yambda-discovery-ranking-two-tower-base / yambda-gemini-3-8-flash-high-r01-b9049e57 Recorded model: gemini-3.8-flash ## Step 1 · user · 2026-09-29T00:30:19.960Z This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app - **Workspace Directories:** - /app - **Directory Structure:** Showing up to 200 items (files + folders). /app/ └───solution/ ## Step 2 · user · 2026-09-29T00:30:20.001Z # Build a latency-efficient PyTorch two-tower neural-network ranker Build a two-tower neural network in CPU PyTorch for the "Made for You" shelf of a music streaming service. Although the supplied benchmark is small, it represents a production system with millions of users and millions of items; the training approach must scale to that setting and therefore must not construct or decompose a global user-item interaction matrix. The production shelf has a strict request-time latency budget. Candidate item embeddings are computed offline and stored in a nearest-neighbor index. At request time, the system computes one user embedding and retrieves items by maximum inner-product search. The model must therefore decompose exactly into a user vector and an independently precomputable item vector of the same dimensionality, with the final relevance score equal to their dot product. Both vectors must be produced by separate trainable user and item towers implemented as PyTorch neural networks. The user tower must derive its representation from interaction history rather than a user-ID lookup. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model implementation, fitted representation, or example submission is supplied. Design and tune the required PyTorch neural two-tower model, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that defines, trains, and applies both towers, along with a concise `/app/solution/README.md`, in `/app/solution/`. The README should state the model architecture, training objective, public validation score, and final submission command. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - The environment includes CPU PyTorch 2.8.0. No accelerator is available. - You have up to 60 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 3 · agent · 2026-09-29T00:30:23.332Z ### Recorded reasoning summary Analyzing Task Requirements I'm now focusing on dissecting the prompt and evaluating the specifics of the task, ensuring I fully grasp the data schema and the intended evaluation metrics to guide my subsequent actions. ### update_topic · 2026-09-29T00:30:23.332Z { "strategic_intent": "Explore task files, understand data schema, evaluation logic, and environment constraints." } ### Tool output · 2026-09-29T00:30:23.344Z Strategic Intent: Explore task files, understand data schema, evaluation logic, and environment constraints. ### list_directory · 2026-09-29T00:30:23.332Z { "dir_path": "/task" } ### Tool error · 2026-09-29T00:30:23.344Z Path not in workspace: Attempted path "/task" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app ### list_directory · 2026-09-29T00:30:23.332Z { "dir_path": "/task/data" } ### Tool error · 2026-09-29T00:30:23.344Z Path not in workspace: Attempted path "/task/data" resolves outside the allowed workspace directories: /app or the project temp directory: /home/agent/.gemini/tmp/app ## Step 4 · agent · 2026-09-29T00:30:25.373Z ### run_shell_command · 2026-09-29T00:30:25.373Z { "description": "List files in /task and /task/data", "command": "ls -la /task && ls -la /task/data" } ### Tool output · 2026-09-29T00:30:25.400Z Output: total 36 dr-xr-xr-x 1 root root 4096 Sep 29 00:27 . drwxr-xr-x 1 root root 4096 Sep 29 00:30 .. -r--r--r-- 1 root root 0 Sep 29 00:13 .keep dr-xr-xr-x 1 root root 4096 Sep 29 00:14 data -r--r--r-- 1 root root 5774 Sep 29 00:13 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 29 00:13 ranking_evaluation.py -r--r--r-- 1 root root 1295 Sep 29 00:13 requirements.txt total 347392 dr-xr-xr-x 1 root root 4096 Sep 29 00:14 . dr-xr-xr-x 1 root root 4096 Sep 29 00:27 .. -r--r--r-- 1 root root 349352 Sep 29 00:13 candidate_catalog.parquet -r--r--r-- 1 root root 84127 Sep 29 00:13 target_users.parquet -r--r--r-- 1 root root 7736341 Sep 29 00:13 test_eligible_candidates.npz -r--r--r-- 1 root root 1092329 Sep 29 00:14 test_preference_state.parquet -r--r--r-- 1 root root 857213 Sep 29 00:13 train_dislikes.parquet -r--r--r-- 1 root root 5374652 Sep 29 00:13 train_likes.parquet -r--r--r-- 1 root root 319183255 Sep 29 00:13 train_listens.parquet -r--r--r-- 1 root root 155988 Sep 29 00:13 train_undislikes.parquet -r--r--r-- 1 root root 1855885 Sep 29 00:13 train_unlikes.parquet -r--r--r-- 1 root root 36571 Sep 29 00:13 val_dislikes.parquet -r--r--r-- 1 root root 298484 Sep 29 00:13 val_likes.parquet -r--r--r-- 1 root root 9947609 Sep 29 00:13 val_listens.parquet -r--r--r-- 1 root root 7845 Sep 29 00:13 val_undislikes.parquet -r--r--r-- 1 root root 72536 Sep 29 00:13 val_unlikes.parquet -r--r--r-- 1 root root 7573821 Sep 29 00:13 validation_eligible_candidates.npz -r--r--r-- 1 root root 1061761 Sep 29 00:14 validation_preference_state.parquet Process Group PGID: 1612 ## Step 5 · agent · 2026-09-29T00:30:28.427Z ### run_shell_command · 2026-09-29T00:30:28.427Z { "command": "cat /task/evaluate_public.py", "description": "View evaluate_public.py" } ### Tool output · 2026-09-29T00:30:28.442Z Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() Process Group PGID: 1625 ## Step 6 · agent · 2026-09-29T00:30:29.929Z ### run_shell_command · 2026-09-29T00:30:29.929Z { "command": "cat /task/ranking_evaluation.py", "description": "View ranking_evaluation.py" } ### Tool output · 2026-09-29T00:30:29.943Z Output: """Shared public primitives for Yambda discovery-ranking evaluation. Both the solver-visible validation evaluator and the private submission judge import this module from the generated task's ``public`` directory. It owns the stage-independent contract: reading a ranking, validating its shape, converting it to per-user rank order, looking up packed candidate eligibility, and computing graded NDCG@10. The module deliberately contains no event-derived labels, private artifact paths, or pass thresholds. Each evaluator supplies its own relevance data and per-cutoff eligibility checks around these common operations. """ from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd __all__ = [ "CandidateSet", "QUALITY_K", "RankingValidationError", "REQUIRED_COLUMNS", "TOP_N", "ndcg_at_k", "ranked_items", "read_ranking", "score_rankings", "validate_ranking", ] TOP_N = 100 QUALITY_K = 10 REQUIRED_COLUMNS = ("uid", "item_id", "rank") class RankingValidationError(ValueError): """Report a ranking-schema or structural-contract violation.""" class CandidateSet: """Provide membership lookups over one packed candidate-set archive. The archive contains aligned ``uids``, ``item_ids``, and ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are copied into memory so they remain valid after the archive is closed. """ def __init__(self, path: Path): """Load an eligibility archive and build ID-to-position indexes. Args: path: Path to a ``.npz`` archive with the candidate-set schema. """ with np.load(path, allow_pickle=False) as artifact: self.uids = artifact["uids"].astype(np.int64, copy=True) self.item_ids = artifact["item_ids"].astype(np.int64, copy=True) self.packed = artifact["packed_eligible"].astype(np.uint8, copy=True) self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)} self.item_column = { int(item): column for column, item in enumerate(self.item_ids) } def is_eligible(self, uid: int, item_id: int) -> bool: """Return whether ``item_id`` is eligible for ``uid``. IDs absent from the target-user or candidate-item arrays are treated as ineligible rather than raising an exception. Args: uid: Target user ID to query. item_id: Candidate item ID to query. Returns: ``True`` only when the corresponding packed eligibility bit is set. """ row = self.uid_row.get(int(uid)) column = self.item_column.get(int(item_id)) if row is None or column is None: return False byte, bit = divmod(column, 8) return bool((int(self.packed[row, byte]) >> bit) & 1) def read_ranking(path: Path) -> pd.DataFrame: """Read a parquet or CSV ranking into a pandas frame. Args: path: Input path. A ``.parquet`` suffix selects parquet; every other suffix is interpreted as CSV. Returns: The ranking frame as loaded by pandas. """ return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) def validate_ranking( frame: pd.DataFrame, targets: set[int], ) -> pd.DataFrame: """Normalize and validate the stage-independent ranking contract. Required values must be finite integers. The user set must exactly match ``targets``; every user must have 100 rows, distinct items, and each rank from 1 through 100 exactly once. Cutoff-specific candidate eligibility is intentionally left to the calling evaluator. Args: frame: Raw ranking frame. Extra columns are discarded from an internal copy; the caller's frame is not modified. targets: Exact set of target user IDs the ranking must cover. Returns: A normalized frame containing only the required ``int64`` columns. Raises: RankingValidationError: If the ranking violates the shared contract. """ required = list(REQUIRED_COLUMNS) if not all(column in frame for column in required): raise RankingValidationError(f"submission requires columns {required}") normalized = frame[required].copy() for column in required: numeric = pd.to_numeric(normalized[column], errors="coerce") values = numeric.to_numpy(dtype=float) if numeric.isna().any() or not np.isfinite(values).all(): raise RankingValidationError( f"submission {column} must be finite numeric values" ) if not np.equal(numeric, np.floor(numeric)).all(): raise RankingValidationError( f"submission {column} must contain integers" ) normalized[column] = numeric.astype(np.int64) submitted_users = set(map(int, normalized.uid.unique())) if submitted_users != targets: raise RankingValidationError( f"submission user set mismatch: missing={len(targets - submitted_users)} " f"extra={len(submitted_users - targets)}" ) counts = normalized.groupby("uid").size() if not (counts == TOP_N).all(): raise RankingValidationError( f"submission must contain exactly {TOP_N} rows for every target user" ) if normalized.duplicated(["uid", "item_id"]).any(): raise RankingValidationError( "submission contains duplicate (uid, item_id) pairs" ) valid_ranks = set(range(1, TOP_N + 1)) ranks_are_valid = normalized.groupby("uid")["rank"].apply( lambda values: set(map(int, values)) == valid_ranks ) if not ranks_are_valid.all(): raise RankingValidationError( f"each user must have every rank from 1 through {TOP_N} exactly once" ) return normalized def ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]: """Convert a validated ranking frame to ordered items keyed by user. Args: frame: Frame that has passed :func:`validate_ranking`. Returns: A ``uid -> item IDs`` mapping ordered by ascending rank. """ ordered = frame.sort_values(["uid", "rank"]) return { int(uid): list(map(int, values)) for uid, values in ordered.groupby("uid").item_id } def ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float: """Compute graded normalized discounted cumulative gain at ``k``. Relevance gain is ``2**grade - 1`` and the discount at one-based rank ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero. Args: ranked: Item IDs in predicted rank order. gold: Mapping from relevant item IDs to integer relevance grades. k: Maximum number of predicted positions to score. Returns: For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns ``0.0`` when the ideal gain is zero. """ gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64) discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2)) dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts)) ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64) ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2)) idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts)) return dcg / idcg if idcg else 0.0 def score_rankings( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Relevant item grades keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ quality = float( np.mean( [ ndcg_at_k(ranked[uid], gold, QUALITY_K) for uid, gold in graded.items() ] ) ) return { "quality_ndcg@10": round(quality, 6), "quality_users": len(graded), } Process Group PGID: 1628 ## Step 7 · agent · 2026-09-29T00:30:31.537Z ### run_shell_command · 2026-09-29T00:30:31.537Z { "command": "cat /task/requirements.txt", "description": "View requirements.txt" } ### Tool output · 2026-09-29T00:30:31.550Z Output: # Exact distribution inventory for the Yambda two-tower task family. # # This is an input to `uv pip sync`, not a list of minimum requirements. A # conforming task environment contains exactly these third-party Python # distributions and no others. Python itself is pinned by task.toml and both # Harbor image Dockerfiles. attrs==26.1.0 bleach==6.4.0 certifi==2026.7.22 chardet==7.6.0 charset-normalizer==3.5.1 fastjsonschema==2.22.2 filelock==3.32.3 fsspec==2026.7.0 idna==3.18 Jinja2==3.1.6 joblib==1.5.3 jsonschema==4.26.0 jsonschema-specifications==2025.9.1 jupyter-core==5.9.1 jupytext==1.19.5 kaggle==2.2.4 kagglesdk==0.1.37 markdown-it-py==4.2.0 MarkupSafe==3.0.3 mdit-py-plugins==0.6.1 mdurl==0.1.2 mpmath==1.3.0 nbformat==5.11.1 networkx==3.6.1 numpy==2.2.6 packaging==26.3 pandas==2.3.3 pillow==12.3.0 pip==26.0.1 platformdirs==4.11.3 protobuf==7.35.1 pyarrow==18.1.0 python-dateutil==2.9.0.post0 python-dotenv==1.2.3 python-slugify==8.0.4 pytz==2026.3.post1 PyYAML==6.0.3 referencing==0.37.0 reportlab==4.2.5 requests==2.34.2 rpds-py==2026.6.3 scikit-learn==1.7.2 scipy==1.15.3 setuptools==84.0.0 six==1.17.0 sympy==1.14.0 text-unidecode==1.3 threadpoolctl==3.6.0 torch==2.8.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 Process Group PGID: 1631 ## Step 8 · agent · 2026-09-29T00:30:33.640Z ### run_shell_command · 2026-09-29T00:30:33.640Z { "command": "lscpu && free -h && df -h", "description": "Check CPU and memory resources" } ### Tool output · 2026-09-29T00:30:33.658Z 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: 5299.99 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 Exit Code: 127 Process Group PGID: 1634 ## Step 9 · agent · 2026-09-29T00:30:35.436Z ### run_shell_command · 2026-09-29T00:30:35.436Z { "command": "cat /proc/meminfo | head -n 5 && df -h", "description": "Check memory and disk space" } ### Tool output · 2026-09-29T00:30:35.452Z Output: MemTotal: 15988972 kB MemFree: 1272528 kB MemAvailable: 14362104 kB Buffers: 249500 kB Cached: 12512984 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: 1638 ## Step 10 · agent · 2026-09-29T00:30:37.076Z ### run_shell_command · 2026-09-29T00:30:37.076Z { "description": "Check python version and path", "command": "python3 --version && which python3" } ### Tool output · 2026-09-29T00:30:37.093Z Output: Python 3.12.13 /opt/task-venv/bin/python3 Process Group PGID: 1655 ## Step 11 · agent · 2026-09-29T00:30:38.597Z ### run_shell_command · 2026-09-29T00:30:38.597Z { "description": "Check /app/solution contents", "command": "ls -la /app/solution" } ### Tool output · 2026-09-29T00:30:38.612Z Output: total 8 drwxr-xr-x 2 agent agent 4096 Sep 29 00:27 . drwxr-xr-x 3 agent agent 4096 Sep 29 00:27 .. Process Group PGID: 1659 ## Step 12 · agent · 2026-09-29T00:30:47.646Z ### Recorded reasoning summary Analyzing Prompt Requirements I'm focusing on understanding the core prompt, particularly the need to construct a two-tower neural network. My immediate next step involves a thorough examination of the provided data files, including their dimensions and content, to inform this architectural decision. ### Recorded reasoning summary Designing User & Item Towers I'm now focused on designing separate PyTorch user and item towers. The user tower will process interaction history to generate embeddings, ensuring scalability and avoiding global matrices. The item tower will also produce independent embeddings. ### Recorded reasoning summary Processing Data Features I'm now detailing the features within the training data, distinguishing between sequential event types like listens and explicit feedback such as likes/dislikes. I am also noting the candidate catalog and user targeting parameters. ### Recorded reasoning summary Defining Evaluation Metrics I am now clearly defining the discovery objective and evaluation metrics. The focus is on ranking candidates based on immediate organic behavior, grading relevance by likes and listen percentages. I will implement the DCG gain and discount calculations for NDCG@10. ### run_shell_command · 2026-09-29T00:30:47.646Z { "description": "Explore catalog, targets, pref states, and eligibility", "command": "python3 -c \"\nimport pandas as pd, numpy as np\ntargets = pd.read_parquet('/task/data/target_users.parquet')\nprint('Target users count:', len(targets), targets.columns.tolist())\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog count:', len(catalog), catalog.columns.tolist())\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref state count:', len(val_pref), val_pref.columns.tolist(), val_pref.head(3))\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref state count:', len(test_pref), test_pref.columns.tolist(), test_pref.head(3))\nval_elig = np.load('/task/data/validation_eligible_candidates.npz')\nprint('Val elig keys:', val_elig.files, 'uids:', len(val_elig['uids']), 'items:', len(val_elig['item_ids']), 'packed shape:', val_elig['packed_eligible'].shape)\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nprint('Test elig keys:', test_elig.files, 'uids:', len(test_elig['uids']), 'items:', len(test_elig['item_ids']), 'packed shape:', test_elig['packed_eligible'].shape)\n\"" } ### Tool output · 2026-09-29T00:30:49.473Z Output: Target users count: 8456 ['uid', 'recent_positive_listens', 'history_positive_listens'] Catalog count: 30000 ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] Val pref state count: 469124 ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked Test pref state count: 484776 ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked Val elig keys: ['uids', 'item_ids', 'packed_eligible'] uids: 8456 items: 30000 packed shape: (8456, 3750) Test elig keys: ['uids', 'item_ids', 'packed_eligible'] uids: 8456 items: 30000 packed shape: (8456, 3750) Process Group PGID: 1662 ## Step 13 · agent · 2026-09-29T00:30:52.153Z ### Recorded reasoning summary Analyzing Data Loads I'm currently examining the event data within the `train_*.parquet` and `val_*.parquet` files to determine row counts. This is to better understand the scale of our dataset beyond the initial catalog and user estimates, aiming for a comprehensive view of the incoming information. ### run_shell_command · 2026-09-29T00:30:52.153Z { "command": "python3 -c \"\nimport pandas as pd\nfor name in ['train_listens', 'train_likes', 'train_dislikes', 'train_unlikes', 'train_undislikes',\n 'val_listens', 'val_likes', 'val_dislikes', 'val_unlikes', 'val_undislikes']:\n df = pd.read_parquet(f'/task/data/{name}.parquet')\n print(f'{name}: shape={df.shape}, cols={list(df.columns)}')\n\"", "description": "Inspect row counts of all event tables" } ### Tool output · 2026-09-29T00:30:54.485Z Output: train_listens: shape=(43484159, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] train_likes: shape=(814831, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] train_dislikes: shape=(101075, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] train_unlikes: shape=(292665, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] train_undislikes: shape=(19960, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] val_listens: shape=(1501697, 6), cols=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct', 'track_length_seconds'] val_likes: shape=(37795, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] val_dislikes: shape=(3563, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] val_unlikes: shape=(9346, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] val_undislikes: shape=(606, 4), cols=['uid', 'timestamp', 'item_id', 'is_organic'] Process Group PGID: 1685 ## Step 14 · agent · 2026-09-29T00:30:57.161Z ### Recorded reasoning summary Assessing Data Scale I'm currently evaluating the sheer volume of our training data, `train_listens`, which is an immense 43 million records. My focus is on understanding its memory footprint and load time, and I'm beginning to investigate the distribution of distinct users within this dataset. ### run_shell_command · 2026-09-29T00:30:57.161Z { "description": "Check target users range and content", "command": "python3 -c \"\nimport pandas as pd\nt = pd.read_parquet('/task/data/target_users.parquet')\nprint('target users min max:', t.uid.min(), t.uid.max(), len(t))\nprint(t.head(5))\n\"" } ### Tool output · 2026-09-29T00:30:58.726Z Output: target users min max: 100 1000000 8456 uid recent_positive_listens history_positive_listens 0 100 844.0 2604 1 200 50.0 286 2 300 168.0 359 3 600 1985.0 2423 4 700 1776.0 6114 Process Group PGID: 1705 ## Step 15 · agent · 2026-09-29T00:31:01.160Z ### Recorded reasoning summary Investigating User Diversity I'm currently exploring the composition of the `train_listens` dataset. My focus is on determining if non-target users are present and quantifying their proportion relative to the target user subset. I'm aiming for an efficient method to assess this without necessarily loading the entire dataset into memory. ### run_shell_command · 2026-09-29T00:31:01.160Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nschema = pq.read_schema('/task/data/train_listens.parquet')\nprint('Schema:', schema)\ntable_meta = pq.read_metadata('/task/data/train_listens.parquet')\nprint('Num row groups:', table_meta.num_row_groups)\nprint('Num rows:', table_meta.num_rows)\n\"", "description": "Check parquet metadata for train_listens" } ### Tool output · 2026-09-29T00:31:01.627Z Output: 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 Num row groups: 42 Num rows: 43484159 Process Group PGID: 1721 ## Step 16 · agent · 2026-09-29T00:31:05.128Z ### Recorded reasoning summary Checking User Counts I'm currently investigating the cardinality of `train_listens` and `target_users`. My next step is to execute a test query using either pyarrow or duckdb/pandas to obtain these counts. I'll first verify which of these libraries is readily available. ### run_shell_command · 2026-09-29T00:31:05.128Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport time\n\nt0 = time.time()\ntargets = set(pq.read_table('/task/data/target_users.parquet', columns=['uid'])['uid'].to_pylist())\nprint(f'Loaded {len(targets)} targets in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Check unique users in a few row groups\nrg0 = pq.read_table('/task/data/train_listens.parquet', columns=['uid'], row_groups=[0])\nuids = set(rg0['uid'].to_pylist())\nprint(f'RG 0 num rows: {len(rg0)}, unique uids: {len(uids)}, overlap with targets: {len(uids & targets)}')\n\"", "description": "Check user distribution in train_listens row group 0" } ### Tool output · 2026-09-29T00:31:06.742Z Output: Traceback (most recent call last): File "", line 12, in TypeError: read_table() got an unexpected keyword argument 'row_groups' Loaded 8456 targets in 1.03s Exit Code: 1 Process Group PGID: 1732 ## Step 17 · agent · 2026-09-29T00:31:09.479Z ### run_shell_command · 2026-09-29T00:31:09.479Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport time\n\nt0 = time.time()\ntargets = set(pq.read_table('/task/data/target_users.parquet', columns=['uid'])['uid'].to_pylist())\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nprint('Num row groups:', pf.num_row_groups)\nrg0 = pf.read_row_group(0, columns=['uid'])\nuids = set(rg0['uid'].to_pylist())\nprint(f'RG 0 num rows: {len(rg0)}, unique uids: {len(uids)}, overlap with targets: {len(uids & targets)}')\n\"", "description": "Check user overlap in train_listens row group 0" } ### Tool output · 2026-09-29T00:31:11.436Z Output: Num row groups: 42 RG 0 num rows: 1048576, unique uids: 242, overlap with targets: 221 Process Group PGID: 1748 ## Step 18 · agent · 2026-09-29T00:31:14.515Z ### Recorded reasoning summary Investigating uid distribution I've noticed an incredibly low number of unique UIDs within row group 0, with a significant majority identified as targets. This strongly suggests the file is either sorted or grouped by UID. I'll now proceed to verify the sorting of `train_listens` to confirm this hypothesis. ### run_shell_command · 2026-09-29T00:31:14.515Z { "description": "Check if train_listens is sorted by uid", "command": "python3 -c \"\nimport pyarrow.parquet as pq\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\nfor i in range(min(5, pf.num_row_groups)):\n rg = pf.read_row_group(i, columns=['uid', 'timestamp'])\n u = rg['uid'].to_numpy()\n ts = rg['timestamp'].to_numpy()\n print(f'RG {i}: rows={len(u)}, uid_min={u.min()}, uid_max={u.max()}, sorted_uid={(u[:-1] <= u[1:]).all()}')\n\"" } ### Tool output · 2026-09-29T00:31:16.121Z Output: RG 0: rows=1048576, uid_min=100, uid_max=26500, sorted_uid=True RG 1: rows=1048576, uid_min=26500, uid_max=50300, sorted_uid=True RG 2: rows=1048576, uid_min=50300, uid_max=73100, sorted_uid=True RG 3: rows=1048576, uid_min=73100, uid_max=98200, sorted_uid=True RG 4: rows=1048576, uid_min=98200, uid_max=122600, sorted_uid=True Process Group PGID: 1770 ## Step 19 · agent · 2026-09-29T00:31:18.616Z ### run_shell_command · 2026-09-29T00:31:18.616Z { "description": "Count total unique uids in train_listens", "command": "python3 -c \"\nimport pyarrow.parquet as pq\n\npf = pq.ParquetFile('/task/data/train_listens.parquet')\ntotal_uids = set()\nfor i in range(pf.num_row_groups):\n rg = pf.read_row_group(i, columns=['uid'])\n total_uids.update(rg['uid'].to_pylist())\nprint(f'Total unique uids in train_listens: {len(total_uids)}')\ntargets = set(pq.read_table('/task/data/target_users.parquet', columns=['uid'])['uid'].to_pylist())\nprint(f'Targets in train_listens: {len(targets & total_uids)} / {len(targets)}')\n\"" } ### Tool output · 2026-09-29T00:31:36.564Z Output: Total unique uids in train_listens: 9238 Targets in train_listens: 8456 / 8456 Process Group PGID: 1783 ## Step 20 · agent · 2026-09-29T00:31:39.357Z ### Recorded reasoning summary Analyzing User and Target Data I'm observing a surprisingly small number of unique users and that all targets are contained within the training set. I'm now curious about the total number of items in the catalog and need to identify their specific IDs. ### run_shell_command · 2026-09-29T00:31:39.357Z { "description": "Inspect candidate catalog details", "command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Catalog item_id min/max/count:', catalog.item_id.min(), catalog.item_id.max(), len(catalog))\nprint('Popularity rank min/max:', catalog.popularity_rank.min(), catalog.popularity_rank.max())\nprint(catalog.head(10))\n\"" } ### Tool output · 2026-09-29T00:31:40.945Z Output: Catalog item_id min/max/count: 206 9390294 30000 Popularity rank min/max: 1 30000 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 Process Group PGID: 1799 ## Step 21 · agent · 2026-09-29T00:31:44.803Z ### run_shell_command · 2026-09-29T00:31:44.803Z { "command": "python3 -c \"\nimport numpy as np\n\nfor name in ['validation_eligible_candidates.npz', 'test_eligible_candidates.npz']:\n data = np.load(f'/task/data/{name}')\n packed = data['packed_eligible']\n item_ids = data['item_ids']\n uids = data['uids']\n # compute number of 1 bits per row\n counts = np.unpackbits(packed, axis=1, count=len(item_ids), bitorder='little').sum(axis=1)\n print(f'{name}: min_eligible={counts.min()}, max_eligible={counts.max()}, mean_eligible={counts.mean():.1f}, total_users={len(uids)}')\n\"", "description": "Check eligible count per user" } ### Tool output · 2026-09-29T00:31:45.590Z Output: validation_eligible_candidates.npz: min_eligible=24657, max_eligible=30000, mean_eligible=29067.3, total_users=8456 test_eligible_candidates.npz: min_eligible=24552, max_eligible=30000, mean_eligible=29041.3, total_users=8456 Process Group PGID: 1816 ## Step 22 · agent · 2026-09-29T00:31:48.632Z ### Recorded reasoning summary Investigating Ineligibility I'm currently examining the factors contributing to item ineligibility for users. The initial data suggests a consistent pattern, with a small but significant portion of items being excluded. My next step is to delve into the `validation_` logs to pinpoint the exact reasons behind this exclusion. ### run_shell_command · 2026-09-29T00:31:48.632Z { "command": "python3 -c \"\nimport pandas as pd\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Preference state shape:', pref.shape)\nprint('States:', pref.state.value_counts())\nprint(pref.head(10))\n\"", "description": "Inspect validation_preference_state" } ### Tool output · 2026-09-29T00:31:50.256Z Output: Preference state shape: (469124, 3) States: state liked 418435 disliked 50689 Name: count, dtype: int64 uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked 5 100 3535902 liked 6 100 3633039 liked 7 100 4099299 liked 8 100 4221787 liked 9 100 4927727 disliked Process Group PGID: 1826 ## Step 23 · agent · 2026-09-29T00:31:56.466Z ### Recorded reasoning summary Testing Popularity Baselines I'm currently focused on establishing a baseline using a simple popularity metric. My next step is to code a small script to generate this baseline and then evaluate its validation performance. ### run_shell_command · 2026-09-29T00:31:56.466Z { "description": "Check catalog sorting and item id alignment", "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nelig = np.load('/task/data/validation_eligible_candidates.npz')\n\n# Catalog items are already sorted by popularity rank (1 to 30000)\n# Let's check catalog order\nprint('Catalog sorted by popularity_rank?', (catalog.popularity_rank.to_numpy() == np.arange(1, len(catalog)+1)).all())\n\n# Check alignment of elig['item_ids'] with catalog['item_id']\ncatalog_items = catalog['item_id'].to_numpy()\nelig_items = elig['item_ids']\nprint('Catalog item_ids match elig item_ids?', np.array_equal(catalog_items, elig_items))\n\"" } ### Tool output · 2026-09-29T00:31:58.043Z Output: Catalog sorted by popularity_rank? True Catalog item_ids match elig item_ids? True Process Group PGID: 1844 ## Step 24 · agent · 2026-09-29T00:32:04.671Z ### Recorded reasoning summary Testing Popularity Rankings I'm currently exploring the impact of aligning popularity rankings between my `catalog` and `eligibility` datasets. I've confirmed their identical ordering and sorting by rank. Next, I'll generate a popularity ranking for validation purposes and proceed to run `evaluate_public.py` to observe the results. ### run_shell_command · 2026-09-29T00:32:04.671Z { "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet('/task/data/target_users.parquet')\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\n\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n top_items = item_ids[mask == 1][:100]\n for rank, item in enumerate(top_items, 1):\n rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/validation_pop.parquet')\nprint('Saved validation_pop.parquet, shape:', df.shape)\n\" && python3 /task/evaluate_public.py /app/solution/validation_pop.parquet", "description": "Evaluate popularity baseline on validation set" } ### Tool output · 2026-09-29T00:32:11.037Z Output: Saved validation_pop.parquet, shape: (845600, 3) { "model": "/app/solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } Process Group PGID: 1861 ## Step 25 · agent · 2026-09-29T00:32:13.794Z ### Recorded reasoning summary Establishing Quality Baseline My current focus is on validating the quality baseline. I've successfully established the `quality_ndcg@10` metric at 0.01071 for 4,263 users. The system is running and validating as expected. Next, I'll confirm the alignment of item IDs within the test eligibility set. ### run_shell_command · 2026-09-29T00:32:13.794Z { "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint('Test elig item_ids match catalog?', np.array_equal(test_elig['item_ids'], catalog['item_id'].to_numpy()))\nprint('Test elig uids match target uids?', np.array_equal(test_elig['uids'], pd.read_parquet('/task/data/target_users.parquet')['uid'].to_numpy()))\n\"", "description": "Check test eligibility array alignment" } ### Tool output · 2026-09-29T00:32:15.369Z Output: Test elig item_ids match catalog? True Test elig uids match target uids? True Process Group PGID: 1895 ## Step 26 · agent · 2026-09-29T00:32:20.058Z ### Recorded reasoning summary Confirming Data Integrity My recent analysis confirms that `test_elig['item_ids']` perfectly aligns with both `catalog['item_id']` and `validation_eligible_candidates.npz['item_ids']`. Furthermore, `test_elig['uids']` has been validated against `target_users.parquet`. With these checks complete, I am proceeding to generate a baseline submission. ### run_shell_command · 2026-09-29T00:32:20.058Z { "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\n\ntest_elig = np.load('/task/data/test_eligible_candidates.npz')\nuids = test_elig['uids']\nitem_ids = test_elig['item_ids']\npacked = test_elig['packed_eligible']\n\nrows = []\nfor r in range(len(uids)):\n uid = uids[r]\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\n top_items = item_ids[mask == 1][:100]\n for rank, item in enumerate(top_items, 1):\n rows.append((uid, item, rank))\n\ndf = pd.DataFrame(rows, columns=['uid', 'item_id', 'rank'])\ndf.to_parquet('/app/solution/submission.parquet')\nprint('Initial valid baseline submission saved:', df.shape)\n\"", "description": "Save initial valid baseline submission" } ### Tool output · 2026-09-29T00:32:23.023Z Output: Initial valid baseline submission saved: (845600, 3) Process Group PGID: 1912 ## Step 27 · agent · 2026-09-29T00:32:27.703Z ### run_shell_command · 2026-09-29T00:32:27.703Z { "command": "python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet, read_ranking, RankingValidationError\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nsub = read_ranking('/app/solution/submission.parquet')\nframe = validate_ranking(sub, targets)\nprint('Structural validation passed! Checking eligibility...')\nfor uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise RankingValidationError(f'Ineligible item {item} for user {uid}')\nprint('All 845,600 user-item pairs are eligible!')\n\"", "description": "Validate initial baseline submission" } ### Tool output · 2026-09-29T00:32:29.236Z Output: Traceback (most recent call last): File "", line 3, in ModuleNotFoundError: No module named 'ranking_evaluation' Exit Code: 1 Process Group PGID: 1923 ## Step 28 · agent · 2026-09-29T00:32:33.668Z ### run_shell_command · 2026-09-29T00:32:33.668Z { "description": "Validate initial submission with PYTHONPATH=/task", "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet, read_ranking, RankingValidationError\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet('/task/data/test_eligible_candidates.npz')\nsub = read_ranking('/app/solution/submission.parquet')\nframe = validate_ranking(sub, targets)\nprint('Structural validation passed! Checking eligibility...')\nfor uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise RankingValidationError(f'Ineligible item {item} for user {uid}')\nprint('All 845,600 user-item pairs are eligible!')\n\"" } ### Tool output · 2026-09-29T00:32:35.372Z Output: Traceback (most recent call last): File "", line 7, in File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^ AttributeError: 'str' object has no attribute 'suffix' Exit Code: 1 Process Group PGID: 1934 ## Step 29 · agent · 2026-09-29T00:32:49.348Z ### run_shell_command · 2026-09-29T00:32:49.348Z { "command": "PYTHONPATH=/task python3 -c \"\nfrom pathlib import Path\nimport pandas as pd\nfrom ranking_evaluation import validate_ranking, CandidateSet, read_ranking, RankingValidationError\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet(Path('/task/data/test_eligible_candidates.npz'))\nsub = read_ranking(Path('/app/solution/submission.parquet'))\nframe = validate_ranking(sub, targets)\nprint('Structural validation passed! Checking eligibility...')\nfor uid, item in frame[['uid', 'item_id']].itertuples(index=False, name=None):\n if not elig.is_eligible(uid, item):\n raise RankingValidationError(f'Ineligible item {item} for user {uid}')\nprint('All 845,600 user-item pairs are eligible!')\n\"", "description": "Validate initial submission with Path object" } ### Tool output · 2026-09-29T00:32:52.021Z Output: Structural validation passed! Checking eligibility... All 845,600 user-item pairs are eligible! Process Group PGID: 1950 ## Step 30 · agent · 2026-09-29T00:32:55.053Z ### Recorded reasoning summary Creating README file I've confirmed the submission file meets all validator requirements. My current focus is on generating a minimal `README.md` within `/app/solution/`, ensuring it adheres to the specified content and structure from the outset. ### write_file · 2026-09-29T00:32:55.053Z { "file_path": "/app/solution/README.md", "content": "# Two-Tower Neural Ranker\n\n## Architecture\nTwo-tower PyTorch neural network ranker with user tower and item tower.\nRelevance score = dot product between user vector and item vector.\n\n## Training Objective\nIn-progress baseline.\n\n## Public Validation Score\nBaseline popularity: 0.010710\n\n## Final Submission Command\n`python /app/solution/train_and_predict.py`\n" } ### Tool output · 2026-09-29T00:32:55.064Z Successfully created and wrote to new file: /app/solution/README.md. Here is the updated code: # Two-Tower Neural Ranker ## Architecture Two-tower PyTorch neural network ranker with user tower and item tower. Relevance score = dot product between user vector and item vector. ## Training Objective In-progress baseline. ## Public Validation Score Baseline popularity: 0.010710 ## Final Submission Command `python /app/solution/train_and_predict.py` ## Step 31 · agent · 2026-09-29T00:33:01.261Z I have initialized the fallback baseline submission (`submission.parquet`) and a preliminary `README.md`. Now let's proceed with research and system design for the two-tower model. Let's enter Plan Mode to carefully analyze: 1. Requirements & Constraints 2. Data characteristics & processing efficiency 3. Architecture of User Tower and Item Tower 4. Loss function and negative sampling strategy 5. Candidate filtering & inner product search 6. Training on train, tuning on val, and refitting on train+val for final submission. Let's call `enter_plan_mode`. ### enter_plan_mode · 2026-09-29T00:33:01.261Z { "reason": "Formulate comprehensive architectural design and implementation plan for the two-tower PyTorch neural ranker." } ### Tool output · 2026-09-29T00:33:02.847Z Switching to Plan mode. ## Step 32 · agent · 2026-09-29T00:33:05.083Z ### update_topic · 2026-09-29T00:33:05.083Z { "strategic_intent": "Deep dive into data statistics, feature extraction, two-tower neural architecture, and training pipeline." } ### Tool output · 2026-09-29T00:33:05.087Z Strategic Intent: Deep dive into data statistics, feature extraction, two-tower neural architecture, and training pipeline. ## Step 33 · agent · 2026-09-29T00:33:16.366Z ### Recorded reasoning summary Analyzing System Constraints I'm currently dissecting the core requirements, specifically focusing on building a two-tower neural network within a CPU PyTorch environment for a music streaming service's 'Made for You' shelf, while also considering the implications of the provided benchmark representing a production system. ### Recorded reasoning summary Refining Two-Tower Architecture I'm zeroing in on how to scale training for millions of users and items without a global matrix, ensuring request-time latency by precomputing item embeddings and only computing user embeddings on the fly. The model needs independent user and item towers, each outputting vectors of the same dimensionality for dot product scoring. The user tower will rely on interaction history, not just IDs. ### Recorded reasoning summary Defining Data Splits I'm now solidifying the data splits. Training will use data up to `24786800`, followed by a 30-minute gap and a 7-day validation window. The hidden test set will follow a similar pattern, utilizing observed validation events as history. I'm also noting the candidate catalog size and target user count. ### Recorded reasoning summary Evaluating Item Features I'm now examining the available item features within the candidate catalog, specifically `positive_listens`, `organic_positive_listens`, and `popularity_rank`. I'm also investigating if track lengths are present in the listens data, as this could inform how item embeddings are learned alongside track IDs and other item attributes. ### Recorded reasoning summary Implementing Item Embeddings I'm confirming that the item tower can indeed leverage `nn.Embedding` for the 30,000 catalog items. Precomputing these item vectors offline is highly efficient, allowing for rapid retrieval during inference. I'm now shifting focus to how the user tower will derive its representation from interaction history. ### run_shell_command · 2026-09-29T00:33:16.366Z { "description": "Analyze preference state and likes/dislikes distributions", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref stats per user:')\npref_user_counts = pref.groupby(['uid', 'state']).size().unstack(fill_value=0)\nprint(pref_user_counts.describe())\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\ndislikes = pd.read_parquet('/task/data/train_dislikes.parquet')\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nt_likes = likes[likes.uid.isin(targets)]\nt_dislikes = dislikes[dislikes.uid.isin(targets)]\nprint(f'Train likes for targets: {len(t_likes)}, unique users: {t_likes.uid.nunique()}')\nprint(f'Train dislikes for targets: {len(t_dislikes)}, unique users: {t_dislikes.uid.nunique()}')\n\"" } ### Tool error · 2026-09-29T00:33:16.368Z Tool "run_shell_command" not found. Did you mean one of: "update_topic", "grep_search", "replace"? ## Step 34 · agent · 2026-09-29T00:33:22.565Z ### Recorded reasoning summary Exploring Plan Mode I've identified that `run_shell_command` is currently absent from the available tools within Plan Mode. My current focus is on understanding the full scope of what is accessible, noting `replace`, `exit_plan_mode`, `glob`, `invoke_agent`, `read_file`, and `list_directory` as present options. ### Recorded reasoning summary Discovering Missing Tools My exploration of Plan Mode continues. I've noted the addition of `grep_search`, `update_topic`, and `write_file` to the available tools. This is particularly important as I've also realized that interactive `ask_user` functionality, which is crucial for the "Consult" phase outlined in the rules, is explicitly unavailable in this non-interactive environment. ### Recorded reasoning summary Identifying Plan Mode Capabilities I've confirmed the following tools are accessible within Plan Mode: `replace`, `exit_plan_mode`, `glob`, `invoke_agent`, `read_file`, `list_directory`, `grep_search`, `update_topic`, and `write_file`. Notably, `run_shell_command` is absent, and interactive `ask_user` functionality is unavailable, necessitating independent judgment. ### list_directory · 2026-09-29T00:33:22.565Z { "dir_path": "/home/agent/.gemini/tmp/app/c8321455-c473-4e52-a372-e5d48d82e44e/plans" } ### Tool output · 2026-09-29T00:33:22.570Z Directory /home/agent/.gemini/tmp/app/c8321455-c473-4e52-a372-e5d48d82e44e/plans is empty. ## Step 35 · agent · 2026-09-29T00:33:59.051Z ### Recorded reasoning summary Analyzing Target Users I'm currently examining the `target_users.parquet` dataset, which contains 8,456 users, each with a minimum of 20 positive listens in the 90 days prior to the training cutoff. This data will be crucial for understanding the user base. ### Recorded reasoning summary Processing Training Data I'm now integrating the various training interaction datasets. Specifically, I'm analyzing the `train_listens.parquet` with over 43 million entries, and combining it with the likes, dislikes, unlikes, and undislikes data to build a comprehensive user interaction profile. ### Recorded reasoning summary Refining Evaluation Metrics My focus has shifted to refining evaluation. I'm noting that listens with >= 80% completion will be graded 1, while organic likes receive a grade 3. Likes will supersede listens, and NDCG@10 is now being calculated specifically for target users with at least one relevant candidate in the window. ### Recorded reasoning summary Developing Model Architecture I'm now focusing on architectural design. This involves mapping item IDs and building the item tower as a PyTorch neural network. The user tower will also be a separate trainable network, deriving its representation from interaction history rather than a simple user ID lookup, with scores computed via dot product. ### Recorded reasoning summary Designing Item Tower I'm mapping item IDs and building the item tower. The plan is to normalize item embeddings, potentially using L2 normalization or a learnable temperature scaling, as this approach works well for two-tower retrieval models and aligns with the dot product scoring mechanism. ### Recorded reasoning summary Incorporating User History I'm now exploring how to represent user history for the user tower, focusing on explicit preferences like likes and dislikes, along with detailed listen history including played ratio, organic status, and recency. The goal is to process this rich data into a single, effective user embedding. ### Recorded reasoning summary Synthesizing User Representations I'm now combining various interaction signals to form the user embedding. This involves a weighted average of item embeddings from recent positive interactions and a representation of negative preferences, followed by a multi-layer perceptron to integrate these features with user statistics. ### Recorded reasoning summary Optimizing User Vector Computation I'm finalizing the user vector generation process. The chosen architecture, utilizing ReLU, Linear, and LayerNorm followed by L2 normalization, efficiently derives user representations solely from interaction history, ensuring effective handling of cold-start scenarios and rapid evaluation. ### Recorded reasoning summary Generating Training Pairs I'm now defining the core training examples. For each user, I'm temporally splitting their interaction history to create user-history/positive-item pairs, using recent interactions as targets to mimic future predictions. I'm also considering including hard or explicit negatives to enrich the training process. ### Recorded reasoning summary Organizing Training Data I'm now focused on organizing the interaction data for training and validation. This involves checking timestamp ranges within `train_listens` and `train_likes` to ensure proper temporal splitting of user histories, accounting for interactions spanning multiple months. ### write_file · 2026-09-29T00:33:59.051Z { "content": "# Plan: Latency-Efficient PyTorch Two-Tower Neural Ranker\n\n## Objective\nBuild, tune, and deploy a latency-efficient two-tower PyTorch neural ranker for music track retrieval and ranking.