Agent trace

Two-tower retriever gemini-3-8-flash-high-03

The full recorded conversation, expanded from start to finish: instructions, agent messages, and tool inputs and responses.

This recording includes 172 readable reasoning summaries, shown in full below.

Browse attempts on this task

Agent: gemini-3.8-flash · Gemini CLI · high effort
Recorded conversation: 59:55 · 82 steps
Private reward: 0.009092 · Failed
Attempt cost: $1.91

Thinking
Model text
Tool call
Tool response

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User context

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User context

<session_context> This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app

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

</session_context>

Task instructions

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Task instructions

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:

Build and tune a validation ranker using only train_* as history and val_* as subsequent outcomes.
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:

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.

update_topic command

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Recorded reasoning summary 34 words

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 46 words

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 41 words

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.

Tool callupdate_topicTool call Returned

Tool call · Input +00:09

{
  "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 response +00:09

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.

Tool callrun_shell_commandInspect CPU, memory, and directories Returned

Tool call · Input +00:09

{
  "description": "Inspect CPU, memory, and directories",
  "command": "lscpu || nproc; free -h; ls -la /task; ls -la /app"
}

Tool response +00:09

<untrusted_context>
Output: Architecture:                            x86_64
CPU op-mode(s):                          32-bit, 64-bit
Address sizes:                           48 bits physical, 48 bits virtual
Byte Order:                              Little Endian
CPU(s):                                  8
On-line CPU(s) list:                     0-7
Vendor ID:                               AuthenticAMD
Model name:                              AMD EPYC 7R13 Processor
CPU family:                              25
Model:                                   1
Thread(s) per core:                      2
Core(s) per socket:                      4
Socket(s):                               1
Stepping:                                1
BogoMIPS:                                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
</untrusted_context>

Tool callread_file/task/evaluate_public.py Returned

Tool call · Input +00:09

{
  "start_line": 1,
  "end_line": 100,
  "file_path": "/task/evaluate_public.py"
}

Tool response +00:09


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 <validation-ranking.parquet|csv>
"""
from __future__ import annotations

import json
import sys
from pathlib import Path

import pandas as pd

from ranking_evaluation import (
    CandidateSet,
    RankingValidationError,
    ranked_items,
    read_ranking,
    score_rankings,
    validate_ranking,
)


HERE = Path(__file__).resolve().parent
DATA = HERE / "data"
LISTEN_STRONG = 80


def validation_truth(
    eligibility: CandidateSet, targets: set[int]
) -> dict[int, dict[int, int]]:
    """Build cutoff-correct graded relevance from public validation events.

    Only target-user events for eligible candidates are retained. Organic
    listens completed to at least 80 percent receive grade 1, and organic likes
    receive grade 3. Repeated user-item events are collapsed, and processing
    likes second ensures that grade 3 wins over grade 1.

    Args:
        eligibility: Validation-cutoff candidate membership service.
        targets: Complete set of target user IDs.

    Returns:
        A nested ``uid -> item_id -> grade`` mapping. Users without an eligible
        relevant event are omitted.
    """
    listens = pd.read_parquet(DATA / "val_listens.parquet")
    likes = pd.read_parquet(DATA / "val_likes.parquet")
    strong = listens[
        listens.uid.isin(targets)
        & (listens.is_organic == 1)
        & (listens.played_ratio_pct >= LISTEN_STRONG)
    ][["uid", "item_id"]].drop_duplicates()
    organic_likes = likes[
        likes.uid.isin(targets) & (likes.is_organic == 1)
    ][["uid", "item_id"]].drop_duplicates()

    graded: dict[int, dict[int, int]] = {}
    for raw_uid, raw_item in strong.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 1
    for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):
        uid, item = int(raw_uid), int(raw_item)
        if eligibility.is_eligible(uid, item):
            graded.setdefault(uid, {})[item] = 3
    return graded


def load_submission(
    path: Path, eligibility: CandidateSet, targets: set[int]
) -> dict[int, list[int]]:
    """Load and validate a public validation ranking.

    Files ending in ``.parquet`` are read as parquet; every other suffix is
    read as CSV. The required columns must be integer-valued, cover exactly the
    target-user set, contain 100 rows and ranks 1 through 100 per user, avoid
    duplicate user-item pairs, and contain only cutoff-eligible items.

Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input +00:09

{
  "end_line": 100,
  "file_path": "/task/ranking_evaluation.py",
  "start_line": 1
}

Tool response +00:09


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)

read_file evaluate_public.py

+00:13

Tool callread_file/task/evaluate_public.py Returned

Tool call · Input +00:13

{
  "start_line": 101,
  "file_path": "/task/evaluate_public.py",
  "end_line": 160
}

Tool response +00:13


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 <validation-ranking.parquet|csv>"
        )
    path = Path(sys.argv[1])
    ranked = load_submission(path, eligibility, targets)
    print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2))


if __name__ == "__main__":
    main()

Tool callread_file/task/ranking_evaluation.py Returned

Tool call · Input +00:13

{
  "file_path": "/task/ranking_evaluation.py",
  "start_line": 101,
  "end_line": 232
}

Tool response +00:13


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),
    }

Tool callread_file/task/requirements.txt Returned

Tool call · Input +00:13

{
  "start_line": 1,
  "file_path": "/task/requirements.txt",
  "end_line": 100
}

Tool response +00:13

# 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

Inspect datasets in /task/data

+00:16

Tool callrun_shell_commandInspect datasets in /task/data Returned

Tool call · Input +00:16

{
  "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 response +00:18

<untrusted_context>
Output: candidate_catalog.parquet: shape=(30000, 4), cols=['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank']
target_users.parquet: shape=(8456, 3), cols=['uid', 'recent_positive_listens', 'history_positive_listens']
test_eligible_candidates.npz: 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
</untrusted_context>

Check system resources

+00:22

Recorded reasoning summary 40 words

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.

Tool callrun_shell_commandCheck system resources Returned

Tool call · Input +00:22

{
  "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 response +00:22

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 2, in <module>
ModuleNotFoundError: No module named 'psutil'
Exit Code: 1
Process Group PGID: 1638
</untrusted_context>

Check memory and disk space via /proc/meminfo

+00:27

Tool callrun_shell_commandCheck memory and disk space via /proc/meminfo Returned

Tool call · Input +00:27

{
  "command": "cat /proc/meminfo | grep -E \"MemTotal|MemAvailable|MemFree\" && df -h",
  "description": "Check memory and disk space via /proc/meminfo"
}

Tool response +00:27

<untrusted_context>
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
</untrusted_context>

Check file sizes in /task/data

+00:29

Recorded reasoning summary 38 words

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.

Tool callrun_shell_commandCheck file sizes in /task/data Returned

Tool call · Input +00:29

{
  "command": "ls -lh /task/data/",
  "description": "Check file sizes in /task/data"
}

Tool response +00:29

<untrusted_context>
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
</untrusted_context>

Explore datasets schema and content

+00:35

Recorded reasoning summary 45 words

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.

Tool callrun_shell_commandExplore datasets schema and content Returned

Tool call · Input +00:35

{
  "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 response +00:37

<untrusted_context>
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
</untrusted_context>

Check unique uids in train_listens

+00:42

Tool callrun_shell_commandCheck unique uids in train_listens Returned

Tool call · Input +00:42

{
  "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 response +00:44

<untrusted_context>
Output: train_listens num unique uids: 9238
overlap with target_users: 8456
Process Group PGID: 1675
</untrusted_context>

Check unique items in train_listens and catalog overlap

+00:48

Recorded reasoning summary 38 words

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.

Tool callrun_shell_commandCheck unique items in train_listens and catalog overlap Returned

Tool call · Input +00:48

{
  "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 response +00:50

<untrusted_context>
Output: train_listens num unique items: 845395
cat items in train_listens: 30000
Process Group PGID: 1697
</untrusted_context>

Check validation ground truth statistics

+00:54

Tool callrun_shell_commandCheck validation ground truth statistics Returned

Tool call · Input +00:54

{
  "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 response +00:56

<untrusted_context>
Output: Num users with gold labels in val: 4263
Total positive labels: 37769, grade 3: 4417, grade 1: 33352
Process Group PGID: 1718
</untrusted_context>

Check eligible candidate counts per user

+01:00

Recorded reasoning summary 48 words

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.

Tool callrun_shell_commandCheck eligible candidate counts per user Returned

Tool call · Input +01:00

{
  "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 response +01:01

<untrusted_context>
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
</untrusted_context>

Check alignment of item_ids and uids across datasets

+01:11

Recorded reasoning summary 38 words

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.

