("item_idx", "count"),
mean_ratio=("played_ratio_pct", "mean"),
organic_ratio=("is_organic", "mean"),
).to_dict(orient="index")
user_to_pos = {u: i for i, u in enumerate(user_ids)}
recent_indices, recent_uids, recent_weights = [], [], []
history_indices, history_uids, history_weights = [], [], []
strong_indices, strong_uids, strong_weights = [], [], []
like_indices, like_uids = [], []
dislike_indices, dislike_uids = [], []
stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)
for i, uid in enumerate(user_ids):
st = uid_counts.get(uid, {"n_listens": 0, "mean_ratio": 50.0, "organic_ratio": 0.5})
n_lik = len(pref_likes.get(uid, []))
n_dis = len(pref_dislikes.get(uid, []))
stats_arr[i] = [
np.log1p(st["n_listens"]),
st["mean_ratio"] / 100.0,
st["organic_ratio"],
np.log1p(n_lik),
np.log1p(n_dis),
(n_lik + 1.0) / (n_lik + n_dis + 2.0),
]
l_items = pref_likes.get(uid, [])
like_indices.extend(l_items)
like_uids.extend([i] * len(l_items))
d_items = pref_dislikes.get(uid, [])
dislike_indices.extend(d_items)
dislike_uids.extend([i] * len(d_items))
for uid, g in agg.groupby("uid"):
if uid not in user_to_pos:
continue
u_idx = user_to_pos[uid]
m_r = g.w_recent > 0
if m_r.any():
recent_indices.extend(g.item_idx[m_r].tolist())
recent_weights.extend(g.w_recent[m_r].tolist())
recent_uids.extend([u_idx] * int(m_r.sum()))
history_indices.extend(g.item_idx.tolist())
history_weights.extend(g.w_history.tolist())
history_uids.extend([u_idx] * len(g))
m_s = g.w_strong > 0
if m_s.any():
strong_indices.extend(g.item_idx[m_s].tolist())
strong_weights.extend(g.w_strong[m_s].tolist())
strong_uids.extend([u_idx] * int(m_s.sum()))
N = len(user_ids)
return {
"num_users": N,
"recent_indices": torch.tensor(recent_indices, dtype=torch.long),
"recent_uids": torch.tensor(recent_uids, dtype=torch.long),
"recent_weights": torch.tensor(recent_weights, dtype=torch.float32),
"history_indices": torch.tensor(history_indices, dtype=torch.long),
"history_uids": torch.tensor(history_uids, dtype=torch.long),
"history_weights": torch.tensor(history_weights, dtype=torch.float32),
"strong_indices": torch.tensor(strong_indices, dtype=torch.long),
"strong_uids": torch.tensor(strong_uids, dtype=torch.long),
"strong_weights": torch.tensor(strong_weights, dtype=torch.float32),
"like_indices": torch.tensor(like_indices, dtype=torch.long),
"like_uids": torch.tensor(like_uids, dtype=torch.long),
"dislike_indices": torch.tensor(dislike_indices, dtype=torch.long),
"dislike_uids": torch.tensor(dislike_uids, dtype=torch.long),
"user_stats": torch.tensor(stats_arr, dtype=torch.float32),
}
# 5. Extract training slices
def extract_slice(cutoff_time):
t_start = time.time()
w_end = cutoff_time + WEEK_SECONDS
prior = listens_df[listens_df.timestamp < cutoff_time]
prior_user_items = set(zip(prior.uid, prior.item_idx))
win_listens = listens_df[
(listens_df.timestamp >= cutoff_time)
& (listens_df.timestamp < w_end)
& (listens_df.is_organic == 1)
& (listens_df.played_ratio_pct >= 80)
]
win_likes = likes_df[
(likes_df.timestamp >= cutoff_time)
& (likes_df.timestamp < w_end)
& (likes_df.is_organic == 1)
]
targets = {}
for u, i in zip(win_listens.uid, win_listens.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 1
for u, i in zip(win_likes.uid, win_likes.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 3
user_positives = {}
for (u, i), grade in targets.items():
user_positives.setdefault(u, []).append((i, grade))
unique_users = sorted(list(user_positives.keys()))
user_feats = build_user_features(cutoff_time, unique_users)
user_to_idx = {u: idx for idx, u in enumerate(unique_users)}
pairs = []
for u, items in user_positives.items():
u_idx = user_to_idx[u]
for item_idx, grade in items:
pairs.append((u_idx, item_idx, float(grade)))
print(f"Slice (cutoff={cutoff_time}): {len(unique_users)} users, {len(pairs)} pairs in {time.time() - t_start:.2f}s")
return user_feats, pairs
print("4. Extracting training slices (6 weeks)...")
train_slices = []
for w in range(1, 7):
s_feats, s_pairs = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)
train_slices.append((s_feats, s_pairs))
# 6. Extract validation features
print("5. Extracting validation features...")
v_pref = pd.read_parquet(DATA_DIR / "validation_preference_state.parquet")
val_likes_dict = {u: [] for u in target_uids}
val_dislikes_dict = {u: [] for u in target_uids}
for uid, item, state in v_pref.itertuples(index=False):
idx = item_to_idx.get(int(item))
if idx is not None:
if state == "liked":
val_likes_dict[int(uid)].append(idx)
elif state == "disliked":
val_dislikes_dict[int(uid)].append(idx)
val_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)
val_eligibility = CandidateSet(DATA_DIR / "validation_eligible_candidates.npz")
targets_set = set(target_users_df.uid.astype(int))
val_gold = validation_truth(val_eligibility, targets_set)
val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder="little")
# 7. Model definition
class ItemTower(nn.Module):
def __init__(self, num_items, embed_dim, dense_dim, out_dim):
super().__init__()
self.item_emb = nn.Embedding(num_items, embed_dim)
nn.init.normal_(self.item_emb.weight, std=0.03)
self.dense_proj = nn.Linear(dense_dim, embed_dim)
self.mlp = nn.Sequential(
nn.Linear(embed_dim * 2, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
def forward(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.mlp(x)
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
class UserTower(nn.Module):
def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):
super().__init__()
self.shared_emb = shared_item_emb
self.embed_dim = embed_dim
self.stats_proj = nn.Linear(stats_dim, embed_dim)
in_dim = embed_dim * 5 + embed_dim # 5 views + stats_proj
self.mlp = nn.Sequential(
nn.Linear(in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool(self, num_users, indices, uids, weights=None):
out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)
if len(indices) == 0:
return out
embs = self.shared_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_user_pooled_views(self, feats):
with torch.no_grad():
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)
def forward_user(self, pooled_views, stats):
s_p = self.stats_proj(stats)
x = torch.cat([pooled_views, s_p], dim=-1)
u = self.mlp(x)
u_norm = F.normalize(u[:, :-1], p=2, dim=-1)
alpha = torch.sigmoid(u[:, -1:])
return torch.cat([u_norm, alpha], dim=-1)
class TwoTowerRanker(nn.Module):
def __init__(self, num_items, embed_dim=64, dense_dim=10, stats_dim=6, out_dim=64):
super().__init__()
self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)
self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.user_tower.compute_user_pooled_views(feats)
return self.user_tower.forward_user(pooled, feats["user_stats"])
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
return self.item_tower(all_ids, dense_features)
model = TwoTowerRanker(num_items, embed_dim=64, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=64)
optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=1e-4)
temperature = 0.07
batch_size = 512
num_epochs = 10
print("\n6. Training Two-Tower Model...")
best_ndcg = 0.0
for epoch in range(1, num_epochs + 1):
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs) in enumerate(train_slices):
slice_pooled = model.user_tower.compute_user_pooled_views(feats)
slice_stats = feats["user_stats"]
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
for b_start in range(0, len(pairs_arr), batch_size):
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
B = len(b_pairs)
u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])
# Deduplicate target items in batch
unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)
# Sample random negatives
neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))
cand_item_ids = torch.cat([unique_pos_items, neg_ids])
cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)
# Logits: (B, num_cand)
logits = torch.matmul(u_vecs, cand_vecs.T) / temperature
# Vectorized false negative mask:
# Mask out other columns where user is the same but target column differs
same_u = (b_u.unsqueeze(1) == b_u.unsqueeze(0))
diff_cand = (item_inverse.unsqueeze(1) != item_inverse.unsqueeze(0))
false_neg_pairs = same_u & diff_cand
row_idx, col_idx = torch.nonzero(false_neg_pairs, as_tuple=True)
if len(row_idx) > 0:
mask = torch.zeros_like(logits, dtype=torch.bool)
mask[row_idx, item_inverse[col_idx]] = True
logits.masked_fill_(mask, -1e9)
targets = item_inverse
loss_weights = torch.where(b_grades >= 3.0, 5.0, 1.0)
loss = (F.cross_entropy(logits, targets, reduction="none") * loss_weights).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
num_batches += 1
# Validation evaluation
with torch.no_grad():
val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()
all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()
ranked_dict = {}
for start_u in range(0, num_targets, 1000):
end_u = min(start_u + 1000, num_targets)
sub_u = val_u_vecs[start_u:end_u]
sub_scores = np.matmul(sub_u, all_v_vecs.T)
sub_mask = val_unpacked[start_u:end_u]
sub_scores[sub_mask == 0] = -1e9
top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]
for row in range(len(sub_u)):
u_row = start_u + row
uid = int(target_uids[u_row])
cand_idx = top100_idx[row]
sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]
ranked_dict[uid] = cat_items[sorted_idx].tolist()
val_score = score(ranked_dict, val_gold)
curr_ndcg = val_score["quality_ndcg@10"]
if curr_ndcg > best_ndcg:
best_ndcg = curr_ndcg
# Save checkpoint
torch.save(model.state_dict(), OUT_DIR / "best_model.pt")
print(
f"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, "
f"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) "
f"[{time.time() - t_ep:.1f}s]"
)
## Step 70 · agent · 2026-09-29T06:27:35.918Z
### run_shell_command · 2026-09-29T06:27:35.918Z
{
"command": "python3 /app/solution/train_improved.py",
"description": "Run train_improved.py"
}
### Tool output · 2026-09-29T06:31:16.828Z
Output: 1. Loading metadata...
