yambda-discovery-ranking-v01 / gemini-3-8-flash-high-basic-fixed-r03-d237f45e Recorded model: gemini-3.8-flash ## Step 1 · user · 2026-09-29T04:19:44.333Z This is the Gemini CLI. We are setting up the context for our chat. Today's date is Tuesday, September 29, 2026 (formatted according to the user's locale). My operating system is: linux The project's temporary directory is: /home/agent/.gemini/tmp/app - **Workspace Directories:** - /app - /task - **Directory Structure:** Showing up to 200 items (files + folders). /app/ └───solution/ Showing up to 200 items (files + folders). /task/ ├───evaluate_public.py ├───ranking_evaluation.py ├───requirements.txt └───data/ ├───candidate_catalog.parquet ├───target_users.parquet ├───test_eligible_candidates.npz ├───test_preference_state.parquet ├───train_dislikes.parquet ├───train_likes.parquet ├───train_listens.parquet ├───train_undislikes.parquet ├───train_unlikes.parquet ├───val_dislikes.parquet ├───val_likes.parquet ├───val_listens.parquet ├───val_undislikes.parquet ├───val_unlikes.parquet ├───validation_eligible_candidates.npz └───validation_preference_state.parquet ## Step 2 · user · 2026-09-29T04:19:44.377Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. 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, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, 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 builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. 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. - You have up to 30 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 · 2026-09-29T04:19:55.761Z ### Recorded reasoning summary Analyzing Task Details I'm currently dissecting the prompt, paying close attention to the machine learning task, competitive aspects, and specific constraints. My focus is on understanding the requirements and the provided workspace setup before proceeding further. ### Recorded reasoning summary Investigating Workspace Constraints I'm now focusing on the strict environment limitations: read-only directories, no new packages, and a tight time budget. My priority is to understand the evaluation script and available tools to quickly establish a baseline and ensure valid submissions are always accessible. ### update_topic · 2026-09-29T04:19:55.761Z { "strategic_intent": "Explore data files, requirements, and evaluation script to understand the problem and setup.", "summary": "Starting initial exploration of the task environment. Inspecting /task directory, dataset shapes, evaluation scripts, and installed packages.", "title": "Initial Exploration and Setup" } ### Tool output · 2026-09-29T04:19:55.801Z Current topic: "Initial Exploration and Setup" Topic summary: Starting initial exploration of the task environment. Inspecting /task directory, dataset shapes, evaluation scripts, and installed packages. Strategic Intent: Explore data files, requirements, and evaluation script to understand the problem and setup. ### read_file · 2026-09-29T04:19:55.761Z { "start_line": 1, "file_path": "/task/evaluate_public.py" } ### Tool output · 2026-09-29T04:19:55.801Z #!/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() ### read_file · 2026-09-29T04:19:55.761Z { "start_line": 1, "file_path": "/task/ranking_evaluation.py" } ### Tool output · 2026-09-29T04:19:55.801Z """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), } ### read_file · 2026-09-29T04:19:55.761Z { "start_line": 1, "file_path": "/task/requirements.txt" } ### Tool output · 2026-09-29T04:19:55.801Z # Exact third-party distribution inventory for yambda-discovery-ranking-v01. # # 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. The same inventory is used for source preparation, # task building, reference scoring, judging, and solver attempts. 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 idna==3.18 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 mdit-py-plugins==0.6.1 mdurl==0.1.2 nbformat==5.11.1 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 six==1.17.0 text-unidecode==1.3 threadpoolctl==3.6.0 tqdm==4.70.0 traitlets==5.16.1 typing-extensions==4.16.0 tzdata==2026.3 urllib3==2.7.0 webencodings==0.6.1 ### run_shell_command · 2026-09-29T04:19:55.761Z { "description": "Check sizes of dataset files in /task/data/", "command": "ls -lh /task/data/" } ### Tool output · 2026-09-29T04:19:55.801Z Output: total 340M -r--r--r-- 1 root root 342K Sep 29 04:13 candidate_catalog.parquet -r--r--r-- 1 root root 83K Sep 29 04:13 target_users.parquet -r--r--r-- 1 root root 7.4M Sep 29 04:13 test_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 29 04:14 test_preference_state.parquet -r--r--r-- 1 root root 838K Sep 29 04:13 train_dislikes.parquet -r--r--r-- 1 root root 5.2M Sep 29 04:13 train_likes.parquet -r--r--r-- 1 root root 305M Sep 29 04:13 train_listens.parquet -r--r--r-- 1 root root 153K Sep 29 04:13 train_undislikes.parquet -r--r--r-- 1 root root 1.8M Sep 29 04:13 train_unlikes.parquet -r--r--r-- 1 root root 36K Sep 29 04:13 val_dislikes.parquet -r--r--r-- 1 root root 292K Sep 29 04:13 val_likes.parquet -r--r--r-- 1 root root 9.5M Sep 29 04:13 val_listens.parquet -r--r--r-- 1 root root 7.7K Sep 29 04:13 val_undislikes.parquet -r--r--r-- 1 root root 71K Sep 29 04:13 val_unlikes.parquet -r--r--r-- 1 root root 7.3M Sep 29 04:13 validation_eligible_candidates.npz -r--r--r-- 1 root root 1.1M Sep 29 04:14 validation_preference_state.parquet Process Group PGID: 1612 ## Step 4 · agent · 2026-09-29T04:20:06.201Z ### run_shell_command · 2026-09-29T04:20:06.201Z { "description": "Check CPU cores and available memory", "command": "nproc && free -h" } ### Tool output · 2026-09-29T04:20:06.218Z Output: 8 /usr/bin/bash: line 4: free: command not found Exit Code: 127 Process Group PGID: 1615 ## Step 5 · agent · 2026-09-29T04:20:09.351Z ### run_shell_command · 2026-09-29T04:20:09.351Z { "command": "grep -i MemTotal /proc/meminfo && grep -i MemAvailable /proc/meminfo", "description": "Check system memory" } ### Tool output · 2026-09-29T04:20:09.366Z Output: MemTotal: 15988972 kB MemAvailable: 14687508 kB Process Group PGID: 1619 ## Step 6 · agent · 2026-09-29T04:20:14.462Z ### Recorded reasoning summary Exploring Data Structures I'm now focusing on understanding the data itself, checking the shapes and types of relevant structures like candidate sets and preference states. I plan to use a quick Python script or a one-liner to get a feel for the data. ### run_shell_command · 2026-09-29T04:20:14.462Z { "description": "Inspect metadata and shapes of catalog, targets, preference states, and eligibility npz", "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nprint(\"catalog:\", catalog.shape, catalog.columns)\nprint(catalog.head())\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nprint(\"targets:\", targets.shape, targets.columns)\nprint(targets.head())\n\nval_pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(\"val_pref:\", val_pref.shape, val_pref.columns)\nprint(val_pref.head())\n\ntest_pref = pd.read_parquet(\"/task/data/test_preference_state.parquet\")\nprint(\"test_pref:\", test_pref.shape, test_pref.columns)\nprint(test_pref.head())\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"val_npz:\", val_npz[\"uids\"].shape, val_npz[\"item_ids\"].shape, val_npz[\"packed_eligible\"].shape)\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nprint(\"test_npz:\", test_npz[\"uids\"].shape, test_npz[\"item_ids\"].shape, test_npz[\"packed_eligible\"].shape)\n'" } ### Tool output · 2026-09-29T04:20:15.480Z Output: catalog: (30000, 4) Index(['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'], dtype='object') 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 targets: (8456, 