Tool callrun_shell_commandCheck alignment of item_ids and uids across datasets Returned

Tool call · Input +01:11

{
  "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 response +01:12

<untrusted_context>
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
</untrusted_context>

Test popularity baseline on validation set

+01:16

Recorded reasoning summary 52 words

Confirming Data Integrity

I've confirmed that the item_ids, 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.

Tool callrun_shell_commandTest popularity baseline on validation set Returned

Tool call · Input +01:16

{
  "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 response +01:19

<untrusted_context>
Output: Popularity baseline NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263}
Process Group PGID: 1765
</untrusted_context>

Validate baseline with evaluate_public.py

+01:25

Recorded reasoning summary 51 words

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.

Tool callrun_shell_commandValidate baseline with evaluate_public.py Returned

Tool call · Input +01:25

{
  "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 response +01:32

<untrusted_context>
Output: {
  "model": "/app/solution/validation_baseline.parquet",
  "scores": {
    "quality_ndcg@10": 0.01071,
    "quality_users": 4263
  }
}
Process Group PGID: 1785
</untrusted_context>

Generate baseline test submission to guarantee a valid submission exists

+01:36

Recorded reasoning summary 35 words

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.

Tool callrun_shell_commandGenerate baseline test submission to guarantee a valid submission exists Returned

Tool call · Input +01:36

{
  "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 response +01:41

<untrusted_context>
Output: Baseline submission generated and verified. Shape: (845600, 3)
Process Group PGID: 1820
</untrusted_context>

Inspect timestamps and preference state

+02:04

Recorded reasoning summary 37 words

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 49 words

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 50 words

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 72 words

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.

Tool callrun_shell_commandInspect timestamps and preference state Returned

Tool call · Input +02:04

{
  "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 response +02:06

<untrusted_context>
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
</untrusted_context>

Inspect target_users.parquet

+02:11

Tool callrun_shell_commandInspect target_users.parquet Returned

Tool call · Input +02:11

{
  "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 response +02:13

<untrusted_context>
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
</untrusted_context>

Check overlap of interactions with catalog items

+02:32

Recorded reasoning summary 48 words

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 58 words

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 41 words

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 40 words

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 52 words

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.

Tool callrun_shell_commandCheck overlap of interactions with catalog items Returned

Tool call · Input +02:32

{
  "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 response +02:44

<untrusted_context>
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
</untrusted_context>

Check test_pref overlap with catalog

+02:47

Recorded reasoning summary 38 words

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.

Tool callrun_shell_commandCheck test_pref overlap with catalog Returned

Tool call · Input +02:47

{
  "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 response +02:49

<untrusted_context>
Output: test_pref in cat: 1.0
Process Group PGID: 1902
</untrusted_context>

Check preference state eligibility

+02:59

Recorded reasoning summary 48 words

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.

Tool callrun_shell_commandCheck preference state eligibility Returned

Tool call · Input +02:59

{
  "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 response +03:02

<untrusted_context>
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
</untrusted_context>

Check test preference state eligibility

+03:05

Recorded reasoning summary 40 words

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.

Tool callrun_shell_commandCheck test preference state eligibility Returned

Tool call · Input +03:05

{
  "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 response +03:07

<untrusted_context>
Output: Test preference state items eligible: 0 / 484776 (0.00%)
Process Group PGID: 1943
</untrusted_context>

Check why items are ineligible

+03:12

Recorded reasoning summary 35 words

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.

Tool callrun_shell_commandCheck why items are ineligible Returned

Tool call · Input +03:12

{
  "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 response +03:14

<untrusted_context>
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
</untrusted_context>

Check candidate eligibility for sample users

+03:17

Recorded reasoning summary 49 words

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.

Tool callrun_shell_commandCheck candidate eligibility for sample users Returned

Tool call · Input +03:17

{
  "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 response +03:19

<untrusted_context>
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
</untrusted_context>

Inspect user history statistics

+03:39

Recorded reasoning summary 39 words

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 36 words

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 73 words

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 44 words

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 54 words

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 47 words

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 61 words

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.