\nThe ranker must:\n1. Decompose strictly into independent user and item towers producing embeddings of identical dimensionality $d$.\n2. Compute relevance scores via the dot product: $s(u, i) = \\mathbf{u}^\\top \\mathbf{v}_i$.\n3. Compute item embeddings $\\mathbf{v}_i$ offline and compute the user embedding $\\mathbf{u}$ from interaction history (not a user-ID lookup).\n4. Rank cutoff-eligible candidates for each target user to maximize graded NDCG@10 on the discovery objective (grade 3 for organic like, grade 1 for organic listen $\\ge 80\\%$).\n5. Validate on the public validation window using `train_*` as history and tuning hyperparameters to maximize public NDCG@10.\n6. Refit the tuned model on `train_* + val_*` with updated `test_preference_state.parquet` to produce the final `/app/solution/submission.parquet`.\n7. Deliver clean code and `/app/solution/README.md`.\n\n---\n\n## Key Files & Context\n- `/task/data/candidate_catalog.parquet`: 30,000 retrieval-catalog items with popularity ranks.\n- `/task/data/target_users.parquet`: 8,456 target users.\n- `/task/data/validation_eligible_candidates.npz`: packed eligibility bitmasks for validation.\n- `/task/data/test_eligible_candidates.npz`: packed eligibility bitmasks for test.\n- `/task/data/validation_preference_state.parquet` / `test_preference_state.parquet`: active liked/disliked items.\n- `/task/data/train_*.parquet` and `val_*.parquet`: listens, likes, dislikes, unlikes, undislikes.\n- `/task/evaluate_public.py` & `/task/ranking_evaluation.py`: ground truth metric calculation (graded NDCG@10).\n- Final deliverable: `/app/solution/submission.parquet` (columns: `uid`, `item_id`, `rank` 1..100) and `/app/solution/README.md`.\n\n---\n\n## Technical Architecture & Modeling Design\n\n### 1. Item Tower\n- **Input:** Catalog item index $i \\in \\{0, \\dots, 29999\\}$ and item features (log popularity, organic ratio).\n- **Network:**\n - Item embedding layer: `nn.Embedding(num_items, embed_dim)`.\n - Item feature projection: `nn.Linear(num_item_features, feature_dim)`.\n - Fusion MLP: `Linear -> LayerNorm -> GELU -> Linear -> LayerNorm`.\n - L2-normalization (or learned scale) so that inner product represents normalized cosine similarity with temperature.\n- **Offline Precomputation:**\n - Precompute all 30,000 item vectors in a single tensor $\\mathbf{V} \\in \\mathbb{R}^{30000 \\times d}$.\n\n### 2. User Tower\n- **Input:**\n - Derived entirely from user interaction history (no `nn.Embedding(num_users, ...)`):\n 1. **Liked items:** List of item IDs from active preference state and organic likes.\n 2. **Disliked items:** List of item IDs from active preference state and dislikes.\n 3. **Recent listens:** Sequence / bag of recently listened items, with completion weights ($w = \\text{played\\_ratio\\_pct} / 100 \\times (1 + \\text{is\\_organic})$) and exponential recency time decay $\\exp(-\\lambda \\Delta t)$.\n 4. **Aggregate user features:** Number of listens, liked count, disliked count, mean played ratio, organic listen proportion.\n- **Network:**\n - Shared or projected item embedding table to embed history items.\n - Attentive / weighted average pooling:\n - $\\mathbf{h}_{\\text{liked}} = \\text{WeightedMean}(\\mathbf{E}[\\text{liked}])$\n - $\\mathbf{h}_{\\text{listens}} = \\sum_k w_k \\mathbf{E}[\\text{listen}_k] / \\sum_k w_k$\n - $\\mathbf{h}_{\\text{disliked}} = \\text{Mean}(\\mathbf{E}[\\text{disliked}])$\n - User projection MLP:\n - Input: $[\\mathbf{h}_{\\text{liked}}, \\mathbf{h}_{\\text{listens}}, \\mathbf{h}_{\\text{disliked}}, \\mathbf{x}_{\\text{user\\_stats}}]$\n - MLP: `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> L2-normalize`.\n - Output: User vector $\\mathbf{u} \\in \\mathbb{R}^d$.\n\n### 3. Training Strategy & Loss Function\n- **Sample Construction:**\n - Temporal training split:\n - Split training history before timestamp $T_{\\text{train\\_split}} = 24786800 - 604800$ (7 days before train cutoff).\n - Features are computed from events before $T_{\\text{train\\_split}}$.\n - Positive labels are organic likes (grade 3) and organic listens $\\ge 80\\%$ (grade 1) occurring between $T_{\\text{train\\_split}}$ and $24786800$.\n - Can also use multiple sliding window slices or positive pairs weighted by discovery grade.\n- **Loss:**\n - Graded sampled softmax / InfoNCE loss with temperature $\\tau$:\n $$\\mathcal{L} = -\\sum_{i \\in \\mathcal{P}_u} w_i \\log \\frac{\\exp(\\mathbf{u}^\\top \\mathbf{v}_i / \\tau)}{\\sum_{j \\in \\mathcal{N}_u \\cup \\{i\\}} \\exp(\\mathbf{u}^\\top \\mathbf{v}_j / \\tau)}$$\n where $w_i = 3.0$ for likes and $1.0$ for strong listens.\n - In-batch negatives + sampled catalog negatives (popularity-aware) + user's explicit dislikes as hard negatives (with negative margin or penalty).\n - Temperature $\\tau \\approx 0.07 - 0.1$.\n - Optimizer: AdamW with cosine learning rate schedule, weight decay for regularization.\n\n---\n\n## Implementation Steps\n\n### Step 1: Fast Feature Extraction & Dataset Builder\n- Create `data_prep.py`:\n - Efficiently parse target users, catalog, preference states.\n - Extract user interaction histories (listens, likes, dislikes) before a specified cutoff timestamp.\n - Weight interactions by recency and completion ratio.\n - Build train dataset (pre-cutoff history $\\rightarrow$ target window organic actions).\n\n### Step 2: Two-Tower PyTorch Model Definition\n- Create `model.py`:\n - `ItemTower(num_items, embed_dim, out_dim)`\n - `UserTower(num_items, embed_dim, out_dim, ...)`\n - `TwoTowerModel` wrapping both towers, ensuring strict decomposition.\n - Forward methods for training (batch loss) and inference (compute user vectors, precompute item vectors).\n\n### Step 3: Retrieval, Eligibility Filtering & Evaluation Script\n- Create `retrieval.py`:\n - Input: User embeddings $\\mathbf{U} \\in \\mathbb{R}^{N \\times d}$, precomputed item embeddings $\\mathbf{V} \\in \\mathbb{R}^{M \\times d}$, candidate eligibility bitmasks.\n - Fast matrix multiplication $\\mathbf{S} = \\mathbf{U} \\mathbf{V}^\\top \\in \\mathbb{R}^{N \\times M}$.\n - Mask out ineligible items ($S_{u, i} = -\\infty$ for ineligible bits).\n - Efficiently extract top 100 items per user using `torch.topk` or `np.argpartition`.\n - Format into pandas DataFrame `(uid, item_id, rank)`.\n - Score using `/task/evaluate_public.py`.\n\n### Step 4: Model Training & Validation Tuning\n- Train model on `train_*` with validation on `val_*` outcomes.\n- Track NDCG@10 progression against baseline (0.010710).\n- Iterate and tune:\n - Dimensionality $d$ (e.g. 64, 128), temperature $\\tau$.\n - Feature weights (organic vs explicit likes, recency decay factor $\\lambda$).\n - Negative sampling strategy (in-batch + catalog negatives).\n - Training epochs and learning rates.\n\n### Step 5: Full Refit on Train + Val for Hidden Test\n- Once optimal architecture and hyperparameters are verified on public validation:\n - Re-extract user histories combining `train_* + val_*` up to the validation cutoff timestamp.\n - Use `test_preference_state.parquet`.\n - Train model on the full historical dataset.\n - Compute test user embeddings and item embeddings.\n - Retrieve top 100 items per user masked by `test_eligible_candidates.npz`.\n - Save `/app/solution/submission.parquet`.\n - Verify formatting, candidate eligibility, and lack of duplicate/invalid entries.\n\n### Step 6: Documentation & Artifact Preservation\n- Ensure complete, standalone pipeline scripts in `/app/solution/`:\n - `model.py`, `dataset.py`, `train_and_predict.py`.\n- Update `/app/solution/README.md` with:\n - Architecture description\n - Training objective and loss formulation\n - Public validation NDCG@10 score\n - Final submission command\n\n---\n\n## Verification & Acceptance\n- Structural checks:\n - Exact target users present (8,456).\n - Exactly 100 rows per user, ranks 1..100 without duplicates.\n - 100% of ranked items are present in `test_eligible_candidates.npz`.\n - Code runs end-to-end on CPU PyTorch within resource limits.\n- Metric check:\n - Validation NDCG@10 improves significantly over the popularity baseline (0.010710).\n", "file_path": "/home/agent/.gemini/tmp/app/c8321455-c473-4e52-a372-e5d48d82e44e/plans/two_tower_ranker_plan.md" } ### Tool output · 2026-09-29T00:33:59.060Z Successfully created and wrote to new file: /home/agent/.gemini/tmp/app/c8321455-c473-4e52-a372-e5d48d82e44e/plans/two_tower_ranker_plan.md. Here is the updated code: # Plan: Latency-Efficient PyTorch Two-Tower Neural Ranker ## Objective Build, tune, and deploy a latency-efficient two-tower PyTorch neural ranker for music track retrieval and ranking. The ranker must: 1. Decompose strictly into independent user and item towers producing embeddings of identical dimensionality $d$. 2. Compute relevance scores via the dot product: $s(u, i) = \mathbf{u}^\top \mathbf{v}_i$. 3. Compute item embeddings $\mathbf{v}_i$ offline and compute the user embedding $\mathbf{u}$ from interaction history (not a user-ID lookup). 4. Rank cutoff-eligible candidates for each target user to maximize graded NDCG@10 on the discovery objective (grade 3 for organic like, grade 1 for organic listen $\ge 80\%$). 5. Validate on the public validation window using `train_*` as history and tuning hyperparameters to maximize public NDCG@10. 6. Refit the tuned model on `train_* + val_*` with updated `test_preference_state.parquet` to produce the final `/app/solution/submission.parquet`. 7. Deliver clean code and `/app/solution/README.md`. --- ## Key Files & Context - `/task/data/candidate_catalog.parquet`: 30,000 retrieval-catalog items with popularity ranks. - `/task/data/target_users.parquet`: 8,456 target users. - `/task/data/validation_eligible_candidates.npz`: packed eligibility bitmasks for validation. - `/task/data/test_eligible_candidates.npz`: packed eligibility bitmasks for test. - `/task/data/validation_preference_state.parquet` / `test_preference_state.parquet`: active liked/disliked items. - `/task/data/train_*.parquet` and `val_*.parquet`: listens, likes, dislikes, unlikes, undislikes. - `/task/evaluate_public.py` & `/task/ranking_evaluation.py`: ground truth metric calculation (graded NDCG@10). - Final deliverable: `/app/solution/submission.parquet` (columns: `uid`, `item_id`, `rank` 1..100) and `/app/solution/README.md`. --- ## Technical Architecture & Modeling Design ### 1. Item Tower - **Input:** Catalog item index $i \in \{0, \dots, 29999\}$ and item features (log popularity, organic ratio). - **Network:** - Item embedding layer: `nn.Embedding(num_items, embed_dim)`. - Item feature projection: `nn.Linear(num_item_features, feature_dim)`. - Fusion MLP: `Linear -> LayerNorm -> GELU -> Linear -> LayerNorm`. - L2-normalization (or learned scale) so that inner product represents normalized cosine similarity with temperature. - **Offline Precomputation:** - Precompute all 30,000 item vectors in a single tensor $\mathbf{V} \in \mathbb{R}^{30000 \times d}$. ### 2. User Tower - **Input:** - Derived entirely from user interaction history (no `nn.Embedding(num_users, ...)`): 1. **Liked items:** List of item IDs from active preference state and organic likes. 2. **Disliked items:** List of item IDs from active preference state and dislikes. 3. **Recent listens:** Sequence / bag of recently listened items, with completion weights ($w = \text{played\_ratio\_pct} / 100 \times (1 + \text{is\_organic})$) and exponential recency time decay $\exp(-\lambda \Delta t)$. 4. **Aggregate user features:** Number of listens, liked count, disliked count, mean played ratio, organic listen proportion. - **Network:** - Shared or projected item embedding table to embed history items. - Attentive / weighted average pooling: - $\mathbf{h}_{\text{liked}} = \text{WeightedMean}(\mathbf{E}[\text{liked}])$ - $\mathbf{h}_{\text{listens}} = \sum_k w_k \mathbf{E}[\text{listen}_k] / \sum_k w_k$ - $\mathbf{h}_{\text{disliked}} = \text{Mean}(\mathbf{E}[\text{disliked}])$ - User projection MLP: - Input: $[\mathbf{h}_{\text{liked}}, \mathbf{h}_{\text{listens}}, \mathbf{h}_{\text{disliked}}, \mathbf{x}_{\text{user\_stats}}]$ - MLP: `Linear -> LayerNorm -> GELU -> Dropout -> Linear -> LayerNorm -> L2-normalize`. - Output: User vector $\mathbf{u} \in \mathbb{R}^d$. ### 3. Training Strategy & Loss Function - **Sample Construction:** - Temporal training split: - Split training history before timestamp $T_{\text{train\_split}} = 24786800 - 604800$ (7 days before train cutoff). - Features are computed from events before $T_{\text{train\_split}}$. - Positive labels are organic likes (grade 3) and organic listens $\ge 80\%$ (grade 1) occurring between $T_{\text{train\_split}}$ and $24786800$. - Can also use multiple sliding window slices or positive pairs weighted by discovery grade. - **Loss:** - Graded sampled softmax / InfoNCE loss with temperature $\tau$: $$\mathcal{L} = -\sum_{i \in \mathcal{P}_u} w_i \log \frac{\exp(\mathbf{u}^\top \mathbf{v}_i / \tau)}{\sum_{j \in \mathcal{N}_u \cup \{i\}} \exp(\mathbf{u}^\top \mathbf{v}_j / \tau)}$$ where $w_i = 3.0$ for likes and $1.0$ for strong listens. - In-batch negatives + sampled catalog negatives (popularity-aware) + user's explicit dislikes as hard negatives (with negative margin or penalty). - Temperature $\tau \approx 0.07 - 0.1$. - Optimizer: AdamW with cosine learning rate schedule, weight decay for regularization. --- ## Implementation Steps ### Step 1: Fast Feature Extraction & Dataset Builder - Create `data_prep.py`: - Efficiently parse target users, catalog, preference states. - Extract user interaction histories (listens, likes, dislikes) before a specified cutoff timestamp. - Weight interactions by recency and completion ratio. - Build train dataset (pre-cutoff history $\rightarrow$ target window organic actions). ### Step 2: Two-Tower PyTorch Model Definition - Create `model.py`: - `ItemTower(num_items, embed_dim, out_dim)` - `UserTower(num_items, embed_dim, out_dim, ...)` - `TwoTowerModel` wrapping both towers, ensuring strict decomposition. - Forward methods for training (batch loss) and inference (compute user vectors, precompute item vectors). ### Step 3: Retrieval, Eligibility Filtering & Evaluation Script - Create `retrieval.py`: - Input: User embeddings $\mathbf{U} \in \mathbb{R}^{N \times d}$, precomputed item embeddings $\mathbf{V} \in \mathbb{R}^{M \times d}$, candidate eligibility bitmasks. - Fast matrix multiplication $\mathbf{S} = \mathbf{U} \mathbf{V}^\top \in \mathbb{R}^{N \times M}$. - Mask out ineligible items ($S_{u, i} = -\infty$ for ineligible bits). - Efficiently extract top 100 items per user using `torch.topk` or `np.argpartition`. - Format into pandas DataFrame `(uid, item_id, rank)`. - Score using `/task/evaluate_public.py`. ### Step 4: Model Training & Validation Tuning - Train model on `train_*` with validation on `val_*` outcomes. - Track NDCG@10 progression against baseline (0.010710). - Iterate and tune: - Dimensionality $d$ (e.g. 64, 128), temperature $\tau$. - Feature weights (organic vs explicit likes, recency decay factor $\lambda$). - Negative sampling strategy (in-batch + catalog negatives). - Training epochs and learning rates. ### Step 5: Full Refit on Train + Val for Hidden Test - Once optimal architecture and hyperparameters are verified on public validation: - Re-extract user histories combining `train_* + val_*` up to the validation cutoff timestamp. - Use `test_preference_state.parquet`. - Train model on the full historical dataset. - Compute test user embeddings and item embeddings. - Retrieve top 100 items per user masked by `test_eligible_candidates.npz`. - Save `/app/solution/submission.parquet`. - Verify formatting, candidate eligibility, and lack of duplicate/invalid entries. ### Step 6: Documentation & Artifact Preservation - Ensure complete, standalone pipeline scripts in `/app/solution/`: - `model.py`, `dataset.py`, `train_and_predict.py`. - Update `/app/solution/README.md` with: - Architecture description - Training objective and loss formulation - Public validation NDCG@10 score - Final submission command --- ## Verification & Acceptance - Structural checks: - Exact target users present (8,456). - Exactly 100 rows per user, ranks 1..100 without duplicates. - 100% of ranked items are present in `test_eligible_candidates.npz`. - Code runs end-to-end on CPU PyTorch within resource limits. - Metric check: - Validation NDCG@10 improves significantly over the popularity baseline (0.010710). ## Step 36 · agent · 2026-09-29T00:34:01.771Z ### Recorded reasoning summary Analyzing Exit Conditions I am currently focused on understanding the precise conditions that trigger the exit from plan mode. My current analysis is centered on the specific rule that mandates reaching an informal user agreement *before* utilizing the `exit_plan_mode` tool for formal approval, and crucially, avoiding its invocation via `run_shell_command`. ### exit_plan_mode · 2026-09-29T00:34:01.771Z { "plan_filename": "two_tower_ranker_plan.md" } ### Tool output · 2026-09-29T00:34:01.778Z Plan approved. Switching to YOLO mode (all tool calls auto-approved). The approved implementation plan is stored at: /home/agent/.gemini/tmp/app/c8321455-c473-4e52-a372-e5d48d82e44e/plans/two_tower_ranker_plan.md Read and follow the plan strictly during implementation. ## Step 37 · agent · 2026-09-29T00:34:04.707Z ### update_topic · 2026-09-29T00:34:04.707Z { "strategic_intent": "Develop and benchmark fast feature extraction and dataset preparation pipeline." } ### Tool output · 2026-09-29T00:34:04.711Z Strategic Intent: Develop and benchmark fast feature extraction and dataset preparation pipeline. ## Step 38 · agent · 2026-09-29T00:34:07.436Z ### Recorded reasoning summary Analyzing Data Extraction I'm currently assessing the performance of data extraction, specifically focusing on the `train_listens.parquet` file and its substantial row count. My priority is to understand the time implications of loading this dataset to ensure efficient processing. ### run_shell_command · 2026-09-29T00:34:07.436Z { "description": "Benchmark reading train_listens.parquet", "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', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read full table in {time.time()-t0:.2f}s, num rows: {len(table)}')\n\"" } ### Tool output · 2026-09-29T00:34:09.441Z Output: Read full table in 1.41s, num rows: 43484159 Process Group PGID: 1970 ## Step 39 · agent · 2026-09-29T00:34:13.275Z ### Recorded reasoning summary Analyzing Timestamps Quickly I'm impressed by how fast loading the data was! Now I'm zeroing in on the timestamps within `train_listens`. My next step is to find the minimum and maximum timestamps for both `train_listens` and `val_listens`. ### run_shell_command · 2026-09-29T00:34:13.275Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\n\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['timestamp'])\nmin_ts = pc.min(t_listens['timestamp']).as_py()\nmax_ts = pc.max(t_listens['timestamp']).as_py()\nprint(f'Train listens timestamp range: {min_ts} to {max_ts} (span: {(max_ts - min_ts) / 86400:.1f} days)')\n\nv_listens = pq.read_table('/task/data/val_listens.parquet', columns=['timestamp'])\nmin_vts = pc.min(v_listens['timestamp']).as_py()\nmax_vts = pc.max(v_listens['timestamp']).as_py()\nprint(f'Val listens timestamp range: {min_vts} to {max_vts} (span: {(max_vts - min_vts) / 86400:.1f} days)')\nprint(f'Gap between train and val: {min_vts - max_ts} seconds ({(min_vts - max_ts) / 60:.1f} minutes)')\n\"", "description": "Check timestamp ranges of train and val listens" } ### Tool output · 2026-09-29T00:34:15.147Z Output: Train listens timestamp range: 0 to 24786795 (span: 286.9 days) Val listens timestamp range: 24788600 to 25393395 (span: 7.0 days) Gap between train and val: 1805 seconds (30.1 minutes) Process Group PGID: 1992 ## Step 40 · agent · 2026-09-29T00:34:19.857Z ### Recorded reasoning summary Analyzing Data Segmentation My focus has shifted to the data split. I'm evaluating a 286-day training span concluding at epoch 24786795. The validation set is precisely 7 days, beginning 30 minutes after training ends, and covers the user base with at least 20 positive listens in the 90 days preceding the training cutoff. ### run_shell_command · 2026-09-29T00:34:19.857Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntargets = set(pq.read_table('/task/data/target_users.parquet', columns=['uid'])['uid'].to_pylist())\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\n\n# Filter by timestamp >= 17010800 (last 90 days)\nmask_90d = pc.greater_equal(table['timestamp'], 17010800)\nt_90d = table.filter(mask_90d)\nprint(f'Rows in last 90 days: {len(t_90d)}')\n\n# Also filter by timestamp >= 24786800 - 30 * 86400 (last 30 days: >= 22194800)\nmask_30d = pc.greater_equal(table['timestamp'], 22194800)\nt_30d = table.filter(mask_30d)\nprint(f'Rows in last 30 days: {len(t_30d)}')\n\n# Also filter by timestamp >= 24786800 - 7 * 86400 (last 7 days: >= 24182000)\nmask_7d = pc.greater_equal(table['timestamp'], 24182000)\nt_7d = table.filter(mask_7d)\nprint(f'Rows in last 7 days: {len(t_7d)}')\n\"", "description": "Count rows in recent training windows" } ### Tool output · 2026-09-29T00:34:22.912Z Output: Rows in last 90 days: 16769274 Rows in last 30 days: 6126103 Rows in last 7 days: 1491699 Process Group PGID: 2013 ## Step 41 · agent · 2026-09-29T00:34:35.350Z ### Recorded reasoning summary Observing Model Behavior My analysis shows a near-identical number of listens between training and validation datasets over the last week, which is an interesting temporal simulation outcome. I'm now verifying that all catalog items are accounted for in the current iteration. ### run_shell_command · 2026-09-29T00:34:35.350Z { "description": "Check catalog items mapping", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_item_set = set(catalog['item_id'])\nprint('Catalog size:', len(catalog_item_set))\n\n# Check item_ids in candidate_catalog\ncatalog_items = catalog['item_id'].to_numpy()\nprint('Item ID min and max:', catalog_items.min(), catalog_items.max())\n\n# Let's map each item_id to an index 0..29999\nitem2idx = {item: idx for idx, item in enumerate(catalog_items)}\n\"" } ### Tool output · 2026-09-29T00:34:36.910Z Output: Catalog size: 30000 Item ID min and max: 206 9390294 Process Group PGID: 2035 ## Step 42 · agent · 2026-09-29T00:34:39.614Z ### run_shell_command · 2026-09-29T00:34:39.614Z { "description": "Check catalog overlap with interactions", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_item_set = set(catalog['item_id'])\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nprint('Likes item in catalog pct:', likes['item_id'].isin(catalog_item_set).mean())\n\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref item in catalog pct:', pref['item_id'].isin(catalog_item_set).mean())\n\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\nprint('Val likes item in catalog pct:', val_likes['item_id'].isin(catalog_item_set).mean())\n\"" } ### Tool output · 2026-09-29T00:34:41.342Z Output: Likes item in catalog pct: 0.6809068383505291 Val pref item in catalog pct: 1.0 Val likes item in catalog pct: 0.5570842704061384 Process Group PGID: 2052 ## Step 43 · agent · 2026-09-29T00:34:43.698Z ### run_shell_command · 2026-09-29T00:34:43.698Z { "command": "python3 -c \"\nimport pandas as pd\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncatalog_item_set = set(catalog['item_id'])\npref = pd.read_parquet('/task/data/test_preference_state.parquet')\nprint('Test pref item in catalog pct:', pref['item_id'].isin(catalog_item_set).mean())\n\"", "description": "Check test preference state overlap with catalog" } ### Tool output · 2026-09-29T00:34:45.336Z Output: Test pref item in catalog pct: 1.0 Process Group PGID: 2069 ## Step 44 · agent · 2026-09-29T00:34:50.586Z ### Recorded reasoning summary Cataloging Preference State I've successfully cataloged all preference state items. My current focus shifts to the items designated for ranking, specifically ensuring only the 30,000 items from `candidate_catalog.parquet` are considered as per the cutoff directive. ### run_shell_command · 2026-09-29T00:34:50.586Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\nuid_to_row = {uid: r for r, uid in enumerate(uids)}\nitem_to_col = {item: c for c, item in enumerate(item_ids)}\n\n# Check if liked items in val_pref are eligible\nliked_eligible = 0\nliked_total = 0\ndisliked_eligible = 0\ndisliked_total = 0\n\nfor uid, item, state in val_pref.itertuples(index=False):\n r = uid_to_row.get(uid)\n c = item_to_col.get(item)\n if r is not None and c is not None:\n byte, bit = divmod(c, 8)\n is_elig = bool((int(packed[r, byte]) >> bit) & 1)\n if state == 'liked':\n liked_total += 1\n if is_elig: liked_eligible += 1\n elif state == 'disliked':\n disliked_total += 1\n if is_elig: disliked_eligible += 1\n\nprint(f'Liked items: total={liked_total}, eligible={liked_eligible} ({liked_eligible/liked_total*100:.1f}%)')\nprint(f'Disliked items: total={disliked_total}, eligible={disliked_eligible} ({disliked_eligible/disliked_total*100:.1f}%)')\n\"", "description": "Check if liked and disliked items are eligible" } ### Tool output · 2026-09-29T00:34:53.044Z Output: Liked items: total=418435, eligible=0 (0.0%) Disliked items: total=50689, eligible=0 (0.0%) Process Group PGID: 2086 ## Step 45 · agent · 2026-09-29T00:34:57.571Z ### Recorded reasoning summary Analyzing Eligibility Filters I've discovered that all liked and disliked items are deemed ineligible. The service specifically excludes items the user has already explicitly interacted with. I'm now investigating what other criteria might be at play for eligibility. ### run_shell_command · 2026-09-29T00:34:57.571Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\nelig = np.load('/task/data/validation_eligible_candidates.npz')\nuids = elig['uids']\nitem_ids = elig['item_ids']\npacked = elig['packed_eligible']\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\n# Let's inspect user 100\nuid = 100\nr = list(uids).index(uid)\nmask = np.unpackbits(packed[r], count=len(item_ids), bitorder='little')\ninelig_items = set(item_ids[mask == 0])\nprint(f'User {uid} has {len(inelig_items)} ineligible items.')\n\npref_user = val_pref[val_pref.uid == uid]\npref_items = set(pref_user.item_id)\nprint(f'User {uid} pref items: {len(pref_items)}, all in inelig_items? {pref_items.issubset(inelig_items)}')\ndiff = inelig_items - pref_items\nprint(f'Remaining {len(diff)} ineligible items. Let us check if they are in train_listens...')\n\ntrain_listens = pd.read_parquet('/task/data/train_listens.parquet', columns=['uid', 'item_id'])\nu_listens = set(train_listens[train_listens.uid == uid]['item_id'])\nprint(f'How many of remaining diff are in train_listens: {len(diff & u_listens)} / {len(diff)}')\n\"", "description": "Analyze ineligible items composition for user 100" } ### Tool output · 2026-09-29T00:34:59.643Z Output: User 100 has 682 ineligible items. User 100 pref items: 15, all in inelig_items? True Remaining 667 ineligible items. Let us check if they are in train_listens... How many of remaining diff are in train_listens: 667 / 667 Process Group PGID: 2104 ## Step 46 · agent · 2026-09-29T00:35:19.951Z ### Recorded reasoning summary Discovering eligibility logic I'm noticing a clear pattern: user 100's ineligible items are solely due to explicit preferences or prior listens in their training data. This reinforces my understanding of the "Made for You" discovery shelf's intent. ### Recorded reasoning summary Refining recommendation signals My analysis indicates that "Made for You" filtering is primarily driven by explicit user feedback and historical listening habits. I'm now exploring how the 80% listening threshold and organic engagement signals in the validation window contribute to this relevance scoring, especially within the context of unseen tracks. ### Recorded reasoning summary Developing user tower architecture I'm focusing on the user tower, ensuring it derives representations solely from interaction history. This is crucial for generating user embeddings independently of item embeddings, aligning with the two-tower architecture's requirements for precomputation and efficient retrieval. ### Recorded reasoning summary Optimizing discovery shelf logic I'm diving into the specifics of candidate eligibility, noting how platform-owned logic dictates what's even considered. My current focus is on how explicit preferences and temporal backtesting within this boundary influence the final recommendations, ensuring efficient use of the 60-minute constraint. ### Recorded reasoning summary Structuring two-tower model I'm investigating the optimal two-tower model architecture. My current focus is on the item tower, exploring its potential features like Item ID embeddings, catalog metadata, and derived listening statistics. I'm also considering a suitable neural network structure for processing these inputs efficiently. ### Recorded reasoning summary Processing item tower outputs I'm looking at how to compute item vectors offline, precomputing a tensor of (30000, out_dim). This will be crucial for efficiently retrieving user recommendations later on. ### Recorded reasoning summary Learning user preferences I'm now understanding how user interactions, especially recent listens and explicit dislikes, are weighted to form user representations. My current focus is on leveraging these signals to accurately capture a user's evolving musical taste within the two-tower model. ### Recorded reasoning summary Integrating user statistical features I'm now incorporating user statistical features like like counts and completion ratios into the user tower. This involves projecting these statistics and concatenating them with pooled embeddings before feeding them into the MLP for final user representation. ### run_shell_command · 2026-09-29T00:35:19.951Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(catalog['item_id'])\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['item_id'])\nitems = t_listens['item_id'].to_numpy()\nin_cat = np.isin(items, list(cat_set))\nprint(f'Train listens in catalog: {in_cat.mean():.4f} ({in_cat.sum()} / {len(items)})')\n\"", "description": "Check fraction of train listens in catalog" } ### Tool output · 2026-09-29T00:35:23.591Z Output: Train listens in catalog: 0.7186 (31249613 / 43484159) Process Group PGID: 2133 ## Step 47 · agent · 2026-09-29T00:35:32.230Z ### Recorded reasoning summary Analyzing Catalog Performance My focus has shifted to understanding the performance of items within and outside our catalog for training. It's fascinating that over 70% of listens are already cataloged. Now, I'm exploring strategies for handling those out-of-catalog items, considering options like a dedicated OOV index or simply ignoring them. ### Recorded reasoning summary Refining Training Objectives I'm now considering the specific training task. The goal is to predict high dot products for items a user will like or strongly engage with within a 7-day window. I'm evaluating whether to use historical 7-day windows for training, including likes and strong listens as positive examples. ### run_shell_command · 2026-09-29T00:35:32.230Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\nT_cut = 24786800\nT_start = T_cut - 7 * 86400\n\n# Train listens in [T_start, T_cut]\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(t_listens['timestamp'], T_start), pc.less_equal(t_listens['timestamp'], T_cut))\nrecent_listens = t_listens.filter(mask)\ndf_listens = recent_listens.to_pandas()\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nstrong = df_listens[df_listens.uid.isin(targets) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\nprint('Strong organic listens in last 7 days:', len(strong), 'unique users:', strong.uid.nunique(), 'unique items:', strong.item_id.nunique())\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nrecent_likes = likes[(likes.timestamp >= T_start) & (likes.timestamp <= T_cut) & likes.uid.isin(targets) & (likes.is_organic == 1)]\nprint('Organic likes in last 7 days:', len(recent_likes), 'unique users:', recent_likes.uid.nunique(), 'unique items:', recent_likes.item_id.nunique())\n\"", "description": "Check positive interactions in last 7 days of training" } ### Tool output · 2026-09-29T00:35:34.935Z Output: Strong organic listens in last 7 days: 428209 unique users: 6084 unique items: 86275 Organic likes in last 7 days: 12022 unique users: 2700 unique items: 8912 Process Group PGID: 2159 ## Step 48 · agent · 2026-09-29T00:35:51.627Z ### run_shell_command · 2026-09-29T00:35:51.627Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(catalog['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nT_cut = 24786800\nT_start = T_cut - 7 * 86400\n\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(t_listens['timestamp'], T_start), pc.less_equal(t_listens['timestamp'], T_cut))\nrecent_listens = t_listens.filter(mask)\ndf_listens = recent_listens.to_pandas()\nstrong = df_listens[df_listens.uid.isin(targets) & (df_listens.is_organic == 1) & (df_listens.played_ratio_pct >= 80)]\nstrong_cat = strong[strong.item_id.isin(cat_set)]\nprint('Strong organic catalog listens in last 7 days:', len(strong_cat), 'unique users:', strong_cat.uid.nunique(), 'unique items:', strong_cat.item_id.nunique())\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nrecent_likes = likes[(likes.timestamp >= T_start) & (likes.timestamp <= T_cut) & likes.uid.isin(targets) & (likes.is_organic == 1)]\nlikes_cat = recent_likes[recent_likes.item_id.isin(cat_set)]\nprint('Organic catalog likes in last 7 days:', len(likes_cat), 'unique users:', likes_cat.uid.nunique(), 'unique items:', likes_cat.item_id.nunique())\n\"", "description": "Check catalog overlap with last 7 days interactions" } ### Tool output · 2026-09-29T00:35:54.401Z Output: Strong organic catalog listens in last 7 days: 304885 unique users: 5842 unique items: 25540 Organic catalog likes in last 7 days: 7774 unique users: 2185 unique items: 4896 Process Group PGID: 2181 ## Step 49 · agent · 2026-09-29T00:35:59.126Z ### Recorded reasoning summary Analyzing Recent Performance I'm pleased to report substantial growth in organic catalog engagement, with over 300,000 listens and 7,774 likes in the last week, covering 25,540 unique tracks. This surge indicates a strong positive trend. ### run_shell_command · 2026-09-29T00:35:59.126Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nT_cut = 24786800\nT_30d = T_cut - 30 * 86400\n\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(t_listens['timestamp'], T_30d), pc.less_equal(t_listens['timestamp'], T_cut))\nrecent = t_listens.filter(mask).to_pandas()\nrecent = recent[recent.uid.isin(targets)]\n\ncounts = recent.groupby('uid').size()\nprint('30-day listen count per user summary:')\nprint(counts.describe())\n\"", "description": "Summary of 30-day listen counts per target user" } ### Tool output · 2026-09-29T00:36:02.264Z Output: 30-day listen count per user summary: count 8237.000000 mean 742.178827 std 794.447442 min 1.000000 25% 185.000000 50% 493.000000 75% 1021.000000 max 9660.000000 dtype: float64 Process Group PGID: 2203 ## Step 50 · agent · 2026-09-29T00:36:19.101Z ### Recorded reasoning summary Analyzing User Interaction I'm currently exploring how to best represent user history, specifically looking at recent interactions like the last 50 or 100 listens, or a weighted bag of items. This is in light of understanding average listener engagement, where the typical user has around 742 listens in the 30 days prior to cutoff. ### Recorded reasoning summary Implementing Embedding Bags I've decided to proceed with `nn.EmbeddingBag` for handling user interaction data. Its efficiency with variable-length sequences and bags of items, especially on CPU, is compelling. I'm currently exploring the specifics of its implementation, including potential weighted summation. ### run_shell_command · 2026-09-29T00:36:19.101Z { "description": "Benchmark EmbeddingBag on CPU", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport time\n\nemb_bag = nn.EmbeddingBag(30001, 64, mode='mean')\n# 8456 users, each having ~50 items on average = ~400,000 items total\nindices = torch.randint(0, 30000, (400000,))\noffsets = torch.arange(0, 400000, 50)\n\nt0 = time.time()\nfor _ in range(10):\n out = emb_bag(indices, offsets)\nprint(f'EmbeddingBag output shape: {out.shape}, time per forward pass: {(time.time()-t0)/10*1000:.2f} ms')\n\"" } ### Tool output · 2026-09-29T00:36:22.147Z Output: EmbeddingBag output shape: torch.Size([8000, 64]), time per forward pass: 2.76 ms Process Group PGID: 2225 ## Step 51 · agent · 2026-09-29T00:36:28.837Z ### Recorded reasoning summary Optimizing inference latency My focus is now on refining the inference speed. I've confirmed that processing all target users takes approximately 3 milliseconds per forward pass, a significant improvement. Next, I'm investigating how `nn.EmbeddingBag` behaves with empty sequences, a crucial edge case for user histories. ### run_shell_command · 2026-09-29T00:36:28.837Z { "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nemb_bag = nn.EmbeddingBag(30002, 64, mode='mean', padding_idx=30001)\n# Suppose user 0 has [1, 2], user 1 has empty (put padding index 30001), user 2 has [3]\nindices = torch.tensor([1, 2, 30001, 3], dtype=torch.long)\noffsets = torch.tensor([0, 2, 3], dtype=torch.long)\nout = emb_bag(indices, offsets)\nprint(out.shape)\nprint('User 1 (empty with pad) norm:', torch.norm(out[1]).item())\n\"", "description": "Test empty sequence with padding_idx in EmbeddingBag" } ### Tool output · 2026-09-29T00:36:31.790Z Output: torch.Size([3, 64]) User 1 (empty with pad) norm: 0.0 Process Group PGID: 2244 ## Step 52 · agent · 2026-09-29T00:36:35.635Z ### run_shell_command · 2026-09-29T00:36:35.635Z { "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\n\nemb_bag = nn.EmbeddingBag(30002, 64, mode='sum', padding_idx=30001)\nindices = torch.tensor([1, 2, 30001, 3], dtype=torch.long)\nweights = torch.tensor([0.8, 0.2, 0.0, 1.0], dtype=torch.float)\noffsets = torch.tensor([0, 2, 3], dtype=torch.long)\nout = emb_bag(indices, offsets, per_sample_weights=weights)\nprint('out shape:', out.shape)\nprint('User 1 norm:', torch.norm(out[1]).item())\n\"", "description": "Test per-sample weights in EmbeddingBag" } ### Tool output · 2026-09-29T00:36:38.586Z Output: out shape: torch.Size([3, 64]) User 1 norm: 0.0 Process Group PGID: 2254 ## Step 53 · agent · 2026-09-29T00:36:42.988Z ### Recorded reasoning summary Exploring Weight Normalization I've confirmed `EmbeddingBag` with `per_sample_weights` functions as expected. My current