3) Index(['uid', 'recent_positive_listens', 'history_positive_listens'], dtype='object') 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 val_pref: (469124, 3) Index(['uid', 'item_id', 'state'], dtype='object') uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked test_pref: (484776, 3) Index(['uid', 'item_id', 'state'], dtype='object') uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked val_npz: (8456,) (30000,) (8456, 3750) test_npz: (8456,) (30000,) (8456, 3750) Process Group PGID: 1623 ## Step 7 · agent · 2026-09-29T04:20:20.399Z ### Recorded reasoning summary Verifying Data Integrity I'm currently cross-referencing the user and item counts between the `val_npz` and `test_npz` datasets. My next step is to confirm the exact match of `uids` between the two and then analyze the average number of eligible items per user. ### run_shell_command · 2026-09-29T04:20:20.399Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\ncounts = np.unpackbits(packed, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"val eligible count per user: min=\", counts.min(), \"max=\", counts.max(), \"mean=\", counts.mean(), \"median=\", np.median(counts))\n\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\npacked_test = test_npz[\"packed_eligible\"]\ncounts_test = np.unpackbits(packed_test, axis=1, count=30000, bitorder=\"little\").sum(axis=1)\nprint(\"test eligible count per user: min=\", counts_test.min(), \"max=\", counts_test.max(), \"mean=\", counts_test.mean(), \"median=\", np.median(counts_test))\n'", "description": "Check distribution of eligible items per user" } ### Tool output · 2026-09-29T04:20:21.473Z Output: val eligible count per user: min= 24657 max= 30000 mean= 29067.306291390727 median= 29242.0 test eligible count per user: min= 24552 max= 30000 mean= 29041.3178807947 median= 29220.0 Process Group PGID: 1640 ## Step 8 · agent · 2026-09-29T04:20:34.070Z ### Recorded reasoning summary Investigating Eligibility Logic I'm currently analyzing the eligibility of catalog items for users. It appears a significant majority, around 29,000 out of 30,000, are typically eligible. My next step is to delve into the specific criteria that determine this eligibility, likely involving user listening history and engagement signals. ### run_shell_command · 2026-09-29T04:20:34.070Z { "description": "Check why items are ineligible", "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\nu0 = uids[0]\nmask0 = np.unpackbits(packed[0], count=len(item_ids), bitorder=\"little\")\nineligible_items = set(item_ids[mask0 == 0])\nprint(f\"User {u0} has {len(ineligible_items)} ineligible items out of {len(item_ids)}\")\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nu0_pref = pref[pref.uid == u0]\nprint(f\"User {u0} pref items: {len(u0_pref)}, in ineligible: {len(set(u0_pref.item_id).intersection(ineligible_items))}\")\n'" } ### Tool output · 2026-09-29T04:20:34.937Z Output: User 100 has 682 ineligible items out of 30000 User 100 pref items: 15, in ineligible: 15 Process Group PGID: 1651 ## Step 9 · agent · 2026-09-29T04:20:41.638Z ### Recorded reasoning summary Refining Model Logic I'm currently focusing on the core problem: building the most robust ranking model for a music streaming service's "Made for You" shelf. My current efforts are directed towards ensuring that previously interacted-with items are correctly excluded from recommendations, as organic behavior in the immediate subsequent window is paramount for relevance. ### Recorded reasoning summary Evaluating Ranking Precision My focus has shifted to precisely implementing the grading system for our ranking model's validation. I'm ensuring that "organic likes" and substantial track listening (over 80%) are correctly mapped to the highest grade, with a keen eye on accurate DCG calculations and per-user NDCG averaging for robust metric assessment. ### run_shell_command · 2026-09-29T04:20:41.638Z { "command": "python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nimport numpy as np\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'", "description": "Check validation labels statistics" } ### Tool output · 2026-09-29T04:20:41.666Z Output: Traceback (most recent call last): File "", line 2, in ModuleNotFoundError: No module named 'evaluate_public' Exit Code: 1 Process Group PGID: 1669 ## Step 10 · agent · 2026-09-29T04:20:45.193Z ### run_shell_command · 2026-09-29T04:20:45.193Z { "command": "PYTHONPATH=/task python3 -c '\nfrom evaluate_public import validation_truth, CandidateSet\nimport pandas as pd\nfrom pathlib import Path\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\nprint(f\"Total target users: {len(targets)}\")\nprint(f\"Users with relevant items in val: {len(graded)}\")\nnum_items = [len(v) for v in graded.values()]\nprint(f\"Items per relevant user: mean={np.mean(num_items):.2f}, median={np.median(num_items)}, max={np.max(num_items)}\")\ngrade3_count = sum(sum(1 for g in v.values() if g == 3) for v in graded.values())\ngrade1_count = sum(sum(1 for g in v.values() if g == 1) for v in graded.values())\nprint(f\"Grade 3 items: {grade3_count}, Grade 1 items: {grade1_count}\")\n'", "description": "Check validation labels statistics with PYTHONPATH" } ### Tool output · 2026-09-29T04:20:46.433Z Output: Total target users: 8456 Users with relevant items in val: 4263 Items per relevant user: mean=8.86, median=4.0, max=442 Grade 3 items: 4417, Grade 1 items: 33352 Process Group PGID: 1672 ## Step 11 · agent · 2026-09-29T04:21:04.328Z ### Recorded reasoning summary Testing Popularity Baseline I'm assessing a simple popularity baseline against our validation set. My focus is on the NDCG@10 metric for users with relevant items, currently standing at 4263 out of 8456 total target users. I'm now calculating its performance. ### run_shell_command · 2026-09-29T04:21:04.328Z { "description": "Compute popularity baseline NDCG@10", "command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings, ndcg_at_k\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Catalog order\ncat_items = catalog[\"item_id\"].to_numpy()\n# Map item_id to index in val_npz item_ids\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\n# For each user, get top 100 eligible items by catalog popularity\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n # which items in cat_cols are eligible?\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Popularity NDCG@10:\", res)\n'" } ### Tool output · 2026-09-29T04:21:06.329Z Output: Popularity NDCG@10: {'quality_ndcg@10': 0.01071, 'quality_users': 4263} Process Group PGID: 1692 ## Step 12 · agent · 2026-09-29T04:21:13.668Z ### Recorded reasoning summary Establishing Baseline Metric My initial thought process has focused on establishing a baseline performance metric. I've identified NDCG@10 at 0.01071 as a promising starting point. My next immediate step is to confirm the exact file format and row count requirements for a valid submission. ### run_shell_command · 2026-09-29T04:21:13.668Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\nuids = test_npz[\"uids\"]\nitem_ids = test_npz[\"item_ids\"]\npacked = test_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/test_eligible_candidates.npz\"))\n\ncat_items = catalog[\"item_id\"].to_numpy()\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\nPath(\"/app/solution\").mkdir(parents=True, exist_ok=True)\ndf.to_parquet(\"/app/solution/submission.parquet\", index=False)\n\n# Validate\nvalidated = validate_ranking(df, targets)\nfor uid, item in validated[[\"uid\", \"item_id\"]].itertuples(index=False, name=None):\n if not eligibility.is_eligible(uid, item):\n raise ValueError(f\"Ineligible: {uid}, {item}\")\nprint(\"Baseline submission successfully created and validated!