Tool callrun_shell_commandInspect user history statistics Returned

Tool call · Input +03:39

{
  "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 response +03:41

<untrusted_context>
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
</untrusted_context>

Check train listens statistics

+03:47

Recorded reasoning summary 44 words

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 53 words

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.

Tool callrun_shell_commandCheck train listens statistics Returned

Tool call · Input +03:47

{
  "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 response +03:49

<untrusted_context>
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
</untrusted_context>

Check strong organic events in catalog

+03:56

Tool callrun_shell_commandCheck strong organic events in catalog Returned

Tool call · Input +03:56

{
  "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 response +04:00

<untrusted_context>
Output: Strong organic listens in cat: 8699160 71.44%
Organic likes in cat: 301527 65.29%
Process Group PGID: 2044
</untrusted_context>

Analyze temporal distribution of training data

+04:53

Recorded reasoning summary 40 words

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 40 words

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 50 words

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 44 words

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 41 words

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 47 words

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 43 words

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 43 words

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 46 words

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 45 words

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 61 words

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 43 words

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.

Tool callrun_shell_commandAnalyze temporal distribution of training data Returned

Tool call · Input +04:53

{
  "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 response +04:54

<untrusted_context>
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
</untrusted_context>

Search for tower references in /task

+05:08

Recorded reasoning summary 40 words

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 53 words

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 47 words

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 45 words

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 50 words

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.

Tool callrun_shell_commandSearch for tower references in /task Returned

Tool call · Input +05:08

{
  "description": "Search for tower references in /task",
  "command": "grep -rn \"tower\" /task/"
}

Tool response +05:08

<untrusted_context>
Output: /task/requirements.txt:1:# Exact distribution inventory for the Yambda two-tower task family.
Process Group PGID: 2091
</untrusted_context>

Inspect sample users and their validation gold items

+05:19

Recorded reasoning summary 37 words

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 47 words

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.

Tool callrun_shell_commandInspect sample users and their validation gold items Returned

Tool call · Input +05:19

{
  "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 response +05:22

<untrusted_context>
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
</untrusted_context>

Benchmark reading train_listens columns

+05:26

Recorded reasoning summary 42 words

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.

Tool callrun_shell_commandBenchmark reading train_listens columns Returned

Tool call · Input +05:26

{
  "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 response +05:28

<untrusted_context>
Output: Read table in 1.36s, rows: 43484159
Process Group PGID: 2114
</untrusted_context>

Measure memory and numpy conversion speed

+05:49

Tool callrun_shell_commandMeasure memory and numpy conversion speed Returned

Tool call · Input +05:49

{
  "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 response +05:51

<untrusted_context>
Output: Converted to numpy in 1.67 s
Shapes: (43484159,) Memory MB: 622.0458841323853
Process Group PGID: 2136
</untrusted_context>

Check positive events count per 7-day window in train

+06:12

Recorded reasoning summary 43 words

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 51 words

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 48 words

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 52 words

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 47 words

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 49 words

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 68 words

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 51 words

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 54 words

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 47 words

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.

Tool callrun_shell_commandCheck positive events count per 7-day window in train Returned

Tool call · Input +06:12

{
  "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 response +06:18

<untrusted_context>
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
</untrusted_context>

Check new discovery items in Week -1

+06:25

Recorded reasoning summary 44 words

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.

Tool callrun_shell_commandCheck new discovery items in Week -1 Returned

Tool call · Input +06:25

{
  "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 response +06:43

<untrusted_context>
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
</untrusted_context>

Benchmark basic two-tower forward/backward speed

+07:23

Recorded reasoning summary 38 words

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 70 words

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 49 words

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 49 words

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 54 words

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 37 words

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 49 words

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 48 words

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 52 words

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.

Tool callrun_shell_commandBenchmark basic two-tower forward/backward speed Returned

Tool call · Input +07:23

{
  "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 response +07:26

<untrusted_context>
Output: 100 batches of size 512 took: 0.33s
Process Group PGID: 2214
</untrusted_context>

Benchmark EmbeddingBag speed on CPU

+09:26

Recorded reasoning summary 40 words

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.

Tool callrun_shell_commandBenchmark EmbeddingBag speed on CPU Returned

Tool call · Input +09:26

{
  "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 response +09:30

<untrusted_context>
Output: 100 batches of EmbeddingBag took: 0.36s
Process Group PGID: 2250
</untrusted_context>

Test loading all interaction data into memory

+09:47

Recorded reasoning summary 60 words

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 49 words

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 50 words

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 50 words

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 43 words

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 53 words

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.