focus is on investigating normalization strategies for these weights, specifically if they can be adjusted to sum to 1.0 or undergo softmax/L2 normalization. ### run_shell_command · 2026-09-29T00:36:42.988Z { "description": "Inspect candidate catalog features stats", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nprint(catalog.head())\nprint('Catalog features stats:')\nprint(catalog[['positive_listens', 'organic_positive_listens', 'popularity_rank']].describe())\n\"" } ### Tool output · 2026-09-29T00:36:44.543Z Output: 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 Catalog features stats: positive_listens organic_positive_listens popularity_rank count 30000.000000 30000.000000 30000.000000 mean 668.544300 313.926433 15000.500000 std 1053.389553 636.039902 8660.398374 min 163.000000 0.000000 1.000000 25% 228.000000 93.000000 7500.750000 50% 354.000000 162.000000 15000.500000 75% 669.250000 309.000000 22500.250000 max 26615.000000 20620.000000 30000.000000 Process Group PGID: 2264 ## Step 54 · agent · 2026-09-29T00:37:03.308Z ### Recorded reasoning summary Analyzing Item Popularity I'm currently exploring the relationship between the sheer volume of positive listens per catalog item and its overall popularity rank. My focus is on identifying the organic factors that drive user preference and engagement. ### Recorded reasoning summary Refining Recommendation Signals I'm delving into item co-occurrence as a primary signal, recognizing its power in inferring artist and genre relationships. The goal is to leverage this for a more robust user vector representation within the two-tower model, aligning user preferences with similar item clusters. ### Recorded reasoning summary Implementing Two-Tower Model I'm now focused on implementing a two-tower neural network architecture for recommendation. Specifically, I'm designing the user tower to learn representations directly from interaction history, avoiding user ID lookups, and ensuring the item tower generates compatible item embeddings for dot product scoring. ### Recorded reasoning summary Training Two-Tower Networks I'm now focusing on the specifics of training the two-tower model, particularly exploring in-batch negative sampling. My current thinking involves utilizing the mini-batch itself to generate negative signals for positive user-item interactions, aiming to optimize the model through a cross-entropy loss. ### Recorded reasoning summary Addressing Popularity Bias I'm currently re-evaluating in-batch negative sampling due to its known popularity bias. My latest thoughts involve augmenting this with explicit catalog negatives, potentially sampled by popularity or uniformly, and even considering explicit user dislikes as negative signals. ### Recorded reasoning summary Weighting Interaction Gradients I'm now focused on how to best assign weights to different interaction types within the loss function. My current thinking is to leverage NDCG gain values, effectively giving "organic likes" significantly more weight than "organic listens," thereby aligning training gradients directly with our evaluation metric. **Sampling Training Data** I'm exploring strategies for constructing training samples from our available listen and like data. My current focus is on defining appropriate time horizons for sampling, considering recent history like the last 90 days, and how this intersects with validation windows. ### Recorded reasoning summary Defining Training Windows I am currently defining multiple training windows, each comprising a target window of 7 days and the preceding user history. This approach generates thousands of user histories and their subsequent positive items, providing rich data for training. I am also refining how user history and target items are constructed for validation and testing. ### run_shell_command · 2026-09-29T00:37:03.308Z { "command": "python3 -c \"\nimport pandas as pd\n\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nprint('Val pref shape:', val_pref.shape)\n\n# Let's check a user\nu = 100\nu_pref = val_pref[val_pref.uid == u].sort_values('item_id')\nprint(f'User {u} val_pref:')\nprint(u_pref)\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\nu_l = likes[likes.uid == u].assign(event='like')\nu_u = unlikes[unlikes.uid == u].assign(event='unlike')\nu_d = dislikes[dislikes.uid == u].assign(event='dislike')\nu_ud = undislikes[undislikes.uid == u].assign(event='undislike')\n\nall_events = pd.concat([u_l, u_u, u_d, u_ud]).sort_values('timestamp')\nprint(f'User {u} explicit events count: {len(all_events)}')\n\"", "description": "Verify preference state generation logic" } ### Tool output · 2026-09-29T00:37:04.932Z Output: Val pref shape: (469124, 3) User 100 val_pref: uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked 5 100 3535902 liked 6 100 3633039 liked 7 100 4099299 liked 8 100 4221787 liked 9 100 4927727 disliked 10 100 4943655 liked 11 100 5590855 liked 12 100 6568592 liked 13 100 8200811 liked 14 100 8661238 disliked User 100 explicit events count: 29 Process Group PGID: 2281 ## Step 55 · agent · 2026-09-29T00:37:13.645Z ### Recorded reasoning summary Confirming Data Integrity My analysis confirms the complete loading of all preference states for both training and testing sets. The training data, with 469,124 entries, aligns with the 24786800 timestamp cutoff. Similarly, the test set contains its full 484,776 records, correctly timestamped at 25393400. ### Recorded reasoning summary Leveraging Provided States It seems I can skip reconstructing the preference states for validation and testing, as they're conveniently provided by the platform. My current focus is on identifying the most valuable user features to extract from their history, considering liked/disliked items and recent listening patterns. ### Recorded reasoning summary Exploring Training Regimes I'm currently investigating two distinct training approaches. The first involves window-based pairs, utilizing historical interactions as features to predict organic likes and listens within a defined recent period. The second explores item-item co-occurrence and session-based predictions, aiming to discern patterns in subsequent track selections. ### run_shell_command · 2026-09-29T00:37:13.645Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(catalog['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nT_train_end = 24786800\nwindow_len = 7 * 86400\ngap = 1800\n\n# Let's test 3 training windows:\n# Window 0: Target = [T_train_end - window_len, T_train_end]\n# Window 1: Target = [T_train_end - 2*window_len, T_train_end - window_len]\n# Window 2: Target = [T_train_end - 3*window_len, T_train_end - 2*window_len]\n\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nt_likes = pd.read_parquet('/task/data/train_likes.parquet')\n\nfor w in range(3):\n w_end = T_train_end - w * window_len\n w_start = w_end - window_len\n \n # filter listens\n mask = pc.and_(pc.greater_equal(t_listens['timestamp'], w_start), pc.less_equal(t_listens['timestamp'], w_end))\n w_listens = t_listens.filter(mask).to_pandas()\n w_listens = w_listens[w_listens.uid.isin(targets) & (w_listens.is_organic == 1) & (w_listens.played_ratio_pct >= 80) & w_listens.item_id.isin(cat_set)]\n \n # filter likes\n w_likes = t_likes[(t_likes.timestamp >= w_start) & (t_likes.timestamp <= w_end) & t_likes.uid.isin(targets) & (t_likes.is_organic == 1) & t_likes.item_id.isin(cat_set)]\n \n print(f'Window {w} ({w_start} to {w_end}): strong listens={len(w_listens)} (unique uids={w_listens.uid.nunique()}), likes={len(w_likes)} (unique uids={w_likes.uid.nunique()})')\n\"", "description": "Check positive pairs across 3 historical windows" } ### Tool output · 2026-09-29T00:37:18.321Z Output: Window 0 (24182000 to 24786800): strong listens=304885 (unique uids=5842), likes=7774 (unique uids=2185) Window 1 (23577200 to 24182000): strong listens=289063 (unique uids=5660), likes=7129 (unique uids=2119) Window 2 (22972400 to 23577200): strong listens=287016 (unique uids=5593), likes=7451 (unique uids=2026) Process Group PGID: 2298 ## Step 56 · agent · 2026-09-29T00:37:23.817Z ### Recorded reasoning summary Analyzing User Interactions My current thoughts are focused on the impressive volume of user engagement observed. Across three distinct windows, I've recorded approximately 1 million positive interactions, comprising strong listens and organic likes. It's noteworthy that the user's historical actions appear to directly precede and potentially influence these interactions within each window. ### run_shell_command · 2026-09-29T00:37:23.817Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\nT_cut = 24786800\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(catalog['item_id'])}\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\n# Load validation preference state\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\npref_likes = val_pref[val_pref.state == 'liked'].groupby('uid')['item_id'].apply(list).to_dict()\npref_dislikes = val_pref[val_pref.state == 'disliked'].groupby('uid')['item_id'].apply(list).to_dict()\nprint(f'Preference state processed in {time.time()-t0:.2f}s')\n\n# Load recent listens in last 30 days before T_cut\nt0 = time.time()\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(t_listens['timestamp'], T_cut - 30 * 86400), pc.less_equal(t_listens['timestamp'], T_cut))\nrecent = t_listens.filter(mask).to_pandas()\nrecent = recent[recent.uid.isin(targets)]\nprint(f'Filtered 30-day listens in {time.time()-t0:.2f}s, rows: {len(recent)}')\n\"", "description": "Benchmark filtering and grouping user history" } ### Tool output · 2026-09-29T00:37:27.218Z Output: Preference state processed in 0.34s Filtered 30-day listens in 1.44s, rows: 6113327 Process Group PGID: 2320 ## Step 57 · agent · 2026-09-29T00:37:33.543Z ### Recorded reasoning summary Processing Recent Activity My focus has shifted to efficiently processing recent user activity into per-user arrays, aiming to optimize speed after noting performance metrics for preference states and 30-day listens. I'm now analyzing how to make user embeddings as informative as possible. ### run_shell_command · 2026-09-29T00:37:33.543Z { "description": "Check unique catalog items per user in 30 days", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncat_set = set(catalog['item_id'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nT_cut = 24786800\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(t_listens['timestamp'], T_cut - 30 * 86400), pc.less_equal(t_listens['timestamp'], T_cut))\nrecent = t_listens.filter(mask).to_pandas()\nrecent = recent[recent.uid.isin(targets) & recent.item_id.isin(cat_set)]\n\nunique_items_per_user = recent.groupby('uid')['item_id'].nunique()\nprint('Unique catalog items per user in 30 days:')\nprint(unique_items_per_user.describe())\n\"" } ### Tool output · 2026-09-29T00:37:37.398Z Output: Unique catalog items per user in 30 days: count 8196.000000 mean 262.256589 std 239.717776 min 1.000000 25% 79.000000 50% 195.000000 75% 379.250000 max 1913.000000 Name: item_id, dtype: float64 Process Group PGID: 2348 ## Step 58 · agent · 2026-09-29T00:37:45.009Z ### Recorded reasoning summary Processing User Listening Habits My current focus is on leveraging the extensive listening data per user, specifically around 262 unique tracks monthly. I'm exploring methods to efficiently incorporate liked, disliked, and top-streamed tracks into our models, considering options like PyTorch's `nn.EmbeddingBag` for optimal processing. ### Recorded reasoning summary Optimizing User Embeddings I've confirmed `nn.EmbeddingBag` efficiently computes weighted averages across user interactions in milliseconds, regardless of the number of items. This allows me to combine liked, disliked, and weighted listened tracks with user stats into a unified user representation $\mathbf{h}_u$, ready for an MLP and normalization. ### Recorded reasoning summary Refining Interaction Scoring I've confirmed the model structure supports a dot product score $s(u, i) = \mathbf{u}^\top \mathbf{v}_i$, utilizing L2 normalized item vectors $\mathbf{v}_i$. This aligns perfectly with the requirement for independently precomputable user and item vectors derived from interaction history, not just IDs, through separate PyTorch towers. ### run_shell_command · 2026-09-29T00:37:45.009Z { "description": "Benchmark MIPS dot-product and topk on CPU", "command": "python3 -c \"\nimport torch\nimport time\n\nU = torch.randn(8456, 128)\nV = torch.randn(30000, 128)\n\nt0 = time.time()\nscores = torch.matmul(U, V.T)\nprint(f'Matrix multiplication 8456 x 30000 took {time.time()-t0:.4f}s')\n\nt0 = time.time()\ntopk = torch.topk(scores, 100, dim=1)\nprint(f'Top-100 selection for 8456 users took {time.time()-t0:.4f}s')\n\"" } ### Tool output · 2026-09-29T00:37:48.684Z Output: Matrix multiplication 8456 x 30000 took 0.3486s Top-100 selection for 8456 users took 0.1660s Process Group PGID: 2370 ## Step 59 · agent · 2026-09-29T00:37:58.471Z ### Recorded reasoning summary Optimizing Retrieval Speed The retrieval process for all 8,456 users and 30,000 items is exceptionally fast, clocking in at approximately 0.5 seconds. This performance comfortably exceeds any latency requirements. My current focus is on analyzing the training process to ensure dot products yield a high NDCG@10. ### Recorded reasoning summary Refining Loss Calculation I'm now scrutinizing the training loss, specifically the cross-entropy formulation. I'm considering how to incorporate sample weights to better distinguish between organic likes and listens, aiming for improved NDCG@10. I'm also beginning to explore potential issues with false negatives arising from in-batch sampling. ### Recorded reasoning summary Addressing Popularity Bias I'm now considering popularity bias. While explicit negatives are useful for embedding dislikes, they are already excluded at inference. I'm examining how to leverage disliked items to push user embeddings away from what they dislike, indirectly addressing potential popularity bias issues during training. ### run_shell_command · 2026-09-29T00:37:58.471Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\nval_listens = pd.read_parquet('/task/data/val_listens.parquet')\nval_likes = pd.read_parquet('/task/data/val_likes.parquet')\n\n# Validation ground truth\nstrong = val_listens[val_listens.uid.isin(targets) & (val_listens.is_organic == 1) & (val_listens.played_ratio_pct >= 80)]\nlikes = val_likes[val_likes.uid.isin(targets) & (val_likes.is_organic == 1)]\n\nprint('Total strong listens in val:', len(strong))\nprint('Total likes in val:', len(likes))\n\nstrong_cat = strong[strong.item_id.isin(item_pop)]\nlikes_cat = likes[likes.item_id.isin(item_pop)]\n\npop_strong = [item_pop[i] for i in strong_cat.item_id]\npop_likes = [item_pop[i] for i in likes_cat.item_id]\n\nprint('Popularity rank of strong listens: median=', np.median(pop_strong), 'mean=', np.mean(pop_strong), 'p25=', np.percentile(pop_strong, 25), 'p75=', np.percentile(pop_strong, 75))\nprint('Popularity rank of likes: median=', np.median(pop_likes), 'mean=', np.mean(pop_likes), 'p25=', np.percentile(pop_likes, 25), 'p75=', np.percentile(pop_likes, 75))\n\"", "description": "Check popularity rank distribution of validation ground truth" } ### Tool output · 2026-09-29T00:38:00.365Z Output: Total strong listens in val: 421615 Total likes in val: 20702 Popularity rank of strong listens: median= 4263.0 mean= 7288.4264337252625 p25= 1059.0 p75= 11431.0 Popularity rank of likes: median= 5377.0 mean= 8370.323947722467 p25= 1584.0 p75= 13568.0 Process Group PGID: 2383 ## Step 60 · agent · 2026-09-29T00:38:13.810Z ### Recorded reasoning summary Observing Distribution Trends I'm currently analyzing the distribution of popular positive items in validation, noting medians around 4,200-5,300 and percentiles suggesting relevant items extend well beyond the top 100 global rankings. ### Recorded reasoning summary Learning Collaborative Signals I'm focusing on how users interact with tracks beyond the top global hits, finding personalized relevance is key. My current exploration centers on learning track embeddings that capture collaborative co-occurrence, and user embeddings that pool individual listening histories. I'm considering end-to-end training of item embeddings within a two-tower model. ### Recorded reasoning summary Refining User Embeddings I'm exploring how to effectively pool historical track embeddings for users. I've confirmed that using the same item embedding table $E$ in both user and item towers creates a shared space where co-occurring tracks are pulled closer. This mechanism mirrors successful architectures like YouTube DNN and Word2Vec, suggesting strong potential for accurate user representation. ### run_shell_command · 2026-09-29T00:38:13.810Z { "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass ItemTower(nn.Module):\n def __init__(self, num_items, embed_dim, feat_dim, out_dim):\n super().__init__()\n self.item_embed = nn.Embedding(num_items, embed_dim)\n self.feat_proj = nn.Linear(feat_dim, embed_dim)\n self.net = nn.Sequential(\n nn.Linear(embed_dim * 2, out_dim),\n nn.LayerNorm(out_dim),\n nn.GELU(),\n nn.Linear(out_dim, out_dim),\n )\n def forward(self, item_ids, item_feats):\n e = self.item_embed(item_ids)\n f = self.feat_proj(item_feats)\n x = torch.cat([e, f], dim=-1)\n out = self.net(x)\n return F.normalize(out, p=2, dim=-1)\n\nit = ItemTower(30000, 64, 4, 64)\nids = torch.randint(0, 30000, (1024,))\nfeats = torch.randn(1024, 4)\nout = it(ids, feats)\nprint('ItemTower out shape:', out.shape, 'norm:', torch.norm(out[0]).item())\n\"", "description": "Test ItemTower architecture" } ### Tool output · 2026-09-29T00:38:16.771Z Output: ItemTower out shape: torch.Size([1024, 64]) norm: 1.0 Process Group PGID: 2403 ## Step 61 · agent · 2026-09-29T00:38:24.117Z ### Recorded reasoning summary Designing User Tower I'm focusing on the `UserTower` design now. It will process liked items, starting with their indices and offsets from the `ItemTower`'s output of normalized vectors in $\mathbb{R}^{64}$. ### Recorded reasoning summary Developing User Tower Logic I'm now concentrating on the `UserTower`'s forward pass. This involves creating embeddings for liked, listened, and disliked items by leveraging the shared `item_embed` weights, and then concatenating these with projected user statistics to feed into the main network. ### run_shell_command · 2026-09-29T00:38:24.117Z { "description": "Test F.embedding_bag with shared embedding weight", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nitem_embed = nn.Embedding(30001, 64, padding_idx=30000)\n# Test F.embedding_bag with item_embed.weight\nindices = torch.tensor([1, 2, 30000, 3], dtype=torch.long)\noffsets = torch.tensor([0, 2, 3], dtype=torch.long)\nweights = torch.tensor([0.5, 0.5, 0.0, 1.0], dtype=torch.float)\n\nout = F.embedding_bag(indices, item_embed.weight, offsets=offsets, per_sample_weights=weights, mode='sum')\nprint('F.embedding_bag shape:', out.shape)\nprint('User 1 norm:', torch.norm(out[1]).item())\n\"" } ### Tool output · 2026-09-29T00:38:28.465Z Output: F.embedding_bag shape: torch.Size([3, 64]) User 1 norm: 0.0 Process Group PGID: 2422 ## Step 62 · agent · 2026-09-29T00:38:42.122Z ### Recorded reasoning summary Unifying Item Embeddings I've realized `F.embedding_bag` can operate directly on `item_embed.weight`. This unification means updates to item embeddings from user history or retrieval loss will benefit both towers concurrently, a significant efficiency gain. ### Recorded reasoning summary Refining Embeddings and Towers I'm exploring how tying item embeddings, a common practice for reducing overfitting and parameter count, influences the system. Both user and item towers output normalized vectors, and relevance is determined by their dot product. This allows for independent computation of item embeddings offline, streamlining the process. ### Recorded reasoning summary Optimizing Data Extraction I'm focusing on accelerating data preparation. My current task is to extract necessary information for target users at the validation cutoff, specifically their liked, disliked items, and recent listens with their associated features, considering potential aggregation strategies for multiple listens. ### Recorded reasoning summary Defining Training Data Windows I'm considering temporal windows to generate training data. For each window preceding the validation cutoff, I'll use user engagement and likes/dislikes to construct positive training examples. This approach aims to capture evolving user preferences effectively for model training. ### run_shell_command · 2026-09-29T00:38:42.122Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_map = {int(item): idx for idx, item in enumerate(catalog['item_id'])}\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\n# Read listens with pyarrow\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nprint(f'Read parquet in {time.time()-t0:.2f}s')\n\nt0 = time.time()\n# Filter only target users and timestamp >= 24786800 - 60*86400\nt_min = 24786800 - 60 * 86400\nt_max = 24786800\nmask = pc.and_(pc.greater_equal(table['timestamp'], t_min), pc.less_equal(table['timestamp'], t_max))\nfiltered = table.filter(mask)\nprint(f'Filtered 60d in {time.time()-t0:.2f}s, rows={len(filtered)}')\n\nt0 = time.time()\ndf = filtered.to_pandas()\ndf = df[df.uid.isin(targets)]\n# Map items\ndf['item_idx'] = df['item_id'].map(item_map)\ndf = df.dropna(subset=['item_idx'])\ndf['item_idx'] = df['item_idx'].astype(np.int32)\nprint(f'Pandas prep in {time.time()-t0:.2f}s, remaining rows: {len(df)}')\n\"", "description": "Benchmark filtering and item mapping" } ### Tool output · 2026-09-29T00:38:46.319Z Output: Read parquet in 0.39s Filtered 60d in 0.65s, rows=11718758 Pandas prep in 1.55s, remaining rows: 8403492 Process Group PGID: 2435 ## Step 63 · agent · 2026-09-29T00:38:52.370Z ### Recorded reasoning summary Measuring Initial Load My initial data loading and preprocessing pipeline is quite performant, completing in under 3 seconds. Now, I'm focusing on the next step: efficiently aggregating user interaction histories to calculate track weights, specifically utilizing played ratio. ### run_shell_command · 2026-09-29T00:38:52.370Z { "description": "Benchmark user-item weight aggregation and top-k selection", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_map = {int(item): idx for idx, item in enumerate(catalog['item_id'])}\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nt_min = 24786800 - 30 * 86400\nt_max = 24786800\nmask = pc.and_(pc.greater_equal(table['timestamp'], t_min), pc.less_equal(table['timestamp'], t_max))\nfiltered = table.filter(mask)\ndf = filtered.to_pandas()\ndf = df[df.uid.isin(targets)]\ndf['item_idx'] = df['item_id'].map(item_map)\ndf = df.dropna(subset=['item_idx'])\ndf['item_idx'] = df['item_idx'].astype(np.int32)\n\n# Compute weight\ndelta_days = (t_max - df['timestamp'].to_numpy()) / 86400.0\n# Half-life of 14 days: lambda = ln(2) / 14 = 0.0495\ndecay = np.exp(-0.0495 * delta_days)\nw = (df['played_ratio_pct'].to_numpy() / 100.0) * (1.0 + 0.5 * df['is_organic'].to_numpy()) * decay\ndf['w'] = w.astype(np.float32)\n\nt1 = time.time()\n# Groupby (uid, item_idx) and sum weights\nuser_item = df.groupby(['uid', 'item_idx'], as_index=False)['w'].sum()\nprint(f'Groupby (uid, item_idx) took {time.time()-t1:.2f}s, unique pairs: {len(user_item)}')\n\n# Sort and select top K items per user\nt2 = time.time()\nuser_item = user_item.sort_values(['uid', 'w'], ascending=[True, False])\ntop_k = user_item.groupby('uid').head(100)\nprint(f'Top-100 per user took {time.time()-t2:.2f}s, total rows: {len(top_k)}')\n\"" } ### Tool output · 2026-09-29T00:38:56.976Z Output: Groupby (uid, item_idx) took 0.54s, unique pairs: 2149455 Top-100 per user took 0.71s, total rows: 687345 Process Group PGID: 2457 ## Step 64 · agent · 2026-09-29T00:39:04.514Z ### Recorded reasoning summary Optimizing Weighted Listens My focus has shifted to the performance of weighted listen extraction. I've successfully benchmarked the groupby and top-100 selection, achieving approximately 3 seconds for 8,456 users. Next, I'll be analyzing the active preference state within `validation_preference_state`, specifically the liked items. ### Recorded reasoning summary Analyzing Training Samples I've been examining the construction of training samples across different temporal windows. Specifically, I'm analyzing how user history and positive items (organic likes and strong listens) are defined within each window to inform model training. My current focus is on window two's data preparation. ### run_shell_command · 2026-09-29T00:39:04.514Z { "description": "Benchmark computing active preference state at arbitrary timestamp", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\nimport time\n\nt0 = time.time()\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\nT = 24182000\n# Filter <= T\nl = likes[likes.timestamp <= T][['uid', 'item_id', 'timestamp']].assign(act=1, typ='like')\nu = unlikes[unlikes.timestamp <= T][['uid', 'item_id', 'timestamp']].assign(act=0, typ='like')\nd = dislikes[dislikes.timestamp <= T][['uid', 'item_id', 'timestamp']].assign(act=1, typ='dislike')\nud = undislikes[undislikes.timestamp <= T][['uid', 'item_id', 'timestamp']].assign(act=0, typ='dislike')\n\nevents = pd.concat([l, u, d, ud])\nevents = events.sort_values('timestamp')\n# latest action per (uid, item_id, typ)\nlatest = events.groupby(['uid', 'item_id', 'typ']).last().reset_index()\nactive = latest[latest.act == 1]\nprint(f'Computed active preference state at T={T} in {time.time()-t0:.2f}s, active count: {len(active)}')\n\"" } ### Tool output · 2026-09-29T00:39:06.561Z Output: Computed active preference state at T=24182000 in 0.51s, active count: 791778 Process Group PGID: 2479 ## Step 65 · agent · 2026-09-29T00:39:22.248Z ### Recorded reasoning summary Optimizing Data Flow The latest insights reveal an impressive 0.51-second processing time, indicating significant efficiency gains. My current focus is on optimizing the construction of training batches. I'm analyzing how user features, specifically `liked_indices` at cutoff $T$, should be integrated for optimal model performance. ### Recorded reasoning summary Refining Batch Construction I'm re-evaluating how to handle multiple positive items per user within a batch. My current thinking is to treat each positive item as a distinct sample for a given user's embedding, ensuring the loss computation accurately reflects all valid positive interactions. ### Recorded reasoning summary Addressing User Sampling I'm considering how to prevent penalizing correct items by ensuring each user is sampled at most once per batch. This approach, along with treating each positive item as a distinct sample, seems most aligned with standard two-tower model practices for efficient loss computation. ### update_topic · 2026-09-29T00:39:22.248Z { "strategic_intent": "Implement two-tower neural network, feature extraction, and validation pipeline." } ### Tool output · 2026-09-29T00:39:22.252Z Strategic Intent: Implement two-tower neural network, feature extraction, and validation pipeline. ## Step 66 · agent · 2026-09-29T00:39:50.721Z ### Recorded reasoning summary Designing the Code I'm focusing on structuring the code within `/app/solution/` for optimal modularity. This involves creating distinct files for data utilities, model components, and evaluation logic. ### Recorded reasoning summary Refining Item Tower Features I'm currently detailing the specific features each model tower will utilize. For the item tower, this includes the catalog size with a designated padding index, alongside four normalized continuous features derived from popularity and listen counts. ### Recorded reasoning summary Implementing Item Tower I've implemented the item tower's structure, incorporating both embedding lookups and feature projections. The architecture utilizes an MLP with LayerNorm and GELU activations, culminating in normalized output vectors. I'm now defining the user tower's input, focusing on interaction history rather than direct user ID mapping. ### Recorded reasoning summary Processing User Inputs I'm now defining how the user tower will process its inputs. This involves calculating decay-weighted embeddings for listened, liked, and disliked items, alongside projecting continuous user statistics. These will then be concatenated and passed through an MLP for final representation. ### Recorded reasoning summary Calculating Relevance Scores I'm now calculating the relevance scores, which are essentially unit vector dot products. This means the candidate retrieval is an exact maximum inner product search. I'm also considering the discovery objective, specifically how to handle ineligible items and mitigate popularity bias. ### Recorded reasoning summary Testing the Model I'm now focusing on prototyping and testing. I've decided to build a prototype within `/app/solution/` to validate the conceptual model on the public validation set. The initial step involves carefully defining the requirements for `data_utils.py`. ### Recorded reasoning summary Processing User Data I'm now refining the extraction of user interaction data. This involves loading recent listens, filtering for target users, and computing weighted interactions. I'm also generating user statistics and packaging them for efficient processing. ### write_file · 2026-09-29T00:39:50.721Z { "file_path": "/app/solution/data_utils.py", "content": "\"\"\"Data loading and preprocessing utilities for the two-tower ranker.\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\n\nCATALOG_SIZE = 30000\nPAD_IDX = 30000\n\n\ndef load_catalog(path: str | Path = \"/task/data/candidate_catalog.parquet\"):\n \"\"\"Load candidate catalog and construct feature tensor.\"\"\"\n catalog = pd.read_parquet(path)\n catalog_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\n item2idx = {int(item): idx for idx, item in enumerate(catalog_items)}\n\n # Features: normalized rank, log positive listens, log organic positive listens, organic ratio\n rank_feat = catalog[\"popularity_rank\"].to_numpy(dtype=np.float32) / 30000.0\n pos_feat = np.log1p(catalog[\"positive_listens\"].to_numpy(dtype=np.float32)) / 10.0\n org_feat = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32)) / 10.0\n ratio_feat = (\n catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n / (catalog[\"positive_listens\"].to_numpy(dtype=np.float32) + 1.0)\n )\n\n feat_matrix = np.column_stack([rank_feat, pos_feat, org_feat, ratio_feat]).astype(np.float32)\n # Add a zero row for padding index\n feat_matrix = np.vstack([feat_matrix, np.zeros((1, feat_matrix.shape[1]), dtype=np.float32)])\n return catalog_items, item2idx, torch.from_numpy(feat_matrix)\n\n\ndef extract_user_histories(\n listens_path: str | Path,\n pref_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: list[int],\n cutoff_timestamp: int,\n window_days: int = 60,\n top_k_listens: int = 100,\n):\n \"\"\"Extract liked, disliked, and top listened items for each target user.\"\"\"\n target_set = set(target_uids)\n uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\n num_users = len(target_uids)\n\n # 1. Preferences\n pref_df = pref_df[pref_df[\"uid\"].isin(target_set)]\n likes_grouped = (\n pref_df[pref_df[\"state\"] == \"liked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n dislikes_grouped = (\n pref_df[pref_df[\"state\"] == \"disliked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n\n # 2. Listens\n t_min = cutoff_timestamp - window_days * 86400\n t_max = cutoff_timestamp\n\n table = pq.read_table(\n listens_path,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n mask = pc.and_(\n pc.greater_equal(table[\"timestamp\"], t_min),\n pc.less_equal(table[\"timestamp\"], t_max),\n )\n filtered = table.filter(mask).to_pandas()\n filtered = filtered[filtered[\"uid\"].isin(target_set)]\n\n # Compute listen stats for user features\n user_listen_counts = filtered.groupby(\"uid\").size().to_dict()\n user_listen_ratios = filtered.groupby(\"uid\")[\"played_ratio_pct\"].mean().to_dict()\n user_organic_ratios = filtered.groupby(\"uid\")[\"is_organic\"].mean().to_dict()\n\n # Map items to index\n filtered[\"item_idx\"] = filtered[\"item_id\"].map(item2idx)\n filtered = filtered.dropna(subset=[\"item_idx\"])\n filtered[\"item_idx\"] = filtered[\"item_idx\"].astype(np.int32)\n\n # Time decay and weight\n delta_days = (t_max - filtered[\"timestamp\"].to_numpy()) / 86400.0\n decay = np.exp(-0.05 * delta_days)\n filtered[\"w\"] = (\n (filtered[\"played_ratio_pct\"].to_numpy() / 100.0)\n * (1.0 + 0.5 * filtered[\"is_organic\"].to_numpy())\n * decay\n ).astype(np.float32)\n\n # Aggregate by (uid, item_idx)\n user_item = (\n filtered.groupby([\"uid\", \"item_idx\"], as_index=False)[\"w\"]\n .sum()\n .sort_values([\"uid\", \"w\"], ascending=[True, False])\n )\n top_user_item = user_item.groupby(\"uid\").head(top_k_listens)\n listens_grouped = top_user_item.groupby(\"uid\")[[\"item_idx\", \"w\"]].apply(\n lambda g: (g[\"item_idx\"].to_numpy(), g[\"w\"].to_numpy())\n ).to_dict()\n\n # Build flat arrays with offsets for EmbeddingBag\n liked_indices, liked_offsets = [], [0]\n disliked_indices, disliked_offsets = [], [0]\n listen_indices, listen_weights, listen_offsets = [], [], [0]\n stats_list = []\n\n for uid in target_uids:\n # Liked\n l_items = likes_grouped.get(uid, [])\n l_idx = [item2idx[i] for i in l_items if i in item2idx]\n if not l_idx:\n liked_indices.append(PAD_IDX)\n else:\n liked_indices.extend(l_idx)\n liked_offsets.append(len(liked_indices))\n\n # Disliked\n d_items = dislikes_grouped.get(uid, [])\n d_idx = [item2idx[i] for i in d_items if i in item2idx]\n if not d_idx:\n disliked_indices.append(PAD_IDX)\n else:\n disliked_indices.extend(d_idx)\n disliked_offsets.append(len(disliked_indices))\n\n # Listens\n if uid in listens_grouped:\n u_items, u_w = listens_grouped[uid]\n # normalize weights so sum is 1.0\n sum_w = u_w.sum()\n norm_w = u_w / (sum_w + 1e-6)\n listen_indices.extend(u_items)\n listen_weights.extend(norm_w)\n else:\n listen_indices.append(PAD_IDX)\n listen_weights.append(0.0)\n listen_offsets.append(len(listen_indices))\n\n # Stats\n num_likes = len(l_idx)\n num_dislikes = len(d_idx)\n num_listens = user_listen_counts.get(uid, 0)\n mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0\n org_ratio = user_organic_ratios.get(uid, 0.0)\n stats_list.append([\n np.log1p(num_likes) / 5.0,\n np.log1p(num_dislikes) / 3.0,\n np.log1p(num_listens) / 10.0,\n mean_ratio,\n org_ratio,\n ])\n\n return {\n \"liked_indices\": torch.tensor(liked_indices, dtype=torch.long),\n \"liked_offsets\": torch.tensor(liked_offsets[:-1], dtype=torch.long),\n \"disliked_indices\": torch.tensor(disliked_indices, dtype=torch.long),\n \"disliked_offsets\": torch.tensor(disliked_offsets[:-1], dtype=torch.long),\n \"listen_indices\": torch.tensor(listen_indices, dtype=torch.long),\n \"listen_weights\": torch.tensor(listen_weights, dtype=torch.float32),\n \"listen_offsets\": torch.tensor(listen_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n \"uid2row\": uid2row,\n }\n" } ### Tool output · 2026-09-29T00:39:50.731Z Successfully created and wrote to new file: /app/solution/data_utils.py. Here is the updated code: """Data loading and preprocessing utilities for the two-tower ranker.""" from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch CATALOG_SIZE = 30000 PAD_IDX = 30000 def load_catalog(path: str | Path = "/task/data/candidate_catalog.parquet"): """Load candidate catalog and construct feature tensor.""" catalog = pd.read_parquet(path) catalog_items = catalog["item_id"].to_numpy(dtype=np.int64) item2idx = {int(item): idx for idx, item in enumerate(catalog_items)} # Features: normalized rank, log positive listens, log organic positive listens, organic ratio rank_feat = catalog["popularity_rank"].to_numpy(dtype=np.float32) / 30000.0 pos_feat = np.log1p(catalog["positive_listens"].to_numpy(dtype=np.float32)) / 10.0 org_feat = np.log1p(catalog["organic_positive_listens"].to_numpy(dtype=np.float32)) / 10.0 ratio_feat = ( catalog["organic_positive_listens"].to_numpy(dtype=np.float32) / (catalog["positive_listens"].to_numpy(dtype=np.float32) + 1.0) ) feat_matrix = np.column_stack([rank_feat, pos_feat, org_feat, ratio_feat]).astype(np.float32) # Add a zero row for padding index feat_matrix = np.vstack([feat_matrix, np.zeros((1, feat_matrix.shape[1]), dtype=np.float32)]) return catalog_items, item2idx, torch.from_numpy(feat_matrix) def extract_user_histories( listens_path: str | Path, pref_df: pd.DataFrame, item2idx: dict[int, int], target_uids: list[int], cutoff_timestamp: int, window_days: int = 60, top_k_listens: int = 100, ): """Extract liked, disliked, and top listened items for each target user.""" target_set = set(target_uids) uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)} num_users = len(target_uids) # 1. Preferences pref_df = pref_df[pref_df["uid"].isin(target_set)] likes_grouped = ( pref_df[pref_df["state"] == "liked"].groupby("uid")["item_id"].apply(list).to_dict() ) dislikes_grouped = ( pref_df[pref_df["state"] == "disliked"].groupby("uid")["item_id"].apply(list).to_dict() ) # 2. Listens t_min = cutoff_timestamp - window_days * 86400 t_max = cutoff_timestamp table = pq.read_table( listens_path, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) mask = pc.and_( pc.greater_equal(table["timestamp"], t_min), pc.less_equal(table["timestamp"], t_max), ) filtered = table.filter(mask).to_pandas() filtered = filtered[filtered["uid"].isin(target_set)] # Compute listen stats for user features user_listen_counts = filtered.groupby("uid").size().to_dict() user_listen_ratios = filtered.groupby("uid")["played_ratio_pct"].mean().to_dict() user_organic_ratios = filtered.groupby("uid")["is_organic"].mean().to_dict() # Map items to index filtered["item_idx"] = filtered["item_id"].map(item2idx) filtered = filtered.dropna(subset=["item_idx"]) filtered["item_idx"] = filtered["item_idx"].astype(np.int32) # Time decay and weight delta_days = (t_max - filtered["timestamp"].to_numpy()) / 86400.0 decay = np.exp(-0.05 * delta_days) filtered["w"] = ( (filtered["played_ratio_pct"].to_numpy() / 100.0) * (1.0 + 0.5 * filtered["is_organic"].to_numpy()) * decay ).astype(np.float32) # Aggregate by (uid, item_idx) user_item = ( filtered.groupby(["uid", "item_idx"], as_index=False)["w"] .sum() .sort_values(["uid", "w"], ascending=[True, False]) ) top_user_item = user_item.groupby("uid").head(top_k_listens) listens_grouped = top_user_item.groupby("uid")[["item_idx", "w"]].apply( lambda g: (g["item_idx"].to_numpy(), g["w"].to_numpy()) ).to_dict() # Build flat arrays with offsets for EmbeddingBag liked_indices, liked_offsets = [], [0] disliked_indices, disliked_offsets = [], [0] listen_indices, listen_weights, listen_offsets = [], [], [0] stats_list = [] for uid in target_uids: # Liked l_items = likes_grouped.get(uid, []) l_idx = [item2idx[i] for i in l_items if i in item2idx] if not l_idx: liked_indices.append(PAD_IDX) else: liked_indices.extend(l_idx) liked_offsets.append(len(liked_indices)) # Disliked d_items = dislikes_grouped.get(uid, []) d_idx = [item2idx[i] for i in d_items if i in item2idx] if not d_idx: disliked_indices.append(PAD_IDX) else: disliked_indices.extend(d_idx) disliked_offsets.append(len(disliked_indices)) # Listens if uid in listens_grouped: u_items, u_w = listens_grouped[uid] # normalize weights so sum is 1.0 sum_w = u_w.sum() norm_w = u_w / (sum_w + 1e-6) listen_indices.extend(u_items) listen_weights.extend(norm_w) else: listen_indices.append(PAD_IDX) listen_weights.append(0.0) listen_offsets.append(len(listen_indices)) # Stats num_likes = len(l_idx) num_dislikes = len(d_idx) num_listens = user_listen_counts.get(uid, 0) mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0 org_ratio = user_organic_ratios.get(uid, 0.0) stats_list.append([ np.log1p(num_likes) / 5.0, np.log1p(num_dislikes) / 3.0, np.log1p(num_listens) / 10.0, mean_ratio, org_ratio, ]) return { "liked_indices": torch.tensor(liked_indices, dtype=torch.long), "liked_offsets": torch.tensor(liked_offsets[:-1], dtype=torch.long), "disliked_indices": torch.tensor(disliked_indices, dtype=torch.long), "disliked_offsets": torch.tensor(disliked_offsets[:-1], dtype=torch.long), "listen_indices": torch.tensor(listen_indices, dtype=torch.long), "listen_weights": torch.tensor(listen_weights, dtype=torch.float32), "listen_offsets": torch.tensor(listen_offsets[:-1], dtype=torch.long), "user_stats": torch.tensor(stats_list, dtype=torch.float32), "uid2row": uid2row, } ## Step 67 · agent · 2026-09-29T00:39:53.974Z ### run_shell_command · 2026-09-29T00:39:53.974Z { "command": "python3 -c \"\nimport time\nimport pandas as pd\nfrom solution.data_utils import load_catalog, extract_user_histories\n\nt0 = time.time()\ncatalog_items, item2idx, feat_matrix = load_catalog()\nprint(f'Catalog loaded in {time.time()-t0:.2f}s, shape: {feat_matrix.shape}')\n\ntargets = pd.read_parquet('/task/data/target_users.parquet')['uid'].tolist()\npref = pd.read_parquet('/task/data/validation_preference_state.parquet')\n\nt0 = time.time()\nhist = extract_user_histories('/task/data/train_listens.parquet', pref, item2idx, targets, 24786800, window_days=60)\nprint(f'User histories extracted in {time.time()-t0:.2f}s')\nprint('liked_indices:', hist['liked_indices'].shape)\nprint('disliked_indices:', hist['disliked_indices'].shape)\nprint('listen_indices:', hist['listen_indices'].shape)\nprint('user_stats:', hist['user_stats'].shape)\n\"", "description": "Test load_catalog and extract_user_histories" } ### Tool output · 2026-09-29T00:40:04.407Z Output: Catalog loaded in 0.03s, shape: torch.Size([30001, 4]) User histories extracted in 6.28s liked_indices: torch.Size([420122]) disliked_indices: torch.Size([54868]) listen_indices: torch.Size([763372]) user_stats: torch.Size([8456, 5]) Process Group PGID: 2504 ## Step 68 · agent · 2026-09-29T00:40:12.112Z ### Recorded reasoning summary Processing User History I've successfully extracted all historical listening features for 8,456 users over 60 days. My next step is to define the precise requirements for `extract_training_targets`, specifically how to generate `(uid, item_idx)` pairs within a given target window. ### run_shell_command · 2026-09-29T00:40:12.112Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(catalog['item_id'])}\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nstart_ts = 24182000\nend_ts = 24786800\n\n# 1. listens\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nmask = pc.and_(pc.greater_equal(table['timestamp'], start_ts), pc.less_equal(table['timestamp'], end_ts))\nfiltered = table.filter(mask).to_pandas()\nstrong = filtered[filtered.uid.isin(targets) & (filtered.is_organic == 1) & (filtered.played_ratio_pct >= 80)]\nstrong = strong.dropna(subset=['item_id'])\nstrong['item_idx'] = strong['item_id'].map(item2idx)\nstrong = strong.dropna(subset=['item_idx'])\nstrong['item_idx'] = strong['item_idx'].astype(int)\n\n# 2. likes\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\norg_likes = likes[(likes.timestamp >= start_ts) & (likes.timestamp <= end_ts) & likes.uid.isin(targets) & (likes.is_organic == 1)]\norg_likes['item_idx'] = org_likes['item_id'].map(item2idx)\norg_likes = org_likes.dropna(subset=['item_idx'])\norg_likes['item_idx'] = org_likes['item_idx'].astype(int)\n\n# Combine\npairs = {}\nfor uid, idx in strong[['uid', 'item_idx']].itertuples(index=False, name=None):\n pairs[(uid, idx)] = 1.0\nfor uid, idx in org_likes[['uid', 'item_idx']].itertuples(index=False, name=None):\n pairs[(uid, idx)] = 3.0\n\nprint(f'Total unique (user, item) positive pairs in target window: {len(pairs)}')\nweights = list(pairs.values())\nprint(f'Grade 3 count: {weights.count(3.0)}, Grade 1 count: {weights.count(1.0)}')\n\"", "description": "Test extracting target positive pairs" } ### Tool output · 2026-09-29T00:40:15.027Z Output: :27: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy Total unique (user, item) positive pairs in target window: 176193 Grade 3 count: 7645, Grade 1 count: 168548 Process Group PGID: 2532 ## Step 69 · agent · 2026-09-29T00:40:31.962Z ### Recorded reasoning summary Designing `model.py` I'm focusing on refining `model.py`. The latest insights reveal a substantial dataset of 176,193 positive (user, item) pairs within the target window, comprising 7,645 Grade 3 organic likes and 168,548 Grade 1 strong listens. This forms a strong foundation for our model architecture. ### Recorded reasoning summary Decomposing Model Architecture I'm breaking down the model into distinct components: an `ItemTower` for generating item embeddings, and a `UserTower` that derives user representations from interaction history, not just IDs. The score function will be a simple dot product. Training will involve positive items and sampled negatives. ### Recorded reasoning summary Sharing Item Embeddings I'm reconsidering the shared item embedding table. It appears `ItemTower` and `UserTower` could leverage a common `nn.Embedding` layer, impacting how item and user features are processed. This could simplify feature interactions and potentially improve efficiency. ### write_file · 2026-09-29T00:40:31.962Z { "content": "\"\"\"Two-tower neural network architecture in PyTorch.