\")\n'", "description": "Create initial baseline submission and validate against test_eligible_candidates" } ### Tool output · 2026-09-29T04:21:16.897Z Output: Baseline submission successfully created and validated! Process Group PGID: 1712 ## Step 13 · agent · 2026-09-29T04:21:19.226Z ### run_shell_command · 2026-09-29T04:21:19.226Z { "command": "python3 -c '\nimport pandas as pd\ndf = pd.read_parquet(\"/app/solution/submission.parquet\")\nprint(df.info())\nprint(df.head())\n'", "description": "Verify submission.parquet format and row count" } ### Tool output · 2026-09-29T04:21:19.976Z Output: RangeIndex: 845600 entries, 0 to 845599 Data columns (total 3 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 uid 845600 non-null int64 1 item_id 845600 non-null uint32 2 rank 845600 non-null int64 dtypes: int64(2), uint32(1) memory usage: 16.1 MB None uid item_id rank 0 100 5862961 1 1 100 6901374 2 2 100 5635052 3 3 100 3542184 4 4 100 8213481 5 Process Group PGID: 1729 ## Step 14 · agent · 2026-09-29T04:21:27.595Z ### Recorded reasoning summary Ensuring Data Type Compliance I'm focusing on ensuring type consistency. Specifically, I'm reviewing the `item_id`'s uint32 type against the `int64` cast in `validate_ranking` and plan to ensure it's int64 for `df.to_parquet` compliance. Next, I'll run `python3 /task/evaluate_public.py` with a popularity baseline validation parquet. ### run_shell_command · 2026-09-29T04:21:27.595Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import validate_ranking, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\n\ncat_items = catalog[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nrecords = []\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n for rank, item in enumerate(top100, 1):\n records.append((uid, item, rank))\n\ndf = pd.DataFrame(records, columns=[\"uid\", \"item_id\", \"rank\"])\ndf.to_parquet(\"/app/solution/val_baseline.parquet\", index=False)\n' && PYTHONPATH=/task python3 /task/evaluate_public.py /app/solution/val_baseline.parquet", "description": "Test evaluate_public.py on val_baseline.parquet" } ### Tool output · 2026-09-29T04:21:32.242Z Output: { "model": "/app/solution/val_baseline.parquet", "scores": { "quality_ndcg@10": 0.01071, "quality_users": 4263 } } Process Group PGID: 1748 ## Step 15 · agent · 2026-09-29T04:21:39.546Z ### Recorded reasoning summary Exploring Dataset Potential My current focus is on a deep dive into the dataset after confirming the successful execution of `evaluate_public.py`, which yielded a score of 0.01071. I'm now examining the available data files, specifically `train_listens.parquet` (305MB) and `train_likes.parquet`, to understand their structure and content more thoroughly. ### run_shell_command · 2026-09-29T04:21:39.546Z { "description": "Inspect train_listens and train_likes", "command": "python3 -c '\nimport pandas as pd\n\ndf_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\nprint(\"train_listens shape:\", df_listens.shape)\nprint(df_listens.head())\nprint(\"min timestamp:\", df_listens.timestamp.min(), \"max timestamp:\", df_listens.timestamp.max())\nprint(\"is_organic distribution:\\n\", df_listens.is_organic.value_counts())\nprint(\"played_ratio_pct distribution:\\n\", df_listens.played_ratio_pct.describe())\n\ndf_likes = pd.read_parquet(\"/task/data/train_likes.parquet\")\nprint(\"train_likes shape:\", df_likes.shape)\nprint(df_likes.head())\nprint(\"min timestamp:\", df_likes.timestamp.min(), \"max timestamp:\", df_likes.timestamp.max())\n'" } ### Tool output · 2026-09-29T04:21:41.708Z Output: train_listens shape: (43484159, 6) uid timestamp item_id is_organic played_ratio_pct track_length_seconds 0 100 39420 8326270 0 100 170 1 100 39420 1441281 0 100 105 2 100 39625 286361 0 100 185 3 100 40110 732449 0 100 240 4 100 40360 3397170 0 46 130 min timestamp: 0 max timestamp: 24786795 is_organic distribution: is_organic 1 22477622 0 21006537 Name: count, dtype: int64 played_ratio_pct distribution: count 4.348416e+07 mean 6.465021e+01 std 4.412183e+01 min 0.000000e+00 25% 7.000000e+00 50% 1.000000e+02 75% 1.000000e+02 max 1.590000e+02 Name: played_ratio_pct, dtype: float64 train_likes shape: (814831, 4) uid timestamp item_id is_organic 0 100 44755 732449 1 1 100 1155860 6568592 0 2 100 1259125 5411243 1 3 100 1260005 7371186 0 4 100 1263935 4943655 0 min timestamp: 80 max timestamp: 24786790 Process Group PGID: 1789 ## Step 16 · agent · 2026-09-29T04:21:46.323Z ### Recorded reasoning summary Processing Massive Datasets I'm currently grappling with the sheer scale of the `train_listens` dataset, which clocks in at an impressive 43.4 million rows. Despite its size, reading it into memory was surprisingly swift thanks to the efficient parquet format, occupying a manageable 305MB. My next step is to investigate the distribution of target users within this substantial data. ### run_shell_command · 2026-09-29T04:21:46.323Z { "description": "Check unique users in train_listens vs target users", "command": "python3 -c '\nimport pandas as pd\ndf_targets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(df_targets.uid)\nprint(f\"Number of target users: {len(target_uids)}\")\n\n# Sample or check total unique users in train_listens\n# Using pyarrow to read only uid column\nimport pyarrow.parquet as pq\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\"])\nunique_uids = set(table[\"uid\"].to_pylist())\nprint(f\"Total unique uids in train_listens: {len(unique_uids)}\")\nprint(f\"Are all target uids in train_listens? {len(target_uids.intersection(unique_uids))}\")\n'" } ### Tool output · 2026-09-29T04:22:03.562Z Output: Number of target users: 8456 Total unique uids in train_listens: 9238 Are all target uids in train_listens? 8456 Process Group PGID: 1809 ## Step 17 · agent · 2026-09-29T04:22:06.856Z ### Recorded reasoning summary Analyzing User Data I'm currently examining the unique items within my training data, `train_listens`, and comparing them against the 30,000 items in the catalog. My focus is on identifying how many unique items are present in `train_listens` and which specific items are candidates for further analysis. ### run_shell_command · 2026-09-29T04:22:06.856Z { "command": "python3 -c '\nimport pandas as pd\nimport pyarrow.parquet as pq\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(cat.item_id)\nprint(\"Catalog items:\", len(cat_items))\n\ntable = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"item_id\"])\ntrain_items = set(table[\"item_id\"].to_pylist())\nprint(\"Train items:\", len(train_items))\nprint(\"Catalog items in train items:\", len(cat_items.intersection(train_items)))\n'", "description": "Check items overlap between catalog and train_listens" } ### Tool output · 2026-09-29T04:22:25.682Z Output: Catalog items: 30000 Train items: 845395 Catalog items in train items: 30000 Process Group PGID: 1830 ## Step 18 · agent · 2026-09-29T04:22:30.293Z ### Recorded reasoning summary Verifying Timestamp Boundaries All 30,000 catalog items are now incorporated into the training set. My current focus is on scrutinizing the date range and timestamps, specifically noting that the training data concludes at timestamp 24786800. I'm investigating the units of this timestamp. ### run_shell_command · 2026-09-29T04:22:30.293Z { "description": "Verify timestamps and units of val_listens", "command": "python3 -c '\nimport pandas as pd\nval_listens = pd.read_parquet(\"/task/data/val_listens.parquet\")\nprint(\"val_listens shape:\", val_listens.shape)\nprint(\"val_listens min ts:\", val_listens.timestamp.min(), \"max ts:\", val_listens.timestamp.max())\nprint(\"difference:\", val_listens.timestamp.max() - val_listens.timestamp.min())\nprint(\"gap