Tool callrun_shell_commandTest loading all interaction data into memory Returned

Tool call · Input +09:47

{
  "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 response +09:49

<untrusted_context>
Output: Loaded all data in 0.48s
Process Group PGID: 2274
</untrusted_context>

Compare reconstructed preference state with validation_preference_state

+09:53

Recorded reasoning summary 46 words

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."

Tool callrun_shell_commandCompare reconstructed preference state with validation_preference_state Returned

Tool call · Input +09:53

{
  "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 response +09:55

<untrusted_context>
Output: v_pref shape: (469124, 3)
v_pref unique uids: 7038
Process Group PGID: 2296
</untrusted_context>

update_topic command

+10:09

Recorded reasoning summary 31 words

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 48 words

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 53 words

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 46 words

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 53 words

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 42 words

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.

Tool callupdate_topicTool call Returned

Tool call · Input +10:09

{
  "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 response +10:09

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.

Benchmark feature extraction from listens for last 60 days

+10:22

Recorded reasoning summary 47 words

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 65 words

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.

Tool callrun_shell_commandBenchmark feature extraction from listens for last 60 days Returned

Tool call · Input +10:22

{
  "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 response +10:25

<untrusted_context>
Output: Filtered listens in last 60 days: 11699019 in 1.80s
Process Group PGID: 2319
</untrusted_context>

Benchmark aggregation speed of user-item weights

+11:01

Recorded reasoning summary 38 words

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.

Tool callrun_shell_commandBenchmark aggregation speed of user-item weights Returned

Tool call · Input +11:01

{
  "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 response +11:05

<untrusted_context>
Output: Filtered to catalog items: 8417391 in 1.37s
Aggregated rows: 3406169 in 2.61s
Process Group PGID: 2347
</untrusted_context>

Benchmark EmbeddingBag for all 8456 users

+11:12

Recorded reasoning summary 39 words

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.

Tool callrun_shell_commandBenchmark EmbeddingBag for all 8456 users Returned

Tool call · Input +11:12

{
  "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 response +11:15

<untrusted_context>
Output: EmbeddingBag forward for all 8456 users took: 0.0072 s, output shape: torch.Size([8455, 64])
Process Group PGID: 2369
</untrusted_context>

Check environment setup for prototype

+11:39

Recorded reasoning summary 40 words

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.

Tool callrun_shell_commandCheck environment setup for prototype Returned

Tool call · Input +11:39

{
  "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 response +11:43

<untrusted_context>
Output: Catalog items: 30000 Target users: 8456
Process Group PGID: 2393
</untrusted_context>

Verify target pair extraction for week -1

+12:17

Recorded reasoning summary 40 words

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 54 words

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 $T$.

Tool callrun_shell_commandVerify target pair extraction for week -1 Returned

Tool call · Input +12:17

{
  "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 response +12:36

<untrusted_context>
Output: Data loaded in 4.08s
Target pairs for week -1: 45238
Grade 3 count: 8639 Grade 1 count: 36599
Process Group PGID: 2423
</untrusted_context>

Check PyTorch environment

+12:47

Recorded reasoning summary 34 words

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 35 words

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 52 words

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 56 words

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.

Tool callrun_shell_commandCheck PyTorch environment Returned

Tool call · Input +12:47

{
  "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 response +12:53

<untrusted_context>
Output: PyTorch version: 2.8.0+cpu
CUDA available: False
Process Group PGID: 2445
</untrusted_context>

Test preparing listens with vectorized mapping

+13:01

Recorded reasoning summary 36 words

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.

Tool callrun_shell_commandTest preparing listens with vectorized mapping Returned

Tool call · Input +13:01

{
  "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 response +13:04

<untrusted_context>
Output: Listens prepared in 1.88s, rows: 12117965
Process Group PGID: 2463
</untrusted_context>

Benchmark multi-weight aggregation in pandas

+13:12

Recorded reasoning summary 39 words

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.

Tool callrun_shell_commandBenchmark multi-weight aggregation in pandas Returned

Tool call · Input +13:12

{
  "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 response +13:18

<untrusted_context>
Output: Computed weights in 2.29s
Aggregated in 1.51s, unique pairs: 4321941
Process Group PGID: 2491
</untrusted_context>

Verify PyTorch EmbeddingBag empty offset handling

+13:28

Recorded reasoning summary 50 words

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 50 words

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.