\"\"\"\nfrom __future__ import annotations\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nCATALOG_SIZE = 30000\nPAD_IDX = 30000\n\n\nclass ItemTower(nn.Module):\n \"\"\"Item tower neural network.\n\n Takes catalog item indices and precomputed continuous item features,\n and maps them to a normalized d-dimensional item embedding vector.\n \"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n num_features: int = 4,\n out_dim: int = 64,\n hidden_dim: int = 128,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.feat_proj = nn.Linear(num_features, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 2, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Linear(hidden_dim, out_dim),\n nn.LayerNorm(out_dim),\n )\n\n def forward(\n self,\n item_indices: torch.Tensor,\n item_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized item embeddings.\n\n Args:\n item_indices: 1D tensor of item indices in [0, CATALOG_SIZE].\n item_features: 2D tensor of shape (len(item_indices), num_features).\n\n Returns:\n Normalized item embedding tensor of shape (len(item_indices), out_dim).\n \"\"\"\n e = self.item_embed(item_indices)\n f = self.feat_proj(item_features)\n x = torch.cat([e, f], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass UserTower(nn.Module):\n \"\"\"User tower neural network.\n\n Derives user representation strictly from interaction history (liked items,\n listened items with recency/completion weights, disliked items, and aggregate\n interaction statistics). Uses NO user-ID lookup.\n \"\"\"\n\n def __init__(\n self,\n item_embed: nn.Embedding,\n embed_dim: int = 64,\n num_stats: int = 5,\n out_dim: int = 64,\n hidden_dim: int = 128,\n dropout: float = 0.1,\n ):\n super().__init__()\n self.item_embed = item_embed\n self.stat_proj = nn.Linear(num_stats, embed_dim)\n self.mlp = nn.Sequential(\n nn.Linear(embed_dim * 4, hidden_dim),\n nn.LayerNorm(hidden_dim),\n nn.GELU(),\n nn.Dropout(dropout),\n nn.Linear(hidden_dim, out_dim),\n nn.LayerNorm(out_dim),\n )\n\n def forward(\n self,\n liked_indices: torch.Tensor,\n liked_offsets: torch.Tensor,\n disliked_indices: torch.Tensor,\n disliked_offsets: torch.Tensor,\n listen_indices: torch.Tensor,\n listen_weights: torch.Tensor,\n listen_offsets: torch.Tensor,\n user_stats: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Compute normalized user embeddings from interaction history.\n\n Returns:\n Normalized user embedding tensor of shape (batch_size, out_dim).\n \"\"\"\n # Pool liked items\n e_like = F.embedding_bag(\n liked_indices,\n self.item_embed.weight,\n offsets=liked_offsets,\n mode=\"mean\",\n )\n # Pool listened items with weighted sum\n e_listen = F.embedding_bag(\n listen_indices,\n self.item_embed.weight,\n offsets=listen_offsets,\n per_sample_weights=listen_weights,\n mode=\"sum\",\n )\n # Pool disliked items\n e_dislike = F.embedding_bag(\n disliked_indices,\n self.item_embed.weight,\n offsets=disliked_offsets,\n mode=\"mean\",\n )\n # Project user statistical features\n s_proj = self.stat_proj(user_stats)\n\n # Concatenate history representations\n x = torch.cat([e_like, e_listen, e_dislike, s_proj], dim=-1)\n out = self.mlp(x)\n return F.normalize(out, p=2, dim=-1)\n\n\nclass TwoTowerModel(nn.Module):\n \"\"\"Complete two-tower recommendation model.\"\"\"\n\n def __init__(\n self,\n catalog_size: int = CATALOG_SIZE,\n embed_dim: int = 64,\n out_dim: int = 64,\n hidden_dim: int = 128,\n num_item_features: int = 4,\n num_user_stats: int = 5,\n temperature: float = 0.07,\n ):\n super().__init__()\n # Shared item embedding table (+1 for padding/OOV index)\n self.item_embed = nn.Embedding(\n catalog_size + 1,\n embed_dim,\n padding_idx=PAD_IDX,\n )\n # Initialize item embeddings with small normal weights\n nn.init.normal_(self.item_embed.weight, std=0.02)\n # Zero out padding vector\n with torch.no_grad():\n self.item_embed.weight[PAD_IDX].fill_(0.0)\n\n self.item_tower = ItemTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n num_features=num_item_features,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n )\n self.user_tower = UserTower(\n item_embed=self.item_embed,\n embed_dim=embed_dim,\n num_stats=num_user_stats,\n out_dim=out_dim,\n hidden_dim=hidden_dim,\n )\n self.temperature = nn.Parameter(torch.tensor(temperature))\n\n def precompute_item_vectors(\n self,\n all_item_features: torch.Tensor,\n ) -> torch.Tensor:\n \"\"\"Offline precomputation of item embeddings for all catalog items.\n\n Returns:\n Precomputed item embedding tensor of shape (CATALOG_SIZE, out_dim).\n \"\"\"\n device = all_item_features.device\n all_indices = torch.arange(CATALOG_SIZE, device=device, dtype=torch.long)\n catalog_feats = all_item_features[:CATALOG_SIZE]\n return self.item_tower(all_indices, catalog_feats)\n", "file_path": "/app/solution/model.py" } ### Tool output · 2026-09-29T00:40:31.971Z Successfully created and wrote to new file: /app/solution/model.py. Here is the updated code: """Two-tower neural network architecture in PyTorch.""" from __future__ import annotations import torch import torch.nn as nn import torch.nn.functional as F CATALOG_SIZE = 30000 PAD_IDX = 30000 class ItemTower(nn.Module): """Item tower neural network. Takes catalog item indices and precomputed continuous item features, and maps them to a normalized d-dimensional item embedding vector. """ def __init__( self, item_embed: nn.Embedding, embed_dim: int = 64, num_features: int = 4, out_dim: int = 64, hidden_dim: int = 128, ): super().__init__() self.item_embed = item_embed self.feat_proj = nn.Linear(num_features, embed_dim) self.mlp = nn.Sequential( nn.Linear(embed_dim * 2, hidden_dim), nn.LayerNorm(hidden_dim), nn.GELU(), nn.Linear(hidden_dim, out_dim), nn.LayerNorm(out_dim), ) def forward( self, item_indices: torch.Tensor, item_features: torch.Tensor, ) -> torch.Tensor: """Compute normalized item embeddings. Args: item_indices: 1D tensor of item indices in [0, CATALOG_SIZE]. item_features: 2D tensor of shape (len(item_indices), num_features). Returns: Normalized item embedding tensor of shape (len(item_indices), out_dim). """ e = self.item_embed(item_indices) f = self.feat_proj(item_features) x = torch.cat([e, f], dim=-1) out = self.mlp(x) return F.normalize(out, p=2, dim=-1) class UserTower(nn.Module): """User tower neural network. Derives user representation strictly from interaction history (liked items, listened items with recency/completion weights, disliked items, and aggregate interaction statistics). Uses NO user-ID lookup. """ def __init__( self, item_embed: nn.Embedding, embed_dim: int = 64, num_stats: int = 5, out_dim: int = 64, hidden_dim: int = 128, dropout: float = 0.1, ): super().__init__() self.item_embed = item_embed self.stat_proj = nn.Linear(num_stats, embed_dim) self.mlp = nn.Sequential( nn.Linear(embed_dim * 4, hidden_dim), nn.LayerNorm(hidden_dim), nn.GELU(), nn.Dropout(dropout), nn.Linear(hidden_dim, out_dim), nn.LayerNorm(out_dim), ) def forward( self, liked_indices: torch.Tensor, liked_offsets: torch.Tensor, disliked_indices: torch.Tensor, disliked_offsets: torch.Tensor, listen_indices: torch.Tensor, listen_weights: torch.Tensor, listen_offsets: torch.Tensor, user_stats: torch.Tensor, ) -> torch.Tensor: """Compute normalized user embeddings from interaction history. Returns: Normalized user embedding tensor of shape (batch_size, out_dim). """ # Pool liked items e_like = F.embedding_bag( liked_indices, self.item_embed.weight, offsets=liked_offsets, mode="mean", ) # Pool listened items with weighted sum e_listen = F.embedding_bag( listen_indices, self.item_embed.weight, offsets=listen_offsets, per_sample_weights=listen_weights, mode="sum", ) # Pool disliked items e_dislike = F.embedding_bag( disliked_indices, self.item_embed.weight, offsets=disliked_offsets, mode="mean", ) # Project user statistical features s_proj = self.stat_proj(user_stats) # Concatenate history representations x = torch.cat([e_like, e_listen, e_dislike, s_proj], dim=-1) out = self.mlp(x) return F.normalize(out, p=2, dim=-1) class TwoTowerModel(nn.Module): """Complete two-tower recommendation model.""" def __init__( self, catalog_size: int = CATALOG_SIZE, embed_dim: int = 64, out_dim: int = 64, hidden_dim: int = 128, num_item_features: int = 4, num_user_stats: int = 5, temperature: float = 0.07, ): super().__init__() # Shared item embedding table (+1 for padding/OOV index) self.item_embed = nn.Embedding( catalog_size + 1, embed_dim, padding_idx=PAD_IDX, ) # Initialize item embeddings with small normal weights nn.init.normal_(self.item_embed.weight, std=0.02) # Zero out padding vector with torch.no_grad(): self.item_embed.weight[PAD_IDX].fill_(0.0) self.item_tower = ItemTower( item_embed=self.item_embed, embed_dim=embed_dim, num_features=num_item_features, out_dim=out_dim, hidden_dim=hidden_dim, ) self.user_tower = UserTower( item_embed=self.item_embed, embed_dim=embed_dim, num_stats=num_user_stats, out_dim=out_dim, hidden_dim=hidden_dim, ) self.temperature = nn.Parameter(torch.tensor(temperature)) def precompute_item_vectors( self, all_item_features: torch.Tensor, ) -> torch.Tensor: """Offline precomputation of item embeddings for all catalog items. Returns: Precomputed item embedding tensor of shape (CATALOG_SIZE, out_dim). """ device = all_item_features.device all_indices = torch.arange(CATALOG_SIZE, device=device, dtype=torch.long) catalog_feats = all_item_features[:CATALOG_SIZE] return self.item_tower(all_indices, catalog_feats) ## Step 70 · agent · 2026-09-29T00:40:37.967Z ### Recorded reasoning summary Developing Retrieval Strategy I've finalized the `solution/model.py` file. My current focus is on refining the retrieval and ranking process. I'm working through how to effectively map the `user_vectors` (8456, d) and `item_vectors` (30000, d) to generate the top 100 ranked items for each user, considering the provided tensor shapes `U` and `V`. ### run_shell_command · 2026-09-29T00:40:37.967Z { "command": "python3 -c \"\nimport numpy as np\nimport time\n\npacked = np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible']\nt0 = time.time()\nmasks = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\nprint(f'Unpacked all 8456 rows in {time.time()-t0:.4f}s, shape: {masks.shape}')\n\"", "description": "Benchmark unpacking all eligibility bitmasks" } ### Tool output · 2026-09-29T00:40:38.507Z Output: Unpacked all 8456 rows in 0.0274s, shape: (8456, 30000) Process Group PGID: 2554 ## Step 71 · agent · 2026-09-29T00:40:43.300Z ### Recorded reasoning summary Processing Masks and Scores I've successfully unpacked all 8,456 masks and am now calculating the dot product between U and V. Transposing V and then performing the matrix multiplication is complete. I'm currently converting the resulting tensor to a NumPy array for efficient manipulation and masking out irrelevant areas with a very low score. The next step involves identifying the indices of the top 100 scores. ### run_shell_command · 2026-09-29T00:40:43.300Z { "command": "python3 -c \"\nimport numpy as np\nimport torch\nimport time\n\npacked = np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible']\nU = torch.randn(8456, 64)\nV = torch.randn(30000, 64)\n\nt0 = time.time()\nscores = torch.matmul(U, V.T).numpy()\nmasks = np.unpackbits(packed, axis=1, count=30000, bitorder='little')\nscores[masks == 0] = -1e9\n\n# Fast top-100 sort\npart = np.argpartition(-scores, 100, axis=1)[:, :100]\nrow_idx = np.arange(len(scores))[:, None]\npart_scores = scores[row_idx, part]\nsort_order = np.argsort(-part_scores, axis=1)\ntop100 = np.take_along_axis(part, sort_order, axis=1)\n\nprint(f'Full retrieval took {time.time()-t0:.4f}s, top100 shape: {top100.shape}')\n\"", "description": "Benchmark end-to-end vector retrieval on CPU" } ### Tool output · 2026-09-29T00:40:48.666Z Output: Full retrieval took 2.1679s, top100 shape: (8456, 100) Process Group PGID: 2564 ## Step 72 · agent · 2026-09-29T00:40:51.799Z ### Recorded reasoning summary Achieving Peak Performance My latest thoughts confirm that full retrieval, even with masking across all target users and items, now completes in a swift 2.16 seconds. I'm now exploring `torch.topk` for even greater speed, anticipating it will leverage multi-threading for a significant boost. ### run_shell_command · 2026-09-29T00:40:51.799Z { "command": "python3 -c \"\nimport numpy as np\nimport torch\nimport time\n\npacked = np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible']\nU = torch.randn(8456, 64)\nV = torch.randn(30000, 64)\n\nt0 = time.time()\nscores = torch.matmul(U, V.T)\nmasks = torch.from_numpy(np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool))\nscores[~masks] = -1e9\ntop100 = torch.topk(scores, 100, dim=1).indices\nprint(f'PyTorch topk retrieval took {time.time()-t0:.4f}s')\n\"", "description": "Benchmark PyTorch topk retrieval" } ### Tool output · 2026-09-29T00:40:55.531Z Output: PyTorch topk retrieval took 0.5756s Process Group PGID: 2577 ## Step 73 · agent · 2026-09-29T00:40:59.126Z ### Recorded reasoning summary Optimizing Data Formatting I'm currently focused on the efficiency of transforming retrieved data into a pandas DataFrame. Initial benchmarks show the retrieval itself is remarkably swift. Now, I'm testing formatting speeds, particularly with large datasets like 845,600 rows, by optimizing the creation of repeated `uid` and `item_id` columns. ### run_shell_command · 2026-09-29T00:40:59.126Z { "command": "python3 -c \"\nimport numpy as np\nimport pandas as pd\nimport time\n\nuids = np.arange(8456)\nitems = np.random.randint(0, 1000000, size=(8456, 100))\n\nt0 = time.time()\ndf = pd.DataFrame({\n 'uid': np.repeat(uids, 100),\n 'item_id': items.flatten(),\n 'rank': np.tile(np.arange(1, 101, dtype=np.int64), len(uids)),\n})\nprint(f'DataFrame created in {time.time()-t0:.4f}s, shape: {df.shape}')\nt0 = time.time()\ndf.to_parquet('/app/solution/temp_test.parquet')\nprint(f'Parquet written in {time.time()-t0:.4f}s')\n\"", "description": "Benchmark DataFrame creation and parquet writing" } ### Tool output · 2026-09-29T00:41:00.753Z Output: DataFrame created in 0.0085s, shape: (845600, 3) Parquet written in 0.1212s Process Group PGID: 2590 ## Step 74 · agent · 2026-09-29T00:41:06.951Z ### run_shell_command · 2026-09-29T00:41:06.951Z { "description": "Remove temp parquet test file", "command": "rm -f /app/solution/temp_test.parquet" } ### Tool output · 2026-09-29T00:41:06.968Z Output: (empty) Process Group PGID: 2607 ## Step 75 · agent · 2026-09-29T00:41:12.881Z ### write_file · 2026-09-29T00:41:12.881Z { "content": "\"\"\"Fast candidate retrieval, masking, and ranking export.\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport torch\n\n\ndef generate_ranking(\n user_embeddings: torch.Tensor,\n item_embeddings: torch.Tensor,\n eligibility_npz_path: str | Path,\n output_path: str | Path | None = None,\n) -> pd.DataFrame:\n \"\"\"Perform MIPS retrieval, mask ineligible items, and generate top-100 ranking.\n\n Args:\n user_embeddings: (num_users, dim) tensor of user representations.\n item_embeddings: (num_items, dim) tensor of candidate representations.\n eligibility_npz_path: Path to eligibility archive (.npz).\n output_path: Optional path to save parquet ranking.\n\n Returns:\n DataFrame with columns ['uid', 'item_id', 'rank'].\n \"\"\"\n elig = np.load(eligibility_npz_path)\n uids = elig[\"uids\"].astype(np.int64)\n catalog_items = elig[\"item_ids\"].astype(np.int64)\n packed = elig[\"packed_eligible\"]\n\n num_users = len(uids)\n num_items = len(catalog_items)\n\n # Compute dot products\n scores = torch.matmul(user_embeddings, item_embeddings.T)\n\n # Unpack eligibility masks and apply\n masks = torch.from_numpy(\n np.unpackbits(packed, axis=1, count=num_items, bitorder=\"little\").astype(bool)\n )\n scores[~masks] = -1e9\n\n # Retrieve top 100 per user\n top100_indices = torch.topk(scores, 100, dim=1).indices.cpu().numpy()\n top100_items = catalog_items[top100_indices]\n\n df = pd.DataFrame({\n \"uid\": np.repeat(uids, 100),\n \"item_id\": top100_items.flatten(),\n \"rank\": np.tile(np.arange(1, 101, dtype=np.int64), num_users),\n })\n\n if output_path is not None:\n df.to_parquet(output_path, index=False)\n\n return df\n", "file_path": "/app/solution/retrieval.py" } ### Tool output · 2026-09-29T00:41:12.889Z Successfully created and wrote to new file: /app/solution/retrieval.py. Here is the updated code: """Fast candidate retrieval, masking, and ranking export.""" from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd import torch def generate_ranking( user_embeddings: torch.Tensor, item_embeddings: torch.Tensor, eligibility_npz_path: str | Path, output_path: str | Path | None = None, ) -> pd.DataFrame: """Perform MIPS retrieval, mask ineligible items, and generate top-100 ranking. Args: user_embeddings: (num_users, dim) tensor of user representations. item_embeddings: (num_items, dim) tensor of candidate representations. eligibility_npz_path: Path to eligibility archive (.npz). output_path: Optional path to save parquet ranking. Returns: DataFrame with columns ['uid', 'item_id', 'rank']. """ elig = np.load(eligibility_npz_path) uids = elig["uids"].astype(np.int64) catalog_items = elig["item_ids"].astype(np.int64) packed = elig["packed_eligible"] num_users = len(uids) num_items = len(catalog_items) # Compute dot products scores = torch.matmul(user_embeddings, item_embeddings.T) # Unpack eligibility masks and apply masks = torch.from_numpy( np.unpackbits(packed, axis=1, count=num_items, bitorder="little").astype(bool) ) scores[~masks] = -1e9 # Retrieve top 100 per user top100_indices = torch.topk(scores, 100, dim=1).indices.cpu().numpy() top100_items = catalog_items[top100_indices] df = pd.DataFrame({ "uid": np.repeat(uids, 100), "item_id": top100_items.flatten(), "rank": np.tile(np.arange(1, 101, dtype=np.int64), num_users), }) if output_path is not None: df.to_parquet(output_path, index=False) return df ## Step 76 · agent · 2026-09-29T00:41:15.936Z ### run_shell_command · 2026-09-29T00:41:15.936Z { "command": "python3 -c \"\nimport torch\nfrom solution.retrieval import generate_ranking\n\nU = torch.randn(8456, 64)\nV = torch.randn(30000, 64)\ndf = generate_ranking(U, V, '/task/data/validation_eligible_candidates.npz', '/app/solution/val_test.parquet')\nprint('Generated test ranking:', df.shape)\n\" && python3 /task/evaluate_public.py /app/solution/val_test.parquet && rm -f /app/solution/val_test.parquet", "description": "Test generate_ranking with evaluate_public.py" } ### Tool output · 2026-09-29T00:41:26.108Z Output: Generated test ranking: (845600, 3) { "model": "/app/solution/val_test.parquet", "scores": { "quality_ndcg@10": 0.000315, "quality_users": 4263 } } Process Group PGID: 2610 ## Step 77 · agent · 2026-09-29T00:41:38.689Z ### Recorded reasoning summary Completing Pipeline Testing The retrieval, masking, ranking, and evaluation pipeline now runs without issue and passes validation with `evaluate_public.py`. My focus is shifting to the training dataset preparation and the training loop itself. ### Recorded reasoning summary Refining Training Strategy I'm now exploring how to best construct training data to boost NDCG@10 beyond the baseline. I'm analyzing the overlap in user history across different windows and considering extracting history at a single point to simplify the data generation process for the two-tower model. ### Recorded reasoning summary Expanding Training Data I've discovered a way to significantly expand my training data by incorporating more positive interactions. I can now combine target pairs from multiple time windows and also leverage individual user's organic listens and likes at a specific cutoff time for training, creating millions of new examples. This is a significant boost over the initial dataset size. ### run_shell_command · 2026-09-29T00:41:38.689Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\nT_split = 24182000\nT_end = 24786800\n\n# Read listens before and after T_split\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\n# Prior history\nm_prior = pc.less(table['timestamp'], T_split)\nt_prior = table.filter(m_prior).to_pandas()\nt_prior = t_prior[t_prior.uid.isin(targets)]\nuser_prior_items = t_prior.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Target window\nm_target = pc.and_(pc.greater_equal(table['timestamp'], T_split), pc.less_equal(table['timestamp'], T_end))\nt_target = table.filter(m_target).to_pandas()\nt_target = t_target[t_target.uid.isin(targets) & (t_target.is_organic == 1) & (t_target.played_ratio_pct >= 80)]\n\nis_new = []\nfor uid, item in t_target[['uid', 'item_id']].itertuples(index=False, name=None):\n prior = user_prior_items.get(uid, set())\n is_new.append(item not in prior)\n\nprint(f'Total target strong listens: {len(is_new)}, fraction new: {np.mean(is_new):.4f}')\n\"", "description": "Check fraction of new items in temporal target window" } ### Tool output · 2026-09-29T00:41:52.543Z Output: Total target strong listens: 428209, fraction new: 0.2594 Process Group PGID: 2643 ## Step 78 · agent · 2026-09-29T00:42:02.210Z ### Recorded reasoning summary Discovering Fresh Music I'm excited to report that a significant portion, over a quarter, of the strong listening activity consists of brand-new music the user hasn't encountered. My eligibility filter is ensuring we only consider unseen items from the candidate catalog for this analysis. ### Recorded reasoning summary Training New Content I'm refining the training data to focus solely on items the user has never heard before, along with their organic likes and strong listens. This approach aims to force the model to truly learn discovery preferences, and I'm testing with multiple historical windows to maximize the dataset size. ### run_shell_command · 2026-09-29T00:42:02.210Z { "description": "Benchmark training throughput on CPU", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport time\nfrom solution.model import TwoTowerModel\n\nmodel = TwoTowerModel(catalog_size=30000, embed_dim=64, out_dim=64, hidden_dim=128)\noptimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-5)\n\n# Simulate 100 batches of size 512\nB = 512\nK_neg = 512\nt0 = time.time()\nfor b in range(50):\n optimizer.zero_grad()\n # Dummy user history inputs for batch\n num_liked = B * 30\n liked_idx = torch.randint(0, 30000, (num_liked,))\n liked_off = torch.arange(0, num_liked, 30)\n \n num_disliked = B * 5\n disliked_idx = torch.randint(0, 30000, (num_disliked,))\n disliked_off = torch.arange(0, num_disliked, 5)\n \n num_listens = B * 50\n listen_idx = torch.randint(0, 30000, (num_listens,))\n listen_w = torch.ones(num_listens, dtype=torch.float32) / 50.0\n listen_off = torch.arange(0, num_listens, 50)\n \n user_stats = torch.randn(B, 5)\n \n # Forward user tower\n u = model.user_tower(liked_idx, liked_off, disliked_idx, disliked_off, listen_idx, listen_w, listen_off, user_stats)\n \n # Forward item tower for positive items\n pos_items = torch.randint(0, 30000, (B,))\n pos_feats = torch.randn(B, 4)\n v_pos = model.item_tower(pos_items, pos_feats)\n \n # Forward item tower for catalog negatives\n neg_items = torch.randint(0, 30000, (K_neg,))\n neg_feats = torch.randn(K_neg, 4)\n v_neg = model.item_tower(neg_items, neg_feats)\n \n v_all = torch.cat([v_pos, v_neg], dim=0) # (B + K_neg, out_dim)\n \n # Scores\n scores = torch.matmul(u, v_all.T) / 0.07 # (B, B + K_neg)\n labels = torch.arange(B)\n loss = F.cross_entropy(scores, labels)\n \n loss.backward()\n optimizer.step()\n\nelapsed = time.time() - t0\nprint(f'50 batches of size {B} ({50 * B} samples) took {elapsed:.2f}s ({50 * B / elapsed:.1f} samples/sec)')\n\"" } ### Tool output · 2026-09-29T00:42:09.088Z Output: 50 batches of size 512 (25600 samples) took 1.11s (23009.5 samples/sec) Process Group PGID: 2671 ## Step 79 · agent · 2026-09-29T00:42:20.505Z ### Recorded reasoning summary Achieving Incredible Speeds I'm thrilled by the latest results. Processing 23,000 samples per second on the CPU means full epochs are now incredibly fast, with 10 epochs completing in under 1.5 minutes. This pace provides ample room for rapid experimentation. ### Recorded reasoning summary Optimizing Batch Construction My focus has shifted to efficiently building training batches. With only 8,456 target users, I'm exploring strategies like pre-indexing user representations or batching user histories to avoid overhead when creating real data batches. ### Recorded reasoning summary Refining User Embedding Calculation I've discovered that computing user embeddings for all 8,456 target users simultaneously takes a mere 5 milliseconds. This allows me to simply index pre-computed embeddings for any training pair, drastically reducing overhead for batch construction. ### Recorded reasoning summary Improving Training Efficiency I've refined the training process. With each epoch now taking a mere 0.2 seconds, thanks to efficient batching of distinct users and items, 10 epochs are completed swiftly. I'm now considering how to best leverage multiple positive items per user and adjust sampling probabilities. ### run_shell_command · 2026-09-29T00:42:20.505Z { "description": "Benchmark batch tensor construction on the fly", "command": "python3 -c \"\nimport torch\nimport torch.nn.functional as F\n\n# Test slicing user histories for a batch of user row indices\n# Suppose we have user_liked = {uid: [items]}, user_disliked = {uid: [items]}, etc.\n# For a batch of uids, we can construct the batch tensors on the fly in Python/NumPy!\nuids = list(range(1024))\nimport time\nt0 = time.time()\n\n# Dummy data\nuser_likes = {u: list(range(u % 20 + 1)) for u in uids}\nuser_listens = {u: (list(range(u % 50 + 1)), [1.0/(u%50+1)]*(u%50+1)) for u in uids}\n\nindices = []\noffsets = [0]\nfor u in uids:\n items = user_likes[u]\n indices.extend(items)\n offsets.append(len(indices))\n\nidx_t = torch.tensor(indices, dtype=torch.long)\noff_t = torch.tensor(offsets[:-1], dtype=torch.long)\nprint(f'Batch tensor construction took {(time.time()-t0)*1000:.2f} ms, total items: {len(indices)}')\n\"" } ### Tool output · 2026-09-29T00:42:23.460Z Output: Batch tensor construction took 2.78 ms, total items: 10720 Process Group PGID: 2684 ## Step 80 · agent · 2026-09-29T00:42:33.677Z ### Recorded reasoning summary Optimizing Batch Tensor Construction I'm focusing on the performance of batch tensor construction, specifically reducing the 2.78ms overhead. My current line of inquiry involves precomputing user history tensors for all target users, leveraging the `hist['liked_indices']` and `hist['liked_offsets']` already generated by `extract_user_histories`. ### Recorded reasoning summary Precomputing User Embeddings I'm exploring precomputing embeddings for all target users at the epoch's start. Initial tests show the user tower handles all 8,456 users in roughly 5ms, significantly reducing overhead. This could allow direct loss computation on mini-batches of user and item embeddings. ### run_shell_command · 2026-09-29T00:42:33.677Z { "description": "Analyze target user coverage across historical windows", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\n# Let's inspect multiple historical windows:\n# Window 0: [24786800 - 7*86400, 24786800] = [24182000, 24786800]\n# Window 1: [24786800 - 14*86400, 24786800 - 7*86400] = [23577200, 24182000]\n# Window 2: [24786800 - 21*86400, 24786800 - 14*86400] = [22972400, 23577200]\n# Window 3: [24786800 - 28*86400, 24786800 - 21*86400] = [22367600, 22972400]\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem2idx = {int(item): idx for idx, item in enumerate(catalog['item_id'])}\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\nt_listens = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nt_likes = pd.read_parquet('/task/data/train_likes.parquet')\n\n# Let's see how many target users have at least 1 positive item across these windows\nfor n_windows in [1, 2, 3, 4]:\n start_ts = 24786800 - n_windows * 7 * 86400\n mask = pc.and_(pc.greater_equal(t_listens['timestamp'], start_ts), pc.less_equal(t_listens['timestamp'], 24786800))\n filtered = t_listens.filter(mask).to_pandas()\n strong = filtered[filtered.uid.isin(targets) & (filtered.is_organic == 1) & (filtered.played_ratio_pct >= 80) & filtered.item_id.isin(item2idx)]\n \n likes = t_likes[(t_likes.timestamp >= start_ts) & (t_likes.timestamp <= 24786800) & t_likes.uid.isin(targets) & (t_likes.is_organic == 1) & t_likes.item_id.isin(item2idx)]\n \n uids_covered = set(strong.uid) | set(likes.uid)\n print(f'{n_windows} windows: strong listens={len(strong)}, likes={len(likes)}, active target users={len(uids_covered)} / {len(targets)} ({len(uids_covered)/len(targets)*100:.1f}%)')\n\"" } ### Tool output · 2026-09-29T00:42:40.517Z Output: 1 windows: strong listens=304885, likes=7774, active target users=5989 / 8456 (70.8%) 2 windows: strong listens=593945, likes=14903, active target users=6752 / 8456 (79.8%) 3 windows: strong listens=880960, likes=22354, active target users=7142 / 8456 (84.5%) 4 windows: strong listens=1170136, likes=29985, active target users=7374 / 8456 (87.2%) Process Group PGID: 2694 ## Step 81 · agent · 2026-09-29T00:43:03.377Z ### Recorded reasoning summary Analyzing User Engagement I've observed strong engagement metrics across 28 days, with over a million listens and nearly 30,000 organic likes, reaching 87.2% of our target users. I'm now focused on understanding the behavior of the remaining 12.8%, investigating their listening patterns in earlier timeframes to ensure comprehensive user coverage. ### Recorded reasoning summary Structuring Training Data I'm now organizing the training dataset. Each epoch will involve mapping user histories to relevant discovery tracks. I'm planning to use a `(user, item, weight)` structure, assigning a weight of 3.0 for organic likes and 1.0 for strong organic listens. This will allow for weighted sampling of positive items per user. ### Recorded reasoning summary Testing Training Strategy I've refined the batching and loss calculation, determining that approximately 14 batches per epoch will be generated. My focus is now on writing and executing a full Python prototype to test this strategy, specifically looking for decreases in training loss and improvements in public validation NDCG@10. ### write_file · 2026-09-29T00:43:03.377Z { "file_path": "/app/solution/experiment_1.py", "content": "\"\"\"Experiment 1: Train two-tower model and evaluate on public validation.\"\"\"\nfrom __future__ import annotations\n\nimport random\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef build_training_pairs(\n listens_path: str | Path,\n likes_path: str | Path,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window.\"\"\"\n # 1. listens\n table = pq.read_table(\n listens_path,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n mask = pc.and_(\n pc.greater_equal(table[\"timestamp\"], start_ts),\n pc.less_equal(table[\"timestamp\"], end_ts),\n )\n filtered = table.filter(mask).to_pandas()\n strong = filtered[\n filtered[\"uid\"].isin(target_uids)\n & (filtered[\"is_organic\"] == 1)\n & (filtered[\"played_ratio_pct\"] >= 80)\n ]\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n # 2. likes\n likes = pd.read_parquet(likes_path)\n org_likes = likes[\n (likes[\"timestamp\"] >= start_ts)\n & (likes[\"timestamp\"] <= end_ts)\n & likes[\"uid\"].isin(target_uids)\n & (likes[\"is_organic\"] == 1)\n ]\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n # Merge pairs: uid -> {item_idx: weight}\n user_positives: dict[int, dict[int, float]] = {}\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 1.0\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 3.0\n\n return user_positives\n\n\ndef main():\n set_seed(42)\n print(\"Loading catalog...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n T_val_cutoff = 24786800\n print(\"Extracting validation user histories at T =\", T_val_cutoff)\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n val_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n print(\"Building training positive interactions (last 14 days of train)...\")\n train_positives = build_training_pairs(\n \"/task/data/train_listens.parquet\",\n \"/task/data/train_likes.parquet\",\n item2idx,\n target_set,\n start_ts=T_val_cutoff - 14 * 86400,\n end_ts=T_val_cutoff,\n )\n print(f\"Users with positive targets: {len(train_positives)}\")\n\n # Extract user histories at training split (T_val_cutoff - 14 days)\n T_train_split = T_val_cutoff - 14 * 86400\n print(\"Extracting training user histories at T =\", T_train_split)\n train_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref, # using val_pref or pre-split\n item2idx,\n target_uids,\n T_train_split,\n window_days=60,\n )\n\n # Initialize model\n model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n\n # Active training users\n active_uids = [u for u in target_uids if u in train_positives]\n uid2row = train_hist[\"uid2row\"]\n\n batch_size = 512\n num_negatives = 512\n epochs = 15\n\n print(f\"Training for {epochs} epochs...\")\n for epoch in range(1, epochs + 1):\n model.train()\n random.shuffle(active_uids)\n total_loss = 0.0\n num_batches = 0\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n # For each user, sample 1 positive item proportional to weights\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = train_positives[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n # Sample proportional to weight\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n # Build batch user history tensors\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n # liked\n l_start = train_hist[\"liked_offsets\"][r].item()\n l_end = (\n train_hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(train_hist[\"liked_offsets\"])\n else len(train_hist[\"liked_indices\"])\n )\n b_liked_idx.extend(train_hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n # disliked\n d_start = train_hist[\"disliked_offsets\"][r].item()\n d_end = (\n train_hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(train_hist[\"disliked_offsets\"])\n else len(train_hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(train_hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n # listens\n ls_start = train_hist[\"listen_offsets\"][r].item()\n ls_end = (\n train_hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(train_hist[\"listen_offsets\"])\n else len(train_hist[\"listen_indices\"])\n )\n b_listen_idx.extend(train_hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(train_hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n # stats\n b_stats.append(train_hist[\"user_stats\"][r])\n\n # Convert to tensors\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n # Sample catalog negatives\n t_neg_items = torch.randint(0, CATALOG_SIZE, (num_negatives,))\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n # Forward towers\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n avg_loss = total_loss / max(1, num_batches)\n if epoch % 3 == 0 or epoch == epochs:\n # Evaluate on validation\n model.eval()\n with torch.no_grad():\n val_u_emb = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v_emb = model.precompute_item_vectors(feat_matrix)\n\n val_ranking_path = \"/app/solution/val_exp1.parquet\"\n generate_ranking(\n val_u_emb,\n val_v_emb,\n \"/task/data/validation_eligible_candidates.npz\",\n val_ranking_path,\n )\n\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | Evaluating validation ranking...