2. Loading interaction logs...
Loaded logs in 3.12s (listens: 18800747)
3. Computing item features...
Item features ready in 4.55s
4. Extracting training slices (6 weeks)...
Slice (cutoff=24182000): 4968 users, 49683 pairs in 12.80s
Slice (cutoff=23577200): 4927 users, 47773 pairs in 12.52s
Slice (cutoff=22972400): 4774 users, 47336 pairs in 12.03s
Slice (cutoff=22367600): 4668 users, 46200 pairs in 11.37s
Slice (cutoff=21762800): 4643 users, 45826 pairs in 10.97s
Slice (cutoff=21158000): 4513 users, 42793 pairs in 10.71s
5. Extracting validation features...
6. Training Two-Tower Model...
Epoch 1: loss=11.0525, val_ndcg@10=0.014643 (best=0.014643) [12.1s]
Epoch 2: loss=10.6424, val_ndcg@10=0.009121 (best=0.014643) [11.5s]
Epoch 3: loss=10.1821, val_ndcg@10=0.015472 (best=0.015472) [11.5s]
Epoch 4: loss=9.8330, val_ndcg@10=0.009633 (best=0.015472) [11.4s]
Epoch 5: loss=9.5499, val_ndcg@10=0.006211 (best=0.015472) [11.5s]
Epoch 6: loss=9.3125, val_ndcg@10=0.005576 (best=0.015472) [11.7s]
Epoch 7: loss=9.1088, val_ndcg@10=0.005108 (best=0.015472) [11.4s]
Epoch 8: loss=8.9413, val_ndcg@10=0.004401 (best=0.015472) [11.4s]
Epoch 9: loss=8.7853, val_ndcg@10=0.003654 (best=0.015472) [11.4s]
Epoch 10: loss=8.6605, val_ndcg@10=0.003811 (best=0.015472) [11.2s]
Process Group PGID: 2974
## Step 71 · agent · 2026-09-29T06:35:45.674Z
### Recorded reasoning summary
Investigating Evaluation Discrepancy
I'm scrutinizing the fine-grained code differences between `prototype_eval.py` and `train_improved.py` to pinpoint why the former achieved a significantly higher `val_ndcg@10` of 0.022638 compared to the latter's 0.015472. My focus is on identifying the precise algorithmic or implementation variations that led to this outcome.
### Recorded reasoning summary
Analyzing Feature Differences
I've identified key discrepancies in feature processing. `prototype_eval.py` uses fewer, unstandardized dense features, including raw `log1p(positive_listens)` values around 10.0. In contrast, `train_improved.py` employs more features, standardized with mean/std. I'm investigating why the simpler, unstandardized approach yielded superior results.
### Recorded reasoning summary
Discovering Divergences
My analysis revealed that categorical features *were* indeed standardized in `prototype_eval.py`. Further investigation shows differing `loss_weights` for grades >= 3.0 (3.0 vs. 5.0) and varying learning rates and weight decays in the AdamW optimizer. I'm now exploring the impact of these adjustments and the false negative mask implementation.
### Recorded reasoning summary
Identifying False Negative Logic
I've discovered a critical divergence in how false negatives are handled. My earlier investigation of `prototype_eval.py` missed that its `u_to_pos` included all positive items for a user for the entire week, not just those within the current mini-batch. This broader context is key.
**Revisiting False Negative Handling**
I'm now re-evaluating the false negative masking implementation. The `prototype_eval.py` approach of considering all positive items from the entire week for a user, rather than just those in the current batch as done in `train_improved.py`, appears to be a significant factor in the observed performance difference.
**Clarifying Positive Item Scope**
My focus has shifted to the scope of positive items used in false negative generation. `prototype_eval.py` incorporates a user's entire week of positive items, a broader definition than the mini-batch specific approach in `train_improved.py`, which likely impacts the model's learning and evaluation.
### Recorded reasoning summary
Investigating Overfitting Trends
I'm now focused on the decay in validation NDCG observed after epoch 2, despite decreasing training loss. This pattern strongly suggests overfitting or distribution drift, and I'm analyzing item embedding updates and MLP behavior within the training loop to understand the root cause.
### run_shell_command · 2026-09-29T06:35:45.674Z
{
"command": "python3 -c \"\nimport torch\nimport numpy as np\n\n# Let's inspect best_model.pt saved from train_improved\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', sd.keys())\n print('item_emb weight norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n\"",
"description": "Check saved best model"
}
### Tool output · 2026-09-29T06:35:48.611Z
Output: Traceback (most recent call last):
File "", line 7, in
NameError: name 'os' is not defined. Did you forget to import 'os'?
Exit Code: 1
Process Group PGID: 3057
## Step 72 · agent · 2026-09-29T06:35:54.101Z
### run_shell_command · 2026-09-29T06:35:54.101Z
{
"command": "python3 -c \"\nimport os, torch\nmodel_path = '/app/solution/best_model.pt'\nif os.path.exists(model_path):\n sd = torch.load(model_path, weights_only=True)\n print('Keys:', list(sd.keys()))\n print('item_emb norm:', torch.norm(sd['item_tower.item_emb.weight']).item())\n print('item_emb mean, std:', sd['item_tower.item_emb.weight'].mean().item(), sd['item_tower.item_emb.weight'].std().item())\n\"",
"description": "Inspect best_model.pt parameters"
}
### Tool output · 2026-09-29T06:35:57.042Z
Output: Keys: ['item_tower.item_emb.weight', 'item_tower.dense_proj.weight', 'item_tower.dense_proj.bias', 'item_tower.mlp.0.weight', 'item_tower.mlp.0.bias', 'item_tower.mlp.1.weight', 'item_tower.mlp.1.bias', 'item_tower.mlp.3.weight', 'item_tower.mlp.3.bias', 'user_tower.shared_emb.weight', 'user_tower.stats_proj.weight', 'user_tower.stats_proj.bias', 'user_tower.mlp.0.weight', 'user_tower.mlp.0.bias', 'user_tower.mlp.1.weight', 'user_tower.mlp.1.bias', 'user_tower.mlp.4.weight', 'user_tower.mlp.4.bias']
item_emb norm: 67.66739654541016
item_emb mean, std: 0.0011243692133575678 0.048823002725839615
Process Group PGID: 3067
## Step 73 · agent · 2026-09-29T06:36:36.172Z
### Recorded reasoning summary
Evaluating Prototype Performance
My recent focus has been on the `prototype_eval.py` script. I've observed that Epoch 2 yielded a score of `0.022638`. I am now proceeding to save the model checkpoint from this epoch and will evaluate its performance using `python /task/evaluate_public.py`. This step is crucial for understanding the current state of the prototype.
### Recorded reasoning summary
Investigating Score Discrepancies
I'm currently examining why `prototype_eval.py` scored 0.022638 while `train_improved.py` achieved 0.015472. My initial thoughts point to the reduced number of slices (3 vs. 6) in `prototype_eval.py` focusing on more recent data, a different learning rate of `1e-3`, and a crucial change in how the `mask_matrix` operates, only masking items present in the user's weekly positive set within the batch.
### run_shell_command · 2026-09-29T06:36:36.172Z
{
"command": "python3 -c \"\nimport time\n\ncand_set = set(range(400))\nu_pos = {i: set(range(i, i+10)) for i in range(512)}\n\nt0 = time.time()\nfor _ in range(100):\n for r in range(512):\n overlap = u_pos[r] & cand_set\nprint('512 set intersections x 100 took:', round(time.time() - t0, 3), 's')\n\"",
"description": "Benchmark set intersection for false negative masking"
}
### Tool output · 2026-09-29T06:36:36.224Z
Output: 512 set intersections x 100 took: 0.018 s
Process Group PGID: 3080
## Step 74 · agent · 2026-09-29T06:36:53.433Z
### Recorded reasoning summary
Optimizing Inference Speed
My recent analysis revealed that set intersection isn't the bottleneck. The slowdown actually stems from the `cand_items_np[c] in pos_set` operation within `prototype_eval.py`. I'm now focusing on optimizing that specific line of code.
### Recorded reasoning summary
Refining User Representations
I've confirmed the set intersection approach drastically improved performance, now taking milliseconds. My focus has shifted to further enhancing the Two-Tower ranker's effectiveness, specifically examining the components within the User Tower, such as the weighting of recent versus historical listens.
### run_shell_command · 2026-09-29T06:36:53.433Z
{
"description": "Check listens in last 3 days",
"command": "python3 -c \"\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncutoff = 24786800\nt_start = cutoff - 3 * 86400\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid'], filters=[('timestamp', '>=', t_start)])\nuids = table['uid'].to_numpy()\nprint('Users with listens in last 3 days:', len(set(uids)))\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users with listens in last 3 days:', len(set(uids) & set(targets.uid)), '/', len(targets))\n\""
}
### Tool output · 2026-09-29T06:36:55.305Z
Output: Users with listens in last 3 days: 6511
Target users with listens in last 3 days: 6400 / 8456
Process Group PGID: 3089
## Step 75 · agent · 2026-09-29T06:37:11.610Z
### Recorded reasoning summary
Analyzing User Activity
My focus has shifted to understanding item co-occurrence. It's interesting to note that a significant portion of our target users, 76%, have shown ultra-recent activity within the last 72 hours. This high engagement is encouraging as I delve into this next analytical step.
### Recorded reasoning summary
Exploring Embedding Initialization
I'm investigating how Two-Tower models link past and future music. The shared item embedding acts as the bridge. I'm considering pre-initializing these embeddings with Word2Vec or SVD on session co-occurrence to potentially improve learning speed and accuracy, as the model needs to learn all item similarities from scratch otherwise.
### run_shell_command · 2026-09-29T06:37:11.610Z
{
"description": "Benchmark item-item co-occurrence computation",
"command": "python3 -c \"\nimport time, scipy.sparse as sp, numpy as np, pandas as pd, pyarrow.parquet as pq\nfrom sklearn.decomposition import TruncatedSVD\n\nt0 = time.time()\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat.item_id)}\ncutoff = 24786800\n\n# Take listens in last 30 days\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'item_id'],\n filters=[('timestamp', '>=', cutoff - 30 * 86400)])\ndf = table.to_pandas()\ndf = df[df.item_id.isin(item_to_idx)]\ndf['item_idx'] = df.item_id.map(item_to_idx)\ndf = df.drop_duplicates(['uid', 'item_idx'])\n\n# User-item sparse matrix (users in last 30 days x 30000 items)\nuid_map = {u: i for i, u in enumerate(df.uid.unique())}\nuser_rows = df.uid.map(uid_map).to_numpy()\nitem_cols = df.item_idx.to_numpy()\ndata = np.ones(len(df), dtype=np.float32)\n\nU = sp.csr_matrix((data, (user_rows, item_cols)), shape=(len(uid_map), 30000))\nprint(f'User-Item matrix created in {time.time() - t0:.2f}s, shape: {U.shape}, nnz: {U.nnz}')\n\n# Item-item co-occurrence: C = U.T @ U\nt1 = time.time()\nC = U.T @ U # shape (30000, 30000)\nprint(f'Item-Item co-occurrence computed in {time.time() - t1:.2f}s, nnz: {C.nnz}')\n\""
}
### Tool output · 2026-09-29T06:37:23.617Z
Output: User-Item matrix created in 1.33s, shape: (8512, 30000), nnz: 2157185
Item-Item co-occurrence computed in 7.33s, nnz: 287150348
Process Group PGID: 3111
## Step 76 · agent · 2026-09-29T06:38:11.527Z
### Recorded reasoning summary
Analyzing Matrix Size
I'm processing the item-item co-occurrence matrix, which currently stands at an impressive 287 million entries. This dense-ish 30,000x30,000 matrix is being handled in roughly 7.3 seconds. I'm keeping in mind the instruction that this benchmark scales to millions of users and items in production.
### Recorded reasoning summary
Refining Training Strategy
My focus has shifted to ensuring the training methodology adheres to the constraint of avoiding global user-item matrix construction or decomposition, which is critical for scalability. I am now examining the Two-Tower model's performance and exploring optimizations for its training recipe to further enhance metrics like NDCG@10.
### Recorded reasoning summary
Exploring Embedding Dimensions
I'm currently considering doubling the embedding dimension to 128. This enhancement should significantly boost the model's capacity to capture nuanced music styles and artist relationships without a substantial memory overhead. The focus is on evaluating its impact on representing richer user and item features within the Two-Tower architecture.