Tool callrun_shell_commandVerify PyTorch EmbeddingBag empty offset handling Returned

Tool call · Input +13:28

{
  "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 response +13:31

<untrusted_context>
Output: Output shape: torch.Size([4, 4])
Row 0: tensor([-4.0461, -2.2352, -2.8407, -1.9140], grad_fn=<SelectBackward0>)
Row 1 (empty): tensor([0., 0., 0., 0.], grad_fn=<SelectBackward0>)
Row 2: tensor([ 1.7677, -1.5779,  0.4143,  2.0241], grad_fn=<SelectBackward0>)
Process Group PGID: 2513
</untrusted_context>

Verify EmbeddingBag with 3 offsets

+19:25

Recorded reasoning summary 36 words

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.

Tool callrun_shell_commandVerify EmbeddingBag with 3 offsets Returned

Tool call · Input +19:25

{
  "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 response +19:28

<untrusted_context>
Output: Output shape: torch.Size([3, 4])
Row 0: tensor([ 1.6807, -2.1469,  1.1582,  1.8539], grad_fn=<SelectBackward0>)
Row 1 (empty): tensor([0., 0., 0., 0.], grad_fn=<SelectBackward0>)
Row 2: tensor([-1.9523, -0.4724,  1.8892,  0.2620], grad_fn=<SelectBackward0>)
Process Group PGID: 2556
</untrusted_context>

Benchmark slice target extraction

+19:54

Recorded reasoning summary 53 words

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 65 words

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 50 words

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 61 words

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.

Tool callrun_shell_commandBenchmark slice target extraction Returned

Tool call · Input +19:54

{
  "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 response +20:11

<untrusted_context>
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
</untrusted_context>

Check distinct items per user in last 14 days

+22:30

Recorded reasoning summary 37 words

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 44 words

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.

Tool callrun_shell_commandCheck distinct items per user in last 14 days Returned

Tool call · Input +22:30

{
  "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 response +22:32

<untrusted_context>
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
</untrusted_context>

Verify data loading for prototype

+22:47

Recorded reasoning summary 42 words

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 59 words

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 47 words

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 47 words

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 40 words

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.

Tool callrun_shell_commandVerify data loading for prototype Returned

Tool call · Input +22:47

{
  "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 response +22:51

<untrusted_context>
Output: Catalog: 30000 items, Targets: 8456 users
Process Group PGID: 2637
</untrusted_context>

write_file train_prototype.py

+23:23

Recorded reasoning summary 63 words

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.

Tool callwrite_file/app/solution/train_prototype.py Returned

Tool call · Input +23:23

{
  "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 response +23:23

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.")

Test index_add autograd gradient flow

+24:35

Recorded reasoning summary 40 words

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.

Tool callrun_shell_commandTest index_add autograd gradient flow Returned

Tool call · Input +24:35

{
  "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 response +24:38

<untrusted_context>
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
</untrusted_context>

Benchmark full index_add forward and backward

+27:18

Tool callrun_shell_commandBenchmark full index_add forward and backward Returned

Tool call · Input +27:18

{
  "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 response +27:22

<untrusted_context>
Output: Full index_add forward + backward took: 0.915 s
Process Group PGID: 2695
</untrusted_context>

write_file prototype_eval.py

+27:58

Recorded reasoning summary 45 words

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 43 words

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 51 words

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.

Tool callwrite_file/app/solution/prototype_eval.py Returned

Tool call · Input +27:58

{
  "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 response +27:59

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)"
    )

Run prototype_eval.py

+28:03

Tool callrun_shell_commandRun prototype_eval.py Returned

Tool call · Input +28:03

{
  "description": "Run prototype_eval.py",
  "command": "python3 /app/solution/prototype_eval.py"
}

Tool response +29:02

<untrusted_context>
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 <module>
    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
</untrusted_context>

replace prototype_eval.py

+29:25

Recorded reasoning summary 52 words

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 52 words

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.

Tool callreplace/app/solution/prototype_eval.py Returned

Tool call · Input +29:25

{
  "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 response +29:25

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)
...