\")\n import subprocess\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_ranking_path],\n capture_output=True,\n text=True,\n )\n print(res.stdout.strip())\n else:\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f}\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:43:03.387Z Successfully created and wrote to new file: /app/solution/experiment_1.py. Here is the updated code: """Experiment 1: Train two-tower model and evaluate on public validation.""" from __future__ import annotations import random import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def build_training_pairs( listens_path: str | Path, likes_path: str | Path, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window.""" # 1. listens table = pq.read_table( listens_path, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) mask = pc.and_( pc.greater_equal(table["timestamp"], start_ts), pc.less_equal(table["timestamp"], end_ts), ) filtered = table.filter(mask).to_pandas() strong = filtered[ filtered["uid"].isin(target_uids) & (filtered["is_organic"] == 1) & (filtered["played_ratio_pct"] >= 80) ] strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) # 2. likes likes = pd.read_parquet(likes_path) org_likes = likes[ (likes["timestamp"] >= start_ts) & (likes["timestamp"] <= end_ts) & likes["uid"].isin(target_uids) & (likes["is_organic"] == 1) ] org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) # Merge pairs: uid -> {item_idx: weight} user_positives: dict[int, dict[int, float]] = {} for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 1.0 for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 3.0 return user_positives def main(): set_seed(42) print("Loading catalog...") catalog_items, item2idx, feat_matrix = load_catalog() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) T_val_cutoff = 24786800 print("Extracting validation user histories at T =", T_val_cutoff) val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") val_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) print("Building training positive interactions (last 14 days of train)...") train_positives = build_training_pairs( "/task/data/train_listens.parquet", "/task/data/train_likes.parquet", item2idx, target_set, start_ts=T_val_cutoff - 14 * 86400, end_ts=T_val_cutoff, ) print(f"Users with positive targets: {len(train_positives)}") # Extract user histories at training split (T_val_cutoff - 14 days) T_train_split = T_val_cutoff - 14 * 86400 print("Extracting training user histories at T =", T_train_split) train_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, # using val_pref or pre-split item2idx, target_uids, T_train_split, window_days=60, ) # Initialize model model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07, ) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4) # Active training users active_uids = [u for u in target_uids if u in train_positives] uid2row = train_hist["uid2row"] batch_size = 512 num_negatives = 512 epochs = 15 print(f"Training for {epochs} epochs...") for epoch in range(1, epochs + 1): model.train() random.shuffle(active_uids) total_loss = 0.0 num_batches = 0 for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue # For each user, sample 1 positive item proportional to weights b_pos_items = [] b_weights = [] for u in b_uids: items_dict = train_positives[u] items = list(items_dict.keys()) weights = list(items_dict.values()) # Sample proportional to weight chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) # Build batch user history tensors b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] # liked l_start = train_hist["liked_offsets"][r].item() l_end = ( train_hist["liked_offsets"][r + 1].item() if r + 1 < len(train_hist["liked_offsets"]) else len(train_hist["liked_indices"]) ) b_liked_idx.extend(train_hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) # disliked d_start = train_hist["disliked_offsets"][r].item() d_end = ( train_hist["disliked_offsets"][r + 1].item() if r + 1 < len(train_hist["disliked_offsets"]) else len(train_hist["disliked_indices"]) ) b_disliked_idx.extend(train_hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) # listens ls_start = train_hist["listen_offsets"][r].item() ls_end = ( train_hist["listen_offsets"][r + 1].item() if r + 1 < len(train_hist["listen_offsets"]) else len(train_hist["listen_indices"]) ) b_listen_idx.extend(train_hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(train_hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) # stats b_stats.append(train_hist["user_stats"][r]) # Convert to tensors t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) # Sample catalog negatives t_neg_items = torch.randint(0, CATALOG_SIZE, (num_negatives,)) t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() # Forward towers u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 avg_loss = total_loss / max(1, num_batches) if epoch % 3 == 0 or epoch == epochs: # Evaluate on validation model.eval() with torch.no_grad(): val_u_emb = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v_emb = model.precompute_item_vectors(feat_matrix) val_ranking_path = "/app/solution/val_exp1.parquet" generate_ranking( val_u_emb, val_v_emb, "/task/data/validation_eligible_candidates.npz", val_ranking_path, ) print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | Evaluating validation ranking...") import subprocess res = subprocess.run( ["python3", "/task/evaluate_public.py", val_ranking_path], capture_output=True, text=True, ) print(res.stdout.strip()) else: print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f}") if __name__ == "__main__": main() ## Step 82 · agent · 2026-09-29T00:43:07.391Z ### run_shell_command · 2026-09-29T00:43:07.391Z { "command": "python3 /app/solution/experiment_1.py", "description": "Run Experiment 1: Two-Tower model training and validation evaluation" } ### Tool output · 2026-09-29T00:43:11.399Z Output: Traceback (most recent call last): File "/app/solution/experiment_1.py", line 15, in from solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX ModuleNotFoundError: No module named 'solution' Exit Code: 1 Process Group PGID: 2722 ## Step 83 · agent · 2026-09-29T00:43:13.590Z ### run_shell_command · 2026-09-29T00:43:13.590Z { "description": "Run Experiment 1 with PYTHONPATH=/app", "command": "PYTHONPATH=/app python3 /app/solution/experiment_1.py" } ### Tool output · 2026-09-29T00:44:04.413Z Output: /app/solution/experiment_1.py:50: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_1.py:62: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Loading catalog... Extracting validation user histories at T = 24786800 Building training positive interactions (last 14 days of train)... Users with positive targets: 6752 Extracting training user histories at T = 23577200 Training for 15 epochs... Epoch 01 | Loss: 6.8552 Epoch 02 | Loss: 6.7795 Epoch 03 | Loss: 6.7581 | Evaluating validation ranking... { "model": "/app/solution/val_exp1.parquet", "scores": { "quality_ndcg@10": 0.006681, "quality_users": 4263 } } Epoch 04 | Loss: 6.7476 Epoch 05 | Loss: 6.7176 Epoch 06 | Loss: 6.6329 | Evaluating validation ranking... { "model": "/app/solution/val_exp1.parquet", "scores": { "quality_ndcg@10": 0.012545, "quality_users": 4263 } } Epoch 07 | Loss: 6.5214 Epoch 08 | Loss: 6.4088 Epoch 09 | Loss: 6.3725 | Evaluating validation ranking... { "model": "/app/solution/val_exp1.parquet", "scores": { "quality_ndcg@10": 0.003572, "quality_users": 4263 } } Epoch 10 | Loss: 6.2552 Epoch 11 | Loss: 6.1783 Epoch 12 | Loss: 6.1162 | Evaluating validation ranking... { "model": "/app/solution/val_exp1.parquet", "scores": { "quality_ndcg@10": 0.005431, "quality_users": 4263 } } Epoch 13 | Loss: 6.0380 Epoch 14 | Loss: 6.0288 Epoch 15 | Loss: 5.9445 | Evaluating validation ranking... { "model": "/app/solution/val_exp1.parquet", "scores": { "quality_ndcg@10": 0.004166, "quality_users": 4263 } } Process Group PGID: 2733 ## Step 84 · agent · 2026-09-29T00:44:21.562Z ### Recorded reasoning summary Investigating NDCG Drop I'm noticing a concerning trend: while NDCG@10 saw an initial boost at Epoch 6, it has since declined. My current focus is on understanding why this happened, specifically examining the constant learning rate and potential overfitting or embedding drift that might be contributing to this performance degradation. ### run_shell_command · 2026-09-29T00:44:21.562Z { "description": "Analyze popularity rank of retrieved items from Experiment 1", "command": "python3 -c \"\nimport pandas as pd\ndf = pd.read_parquet('/app/solution/val_exp1.parquet')\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\nranks = [item_pop[i] for i in df.item_id]\nimport numpy as np\nprint('Retrieved items popularity rank: mean=', np.mean(ranks), 'median=', np.median(ranks), 'min=', np.min(ranks), 'max=', np.max(ranks))\nprint('Top 10 most frequent items in ranking:')\nprint(df.item_id.value_counts().head(10))\n\"" } ### Tool output · 2026-09-29T00:44:23.444Z Output: Retrieved items popularity rank: mean= 7988.541078524125 median= 5267.0 min= 1 max= 29992 Top 10 most frequent items in ranking: item_id 3257008 1321 2966164 1312 1308225 1238 5111954 1221 8961797 1190 7340873 1130 2275849 1129 6694551 1117 641052 1115 7873233 1095 Name: count, dtype: int64 Process Group PGID: 2854 ## Step 85 · agent · 2026-09-29T00:44:43.258Z ### Recorded reasoning summary Analyzing Recommendation Performance I've been examining the performance metrics, and the retrieved items show a strong median popularity rank mirroring the ground truth. It's interesting to note the significant reach of certain items, with some being recommended to over a thousand users. My next step is to delve into the specifics of these high-impact recommendations. ### Recorded reasoning summary Investigating Model Drift I'm scrutinizing the `UserTower`'s embedding computations. A key observation: if a user has no dislikes, their dislike embedding becomes a zero vector due to padding. This disparity between zero and averaged/weighted embeddings could be a significant factor in model drift and overfitting. ### Recorded reasoning summary Aligning Item and User Representations I've identified a critical asymmetry: the `UserTower` was averaging raw item embeddings, while the `ItemTower` processes them through a non-linear MLP. This mismatch, where user history items aren't represented the same way as candidate items, is likely impacting performance. I'm now exploring how to align these representations correctly. ### Recorded reasoning summary Revising Tower Architectures I'm contemplating the internal mechanics of both towers. Specifically, whether the item tower employs a linear or shared projection. I'm also exploring the possibility of pooling item tower outputs within the user tower, allowing user representations to emerge from the same embedding space as item representations. This could significantly enhance the direct comparison of user preferences and candidate item similarities. ### Recorded reasoning summary Refining User Preferences I'm now incorporating dislike preferences, scaling them by $\alpha$ and subtracting from the like preferences. This refinement will also account for user engagement and organic ratios. I'm testing this updated mathematical formulation for user preference vector generation and relevance scoring against candidate items. ### Recorded reasoning summary Adjusting Preference Scaling I'm recalibrating how I combine like and dislike signals. Specifically, I'm experimenting with different scaling factors ($\alpha$) for dislikes and factoring in engagement ratios to create a more nuanced user preference vector. The goal is to improve the accuracy of the relevance scores. ### Recorded reasoning summary Validating Representation Alignment I've confirmed that the User Tower was indeed pooling untransformed item embeddings while the Item Tower used an MLP. This created an alignment issue, forcing an arbitrary cross-space mapping and contributing to instability. I'm now verifying that both towers operate on the same transformed item representations. ### run_shell_command · 2026-09-29T00:44:43.258Z { "description": "Test autograd through dynamically computed item vectors V", "command": "python3 -c \"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# Test end-to-end autograd through dynamically computed item vectors V\nitem_embed = nn.Embedding(100, 32)\nfeat_proj = nn.Linear(4, 32)\nfeats = torch.randn(100, 4)\n\n# Item tower computes V\nidx = torch.arange(100)\nV = F.normalize(item_embed(idx) + feat_proj(feats), p=2, dim=-1)\n\n# User tower uses V in embedding_bag\nliked_idx = torch.tensor([1, 2, 5, 10], dtype=torch.long)\nliked_off = torch.tensor([0, 2], dtype=torch.long)\nu_like = F.embedding_bag(liked_idx, V, offsets=liked_off, mode='mean')\n\n# User MLP\nuser_mlp = nn.Linear(32, 32)\nu = F.normalize(user_mlp(u_like), p=2, dim=-1)\n\n# Loss: dot product with positive item 1 for user 0, item 5 for user 1\npos_items = torch.tensor([1, 5])\nv_pos = V[pos_items]\nscores = torch.sum(u * v_pos, dim=-1)\nloss = -scores.mean()\n\nloss.backward()\nprint('Autograd succeeded!')\nprint('item_embed grad norm:', item_embed.weight.grad.norm().item())\nprint('feat_proj grad norm:', feat_proj.weight.grad.norm().item())\nprint('user_mlp grad norm:', user_mlp.weight.grad.norm().item())\n\"" } ### Tool output · 2026-09-29T00:44:46.205Z Output: Autograd succeeded! item_embed grad norm: 0.12581439316272736 feat_proj grad norm: 0.165973499417305 user_mlp grad norm: 0.8690409064292908 Process Group PGID: 2872 ## Step 86 · agent · 2026-09-29T00:45:03.794Z ### Recorded reasoning summary Confirming gradient flow The autograd system has successfully processed `F.embedding_bag` with my tensor `V`, ensuring all gradients are flowing as expected. This confirms a critical pathway for learning. Now, I'm turning my attention to the challenges of effectively learning item embeddings given a large corpus of 30,000 items. ### Recorded reasoning summary Exploring co-occurrence relationships I'm delving into how track embeddings are learned in music recommendation, particularly focusing on the co-occurrence patterns within listening logs as a primary relationship. My training setup implicitly learns this co-occurrence structure by predicting the next item from a user's history, and I'm considering pre-training item embeddings using methods like Word2Vec. ### Recorded reasoning summary Designing two-tower architecture My focus is now on a two-tower neural network design, strictly avoiding global matrix factorization. The user tower will learn from interaction history, not just IDs, while item embeddings will be precomputed offline for low latency retrieval, ensuring relevance is the dot product of independently computed user and item vectors. ### Recorded reasoning summary Investigating performance dip My recent analysis reveals that Experiment 1's NDCG@10 score peaked and then declined due to a constant learning rate and insufficient regularization. I'm now prioritizing a cosine annealing schedule and increased weight decay, alongside implementing best checkpointing to prevent embedding drift and ensure sustained improvement. ### Recorded reasoning summary Refining negative sampling I'm realizing uniform negative sampling is inefficient. It trains me to distinguish popular from obscure, but not nuanced preferences within popular items. Industry best practices suggest sampling negatives proportionally to popularity, which will force me to discriminate between genuinely relevant high-quality candidates. ### Recorded reasoning summary Refining negative sampling I'm realizing uniform negative sampling is inefficient. It trains me to distinguish popular from obscure, but not nuanced preferences within popular items. Industry best practices suggest sampling negatives proportionally to popularity, which will force me to discriminate between genuinely relevant high-quality candidates. **Exploring multi-window data** I'm considering using multiple temporal or sliding windows, such as 28 days of history, to increase training data by three times and expose the model to more diverse listening contexts. This contrasts with the single 14-day window used previously. **Implementing discovery filtering** I'm realizing that training on all positive items, including repeats, might confuse the discovery tower. I will now focus on training with only eligible or new items for the user at the cutoff, to better align with the discovery shelf's purpose. ### run_shell_command · 2026-09-29T00:45:03.794Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport pandas as pd\nimport numpy as np\n\nT_split = 24182000\nT_end = 24786800\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'])\n\n# Prior items up to T_split\nm_prior = pc.less(table['timestamp'], T_split)\nt_prior = table.filter(m_prior).to_pandas()\nt_prior = t_prior[t_prior.uid.isin(targets)]\nuser_prior = t_prior.groupby('uid')['item_id'].apply(set).to_dict()\n\n# Target items\nm_target = pc.and_(pc.greater_equal(table['timestamp'], T_split), pc.less_equal(table['timestamp'], T_end))\nt_target = table.filter(m_target).to_pandas()\nt_target = t_target[t_target.uid.isin(targets) & (t_target.is_organic == 1) & (t_target.played_ratio_pct >= 80)]\n\nlikes = pd.read_parquet('/task/data/train_likes.parquet')\nt_likes = likes[(likes.timestamp >= T_split) & (likes.timestamp <= T_end) & likes.uid.isin(targets) & (likes.is_organic == 1)]\n\n# Check how many are new\nnew_listens = 0\ntotal_listens = len(t_target)\nfor u, item in t_target[['uid', 'item_id']].itertuples(index=False, name=None):\n if item not in user_prior.get(u, set()):\n new_listens += 1\n\nnew_likes = 0\ntotal_likes = len(t_likes)\nfor u, item in t_likes[['uid', 'item_id']].itertuples(index=False, name=None):\n if item not in user_prior.get(u, set()):\n new_likes += 1\n\nprint(f'Listens: total={total_listens}, new={new_listens} ({new_listens/total_listens*100:.1f}%)')\nprint(f'Likes: total={total_likes}, new={new_likes} ({new_likes/total_likes*100:.1f}%)')\n\"", "description": "Check new vs repeat items in training target window" } ### Tool output · 2026-09-29T00:45:17.349Z Output: Listens: total=428209, new=111095 (25.9%) Likes: total=12022, new=8901 (74.0%) Process Group PGID: 2891 ## Step 87 · agent · 2026-09-29T00:45:22.470Z ### Recorded reasoning summary Analyzing New Engagement My current focus is on the impressive influx of entirely novel engagement. Over the past week alone, I've recorded over 111,000 fresh listens and nearly 9,000 new likes, with a significant portion originating from new content. Extending this to a 14-day window reveals over 220,000 new listens and roughly 17,000 new likes. This trend appears to be holding steady as I continue to observe the discovery ranking task. ### run_shell_command · 2026-09-29T00:45:22.470Z { "description": "Test popularity-biased negative sampling probabilities", "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncounts = catalog['positive_listens'].to_numpy(dtype=np.float64)\nprobs = np.power(counts, 0.6)\nprobs /= probs.sum()\n\nprint('Probs min:', probs.min(), 'max:', probs.max(), 'ratio max/min:', probs.max() / probs.min())\nsampled = np.random.choice(len(catalog), size=10000, p=probs)\nprint('Sampled indices min/median/max:', sampled.min(), np.median(sampled), sampled.max())\n\"" } ### Tool output · 2026-09-29T00:45:24.025Z Output: Probs min: 1.6143685372072397e-05 max: 0.00034337289066762314 ratio max/min: 21.26979575937707 Sampled indices min/median/max: 0 8303.0 29982 Process Group PGID: 2913 ## Step 88 · agent · 2026-09-29T00:46:11.385Z ### Recorded reasoning summary Sampling Tracks Progress My sampling strategy is proving effective, with the median index at 8,303. This range genuinely represents the diverse distribution of tracks, from popular to obscure, avoiding an overemphasis on noise. ### Recorded reasoning summary Aligning Item-User Embeddings I'm currently focused on ensuring geometric alignment between my Item and User tower representations. I've examined the Item Tower's architecture, noting its feature projection and concatenation steps. The next step involves analyzing how the User Tower pools item embeddings to establish this crucial alignment. ### Recorded reasoning summary Refining User Representations I'm exploring enhancements for the User Tower, considering attentive pooling and residual connections. The current approach concatenates aggregated liked, listened, and disliked item embeddings with user statistics. I'm evaluating how to better integrate positive preferences like liked and recent listens into the final user representation. ### Recorded reasoning summary Implementing UserMLP I'm now implementing the UserMLP in `solution/model.py` to better preserve positive taste while penalizing dislikes. I'm also configuring dimensions, temperature settings for InfoNCE loss, and evaluating AdamW with specific learning rates, weight decay, and a CosineAnnealingLR scheduler. ### write_file · 2026-09-29T00:46:11.385Z { "file_path": "/app/solution/experiment_2.py", "content": "\"\"\"Experiment 2: Multi-window training, popularity-based negatives, and cosine LR.\"\"\"\nfrom __future__ import annotations\n\nimport random\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef extract_window_positives(\n listens_table,\n likes_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window, marking new items.\"\"\"\n # Listens in window\n mask = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts),\n pc.less_equal(listens_table[\"timestamp\"], end_ts),\n )\n t_listens = listens_table.filter(mask).to_pandas()\n strong = t_listens[\n t_listens[\"uid\"].isin(target_uids)\n & (t_listens[\"is_organic\"] == 1)\n & (t_listens[\"played_ratio_pct\"] >= 80)\n ]\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n # Likes in window\n org_likes = likes_df[\n (likes_df[\"timestamp\"] >= start_ts)\n & (likes_df[\"timestamp\"] <= end_ts)\n & likes_df[\"uid\"].isin(target_uids)\n & (likes_df[\"is_organic\"] == 1)\n ]\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n # User prior items (before start_ts) to identify new items\n m_prior = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts - 60 * 86400),\n pc.less(listens_table[\"timestamp\"], start_ts),\n )\n t_prior = listens_table.filter(m_prior).to_pandas()\n t_prior = t_prior[t_prior[\"uid\"].isin(target_uids)]\n user_prior = t_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n user_positives: dict[int, dict[int, float]] = {}\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n idx2item = catalog[\"item_id\"].to_numpy()\n\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n raw_item = idx2item[idx]\n is_new = raw_item not in user_prior.get(uid, set())\n # Weight: 2.0 for new strong listen, 1.0 for repeat\n w = 2.0 if is_new else 1.0\n user_positives.setdefault(uid, {})[idx] = w\n\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n # Weight: 4.0 for organic like (grade 3 discovery)\n user_positives.setdefault(uid, {})[idx] = 4.0\n\n return user_positives\n\n\ndef main():\n set_seed(42)\n print(\"Loading catalog...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n\n # Precompute popularity sampling distribution for negatives\n catalog_df = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n counts = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float64)\n neg_probs = np.power(counts, 0.6)\n neg_probs /= neg_probs.sum()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n T_val_cutoff = 24786800\n print(\"Extracting validation user histories at T =\", T_val_cutoff)\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n val_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n print(\"Reading listens parquet...\")\n listens_table = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n likes_df = pd.read_parquet(\"/task/data/train_likes.parquet\")\n\n # Build 2 training windows:\n # Window 0: Target = [T_val_cutoff - 7d, T_val_cutoff], History at T_val_cutoff - 7d\n # Window 1: Target = [T_val_cutoff - 14d, T_val_cutoff - 7d], History at T_val_cutoff - 14d\n T_w0_split = T_val_cutoff - 7 * 86400\n T_w1_split = T_val_cutoff - 14 * 86400\n\n print(\"Building Window 0 positive targets...\")\n w0_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff\n )\n print(\"Building Window 1 positive targets...\")\n w1_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split\n )\n\n print(\"Extracting Window 0 history...\")\n w0_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w0_split,\n window_days=60,\n )\n\n print(\"Extracting Window 1 history...\")\n w1_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w1_split,\n window_days=60,\n )\n\n # Initialize model\n model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4)\n epochs = 12\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=5e-5)\n\n batch_size = 512\n num_negatives = 512\n best_score = 0.0\n\n print(f\"Training for {epochs} epochs across 2 temporal windows...\")\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n # Alternate or combine both windows\n windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)]\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n # Build batch user history tensors\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n # liked\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n # disliked\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n # listens\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n # stats\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n # Sample popularity-biased catalog negatives\n neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs)\n t_neg_items = torch.from_numpy(neg_indices).long()\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n # Evaluate on validation every 2 epochs\n if epoch % 2 == 0 or epoch == epochs:\n model.eval()\n with torch.no_grad():\n val_u_emb = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v_emb = model.precompute_item_vectors(feat_matrix)\n\n val_ranking_path = f\"/app/solution/val_exp2_ep{epoch}.parquet\"\n generate_ranking(\n val_u_emb,\n val_v_emb,\n \"/task/data/validation_eligible_candidates.npz\",\n val_ranking_path,\n )\n\n import subprocess, json\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_ranking_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_model_exp2.pt\")\n print(f\" *** New best model saved! Score: {score:.6f} ***\")\n else:\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f}\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:46:11.398Z Successfully created and wrote to new file: /app/solution/experiment_2.py. Here is the updated code: """Experiment 2: Multi-window training, popularity-based negatives, and cosine LR.""" from __future__ import annotations import random import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def extract_window_positives( listens_table, likes_df: pd.DataFrame, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window, marking new items.""" # Listens in window mask = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts), pc.less_equal(listens_table["timestamp"], end_ts), ) t_listens = listens_table.filter(mask).to_pandas() strong = t_listens[ t_listens["uid"].isin(target_uids) & (t_listens["is_organic"] == 1) & (t_listens["played_ratio_pct"] >= 80) ] strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) # Likes in window org_likes = likes_df[ (likes_df["timestamp"] >= start_ts) & (likes_df["timestamp"] <= end_ts) & likes_df["uid"].isin(target_uids) & (likes_df["is_organic"] == 1) ] org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) # User prior items (before start_ts) to identify new items m_prior = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts - 60 * 86400), pc.less(listens_table["timestamp"], start_ts), ) t_prior = listens_table.filter(m_prior).to_pandas() t_prior = t_prior[t_prior["uid"].isin(target_uids)] user_prior = t_prior.groupby("uid")["item_id"].apply(set).to_dict() user_positives: dict[int, dict[int, float]] = {} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") idx2item = catalog["item_id"].to_numpy() for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): raw_item = idx2item[idx] is_new = raw_item not in user_prior.get(uid, set()) # Weight: 2.0 for new strong listen, 1.0 for repeat w = 2.0 if is_new else 1.0 user_positives.setdefault(uid, {})[idx] = w for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): # Weight: 4.0 for organic like (grade 3 discovery) user_positives.setdefault(uid, {})[idx] = 4.0 return user_positives def main(): set_seed(42) print("Loading catalog...") catalog_items, item2idx, feat_matrix = load_catalog() # Precompute popularity sampling distribution for negatives catalog_df = pd.read_parquet("/task/data/candidate_catalog.parquet") counts = catalog_df["positive_listens"].to_numpy(dtype=np.float64) neg_probs = np.power(counts, 0.6) neg_probs /= neg_probs.sum() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) T_val_cutoff = 24786800 print("Extracting validation user histories at T =", T_val_cutoff) val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") val_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) print("Reading listens parquet...") listens_table = pq.read_table( "/task/data/train_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) likes_df = pd.read_parquet("/task/data/train_likes.parquet") # Build 2 training windows: # Window 0: Target = [T_val_cutoff - 7d, T_val_cutoff], History at T_val_cutoff - 7d # Window 1: Target = [T_val_cutoff - 14d, T_val_cutoff - 7d], History at T_val_cutoff - 14d T_w0_split = T_val_cutoff - 7 * 86400 T_w1_split = T_val_cutoff - 14 * 86400 print("Building Window 0 positive targets...") w0_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff ) print("Building Window 1 positive targets...") w1_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split ) print("Extracting Window 0 history...") w0_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w0_split, window_days=60, ) print("Extracting Window 1 history...") w1_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w1_split, window_days=60, ) # Initialize model model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07, ) optimizer = torch.optim.AdamW(model.parameters(), lr=1e-3, weight_decay=1e-4) epochs = 12 scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=5e-5) batch_size = 512 num_negatives = 512 best_score = 0.0 print(f"Training for {epochs} epochs across 2 temporal windows...") for epoch in range(1, epochs + 1): model.train() total_loss = 0.0 num_batches = 0 # Alternate or combine both windows windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)] for hist, pos_dict in windows: active_uids = [u for u in target_uids if u in pos_dict] random.shuffle(active_uids) uid2row = hist["uid2row"] for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue b_pos_items = [] b_weights = [] for u in b_uids: items_dict = pos_dict[u] items = list(items_dict.keys()) weights = list(items_dict.values()) chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) # Build batch user history tensors b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] # liked l_start = hist["liked_offsets"][r].item() l_end = ( hist["liked_offsets"][r + 1].item() if r + 1 < len(hist["liked_offsets"]) else len(hist["liked_indices"]) ) b_liked_idx.extend(hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) # disliked d_start = hist["disliked_offsets"][r].item() d_end = ( hist["disliked_offsets"][r + 1].item() if r + 1 < len(hist["disliked_offsets"]) else len(hist["disliked_indices"]) ) b_disliked_idx.extend(hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) # listens ls_start = hist["listen_offsets"][r].item() ls_end = ( hist["listen_offsets"][r + 1].item() if r + 1 < len(hist["listen_offsets"]) else len(hist["listen_indices"]) ) b_listen_idx.extend(hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) # stats b_stats.append(hist["user_stats"][r]) t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) # Sample popularity-biased catalog negatives neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs) t_neg_items = torch.from_numpy(neg_indices).long() t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 scheduler.step() avg_loss = total_loss / max(1, num_batches) current_lr = scheduler.get_last_lr()[0] # Evaluate on validation every 2 epochs if epoch % 2 == 0 or epoch == epochs: model.eval() with torch.no_grad(): val_u_emb = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v_emb = model.precompute_item_vectors(feat_matrix) val_ranking_path = f"/app/solution/val_exp2_ep{epoch}.parquet" generate_ranking( val_u_emb, val_v_emb, "/task/data/validation_eligible_candidates.npz", val_ranking_path, ) import subprocess, json res = subprocess.run( ["python3", "/task/evaluate_public.py", val_ranking_path], capture_output=True, text=True, ) score_data = json.loads(res.stdout.strip()) score = score_data["scores"]["quality_ndcg@10"] print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}") if score > best_score: best_score = score torch.save(model.state_dict(), "/app/solution/best_model_exp2.pt") print(f" *** New best model saved! Score: {score:.6f} ***") else: print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f}") if __name__ == "__main__": main() ## Step 89 · agent · 2026-09-29T00:46:14.756Z ### run_shell_command · 2026-09-29T00:46:14.756Z { "command": "PYTHONPATH=/app python3 /app/solution/experiment_2.py", "description": "Run Experiment 2" } ### Tool output · 2026-09-29T00:47:27.238Z Output: /app/solution/experiment_2.py:46: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_2.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_2.py:46: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_2.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Loading catalog... Extracting validation user histories at T = 24786800 Reading listens parquet... Building Window 0 positive targets... Building Window 1 positive targets... Extracting Window 0 history... Extracting Window 1 history... Training for 12 epochs across 2 temporal windows... Epoch 01 | Loss: 6.8993 | LR: 0.000984 Epoch 02 | Loss: 6.8558 | LR: 0.000936 | Val NDCG@10: 0.021558 *** New best model saved! Score: 0.021558 *** Epoch 03 | Loss: 6.8083 | LR: 0.000861 Epoch 04 | Loss: 6.7082 | LR: 0.000762 | Val NDCG@10: 0.013508 Epoch 05 | Loss: 6.5531 | LR: 0.000648 Epoch 06 | Loss: 6.4403 | LR: 0.000525 | Val NDCG@10: 0.009593 Epoch 07 | Loss: 6.3174 | LR: 0.000402 Epoch 08 | Loss: 6.2112 | LR: 0.000288 | Val NDCG@10: 0.006897 Epoch 09 | Loss: 6.1415 | LR: 0.000189 Epoch 10 | Loss: 6.0488 | LR: 0.000114 | Val NDCG@10: 0.008065 Epoch 11 | Loss: 6.0350 | LR: 0.000066 Epoch 12 | Loss: 5.9916 | LR: 0.000050 | Val NDCG@10: 0.006739 Process Group PGID: 2936 ## Step 90 · agent · 2026-09-29T00:47:32.782Z ### Recorded reasoning summary Analyzing NDCG Dip My NDCG@10 score has more than doubled, reaching 0.021558! It peaked at Epoch 2, but then surprisingly dropped as loss decreased. I'm now investigating why NDCG declined despite falling loss values from 6.85 to 5. ### run_shell_command · 2026-09-29T00:47:32.782Z { "command": "python3 -c \"\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\nitem_pop = dict(zip(catalog.item_id, catalog.popularity_rank))\n\nfor name in ['val_exp2_ep2.parquet', 'val_exp2_ep12.parquet']:\n df = pd.read_parquet(f'/app/solution/{name}')\n ranks = [item_pop[i] for i in df.item_id]\n print(f'{name}:')\n print(' popularity rank: mean=', np.mean(ranks), 'median=', np.median(ranks), 'p25=', np.percentile(ranks, 25), 'p75=', np.percentile(ranks, 75))\n print(' unique items in top 100 across all users:', df.item_id.nunique())\n print(' top item max frequency:', df.item_id.value_counts().iloc[0])\n\"", "description": "Compare retrieved item distributions at epoch 2 vs 12" } ### Tool output · 2026-09-29T00:47:34.978Z Output: val_exp2_ep2.parquet: popularity rank: mean= 3155.7402459791865 median= 1683.0 p25= 291.0 p75= 4140.0 unique items in top 100 across all users: 1075 top item max frequency: 7786 val_exp2_ep12.parquet: popularity rank: mean= 12472.392960028383 median= 11374.0 p25= 4341.0 p75= 20158.0 unique items in top 100 across all users: 8876 top item max frequency: 1638 Process Group PGID: 3075 ## Step 91 · agent · 2026-09-29T00:48:00.286Z ### Recorded reasoning summary Observing Significant Rank Degradation I'm seeing a substantial shift in popularity ranks. Between Epoch 2 and Epoch 12, the median rank has worsened significantly, moving from 1,683 to 11,374. The 75th percentile experienced a similar decline, increasing from 4,140 to 20,158, indicating a drift towards less popular items. ### Recorded reasoning summary Investigating Popularity Suppression It appears my negative sampling strategy, specifically combining catalog negatives proportional to listens with in-batch negatives, inadvertently penalized popular items too aggressively. This led to a significant drift in rankings, pushing median popularity from 1,683 to over 11,000. I need to recalibrate to prevent this over-suppression. ### Recorded reasoning summary Adjusting Negative Sampling Strategy I'm considering that the in-batch negatives already have some popularity bias. Adding proportional catalog negatives on top might be overly penalizing popular items. I'm exploring alternatives like uniform catalog negatives or a lower learning rate with increased weight decay to mitigate this, alongside investigating temperature scaling and evaluating each epoch. I will test these in experiment_3.py. ### write_file · 2026-09-29T00:48:00.286Z { "content": "\"\"\"Experiment 3: Calibrated negative sampling, moderate LR, and per-epoch evaluation.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport random\nimport subprocess\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef extract_window_positives(\n listens_table,\n likes_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window, marking new items.\"\"\"\n mask = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts),\n pc.less_equal(listens_table[\"timestamp\"], end_ts),\n )\n t_listens = listens_table.filter(mask).to_pandas()\n strong = t_listens[\n t_listens[\"uid\"].isin(target_uids)\n & (t_listens[\"is_organic\"] == 1)\n & (t_listens[\"played_ratio_pct\"] >= 80)\n ]\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n org_likes = likes_df[\n (likes_df[\"timestamp\"] >= start_ts)\n & (likes_df[\"timestamp\"] <= end_ts)\n & likes_df[\"uid\"].isin(target_uids)\n & (likes_df[\"is_organic\"] == 1)\n ]\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n m_prior = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts - 60 * 86400),\n pc.less(listens_table[\"timestamp\"], start_ts),\n )\n t_prior = listens_table.filter(m_prior).to_pandas()\n t_prior = t_prior[t_prior[\"uid\"].isin(target_uids)]\n user_prior = t_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n user_positives: dict[int, dict[int, float]] = {}\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n idx2item = catalog[\"item_id\"].to_numpy()\n\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n raw_item = idx2item[idx]\n is_new = raw_item not in user_prior.get(uid, set())\n w = 2.0 if is_new else 1.0\n user_positives.setdefault(uid, {})[idx] = w\n\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 4.0\n\n return user_positives\n\n\ndef main():\n set_seed(42)\n print(\"Loading catalog...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n T_val_cutoff = 24786800\n print(\"Extracting validation user histories at T =\", T_val_cutoff)\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n val_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n print(\"Reading listens parquet...\")\n listens_table = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n likes_df = pd.read_parquet(\"/task/data/train_likes.parquet\")\n\n T_w0_split = T_val_cutoff - 7 * 86400\n T_w1_split = T_val_cutoff - 14 * 86400\n\n print(\"Building Window 0 & 1 targets and histories...\")\n w0_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff\n )\n w1_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split\n )\n\n w0_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w0_split,\n window_days=60,\n )\n w1_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w1_split,\n window_days=60,\n )\n\n model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.08,\n )\n # Moderate learning rate and higher weight decay\n optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-3)\n epochs = 10\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5)\n\n batch_size = 512\n num_negatives = 128 # moderate catalog negatives to avoid over-penalizing head items\n best_score = 0.0\n best_ranking_path = \"/app/solution/val_best.parquet\"\n\n print(f\"Training for {epochs} epochs...\")\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)]\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n # Uniform catalog negatives\n t_neg_items = torch.randint(0, CATALOG_SIZE, (num_negatives,))\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n # Evaluate on validation every single epoch\n model.eval()\n with torch.no_grad():\n val_u_emb = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v_emb = model.precompute_item_vectors(feat_matrix)\n\n val_ranking_path = f\"/app/solution/val_exp3_ep{epoch}.parquet\"\n generate_ranking(\n val_u_emb,\n val_v_emb,\n \"/task/data/validation_eligible_candidates.npz\",\n val_ranking_path,\n )\n\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_ranking_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_model_exp3.pt\")\n subprocess.run([\"cp\", val_ranking_path, best_ranking_path])\n print(f\" *** New best model saved! Score: {score:.6f} ***\")\n\n\nif __name__ == \"__main__\":\n main()\n", "file_path": "/app/solution/experiment_3.py" } ### Tool output · 2026-09-29T00:48:02.555Z Successfully created and wrote to new file: /app/solution/experiment_3.py. Here is the updated code: """Experiment 3: Calibrated negative sampling, moderate LR, and per-epoch evaluation.""" from __future__ import annotations import json import random import subprocess import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def extract_window_positives( listens_table, likes_df: pd.DataFrame, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window, marking new items.""" mask = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts), pc.less_equal(listens_table["timestamp"], end_ts), ) t_listens = listens_table.filter(mask).to_pandas() strong = t_listens[ t_listens["uid"].isin(target_uids) & (t_listens["is_organic"] == 1) & (t_listens["played_ratio_pct"] >= 80) ] strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) org_likes = likes_df[ (likes_df["timestamp"] >= start_ts) & (likes_df["timestamp"] <= end_ts) & likes_df["uid"].isin(target_uids) & (likes_df["is_organic"] == 1) ] org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) m_prior = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts - 60 * 86400), pc.less(listens_table["timestamp"], start_ts), ) t_prior = listens_table.filter(m_prior).to_pandas() t_prior = t_prior[t_prior["uid"].isin(target_uids)] user_prior = t_prior.groupby("uid")["item_id"].apply(set).to_dict() user_positives: dict[int, dict[int, float]] = {} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") idx2item = catalog["item_id"].to_numpy() for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): raw_item = idx2item[idx] is_new = raw_item not in user_prior.get(uid, set()) w = 2.0 if is_new else 1.0 user_positives.setdefault(uid, {})[idx] = w for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 4.0 return user_positives def main(): set_seed(42) print("Loading catalog...") catalog_items, item2idx, feat_matrix = load_catalog() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) T_val_cutoff = 24786800 print("Extracting validation user histories at T =", T_val_cutoff) val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") val_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) print("Reading listens parquet...") listens_table = pq.read_table( "/task/data/train_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) likes_df = pd.read_parquet("/task/data/train_likes.parquet") T_w0_split = T_val_cutoff - 7 * 86400 T_w1_split = T_val_cutoff - 14 * 86400 print("Building Window 0 & 1 targets and histories...") w0_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff ) w1_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split ) w0_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w0_split, window_days=60, ) w1_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w1_split, window_days=60, ) model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.08, ) # Moderate learning rate and higher weight decay optimizer = torch.optim.AdamW(model.parameters(), lr=3e-4, weight_decay=1e-3) epochs = 10 scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5) batch_size = 512 num_negatives = 128 # moderate catalog negatives to avoid over-penalizing head items best_score = 0.0 best_ranking_path = "/app/solution/val_best.parquet" print(f"Training for {epochs} epochs...") for epoch in range(1, epochs + 1): model.train() total_loss = 0.0 num_batches = 0 windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)] for hist, pos_dict in windows: active_uids = [u for u in target_uids if u in pos_dict] random.shuffle(active_uids) uid2row = hist["uid2row"] for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue b_pos_items = [] b_weights = [] for u in b_uids: items_dict = pos_dict[u] items = list(items_dict.keys()) weights = list(items_dict.values()) chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] l_start = hist["liked_offsets"][r].item() l_end = ( hist["liked_offsets"][r + 1].item() if r + 1 < len(hist["liked_offsets"]) else len(hist["liked_indices"]) ) b_liked_idx.extend(hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) d_start = hist["disliked_offsets"][r].item() d_end = ( hist["disliked_offsets"][r + 1].item() if r + 1 < len(hist["disliked_offsets"]) else len(hist["disliked_indices"]) ) b_disliked_idx.extend(hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) ls_start = hist["listen_offsets"][r].item() ls_end = ( hist["listen_offsets"][r + 1].item() if r + 1 < len(hist["listen_offsets"]) else len(hist["listen_indices"]) ) b_listen_idx.extend(hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) b_stats.append(hist["user_stats"][r]) t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) # Uniform catalog negatives t_neg_items = torch.randint(0, CATALOG_SIZE, (num_negatives,)) t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 scheduler.step() avg_loss = total_loss / max(1, num_batches) current_lr = scheduler.get_last_lr()[0] # Evaluate on validation every single epoch model.eval() with torch.no_grad(): val_u_emb = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v_emb = model.precompute_item_vectors(feat_matrix) val_ranking_path = f"/app/solution/val_exp3_ep{epoch}.parquet" generate_ranking( val_u_emb, val_v_emb, "/task/data/validation_eligible_candidates.npz", val_ranking_path, ) res = subprocess.run( ["python3", "/task/evaluate_public.py", val_ranking_path], capture_output=True, text=True, ) score_data = json.loads(res.stdout.strip()) score = score_data["scores"]["quality_ndcg@10"] print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}") if score > best_score: best_score = score torch.save(model.state_dict(), "/app/solution/best_model_exp3.pt") subprocess.run(["cp", val_ranking_path, best_ranking_path]) print(f" *** New best model saved! Score: {score:.6f} ***") if __name__ == "__main__": main() ## Step 92 · agent · 2026-09-29T00:48:04.991Z ### run_shell_command · 2026-09-29T00:48:04.991Z { "description": "Run Experiment 3", "command": "PYTHONPATH=/app python3 /app/solution/experiment_3.py" } ### Tool output · 2026-09-29T00:49:31.352Z Output: /app/solution/experiment_3.py:47: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_3.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_3.py:47: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_3.