from train end (24786800):\", val_listens.timestamp.min() - 24786800)\n'" } ### Tool output · 2026-09-29T04:22:31.066Z Output: val_listens shape: (1501697, 6) val_listens min ts: 24788600 max ts: 25393395 difference: 604795 gap from train end (24786800): 1800 Process Group PGID: 1857 ## Step 19 · agent · 2026-09-29T04:22:36.125Z ### Recorded reasoning summary Analyzing Data Gaps I'm currently examining the `val_listens` dataset, noting its substantial size over seven days and its specific start and end times. I'm also paying close attention to the defined gaps between training and testing windows. The instruction regarding distinct roles for public windows is also a key focus. ### run_shell_command · 2026-09-29T04:22:36.125Z { "description": "Check values in validation preference state", "command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\nprint(pref[\"state\"].value_counts())\n'" } ### Tool output · 2026-09-29T04:22:36.886Z Output: state liked 418435 disliked 50689 Name: count, dtype: int64 Process Group PGID: 1877 ## Step 20 · agent · 2026-09-29T04:22:45.020Z ### Recorded reasoning summary Analyzing User Interaction Data I'm currently investigating user interactions within the validation phase, specifically focusing on whether these interactions overlap with tracks previously engaged during training. My next step is to verify the extent of this overlap. ### Recorded reasoning summary Investigating Validation Item Eligibility I'm digging into the implications of an item being "eligible" in validation. This means I need to clarify what triggers eligibility and how it relates to past interactions. My focus is on understanding the criteria for an item's inclusion in the validation set, especially if it was already encountered during training. ### run_shell_command · 2026-09-29T04:22:45.020Z { "description": "Check if graded items appeared in user train_listens", "command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Let us check train_listens for these (uid, item) pairs\ntrain_listens = pd.read_parquet(\"/task/data/train_listens.parquet\")\n# sample 1000 graded pairs\npairs = []\nfor u, items in list(graded.items())[:500]:\n for it in items:\n pairs.append((u, it))\nsample_pairs = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total sample graded pairs: {len(sample_pairs)}\")\nmerged = sample_pairs.merge(train_listens[[\"uid\", \"item_id\", \"timestamp\"]], on=[\"uid\", \"item_id\"])\nprint(f\"Sample pairs present in train_listens: {len(merged)}\")\nif len(merged) > 0:\n print(merged.head())\n'" } ### Tool output · 2026-09-29T04:22:51.072Z Output: Total sample graded pairs: 5013 Sample pairs present in train_listens: 0 Process Group PGID: 1894 ## Step 21 · agent · 2026-09-29T04:22:55.241Z ### run_shell_command · 2026-09-29T04:22:55.241Z { "description": "Check overlap across all graded pairs and train_listens", "command": "PYTHONPATH=/task python3 -c '\nimport pandas as pd\nimport numpy as np\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\nimport pyarrow.parquet as pq\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Collect all (uid, item_id) in graded\npairs = []\nfor u, items in graded.items():\n for it in items:\n pairs.append((u, it))\ngraded_df = pd.DataFrame(pairs, columns=[\"uid\", \"item_id\"])\nprint(f\"Total graded pairs: {len(graded_df)}\")\n\n# Check train_listens\nt_listens = pq.read_table(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\"])\ntrain_pairs = set(zip(t_listens[\"uid\"].to_pylist(), t_listens[\"item_id\"].to_pylist()))\noverlap = sum(1 for row in graded_df.itertuples(index=False) if (row.uid, row.item_id) in train_pairs)\nprint(f\"Overlap between graded pairs and train_listens: {overlap}\")\n'" } ### Tool output · 2026-09-29T04:23:42.049Z Output: Total graded pairs: 37769 Overlap between graded pairs and train_listens: 0 Process Group PGID: 1914 ## Step 22 · agent · 2026-09-29T04:23:52.724Z ### Recorded reasoning summary Refining Discovery Strategy I've confirmed a crucial detail: only items the user hasn't encountered during training are considered for discovery. This ensures a truly fresh and exploratory experience, prioritizing novel recommendations. ### Recorded reasoning summary Filtering Candidate Items My focus has shifted to refining how candidate items are filtered. I've confirmed that the system now excludes items already consumed or explicitly liked by the user. This ensures the discovery process prioritizes genuinely new content for each user. ### Recorded reasoning summary Exploring Item-Item Similarity I'm investigating the potential of item-item collaborative filtering for discovery, specifically how to identify items with the highest similarity that the user hasn't encountered. I'm also considering efficient ways to handle a large item catalog, possibly through sparse representations or low-rank factorization to manage memory constraints. ### update_topic · 2026-09-29T04:23:52.724Z { "title": "Researching Collaborative Filtering & Recency Models", "summary": "Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms.", "strategic_intent": "Analyze user interaction patterns, recency, and test collaborative filtering methods." } ### Tool output · 2026-09-29T04:23:52.726Z Current topic: "Researching Collaborative Filtering & Recency Models" Topic summary: Completed initial exploration: confirmed 8,456 target users and 30,000 catalog candidates. Established that eligible items are strictly discovery tracks (never previously listened to). Built and verified an initial baseline submission scoring 0.01071 on validation. Now moving to research and feature engineering: investigating interaction types, recency weighting, and collaborative filtering algorithms. Strategic Intent: Analyze user interaction patterns, recency, and test collaborative filtering methods. ## Step 23 · agent · 2026-09-29T04:23:58.664Z ### Recorded reasoning summary Analyzing Recent Interactions I'm currently exploring recent interactions, specifically aiming to script a Python test. My focus is on analyzing the temporal distribution of events within `train_listens` and `train_likes`. I'll also investigate processing speed by filtering `train_listens` by target users and timestamps. ### run_shell_command · 2026-09-29T04:23:58.664Z { "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets_frame.uid.astype(int))\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"])\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\ndays30 = cutoff - 30 * 86400\ndays14 = cutoff - 14 * 86400\ndays7 = cutoff - 7 * 86400\n\nprint(f\"Total rows: {len(listens)}\")\nt_in_target = listens.uid.isin(target_uids)\nprint(f\"Rows for target users: {t_in_target.sum()} ({t_in_target.mean():.1%})\")\nprint(f\"Rows in last 90 days: {(listens.timestamp >= days90).sum()}\")\nprint(f\"Rows in last 30 days: {(listens.timestamp >= days30).sum()}\")\nprint(f\"Rows in last 14 days: {(listens.timestamp >= days14).sum()}\")\nprint(f\"Rows in last 7 days: {(listens.timestamp >= days7).sum()}\")\n'", "description": "Analyze rows by time window and target users" } ### Tool output · 2026-09-29T04:24:02.842Z Output: Total rows: 43484159 Rows for target users: 43222297 (99.4%) Rows in last 90 days: 16769274 Rows in last 30 days: 6126103 Rows in last 14 days: 2920227 Rows in last 7 days: 1491699 Process Group PGID: 1944 ## Step 24 · agent · 2026-09-29T04:24:45.876Z ### Recorded reasoning summary Analyzing Listen Data My current focus is on understanding user listening habits. I'm reviewing recent data, particularly the last 90 and 30 days, noting the significant volume of activity. I'm investigating which