### Recorded reasoning summary
Adapting Interaction Score
I'm refining the score calculation to integrate item popularity and bias directly into the user and item embeddings for a more unified dot product. This change aims to streamline the relevance scoring mechanism.
**Optimizing Training Objectives**
My current task involves experimenting with the InfoNCE loss and sampled softmax, particularly concerning batch negative sampling strategies and graded loss weighting. The goal is to improve the model's ability to distinguish relevant items.
### Recorded reasoning summary
Tuning Two-Tower Parameters
I'm now systematically testing configurations for embedding dimension, temperature, learning rate, and weight decay. My goal is to optimize NDCG@10 through early stopping based on validation performance, and I'm excited to see how these variations impact the results, especially with efficient top-100 generation.
### write_file · 2026-09-29T06:38:11.527Z
{
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\ntarget_uid_set = set(target_uids)\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\nunlikes_df = pd.read_parquet(DATA_DIR / \"train_unlikes.parquet\")\nunlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()\nunlikes_df[\"item_idx\"] = unlikes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nundislikes_df = pd.read_parquet(DATA_DIR / \"train_undislikes.parquet\")\nundislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()\nundislikes_df[\"item_idx\"] = undislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\", \"track_length_seconds\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\nprint(\"3. Computing item features...\")\nl_grouped = listens_df.groupby(\"item_idx\").agg(\n total_plays=(\"played_ratio_pct\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n strong_ratio=(\"played_ratio_pct\", lambda x: (x >= 80).mean()),\n organic_ratio=(\"is_organic\", \"mean\"),\n med_length=(\"track_length_seconds\", \"median\"),\n).reset_index()\n\nitem_stats_dict = l_grouped.set_index(\"item_idx\").to_dict(orient=\"index\")\nlike_counts = likes_df.groupby(\"item_idx\").size().to_dict()\ndislike_counts = dislikes_df.groupby(\"item_idx\").size().to_dict()\n\nNUM_ITEM_FEATS = 10\nitem_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)\nfor idx in range(num_items):\n pos_l = cat_df.positive_listens.iloc[idx]\n org_l = cat_df.organic_positive_listens.iloc[idx]\n pop_r = cat_df.popularity_rank.iloc[idx]\n st = item_stats_dict.get(idx, {\"total_plays\": 0, \"mean_ratio\": 50.0, \"strong_ratio\": 0.5, \"organic_ratio\": 0.5, \"med_length\": 180.0})\n n_lik = like_counts.get(idx, 0)\n n_dis = dislike_counts.get(idx, 0)\n\n item_dense[idx, 0] = np.log1p(pos_l)\n item_dense[idx, 1] = np.log1p(org_l)\n item_dense[idx, 2] = pop_r / float(num_items)\n item_dense[idx, 3] = np.log1p(st[\"total_plays\"])\n item_dense[idx, 4] = st[\"mean_ratio\"] / 100.0\n item_dense[idx, 5] = st[\"strong_ratio\"]\n item_dense[idx, 6] = st[\"organic_ratio\"]\n item_dense[idx, 7] = min(st[\"med_length\"] / 300.0, 3.0)\n item_dense[idx, 8] = np.log1p(n_lik)\n item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)\n\nfor c in range(NUM_ITEM_FEATS):\n m = item_dense[:, c].mean()\n s = item_dense[:, c].std() + 1e-6\n item_dense[:, c] = (item_dense[:, c] - m) / s\n\nitem_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)\n\n\ndef get_active_preference_state(cutoff_time, user_set):\n l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]\n ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]\n d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]\n ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]\n\n l_act = pd.concat([\n l[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ul[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_likes = l_act[l_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n d_act = pd.concat([\n d[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=1),\n ud[[\"uid\", \"item_idx\", \"timestamp\"]].assign(act=0)\n ]).sort_values(\"timestamp\").groupby([\"uid\", \"item_idx\"]).last().reset_index()\n active_dislikes = d_act[d_act.act == 1].groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n return active_likes, active_dislikes\n\n\ndef build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n if pref_likes is None or pref_dislikes is None:\n pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_lik = len(pref_likes.get(uid, []))\n n_dis = len(pref_dislikes.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_lik),\n np.log1p(n_dis),\n (n_lik + 1.0) / (n_lik + n_dis + 2.0),\n ]\n l_items = pref_likes.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = pref_dislikes.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"4. Extracting recent slices (3 weeks)...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nprint(\"5. Extracting validation features...\")\nv_pref = pd.read_parquet(DATA_DIR / \"validation_preference_state.parquet\")\nval_likes_dict = {u: [] for u in target_uids}\nval_dislikes_dict = {u: [] for u in target_uids}\nfor uid, item, state in v_pref.itertuples(index=False):\n idx = item_to_idx.get(int(item))\n if idx is not None:\n if state == \"liked\":\n val_likes_dict[int(uid)].append(idx)\n elif state == \"disliked\":\n val_dislikes_dict[int(uid)].append(idx)\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.1),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\ndef train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):\n print(f\"\\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---\")\n model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)\n batch_size = 512\n best_ndcg = 0.0\n\n for epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Fast false negative masking via set intersection\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n scheduler.step()\n\n # Validation eval\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n ranked_dict = {}\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_scores = np.matmul(sub_u, all_v_vecs.T)\n sub_mask = val_unpacked[start_u:end_u]\n sub_scores[sub_mask == 0] = -1e9\n\n top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n cand_idx = top100_idx[row]\n sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]\n ranked_dict[uid] = cat_items[sorted_idx].tolist()\n\n val_score = score(ranked_dict, val_gold)\n curr_ndcg = val_score[\"quality_ndcg@10\"]\n if curr_ndcg > best_ndcg:\n best_ndcg = curr_ndcg\n torch.save(model.state_dict(), OUT_DIR / \"best_model.pt\")\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n\n return best_ndcg\n\n\ntrain_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\ntrain_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)\n",
"instruction": "Write tune_two_tower.py to benchmark embed_dim=64 vs 128 with cosine decay and fast set masking",
"file_path": "/app/solution/tune_two_tower.py"
}
### Tool output · 2026-09-29T06:38:13.953Z
Successfully created and wrote to new file: /app/solution/tune_two_tower.py. Here is the updated code:
import os
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append("/task")
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet
torch.set_num_threads(8)
DATA_DIR = Path("/task/data")
OUT_DIR = Path("/app/solution")
OUT_DIR.mkdir(parents=True, exist_ok=True)
CUTOFF_VAL = 24786800
WEEK_SECONDS = 7 * 86400
print("1. Loading metadata...")
cat_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
cat_items = cat_df.item_id.to_numpy()
item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}
num_items = len(cat_items)
target_users_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = target_users_df.uid.to_numpy()
target_uid_set = set(target_uids)
num_targets = len(target_uids)
print("2. Loading interaction logs...")
likes_df = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()
likes_df["item_idx"] = likes_df.item_id.map(item_to_idx)
unlikes_df = pd.read_parquet(DATA_DIR / "train_unlikes.parquet")
unlikes_df = unlikes_df[unlikes_df.item_id.isin(item_to_idx)].copy()
unlikes_df["item_idx"] = unlikes_df.item_id.map(item_to_idx)
dislikes_df = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()
dislikes_df["item_idx"] = dislikes_df.item_id.map(item_to_idx)
undislikes_df = pd.read_parquet(DATA_DIR / "train_undislikes.parquet")
undislikes_df = undislikes_df[undislikes_df.item_id.isin(item_to_idx)].copy()
undislikes_df["item_idx"] = undislikes_df.item_id.map(item_to_idx)
min_listen_time = CUTOFF_VAL - 120 * 86400
table = pq.read_table(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic", "track_length_seconds"],
filters=[("timestamp", ">=", min_listen_time)],
)
listens_df = table.to_pandas()
listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()
listens_df["item_idx"] = listens_df.item_id.map(item_to_idx)
# Item features
print("3. Computing item features...")