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Loading catalog... Extracting validation user histories at T = 24786800 Reading listens parquet... Building Window 0 & 1 targets and histories... Training for 10 epochs... Epoch 01 | Loss: 6.4153 | LR: 0.000293 | Val NDCG@10: 0.001903 *** New best model saved! Score: 0.001903 *** Epoch 02 | Loss: 6.3950 | LR: 0.000272 | Val NDCG@10: 0.004009 *** New best model saved! Score: 0.004009 *** Epoch 03 | Loss: 6.3837 | LR: 0.000240 | Val NDCG@10: 0.005012 *** New best model saved! Score: 0.005012 *** Epoch 04 | Loss: 6.3827 | LR: 0.000200 | Val NDCG@10: 0.006057 *** New best model saved! Score: 0.006057 *** Epoch 05 | Loss: 6.3621 | LR: 0.000155 | Val NDCG@10: 0.006964 *** New best model saved! Score: 0.006964 *** Epoch 06 | Loss: 6.3411 | LR: 0.000110 | Val NDCG@10: 0.008808 *** New best model saved! Score: 0.008808 *** Epoch 07 | Loss: 6.2868 | LR: 0.000070 | Val NDCG@10: 0.005519 Epoch 08 | Loss: 6.2399 | LR: 0.000038 | Val NDCG@10: 0.007042 Epoch 09 | Loss: 6.2088 | LR: 0.000017 | Val NDCG@10: 0.006860 Epoch 10 | Loss: 6.1758 | LR: 0.000010 | Val NDCG@10: 0.006043 Process Group PGID: 3100 ## Step 93 · agent · 2026-09-29T00:49:43.905Z ### Recorded reasoning summary Analyzing Experiment Results My current focus is on understanding the significant performance difference observed between Experiment 2 (0.021558 with popularity negatives) and Experiment 3 (0.008808 with uniform negatives). I'm investigating how popularity-biased negative sampling pushes the model towards learning nuanced preferences between plausible options. ### Recorded reasoning summary Investigating Overfitting I'm exploring why my model peaked early and then declined. My current hypothesis is that while initial training with popularity negatives helps capture broad categories, further reduction in loss leads to memorization of training data, negatively impacting performance by penalizing popular items too heavily. I plan to test this by observing validation scores at finer intervals with varied learning rates. ### run_shell_command · 2026-09-29T00:49:43.905Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, read_ranking, validate_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\n\nsub = read_ranking(Path('/app/solution/val_exp2_ep2.parquet'))\nranked = ranked_items(validate_ranking(sub, targets))\n\nscores = [ndcg_at_k(ranked[uid], truth[uid], 10) for uid in truth]\nimport numpy as np\nprint('Num users scored:', len(scores))\nprint('Mean NDCG@10:', np.mean(scores))\nprint('Users with NDCG > 0:', np.sum(np.array(scores) > 0), 'fraction:', np.mean(np.array(scores) > 0))\nprint('Users with NDCG == 1.0:', np.sum(np.array(scores) == 1.0))\n\"", "description": "Inspect per-user NDCG scores for val_exp2_ep2.parquet" } ### Tool output · 2026-09-29T00:49:46.638Z Output: Num users scored: 4263 Mean NDCG@10: 0.021558411684225478 Users with NDCG > 0: 382 fraction: 0.08960825709594182 Users with NDCG == 1.0: 7 Process Group PGID: 3323 ## Step 94 · agent · 2026-09-29T00:50:12.196Z ### run_shell_command · 2026-09-29T00:50:12.196Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, read_ranking, validate_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\n\nsub = read_ranking(Path('/app/solution/validation_pop.parquet'))\nranked = ranked_items(validate_ranking(sub, targets))\n\nscores = [ndcg_at_k(ranked[uid], truth[uid], 10) for uid in truth]\nimport numpy as np\nprint('Popularity Baseline:')\nprint('Mean NDCG@10:', np.mean(scores))\nprint('Users with NDCG > 0:', np.sum(np.array(scores) > 0), 'fraction:', np.mean(np.array(scores) > 0))\n\"", "description": "Check popularity baseline per-user NDCG" } ### Tool output · 2026-09-29T00:50:14.925Z Output: Popularity Baseline: Mean NDCG@10: 0.010709951701903768 Users with NDCG > 0: 229 fraction: 0.053718038939713816 Process Group PGID: 3343 ## Step 95 · agent · 2026-09-29T00:50:29.794Z ### Recorded reasoning summary Improving Recommendation Performance My current focus is on the significant improvements observed with the two-tower model. I'm thrilled that it's more than doubled the popularity baseline's NDCG@10 and substantially increased the number of users showing positive results. ### Recorded reasoning summary Exploring Popularity Integration I'm investigating how to further enhance recommendations. A key idea is to see if incorporating item popularity directly into the `ItemTower`'s features, and then allowing the `UserTower` to learn a preference for it through its dot product with the item embedding, could yield even better results. ### Recorded reasoning summary Testing Popularity Embeddings I'm exploring a new approach by dedicating specific dimensions within user and item embeddings to represent popularity. The idea is for the user embedding's popularity dimension to capture their affinity for mainstream or niche content, while the item embedding's dimension reflects its inherent popularity, allowing for a learned interaction. ### run_shell_command · 2026-09-29T00:50:29.794Z { "command": "PYTHONPATH=/task python3 -c \"\nimport pandas as pd\nimport numpy as np\nfrom pathlib import Path\nfrom ranking_evaluation import CandidateSet, read_ranking, validate_ranking, ranked_items, ndcg_at_k\nfrom evaluate_public import validation_truth\n\ntargets = set(pd.read_parquet('/task/data/target_users.parquet')['uid'].astype(int))\nelig = CandidateSet(Path('/task/data/validation_eligible_candidates.npz'))\ntruth = validation_truth(elig, targets)\n\n# Load model weights from experiment 2\nfrom solution.model import TwoTowerModel\nfrom solution.data_utils import load_catalog, extract_user_histories\nimport torch\n\ncatalog_items, item2idx, feat_matrix = load_catalog()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df['uid'].tolist()\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nval_hist = extract_user_histories('/task/data/train_listens.parquet', val_pref, item2idx, target_uids, 24786800, window_days=60)\n\nmodel = TwoTowerModel(embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07)\nmodel.load_state_dict(torch.load('/app/solution/best_model_exp2.pt'))\nmodel.eval()\n\nwith torch.no_grad():\n val_u = model.user_tower(\n val_hist['liked_indices'], val_hist['liked_offsets'],\n val_hist['disliked_indices'], val_hist['disliked_offsets'],\n val_hist['listen_indices'], val_hist['listen_weights'], val_hist['listen_offsets'],\n val_hist['user_stats']\n )\n val_v = model.precompute_item_vectors(feat_matrix)\n\n# Compute raw dot products\nscores_two_tower = torch.matmul(val_u, val_v.T) # (8456, 30000)\n\n# Catalog popularity score\ncatalog = pd.read_parquet('/task/data/candidate_catalog.parquet')\npop_rank = catalog['popularity_rank'].to_numpy(dtype=np.float32) # 1 to 30000\n# Normalized log popularity\nlog_pop = torch.from_numpy(np.log1p(30000 - pop_rank)).unsqueeze(0) # (1, 30000)\n\npacked = np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible']\nmasks = torch.from_numpy(np.unpackbits(packed, axis=1, count=30000, bitorder='little').astype(bool))\n\n# Test different blending alpha: score = scores_two_tower + alpha * log_pop\nfor alpha in [0.0, 0.01, 0.02, 0.05, 0.1, 0.2]:\n comb = scores_two_tower + alpha * log_pop\n comb[~masks] = -1e9\n top100 = torch.topk(comb, 100, dim=1).indices.cpu().numpy()\n \n # Format ranking dict\n uids_arr = np.array(target_uids)\n cat_items = catalog['item_id'].to_numpy()\n ranked = {int(uids_arr[r]): [int(cat_items[i]) for i in top100[r]] for r in range(len(uids_arr))}\n \n ndcg = np.mean([ndcg_at_k(ranked[uid], truth[uid], 10) for uid in truth])\n print(f'Alpha {alpha:0.2f} -> NDCG@10: {ndcg:.6f}')\n\"", "description": "Test blending two-tower scores with popularity prior" } ### Tool output · 2026-09-29T00:50:45.829Z Output: Alpha 0.00 -> NDCG@10: 0.021558 Alpha 0.01 -> NDCG@10: 0.021194 Alpha 0.02 -> NDCG@10: 0.020287 Alpha 0.05 -> NDCG@10: 0.018041 Alpha 0.10 -> NDCG@10: 0.013940 Alpha 0.20 -> NDCG@10: 0.010370 Process Group PGID: 3363 ## Step 96 · agent · 2026-09-29T00:51:12.600Z ### Recorded reasoning summary Discovering Embedding Power I'm realizing the pure two-tower model, with zero popularity boost, is outperforming. Its learned user preference embeddings seem to capture personalized signals far more effectively than any popularity weighting could. ### Recorded reasoning summary Expanding Model Capacity I'm now investigating how to enhance the existing two-tower architecture. Specifically, I'm exploring increasing embedding dimensions, fine-tuning learning rates with more frequent evaluations, and incorporating additional historical data windows to capture richer user behavior. ### write_file · 2026-09-29T00:51:12.600Z { "file_path": "/app/solution/experiment_4.py", "content": "\"\"\"Experiment 4: Higher embedding dimension (128), 3 temporal windows, and fine-grained epoch search.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport random\nimport subprocess\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef extract_window_positives(\n listens_table,\n likes_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window, marking new items.\"\"\"\n mask = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts),\n pc.less_equal(listens_table[\"timestamp\"], end_ts),\n )\n t_listens = listens_table.filter(mask).to_pandas()\n strong = t_listens[\n t_listens[\"uid\"].isin(target_uids)\n & (t_listens[\"is_organic\"] == 1)\n & (t_listens[\"played_ratio_pct\"] >= 80)\n ]\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n org_likes = likes_df[\n (likes_df[\"timestamp\"] >= start_ts)\n & (likes_df[\"timestamp\"] <= end_ts)\n & likes_df[\"uid\"].isin(target_uids)\n & (likes_df[\"is_organic\"] == 1)\n ]\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n m_prior = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts - 60 * 86400),\n pc.less(listens_table[\"timestamp\"], start_ts),\n )\n t_prior = listens_table.filter(m_prior).to_pandas()\n t_prior = t_prior[t_prior[\"uid\"].isin(target_uids)]\n user_prior = t_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n user_positives: dict[int, dict[int, float]] = {}\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n idx2item = catalog[\"item_id\"].to_numpy()\n\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n raw_item = idx2item[idx]\n is_new = raw_item not in user_prior.get(uid, set())\n w = 2.0 if is_new else 1.0\n user_positives.setdefault(uid, {})[idx] = w\n\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 4.0\n\n return user_positives\n\n\ndef main():\n set_seed(42)\n print(\"Loading catalog...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n\n catalog_df = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n counts = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float64)\n neg_probs = np.power(counts, 0.6)\n neg_probs /= neg_probs.sum()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n T_val_cutoff = 24786800\n print(\"Extracting validation user histories at T =\", T_val_cutoff)\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n val_hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n print(\"Reading listens parquet...\")\n listens_table = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n likes_df = pd.read_parquet(\"/task/data/train_likes.parquet\")\n\n # 3 temporal windows\n w_splits = [\n (T_val_cutoff - 7 * 86400, T_val_cutoff),\n (T_val_cutoff - 14 * 86400, T_val_cutoff - 7 * 86400),\n (T_val_cutoff - 21 * 86400, T_val_cutoff - 14 * 86400),\n ]\n\n windows = []\n for w_idx, (w_start, w_end) in enumerate(w_splits):\n print(f\"Building Window {w_idx} (target {w_start} to {w_end})...\")\n pos = extract_window_positives(listens_table, likes_df, item2idx, target_set, w_start, w_end)\n hist = extract_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n w_start,\n window_days=60,\n )\n windows.append((hist, pos))\n\n # Initialize model with dim=128\n model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=128,\n out_dim=128,\n hidden_dim=256,\n temperature=0.07,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=2e-4)\n epochs = 8\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5)\n\n batch_size = 512\n num_negatives = 256\n best_score = 0.0\n\n print(f\"Training for {epochs} epochs (embed_dim=128)...\")\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs)\n t_neg_items = torch.from_numpy(neg_indices).long()\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n # Evaluate on validation\n model.eval()\n with torch.no_grad():\n val_u_emb = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v_emb = model.precompute_item_vectors(feat_matrix)\n\n val_ranking_path = f\"/app/solution/val_exp4_ep{epoch}.parquet\"\n generate_ranking(\n val_u_emb,\n val_v_emb,\n \"/task/data/validation_eligible_candidates.npz\",\n val_ranking_path,\n )\n\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_ranking_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_model_exp4.pt\")\n subprocess.run([\"cp\", val_ranking_path, \"/app/solution/val_best.parquet\"])\n print(f\" *** New best model saved! Score: {score:.6f} ***\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:51:12.614Z Successfully created and wrote to new file: /app/solution/experiment_4.py. Here is the updated code: """Experiment 4: Higher embedding dimension (128), 3 temporal windows, and fine-grained epoch search.""" from __future__ import annotations import json import random import subprocess import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE, PAD_IDX from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def extract_window_positives( listens_table, likes_df: pd.DataFrame, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window, marking new items.""" mask = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts), pc.less_equal(listens_table["timestamp"], end_ts), ) t_listens = listens_table.filter(mask).to_pandas() strong = t_listens[ t_listens["uid"].isin(target_uids) & (t_listens["is_organic"] == 1) & (t_listens["played_ratio_pct"] >= 80) ] strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) org_likes = likes_df[ (likes_df["timestamp"] >= start_ts) & (likes_df["timestamp"] <= end_ts) & likes_df["uid"].isin(target_uids) & (likes_df["is_organic"] == 1) ] org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) m_prior = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts - 60 * 86400), pc.less(listens_table["timestamp"], start_ts), ) t_prior = listens_table.filter(m_prior).to_pandas() t_prior = t_prior[t_prior["uid"].isin(target_uids)] user_prior = t_prior.groupby("uid")["item_id"].apply(set).to_dict() user_positives: dict[int, dict[int, float]] = {} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") idx2item = catalog["item_id"].to_numpy() for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): raw_item = idx2item[idx] is_new = raw_item not in user_prior.get(uid, set()) w = 2.0 if is_new else 1.0 user_positives.setdefault(uid, {})[idx] = w for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 4.0 return user_positives def main(): set_seed(42) print("Loading catalog...") catalog_items, item2idx, feat_matrix = load_catalog() catalog_df = pd.read_parquet("/task/data/candidate_catalog.parquet") counts = catalog_df["positive_listens"].to_numpy(dtype=np.float64) neg_probs = np.power(counts, 0.6) neg_probs /= neg_probs.sum() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) T_val_cutoff = 24786800 print("Extracting validation user histories at T =", T_val_cutoff) val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") val_hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) print("Reading listens parquet...") listens_table = pq.read_table( "/task/data/train_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) likes_df = pd.read_parquet("/task/data/train_likes.parquet") # 3 temporal windows w_splits = [ (T_val_cutoff - 7 * 86400, T_val_cutoff), (T_val_cutoff - 14 * 86400, T_val_cutoff - 7 * 86400), (T_val_cutoff - 21 * 86400, T_val_cutoff - 14 * 86400), ] windows = [] for w_idx, (w_start, w_end) in enumerate(w_splits): print(f"Building Window {w_idx} (target {w_start} to {w_end})...") pos = extract_window_positives(listens_table, likes_df, item2idx, target_set, w_start, w_end) hist = extract_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, w_start, window_days=60, ) windows.append((hist, pos)) # Initialize model with dim=128 model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=128, out_dim=128, hidden_dim=256, temperature=0.07, ) optimizer = torch.optim.AdamW(model.parameters(), lr=5e-4, weight_decay=2e-4) epochs = 8 scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5) batch_size = 512 num_negatives = 256 best_score = 0.0 print(f"Training for {epochs} epochs (embed_dim=128)...") for epoch in range(1, epochs + 1): model.train() total_loss = 0.0 num_batches = 0 for hist, pos_dict in windows: active_uids = [u for u in target_uids if u in pos_dict] random.shuffle(active_uids) uid2row = hist["uid2row"] for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue b_pos_items = [] b_weights = [] for u in b_uids: items_dict = pos_dict[u] items = list(items_dict.keys()) weights = list(items_dict.values()) chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] l_start = hist["liked_offsets"][r].item() l_end = ( hist["liked_offsets"][r + 1].item() if r + 1 < len(hist["liked_offsets"]) else len(hist["liked_indices"]) ) b_liked_idx.extend(hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) d_start = hist["disliked_offsets"][r].item() d_end = ( hist["disliked_offsets"][r + 1].item() if r + 1 < len(hist["disliked_offsets"]) else len(hist["disliked_indices"]) ) b_disliked_idx.extend(hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) ls_start = hist["listen_offsets"][r].item() ls_end = ( hist["listen_offsets"][r + 1].item() if r + 1 < len(hist["listen_offsets"]) else len(hist["listen_indices"]) ) b_listen_idx.extend(hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) b_stats.append(hist["user_stats"][r]) t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs) t_neg_items = torch.from_numpy(neg_indices).long() t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 scheduler.step() avg_loss = total_loss / max(1, num_batches) current_lr = scheduler.get_last_lr()[0] # Evaluate on validation model.eval() with torch.no_grad(): val_u_emb = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v_emb = model.precompute_item_vectors(feat_matrix) val_ranking_path = f"/app/solution/val_exp4_ep{epoch}.parquet" generate_ranking( val_u_emb, val_v_emb, "/task/data/validation_eligible_candidates.npz", val_ranking_path, ) res = subprocess.run( ["python3", "/task/evaluate_public.py", val_ranking_path], capture_output=True, text=True, ) score_data = json.loads(res.stdout.strip()) score = score_data["scores"]["quality_ndcg@10"] print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}") if score > best_score: best_score = score torch.save(model.state_dict(), "/app/solution/best_model_exp4.pt") subprocess.run(["cp", val_ranking_path, "/app/solution/val_best.parquet"]) print(f" *** New best model saved! Score: {score:.6f} ***") if __name__ == "__main__": main() ## Step 97 · agent · 2026-09-29T00:51:15.813Z ### run_shell_command · 2026-09-29T00:51:15.813Z { "description": "Run Experiment 4", "command": "PYTHONPATH=/app python3 /app/solution/experiment_4.py" } ### Tool output · 2026-09-29T00:52:50.504Z Output: /app/solution/experiment_4.py:47: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_4.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_4.py:47: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_4.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_4.py:47: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_4.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Loading catalog... Extracting validation user histories at T = 24786800 Reading listens parquet... Building Window 0 (target 24182000 to 24786800)... Building Window 1 (target 23577200 to 24182000)... Building Window 2 (target 22972400 to 23577200)... Training for 8 epochs (embed_dim=128)... Epoch 01 | Loss: 6.5968 | LR: 0.000481 | Val NDCG@10: 0.010315 *** New best model saved! Score: 0.010315 *** Epoch 02 | Loss: 6.5672 | LR: 0.000428 | Val NDCG@10: 0.015265 *** New best model saved! Score: 0.015265 *** Epoch 03 | Loss: 6.5082 | LR: 0.000349 | Val NDCG@10: 0.017334 *** New best model saved! Score: 0.017334 *** Epoch 04 | Loss: 6.3595 | LR: 0.000255 | Val NDCG@10: 0.007595 Epoch 05 | Loss: 6.2474 | LR: 0.000161 | Val NDCG@10: 0.007460 Epoch 06 | Loss: 6.1453 | LR: 0.000082 | Val NDCG@10: 0.006721 Epoch 07 | Loss: 6.0621 | LR: 0.000029 | Val NDCG@10: 0.005028 Epoch 08 | Loss: 6.0322 | LR: 0.000010 | Val NDCG@10: 0.004814 Process Group PGID: 3400 ## Step 98 · agent · 2026-09-29T00:53:02.844Z ### Recorded reasoning summary Observing NDCG Peaks I'm noticing a consistent trend: NDCG peaks in every experiment when the loss hovers around 6.5 to 6.8. It's curious that as the loss drops below 6.4, the NDCG performance declines. I need to investigate why this occurs, particularly how minimizing InfoNCE loss below this threshold affects results. ### Recorded reasoning summary Investigating NDCG Declines I'm digging into why NDCG drops when the InfoNCE loss goes below 6.4. It seems my user embeddings are over-specializing on the specific tracks from the training set, which are different from those in the validation set. This specialization is hindering generalization. I'm considering regularization or validation-guided early stopping to address this. ### Recorded reasoning summary Refining Training Data I've identified a critical issue: recent listens with low `played_ratio_pct`, indicating skips, are negatively impacting training. I'm now excluding these skips from the user's positive listen history, aiming to improve the quality of my training data and, consequently, model performance. ### run_shell_command · 2026-09-29T00:53:02.844Z { "description": "Check played ratio distribution in train_listens", "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport numpy as np\n\ntable = pq.read_table('/task/data/train_listens.parquet', columns=['played_ratio_pct'])\nratios = table['played_ratio_pct'].to_numpy()\nprint('Played ratio stats:')\nprint('Total rows:', len(ratios))\nprint('Ratio < 50%:', np.mean(ratios < 50))\nprint('Ratio >= 50%:', np.mean(ratios >= 50))\nprint('Ratio >= 80%:', np.mean(ratios >= 80))\nprint('Ratio == 100%:', np.mean(ratios == 100))\n\"" } ### Tool output · 2026-09-29T00:53:04.690Z Output: Played ratio stats: Total rows: 43484159 Ratio < 50%: 0.3675684287696584 Ratio >= 50%: 0.6324315712303417 Ratio >= 80%: 0.5922891828263254 Ratio == 100%: 0.5317305090343359 Process Group PGID: 3590 ## Step 99 · agent · 2026-09-29T00:53:59.787Z ### Recorded reasoning summary Analyzing Skip Data My analysis reveals a significant portion, over 36%, of all listens were skipped before 50% completion. This suggests our current approach to user listening history may be unintentionally skewing positive signals by including these skipped tracks. ### write_file · 2026-09-29T00:53:59.787Z { "file_path": "/app/solution/experiment_5.py", "content": "\"\"\"Experiment 5: Clean user history (filter out skips < 50%), organic-boosted weights, and evaluate.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport random\nimport subprocess\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import load_catalog, CATALOG_SIZE, PAD_IDX\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef extract_clean_user_histories(\n listens_path: str | Path,\n pref_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: list[int],\n cutoff_timestamp: int,\n window_days: int = 60,\n top_k_listens: int = 100,\n):\n \"\"\"Extract clean user history filtering out skips (< 50% played ratio).\"\"\"\n target_set = set(target_uids)\n uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\n\n # 1. Preferences\n pref_df = pref_df[pref_df[\"uid\"].isin(target_set)]\n likes_grouped = (\n pref_df[pref_df[\"state\"] == \"liked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n dislikes_grouped = (\n pref_df[pref_df[\"state\"] == \"disliked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n\n # 2. Listens\n t_min = cutoff_timestamp - window_days * 86400\n t_max = cutoff_timestamp\n\n table = pq.read_table(\n listens_path,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n mask = pc.and_(\n pc.greater_equal(table[\"timestamp\"], t_min),\n pc.less_equal(table[\"timestamp\"], t_max),\n )\n filtered = table.filter(mask).to_pandas()\n filtered = filtered[filtered[\"uid\"].isin(target_set)]\n\n # Compute listen stats before skip filter\n user_listen_counts = filtered.groupby(\"uid\").size().to_dict()\n user_listen_ratios = filtered.groupby(\"uid\")[\"played_ratio_pct\"].mean().to_dict()\n user_organic_ratios = filtered.groupby(\"uid\")[\"is_organic\"].mean().to_dict()\n\n # Filter out skips! Keep only positive listens (played_ratio >= 50%)\n filtered = filtered[filtered[\"played_ratio_pct\"] >= 50]\n\n # Map items to index\n filtered[\"item_idx\"] = filtered[\"item_id\"].map(item2idx)\n filtered = filtered.dropna(subset=[\"item_idx\"])\n filtered[\"item_idx\"] = filtered[\"item_idx\"].astype(np.int32)\n\n # Time decay and weight: higher weight for high completion and organic\n delta_days = (t_max - filtered[\"timestamp\"].to_numpy()) / 86400.0\n decay = np.exp(-0.04 * delta_days)\n ratio_score = (filtered[\"played_ratio_pct\"].to_numpy() - 40.0) / 60.0\n org_multiplier = 1.0 + 1.0 * filtered[\"is_organic\"].to_numpy()\n filtered[\"w\"] = (ratio_score * org_multiplier * decay).astype(np.float32)\n\n # Aggregate by (uid, item_idx)\n user_item = (\n filtered.groupby([\"uid\", \"item_idx\"], as_index=False)[\"w\"]\n .sum()\n .sort_values([\"uid\", \"w\"], ascending=[True, False])\n )\n top_user_item = user_item.groupby(\"uid\").head(top_k_listens)\n listens_grouped = top_user_item.groupby(\"uid\")[[\"item_idx\", \"w\"]].apply(\n lambda g: (g[\"item_idx\"].to_numpy(), g[\"w\"].to_numpy())\n ).to_dict()\n\n # Build flat arrays with offsets for EmbeddingBag\n liked_indices, liked_offsets = [], [0]\n disliked_indices, disliked_offsets = [], [0]\n listen_indices, listen_weights, listen_offsets = [], [], [0]\n stats_list = []\n\n for uid in target_uids:\n # Liked\n l_items = likes_grouped.get(uid, [])\n l_idx = [item2idx[i] for i in l_items if i in item2idx]\n if not l_idx:\n liked_indices.append(PAD_IDX)\n else:\n liked_indices.extend(l_idx)\n liked_offsets.append(len(liked_indices))\n\n # Disliked\n d_items = dislikes_grouped.get(uid, [])\n d_idx = [item2idx[i] for i in d_items if i in item2idx]\n if not d_idx:\n disliked_indices.append(PAD_IDX)\n else:\n disliked_indices.extend(d_idx)\n disliked_offsets.append(len(disliked_indices))\n\n # Listens\n if uid in listens_grouped:\n u_items, u_w = listens_grouped[uid]\n sum_w = u_w.sum()\n norm_w = u_w / (sum_w + 1e-6)\n listen_indices.extend(u_items)\n listen_weights.extend(norm_w)\n else:\n listen_indices.append(PAD_IDX)\n listen_weights.append(0.0)\n listen_offsets.append(len(listen_indices))\n\n num_likes = len(l_idx)\n num_dislikes = len(d_idx)\n num_listens = user_listen_counts.get(uid, 0)\n mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0\n org_ratio = user_organic_ratios.get(uid, 0.0)\n stats_list.append([\n np.log1p(num_likes) / 5.0,\n np.log1p(num_dislikes) / 3.0,\n np.log1p(num_listens) / 10.0,\n mean_ratio,\n org_ratio,\n ])\n\n return {\n \"liked_indices\": torch.tensor(liked_indices, dtype=torch.long),\n \"liked_offsets\": torch.tensor(liked_offsets[:-1], dtype=torch.long),\n \"disliked_indices\": torch.tensor(disliked_indices, dtype=torch.long),\n \"disliked_offsets\": torch.tensor(disliked_offsets[:-1], dtype=torch.long),\n \"listen_indices\": torch.tensor(listen_indices, dtype=torch.long),\n \"listen_weights\": torch.tensor(listen_weights, dtype=torch.float32),\n \"listen_offsets\": torch.tensor(listen_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n \"uid2row\": uid2row,\n }\n\n\ndef extract_window_positives(\n listens_table,\n likes_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window, marking new items.\"\"\"\n mask = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts),\n pc.less_equal(listens_table[\"timestamp\"], end_ts),\n )\n t_listens = listens_table.filter(mask).to_pandas()\n strong = t_listens[\n t_listens[\"uid\"].isin(target_uids)\n & (t_listens[\"is_organic\"] == 1)\n & (t_listens[\"played_ratio_pct\"] >= 80)\n ]\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n org_likes = likes_df[\n (likes_df[\"timestamp\"] >= start_ts)\n & (likes_df[\"timestamp\"] <= end_ts)\n & likes_df[\"uid\"].isin(target_uids)\n & (likes_df[\"is_organic\"] == 1)\n ]\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n m_prior = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts - 60 * 86400),\n pc.less(listens_table[\"timestamp\"], start_ts),\n )\n t_prior = listens_table.filter(m_prior).to_pandas()\n t_prior = t_prior[t_prior[\"uid\"].isin(target_uids)]\n user_prior = t_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n user_positives: dict[int, dict[int, float]] = {}\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n idx2item = catalog[\"item_id\"].to_numpy()\n\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n raw_item = idx2item[idx]\n is_new = raw_item not in user_prior.get(uid, set())\n w = 2.0 if is_new else 1.0\n user_positives.setdefault(uid, {})[idx] = w\n\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 4.0\n\n return user_positives\n\n\ndef main():\n set_seed(42)\n print(\"Loading catalog...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n\n catalog_df = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n counts = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float64)\n neg_probs = np.power(counts, 0.6)\n neg_probs /= neg_probs.sum()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n T_val_cutoff = 24786800\n print(\"Extracting validation user histories at T =\", T_val_cutoff)\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n val_hist = extract_clean_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n print(\"Reading listens parquet...\")\n listens_table = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n likes_df = pd.read_parquet(\"/task/data/train_likes.parquet\")\n\n T_w0_split = T_val_cutoff - 7 * 86400\n T_w1_split = T_val_cutoff - 14 * 86400\n\n print(\"Building Window 0 & 1 targets and histories...\")\n w0_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff\n )\n w1_pos = extract_window_positives(\n listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split\n )\n\n w0_hist = extract_clean_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w0_split,\n window_days=60,\n )\n w1_hist = extract_clean_user_histories(\n \"/task/data/train_listens.parquet\",\n val_pref,\n item2idx,\n target_uids,\n T_w1_split,\n window_days=60,\n )\n\n model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n optimizer = torch.optim.AdamW(model.parameters(), lr=8e-4, weight_decay=2e-4)\n epochs = 6\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5)\n\n batch_size = 512\n num_negatives = 512\n best_score = 0.0\n\n print(f\"Training for {epochs} epochs...\")\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)]\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs)\n t_neg_items = torch.from_numpy(neg_indices).long()\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n # Evaluate on validation\n model.eval()\n with torch.no_grad():\n val_u_emb = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v_emb = model.precompute_item_vectors(feat_matrix)\n\n val_ranking_path = f\"/app/solution/val_exp5_ep{epoch}.parquet\"\n generate_ranking(\n val_u_emb,\n val_v_emb,\n \"/task/data/validation_eligible_candidates.npz\",\n val_ranking_path,\n )\n\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_ranking_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n torch.save(model.state_dict(), \"/app/solution/best_model_exp5.pt\")\n subprocess.run([\"cp\", val_ranking_path, \"/app/solution/val_best.parquet\"])\n print(f\" *** New best model saved! Score: {score:.6f} ***\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:53:59.801Z Successfully created and wrote to new file: /app/solution/experiment_5.py. Here is the updated code: """Experiment 5: Clean user history (filter out skips < 50%), organic-boosted weights, and evaluate.""" from __future__ import annotations import json import random import subprocess import time from pathlib import Path import numpy as np import pandas as pd import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import load_catalog, CATALOG_SIZE, PAD_IDX from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def extract_clean_user_histories( listens_path: str | Path, pref_df: pd.DataFrame, item2idx: dict[int, int], target_uids: list[int], cutoff_timestamp: int, window_days: int = 60, top_k_listens: int = 100, ): """Extract clean user history filtering out skips (< 50% played ratio).""" target_set = set(target_uids) uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)} # 1. Preferences pref_df = pref_df[pref_df["uid"].isin(target_set)] likes_grouped = ( pref_df[pref_df["state"] == "liked"].groupby("uid")["item_id"].apply(list).to_dict() ) dislikes_grouped = ( pref_df[pref_df["state"] == "disliked"].groupby("uid")["item_id"].apply(list).to_dict() ) # 2. Listens t_min = cutoff_timestamp - window_days * 86400 t_max = cutoff_timestamp table = pq.read_table( listens_path, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) mask = pc.and_( pc.greater_equal(table["timestamp"], t_min), pc.less_equal(table["timestamp"], t_max), ) filtered = table.filter(mask).to_pandas() filtered = filtered[filtered["uid"].isin(target_set)] # Compute listen stats before skip filter user_listen_counts = filtered.groupby("uid").size().to_dict() user_listen_ratios = filtered.groupby("uid")["played_ratio_pct"].mean().to_dict() user_organic_ratios = filtered.groupby("uid")["is_organic"].mean().to_dict() # Filter out skips! Keep only positive listens (played_ratio >= 50%) filtered = filtered[filtered["played_ratio_pct"] >= 50] # Map items to index filtered["item_idx"] = filtered["item_id"].map(item2idx) filtered = filtered.dropna(subset=["item_idx"]) filtered["item_idx"] = filtered["item_idx"].astype(np.int32) # Time decay and weight: higher weight for high completion and organic delta_days = (t_max - filtered["timestamp"].to_numpy()) / 86400.0 decay = np.exp(-0.04 * delta_days) ratio_score = (filtered["played_ratio_pct"].to_numpy() - 40.0) / 60.0 org_multiplier = 1.0 + 1.0 * filtered["is_organic"].to_numpy() filtered["w"] = (ratio_score * org_multiplier * decay).astype(np.float32) # Aggregate by (uid, item_idx) user_item = ( filtered.groupby(["uid", "item_idx"], as_index=False)["w"] .sum() .sort_values(["uid", "w"], ascending=[True, False]) ) top_user_item = user_item.groupby("uid").head(top_k_listens) listens_grouped = top_user_item.groupby("uid")[["item_idx", "w"]].apply( lambda g: (g["item_idx"].to_numpy(), g["w"].to_numpy()) ).to_dict() # Build flat arrays with offsets for EmbeddingBag liked_indices, liked_offsets = [], [0] disliked_indices, disliked_offsets = [], [0] listen_indices, listen_weights, listen_offsets = [], [], [0] stats_list = [] for uid in target_uids: # Liked l_items = likes_grouped.get(uid, []) l_idx = [item2idx[i] for i in l_items if i in item2idx] if not l_idx: liked_indices.append(PAD_IDX) else: liked_indices.extend(l_idx) liked_offsets.append(len(liked_indices)) # Disliked d_items = dislikes_grouped.get(uid, []) d_idx = [item2idx[i] for i in d_items if i in item2idx] if not d_idx: disliked_indices.append(PAD_IDX) else: disliked_indices.extend(d_idx) disliked_offsets.append(len(disliked_indices)) # Listens if uid in listens_grouped: u_items, u_w = listens_grouped[uid] sum_w = u_w.sum() norm_w = u_w / (sum_w + 1e-6) listen_indices.extend(u_items) listen_weights.extend(norm_w) else: listen_indices.append(PAD_IDX) listen_weights.append(0.0) listen_offsets.append(len(listen_indices)) num_likes = len(l_idx) num_dislikes = len(d_idx) num_listens = user_listen_counts.get(uid, 0) mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0 org_ratio = user_organic_ratios.get(uid, 0.0) stats_list.append([ np.log1p(num_likes) / 5.0, np.log1p(num_dislikes) / 3.0, np.log1p(num_listens) / 10.0, mean_ratio, org_ratio, ]) return { "liked_indices": torch.tensor(liked_indices, dtype=torch.long), "liked_offsets": torch.tensor(liked_offsets[:-1], dtype=torch.long), "disliked_indices": torch.tensor(disliked_indices, dtype=torch.long), "disliked_offsets": torch.tensor(disliked_offsets[:-1], dtype=torch.long), "listen_indices": torch.tensor(listen_indices, dtype=torch.long), "listen_weights": torch.tensor(listen_weights, dtype=torch.float32), "listen_offsets": torch.tensor(listen_offsets[:-1], dtype=torch.long), "user_stats": torch.tensor(stats_list, dtype=torch.float32), "uid2row": uid2row, } def extract_window_positives( listens_table, likes_df: pd.DataFrame, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window, marking new items.""" mask = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts), pc.less_equal(listens_table["timestamp"], end_ts), ) t_listens = listens_table.filter(mask).to_pandas() strong = t_listens[ t_listens["uid"].isin(target_uids) & (t_listens["is_organic"] == 1) & (t_listens["played_ratio_pct"] >= 80) ] strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) org_likes = likes_df[ (likes_df["timestamp"] >= start_ts) & (likes_df["timestamp"] <= end_ts) & likes_df["uid"].isin(target_uids) & (likes_df["is_organic"] == 1) ] org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) m_prior = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts - 60 * 86400), pc.less(listens_table["timestamp"], start_ts), ) t_prior = listens_table.filter(m_prior).to_pandas() t_prior = t_prior[t_prior["uid"].isin(target_uids)] user_prior = t_prior.groupby("uid")["item_id"].apply(set).to_dict() user_positives: dict[int, dict[int, float]] = {} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") idx2item = catalog["item_id"].to_numpy() for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): raw_item = idx2item[idx] is_new = raw_item not in user_prior.get(uid, set()) w = 2.0 if is_new else 1.0 user_positives.setdefault(uid, {})[idx] = w for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 4.0 return user_positives def main(): set_seed(42) print("Loading catalog...") catalog_items, item2idx, feat_matrix = load_catalog() catalog_df = pd.read_parquet("/task/data/candidate_catalog.parquet") counts = catalog_df["positive_listens"].to_numpy(dtype=np.float64) neg_probs = np.power(counts, 0.6) neg_probs /= neg_probs.sum() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) T_val_cutoff = 24786800 print("Extracting validation user histories at T =", T_val_cutoff) val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") val_hist = extract_clean_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) print("Reading listens parquet...") listens_table = pq.read_table( "/task/data/train_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) likes_df = pd.read_parquet("/task/data/train_likes.parquet") T_w0_split = T_val_cutoff - 7 * 86400 T_w1_split = T_val_cutoff - 14 * 86400 print("Building Window 0 & 1 targets and histories...") w0_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w0_split, T_val_cutoff ) w1_pos = extract_window_positives( listens_table, likes_df, item2idx, target_set, T_w1_split, T_w0_split ) w0_hist = extract_clean_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w0_split, window_days=60, ) w1_hist = extract_clean_user_histories( "/task/data/train_listens.parquet", val_pref, item2idx, target_uids, T_w1_split, window_days=60, ) model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07, ) optimizer = torch.optim.AdamW(model.parameters(), lr=8e-4, weight_decay=2e-4) epochs = 6 scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5) batch_size = 512 num_negatives = 512 best_score = 0.0 print(f"Training for {epochs} epochs...") for epoch in range(1, epochs + 1): model.train() total_loss = 0.0 num_batches = 0 windows = [(w0_hist, w0_pos), (w1_hist, w1_pos)] for hist, pos_dict in windows: active_uids = [u for u in target_uids if u in pos_dict] random.shuffle(active_uids) uid2row = hist["uid2row"] for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue b_pos_items = [] b_weights = [] for u in b_uids: items_dict = pos_dict[u] items = list(items_dict.keys()) weights = list(items_dict.values()) chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] l_start = hist["liked_offsets"][r].item() l_end = ( hist["liked_offsets"][r + 1].item() if r + 1 < len(hist["liked_offsets"]) else len(hist["liked_indices"]) ) b_liked_idx.extend(hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) d_start = hist["disliked_offsets"][r].item() d_end = ( hist["disliked_offsets"][r + 1].item() if r + 1 < len(hist["disliked_offsets"]) else len(hist["disliked_indices"]) ) b_disliked_idx.extend(hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) ls_start = hist["listen_offsets"][r].item() ls_end = ( hist["listen_offsets"][r + 1].item() if r + 1 < len(hist["listen_offsets"]) else len(hist["listen_indices"]) ) b_listen_idx.extend(hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) b_stats.append(hist["user_stats"][r]) t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs) t_neg_items = torch.from_numpy(neg_indices).long() t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 scheduler.step() avg_loss = total_loss / max(1, num_batches) current_lr = scheduler.get_last_lr()[0] # Evaluate on validation model.eval() with torch.no_grad(): val_u_emb = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v_emb = model.precompute_item_vectors(feat_matrix) val_ranking_path = f"/app/solution/val_exp5_ep{epoch}.parquet" generate_ranking( val_u_emb, val_v_emb, "/task/data/validation_eligible_candidates.npz", val_ranking_path, ) res = subprocess.run( ["python3", "/task/evaluate_public.py", val_ranking_path], capture_output=True, text=True, ) score_data = json.loads(res.stdout.strip()) score = score_data["scores"]["quality_ndcg@10"] print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}") if score > best_score: best_score = score torch.save(model.state_dict(), "/app/solution/best_model_exp5.pt") subprocess.run(["cp", val_ranking_path, "/app/solution/val_best.parquet"]) print(f" *** New best model saved! Score: {score:.6f} ***") if __name__ == "__main__": main() ## Step 100 · agent · 2026-09-29T00:54:06.081Z ### run_shell_command · 2026-09-29T00:54:06.081Z { "command": "PYTHONPATH=/app python3 /app/solution/experiment_5.py", "description": "Run Experiment 5" } ### Tool output · 2026-09-29T00:55:08.827Z Output: /app/solution/experiment_5.py:178: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_5.py:188: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_5.py:178: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_5.py:188: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Loading catalog... Extracting validation user histories at T = 24786800 Reading listens parquet... Building Window 0 & 1 targets and histories... Training for 6 epochs... Epoch 01 | Loss: 6.8938 | LR: 0.000747 | Val NDCG@10: 0.009585 *** New best model saved! Score: 0.009585 *** Epoch 02 | Loss: 6.8561 | LR: 0.000603 | Val NDCG@10: 0.018391 *** New best model saved! Score: 0.018391 *** Epoch 03 | Loss: 6.8245 | LR: 0.000405 | Val NDCG@10: 0.018022 Epoch 04 | Loss: 6.7746 | LR: 0.000208 | Val NDCG@10: 0.020800 *** New best model saved! Score: 0.020800 *** Epoch 05 | Loss: 6.6858 | LR: 0.000063 | Val NDCG@10: 0.014958 Epoch 06 | Loss: 6.6530 | LR: 0.000010 | Val NDCG@10: 0.015548 Process Group PGID: 3617 ## Step 101 · agent · 2026-09-29T00:55:20.049Z ### Recorded reasoning summary Observing Initial Results I'm seeing promising stability in my NDCG@10 scores, holding steady between 0.018 and 0.021 across epochs 2, 3, and 4. This suggests the clean history filtering is indeed enhancing training robustness. Now, I'm ready to move on to comparing Experiment 2 with Experiment 5. ### Recorded reasoning summary Comparing Experiment Outcomes My recent comparisons show Experiment 2 achieved 0.021558 and Experiment 5 reached 0.020800, both significantly outperforming the baseline. I'm now focusing on the refitting requirements, specifically how to leverage both `train_*` and `val_*` datasets for the final hidden-test ranker after initial tuning. ### Recorded reasoning summary Evaluating Refitting Strategies I've identified that using `val_*` events as features during validation measurement is problematic. However, these events are crucial for refitting the final hidden-test ranker. My next step is to implement this distinction, ensuring the final submission adheres to the specified format and candidate sets. ### Recorded reasoning summary Refining Training Windows I'm now defining the training windows for refitting the final ranker. The validation period will become the most recent training target, using historical data prior to it for the feature set. This clearly separates the evaluation and refitting phases. ### Recorded reasoning summary Defining Refitting Windows I'm now defining the specific windows for refitting the final ranker. Window 1 targets the seven days prior to validation, with history preceding it. Window 2 covers the period just before that. Training on these windows will include validation interactions as targets, preparing for test inference. ### run_shell_command · 2026-09-29T00:55:20.049Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow as pa\nimport time\n\nt0 = time.time()\nt1 = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nt2 = pq.read_table('/task/data/val_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ncombined = pa.concat_tables([t1, t2])\nprint(f'Combined listens table in {time.time()-t0:.2f}s, total rows: {len(combined)}')\n\"", "description": "Benchmark concatenating train and val listens" } ### Tool output · 2026-09-29T00:55:22.025Z Output: Combined listens table in 1.39s, total rows: 44985856 Process Group PGID: 3759 ## Step 102 · agent · 2026-09-29T00:55:28.551Z ### Recorded reasoning summary Optimizing PyArrow Efficiency I'm thrilled to report that I've successfully integrated the PyArrow Table directly into the `extract_clean_user_histories` function. This eliminates the need for intermediate file reads, dramatically speeding up the process. My tests show a swift combination of all 44.98 million rows in just 1.39 seconds! ### run_shell_command · 2026-09-29T00:55:28.551Z { "command": "python3 -c \"\nimport pyarrow.parquet as pq\nimport pyarrow as pa\nimport pandas as pd\nimport time\nfrom solution.data_utils import load_catalog\nfrom solution.experiment_5 import extract_clean_user_histories\n\nt0 = time.time()\ncatalog_items, item2idx, feat_matrix = load_catalog()\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df['uid'].tolist()\n\nt1 = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nt2 = pq.read_table('/task/data/val_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\ncombined = pa.concat_tables([t1, t2])\n\ntest_pref = pd.read_parquet('/task/data/test_preference_state.parquet')\nT_test_cutoff = 25393400\n\nt_start = time.time()\ntest_hist = extract_clean_user_histories(\n combined,\n test_pref,\n item2idx,\n target_uids,\n T_test_cutoff,\n window_days=60\n)\nprint(f'Test histories extracted in {time.time()-t_start:.2f}s')\nprint('liked_indices:', test_hist['liked_indices'].shape)\nprint('disliked_indices:', test_hist['disliked_indices'].shape)\nprint('listen_indices:', test_hist['listen_indices'].shape)\nprint('user_stats:', test_hist['user_stats'].shape)\n\"", "description": "Benchmark extracting test user histories" } ### Tool output · 2026-09-29T00:55:33.393Z Output: Traceback (most recent call last): File "", line 22, in File "/app/solution/experiment_5.py", line 54, in extract_clean_user_histories table = pq.read_table( ^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1793, in read_table dataset = ParquetDataset( ^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pyarrow/parquet/core.py", line 1357, in __init__ fragment = parquet_format.make_fragment(single_file, filesystem) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "pyarrow/_dataset_parquet.pyx", line 272, in pyarrow._dataset_parquet.ParquetFileFormat.make_fragment File "pyarrow/_dataset.pyx", line 1337, in pyarrow._dataset.FileFormat.make_fragment File "pyarrow/_dataset.pyx", line 132, in pyarrow._dataset._make_file_source TypeError: cannot construct a FileSource from pyarrow.Table uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 played_ratio_pct: uint16 ---- uid: [[100,100,100,100,100,...,3900,3900,3900,3900,3900],[3900,3900,3900,3900,3900,...,7200,7200,7200,7200,7200],...,[870100,870100,870100,870100,870100,...,964200,964200,964200,964200,964200],[964200,964200,964200,964200,964200,...,1000000,1000000,1000000,1000000,1000000]] timestamp: [[39420,39420,39625,40110,40360,...,19569630,19569630,19569630,19569630,19569630],[19569630,19569630,19569630,19569635,19569635,...,19221395,19221615,19222015,19222110,19222225],...,[25006525,25006545,25006735,25006885,25007075,...,24878435,24878555,24878770,24879360,24879360],[24879445,24912315,24941025,24943175,24943390,...,25266700,25276965,25276990,25277150,25277170]] item_id: [[8326270,1441281,286361,732449,3397170,...,1173379,6183553,3263569,5300983,7124069],[1870967,4660740,2289350,5315938,1005397,...,4057026,6131831,2167211,4511149,57746],...,[5351040,229959,1548489,3796971,1751471,...,4059962,3338511,1762527,5289260,7375537],[709505,290137,519317,7928634,650900,...,8732545,6481109,7696733,7479030,9295648]] is_organic: [[0,0,0,0,0,...,1,1,1,1,1],[1,1,1,1,1,...,0,0,0,0,0],...,[0,0,0,0,0,...,1,1,1,1,1],[1,1,1,1,1,...,1,0,0,0,0]] played_ratio_pct: [[100,100,100,100,46,...,100,100,100,100,100],[100,100,100,100,47,...,100,100,100,100,100],...,[100,100,100,81,100,...,100,58,100,100,100],[100,1,100,100,100,...,72,99,6,99,15]] Exit Code: 1 Process Group PGID: 3781 ## Step 103 · agent · 2026-09-29T00:55:51.691Z ### Recorded reasoning summary Accepting Paths and Tables I've refined `extract_clean_user_histories` to be more flexible. It can now handle either a file path string or a `Path` object directly, or accept an already loaded PyArrow Table. This allows for greater convenience in how users provide their data. ### Recorded reasoning summary Developing Data Utilities I'm focusing on enhancing `data_utils.py`. My current thoughts revolve around refining `extract_user_histories` to seamlessly accept file paths or PyArrow Tables, incorporate a "clean positive listens" filter (played_ratio_pct >= 50), and return the complete structured dictionary. Testing within the module is the next immediate step. ### write_file · 2026-09-29T00:55:51.691Z { "file_path": "/app/solution/data_utils.py", "content": "\"\"\"Data loading and preprocessing utilities for the two-tower ranker.\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\n\nCATALOG_SIZE = 30000\nPAD_IDX = 30000\n\n\ndef load_catalog(path: str | Path = \"/task/data/candidate_catalog.parquet\"):\n \"\"\"Load candidate catalog and construct feature tensor.\"\"\"\n catalog = pd.read_parquet(path)\n catalog_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\n item2idx = {int(item): idx for idx, item in enumerate(catalog_items)}\n\n # Features: normalized rank, log positive listens, log organic positive listens, organic ratio\n rank_feat = catalog[\"popularity_rank\"].to_numpy(dtype=np.float32) / 30000.0\n pos_feat = np.log1p(catalog[\"positive_listens\"].to_numpy(dtype=np.float32)) / 10.0\n org_feat = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32)) / 10.0\n ratio_feat = (\n catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32)\n / (catalog[\"positive_listens\"].to_numpy(dtype=np.float32) + 1.0)\n )\n\n feat_matrix = np.column_stack([rank_feat, pos_feat, org_feat, ratio_feat]).astype(np.float32)\n # Add a zero row for padding index\n feat_matrix = np.vstack([feat_matrix, np.zeros((1, feat_matrix.shape[1]), dtype=np.float32)])\n return catalog_items, item2idx, torch.from_numpy(feat_matrix)\n\n\ndef extract_user_histories(\n listens_input: str | Path | pa.Table,\n pref_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: list[int],\n cutoff_timestamp: int,\n window_days: int = 60,\n top_k_listens: int = 100,\n):\n \"\"\"Extract clean user history filtering out skips (< 50% played ratio).\"\"\"\n target_set = set(target_uids)\n uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)}\n\n # 1. Preferences\n pref_df = pref_df[pref_df[\"uid\"].isin(target_set)]\n likes_grouped = (\n pref_df[pref_df[\"state\"] == \"liked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n dislikes_grouped = (\n pref_df[pref_df[\"state\"] == \"disliked\"].groupby(\"uid\")[\"item_id\"].apply(list).to_dict()\n )\n\n # 2. Listens\n t_min = cutoff_timestamp - window_days * 86400\n t_max = cutoff_timestamp\n\n if isinstance(listens_input, (str, Path)):\n table = pq.read_table(\n listens_input,\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n else:\n table = listens_input\n\n mask = pc.and_(\n pc.greater_equal(table[\"timestamp\"], t_min),\n pc.less_equal(table[\"timestamp\"], t_max),\n )\n filtered = table.filter(mask).to_pandas()\n filtered = filtered[filtered[\"uid\"].isin(target_set)]\n\n # Compute listen stats before skip filter\n user_listen_counts = filtered.groupby(\"uid\").size().to_dict()\n user_listen_ratios = filtered.groupby(\"uid\")[\"played_ratio_pct\"].mean().to_dict()\n user_organic_ratios = filtered.groupby(\"uid\")[\"is_organic\"].mean().to_dict()\n\n # Filter out skips! Keep only positive listens (played_ratio >= 50%)\n filtered = filtered[filtered[\"played_ratio_pct\"] >= 50]\n\n # Map items to index\n filtered[\"item_idx\"] = filtered[\"item_id\"].map(item2idx)\n filtered = filtered.dropna(subset=[\"item_idx\"])\n filtered[\"item_idx\"] = filtered[\"item_idx\"].astype(np.int32)\n\n # Time decay and weight: higher weight for high completion and organic\n delta_days = (t_max - filtered[\"timestamp\"].to_numpy()) / 86400.0\n decay = np.exp(-0.04 * delta_days)\n ratio_score = (filtered[\"played_ratio_pct\"].to_numpy() - 40.0) / 60.0\n org_multiplier = 1.0 + 1.0 * filtered[\"is_organic\"].to_numpy()\n filtered[\"w\"] = (ratio_score * org_multiplier * decay).astype(np.float32)\n\n # Aggregate by (uid, item_idx)\n user_item = (\n filtered.groupby([\"uid\", \"item_idx\"], as_index=False)[\"w\"]\n .sum()\n .sort_values([\"uid\", \"w\"], ascending=[True, False])\n )\n top_user_item = user_item.groupby(\"uid\").head(top_k_listens)\n listens_grouped = top_user_item.groupby(\"uid\")[[\"item_idx\", \"w\"]].apply(\n lambda g: (g[\"item_idx\"].to_numpy(), g[\"w\"].to_numpy())\n ).to_dict()\n\n # Build flat arrays with offsets for EmbeddingBag\n liked_indices, liked_offsets = [], [0]\n disliked_indices, disliked_offsets = [], [0]\n listen_indices, listen_weights, listen_offsets = [], [], [0]\n stats_list = []\n\n for uid in target_uids:\n # Liked\n l_items = likes_grouped.get(uid, [])\n l_idx = [item2idx[i] for i in l_items if i in item2idx]\n if not l_idx:\n liked_indices.append(PAD_IDX)\n else:\n liked_indices.extend(l_idx)\n liked_offsets.append(len(liked_indices))\n\n # Disliked\n d_items = dislikes_grouped.get(uid, [])\n d_idx = [item2idx[i] for i in d_items if i in item2idx]\n if not d_idx:\n disliked_indices.append(PAD_IDX)\n else:\n disliked_indices.extend(d_idx)\n disliked_offsets.append(len(disliked_indices))\n\n # Listens\n if uid in listens_grouped:\n u_items, u_w = listens_grouped[uid]\n sum_w = u_w.sum()\n norm_w = u_w / (sum_w + 1e-6)\n listen_indices.extend(u_items)\n listen_weights.extend(norm_w)\n else:\n listen_indices.append(PAD_IDX)\n listen_weights.append(0.0)\n listen_offsets.append(len(listen_indices))\n\n num_likes = len(l_idx)\n num_dislikes = len(d_idx)\n num_listens = user_listen_counts.get(uid, 0)\n mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0\n org_ratio = user_organic_ratios.get(uid, 0.0)\n stats_list.append([\n np.log1p(num_likes) / 5.0,\n np.log1p(num_dislikes) / 3.0,\n np.log1p(num_listens) / 10.0,\n mean_ratio,\n org_ratio,\n ])\n\n return {\n \"liked_indices\": torch.tensor(liked_indices, dtype=torch.long),\n \"liked_offsets\": torch.tensor(liked_offsets[:-1], dtype=torch.long),\n \"disliked_indices\": torch.tensor(disliked_indices, dtype=torch.long),\n \"disliked_offsets\": torch.tensor(disliked_offsets[:-1], dtype=torch.long),\n \"listen_indices\": torch.tensor(listen_indices, dtype=torch.long),\n \"listen_weights\": torch.tensor(listen_weights, dtype=torch.float32),\n \"listen_offsets\": torch.tensor(listen_offsets[:-1], dtype=torch.long),\n \"user_stats\": torch.tensor(stats_list, dtype=torch.float32),\n \"uid2row\": uid2row,\n }\n\n\ndef extract_window_positives(\n listens_table: pa.Table,\n likes_df: pd.DataFrame,\n item2idx: dict[int, int],\n target_uids: set[int],\n start_ts: int,\n end_ts: int,\n):\n \"\"\"Extract (uid -> list of (item_idx, weight)) in target window, marking new items.\"\"\"\n mask = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts),\n pc.less_equal(listens_table[\"timestamp\"], end_ts),\n )\n t_listens = listens_table.filter(mask).to_pandas()\n strong = t_listens[\n t_listens[\"uid\"].isin(target_uids)\n & (t_listens[\"is_organic\"] == 1)\n & (t_listens[\"played_ratio_pct\"] >= 80)\n ].copy()\n strong[\"item_idx\"] = strong[\"item_id\"].map(item2idx)\n strong = strong.dropna(subset=[\"item_idx\"])\n strong[\"item_idx\"] = strong[\"item_idx\"].astype(int)\n\n org_likes = likes_df[\n (likes_df[\"timestamp\"] >= start_ts)\n & (likes_df[\"timestamp\"] <= end_ts)\n & likes_df[\"uid\"].isin(target_uids)\n & (likes_df[\"is_organic\"] == 1)\n ].copy()\n org_likes[\"item_idx\"] = org_likes[\"item_id\"].map(item2idx)\n org_likes = org_likes.dropna(subset=[\"item_idx\"])\n org_likes[\"item_idx\"] = org_likes[\"item_idx\"].astype(int)\n\n m_prior = pc.and_(\n pc.greater_equal(listens_table[\"timestamp\"], start_ts - 60 * 86400),\n pc.less(listens_table[\"timestamp\"], start_ts),\n )\n t_prior = listens_table.filter(m_prior).to_pandas()\n t_prior = t_prior[t_prior[\"uid\"].isin(target_uids)]\n user_prior = t_prior.groupby(\"uid\")[\"item_id\"].apply(set).to_dict()\n\n user_positives: dict[int, dict[int, float]] = {}\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n idx2item = catalog[\"item_id\"].to_numpy()\n\n for uid, idx in strong[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n raw_item = idx2item[idx]\n is_new = raw_item not in user_prior.get(uid, set())\n w = 2.0 if is_new else 1.0\n user_positives.setdefault(uid, {})[idx] = w\n\n for uid, idx in org_likes[[\"uid\", \"item_idx\"]].itertuples(index=False, name=None):\n user_positives.setdefault(uid, {})[idx] = 4.0\n\n return user_positives\n" } ### Tool output · 2026-09-29T00:55:51.706Z Successfully overwrote file: /app/solution/data_utils.py. Here is the updated code: ... from __future__ import annotations from pathlib import Path import numpy as np import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import pyarrow.compute as pc import torch CATALOG_SIZE = 30000 ... feat_matrix = np.vstack([feat_matrix, np.zeros((1, feat_matrix.shape[1]), dtype=np.float32)]) return catalog_items, item2idx, torch.from_numpy(feat_matrix) def extract_user_histories( listens_input: str | Path | pa.Table, pref_df: pd.DataFrame, item2idx: dict[int, int], target_uids: list[int], cutoff_timestamp: int, window_days: int = 60, top_k_listens: int = 100, ): """Extract clean user history filtering out skips (< 50% played ratio).""" target_set = set(target_uids) uid2row = {int(uid): idx for idx, uid in enumerate(target_uids)} # 1. Preferences pref_df = pref_df[pref_df["uid"].isin(target_set)] likes_grouped = ( pref_df[pref_df["state"] == "liked"].groupby("uid")["item_id"].apply(list).to_dict() ... # 2. Listens t_min = cutoff_timestamp - window_days * 86400 t_max = cutoff_timestamp if isinstance(listens_input, (str, Path)): table = pq.read_table( listens_input, columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) else: table = listens_input mask = pc.and_( pc.greater_equal(table["timestamp"], t_min), pc.less_equal(table["timestamp"], t_max), ) filtered = table.filter(mask).to_pandas() filtered = filtered[filtered["uid"].isin(target_set)] # Compute listen stats before skip filter user_listen_counts = filtered.groupby("uid").size().to_dict() user_listen_ratios = filtered.groupby("uid")["played_ratio_pct"].mean().to_dict() user_organic_ratios = filtered.groupby("uid")["is_organic"].mean().to_dict() # Filter out skips! Keep only positive listens (played_ratio >= 50%) filtered = filtered[filtered["played_ratio_pct"] >= 50] # Map items to index filtered["item_idx"] = filtered["item_id"].map(item2idx) filtered = filtered.dropna(subset=["item_idx"]) filtered["item_idx"] = filtered["item_idx"].astype(np.int32) # Time decay and weight: higher weight for high completion and organic delta_days = (t_max - filtered["timestamp"].to_numpy()) / 86400.0 decay = np.exp(-0.04 * delta_days) ratio_score = (filtered["played_ratio_pct"].to_numpy() - 40.0) / 60.0 org_multiplier = 1.0 + 1.0 * filtered["is_organic"].to_numpy() filtered["w"] = (ratio_score * org_multiplier * decay).astype(np.float32) # Aggregate by (uid, item_idx) user_item = ( filtered.groupby(["uid", "item_idx"], as_index=False)["w"] .sum() ... disliked_offsets.append(len(disliked_indices)) # Listens if uid in listens_grouped: u_items, u_w = listens_grouped[uid] sum_w = u_w.sum() norm_w = u_w / (sum_w + 1e-6) listen_indices.extend(u_items) listen_weights.extend(norm_w) else: listen_indices.append(PAD_IDX) listen_weights.append(0.0) listen_offsets.append(len(listen_indices)) num_likes = len(l_idx) num_dislikes = len(d_idx) num_listens = user_listen_counts.get(uid, 0) mean_ratio = user_listen_ratios.get(uid, 0.0) / 100.0 org_ratio = user_organic_ratios.get(uid, 0.0) ... "listen_weights": torch.tensor(listen_weights, dtype=torch.float32), "listen_offsets": torch.tensor(listen_offsets[:-1], dtype=torch.long), "user_stats": torch.tensor(stats_list, dtype=torch.float32), "uid2row": uid2row, } def extract_window_positives( listens_table: pa.Table, likes_df: pd.DataFrame, item2idx: dict[int, int], target_uids: set[int], start_ts: int, end_ts: int, ): """Extract (uid -> list of (item_idx, weight)) in target window, marking new items.""" mask = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts), pc.less_equal(listens_table["timestamp"], end_ts), ) t_listens = listens_table.filter(mask).to_pandas() strong = t_listens[ t_listens["uid"].isin(target_uids) & (t_listens["is_organic"] == 1) & (t_listens["played_ratio_pct"] >= 80) ].copy() strong["item_idx"] = strong["item_id"].map(item2idx) strong = strong.dropna(subset=["item_idx"]) strong["item_idx"] = strong["item_idx"].astype(int) org_likes = likes_df[ (likes_df["timestamp"] >= start_ts) & (likes_df["timestamp"] <= end_ts) & likes_df["uid"].isin(target_uids) & (likes_df["is_organic"] == 1) ].copy() org_likes["item_idx"] = org_likes["item_id"].map(item2idx) org_likes = org_likes.dropna(subset=["item_idx"]) org_likes["item_idx"] = org_likes["item_idx"].astype(int) m_prior = pc.and_( pc.greater_equal(listens_table["timestamp"], start_ts - 60 * 86400), pc.less(listens_table["timestamp"], start_ts), ) t_prior = listens_table.filter(m_prior).to_pandas() t_prior = t_prior[t_prior["uid"].isin(target_uids)] user_prior = t_prior.groupby("uid")["item_id"].apply(set).to_dict() user_positives: dict[int, dict[int, float]] = {} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") idx2item = catalog["item_id"].to_numpy() for uid, idx in strong[["uid", "item_idx"]].itertuples(index=False, name=None): raw_item = idx2item[idx] is_new = raw_item not in user_prior.get(uid, set()) w = 2.0 if is_new else 1.0 user_positives.setdefault(uid, {})[idx] = w for uid, idx in org_likes[["uid", "item_idx"]].itertuples(index=False, name=None): user_positives.setdefault(uid, {})[idx] = 4.0 return user_positives ## Step 104 · agent · 2026-09-29T00:56:22.158Z ### Recorded reasoning summary Developing Pipeline Script I've updated `data_utils.py` and am now focused on crafting `train_and_predict.py`. This script will integrate the entire process, starting with loading the catalog to build candidate feature representations, then extracting training windows and validating against the public window, meticulously recording the validation score. ### Recorded reasoning summary Finalizing Submission Generation My focus has shifted to the final stages, specifically refitting the model on combined historical data. I'm now working on generating the submission file, ensuring it meets all eligibility criteria and ranking constraints. The subsequent steps involve validation, README generation, and ensuring the solution directory is complete with all necessary code and documentation. ### write_file · 2026-09-29T00:56:22.158Z { "file_path": "/app/solution/train_and_predict.py", "content": "\"\"\"Main script to define, train, evaluate, refit, and generate final submission.\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport random\nimport subprocess\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import (\n load_catalog,\n extract_user_histories,\n extract_window_positives,\n CATALOG_SIZE,\n PAD_IDX,\n)\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef train_two_tower_model(\n model: TwoTowerModel,\n feat_matrix: torch.Tensor,\n neg_probs: np.ndarray,\n target_uids: list[int],\n windows: list[tuple[dict, dict]],\n epochs: int = 4,\n lr: float = 8e-4,\n weight_decay: float = 2e-4,\n batch_size: int = 512,\n num_negatives: int = 512,\n val_hist: dict | None = None,\n val_output_path: str | None = None,\n):\n \"\"\"Train the two-tower model over temporal windows.\"\"\"\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5)\n\n best_score = 0.0\n best_state = None\n\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs)\n t_neg_items = torch.from_numpy(neg_indices).long()\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n if val_hist is not None and val_output_path is not None:\n model.eval()\n with torch.no_grad():\n val_u = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v = model.precompute_item_vectors(feat_matrix)\n\n generate_ranking(\n val_u,\n val_v,\n \"/task/data/validation_eligible_candidates.npz\",\n val_output_path,\n )\n\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_output_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n else:\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f}\")\n\n if best_state is not None:\n model.load_state_dict(best_state)\n return best_score\n\n\ndef main():\n set_seed(42)\n start_time = time.time()\n print(\"=\" * 60)\n print(\"Two-Tower Neural Network Ranker: Training & Refit Pipeline\")\n print(\"=\" * 60)\n\n # 1. Load catalog\n print(\"\\n[1/5] Loading catalog & precomputing features...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n catalog_df = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n counts = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float64)\n neg_probs = np.power(counts, 0.6)\n neg_probs /= neg_probs.sum()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n # 2. Stage 1: Validation Tuning\n print(\"\\n[2/5] Stage 1: Tuning two-tower model on train_* with validation evaluation...\")\n train_listens = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n train_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n\n T_val_cutoff = 24786800\n val_hist = extract_user_histories(\n train_listens,\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n # Historical training windows\n T_w0 = T_val_cutoff - 7 * 86400\n T_w1 = T_val_cutoff - 14 * 86400\n\n w0_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w0, T_val_cutoff)\n w1_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w1, T_w0)\n w0_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w0, window_days=60)\n w1_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w1, window_days=60)\n\n val_model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n\n val_ranking_path = \"/app/solution/validation.parquet\"\n best_val_ndcg = train_two_tower_model(\n model=val_model,\n feat_matrix=feat_matrix,\n neg_probs=neg_probs,\n target_uids=target_uids,\n windows=[(w0_hist, w0_pos), (w1_hist, w1_pos)],\n epochs=4,\n lr=8e-4,\n weight_decay=2e-4,\n batch_size=512,\n num_negatives=512,\n val_hist=val_hist,\n val_output_path=val_ranking_path,\n )\n print(f\"Validation Tuning Complete. Best Public NDCG@10: {best_val_ndcg:.6f}\")\n\n # 3. Stage 2: Full Refit on train_* + val_*\n print(\"\\n[3/5] Stage 2: Refitting two-tower model on combined train_* + val_*...\")\n val_listens = pq.read_table(\n \"/task/data/val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n val_likes = pd.read_parquet(\"/task/data/val_likes.parquet\")\n combined_listens = pa.concat_tables([train_listens, val_listens])\n combined_likes = pd.concat([train_likes, val_likes], ignore_index=True)\n\n T_test_cutoff = 25393400\n test_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\n\n # The validation window [24786800, 25393400] is now observed training history!\n refit_w0_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, 24786800, T_test_cutoff)\n refit_w1_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, T_w0, 24786800)\n\n # Histories at the respective split cutoffs\n refit_w0_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, 24786800, window_days=60)\n refit_w1_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, T_w0, window_days=60)\n\n final_model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n\n train_two_tower_model(\n model=final_model,\n feat_matrix=feat_matrix,\n neg_probs=neg_probs,\n target_uids=target_uids,\n windows=[(refit_w0_hist, refit_w0_pos), (refit_w1_hist, refit_w1_pos)],\n epochs=3,\n lr=8e-4,\n weight_decay=2e-4,\n batch_size=512,\n num_negatives=512,\n )\n\n # Save final model weights\n torch.save(final_model.state_dict(), \"/app/solution/two_tower_model.pt\")\n print(\"Final refitted model saved to /app/solution/two_tower_model.pt\")\n\n # 4. Generate Final Test Submission\n print(\"\\n[4/5] Extracting test user histories and generating final submission...\")\n test_hist = extract_user_histories(\n combined_listens,\n test_pref,\n item2idx,\n target_uids,\n T_test_cutoff,\n window_days=60,\n )\n\n final_model.eval()\n with torch.no_grad():\n test_u = final_model.user_tower(\n test_hist[\"liked_indices\"],\n test_hist[\"liked_offsets\"],\n test_hist[\"disliked_indices\"],\n test_hist[\"disliked_offsets\"],\n test_hist[\"listen_indices\"],\n test_hist[\"listen_weights\"],\n test_hist[\"listen_offsets\"],\n test_hist[\"user_stats\"],\n )\n test_v = final_model.precompute_item_vectors(feat_matrix)\n\n submission_path = \"/app/solution/submission.parquet\"\n generate_ranking(\n test_u,\n test_v,\n \"/task/data/test_eligible_candidates.npz\",\n submission_path,\n )\n\n print(f\"Submission saved to {submission_path}\")\n\n # 5. Exhaustive Verification\n print(\"\\n[5/5] Running strict acceptance validation on submission...\")\n from ranking_evaluation import CandidateSet, read_ranking, validate_ranking, RankingValidationError\n\n sub_frame = read_ranking(Path(submission_path))\n val_frame = validate_ranking(sub_frame, target_set)\n test_elig = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\n for uid, item in val_frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not test_elig.is_eligible(uid, item):\n raise RankingValidationError(f\"Ineligible candidate item {item} for user {uid}\")\n\n print(\"Verification PASSED:\")\n print(f\" - Target users: exactly {len(target_set)}\")\n print(f\" - Total rows: exactly {len(val_frame)}\")\n print(\" - Ranks 1-100 per user without duplicates: verified\")\n print(\" - Candidate eligibility: 100% verified\")\n print(f\" - Total pipeline time: {time.time() - start_time:.1f}s\")\n\n # Update README\n readme_content = f\"\"\"# Latency-Efficient PyTorch Two-Tower Neural Ranker\n\n## Architecture\nThe system implements a production-grade, latency-efficient two-tower PyTorch neural network ranker designed for max inner-product search (MIPS):\n- **Item Tower**: Maps catalog item indices (30,000 items) and precomputed continuous item features (popularity rank, log positive listens, log organic positive listens, and organic ratio) through an item projection MLP with LayerNorm to output normalized item vectors $\\\\mathbf{{v}}_i \\\\in \\\\mathbb{{R}}^{{64}}$ with $\\\\|\\\\mathbf{{v}}_i\\\\|_2 = 1$. All 30,000 item vectors are independently precomputed offline.\n- **User Tower**: Strictly derives user representations from interaction history without user-ID lookup:\n - Actively liked items from cutoff preference state pooled via `nn.EmbeddingBag`.\n - Actively disliked items from cutoff preference state pooled via `nn.EmbeddingBag`.\n - Cleaned positive listens (completed $\\\\ge 50\\\\%$) with exponential time decay, played ratio completion, and organic listening boost.\n - Aggregate user continuous engagement statistics.\n - Concatenation through a deep user projection MLP with LayerNorm, GELU, and Dropout to output normalized user vector $\\\\mathbf{{u}} \\\\in \\\\mathbb{{R}}^{{64}}$ with $\\\\|\\\\mathbf{{u}}\\\\|_2 = 1$.\n- **Relevance Scoring & Retrieval**: Final relevance score decomposes exactly into the dot product $s(u, i) = \\\\mathbf{{u}}^\\\\top \\\\mathbf{{v}}_i$. At request time, candidate items are retrieved via MIPS and filtered by platform eligibility bitmasks.\n\n## Training Objective\nThe model is trained with an InfoNCE / sampled softmax loss over temporal training windows, aligning with discovery relevance (weight 4.0 for organic likes, 2.0 for new strong organic listens, 1.0 for repeat strong listens):\n$$\\\\mathcal{{L}} = -\\\\sum_{{b}} w_b \\\\log \\\\frac{{\\\\exp(\\\\mathbf{{u}}_b^\\\\top \\\\mathbf{{v}}_b / \\\\tau)}}{{\\\\sum_{{j}} \\\\exp(\\\\mathbf{{u}}_b^\\\\top \\\\mathbf{{v}}_j / \\\\tau)}}$$\nNegative items are sampled using a combination of in-batch negatives and popularity-scaled catalog negatives ($P(i) \\\\propto \\\\text{{listens}}^{{0.6}}$) to prevent popularity bias while ensuring fine-grained musical discrimination.\n\n## Public Validation Score\n- **Baseline Popularity NDCG@10**: 0.010710\n- **Two-Tower Neural Ranker NDCG@10**: {best_val_ndcg:.6f} (over 90% relative improvement)\n\n## Final Submission Command\nTo reproduce training, validation, refit on `train_* + val_*`, and test submission generation:\n```sh\nPYTHONPATH=/app python3 /app/solution/train_and_predict.py\n```\n\"\"\"\n with open(\"/app/solution/README.md\", \"w\") as f:\n f.write(readme_content)\n print(\"README.md updated successfully.\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:56:22.172Z Successfully created and wrote to new file: /app/solution/train_and_predict.py. Here is the updated code: """Main script to define, train, evaluate, refit, and generate final submission.""" from __future__ import annotations import json import random import subprocess import time from pathlib import Path import numpy as np import pandas as pd import pyarrow as pa import pyarrow.parquet as pq import pyarrow.compute as pc import torch import torch.nn as nn import torch.nn.functional as F from solution.data_utils import ( load_catalog, extract_user_histories, extract_window_positives, CATALOG_SIZE, PAD_IDX, ) from solution.model import TwoTowerModel from solution.retrieval import generate_ranking def set_seed(seed: int = 42): random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) def train_two_tower_model( model: TwoTowerModel, feat_matrix: torch.Tensor, neg_probs: np.ndarray, target_uids: list[int], windows: list[tuple[dict, dict]], epochs: int = 4, lr: float = 8e-4, weight_decay: float = 2e-4, batch_size: int = 512, num_negatives: int = 512, val_hist: dict | None = None, val_output_path: str | None = None, ): """Train the two-tower model over temporal windows.""" optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5) best_score = 0.0 best_state = None for epoch in range(1, epochs + 1): model.train() total_loss = 0.0 num_batches = 0 for hist, pos_dict in windows: active_uids = [u for u in target_uids if u in pos_dict] random.shuffle(active_uids) uid2row = hist["uid2row"] for b_start in range(0, len(active_uids), batch_size): b_uids = active_uids[b_start : b_start + batch_size] if len(b_uids) < 32: continue b_pos_items = [] b_weights = [] for u in b_uids: items_dict = pos_dict[u] items = list(items_dict.keys()) weights = list(items_dict.values()) chosen = random.choices(items, weights=weights, k=1)[0] b_pos_items.append(chosen) b_weights.append(items_dict[chosen]) b_liked_idx, b_liked_off = [], [0] b_disliked_idx, b_disliked_off = [], [0] b_listen_idx, b_listen_w, b_listen_off = [], [], [0] b_stats = [] for u in b_uids: r = uid2row[u] l_start = hist["liked_offsets"][r].item() l_end = ( hist["liked_offsets"][r + 1].item() if r + 1 < len(hist["liked_offsets"]) else len(hist["liked_indices"]) ) b_liked_idx.extend(hist["liked_indices"][l_start:l_end].tolist()) b_liked_off.append(len(b_liked_idx)) d_start = hist["disliked_offsets"][r].item() d_end = ( hist["disliked_offsets"][r + 1].item() if r + 1 < len(hist["disliked_offsets"]) else len(hist["disliked_indices"]) ) b_disliked_idx.extend(hist["disliked_indices"][d_start:d_end].tolist()) b_disliked_off.append(len(b_disliked_idx)) ls_start = hist["listen_offsets"][r].item() ls_end = ( hist["listen_offsets"][r + 1].item() if r + 1 < len(hist["listen_offsets"]) else len(hist["listen_indices"]) ) b_listen_idx.extend(hist["listen_indices"][ls_start:ls_end].tolist()) b_listen_w.extend(hist["listen_weights"][ls_start:ls_end].tolist()) b_listen_off.append(len(b_listen_idx)) b_stats.append(hist["user_stats"][r]) t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long) t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long) t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long) t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long) t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long) t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32) t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long) t_stats = torch.stack(b_stats, dim=0) t_pos_items = torch.tensor(b_pos_items, dtype=torch.long) t_pos_feats = feat_matrix[t_pos_items] t_weights = torch.tensor(b_weights, dtype=torch.float32) neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs) t_neg_items = torch.from_numpy(neg_indices).long() t_neg_feats = feat_matrix[t_neg_items] optimizer.zero_grad() u_emb = model.user_tower( t_liked_idx, t_liked_off, t_disliked_idx, t_disliked_off, t_listen_idx, t_listen_w, t_listen_off, t_stats, ) v_pos = model.item_tower(t_pos_items, t_pos_feats) v_neg = model.item_tower(t_neg_items, t_neg_feats) v_all = torch.cat([v_pos, v_neg], dim=0) B = len(b_uids) scores = torch.matmul(u_emb, v_all.T) / model.temperature labels = torch.arange(B, device=scores.device) loss = F.cross_entropy(scores, labels, reduction="none") weighted_loss = (loss * t_weights).sum() / t_weights.sum() weighted_loss.backward() optimizer.step() total_loss += weighted_loss.item() num_batches += 1 scheduler.step() avg_loss = total_loss / max(1, num_batches) current_lr = scheduler.get_last_lr()[0] if val_hist is not None and val_output_path is not None: model.eval() with torch.no_grad(): val_u = model.user_tower( val_hist["liked_indices"], val_hist["liked_offsets"], val_hist["disliked_indices"], val_hist["disliked_offsets"], val_hist["listen_indices"], val_hist["listen_weights"], val_hist["listen_offsets"], val_hist["user_stats"], ) val_v = model.precompute_item_vectors(feat_matrix) generate_ranking( val_u, val_v, "/task/data/validation_eligible_candidates.npz", val_output_path, ) res = subprocess.run( ["python3", "/task/evaluate_public.py", val_output_path], capture_output=True, text=True, ) score_data = json.loads(res.stdout.strip()) score = score_data["scores"]["quality_ndcg@10"] print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}") if score > best_score: best_score = score best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()} else: print(f"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f}") if best_state is not None: model.load_state_dict(best_state) return best_score def main(): set_seed(42) start_time = time.time() print("=" * 60) print("Two-Tower Neural Network Ranker: Training & Refit Pipeline") print("=" * 60) # 1. Load catalog print("\n[1/5] Loading catalog & precomputing features...") catalog_items, item2idx, feat_matrix = load_catalog() catalog_df = pd.read_parquet("/task/data/candidate_catalog.parquet") counts = catalog_df["positive_listens"].to_numpy(dtype=np.float64) neg_probs = np.power(counts, 0.6) neg_probs /= neg_probs.sum() targets_df = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_df["uid"].tolist() target_set = set(target_uids) # 2. Stage 1: Validation Tuning print("\n[2/5] Stage 1: Tuning two-tower model on train_* with validation evaluation...") train_listens = pq.read_table( "/task/data/train_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) train_likes = pd.read_parquet("/task/data/train_likes.parquet") val_pref = pd.read_parquet("/task/data/validation_preference_state.parquet") T_val_cutoff = 24786800 val_hist = extract_user_histories( train_listens, val_pref, item2idx, target_uids, T_val_cutoff, window_days=60, ) # Historical training windows T_w0 = T_val_cutoff - 7 * 86400 T_w1 = T_val_cutoff - 14 * 86400 w0_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w0, T_val_cutoff) w1_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w1, T_w0) w0_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w0, window_days=60) w1_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w1, window_days=60) val_model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07, ) val_ranking_path = "/app/solution/validation.parquet" best_val_ndcg = train_two_tower_model( model=val_model, feat_matrix=feat_matrix, neg_probs=neg_probs, target_uids=target_uids, windows=[(w0_hist, w0_pos), (w1_hist, w1_pos)], epochs=4, lr=8e-4, weight_decay=2e-4, batch_size=512, num_negatives=512, val_hist=val_hist, val_output_path=val_ranking_path, ) print(f"Validation Tuning Complete. Best Public NDCG@10: {best_val_ndcg:.6f}") # 3. Stage 2: Full Refit on train_* + val_* print("\n[3/5] Stage 2: Refitting two-tower model on combined train_* + val_*...") val_listens = pq.read_table( "/task/data/val_listens.parquet", columns=["uid", "timestamp", "item_id", "is_organic", "played_ratio_pct"], ) val_likes = pd.read_parquet("/task/data/val_likes.parquet") combined_listens = pa.concat_tables([train_listens, val_listens]) combined_likes = pd.concat([train_likes, val_likes], ignore_index=True) T_test_cutoff = 25393400 test_pref = pd.read_parquet("/task/data/test_preference_state.parquet") # The validation window [24786800, 25393400] is now observed training history! refit_w0_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, 24786800, T_test_cutoff) refit_w1_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, T_w0, 24786800) # Histories at the respective split cutoffs refit_w0_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, 24786800, window_days=60) refit_w1_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, T_w0, window_days=60) final_model = TwoTowerModel( catalog_size=CATALOG_SIZE, embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07, ) train_two_tower_model( model=final_model, feat_matrix=feat_matrix, neg_probs=neg_probs, target_uids=target_uids, windows=[(refit_w0_hist, refit_w0_pos), (refit_w1_hist, refit_w1_pos)], epochs=3, lr=8e-4, weight_decay=2e-4, batch_size=512, num_negatives=512, ) # Save final model weights torch.save(final_model.state_dict(), "/app/solution/two_tower_model.pt") print("Final refitted model saved to /app/solution/two_tower_model.pt") # 4. Generate Final Test Submission print("\n[4/5] Extracting test user histories and generating final submission...") test_hist = extract_user_histories( combined_listens, test_pref, item2idx, target_uids, T_test_cutoff, window_days=60, ) final_model.eval() with torch.no_grad(): test_u = final_model.user_tower( test_hist["liked_indices"], test_hist["liked_offsets"], test_hist["disliked_indices"], test_hist["disliked_offsets"], test_hist["listen_indices"], test_hist["listen_weights"], test_hist["listen_offsets"], test_hist["user_stats"], ) test_v = final_model.precompute_item_vectors(feat_matrix) submission_path = "/app/solution/submission.parquet" generate_ranking( test_u, test_v, "/task/data/test_eligible_candidates.npz", submission_path, ) print(f"Submission saved to {submission_path}") # 5. Exhaustive Verification print("\n[5/5] Running strict acceptance validation on submission...") from ranking_evaluation import CandidateSet, read_ranking, validate_ranking, RankingValidationError sub_frame = read_ranking(Path(submission_path)) val_frame = validate_ranking(sub_frame, target_set) test_elig = CandidateSet(Path("/task/data/test_eligible_candidates.npz")) for uid, item in val_frame[["uid", "item_id"]].itertuples(index=False, name=None): if not test_elig.is_eligible(uid, item): raise RankingValidationError(f"Ineligible candidate item {item} for user {uid}") print("Verification PASSED:") print(f" - Target users: exactly {len(target_set)}") print(f" - Total rows: exactly {len(val_frame)}") print(" - Ranks 1-100 per user without duplicates: verified") print(" - Candidate eligibility: 100% verified") print(f" - Total pipeline time: {time.time() - start_time:.1f}s") # Update README readme_content = f"""# Latency-Efficient PyTorch Two-Tower Neural Ranker ## Architecture The system implements a production-grade, latency-efficient two-tower PyTorch neural network ranker designed for max inner-product search (MIPS): - **Item Tower**: Maps catalog item indices (30,000 items) and precomputed continuous item features (popularity rank, log positive listens, log organic positive listens, and organic ratio) through an item projection MLP with LayerNorm to output normalized item vectors $\\mathbf{{v}}_i \\in \\mathbb{{R}}^{{64}}$ with $\\|\\mathbf{{v}}_i\\|_2 = 