Run prototype_eval.py training and validation

+29:28

Tool callrun_shell_commandRun prototype_eval.py training and validation Returned

Tool call · Input +29:28

{
  "command": "python3 /app/solution/prototype_eval.py",
  "description": "Run prototype_eval.py training and validation"
}

Tool response +31:11

<untrusted_context>
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
</untrusted_context>

Verify unique_items and item_inverse in PyTorch

+31:42

Recorded reasoning summary 41 words

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 45 words

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 43 words

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 69 words

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 50 words

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 54 words

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 41 words

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 61 words

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.

Tool callrun_shell_commandVerify unique_items and item_inverse in PyTorch Returned

Tool call · Input +31:42

{
  "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 response +31:47

<untrusted_context>
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
</untrusted_context>

replace prototype_eval.py

+32:16

Recorded reasoning summary 45 words

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.

Tool callreplace/app/solution/prototype_eval.py Returned

Tool call · Input +32:16

{
  "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 response +32:16

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()
...

Run prototype_eval.py with deduplicated items and bias dimension

+32:27

Tool callrun_shell_commandRun prototype_eval.py with deduplicated items and bias dimension Returned

Tool call · Input +32:27

{
  "description": "Run prototype_eval.py with deduplicated items and bias dimension",
  "command": "python3 /app/solution/prototype_eval.py"
}

Tool response +37:00

<untrusted_context>
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
</untrusted_context>

update_topic command

+37:33

Recorded reasoning summary 49 words

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 51 words

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 42 words

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 46 words

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 52 words

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.

Tool callupdate_topicTool call Returned

Tool call · Input +37:33

{
  "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 response +37:33

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.

Test vectorized false negative masking

+37:46

Recorded reasoning summary 46 words

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 47 words

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.

Tool callrun_shell_commandTest vectorized false negative masking Returned

Tool call · Input +37:46

{
  "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 response +37:49

<untrusted_context>
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
</untrusted_context>

Verify reconstruction of active preference state

+37:59

Recorded reasoning summary 32 words

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 50 words

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.

Tool callrun_shell_commandVerify reconstruction of active preference state Returned

Tool call · Input +37:59

{
  "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 response +38:01

<untrusted_context>
Output: User 100 pref state liked count: 13
Reconstructed liked count: 19
Match exactly? False
Process Group PGID: 2906
</untrusted_context>

Check catalog filtering on preference state

+38:05

Tool callrun_shell_commandCheck catalog filtering on preference state Returned

Tool call · Input +38:05

{
  "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 response +38:06

<untrusted_context>
Output: Catalog filtered reconstructed: 13
Match exactly? True
Process Group PGID: 2923
</untrusted_context>

Test computing rich item features

+38:12

Recorded reasoning summary 39 words

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 $T_w$.

Tool callrun_shell_commandTest computing rich item features Returned

Tool call · Input +38:12

{
  "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 response +38:22

<untrusted_context>
Output: Item features computed in 8.26s
Process Group PGID: 2940
</untrusted_context>

write_file train_improved.py

+39:07

Recorded reasoning summary 28 words

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 41 words

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 46 words

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.

Tool callwrite_file/app/solution/train_improved.py Returned

Tool call · Input +39:07

{
  "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 response +39:07

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]"
    )

Run train_improved.py

+39:29

Tool callrun_shell_commandRun train_improved.py Returned

Tool call · Input +39:29

{
  "command": "python3 /app/solution/train_improved.py",
  "description": "Run train_improved.py"
}

Tool response +43:10

<untrusted_context>
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
</untrusted_context>

Check saved best model

+47:39

Recorded reasoning summary 46 words

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 41 words

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 50 words

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 143 words

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 46 words

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.

Tool callrun_shell_commandCheck saved best model Returned

Tool call · Input +47:39

{
  "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 response +47:42

<untrusted_context>
Output: Traceback (most recent call last):
  File "<string>", line 7, in <module>
NameError: name 'os' is not defined. Did you forget to import 'os'?
Exit Code: 1
Process Group PGID: 3057
</untrusted_context>

Inspect best_model.pt parameters

+47:47

Tool callrun_shell_commandInspect best_model.pt parameters Returned

Tool call · Input +47:47

{
  "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 response +47:50

<untrusted_context>
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
</untrusted_context>

Benchmark set intersection for false negative masking

+48:29

Recorded reasoning summary 54 words

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 62 words

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.