l_grouped = listens_df.groupby("item_idx").agg(
total_plays=("played_ratio_pct", "count"),
mean_ratio=("played_ratio_pct", "mean"),
strong_ratio=("played_ratio_pct", lambda x: (x >= 80).mean()),
organic_ratio=("is_organic", "mean"),
med_length=("track_length_seconds", "median"),
).reset_index()
item_stats_dict = l_grouped.set_index("item_idx").to_dict(orient="index")
like_counts = likes_df.groupby("item_idx").size().to_dict()
dislike_counts = dislikes_df.groupby("item_idx").size().to_dict()
NUM_ITEM_FEATS = 10
item_dense = np.zeros((num_items, NUM_ITEM_FEATS), dtype=np.float32)
for idx in range(num_items):
pos_l = cat_df.positive_listens.iloc[idx]
org_l = cat_df.organic_positive_listens.iloc[idx]
pop_r = cat_df.popularity_rank.iloc[idx]
st = item_stats_dict.get(idx, {"total_plays": 0, "mean_ratio": 50.0, "strong_ratio": 0.5, "organic_ratio": 0.5, "med_length": 180.0})
n_lik = like_counts.get(idx, 0)
n_dis = dislike_counts.get(idx, 0)
item_dense[idx, 0] = np.log1p(pos_l)
item_dense[idx, 1] = np.log1p(org_l)
item_dense[idx, 2] = pop_r / float(num_items)
item_dense[idx, 3] = np.log1p(st["total_plays"])
item_dense[idx, 4] = st["mean_ratio"] / 100.0
item_dense[idx, 5] = st["strong_ratio"]
item_dense[idx, 6] = st["organic_ratio"]
item_dense[idx, 7] = min(st["med_length"] / 300.0, 3.0)
item_dense[idx, 8] = np.log1p(n_lik)
item_dense[idx, 9] = (n_lik + 1.0) / (n_lik + n_dis + 2.0)
for c in range(NUM_ITEM_FEATS):
m = item_dense[:, c].mean()
s = item_dense[:, c].std() + 1e-6
item_dense[:, c] = (item_dense[:, c] - m) / s
item_dense_tensor = torch.tensor(item_dense, dtype=torch.float32)
def get_active_preference_state(cutoff_time, user_set):
l = likes_df[(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(user_set))]
ul = unlikes_df[(unlikes_df.timestamp < cutoff_time) & (unlikes_df.uid.isin(user_set))]
d = dislikes_df[(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(user_set))]
ud = undislikes_df[(undislikes_df.timestamp < cutoff_time) & (undislikes_df.uid.isin(user_set))]
l_act = pd.concat([
l[["uid", "item_idx", "timestamp"]].assign(act=1),
ul[["uid", "item_idx", "timestamp"]].assign(act=0)
]).sort_values("timestamp").groupby(["uid", "item_idx"]).last().reset_index()
active_likes = l_act[l_act.act == 1].groupby("uid")["item_idx"].apply(list).to_dict()
d_act = pd.concat([
d[["uid", "item_idx", "timestamp"]].assign(act=1),
ud[["uid", "item_idx", "timestamp"]].assign(act=0)
]).sort_values("timestamp").groupby(["uid", "item_idx"]).last().reset_index()
active_dislikes = d_act[d_act.act == 1].groupby("uid")["item_idx"].apply(list).to_dict()
return active_likes, active_dislikes
def build_user_features(cutoff_time, user_ids, pref_likes=None, pref_dislikes=None):
u_set = set(user_ids)
t_min = cutoff_time - 90 * 86400
sub_listens = listens_df[
(listens_df.timestamp >= t_min)
& (listens_df.timestamp < cutoff_time)
& (listens_df.uid.isin(u_set))
].copy()
sub_listens["days_ago"] = (cutoff_time - sub_listens.timestamp) / 86400.0
sub_listens["w_recent"] = np.where(
sub_listens.days_ago <= 14.0,
np.exp(-0.1 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0),
0.0,
)
sub_listens["w_history"] = (
np.exp(-0.02 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0)
)
sub_listens["w_strong"] = np.where(
(sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),
np.exp(-0.05 * sub_listens.days_ago),
0.0,
)
agg = (
sub_listens.groupby(["uid", "item_idx"])[["w_recent", "w_history", "w_strong"]]
.sum()
.reset_index()
)
if pref_likes is None or pref_dislikes is None:
pref_likes, pref_dislikes = get_active_preference_state(cutoff_time, u_set)
uid_counts = sub_listens.groupby("uid").agg(
n_listens=("item_idx", "count"),
mean_ratio=("played_ratio_pct", "mean"),
organic_ratio=("is_organic", "mean"),
).to_dict(orient="index")
user_to_pos = {u: i for i, u in enumerate(user_ids)}
recent_indices, recent_uids, recent_weights = [], [], []
history_indices, history_uids, history_weights = [], [], []
strong_indices, strong_uids, strong_weights = [], [], []
like_indices, like_uids = [], []
dislike_indices, dislike_uids = [], []
stats_arr = np.zeros((len(user_ids), 6), dtype=np.float32)
for i, uid in enumerate(user_ids):
st = uid_counts.get(uid, {"n_listens": 0, "mean_ratio": 50.0, "organic_ratio": 0.5})
n_lik = len(pref_likes.get(uid, []))
n_dis = len(pref_dislikes.get(uid, []))
stats_arr[i] = [
np.log1p(st["n_listens"]),
st["mean_ratio"] / 100.0,
st["organic_ratio"],
np.log1p(n_lik),
np.log1p(n_dis),
(n_lik + 1.0) / (n_lik + n_dis + 2.0),
]
l_items = pref_likes.get(uid, [])
like_indices.extend(l_items)
like_uids.extend([i] * len(l_items))
d_items = pref_dislikes.get(uid, [])
dislike_indices.extend(d_items)
dislike_uids.extend([i] * len(d_items))
for uid, g in agg.groupby("uid"):
if uid not in user_to_pos:
continue
u_idx = user_to_pos[uid]
m_r = g.w_recent > 0
if m_r.any():
recent_indices.extend(g.item_idx[m_r].tolist())
recent_weights.extend(g.w_recent[m_r].tolist())
recent_uids.extend([u_idx] * int(m_r.sum()))
history_indices.extend(g.item_idx.tolist())
history_weights.extend(g.w_history.tolist())
history_uids.extend([u_idx] * len(g))
m_s = g.w_strong > 0
if m_s.any():
strong_indices.extend(g.item_idx[m_s].tolist())
strong_weights.extend(g.w_strong[m_s].tolist())
strong_uids.extend([u_idx] * int(m_s.sum()))
N = len(user_ids)
return {
"num_users": N,
"recent_indices": torch.tensor(recent_indices, dtype=torch.long),
"recent_uids": torch.tensor(recent_uids, dtype=torch.long),
"recent_weights": torch.tensor(recent_weights, dtype=torch.float32),
"history_indices": torch.tensor(history_indices, dtype=torch.long),
"history_uids": torch.tensor(history_uids, dtype=torch.long),
"history_weights": torch.tensor(history_weights, dtype=torch.float32),
"strong_indices": torch.tensor(strong_indices, dtype=torch.long),
"strong_uids": torch.tensor(strong_uids, dtype=torch.long),
"strong_weights": torch.tensor(strong_weights, dtype=torch.float32),
"like_indices": torch.tensor(like_indices, dtype=torch.long),
"like_uids": torch.tensor(like_uids, dtype=torch.long),
"dislike_indices": torch.tensor(dislike_indices, dtype=torch.long),
"dislike_uids": torch.tensor(dislike_uids, dtype=torch.long),
"user_stats": torch.tensor(stats_arr, dtype=torch.float32),
}
def extract_slice(cutoff_time):
w_end = cutoff_time + WEEK_SECONDS
prior = listens_df[listens_df.timestamp < cutoff_time]
prior_user_items = set(zip(prior.uid, prior.item_idx))
win_listens = listens_df[
(listens_df.timestamp >= cutoff_time)
& (listens_df.timestamp < w_end)
& (listens_df.is_organic == 1)
& (listens_df.played_ratio_pct >= 80)
]
win_likes = likes_df[
(likes_df.timestamp >= cutoff_time)
& (likes_df.timestamp < w_end)
& (likes_df.is_organic == 1)
]
targets = {}
for u, i in zip(win_listens.uid, win_listens.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 1
for u, i in zip(win_likes.uid, win_likes.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 3
user_positives = {}
for (u, i), grade in targets.items():
user_positives.setdefault(u, []).append((i, grade))
unique_users = sorted(list(user_positives.keys()))
user_feats = build_user_features(cutoff_time, unique_users)
user_to_idx = {u: idx for idx, u in enumerate(unique_users)}
pairs = []
user_all_pos = {idx: set() for idx in range(len(unique_users))}
for u, items in user_positives.items():
u_idx = user_to_idx[u]
for item_idx, grade in items:
pairs.append((u_idx, item_idx, float(grade)))
user_all_pos[u_idx].add(item_idx)
return user_feats, pairs, user_all_pos
print("4. Extracting recent slices (3 weeks)...")
train_slices = []
for w in range(1, 4):
s_feats, s_pairs, s_pos = extract_slice(CUTOFF_VAL - w * WEEK_SECONDS)
train_slices.append((s_feats, s_pairs, s_pos))
print("5. Extracting validation features...")
v_pref = pd.read_parquet(DATA_DIR / "validation_preference_state.parquet")
val_likes_dict = {u: [] for u in target_uids}
val_dislikes_dict = {u: [] for u in target_uids}
for uid, item, state in v_pref.itertuples(index=False):
idx = item_to_idx.get(int(item))
if idx is not None:
if state == "liked":
val_likes_dict[int(uid)].append(idx)
elif state == "disliked":
val_dislikes_dict[int(uid)].append(idx)
val_user_feats = build_user_features(CUTOFF_VAL, target_uids, val_likes_dict, val_dislikes_dict)
val_eligibility = CandidateSet(DATA_DIR / "validation_eligible_candidates.npz")
targets_set = set(target_users_df.uid.astype(int))
val_gold = validation_truth(val_eligibility, targets_set)
val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder="little")
class ItemTower(nn.Module):
def __init__(self, num_items, embed_dim, dense_dim, out_dim):
super().__init__()
self.item_emb = nn.Embedding(num_items, embed_dim)
nn.init.normal_(self.item_emb.weight, std=0.05)
self.dense_proj = nn.Linear(dense_dim, embed_dim)
self.mlp = nn.Sequential(
nn.Linear(embed_dim * 2, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
def forward(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.mlp(x)
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
class UserTower(nn.Module):
def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):
super().__init__()
self.shared_emb = shared_item_emb
self.embed_dim = embed_dim
self.stats_proj = nn.Linear(stats_dim, embed_dim)
in_dim = embed_dim * 5 + embed_dim
self.mlp = nn.Sequential(
nn.Linear(in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool(self, num_users, indices, uids, weights=None):
out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)
if len(indices) == 0:
return out
embs = self.shared_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_user_pooled_views(self, feats):
with torch.no_grad():
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)
def forward_user(self, pooled_views, stats):
s_p = self.stats_proj(stats)
x = torch.cat([pooled_views, s_p], dim=-1)
u = self.mlp(x)
u_norm = F.normalize(u[:, :-1], p=2, dim=-1)
alpha = torch.sigmoid(u[:, -1:])
return torch.cat([u_norm, alpha], dim=-1)
class TwoTowerRanker(nn.Module):
def __init__(self, num_items, embed_dim=128, dense_dim=10, stats_dim=6, out_dim=128):
super().__init__()
self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)
self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.user_tower.compute_user_pooled_views(feats)
return self.user_tower.forward_user(pooled, feats["user_stats"])
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
return self.item_tower(all_ids, dense_features)
def train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=6):
print(f"\n--- Testing config: dim={embed_dim}, lr={lr}, temp={temperature}, wd={weight_decay} ---")
model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=NUM_ITEM_FEATS, stats_dim=6, out_dim=embed_dim)
optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs, eta_min=lr * 0.1)
batch_size = 512
best_ndcg = 0.0
for epoch in range(1, num_epochs + 1):
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):
slice_pooled = model.user_tower.compute_user_pooled_views(feats)
slice_stats = feats["user_stats"]
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
for b_start in range(0, len(pairs_arr), batch_size):
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])
unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)
neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))
cand_item_ids = torch.cat([unique_pos_items, neg_ids])
cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)
logits = torch.matmul(u_vecs, cand_vecs.T) / temperature
# Fast false negative masking via set intersection
cand_set = set(unique_pos_items.tolist())
cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}
b_u_list = b_u.tolist()
inv_list = item_inverse.tolist()
mask = torch.zeros_like(logits, dtype=torch.bool)
for r, u in enumerate(b_u_list):
overlap = u_all_pos[u] & cand_set
tgt_col = inv_list[r]
for item in overlap:
c = cand_pos_map[item]
if c != tgt_col:
mask[r, c] = True
logits.masked_fill_(mask, -1e9)
targets = item_inverse
loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)
loss = (F.cross_entropy(logits, targets, reduction="none") * loss_weights).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
num_batches += 1
scheduler.step()
# Validation eval
with torch.no_grad():
val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()
all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()
ranked_dict = {}
for start_u in range(0, num_targets, 1000):
end_u = min(start_u + 1000, num_targets)
sub_u = val_u_vecs[start_u:end_u]
sub_scores = np.matmul(sub_u, all_v_vecs.T)
sub_mask = val_unpacked[start_u:end_u]
sub_scores[sub_mask == 0] = -1e9
top100_idx = np.argpartition(-sub_scores, 100, axis=1)[:, :100]
for row in range(len(sub_u)):
u_row = start_u + row
uid = int(target_uids[u_row])
cand_idx = top100_idx[row]
sorted_idx = cand_idx[np.argsort(-sub_scores[row, cand_idx])]
ranked_dict[uid] = cat_items[sorted_idx].tolist()
val_score = score(ranked_dict, val_gold)
curr_ndcg = val_score["quality_ndcg@10"]
if curr_ndcg > best_ndcg:
best_ndcg = curr_ndcg
torch.save(model.state_dict(), OUT_DIR / "best_model.pt")
print(
f"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, "
f"val_ndcg@10={curr_ndcg:.6f} (best={best_ndcg:.6f}) "
f"[{time.time() - t_ep:.1f}s]"
)
return best_ndcg
train_and_eval(embed_dim=64, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)
train_and_eval(embed_dim=128, lr=1e-3, temperature=0.07, weight_decay=1e-5, num_epochs=5)
## Step 77 · agent · 2026-09-29T06:38:16.839Z
### run_shell_command · 2026-09-29T06:38:16.839Z
{
"command": "python3 /app/solution/tune_two_tower.py",
"description": "Run tune_two_tower.py"
}
### Tool output · 2026-09-29T06:41:26.701Z
Output: 1. Loading metadata...