1$. All 30,000 item vectors are independently precomputed offline. - **User Tower**: Strictly derives user representations from interaction history without user-ID lookup: - Actively liked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Actively disliked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Cleaned positive listens (completed $\\ge 50\\%$) with exponential time decay, played ratio completion, and organic listening boost. - Aggregate user continuous engagement statistics. - Concatenation through a deep user projection MLP with LayerNorm, GELU, and Dropout to output normalized user vector $\\mathbf{{u}} \\in \\mathbb{{R}}^{{64}}$ with $\\|\\mathbf{{u}}\\|_2 = 1$. - **Relevance Scoring & Retrieval**: Final relevance score decomposes exactly into the dot product $s(u, i) = \\mathbf{{u}}^\\top \\mathbf{{v}}_i$. At request time, candidate items are retrieved via MIPS and filtered by platform eligibility bitmasks. ## Training Objective The model is trained with an InfoNCE / sampled softmax loss over temporal training windows, aligning with discovery relevance (weight 4.0 for organic likes, 2.0 for new strong organic listens, 1.0 for repeat strong listens): $$\\mathcal{{L}} = -\\sum_{{b}} w_b \\log \\frac{{\\exp(\\mathbf{{u}}_b^\\top \\mathbf{{v}}_b / \\tau)}}{{\\sum_{{j}} \\exp(\\mathbf{{u}}_b^\\top \\mathbf{{v}}_j / \\tau)}}$$ Negative items are sampled using a combination of in-batch negatives and popularity-scaled catalog negatives ($P(i) \\propto \\text{{listens}}^{{0.6}}$) to prevent popularity bias while ensuring fine-grained musical discrimination. ## Public Validation Score - **Baseline Popularity NDCG@10**: 0.010710 - **Two-Tower Neural Ranker NDCG@10**: {best_val_ndcg:.6f} (over 90% relative improvement) ## Final Submission Command To reproduce training, validation, refit on `train_* + val_*`, and test submission generation: ```sh PYTHONPATH=/app python3 /app/solution/train_and_predict.py ``` """ with open("/app/solution/README.md", "w") as f: f.write(readme_content) print("README.md updated successfully.") if __name__ == "__main__": main() ## Step 105 · agent · 2026-09-29T00:56:25.864Z ### run_shell_command · 2026-09-29T00:56:25.864Z { "description": "Run complete train, evaluate, refit, and submission pipeline", "command": "PYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py" } ### Tool output · 2026-09-29T00:57:45.513Z Output: ============================================================ Two-Tower Neural Network Ranker: Training & Refit Pipeline ============================================================ [1/5] Loading catalog & precomputing features... [2/5] Stage 1: Tuning two-tower model on train_* with validation evaluation... Epoch 01 | Loss: 6.8938 | LR: 0.000684 | Val NDCG@10: 0.009585 Epoch 02 | Loss: 6.8566 | LR: 0.000405 | Val NDCG@10: 0.018100 Epoch 03 | Loss: 6.8329 | LR: 0.000126 | Val NDCG@10: 0.017370 Epoch 04 | Loss: 6.8135 | LR: 0.000010 | Val NDCG@10: 0.018117 Validation Tuning Complete. Best Public NDCG@10: 0.018117 [3/5] Stage 2: Refitting two-tower model on combined train_* + val_*... Epoch 01 | Loss: 6.8723 | LR: 0.000603 Epoch 02 | Loss: 6.8483 | LR: 0.000208 Epoch 03 | Loss: 6.8225 | LR: 0.000010 Final refitted model saved to /app/solution/two_tower_model.pt [4/5] Extracting test user histories and generating final submission... Submission saved to /app/solution/submission.parquet [5/5] Running strict acceptance validation on submission... Verification PASSED: - Target users: exactly 8456 - Total rows: exactly 845600 - Ranks 1-100 per user without duplicates: verified - Candidate eligibility: 100% verified - Total pipeline time: 75.4s README.md updated successfully. Process Group PGID: 3809 ## Step 106 · agent · 2026-09-29T00:57:51.514Z ### Recorded reasoning summary Confirming Pipeline Performance The pipeline ran exceptionally fast, completing in just 75.4 seconds. It successfully verified all 8,456 target users and 845,600 rows. Ranks 1-100 for each user were confirmed unique, and candidate eligibility also passed 100% verification. ### run_shell_command · 2026-09-29T00:57:51.514Z { "command": "python3 -c \"\nimport subprocess, json\nfor f in ['val_exp2_ep2.parquet', 'val_exp5_ep4.parquet', 'val_exp1.parquet', 'validation.parquet']:\n res = subprocess.run(['python3', '/task/evaluate_public.py', f'/app/solution/{f}'], capture_output=True, text=True)\n score = json.loads(res.stdout.strip())['scores']['quality_ndcg@10']\n print(f'{f}: NDCG@10 = {score}')\n\"", "description": "Compare validation NDCG across saved experiments" } ### Tool output · 2026-09-29T00:58:06.617Z Output: val_exp2_ep2.parquet: NDCG@10 = 0.021558 val_exp5_ep4.parquet: NDCG@10 = 0.0208 val_exp1.parquet: NDCG@10 = 0.004166 validation.parquet: NDCG@10 = 0.018117 Process Group PGID: 3918 ## Step 107 · agent · 2026-09-29T00:58:18.635Z ### Recorded reasoning summary Analyzing Historical Weights I'm examining the precise historical weighting differences between `val_exp2_ep2` and `val_exp5_ep4` after noting the former's impressive 0.021558 performance. My current focus is on understanding how the `delta_days` calculation in `experiment_2.py` might have contributed to this disparity. ### Recorded reasoning summary Investigating Performance Gains I'm exploring how a higher learning rate in early epochs, specifically `lr = 1e-3`, could have accelerated embedding alignment in `experiment_2.py`'s epoch 2, leading to the observed performance of 0.021558. I'm also analyzing the `decay` function's influence. ### run_shell_command · 2026-09-29T00:58:18.635Z { "command": "python3 -c \"\n# Let us test if epoch 2 with lr=1e-3 consistently achieves >= 0.0215\nfrom solution.experiment_2 import extract_window_positives, set_seed\nfrom solution.data_utils import load_catalog, extract_user_histories, CATALOG_SIZE\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\nimport pandas as pd, pyarrow.parquet as pq, numpy as np, torch, random, subprocess, json\n\nset_seed(42)\ncatalog_items, item2idx, feat_matrix = load_catalog()\ncatalog_df = pd.read_parquet('/task/data/candidate_catalog.parquet')\ncounts = catalog_df['positive_listens'].to_numpy(dtype=np.float64)\nneg_probs = np.power(counts, 0.6)\nneg_probs /= neg_probs.sum()\n\ntargets_df = pd.read_parquet('/task/data/target_users.parquet')\ntarget_uids = targets_df['uid'].tolist()\ntarget_set = set(target_uids)\n\nT_val_cutoff = 24786800\nval_pref = pd.read_parquet('/task/data/validation_preference_state.parquet')\nlistens_table = pq.read_table('/task/data/train_listens.parquet', columns=['uid', 'timestamp', 'item_id', 'is_organic', 'played_ratio_pct'])\nlikes_df = pd.read_parquet('/task/data/train_likes.parquet')\n\nT_w0 = T_val_cutoff - 7 * 86400\nT_w1 = T_val_cutoff - 14 * 86400\n\nw0_pos = extract_window_positives(listens_table, likes_df, item2idx, target_set, T_w0, T_val_cutoff)\nw1_pos = extract_window_positives(listens_table, likes_df, item2idx, target_set, T_w1, T_w0)\nw0_hist = extract_user_histories(listens_table, val_pref, item2idx, target_uids, T_w0, window_days=60)\nw1_hist = extract_user_histories(listens_table, val_pref, item2idx, target_uids, T_w1, window_days=60)\nval_hist = extract_user_histories(listens_table, val_pref, item2idx, target_uids, T_val_cutoff, window_days=60)\n\nmodel = TwoTowerModel(embed_dim=64, out_dim=64, hidden_dim=128, temperature=0.07)\nfrom solution.train_and_predict import train_two_tower_model\n\nscore = train_two_tower_model(\n model, feat_matrix, neg_probs, target_uids,\n windows=[(w0_hist, w0_pos), (w1_hist, w1_pos)],\n epochs=3,\n lr=1e-3,\n weight_decay=2e-4,\n batch_size=512,\n num_negatives=512,\n val_hist=val_hist,\n val_output_path='/app/solution/validation_tuned.parquet'\n)\nprint('Tuned score:', score)\n\"", "description": "Test tuned training run with lr=1e-3" } ### Tool output · 2026-09-29T00:59:04.577Z Output: /app/solution/experiment_2.py:46: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_2.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) /app/solution/experiment_2.py:46: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy strong["item_idx"] = strong["item_id"].map(item2idx) /app/solution/experiment_2.py:57: SettingWithCopyWarning: A value is trying to be set on a copy of a slice from a DataFrame. Try using .loc[row_indexer,col_indexer] = value instead See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy org_likes["item_idx"] = org_likes["item_id"].map(item2idx) Epoch 01 | Loss: 6.8993 | LR: 0.000753 | Val NDCG@10: 0.010302 Epoch 02 | Loss: 6.8577 | LR: 0.000258 | Val NDCG@10: 0.020427 Epoch 03 | Loss: 6.8380 | LR: 0.000010 | Val NDCG@10: 0.021567 Tuned score: 0.021567 Process Group PGID: 3993 ## Step 108 · agent · 2026-09-29T00:59:08.639Z ### Recorded reasoning summary Observing NDCG Gains My current focus is on the steady improvement of Val NDCG@10, which has now reached 0.021567 at epoch 3. This upward trend validates the chosen learning rate of 1e-3 and the use of CosineAnnealingLR over three epochs. ### run_shell_command · 2026-09-29T00:59:08.639Z { "command": "ls -la /app/solution", "description": "List all files in /app/solution" } ### Tool output · 2026-09-29T00:59:08.659Z Output: total 96016 drwxr-xr-x 1 agent agent 4096 Sep 29 00:58 . drwxr-xr-x 1 agent agent 4096 Sep 29 00:27 .. -rw-r--r-- 1 agent agent 2464 Sep 29 00:57 README.md -rw-r--r-- 1 agent agent 7958145 Sep 29 00:46 best_model_exp2.pt -rw-r--r-- 1 agent agent 7958145 Sep 29 00:49 best_model_exp3.pt -rw-r--r-- 1 agent agent 16432321 Sep 29 00:52 best_model_exp4.pt -rw-r--r-- 1 agent agent 7958145 Sep 29 00:54 best_model_exp5.pt -rw-r--r-- 1 agent agent 8745 Sep 29 00:55 data_utils.py -rw-r--r-- 1 agent agent 10467 Sep 29 00:43 experiment_1.py -rw-r--r-- 1 agent agent 12826 Sep 29 00:46 experiment_2.py -rw-r--r-- 1 agent agent 11585 Sep 29 00:48 experiment_3.py -rw-r--r-- 1 agent agent 11625 Sep 29 00:51 experiment_4.py -rw-r--r-- 1 agent agent 16699 Sep 29 00:53 experiment_5.py -rw-r--r-- 1 agent agent 5869 Sep 29 00:40 model.py -rw-r--r-- 1 agent agent 1774 Sep 29 00:41 retrieval.py -rw-r--r-- 1 agent agent 1175126 Sep 29 00:57 submission.parquet -rw-r--r-- 1 agent agent 17218 Sep 29 00:56 train_and_predict.py -rw-r--r-- 1 agent agent 7958145 Sep 29 00:57 two_tower_model.pt -rw-r--r-- 1 agent agent 1286022 Sep 29 00:54 val_best.parquet -rw-r--r-- 1 agent agent 1639244 Sep 29 00:44 val_exp1.parquet -rw-r--r-- 1 agent agent 1525123 Sep 29 00:47 val_exp2_ep10.parquet -rw-r--r-- 1 agent agent 1640920 Sep 29 00:47 val_exp2_ep12.parquet -rw-r--r-- 1 agent agent 1283314 Sep 29 00:46 val_exp2_ep2.parquet -rw-r--r-- 1 agent agent 1287930 Sep 29 00:46 val_exp2_ep4.parquet -rw-r--r-- 1 agent agent 1404141 Sep 29 00:47 val_exp2_ep6.parquet -rw-r--r-- 1 agent agent 1522068 Sep 29 00:47 val_exp2_ep8.parquet -rw-r--r-- 1 agent agent 1288188 Sep 29 00:48 val_exp3_ep1.parquet -rw-r--r-- 1 agent agent 1526172 Sep 29 00:49 val_exp3_ep10.parquet -rw-r--r-- 1 agent agent 1395066 Sep 29 00:48 val_exp3_ep2.parquet -rw-r--r-- 1 agent agent 1395769 Sep 29 00:48 val_exp3_ep3.parquet -rw-r--r-- 1 agent agent 1398063 Sep 29 00:48 val_exp3_ep4.parquet -rw-r--r-- 1 agent agent 1397394 Sep 29 00:49 val_exp3_ep5.parquet -rw-r--r-- 1 agent agent 1400924 Sep 29 00:49 val_exp3_ep6.parquet -rw-r--r-- 1 agent agent 1515999 Sep 29 00:49 val_exp3_ep7.parquet -rw-r--r-- 1 agent agent 1522421 Sep 29 00:49 val_exp3_ep8.parquet -rw-r--r-- 1 agent agent 1524404 Sep 29 00:49 val_exp3_ep9.parquet -rw-r--r-- 1 agent agent 1286430 Sep 29 00:52 val_exp4_ep1.parquet -rw-r--r-- 1 agent agent 1283528 Sep 29 00:52 val_exp4_ep2.parquet -rw-r--r-- 1 agent agent 1396269 Sep 29 00:52 val_exp4_ep3.parquet -rw-r--r-- 1 agent agent 1512612 Sep 29 00:52 val_exp4_ep4.parquet -rw-r--r-- 1 agent agent 1519325 Sep 29 00:52 val_exp4_ep5.parquet -rw-r--r-- 1 agent agent 1528966 Sep 29 00:52 val_exp4_ep6.parquet -rw-r--r-- 1 agent agent 1648118 Sep 29 00:52 val_exp4_ep7.parquet -rw-r--r-- 1 agent agent 1641283 Sep 29 00:52 val_exp4_ep8.parquet -rw-r--r-- 1 agent agent 1283505 Sep 29 00:54 val_exp5_ep1.parquet -rw-r--r-- 1 agent agent 1284796 Sep 29 00:54 val_exp5_ep2.parquet -rw-r--r-- 1 agent agent 1177296 Sep 29 00:54 val_exp5_ep3.parquet -rw-r--r-- 1 agent agent 1286022 Sep 29 00:54 val_exp5_ep4.parquet -rw-r--r-- 1 agent agent 1287381 Sep 29 00:54 val_exp5_ep5.parquet -rw-r--r-- 1 agent agent 1288113 Sep 29 00:55 val_exp5_ep6.parquet -rw-r--r-- 1 agent agent 1283499 Sep 29 00:57 validation.parquet -rw-r--r-- 1 agent agent 724231 Sep 29 00:32 validation_pop.parquet -rw-r--r-- 1 agent agent 1283768 Sep 29 00:59 validation_tuned.parquet Process Group PGID: 4078 ## Step 109 · agent · 2026-09-29T00:59:37.173Z ### Recorded reasoning summary Cleaning up project files I'm focusing on tidying up our project structure by removing temporary files. Specifically, I'm identifying and preparing to delete the `val_exp*.parquet` files and the `experiment_*.py` scripts, while ensuring the core codebase files like `model.py`, `data_utils.py`, `retrieval.py`, and `train_and_predict` remain intact. ### write_file · 2026-09-29T00:59:37.173Z { "file_path": "/app/solution/train_and_predict.py", "content": "\"\"\"Production Two-Tower Neural Network Ranker for Music Track Retrieval and Ranking.\n\nDefines, trains, evaluates on public validation, refits on full history (train_* + val_*),\nand generates the final test submission.\n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport random\nimport subprocess\nimport time\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\nimport pyarrow as pa\nimport pyarrow.parquet as pq\nimport pyarrow.compute as pc\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom solution.data_utils import (\n load_catalog,\n extract_user_histories,\n extract_window_positives,\n CATALOG_SIZE,\n PAD_IDX,\n)\nfrom solution.model import TwoTowerModel\nfrom solution.retrieval import generate_ranking\n\n\ndef set_seed(seed: int = 42):\n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n\n\ndef train_two_tower_model(\n model: TwoTowerModel,\n feat_matrix: torch.Tensor,\n neg_probs: np.ndarray,\n target_uids: list[int],\n windows: list[tuple[dict, dict]],\n epochs: int = 3,\n lr: float = 1e-3,\n weight_decay: float = 2e-4,\n batch_size: int = 512,\n num_negatives: int = 512,\n val_hist: dict | None = None,\n val_output_path: str | None = None,\n):\n \"\"\"Train the two-tower model over temporal windows.\"\"\"\n optimizer = torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay)\n scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=epochs, eta_min=1e-5)\n\n best_score = 0.0\n best_state = None\n\n for epoch in range(1, epochs + 1):\n model.train()\n total_loss = 0.0\n num_batches = 0\n\n for hist, pos_dict in windows:\n active_uids = [u for u in target_uids if u in pos_dict]\n random.shuffle(active_uids)\n uid2row = hist[\"uid2row\"]\n\n for b_start in range(0, len(active_uids), batch_size):\n b_uids = active_uids[b_start : b_start + batch_size]\n if len(b_uids) < 32:\n continue\n\n b_pos_items = []\n b_weights = []\n for u in b_uids:\n items_dict = pos_dict[u]\n items = list(items_dict.keys())\n weights = list(items_dict.values())\n chosen = random.choices(items, weights=weights, k=1)[0]\n b_pos_items.append(chosen)\n b_weights.append(items_dict[chosen])\n\n b_liked_idx, b_liked_off = [], [0]\n b_disliked_idx, b_disliked_off = [], [0]\n b_listen_idx, b_listen_w, b_listen_off = [], [], [0]\n b_stats = []\n\n for u in b_uids:\n r = uid2row[u]\n l_start = hist[\"liked_offsets\"][r].item()\n l_end = (\n hist[\"liked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"liked_offsets\"])\n else len(hist[\"liked_indices\"])\n )\n b_liked_idx.extend(hist[\"liked_indices\"][l_start:l_end].tolist())\n b_liked_off.append(len(b_liked_idx))\n\n d_start = hist[\"disliked_offsets\"][r].item()\n d_end = (\n hist[\"disliked_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"disliked_offsets\"])\n else len(hist[\"disliked_indices\"])\n )\n b_disliked_idx.extend(hist[\"disliked_indices\"][d_start:d_end].tolist())\n b_disliked_off.append(len(b_disliked_idx))\n\n ls_start = hist[\"listen_offsets\"][r].item()\n ls_end = (\n hist[\"listen_offsets\"][r + 1].item()\n if r + 1 < len(hist[\"listen_offsets\"])\n else len(hist[\"listen_indices\"])\n )\n b_listen_idx.extend(hist[\"listen_indices\"][ls_start:ls_end].tolist())\n b_listen_w.extend(hist[\"listen_weights\"][ls_start:ls_end].tolist())\n b_listen_off.append(len(b_listen_idx))\n\n b_stats.append(hist[\"user_stats\"][r])\n\n t_liked_idx = torch.tensor(b_liked_idx, dtype=torch.long)\n t_liked_off = torch.tensor(b_liked_off[:-1], dtype=torch.long)\n t_disliked_idx = torch.tensor(b_disliked_idx, dtype=torch.long)\n t_disliked_off = torch.tensor(b_disliked_off[:-1], dtype=torch.long)\n t_listen_idx = torch.tensor(b_listen_idx, dtype=torch.long)\n t_listen_w = torch.tensor(b_listen_w, dtype=torch.float32)\n t_listen_off = torch.tensor(b_listen_off[:-1], dtype=torch.long)\n t_stats = torch.stack(b_stats, dim=0)\n\n t_pos_items = torch.tensor(b_pos_items, dtype=torch.long)\n t_pos_feats = feat_matrix[t_pos_items]\n t_weights = torch.tensor(b_weights, dtype=torch.float32)\n\n neg_indices = np.random.choice(CATALOG_SIZE, size=num_negatives, p=neg_probs)\n t_neg_items = torch.from_numpy(neg_indices).long()\n t_neg_feats = feat_matrix[t_neg_items]\n\n optimizer.zero_grad()\n u_emb = model.user_tower(\n t_liked_idx,\n t_liked_off,\n t_disliked_idx,\n t_disliked_off,\n t_listen_idx,\n t_listen_w,\n t_listen_off,\n t_stats,\n )\n v_pos = model.item_tower(t_pos_items, t_pos_feats)\n v_neg = model.item_tower(t_neg_items, t_neg_feats)\n\n v_all = torch.cat([v_pos, v_neg], dim=0)\n B = len(b_uids)\n scores = torch.matmul(u_emb, v_all.T) / model.temperature\n labels = torch.arange(B, device=scores.device)\n\n loss = F.cross_entropy(scores, labels, reduction=\"none\")\n weighted_loss = (loss * t_weights).sum() / t_weights.sum()\n\n weighted_loss.backward()\n optimizer.step()\n\n total_loss += weighted_loss.item()\n num_batches += 1\n\n scheduler.step()\n avg_loss = total_loss / max(1, num_batches)\n current_lr = scheduler.get_last_lr()[0]\n\n if val_hist is not None and val_output_path is not None:\n model.eval()\n with torch.no_grad():\n val_u = model.user_tower(\n val_hist[\"liked_indices\"],\n val_hist[\"liked_offsets\"],\n val_hist[\"disliked_indices\"],\n val_hist[\"disliked_offsets\"],\n val_hist[\"listen_indices\"],\n val_hist[\"listen_weights\"],\n val_hist[\"listen_offsets\"],\n val_hist[\"user_stats\"],\n )\n val_v = model.precompute_item_vectors(feat_matrix)\n\n generate_ranking(\n val_u,\n val_v,\n \"/task/data/validation_eligible_candidates.npz\",\n val_output_path,\n )\n\n res = subprocess.run(\n [\"python3\", \"/task/evaluate_public.py\", val_output_path],\n capture_output=True,\n text=True,\n )\n score_data = json.loads(res.stdout.strip())\n score = score_data[\"scores\"][\"quality_ndcg@10\"]\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f} | Val NDCG@10: {score:.6f}\")\n if score > best_score:\n best_score = score\n best_state = {k: v.cpu().clone() for k, v in model.state_dict().items()}\n else:\n print(f\"Epoch {epoch:02d} | Loss: {avg_loss:.4f} | LR: {current_lr:.6f}\")\n\n if best_state is not None:\n model.load_state_dict(best_state)\n return best_score\n\n\ndef main():\n set_seed(42)\n start_time = time.time()\n print(\"=\" * 60)\n print(\"Two-Tower Neural Network Ranker: Training & Refit Pipeline\")\n print(\"=\" * 60)\n\n # 1. Load catalog\n print(\"\\n[1/5] Loading catalog & precomputing features...\")\n catalog_items, item2idx, feat_matrix = load_catalog()\n catalog_df = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n counts = catalog_df[\"positive_listens\"].to_numpy(dtype=np.float64)\n neg_probs = np.power(counts, 0.6)\n neg_probs /= neg_probs.sum()\n\n targets_df = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_df[\"uid\"].tolist()\n target_set = set(target_uids)\n\n # 2. Stage 1: Validation Tuning\n print(\"\\n[2/5] Stage 1: Tuning two-tower model on train_* with validation evaluation...\")\n train_listens = pq.read_table(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n train_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\n val_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n\n T_val_cutoff = 24786800\n val_hist = extract_user_histories(\n train_listens,\n val_pref,\n item2idx,\n target_uids,\n T_val_cutoff,\n window_days=60,\n )\n\n # Historical training windows\n T_w0 = T_val_cutoff - 7 * 86400\n T_w1 = T_val_cutoff - 14 * 86400\n\n w0_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w0, T_val_cutoff)\n w1_pos = extract_window_positives(train_listens, train_likes, item2idx, target_set, T_w1, T_w0)\n w0_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w0, window_days=60)\n w1_hist = extract_user_histories(train_listens, val_pref, item2idx, target_uids, T_w1, window_days=60)\n\n val_model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n\n val_ranking_path = \"/app/solution/validation.parquet\"\n best_val_ndcg = train_two_tower_model(\n model=val_model,\n feat_matrix=feat_matrix,\n neg_probs=neg_probs,\n target_uids=target_uids,\n windows=[(w0_hist, w0_pos), (w1_hist, w1_pos)],\n epochs=3,\n lr=1e-3,\n weight_decay=2e-4,\n batch_size=512,\n num_negatives=512,\n val_hist=val_hist,\n val_output_path=val_ranking_path,\n )\n print(f\"Validation Tuning Complete. Best Public NDCG@10: {best_val_ndcg:.6f}\")\n\n # 3. Stage 2: Full Refit on train_* + val_*\n print(\"\\n[3/5] Stage 2: Refitting two-tower model on combined train_* + val_*...\")\n val_listens = pq.read_table(\n \"/task/data/val_listens.parquet\",\n columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"],\n )\n val_likes = pd.read_parquet(\"/task/data/val_likes.parquet\")\n combined_listens = pa.concat_tables([train_listens, val_listens])\n combined_likes = pd.concat([train_likes, val_likes], ignore_index=True)\n\n T_test_cutoff = 25393400\n test_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\n\n # The validation window [24786800, 25393400] is now observed training history!\n refit_w0_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, 24786800, T_test_cutoff)\n refit_w1_pos = extract_window_positives(combined_listens, combined_likes, item2idx, target_set, T_w0, 24786800)\n\n # Histories at the respective split cutoffs\n refit_w0_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, 24786800, window_days=60)\n refit_w1_hist = extract_user_histories(combined_listens, val_pref, item2idx, target_uids, T_w0, window_days=60)\n\n final_model = TwoTowerModel(\n catalog_size=CATALOG_SIZE,\n embed_dim=64,\n out_dim=64,\n hidden_dim=128,\n temperature=0.07,\n )\n\n train_two_tower_model(\n model=final_model,\n feat_matrix=feat_matrix,\n neg_probs=neg_probs,\n target_uids=target_uids,\n windows=[(refit_w0_hist, refit_w0_pos), (refit_w1_hist, refit_w1_pos)],\n epochs=3,\n lr=1e-3,\n weight_decay=2e-4,\n batch_size=512,\n num_negatives=512,\n )\n\n # Save final model weights\n torch.save(final_model.state_dict(), \"/app/solution/two_tower_model.pt\")\n print(\"Final refitted model saved to /app/solution/two_tower_model.pt\")\n\n # 4. Generate Final Test Submission\n print(\"\\n[4/5] Extracting test user histories and generating final submission...\")\n test_hist = extract_user_histories(\n combined_listens,\n test_pref,\n item2idx,\n target_uids,\n T_test_cutoff,\n window_days=60,\n )\n\n final_model.eval()\n with torch.no_grad():\n test_u = final_model.user_tower(\n test_hist[\"liked_indices\"],\n test_hist[\"liked_offsets\"],\n test_hist[\"disliked_indices\"],\n test_hist[\"disliked_offsets\"],\n test_hist[\"listen_indices\"],\n test_hist[\"listen_weights\"],\n test_hist[\"listen_offsets\"],\n test_hist[\"user_stats\"],\n )\n test_v = final_model.precompute_item_vectors(feat_matrix)\n\n submission_path = \"/app/solution/submission.parquet\"\n generate_ranking(\n test_u,\n test_v,\n \"/task/data/test_eligible_candidates.npz\",\n submission_path,\n )\n\n print(f\"Submission saved to {submission_path}\")\n\n # 5. Exhaustive Verification\n print(\"\\n[5/5] Running strict acceptance validation on submission...\")\n from ranking_evaluation import CandidateSet, read_ranking, validate_ranking, RankingValidationError\n\n sub_frame = read_ranking(Path(submission_path))\n val_frame = validate_ranking(sub_frame, target_set)\n test_elig = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\n for uid, item in val_frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not test_elig.is_eligible(uid, item):\n raise RankingValidationError(f\"Ineligible candidate item {item} for user {uid}\")\n\n print(\"Verification PASSED:\")\n print(f\" - Target users: exactly {len(target_set)}\")\n print(f\" - Total rows: exactly {len(val_frame)}\")\n print(\" - Ranks 1-100 per user without duplicates: verified\")\n print(\" - Candidate eligibility: 100% verified\")\n print(f\" - Total pipeline time: {time.time() - start_time:.1f}s\")\n\n # Update README\n readme_content = f\"\"\"# Latency-Efficient PyTorch Two-Tower Neural Ranker\n\n## Architecture\nThe system implements a production-grade, latency-efficient two-tower PyTorch neural network ranker designed for max inner-product search (MIPS):\n- **Item Tower**: Maps catalog item indices (30,000 items) and precomputed continuous item features (popularity rank, log positive listens, log organic positive listens, and organic ratio) through an item projection MLP with LayerNorm to output normalized item vectors $\\\\mathbf{{v}}_i \\\\in \\\\mathbb{{R}}^{{64}}$ with $\\\\|\\\\mathbf{{v}}_i\\\\|_2 = 1$. All 30,000 item vectors are independently precomputed offline.\n- **User Tower**: Strictly derives user representations from interaction history without user-ID lookup:\n - Actively liked items from cutoff preference state pooled via `nn.EmbeddingBag`.\n - Actively disliked items from cutoff preference state pooled via `nn.EmbeddingBag`.\n - Cleaned positive listens (completed $\\\\ge 50\\\\%$) with exponential time decay, played ratio completion, and organic listening boost.\n - Aggregate user continuous engagement statistics.\n - Concatenation through a deep user projection MLP with LayerNorm, GELU, and Dropout to output normalized user vector $\\\\mathbf{{u}} \\\\in \\\\mathbb{{R}}^{{64}}$ with $\\\\|\\\\mathbf{{u}}\\\\|_2 = 1$.\n- **Relevance Scoring & Retrieval**: Final relevance score decomposes exactly into the dot product $s(u, i) = \\\\mathbf{{u}}^\\\\top \\\\mathbf{{v}}_i$. At request time, candidate items are retrieved via MIPS and filtered by platform eligibility bitmasks.\n\n## Training Objective\nThe model is trained with an InfoNCE / sampled softmax loss over temporal training windows, aligning with discovery relevance (weight 4.0 for organic likes, 2.0 for new strong organic listens, 1.0 for repeat strong listens):\n$$\\\\mathcal{{L}} = -\\\\sum_{{b}} w_b \\\\log \\\\frac{{\\\\exp(\\\\mathbf{{u}}_b^\\\\top \\\\mathbf{{v}}_b / \\\\tau)}}{{\\\\sum_{{j}} \\\\exp(\\\\mathbf{{u}}_b^\\\\top \\\\mathbf{{v}}_j / \\\\tau)}}$$\nNegative items are sampled using a combination of in-batch negatives and popularity-scaled catalog negatives ($P(i) \\\\propto \\\\text{{listens}}^{{0.6}}$) to prevent popularity bias while ensuring fine-grained musical discrimination.\n\n## Public Validation Score\n- **Baseline Popularity NDCG@10**: 0.010710\n- **Two-Tower Neural Ranker NDCG@10**: {best_val_ndcg:.6f} (over 100% relative improvement over baseline)\n\n## Final Submission Command\nTo reproduce training, validation, refit on `train_* + val_*`, and test submission generation:\n```sh\nPYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py\n```\n\"\"\"\n with open(\"/app/solution/README.md\", \"w\") as f:\n f.write(readme_content)\n print(\"README.md updated successfully.\")\n\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T00:59:37.186Z Successfully overwrote file: /app/solution/train_and_predict.py. Here is the updated code: """Production Two-Tower Neural Network Ranker for Music Track Retrieval and Ranking. Defines, trains, evaluates on public validation, refits on full history (train_* + val_*), and generates the final test submission. """ from __future__ import annotations import json import random import subprocess ... model: TwoTowerModel, feat_matrix: torch.Tensor, neg_probs: np.ndarray, target_uids: list[int], windows: list[tuple[dict, dict]], epochs: int = 3, lr: float = 1e-3, weight_decay: float = 2e-4, batch_size: int = 512, num_negatives: int = 512, val_hist: dict | None = None, val_output_path: str | None = None, ... model=val_model, feat_matrix=feat_matrix, neg_probs=neg_probs, target_uids=target_uids, windows=[(w0_hist, w0_pos), (w1_hist, w1_pos)], epochs=3, lr=1e-3, weight_decay=2e-4, batch_size=512, num_negatives=512, val_hist=val_hist, val_output_path=val_ranking_path, ... feat_matrix=feat_matrix, neg_probs=neg_probs, target_uids=target_uids, windows=[(refit_w0_hist, refit_w0_pos), (refit_w1_hist, refit_w1_pos)], epochs=3, lr=1e-3, weight_decay=2e-4, batch_size=512, num_negatives=512, ) ... $$\\mathcal{{L}} = -\\sum_{{b}} w_b \\log \\frac{{\\exp(\\mathbf{{u}}_b^\\top \\mathbf{{v}}_b / \\tau)}}{{\\sum_{{j}} \\exp(\\mathbf{{u}}_b^\\top \\mathbf{{v}}_j / \\tau)}}$$ Negative items are sampled using a combination of in-batch negatives and popularity-scaled catalog negatives ($P(i) \\propto \\text{{listens}}^{{0.6}}$) to prevent popularity bias while ensuring fine-grained musical discrimination. ## Public Validation Score - **Baseline Popularity NDCG@10**: 0.010710 - **Two-Tower Neural Ranker NDCG@10**: {best_val_ndcg:.6f} (over 100% relative improvement over baseline) ## Final Submission Command To reproduce training, validation, refit on `train_* + val_*`, and test submission generation: ```sh PYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py ``` """ with open("/app/solution/README.md", "w") as f: f.write(readme_content) print("README.md updated successfully.") ... ## Step 110 · agent · 2026-09-29T00:59:40.315Z ### run_shell_command · 2026-09-29T00:59:40.315Z { "command": "rm -f /app/solution/experiment_*.py /app/solution/val_exp*.parquet /app/solution/val_best.parquet /app/solution/validation_tuned.parquet /app/solution/best_model_exp*.pt", "description": "Clean up temporary experiment artifacts" } ### Tool output · 2026-09-29T00:59:40.341Z Output: (empty) Process Group PGID: 4087 ## Step 111 · agent · 2026-09-29T00:59:44.484Z ### run_shell_command · 2026-09-29T00:59:44.484Z { "description": "List contents of /app/solution", "command": "ls -la /app/solution" } ### Tool output · 2026-09-29T00:59:44.503Z Output: total 10944 drwxr-xr-x 1 agent agent 4096 Sep 29 00:59 . drwxr-xr-x 1 agent agent 4096 Sep 29 00:27 .. -rw-r--r-- 1 agent agent 2464 Sep 29 00:57 README.md -rw-r--r-- 1 agent agent 8745 Sep 29 00:55 data_utils.py -rw-r--r-- 1 agent agent 5869 Sep 29 00:40 model.py -rw-r--r-- 1 agent agent 1774 Sep 29 00:41 retrieval.py -rw-r--r-- 1 agent agent 1175126 Sep 29 00:57 submission.parquet -rw-r--r-- 1 agent agent 17377 Sep 29 00:59 train_and_predict.py -rw-r--r-- 1 agent agent 7958145 Sep 29 00:57 two_tower_model.pt -rw-r--r-- 1 agent agent 1283499 Sep 29 00:57 validation.parquet -rw-r--r-- 1 agent agent 724231 Sep 29 00:32 validation_pop.parquet Process Group PGID: 4090 ## Step 112 · agent · 2026-09-29T00:59:52.152Z ### run_shell_command · 2026-09-29T00:59:52.152Z { "description": "Remove validation_pop.parquet", "command": "rm -f /app/solution/validation_pop.parquet" } ### Tool output · 2026-09-29T00:59:52.168Z Output: (empty) Process Group PGID: 4093 ## Step 113 · agent · 2026-09-29T00:59:55.178Z ### run_shell_command · 2026-09-29T00:59:55.178Z { "description": "Run end-to-end training and prediction pipeline", "command": "PYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py" } ### Tool output · 2026-09-29T01:01:09.207Z Output: ============================================================ Two-Tower Neural Network Ranker: Training & Refit Pipeline ============================================================ [1/5] Loading catalog & precomputing features... [2/5] Stage 1: Tuning two-tower model on train_* with validation evaluation... Epoch 01 | Loss: 6.8993 | LR: 0.000753 | Val NDCG@10: 0.010302 Epoch 02 | Loss: 6.8577 | LR: 0.000258 | Val NDCG@10: 0.020427 Epoch 03 | Loss: 6.8380 | LR: 0.000010 | Val NDCG@10: 0.021567 Validation Tuning Complete. Best Public NDCG@10: 0.021567 [3/5] Stage 2: Refitting two-tower model on combined train_* + val_*... Epoch 01 | Loss: 6.8908 | LR: 0.000753 Epoch 02 | Loss: 6.8522 | LR: 0.000258 Epoch 03 | Loss: 6.8364 | LR: 0.000010 Final refitted model saved to /app/solution/two_tower_model.pt [4/5] Extracting test user histories and generating final submission... Submission saved to /app/solution/submission.parquet [5/5] Running strict acceptance validation on submission... Verification PASSED: - Target users: exactly 8456 - Total rows: exactly 845600 - Ranks 1-100 per user without duplicates: verified - Candidate eligibility: 100% verified - Total pipeline time: 69.8s README.md updated successfully. Process Group PGID: 4096 ## Step 114 · agent · 2026-09-29T01:01:18.125Z ### Recorded reasoning summary Tuning Two-Tower Model My current focus is on the initial tuning of the two-tower model using the training data. I'm observing a steady improvement in validation NDCG@10, reaching 0.021567 by Epoch 03, which indicates promising progress for Stage 1. ### run_shell_command · 2026-09-29T01:01:18.125Z { "description": "Evaluate validation.parquet with evaluate_public.py", "command": "python3 /task/evaluate_public.py /app/solution/validation.parquet" } ### Tool output · 2026-09-29T01:01:21.564Z Output: { "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.021567, "quality_users": 4263 } } Process Group PGID: 4187 ## Step 115 · agent · 2026-09-29T01:01:28.175Z ### run_shell_command · 2026-09-29T01:01:28.175Z { "command": "cat /app/solution/README.md", "description": "View README.md" } ### Tool output · 2026-09-29T01:01:28.194Z Output: # Latency-Efficient PyTorch Two-Tower Neural Ranker ## Architecture The system implements a production-grade, latency-efficient two-tower PyTorch neural network ranker designed for max inner-product search (MIPS): - **Item Tower**: Maps catalog item indices (30,000 items) and precomputed continuous item features (popularity rank, log positive listens, log organic positive listens, and organic ratio) through an item projection MLP with LayerNorm to output normalized item vectors $\mathbf{v}_i \in \mathbb{R}^{64}$ with $\|\mathbf{v}_i\|_2 = 1$. All 30,000 item vectors are independently precomputed offline. - **User Tower**: Strictly derives user representations from interaction history without user-ID lookup: - Actively liked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Actively disliked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Cleaned positive listens (completed $\ge 50\%$) with exponential time decay, played ratio completion, and organic listening boost. - Aggregate user continuous engagement statistics. - Concatenation through a deep user projection MLP with LayerNorm, GELU, and Dropout to output normalized user vector $\mathbf{u} \in \mathbb{R}^{64}$ with $\|\mathbf{u}\|_2 = 1$. - **Relevance Scoring & Retrieval**: Final relevance score decomposes exactly into the dot product $s(u, i) = \mathbf{u}^\top \mathbf{v}_i$. At request time, candidate items are retrieved via MIPS and filtered by platform eligibility bitmasks. ## Training Objective The model is trained with an InfoNCE / sampled softmax loss over temporal training windows, aligning with discovery relevance (weight 4.0 for organic likes, 2.0 for new strong organic listens, 1.0 for repeat strong listens): $$\mathcal{L} = -\sum_{b} w_b \log \frac{\exp(\mathbf{u}_b^\top \mathbf{v}_b / \tau)}{\sum_{j} \exp(\mathbf{u}_b^\top \mathbf{v}_j / \tau)}$$ Negative items are sampled using a combination of in-batch negatives and popularity-scaled catalog negatives ($P(i) \propto \text{listens}^{0.6}$) to prevent popularity bias while ensuring fine-grained musical discrimination. ## Public Validation Score - **Baseline Popularity NDCG@10**: 0.010710 - **Two-Tower Neural Ranker NDCG@10**: 0.021567 (over 100% relative improvement over baseline) ## Final Submission Command To reproduce training, validation, refit on `train_* + val_*`, and test submission generation: ```sh PYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py ``` Process Group PGID: 4207 ## Step 116 · agent · 2026-09-29T01:01:36.407Z ### Recorded reasoning summary Verifying File Integrity I'm currently ensuring that no unintended modifications have occurred outside the designated `/app/solution/` directory, strictly adhering to the prompt's file system restrictions. ### run_shell_command · 2026-09-29T01:01:36.407Z { "description": "Final check of /app/solution", "command": "ls -la /app/solution" } ### Tool output · 2026-09-29T01:01:36.426Z Output: total 10240 drwxr-xr-x 1 agent agent 4096 Sep 29 00:59 . drwxr-xr-x 1 agent agent 4096 Sep 29 00:27 .. -rw-r--r-- 1 agent agent 2485 Sep 29 01:01 README.md -rw-r--r-- 1 agent agent 8745 Sep 29 00:55 data_utils.py -rw-r--r-- 1 agent agent 5869 Sep 29 00:40 model.py -rw-r--r-- 1 agent agent 1774 Sep 29 00:41 retrieval.py -rw-r--r-- 1 agent agent 1176956 Sep 29 01:01 submission.parquet -rw-r--r-- 1 agent agent 17377 Sep 29 00:59 train_and_predict.py -rw-r--r-- 1 agent agent 7958145 Sep 29 01:01 two_tower_model.pt -rw-r--r-- 1 agent agent 1283768 Sep 29 01:00 validation.parquet Process Group PGID: 4210 ## Step 117 · agent · 2026-09-29T01:01:40.345Z ### Recorded reasoning summary Concluding the Topic I'm now moving to finalize our work. The next step involves calling `update_topic` to consolidate all completed tasks and bring this discussion to a close. ### update_topic · 2026-09-29T01:01:40.345Z { "summary": "Built a production-grade, latency-efficient PyTorch two-tower neural ranker for track discovery. The system features an Item Tower that maps 30,000 catalog items and continuous popularity features to normalized embeddings, and a User Tower that derives representations strictly from interaction history (liked/disliked explicit state and weighted positive listens) without user-ID lookups. The model optimizes an InfoNCE loss with discovery-weighted positive sampling and popularity-scaled negative sampling, decomposing exactly into inner products for MIPS candidate retrieval. Public validation NDCG@10 improved by over 100% from 0.010710 (baseline) to 0.021567. The model was refitted on the combined history (train + val), and the final submission was generated and verified for 100% eligibility across all 8,456 target users with exactly 100 unique ranked items each.", "strategic_intent": "Complete task: verify final deliverable, validate schema and candidate eligibility, and summarize results." } ### Tool output · 2026-09-29T01:01:40.350Z Strategic Intent: Complete task: verify final deliverable, validate schema and candidate eligibility, and summarize results. ## Step 118 · agent · 2026-09-29T01:01:49.275Z ### Solution Overview A latency-efficient, scalable PyTorch two-tower neural network ranker has been implemented and deployed for the "Made for You" discovery shelf. #### 1. Architecture & Decomposition - **Exact Vector Decomposition**: The ranker decomposes into an independent **User Tower** producing $\mathbf{u} \in \mathbb{R}^{64}$ and an **Item Tower** producing $\mathbf{v}_i \in \mathbb{R}^{64}$ with $\|\mathbf{u}\|_2 = \|\mathbf{v}_i\|_2 = 1$. Final relevance scores equal their exact dot product $s(u, i) = \mathbf{u}^\top \mathbf{v}_i$. - **Item Tower (`ItemTower` in `solution/model.py`)**: Maps each catalog item index (30,000 items) and continuous track features (popularity rank, log positive listens, log organic positive listens, and organic ratio) through projection and dense MLP layers with LayerNorm. Precomputing all 30,000 catalog item vectors offline takes $\approx 5\text{ ms}$. - **User Tower (`UserTower` in `solution/model.py`)**: Strictly derives user representations from interaction history without user-ID lookup: - Actively liked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Actively disliked items from cutoff preference state pooled via `nn.EmbeddingBag`. - Cleaned positive listens ($\ge 50\%$ completion ratio) with exponential recency decay $\exp(-0.04 \Delta t)$, played ratio completion, and organic multiplier. - User continuous interaction statistics (like count, dislike count, listen count, mean completion, organic ratio). - Concatenation through a deep user projection MLP with LayerNorm, GELU, and Dropout. - **Request-time MIPS**: Inner-product matrix multiplication $\mathbf{U} \mathbf{V}^\top$ followed by fast Boolean eligibility bitmask filtering and `torch.topk` retrieves top-100 items for all 8,456 target users in under $0.6\text{ seconds}$ on CPU. #### 2. Training Objective - Trained with discovery-aligned InfoNCE / sampled softmax loss over multiple temporal training windows: $$\mathcal{L} = -\sum_{b} w_b \log \frac{\exp(\mathbf{u}_b^\top \mathbf{v}_b / \tau)}{\sum_{j} \exp(\mathbf{u}_b^\top \mathbf{v}_j / \tau)}$$ with temperature $\tau = 0.07$. - Samples are weighted to mirror discovery evaluation grades: weight $4.0$ for organic likes (grade 3), $2.0$ for new strong organic listens (grade 1 discovery), and $1.0$ for repeat strong listens. - Negatives combine in-batch negatives with popularity-scaled catalog negatives ($P(i) \propto (\text{positive\_listens}_i)^{0.6}$) to avoid popularity distortion and drive fine-grained taste discrimination. #### 3. Public Validation & Final Refit - **Baseline Popularity NDCG@10**: `0.010710` - **Two-Tower Neural Ranker NDCG@10**: `0.021567` (over 100% relative improvement) - **Refit on `train_* + val_*`**: Refitted on the complete historical data incorporating validation events as observed history up to cutoff `25393400` with `test_preference_state.parquet`. #### 4. Deliverables & Verification The following files are located in `/app/solution/`: - `model.py`: PyTorch `ItemTower`, `UserTower`, and `TwoTowerModel`. - `data_utils.py`: Fast data loading, skip-filtering, history feature extraction, and window target preparation. - `retrieval.py`: MIPS inner-product search and candidate eligibility bitmasking. - `train_and_predict.py`: Complete pipeline to train, validate, refit on `train_* + val_*`, and export submissions. - `submission.parquet`: Validated submission covering all 8,456 target users, exactly 100 ranks per user (1–100 without ties/duplicates), with 100% candidate eligibility verified via `test_eligible_candidates.npz`. - `README.md`: Concise documentation of model architecture, loss formulation, validation score, and reproduction command (`PYTHONPATH=/app:/task python3 /app/solution/train_and_predict.py`). ## Recorded session usage [ { "messageId": "340270ae-8b5d-423d-9c21-7526ebab92b6", "timestamp": "2026-09-29T00:30:23.332Z", "model": 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