specific items users are engaging with and if these align with our training data. ### run_shell_command · 2026-09-29T04:24:45.876Z { "description": "Check fraction of recent listens in catalog", "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"timestamp\", \"item_id\"])\nrecent = listens[listens.timestamp >= days90]\nin_cat = recent.item_id.isin(cat_items)\nprint(f\"Recent listens in catalog: {in_cat.sum()} / {len(recent)} ({in_cat.mean():.1%})\")\n'" } ### Tool output · 2026-09-29T04:24:48.456Z Output: Recent listens in catalog: 12117965 / 16769274 (72.3%) Process Group PGID: 1965 ## Step 25 · agent · 2026-09-29T04:24:52.863Z ### Recorded reasoning summary Analyzing Catalog Performance My recent analysis reveals a strong focus on the candidate catalog, with over 72% of all listens originating from it. I'm now shifting to examine the `train_likes` and `validation_preference_state` datasets, with `train_likes` already showing a substantial 814,831 rows. ### run_shell_command · 2026-09-29T04:24:52.863Z { "command": "python3 -c '\nimport pandas as pd\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nin_cat = pref.item_id.isin(cat_items)\nprint(\"Pref items in catalog:\", in_cat.sum(), f\"({in_cat.mean():.1%})\")\nprint(\"Unique items in pref:\", pref.item_id.nunique())\n'", "description": "Check pref items in catalog" } ### Tool output · 2026-09-29T04:24:53.692Z Output: Pref items in catalog: 469124 (100.0%) Unique items in pref: 28814 Process Group PGID: 1992 ## Step 26 · agent · 2026-09-29T04:25:00.908Z ### Recorded reasoning summary Completing Preference State All active preference state items are now present in the candidate catalog. I'm currently verifying the extent to which catalog items are represented within the preference state and exploring the possibility of users unliking items after initial likes. ### Recorded reasoning summary Refining Candidate Generation I've confirmed that `validation_preference_state` precisely captures users' current explicit likes and dislikes. Coupled with `train_listens` detailing listening habits, I'm now defining the discovery problem: recommending 100 new catalog tracks per user that they'll organically engage with or like within a week. My current focus is on how item popularity, both globally and recently, influences this prediction. ### run_shell_command · 2026-09-29T04:25:00.908Z { "command": "PYTHONPATH=/task python3 -c '\nimport numpy as np\nimport pandas as pd\nfrom pathlib import Path\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntargets = set(targets_frame.uid.astype(int))\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nuids = val_npz[\"uids\"]\nitem_ids = val_npz[\"item_ids\"]\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, targets)\n\n# Sort catalog by organic_positive_listens descending\ncat_sorted = catalog.sort_values(\"organic_positive_listens\", ascending=False)\ncat_items = cat_sorted[\"item_id\"].to_numpy(dtype=np.int64)\nitem_id_to_col = {item: i for i, item in enumerate(item_ids)}\ncat_cols = np.array([item_id_to_col[item] for item in cat_items])\n\nranked = {}\nfor r, uid in enumerate(uids):\n uid = int(uid)\n mask = np.unpackbits(packed[r], count=len(item_ids), bitorder=\"little\")\n eligible_mask = mask[cat_cols] == 1\n top100 = cat_items[eligible_mask][:100]\n ranked[uid] = list(top100)\n\nres = score_rankings(ranked, graded)\nprint(\"Organic popularity NDCG@10:\", res)\n'", "description": "Check organic popularity baseline NDCG@10" } ### Tool output · 2026-09-29T04:25:02.964Z Output: Organic popularity NDCG@10: {'quality_ndcg@10': 0.010578, 'quality_users': 4263} Process Group PGID: 2009 ## Step 27 · agent · 2026-09-29T04:25:09.581Z ### Recorded reasoning summary Exploring Personalization I'm now considering the significant impact of personalized collaborative filtering. Given the strong influence of individual user tastes in music, this approach is crucial and likely to yield substantial gains beyond just global popularity metrics. ### Recorded reasoning summary Evaluating Collaborative Filtering I'm currently exploring the feasibility of implementing item-item collaborative filtering. My primary concern is the sparsity of the user-item interaction matrix given the scale of users and the catalog, which could impact the effectiveness of similarity calculations. ### run_shell_command · 2026-09-29T04:25:09.581Z { "description": "Check unique pairs in recent listens", "command": "python3 -c '\nimport pandas as pd\nimport numpy as np\nimport scipy.sparse as sp\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\nitem_to_idx = {item: i for i, item in enumerate(catalog.item_id)}\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nuid_to_idx = {uid: i for i, uid in enumerate(targets.uid)}\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(uid_to_idx))]\n\nprint(f\"Recent catalog listens for targets: {len(recent)}\")\nprint(f\"Unique (uid, item) pairs: {recent.groupby(['uid', 'item_id']).ngroups}\")\n'" } ### Tool output · 2026-09-29T04:25:17.303Z Output: Traceback (most recent call last): File "", line 20, in NameError: name 'uid' is not defined. Did you mean: 'id'? Recent catalog listens for targets: 12099707 Exit Code: 1 Process Group PGID: 2029 ## Step 28 · agent · 2026-09-29T04:25:20.799Z ### run_shell_command · 2026-09-29T04:25:20.799Z { "command": "python3 -c '\nimport pandas as pd\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = set(catalog.item_id)\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = set(targets.uid)\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\"])\nrecent = listens[(listens.timestamp >= days90) & (listens.item_id.isin(cat_items)) & (listens.uid.isin(target_uids))]\n\nn_unique = len(recent.drop_duplicates(subset=[\"uid\", \"item_id\"]))\nprint(f\"Unique (uid, item) pairs in last 90 days: {n_unique}\")\n'", "description": "Check unique pairs in recent listens without error" } ### Tool output · 2026-09-29T04:25:28.874Z Output: Unique (uid, item) pairs in last 90 days: 4306717 Process Group PGID: 2057 ## Step 29 · agent · 2026-09-29T04:25:34.157Z ### Recorded reasoning summary Exploring Item-Item Similarity I'm currently investigating item-item similarity approaches, specifically cosine and BM25, to understand potential patterns. This analysis stems from the observed user engagement data showing an average of around 500 items per user, with 4.3 million unique pairs across 8,456 users and 30,000 items. ### run_shell_command · 2026-09-29T04:25:34.157Z { "description": "Benchmark sparse matrix multiplication speed", "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\n\n# test sparse dot\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nRtR = R.T @ R\nprint(f\"RtR shape: {RtR.shape}, nnz: {RtR.nnz}, time: {time.time() - t0:.2f}s\")\n'" } ### Tool output · 2026-09-29T04:25:56.208Z Output: RtR shape: (30000, 30000), nnz: 817434538, time: 21.02s Process Group PGID: 2078 ## Step 30 · agent · 2026-09-29T04:26:01.291Z ### Recorded reasoning summary Exploring Real-World Data My recent experiments with uniformly random data yielded a high density of nonzeros, as expected due to the lack of inherent structure. I'm now shifting focus to real-world datasets, anticipating sparsity and community-driven clustering. The next step involves investigating the feasibility of applying SVD. ### run_shell_command · 2026-09-29T04:26:01.291Z { "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\nN, M = 8456, 30000\nnnz = 4300000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"TruncatedSVD k=64 time: {time.time() - t0:.2f}s, U shape: {U.shape}, V shape: {V.shape}\")\n'", "description": "Benchmark TruncatedSVD speed" } ### Tool output · 2026-09-29T04:26:05.178Z Output: TruncatedSVD k=64 time: 2.12s, U shape: (8456, 64), V shape: (64, 30000) Process Group PGID: 2101 ## Step 31 · agent · 2026-09-29T04:26:10.099Z ### Recorded reasoning summary Achieving Efficient SVD I'm pleased to report that a TruncatedSVD with k=64 completed in a swift 2.12 seconds. This yields $U$ (8456x64) and $V$ (64x30000), enabling rapid calculation of all predicted scores ($\hat{Y}$) via $U @ V$. ### run_shell_command · 2026-09-29T04:26:10.099Z { "command": "python3 -c '\nimport time\nimport numpy as np\n\nU = np.random.randn(8456, 64).astype(np.float32)\nV = np.random.randn(64, 30000).astype(np.float32)\n\nt0 = time.time()\nscores = U @ V\nprint(f\"Matrix multiply time: {time.time() - t0:.3f}s, scores shape: {scores.shape}\")\n\nt0 = time.time()\n# test argpartition for top 100\ntop100_idx = np.argpartition(-scores, 100, axis=1)[:, :100]\nprint(f\"Top 100 argpartition time: {time.time() - t0:.3f}s\")\n'", "description": "Benchmark score calculation and top100 argpartition" } ### Tool output · 2026-09-29T04:26:12.938Z Output: Matrix multiply time: 0.204s, scores shape: (8456, 30000) Top 100 argpartition time: 0.968s Process Group PGID: 2119 ## Step 32 · agent · 2026-09-29T04:26:20.948Z ### Recorded reasoning summary Achieving Rapid Iteration I'm thrilled by the performance gains; the entire evaluation cycle now takes mere seconds. This speed allows for incredibly swift iteration, enabling experimentation with various weighting schemes, recency half-lives, and interaction models. ### Recorded reasoning summary Exploring Feature Dimensions I'm now considering a broader spectrum of signal types, including explicit likes and implicit completion rates. I'm also evaluating different dimensionality reduction techniques and normalization strategies, aiming to integrate these with popularity biases for a more robust hybrid approach. Direct evaluation via `evaluate_public.py` is key. ### run_shell_command · 2026-09-29T04:26:20.948Z { "command": "python3 -c '\nimport time\nimport numpy as np\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\n\n# test 43M nonzeros\nN, M = 8456, 845000\nnnz = 20000000 # test 20M\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\nt0 = time.time()\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nprint(f\"TruncatedSVD 20M nnz time: {time.time() - t0:.2f}s\")\n'", "description": "Benchmark TruncatedSVD on large sparse matrix" } ### Tool output · 2026-09-29T04:26:48.967Z Output: TruncatedSVD 20M nnz time: 25.17s Process Group PGID: 2129 ## Step 33 · agent · 2026-09-29T04:26:53.304Z ### Recorded reasoning summary Evaluating Candidate Performance My recent focus has been on understanding the performance implications of candidate item selection. I've noted that processing 20 million non-zero entries took 25 seconds. I'm now analyzing the candidate items, recognizing that our ranking operations are effectively confined to a 30,000-item catalog, as the test and validation sets exclusively draw from this subset. ### run_shell_command · 2026-09-29T04:26:53.304Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"cat len:\", len(cat))\nprint(\"val len:\", len(val_npz[\"item_ids\"]))\nprint(\"test len:\", len(test_npz[\"item_ids\"]))\nprint(\"val == test item_ids:\", np.array_equal(val_npz[\"item_ids\"], test_npz[\"item_ids\"]))\nprint(\"cat items == set(val item_ids):\", set(cat.item_id) == set(val_npz[\"item_ids\"]))\n'", "description": "Verify candidate items alignment" } ### Tool output · 2026-09-29T04:26:54.066Z Output: cat len: 30000 val len: 30000 test len: 30000 val == test item_ids: True cat items == set(val item_ids): True Process Group PGID: 2147 ## Step 34 · agent · 2026-09-29T04:26:57.552Z ### Recorded reasoning summary Confirming Data Integrity I've confirmed that the `item_ids` in both `val_npz` and `test_npz` perfectly match the 30,000 items listed in `candidate_catalog.parquet`. Similarly, the `uids` across `val_npz`, `test_npz`, and `target_users` are identical. My next step is to verify the order of these `uids`. ### run_shell_command · 2026-09-29T04:26:57.552Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ntargets = pd.read_parquet(\"/task/data/target_users.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\ntest_npz = np.load(\"/task/data/test_eligible_candidates.npz\")\n\nprint(\"val == test uids:\", np.array_equal(val_npz[\"uids\"], test_npz[\"uids\"]))\nprint(\"val == targets uids:\", np.array_equal(val_npz[\"uids\"], targets[\"uid\"].to_numpy()))\n'", "description": "Verify target uids alignment" } ### Tool output · 2026-09-29T04:26:58.298Z Output: val == test uids: True val == targets uids: True Process Group PGID: 2164 ## Step 35 · agent · 2026-09-29T04:27:02.087Z ### Recorded reasoning summary Confirming Data Alignment I've confirmed that all rows and columns across `val_npz`, `test_npz`, `target_users`, and `candidate_catalog` are perfectly aligned. The user IDs (`uids`) count 8456, and item IDs (`item_ids`) are 30000. Each row index $r$ corresponds to user $r$, and column index $c$ to item $c$. ### run_shell_command · 2026-09-29T04:27:02.087Z { "description": "Check item_ids order between catalog and npz", "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\n\ncat = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\nprint(\"Are item_ids in exact same order as catalog?\", np.array_equal(cat[\"item_id\"].to_numpy(), val_npz[\"item_ids\"]))\n'" } ### Tool output · 2026-09-29T04:27:02.842Z Output: Are item_ids in exact same order as catalog? True Process Group PGID: 2180 ## Step 36 · agent · 2026-09-29T04:27:17.064Z ### Recorded reasoning summary Confirming Index Alignment I've confirmed that the `item_ids` in both validation and test eligible candidate files perfectly align with the `candidate_catalog.parquet`. This direct correspondence simplifies the mapping process significantly for subsequent steps. ### Recorded reasoning summary Leveraging User-Item Matrix My current focus is on constructing a comprehensive user-item matrix using a combination of `train_listens`, `train_likes`, and `validation_preference_state`. This unified data source will allow me to evaluate explicit positive and negative signals from `validation_preference_state`, alongside the quality signals from organic `train_likes` for my experiments. ### Recorded reasoning summary Exploring Factorization Methods I'm now exploring low-rank matrix factorization techniques like TruncatedSVD, SVD, and iALS. The goal is to leverage the constructed user-item matrix, incorporating factors like listen completion, organic engagement, and recency, to generate predictions for eligible candidates. ### run_shell_command · 2026-09-29T04:27:17.064Z { "description": "Initial SVD model experiment on recent listens", "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\n# Group by uid, item_id and compute count of listens with played_ratio_pct >= 50\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = [uid_to_row[u] for u in grouped.uid]\ncols = [item_to_col[it] for it in grouped.item_id]\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (len(target_uids), len(cat_items))), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\n# Run TruncatedSVD\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD explained variance ratio sum: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\n# Predict scores\nscores = U @ V\n\n# Generate top 100 for each user\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n # top 100 indices\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n # sort top 100\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'" } ### Tool output · 2026-09-29T04:27:25.619Z Output: Traceback (most recent call last): File "", line 49, in File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_compressed.py", line 56, in __init__ coo = self._coo_container(arg1, shape=shape, dtype=dtype) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 101, in __init__ self._check() File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 219, in _check if self.nnz > 0: ^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_base.py", line 349, in nnz return self._getnnz() ^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 168, in _getnnz if any(len(idx) != nnz for idx in self.coords): ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/scipy/sparse/_coo.py", line 168, in if any(len(idx) != nnz for idx in self.coords): ^^^^^^^^ TypeError: len() of unsized object Loading train listens (last 90 days)... [0.7s] Recent listens: 16769274 [1.3s] Filtered listens: 12099707 [5.1s] Exit Code: 1 Process Group PGID: 2197 ## Step 37 · agent · 2026-09-29T04:27:34.077Z ### Recorded reasoning summary Fixing SciPy Array Types I'm addressing a recent discovery in SciPy version 1.15.3 regarding array types. It seems `rows` and `cols` are now expected to be NumPy arrays, not lists. I'm updating the code to reflect this, ensuring `rows` and `cols` are correctly converted to `np.int32` arrays. ### run_shell_command · 2026-09-29T04:27:34.077Z { "command": "python3 -c '\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nimport time\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\nt0 = time.time()\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\n# Load train_listens in the last 90 days\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nprint(f\"Loading train listens (last 90 days)... [{time.time()-t0:.1f}s]\")\nlistens = pd.read_parquet(\"/task/data/train_listens.parquet\", columns=[\"uid\", \"item_id\", \"timestamp\", \"played_ratio_pct\"])\nrecent_listens = listens[listens.timestamp >= days90]\nprint(f\"Recent listens: {len(recent_listens)} [{time.time()-t0:.1f}s]\")\n\n# Filter to target users and catalog items\nmask = recent_listens.item_id.isin(item_to_col) & recent_listens.uid.isin(uid_to_row)\nfiltered = recent_listens[mask]\nprint(f\"Filtered listens: {len(filtered)} [{time.time()-t0:.1f}s]\")\n\npos = filtered[filtered.played_ratio_pct >= 50]\ngrouped = pos.groupby([\"uid\", \"item_id\"]).size().reset_index(name=\"count\")\n\nrows = np.array([uid_to_row[u] for u in grouped.uid], dtype=np.int32)\ncols = np.array([item_to_col[it] for it in grouped.item_id], dtype=np.int32)\ndata = np.log1p(grouped[\"count\"].to_numpy(dtype=np.float32))\n\nR = sp.csr_matrix((data, (rows, cols)), shape=(len(target_uids), len(cat_items)))\nprint(f\"R shape: {R.shape}, nnz: {R.nnz} [{time.time()-t0:.1f}s]\")\n\nsvd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\nU = svd.fit_transform(R)\nV = svd.components_\nprint(f\"SVD fitted. Explained var: {svd.explained_variance_ratio_.sum():.4f} [{time.time()-t0:.1f}s]\")\n\nscores = U @ V\nprint(f\"Scores computed. [{time.time()-t0:.1f}s]\")\n\nranked = {}\nfor r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n\nres = score_rankings(ranked, graded)\nprint(f\"TruncatedSVD k=64 NDCG@10: {res} [{time.time()-t0:.1f}s]\")\n'", "description": "Run initial SVD experiment with fixed sparse matrix creation" } ### Tool output · 2026-09-29T04:27:46.219Z Output: Loading train listens (last 90 days)... [0.7s] Recent listens: 16769274 [1.3s] Filtered listens: 12099707 [5.0s] R shape: (8456, 30000), nnz: 3000810 [7.2s] SVD fitted. Explained var: 0.2716 [9.0s] Scores computed. [9.2s] TruncatedSVD k=64 NDCG@10: {'quality_ndcg@10': 0.02495, 'quality_users': 4263} [10.5s] Process Group PGID: 2233 ## Step 38 · agent · 2026-09-29T04:27:57.159Z ### Recorded reasoning summary Improving NDCG Performance My recent experiments show a significant jump in NDCG@10 from a mere 0.01071 (using popularity) to an impressive 0.02495 with a basic TruncatedSVD. This is over a 2.3x improvement! Next, I'm focusing on incorporating user preference data like likes to further enhance these results. ### Recorded reasoning summary Exploring Item-Item Interactions I'm now considering the impact of explicit signals like likes, and weighting organic listens higher. I'm also exploring recency weighting and tuning the number of components for SVD. A key next step is comparing Item-Item Collaborative Filtering with cosine similarity against my current SVD approach. ### run_shell_command · 2026-09-29T04:27:57.159Z { "description": "Benchmark Item-Item CF via associative multiply", "command": "python3 -c '\nimport numpy as np\nimport scipy.sparse as sp\nimport time\n\nN, M = 8456, 30000\nnnz = 3000000\nrows = np.random.randint(0, N, size=nnz)\ncols = np.random.randint(0, M, size=nnz)\ndata = np.ones(nnz, dtype=np.float32)\nR = sp.csr_matrix((data, (rows, cols)), shape=(N, M))\n\n# Normalize items\ncol_norms = np.asarray(np.sqrt(R.power(2).sum(axis=0))).flatten()\ncol_norms[col_norms == 0] = 1.0\nP = R.multiply(1.0 / col_norms).tocsr()\n\nt0 = time.time()\n# Associative multiply: (R @ P.T) @ P\nRP = R @ P.T # 8456 x 8456 sparse\nprint(f\"R @ P.T nnz: {RP.nnz}, time: {time.time()-t0:.2f}s\")\nt1 = time.time()\nscores = RP @ P # 8456 x 30000\nprint(f\"RP @ P shape: {scores.shape}, nnz: {scores.nnz}, time: {time.time()-t1:.2f}s\")\n'" } ### Tool output · 2026-09-29T04:28:58.349Z Output: R @ P.T nnz: 70371236, time: 2.04s RP @ P shape: (8456, 30000), nnz: 253680000, time: 58.20s Process Group PGID: 2263 ## Step 39 · agent · 2026-09-29T04:29:04.214Z ### Recorded reasoning summary Exploring SVD Efficiency I'm observing a significant performance disparity. RP@P is proving quite slow due to dense matrix expansion, whereas TruncatedSVD is remarkably fast for fitting and multiplication. I'm now investigating TruncatedSVD's behavior with normalized data. ### Recorded reasoning summary Testing Normalization Techniques I'm now exploring how TF-IDF and BM25 transformations, alongside various normalization and feature engineering strategies like played ratio and recency, impact SVD performance and NDCG@ metrics. ### update_topic · 2026-09-29T04:29:04.214Z { "summary": "Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state.", "strategic_intent": "Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states.", "title": "Feature Engineering and Matrix Weighting Optimization" } ### Tool output · 2026-09-29T04:29:04.217Z Current topic: "Feature Engineering and Matrix Weighting Optimization" Topic summary: Established baseline NDCG@10: popularity (0.01071), basic SVD (0.02495). Now systematically testing interaction feature engineering: recency decay, organic listen weights, >=80% completion boosts, and integrating explicit likes/dislikes from preference state. Strategic Intent: Test weighting strategies: organic flag, completion rate, recency decay, and explicit preference states. ## Step 40 · agent · 2026-09-29T04:29:22.240Z ### Recorded reasoning summary Designing Evaluation Harness I'm currently focused on developing the Python evaluation harness, specifically the `/app/solution/tune_weights.py` script. My primary goal is to efficiently load the data once and then construct the sparse user-item interaction matrix, taking into account the recency half-life parameter. ### write_file · 2026-09-29T04:29:22.240Z { "file_path": "/app/solution/tune_weights.py", "instruction": "Write hyperparameter tuning script for weighting and SVD ranker", "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.feature_extraction.text import TfidfTransformer\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n # Filter to recent 90 days, target users, catalog items\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n print(f\"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]\")\n\n # Convert uids and item_ids to row and col indices\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n\n # Helper function to evaluate given weights\n def evaluate_model(\n half_life_days=30.