2. Loading interaction logs...
3. Computing item features...
4. Extracting recent slices (3 weeks)...
5. Extracting validation features...
--- Testing config: dim=64, lr=0.001, temp=0.07, wd=1e-05 ---
Epoch 1: loss=8.7934, val_ndcg@10=0.012061 (best=0.012061) [10.4s]
Epoch 2: loss=8.4039, val_ndcg@10=0.015618 (best=0.015618) [10.3s]
Epoch 3: loss=7.9697, val_ndcg@10=0.010337 (best=0.015618) [10.3s]
Epoch 4: loss=7.6199, val_ndcg@10=0.012190 (best=0.015618) [10.9s]
Epoch 5: loss=7.3775, val_ndcg@10=0.011007 (best=0.015618) [10.3s]
--- Testing config: dim=128, lr=0.001, temp=0.07, wd=1e-05 ---
Epoch 1: loss=8.7660, val_ndcg@10=0.014446 (best=0.014446) [15.1s]
Epoch 2: loss=8.3346, val_ndcg@10=0.017088 (best=0.017088) [14.7s]
Epoch 3: loss=7.8490, val_ndcg@10=0.012533 (best=0.017088) [14.6s]
Epoch 4: loss=7.4356, val_ndcg@10=0.010879 (best=0.017088) [15.2s]
Epoch 5: loss=7.1400, val_ndcg@10=0.008310 (best=0.017088) [14.6s]
Process Group PGID: 3146
## Step 78 · agent · 2026-09-29T06:41:41.004Z
### Recorded reasoning summary
Analyzing Dimension Impact
I've observed that a dimension of 128 yields better NDCG results compared to the previous setting. My current focus is on dissecting the discrepancies between my current evaluation and the `prototype_eval.py` script's output to pinpoint the root causes of the performance gap.
### Recorded reasoning summary
Investigating Feature Divergence
I'm finding that the feature sets used in `tune_two_tower.py` and `prototype_eval.py` differ significantly, particularly in categorical and user statistics. A critical divergence appears in how user history, specifically likes and unlikes, is handled, impacting validation data representation.
### run_shell_command · 2026-09-29T06:41:41.004Z
{
"description": "Compare train_likes and v_pref liked counts",
"command": "python3 -c \"\nimport pandas as pd\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nv_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(cat.item_id)\n\nlikes_cat = likes[likes.item_id.isin(cat_set)]\nv_liked = v_pref[v_pref.state == 'liked']\n\nprint('train_likes in catalog per user:', likes_cat.groupby('uid').size().mean())\nprint('v_pref liked per user:', v_liked.groupby('uid').size().mean())\n\""
}
### Tool output · 2026-09-29T06:41:42.736Z
Output: train_likes in catalog per user: 70.22199721554233
v_pref liked per user: 61.816368739843405
Process Group PGID: 3199
## Step 79 · agent · 2026-09-29T06:41:46.854Z
### read_file · 2026-09-29T06:41:46.854Z
{
"start_line": 270,
"file_path": "/app/solution/prototype_eval.py",
"end_line": 330
}
### Tool output · 2026-09-29T06:41:46.864Z
IMPORTANT: The file content has been truncated.
Status: Showing lines 270-330 of 482 total lines.
Action: To read more of the file, you can use the 'start_line' and 'end_line' parameters in a subsequent 'read_file' call. For example, to read the next section of the file, use start_line: 331.
--- FILE CONTENT (truncated) ---
nn.Linear(user_in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool(self, num_users, indices, uids, weights=None):
out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)
if len(indices) == 0:
return out
embs = self.item_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_user_pooled_views(self, feats):
with torch.no_grad():
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)
def forward_user(self, pooled_views_batch, stats_batch):
s_p = self.user_stats_proj(stats_batch)
x = torch.cat([pooled_views_batch, s_p], dim=-1)
u = self.user_mlp(x)
return F.normalize(u, p=2, dim=-1)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.compute_user_pooled_views(feats)
s_p = self.user_stats_proj(feats["user_stats"])
x = torch.cat([pooled, s_p], dim=-1)
u = self.user_mlp(x)
return F.normalize(u, p=2, dim=-1)
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
embs = self.item_emb(all_ids)
d_p = self.item_dense_proj(dense_features)
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
return F.normalize(v, p=2, dim=-1)
def forward_items(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.item_dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.item_mlp(x)
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
def forward_user(self, pooled_views_batch, stats_batch):
## Step 80 · agent · 2026-09-29T06:41:55.913Z
### Recorded reasoning summary
Detecting Code Redundancy
I've identified a significant issue where the `forward_user` function was inadvertently defined twice within `prototype_eval.py`. This duplication, occurring at lines 302-306 and again at lines 327-330, necessitates immediate correction to ensure the script functions as intended.
### Recorded reasoning summary
Resolving Function Conflicts
I've discovered that a duplicate `forward_user` definition in `prototype_eval.py` led to the later definition overwriting the earlier one. My immediate focus is on rectifying this, ensuring the correct implementation is utilized while also examining how `compute_all_user_vectors` and `compute_all_item_vectors` are affected.
### Recorded reasoning summary
Analyzing Evaluation Scores
My current focus is on understanding the surprisingly low evaluation score. It appears that during evaluation, both user and item vectors were purely normalized unit vectors, meaning only taste alignment was considered, without any bias component. This lack of bias likely contributed to the suboptimal performance observed.
### Recorded reasoning summary
Investigating Logit Adjustment
I've uncovered that during training, a logit adjustment mechanism involving bias terms was applied to user and item vectors in `prototype_eval.py`. My current focus is on verifying how this impacts evaluation when only normalized vectors are used, potentially explaining the observed score discrepancies.
### run_shell_command · 2026-09-29T06:41:55.913Z
{
"description": "Run prototype_eval to see epoch 2 output",
"command": "python3 -c \"\nimport sys\nsys.path.append('/app/solution')\n# Let's run prototype_eval for 2 epochs and save validation.parquet\nfrom prototype_eval import *\n\""
}
### Tool output · 2026-09-29T06:46:28.887Z
Output: Loading metadata...
Loading raw events...
Events loaded in 2.47s
Extracting training slices...
Starting training...
Epoch 1: loss=8.8844, val_ndcg@10=0.011395 (26.8s)
Epoch 2: loss=8.5874, val_ndcg@10=0.017676 (26.7s)
Epoch 3: loss=8.1819, val_ndcg@10=0.019507 (26.6s)
Epoch 4: loss=7.8262, val_ndcg@10=0.013666 (27.1s)
Epoch 5: loss=7.5620, val_ndcg@10=0.014345 (27.0s)
Epoch 6: loss=7.3452, val_ndcg@10=0.013599 (27.1s)
Epoch 7: loss=7.1613, val_ndcg@10=0.008978 (26.5s)
Epoch 8: loss=7.0053, val_ndcg@10=0.007581 (26.6s)
Process Group PGID: 3218
## Step 81 · agent · 2026-09-29T06:46:41.595Z
### Recorded reasoning summary
Investigating Performance Fluctuations
I'm currently analyzing the training progression, specifically focusing on the fluctuating validation scores. Epoch 4 showed a notable improvement, dropping to 0.013666, though the overall range between epochs 2 and 3 remained between 0.0195 and 0.0226. I'm now digging into the loss function at epoch 1 (value 8) to understand why overfitting might be occurring after epoch 3.
### Recorded reasoning summary
Unraveling Representation Collapse
I'm now understanding the issue: my contrastive loss is overfitting the item embeddings in recommendation retrieval. This is causing representation collapse, where positive items are pulled too close to specific training users, distorting their embeddings. The existing low weight decay likely exacerbated this, and I'm exploring how dropout and LayerNorm can mitigate this drift.
### Recorded reasoning summary
Adjusting Training Dynamics
I'm now exploring how to prevent the MLP from memorizing specific item combinations. My focus is on reducing training steps to 1-3 epochs and utilizing a smaller learning rate with cosine decay to avoid large parameter jumps. I'm also investigating item popularity regularization by subtracting $\log P(i)$ from logits, aiming to learn PMI instead of just popularity.
### run_shell_command · 2026-09-29T06:46:41.595Z
{
"description": "Inspect log_q distribution",
"command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\ncat = pd.read_parquet('/task/data/candidate_catalog.parquet')\npos_l = cat.positive_listens.to_numpy().astype(np.float64)\n# Probability of each item\nprob = pos_l / pos_l.sum()\nlog_q = np.log(prob + 1e-10)\nprint('log_q min, median, max:', log_q.min(), np.median(log_q), log_q.max())\n\""
}
### Tool output · 2026-09-29T06:46:43.132Z
Output: log_q min, median, max: -11.720292817526948 -10.944752744006943 -6.624825001790236
Process Group PGID: 3284
## Step 82 · agent · 2026-09-29T06:48:01.818Z
### Recorded reasoning summary
Developing Training Logic
I'm now focusing on implementing the `train_logq.py` script. The core of this involves integrating the `log_q` correction directly into the training loop. This means carefully handling the logits calculation for both in-batch positive items and sampled negatives, specifically using the formula $S_{b, c} = \frac{\mathbf{u}_b \cdot \mathbf{v}_c}{\tau}$.
### Recorded reasoning summary
Implementing Correction Factors
I'm currently integrating the log probability correction into the training process, specifically by adding $\lambda \log P(c)$. This addresses the higher probability of popular items appearing in candidate sets. At inference, I'm exploring how to combine the dot product with a pre-computed item score, controlled by a learned scalar from the user tower.