Tool callrun_shell_commandBenchmark set intersection for false negative masking Returned

Tool call · Input +48:29

{
  "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 response +48:29

<untrusted_context>
Output: 512 set intersections x 100 took: 0.018 s
Process Group PGID: 3080
</untrusted_context>

Check listens in last 3 days

+48:46

Recorded reasoning summary 35 words

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 43 words

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.

Tool callrun_shell_commandCheck listens in last 3 days Returned

Tool call · Input +48:46

{
  "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 response +48:48

<untrusted_context>
Output: Users with listens in last 3 days: 6511
Target users with listens in last 3 days: 6400 / 8456
Process Group PGID: 3089
</untrusted_context>

Benchmark item-item co-occurrence computation

+49:05

Recorded reasoning summary 46 words

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 52 words

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.

Tool callrun_shell_commandBenchmark item-item co-occurrence computation Returned

Tool call · Input +49:05

{
  "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 response +49:17

<untrusted_context>
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
</untrusted_context>

write_file tune_two_tower.py

+50:05

Recorded reasoning summary 47 words

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 50 words

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 51 words

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 73 words

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 46 words

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.

Tool callwrite_file/app/solution/tune_two_tower.py Returned

Tool call · Input +50:05

{
  "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 response +50:07

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)

Run tune_two_tower.py

+50:10

Tool callrun_shell_commandRun tune_two_tower.py Returned

Tool call · Input +50:10

{
  "command": "python3 /app/solution/tune_two_tower.py",
  "description": "Run tune_two_tower.py"
}

Tool response +53:20

<untrusted_context>
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
</untrusted_context>

Compare train_likes and v_pref liked counts

+53:34

Recorded reasoning summary 45 words

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 40 words

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.

Tool callrun_shell_commandCompare train_likes and v_pref liked counts Returned

Tool call · Input +53:34

{
  "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 response +53:36

<untrusted_context>
Output: train_likes in catalog per user: 70.22199721554233
v_pref liked per user: 61.816368739843405
Process Group PGID: 3199
</untrusted_context>

read_file prototype_eval.py

+53:40

Tool callread_file/app/solution/prototype_eval.py Returned

Tool call · Input +53:40

{
  "start_line": 270,
  "file_path": "/app/solution/prototype_eval.py",
  "end_line": 330
}

Tool response +53:40


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):

Run prototype_eval to see epoch 2 output

+53:49

Recorded reasoning summary 39 words

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 43 words

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 50 words

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 46 words

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.

Tool callrun_shell_commandRun prototype_eval to see epoch 2 output Returned

Tool call · Input +53:49

{
  "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 response +58:22

<untrusted_context>
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
</untrusted_context>

Inspect log_q distribution

+58:35

Recorded reasoning summary 61 words

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 57 words

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 60 words

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.

Tool callrun_shell_commandInspect log_q distribution Returned

Tool call · Input +58:35

{
  "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 response +58:36

<untrusted_context>
Output: log_q min, median, max: -11.720292817526948 -10.944752744006943 -6.624825001790236
Process Group PGID: 3284
</untrusted_context>

write_file train_logq.py

+59:55

Recorded reasoning summary 50 words

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 56 words

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.

Tool callwrite_file/app/solution/train_logq.py Returned

Tool call · Input +59:55

{
  "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 response +59:55

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]"
    )

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About this recording

The full recorded conversation: task prompt, agent messages, thinking text when available, tool inputs and outputs, and harness context. Empty thinking entries are labeled unavailable, not reconstructed from tool calls. Disclosures change the view; they do not shorten the content. Any truncation already present in tool output is preserved.

Steps follow the recorded model messages and reasoning events. Tool results appear with their originating call. Elapsed time starts with the first recorded event and excludes verification.

Attempt: gemini-3-8-flash-high-base-fixed-r03-3d6fca60
Recorded model: gemini-3.8-flash
Harness version: 0.61.0
Session: db9aeacf-0e46-459f-a908-edb45638eea0
Source SHA-256: d8b5cd93e13aac009ba58852b4e1564dd03f2640afe3afc059327c0e45243d87

Session bookkeeping and encrypted reasoning or signatures are excluded from the reading export. The harness’s system prompt is not present in this session archive.

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