### write_file · 2026-09-29T06:48:01.818Z
{
"file_path": "/app/solution/train_logq.py",
"content": "import os\nimport sys\nimport time\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nsys.path.append(\"/task\")\nfrom evaluate_public import score, validation_truth\nfrom ranking_evaluation import CandidateSet\n\ntorch.set_num_threads(8)\n\nDATA_DIR = Path(\"/task/data\")\nOUT_DIR = Path(\"/app/solution\")\nOUT_DIR.mkdir(parents=True, exist_ok=True)\n\nCUTOFF_VAL = 24786800\nWEEK_SECONDS = 7 * 86400\n\nprint(\"1. Loading metadata...\")\ncat_df = pd.read_parquet(DATA_DIR / \"candidate_catalog.parquet\")\ncat_items = cat_df.item_id.to_numpy()\nitem_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}\nnum_items = len(cat_items)\n\n# Item popularity prob\npos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)\nitem_probs = pos_listens / pos_listens.sum()\nlog_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)\n\ntarget_users_df = pd.read_parquet(DATA_DIR / \"target_users.parquet\")\ntarget_uids = target_users_df.uid.to_numpy()\nnum_targets = len(target_uids)\n\nprint(\"2. Loading interaction logs...\")\nlikes_df = pd.read_parquet(DATA_DIR / \"train_likes.parquet\")\nlikes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()\nlikes_df[\"item_idx\"] = likes_df.item_id.map(item_to_idx)\n\ndislikes_df = pd.read_parquet(DATA_DIR / \"train_dislikes.parquet\")\ndislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()\ndislikes_df[\"item_idx\"] = dislikes_df.item_id.map(item_to_idx)\n\nmin_listen_time = CUTOFF_VAL - 120 * 86400\ntable = pq.read_table(\n DATA_DIR / \"train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\", \"is_organic\"],\n filters=[(\"timestamp\", \">=\", min_listen_time)],\n)\nlistens_df = table.to_pandas()\nlistens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()\nlistens_df[\"item_idx\"] = listens_df.item_id.map(item_to_idx)\n\n# Item features\ncat_features = np.zeros((num_items, 4), dtype=np.float32)\ncat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))\ncat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)\ncat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)\nfor c in range(4):\n mean = cat_features[:, c].mean()\n std = cat_features[:, c].std() + 1e-6\n cat_features[:, c] = (cat_features[:, c] - mean) / std\n\nitem_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)\n\n\ndef build_user_features(cutoff_time, user_ids):\n u_set = set(user_ids)\n t_min = cutoff_time - 90 * 86400\n sub_listens = listens_df[\n (listens_df.timestamp >= t_min)\n & (listens_df.timestamp < cutoff_time)\n & (listens_df.uid.isin(u_set))\n ].copy()\n\n sub_listens[\"days_ago\"] = (cutoff_time - sub_listens.timestamp) / 86400.0\n\n sub_listens[\"w_recent\"] = np.where(\n sub_listens.days_ago <= 14.0,\n np.exp(-0.1 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0),\n 0.0,\n )\n\n sub_listens[\"w_history\"] = (\n np.exp(-0.02 * sub_listens.days_ago)\n * (1.0 + sub_listens.is_organic)\n * (sub_listens.played_ratio_pct / 100.0)\n )\n\n sub_listens[\"w_strong\"] = np.where(\n (sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),\n np.exp(-0.05 * sub_listens.days_ago),\n 0.0,\n )\n\n agg = (\n sub_listens.groupby([\"uid\", \"item_idx\"])[[\"w_recent\", \"w_history\", \"w_strong\"]]\n .sum()\n .reset_index()\n )\n\n sub_likes = likes_df[\n (likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))\n ]\n likes_grouped = sub_likes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n sub_dislikes = dislikes_df[\n (dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))\n ]\n dislikes_grouped = sub_dislikes.groupby(\"uid\")[\"item_idx\"].apply(list).to_dict()\n\n uid_counts = sub_listens.groupby(\"uid\").agg(\n n_listens=(\"item_idx\", \"count\"),\n mean_ratio=(\"played_ratio_pct\", \"mean\"),\n organic_ratio=(\"is_organic\", \"mean\"),\n ).to_dict(orient=\"index\")\n\n user_to_pos = {u: i for i, u in enumerate(user_ids)}\n recent_indices, recent_uids, recent_weights = [], [], []\n history_indices, history_uids, history_weights = [], [], []\n strong_indices, strong_uids, strong_weights = [], [], []\n like_indices, like_uids = [], []\n dislike_indices, dislike_uids = [], []\n stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)\n\n for i, uid in enumerate(user_ids):\n st = uid_counts.get(uid, {\"n_listens\": 0, \"mean_ratio\": 50.0, \"organic_ratio\": 0.5})\n n_likes = len(likes_grouped.get(uid, []))\n n_dislikes = len(dislikes_grouped.get(uid, []))\n stats_arr[i] = [\n np.log1p(st[\"n_listens\"]),\n st[\"mean_ratio\"] / 100.0,\n st[\"organic_ratio\"],\n np.log1p(n_likes),\n np.log1p(n_dislikes),\n ]\n l_items = likes_grouped.get(uid, [])\n like_indices.extend(l_items)\n like_uids.extend([i] * len(l_items))\n\n d_items = dislikes_grouped.get(uid, [])\n dislike_indices.extend(d_items)\n dislike_uids.extend([i] * len(d_items))\n\n for uid, g in agg.groupby(\"uid\"):\n if uid not in user_to_pos:\n continue\n u_idx = user_to_pos[uid]\n m_r = g.w_recent > 0\n if m_r.any():\n recent_indices.extend(g.item_idx[m_r].tolist())\n recent_weights.extend(g.w_recent[m_r].tolist())\n recent_uids.extend([u_idx] * int(m_r.sum()))\n\n history_indices.extend(g.item_idx.tolist())\n history_weights.extend(g.w_history.tolist())\n history_uids.extend([u_idx] * len(g))\n\n m_s = g.w_strong > 0\n if m_s.any():\n strong_indices.extend(g.item_idx[m_s].tolist())\n strong_weights.extend(g.w_strong[m_s].tolist())\n strong_uids.extend([u_idx] * int(m_s.sum()))\n\n N = len(user_ids)\n return {\n \"num_users\": N,\n \"recent_indices\": torch.tensor(recent_indices, dtype=torch.long),\n \"recent_uids\": torch.tensor(recent_uids, dtype=torch.long),\n \"recent_weights\": torch.tensor(recent_weights, dtype=torch.float32),\n \"history_indices\": torch.tensor(history_indices, dtype=torch.long),\n \"history_uids\": torch.tensor(history_uids, dtype=torch.long),\n \"history_weights\": torch.tensor(history_weights, dtype=torch.float32),\n \"strong_indices\": torch.tensor(strong_indices, dtype=torch.long),\n \"strong_uids\": torch.tensor(strong_uids, dtype=torch.long),\n \"strong_weights\": torch.tensor(strong_weights, dtype=torch.float32),\n \"like_indices\": torch.tensor(like_indices, dtype=torch.long),\n \"like_uids\": torch.tensor(like_uids, dtype=torch.long),\n \"dislike_indices\": torch.tensor(dislike_indices, dtype=torch.long),\n \"dislike_uids\": torch.tensor(dislike_uids, dtype=torch.long),\n \"user_stats\": torch.tensor(stats_arr, dtype=torch.float32),\n }\n\n\ndef extract_slice_data(cutoff_time):\n w_end = cutoff_time + WEEK_SECONDS\n prior = listens_df[listens_df.timestamp < cutoff_time]\n prior_user_items = set(zip(prior.uid, prior.item_idx))\n\n win_listens = listens_df[\n (listens_df.timestamp >= cutoff_time)\n & (listens_df.timestamp < w_end)\n & (listens_df.is_organic == 1)\n & (listens_df.played_ratio_pct >= 80)\n ]\n win_likes = likes_df[\n (likes_df.timestamp >= cutoff_time)\n & (likes_df.timestamp < w_end)\n & (likes_df.is_organic == 1)\n ]\n\n targets = {}\n for u, i in zip(win_listens.uid, win_listens.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 1\n for u, i in zip(win_likes.uid, win_likes.item_idx):\n if (u, i) not in prior_user_items:\n targets[(u, i)] = 3\n\n user_positives = {}\n for (u, i), grade in targets.items():\n user_positives.setdefault(u, []).append((i, grade))\n\n unique_users = sorted(list(user_positives.keys()))\n user_feats = build_user_features(cutoff_time, unique_users)\n user_to_idx = {u: idx for idx, u in enumerate(unique_users)}\n\n pairs = []\n user_all_pos = {idx: set() for idx in range(len(unique_users))}\n for u, items in user_positives.items():\n u_idx = user_to_idx[u]\n for item_idx, grade in items:\n pairs.append((u_idx, item_idx, float(grade)))\n user_all_pos[u_idx].add(item_idx)\n\n return user_feats, pairs, user_all_pos\n\n\nprint(\"Extracting training slices...\")\ntrain_slices = []\nfor w in range(1, 4):\n s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)\n train_slices.append((s_feats, s_pairs, s_pos))\n\nval_user_feats = build_user_features(CUTOFF_VAL, target_uids)\nval_eligibility = CandidateSet(DATA_DIR / \"validation_eligible_candidates.npz\")\ntargets_set = set(target_users_df.uid.astype(int))\nval_gold = validation_truth(val_eligibility, targets_set)\nval_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder=\"little\")\n\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, dense_dim, out_dim):\n super().__init__()\n self.item_emb = nn.Embedding(num_items, embed_dim)\n nn.init.normal_(self.item_emb.weight, std=0.05)\n self.dense_proj = nn.Linear(dense_dim, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, embed_dim),\n nn.LayerNorm(embed_dim),\n nn.GELU(),\n nn.Linear(embed_dim, out_dim),\n )\n\n def forward(self, item_ids, dense_features):\n embs = self.item_emb(item_ids)\n d_p = self.dense_proj(dense_features[item_ids])\n x = torch.cat([embs, d_p], dim=-1)\n v = self.mlp(x)\n # Normalize first out_dim - 1 dimensions\n v_norm = F.normalize(v[:, :-1], p=2, dim=-1)\n # Last dimension is learned item bias\n b = v[:, -1:]\n return torch.cat([v_norm, b], dim=-1)\n\n\nclass UserTower(nn.Module):\n def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):\n super().__init__()\n self.shared_emb = shared_item_emb\n self.embed_dim = embed_dim\n self.stats_proj = nn.Linear(stats_dim, embed_dim)\n in_dim = embed_dim * 5 + embed_dim\n self.mlp = nn.Sequential(\n nn.Linear(in_dim, embed_dim * 2),\n nn.LayerNorm(embed_dim * 2),\n nn.GELU(),\n nn.Dropout(0.15),\n nn.Linear(embed_dim * 2, out_dim),\n )\n\n def _pool(self, num_users, indices, uids, weights=None):\n out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)\n if len(indices) == 0:\n return out\n embs = self.shared_emb(indices)\n if weights is not None:\n embs = embs * weights.unsqueeze(-1)\n out = torch.index_add(out, 0, uids, embs)\n norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6\n return out / norms\n\n def compute_user_pooled_views(self, feats):\n with torch.no_grad():\n N = feats[\"num_users\"]\n h_rec = self._pool(N, feats[\"recent_indices\"], feats[\"recent_uids\"], feats[\"recent_weights\"])\n h_his = self._pool(N, feats[\"history_indices\"], feats[\"history_uids\"], feats[\"history_weights\"])\n h_str = self._pool(N, feats[\"strong_indices\"], feats[\"strong_uids\"], feats[\"strong_weights\"])\n h_lik = self._pool(N, feats[\"like_indices\"], feats[\"like_uids\"])\n h_dis = self._pool(N, feats[\"dislike_indices\"], feats[\"dislike_uids\"])\n return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)\n\n def forward_user(self, pooled_views, stats):\n s_p = self.stats_proj(stats)\n x = torch.cat([pooled_views, s_p], dim=-1)\n u = self.mlp(x)\n u_norm = F.normalize(u[:, :-1], p=2, dim=-1)\n # Learnable scaling for item bias\n alpha = torch.sigmoid(u[:, -1:])\n return torch.cat([u_norm, alpha], dim=-1)\n\n\nclass TwoTowerRanker(nn.Module):\n def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):\n super().__init__()\n self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)\n self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)\n\n def compute_all_user_vectors(self, feats):\n with torch.no_grad():\n pooled = self.user_tower.compute_user_pooled_views(feats)\n return self.user_tower.forward_user(pooled, feats[\"user_stats\"])\n\n def compute_all_item_vectors(self, dense_features):\n with torch.no_grad():\n all_ids = torch.arange(len(dense_features), dtype=torch.long)\n return self.item_tower(all_ids, dense_features)\n\n\nembed_dim = 64\nout_dim = 64\nmodel = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\ntemperature = 0.07\nbatch_size = 512\nnum_epochs = 6\n\nprint(\"\\nStarting training with fast set masking...\")\nbest_ndcg = 0.0\n\nfor epoch in range(1, num_epochs + 1):\n t_ep = time.time()\n total_loss = 0.0\n num_batches = 0\n\n for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):\n slice_pooled = model.user_tower.compute_user_pooled_views(feats)\n slice_stats = feats[\"user_stats\"]\n\n np.random.seed(epoch * 100 + s_idx)\n perm = np.random.permutation(len(pairs))\n pairs_arr = np.array(pairs)[perm]\n\n for b_start in range(0, len(pairs_arr), batch_size):\n b_pairs = pairs_arr[b_start : b_start + batch_size]\n b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)\n b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)\n b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)\n\n u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])\n unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)\n\n neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))\n cand_item_ids = torch.cat([unique_pos_items, neg_ids])\n\n cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)\n\n # Similarity logits: dot product\n logits = torch.matmul(u_vecs, cand_vecs.T) / temperature\n\n # Mask out other weekly positives for this user\n cand_set = set(unique_pos_items.tolist())\n cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}\n b_u_list = b_u.tolist()\n inv_list = item_inverse.tolist()\n\n mask = torch.zeros_like(logits, dtype=torch.bool)\n for r, u in enumerate(b_u_list):\n overlap = u_all_pos[u] & cand_set\n tgt_col = inv_list[r]\n for item in overlap:\n c = cand_pos_map[item]\n if c != tgt_col:\n mask[r, c] = True\n\n logits.masked_fill_(mask, -1e9)\n\n targets = item_inverse\n loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)\n loss = (F.cross_entropy(logits, targets, reduction=\"none\") * loss_weights).mean()\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n num_batches += 1\n\n # Validation evaluation: test BOTH pure cosine AND dot product\n with torch.no_grad():\n val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()\n all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()\n\n # Mode A: full dot product (cosine + alpha * b)\n ranked_dict_dot = {}\n # Mode B: pure cosine (first 63 dimensions)\n ranked_dict_cos = {}\n\n for start_u in range(0, num_targets, 1000):\n end_u = min(start_u + 1000, num_targets)\n sub_u = val_u_vecs[start_u:end_u]\n sub_mask = val_unpacked[start_u:end_u]\n\n # Full dot product\n sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)\n sub_scores_dot[sub_mask == 0] = -1e9\n top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]\n\n # Pure cosine\n sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)\n sub_scores_cos[sub_mask == 0] = -1e9\n top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]\n\n for row in range(len(sub_u)):\n u_row = start_u + row\n uid = int(target_uids[u_row])\n\n cand_dot = top100_idx_dot[row]\n sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]\n ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()\n\n cand_cos = top100_idx_cos[row]\n sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]\n ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()\n\n score_dot = score(ranked_dict_dot, val_gold)[\"quality_ndcg@10\"]\n score_cos = score(ranked_dict_cos, val_gold)[\"quality_ndcg@10\"]\n\n print(\n f\"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, \"\n f\"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} \"\n f\"[{time.time() - t_ep:.1f}s]\"\n )\n",
"instruction": "Write train_logq.py comparing dot product vs pure cosine at validation"
}
### Tool output · 2026-09-29T06:48:01.829Z
Successfully created and wrote to new file: /app/solution/train_logq.py. Here is the updated code:
import os
import sys
import time
from pathlib import Path
import numpy as np
import pandas as pd
import pyarrow.parquet as pq
import torch
import torch.nn as nn
import torch.nn.functional as F
sys.path.append("/task")
from evaluate_public import score, validation_truth
from ranking_evaluation import CandidateSet
torch.set_num_threads(8)
DATA_DIR = Path("/task/data")
OUT_DIR = Path("/app/solution")
OUT_DIR.mkdir(parents=True, exist_ok=True)
CUTOFF_VAL = 24786800
WEEK_SECONDS = 7 * 86400
print("1. Loading metadata...")
cat_df = pd.read_parquet(DATA_DIR / "candidate_catalog.parquet")
cat_items = cat_df.item_id.to_numpy()
item_to_idx = {int(item): idx for idx, item in enumerate(cat_items)}
num_items = len(cat_items)
# Item popularity prob
pos_listens = cat_df.positive_listens.to_numpy().astype(np.float32)
item_probs = pos_listens / pos_listens.sum()
log_q_tensor = torch.tensor(np.log(item_probs + 1e-10), dtype=torch.float32)
target_users_df = pd.read_parquet(DATA_DIR / "target_users.parquet")
target_uids = target_users_df.uid.to_numpy()
num_targets = len(target_uids)
print("2. Loading interaction logs...")
likes_df = pd.read_parquet(DATA_DIR / "train_likes.parquet")
likes_df = likes_df[likes_df.item_id.isin(item_to_idx)].copy()
likes_df["item_idx"] = likes_df.item_id.map(item_to_idx)
dislikes_df = pd.read_parquet(DATA_DIR / "train_dislikes.parquet")
dislikes_df = dislikes_df[dislikes_df.item_id.isin(item_to_idx)].copy()
dislikes_df["item_idx"] = dislikes_df.item_id.map(item_to_idx)
min_listen_time = CUTOFF_VAL - 120 * 86400
table = pq.read_table(
DATA_DIR / "train_listens.parquet",
columns=["uid", "item_id", "timestamp", "played_ratio_pct", "is_organic"],
filters=[("timestamp", ">=", min_listen_time)],
)
listens_df = table.to_pandas()
listens_df = listens_df[listens_df.item_id.isin(item_to_idx)].copy()
listens_df["item_idx"] = listens_df.item_id.map(item_to_idx)
# Item features
cat_features = np.zeros((num_items, 4), dtype=np.float32)
cat_features[:, 0] = np.log1p(cat_df.positive_listens.to_numpy().astype(np.float32))
cat_features[:, 1] = np.log1p(cat_df.organic_positive_listens.to_numpy().astype(np.float32))
cat_features[:, 2] = cat_df.popularity_rank.to_numpy().astype(np.float32) / float(num_items)
cat_features[:, 3] = (cat_features[:, 1] / (cat_features[:, 0] + 1e-5)).astype(np.float32)
for c in range(4):
mean = cat_features[:, c].mean()
std = cat_features[:, c].std() + 1e-6
cat_features[:, c] = (cat_features[:, c] - mean) / std
item_dense_tensor = torch.tensor(cat_features, dtype=torch.float32)
def build_user_features(cutoff_time, user_ids):
u_set = set(user_ids)
t_min = cutoff_time - 90 * 86400
sub_listens = listens_df[
(listens_df.timestamp >= t_min)
& (listens_df.timestamp < cutoff_time)
& (listens_df.uid.isin(u_set))
].copy()
sub_listens["days_ago"] = (cutoff_time - sub_listens.timestamp) / 86400.0
sub_listens["w_recent"] = np.where(
sub_listens.days_ago <= 14.0,
np.exp(-0.1 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0),
0.0,
)
sub_listens["w_history"] = (
np.exp(-0.02 * sub_listens.days_ago)
* (1.0 + sub_listens.is_organic)
* (sub_listens.played_ratio_pct / 100.0)
)
sub_listens["w_strong"] = np.where(
(sub_listens.played_ratio_pct >= 80) & (sub_listens.is_organic == 1),
np.exp(-0.05 * sub_listens.days_ago),
0.0,
)
agg = (
sub_listens.groupby(["uid", "item_idx"])[["w_recent", "w_history", "w_strong"]]
.sum()
.reset_index()
)
sub_likes = likes_df[
(likes_df.timestamp < cutoff_time) & (likes_df.uid.isin(u_set))
]
likes_grouped = sub_likes.groupby("uid")["item_idx"].apply(list).to_dict()
sub_dislikes = dislikes_df[
(dislikes_df.timestamp < cutoff_time) & (dislikes_df.uid.isin(u_set))
]
dislikes_grouped = sub_dislikes.groupby("uid")["item_idx"].apply(list).to_dict()
uid_counts = sub_listens.groupby("uid").agg(
n_listens=("item_idx", "count"),
mean_ratio=("played_ratio_pct", "mean"),
organic_ratio=("is_organic", "mean"),
).to_dict(orient="index")
user_to_pos = {u: i for i, u in enumerate(user_ids)}
recent_indices, recent_uids, recent_weights = [], [], []
history_indices, history_uids, history_weights = [], [], []
strong_indices, strong_uids, strong_weights = [], [], []
like_indices, like_uids = [], []
dislike_indices, dislike_uids = [], []
stats_arr = np.zeros((len(user_ids), 5), dtype=np.float32)
for i, uid in enumerate(user_ids):
st = uid_counts.get(uid, {"n_listens": 0, "mean_ratio": 50.0, "organic_ratio": 0.5})
n_likes = len(likes_grouped.get(uid, []))
n_dislikes = len(dislikes_grouped.get(uid, []))
stats_arr[i] = [
np.log1p(st["n_listens"]),
st["mean_ratio"] / 100.0,
st["organic_ratio"],
np.log1p(n_likes),
np.log1p(n_dislikes),
]
l_items = likes_grouped.get(uid, [])
like_indices.extend(l_items)
like_uids.extend([i] * len(l_items))
d_items = dislikes_grouped.get(uid, [])
dislike_indices.extend(d_items)
dislike_uids.extend([i] * len(d_items))
for uid, g in agg.groupby("uid"):
if uid not in user_to_pos:
continue
u_idx = user_to_pos[uid]
m_r = g.w_recent > 0
if m_r.any():
recent_indices.extend(g.item_idx[m_r].tolist())
recent_weights.extend(g.w_recent[m_r].tolist())
recent_uids.extend([u_idx] * int(m_r.sum()))
history_indices.extend(g.item_idx.tolist())
history_weights.extend(g.w_history.tolist())
history_uids.extend([u_idx] * len(g))
m_s = g.w_strong > 0
if m_s.any():
strong_indices.extend(g.item_idx[m_s].tolist())
strong_weights.extend(g.w_strong[m_s].tolist())
strong_uids.extend([u_idx] * int(m_s.sum()))
N = len(user_ids)
return {
"num_users": N,
"recent_indices": torch.tensor(recent_indices, dtype=torch.long),
"recent_uids": torch.tensor(recent_uids, dtype=torch.long),
"recent_weights": torch.tensor(recent_weights, dtype=torch.float32),
"history_indices": torch.tensor(history_indices, dtype=torch.long),
"history_uids": torch.tensor(history_uids, dtype=torch.long),
"history_weights": torch.tensor(history_weights, dtype=torch.float32),
"strong_indices": torch.tensor(strong_indices, dtype=torch.long),
"strong_uids": torch.tensor(strong_uids, dtype=torch.long),
"strong_weights": torch.tensor(strong_weights, dtype=torch.float32),
"like_indices": torch.tensor(like_indices, dtype=torch.long),
"like_uids": torch.tensor(like_uids, dtype=torch.long),
"dislike_indices": torch.tensor(dislike_indices, dtype=torch.long),
"dislike_uids": torch.tensor(dislike_uids, dtype=torch.long),
"user_stats": torch.tensor(stats_arr, dtype=torch.float32),
}
def extract_slice_data(cutoff_time):
w_end = cutoff_time + WEEK_SECONDS
prior = listens_df[listens_df.timestamp < cutoff_time]
prior_user_items = set(zip(prior.uid, prior.item_idx))
win_listens = listens_df[
(listens_df.timestamp >= cutoff_time)
& (listens_df.timestamp < w_end)
& (listens_df.is_organic == 1)
& (listens_df.played_ratio_pct >= 80)
]
win_likes = likes_df[
(likes_df.timestamp >= cutoff_time)
& (likes_df.timestamp < w_end)
& (likes_df.is_organic == 1)
]
targets = {}
for u, i in zip(win_listens.uid, win_listens.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 1
for u, i in zip(win_likes.uid, win_likes.item_idx):
if (u, i) not in prior_user_items:
targets[(u, i)] = 3
user_positives = {}
for (u, i), grade in targets.items():
user_positives.setdefault(u, []).append((i, grade))
unique_users = sorted(list(user_positives.keys()))
user_feats = build_user_features(cutoff_time, unique_users)
user_to_idx = {u: idx for idx, u in enumerate(unique_users)}
pairs = []
user_all_pos = {idx: set() for idx in range(len(unique_users))}
for u, items in user_positives.items():
u_idx = user_to_idx[u]
for item_idx, grade in items:
pairs.append((u_idx, item_idx, float(grade)))
user_all_pos[u_idx].add(item_idx)
return user_feats, pairs, user_all_pos
print("Extracting training slices...")
train_slices = []
for w in range(1, 4):
s_feats, s_pairs, s_pos = extract_slice_data(CUTOFF_VAL - w * WEEK_SECONDS)
train_slices.append((s_feats, s_pairs, s_pos))
val_user_feats = build_user_features(CUTOFF_VAL, target_uids)
val_eligibility = CandidateSet(DATA_DIR / "validation_eligible_candidates.npz")
targets_set = set(target_users_df.uid.astype(int))
val_gold = validation_truth(val_eligibility, targets_set)
val_unpacked = np.unpackbits(val_eligibility.packed, axis=1, count=num_items, bitorder="little")
class ItemTower(nn.Module):
def __init__(self, num_items, embed_dim, dense_dim, out_dim):
super().__init__()
self.item_emb = nn.Embedding(num_items, embed_dim)
nn.init.normal_(self.item_emb.weight, std=0.05)
self.dense_proj = nn.Linear(dense_dim, embed_dim)
self.mlp = nn.Sequential(
nn.Linear(embed_dim * 2, embed_dim),
nn.LayerNorm(embed_dim),
nn.GELU(),
nn.Linear(embed_dim, out_dim),
)
def forward(self, item_ids, dense_features):
embs = self.item_emb(item_ids)
d_p = self.dense_proj(dense_features[item_ids])
x = torch.cat([embs, d_p], dim=-1)
v = self.mlp(x)
# Normalize first out_dim - 1 dimensions
v_norm = F.normalize(v[:, :-1], p=2, dim=-1)
# Last dimension is learned item bias
b = v[:, -1:]
return torch.cat([v_norm, b], dim=-1)
class UserTower(nn.Module):
def __init__(self, embed_dim, stats_dim, out_dim, shared_item_emb):
super().__init__()
self.shared_emb = shared_item_emb
self.embed_dim = embed_dim
self.stats_proj = nn.Linear(stats_dim, embed_dim)
in_dim = embed_dim * 5 + embed_dim
self.mlp = nn.Sequential(
nn.Linear(in_dim, embed_dim * 2),
nn.LayerNorm(embed_dim * 2),
nn.GELU(),
nn.Dropout(0.15),
nn.Linear(embed_dim * 2, out_dim),
)
def _pool(self, num_users, indices, uids, weights=None):
out = torch.zeros((num_users, self.embed_dim), dtype=torch.float32)
if len(indices) == 0:
return out
embs = self.shared_emb(indices)
if weights is not None:
embs = embs * weights.unsqueeze(-1)
out = torch.index_add(out, 0, uids, embs)
norms = torch.norm(out, p=2, dim=-1, keepdim=True) + 1e-6
return out / norms
def compute_user_pooled_views(self, feats):
with torch.no_grad():
N = feats["num_users"]
h_rec = self._pool(N, feats["recent_indices"], feats["recent_uids"], feats["recent_weights"])
h_his = self._pool(N, feats["history_indices"], feats["history_uids"], feats["history_weights"])
h_str = self._pool(N, feats["strong_indices"], feats["strong_uids"], feats["strong_weights"])
h_lik = self._pool(N, feats["like_indices"], feats["like_uids"])
h_dis = self._pool(N, feats["dislike_indices"], feats["dislike_uids"])
return torch.cat([h_rec, h_his, h_str, h_lik, h_dis], dim=-1)
def forward_user(self, pooled_views, stats):
s_p = self.stats_proj(stats)
x = torch.cat([pooled_views, s_p], dim=-1)
u = self.mlp(x)
u_norm = F.normalize(u[:, :-1], p=2, dim=-1)
# Learnable scaling for item bias
alpha = torch.sigmoid(u[:, -1:])
return torch.cat([u_norm, alpha], dim=-1)
class TwoTowerRanker(nn.Module):
def __init__(self, num_items, embed_dim=64, dense_dim=4, stats_dim=5, out_dim=64):
super().__init__()
self.item_tower = ItemTower(num_items, embed_dim, dense_dim, out_dim)
self.user_tower = UserTower(embed_dim, stats_dim, out_dim, self.item_tower.item_emb)
def compute_all_user_vectors(self, feats):
with torch.no_grad():
pooled = self.user_tower.compute_user_pooled_views(feats)
return self.user_tower.forward_user(pooled, feats["user_stats"])
def compute_all_item_vectors(self, dense_features):
with torch.no_grad():
all_ids = torch.arange(len(dense_features), dtype=torch.long)
return self.item_tower(all_ids, dense_features)
embed_dim = 64
out_dim = 64
model = TwoTowerRanker(num_items, embed_dim=embed_dim, dense_dim=4, stats_dim=5, out_dim=out_dim)
optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)
temperature = 0.07
batch_size = 512
num_epochs = 6
print("\nStarting training with fast set masking...")
best_ndcg = 0.0
for epoch in range(1, num_epochs + 1):
t_ep = time.time()
total_loss = 0.0
num_batches = 0
for s_idx, (feats, pairs, u_all_pos) in enumerate(train_slices):
slice_pooled = model.user_tower.compute_user_pooled_views(feats)
slice_stats = feats["user_stats"]
np.random.seed(epoch * 100 + s_idx)
perm = np.random.permutation(len(pairs))
pairs_arr = np.array(pairs)[perm]
for b_start in range(0, len(pairs_arr), batch_size):
b_pairs = pairs_arr[b_start : b_start + batch_size]
b_u = torch.tensor(b_pairs[:, 0], dtype=torch.long)
b_i = torch.tensor(b_pairs[:, 1], dtype=torch.long)
b_grades = torch.tensor(b_pairs[:, 2], dtype=torch.float32)
u_vecs = model.user_tower.forward_user(slice_pooled[b_u], slice_stats[b_u])
unique_pos_items, item_inverse = torch.unique(b_i, return_inverse=True)
neg_ids = torch.randint(0, num_items, (len(unique_pos_items),))
cand_item_ids = torch.cat([unique_pos_items, neg_ids])
cand_vecs = model.item_tower(cand_item_ids, item_dense_tensor)
# Similarity logits: dot product
logits = torch.matmul(u_vecs, cand_vecs.T) / temperature
# Mask out other weekly positives for this user
cand_set = set(unique_pos_items.tolist())
cand_pos_map = {item: idx for idx, item in enumerate(unique_pos_items.tolist())}
b_u_list = b_u.tolist()
inv_list = item_inverse.tolist()
mask = torch.zeros_like(logits, dtype=torch.bool)
for r, u in enumerate(b_u_list):
overlap = u_all_pos[u] & cand_set
tgt_col = inv_list[r]
for item in overlap:
c = cand_pos_map[item]
if c != tgt_col:
mask[r, c] = True
logits.masked_fill_(mask, -1e9)
targets = item_inverse
loss_weights = torch.where(b_grades >= 3.0, 3.0, 1.0)
loss = (F.cross_entropy(logits, targets, reduction="none") * loss_weights).mean()
optimizer.zero_grad()
loss.backward()
optimizer.step()
total_loss += loss.item()
num_batches += 1
# Validation evaluation: test BOTH pure cosine AND dot product
with torch.no_grad():
val_u_vecs = model.compute_all_user_vectors(val_user_feats).numpy()
all_v_vecs = model.compute_all_item_vectors(item_dense_tensor).numpy()
# Mode A: full dot product (cosine + alpha * b)
ranked_dict_dot = {}
# Mode B: pure cosine (first 63 dimensions)
ranked_dict_cos = {}
for start_u in range(0, num_targets, 1000):
end_u = min(start_u + 1000, num_targets)
sub_u = val_u_vecs[start_u:end_u]
sub_mask = val_unpacked[start_u:end_u]
# Full dot product
sub_scores_dot = np.matmul(sub_u, all_v_vecs.T)
sub_scores_dot[sub_mask == 0] = -1e9
top100_idx_dot = np.argpartition(-sub_scores_dot, 100, axis=1)[:, :100]
# Pure cosine
sub_scores_cos = np.matmul(sub_u[:, :-1], all_v_vecs[:, :-1].T)
sub_scores_cos[sub_mask == 0] = -1e9
top100_idx_cos = np.argpartition(-sub_scores_cos, 100, axis=1)[:, :100]
for row in range(len(sub_u)):
u_row = start_u + row
uid = int(target_uids[u_row])
cand_dot = top100_idx_dot[row]
sorted_dot = cand_dot[np.argsort(-sub_scores_dot[row, cand_dot])]
ranked_dict_dot[uid] = cat_items[sorted_dot].tolist()
cand_cos = top100_idx_cos[row]
sorted_cos = cand_cos[np.argsort(-sub_scores_cos[row, cand_cos])]
ranked_dict_cos[uid] = cat_items[sorted_cos].tolist()
score_dot = score(ranked_dict_dot, val_gold)["quality_ndcg@10"]
score_cos = score(ranked_dict_cos, val_gold)["quality_ndcg@10"]
print(
f"Epoch {epoch:2d}: loss={total_loss / num_batches:.4f}, "
f"val_ndcg (dot)={score_dot:.6f}, val_ndcg (cos)={score_cos:.6f} "
f"[{time.time() - t_ep:.1f}s]"
)
## Recorded session usage
[
{
"messageId": "63f91fdc-a156-4e35-8ebe-66850a3be0e0",
"timestamp": "2026-09-29T05:48:15.637Z",
"model": "gemini-3.8-flash",
"tokens": {
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