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n n_components=128,\n use_tfidf=False,\n norm_rows=False,\n pop_alpha=0.0\n ):\n t_start = time.time()\n # Compute event weights\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n \n # Base completion weight\n completion = listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0\n completion = np.clip(completion, 0.0, 1.5)\n # Strong listen boost\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n # Organic boost\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n \n event_weight = recency_weight * completion * strong_boost * organic_boost\n \n # Aggregate weights per (row, col)\n # Using coo matrix summing duplicates\n rows = listens[\"row\"].to_numpy()\n cols = listens[\"col\"].to_numpy()\n \n # Add likes\n like_rows = likes_only[\"row\"].to_numpy()\n like_cols = likes_only[\"col\"].to_numpy()\n like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)\n \n all_rows = np.concatenate([rows, like_rows])\n all_cols = np.concatenate([cols, like_cols])\n all_weights = np.concatenate([event_weight, like_weights])\n \n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Log1p transform\n R.data = np.log1p(R.data)\n \n if use_tfidf:\n tfidf = TfidfTransformer(norm=\"l2\", sublinear_tf=True)\n R_transformed = tfidf.fit_transform(R)\n elif norm_rows:\n from sklearn.preprocessing import normalize\n R_transformed = normalize(R, norm=\"l2\", axis=1)\n else:\n R_transformed = R\n\n svd = TruncatedSVD(n_components=n_components, algorithm=\"randomized\", random_state=42)\n U = svd.fit_transform(R_transformed)\n V = svd.components_\n \n scores = U @ V\n \n if pop_alpha > 0.0:\n # Add popularity prior\n pop = np.log1p(catalog[\"organic_positive_listens\"].to_numpy(dtype=np.float32))\n pop_std = pop.std()\n pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0)\n scores = scores + pop_alpha * pop_norm\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)\")\n return score\n\n print(\"\\n--- Running parameter experiments ---\")\n evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64)\n evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True)\n evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True)\n\nif __name__ == \"__main__\":\n main()\n" } ### Tool output · 2026-09-29T04:29:22.255Z Successfully created and wrote to new file: /app/solution/tune_weights.py. Here is the updated code: import time import numpy as np import pandas as pd import scipy.sparse as sp from sklearn.decomposition import TruncatedSVD from sklearn.feature_extraction.text import TfidfTransformer import sys sys.path.append("/task") from ranking_evaluation import score_rankings from evaluate_public import validation_truth, CandidateSet from pathlib import Path def main(): t0 = time.time() targets_frame = pd.read_parquet("/task/data/target_users.parquet") target_uids = targets_frame.uid.to_numpy() uid_to_row = {uid: i for i, uid in enumerate(target_uids)} catalog = pd.read_parquet("/task/data/candidate_catalog.parquet") cat_items = catalog.item_id.to_numpy() item_to_col = {item: i for i, item in enumerate(cat_items)} val_npz = np.load("/task/data/validation_eligible_candidates.npz") packed = val_npz["packed_eligible"] eligibility = CandidateSet(Path("/task/data/validation_eligible_candidates.npz")) graded = validation_truth(eligibility, set(target_uids)) cutoff = 24786800 days90 = cutoff - 90 * 86400 print(f"Loading data... [{time.time()-t0:.1f}s]") listens = pd.read_parquet( "/task/data/train_listens.parquet", columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"] ) # Filter to recent 90 days, target users, catalog items mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row) listens = listens[mask].copy() print(f"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]") # Load preference state pref = pd.read_parquet("/task/data/validation_preference_state.parquet") pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy() print(f"Pref state rows: {len(pref_targets)} [{time.time()-t0:.1f}s]") # Convert uids and item_ids to row and col indices listens["row"] = listens.uid.map(uid_to_row).astype(np.int32) listens["col"] = listens.item_id.map(item_to_col).astype(np.int32) pref_targets["row"] = pref_targets.uid.map(uid_to_row).astype(np.int32) pref_targets["col"] = pref_targets.item_id.map(item_to_col).astype(np.int32) likes_only = pref_targets[pref_targets.state == "liked"] # Helper function to evaluate given weights def evaluate_model( half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=False, norm_rows=False, pop_alpha=0.0 ): t_start = time.time() # Compute event weights delta_days = (cutoff - listens["timestamp"].to_numpy()) / 86400.0 recency_weight = np.exp(-np.log(2) * delta_days / half_life_days) # Base completion weight completion = listens["played_ratio_pct"].to_numpy(dtype=np.float32) / 100.0 completion = np.clip(completion, 0.0, 1.5) # Strong listen boost strong_boost = np.where(listens["played_ratio_pct"].to_numpy() >= 80, strong_mult, 1.0) # Organic boost organic_boost = np.where(listens["is_organic"].to_numpy() == 1, organic_mult, 1.0) event_weight = recency_weight * completion * strong_boost * organic_boost # Aggregate weights per (row, col) # Using coo matrix summing duplicates rows = listens["row"].to_numpy() cols = listens["col"].to_numpy() # Add likes like_rows = likes_only["row"].to_numpy() like_cols = likes_only["col"].to_numpy() like_weights = np.full(len(likes_only), fill_value=like_weight, dtype=np.float32) all_rows = np.concatenate([rows, like_rows]) all_cols = np.concatenate([cols, like_cols]) all_weights = np.concatenate([event_weight, like_weights]) R = sp.csr_matrix( (all_weights, (all_rows, all_cols)), shape=(len(target_uids), len(cat_items)), dtype=np.float32 ) # Log1p transform R.data = np.log1p(R.data) if use_tfidf: tfidf = TfidfTransformer(norm="l2", sublinear_tf=True) R_transformed = tfidf.fit_transform(R) elif norm_rows: from sklearn.preprocessing import normalize R_transformed = normalize(R, norm="l2", axis=1) else: R_transformed = R svd = TruncatedSVD(n_components=n_components, algorithm="randomized", random_state=42) U = svd.fit_transform(R_transformed) V = svd.components_ scores = U @ V if pop_alpha > 0.0: # Add popularity prior pop = np.log1p(catalog["organic_positive_listens"].to_numpy(dtype=np.float32)) pop_std = pop.std() pop_norm = (pop - pop.mean()) / (pop_std if pop_std > 0 else 1.0) scores = scores + pop_alpha * pop_norm ranked = {} for r in range(len(target_uids)): uid = int(target_uids[r]) mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder="little") == 1 user_scores = scores[r].copy() user_scores[~mask_eligible] = -1e9 top100_idx = np.argpartition(-user_scores, 100)[:100] top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])] ranked[uid] = list(cat_items[top100_idx]) res = score_rankings(ranked, graded) score = res["quality_ndcg@10"] print(f"HL={half_life_days}d org={organic_mult} str={strong_mult} like={like_weight} k={n_components} tfidf={use_tfidf} norm={norm_rows} pop={pop_alpha} -> NDCG@10: {score:.5f} ({time.time()-t_start:.1f}s)") return score print("\n--- Running parameter experiments ---") evaluate_model(half_life_days=30.0, organic_mult=1.0, strong_mult=1.0, like_weight=0.0, n_components=64) evaluate_model(half_life_days=30.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64) evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64) evaluate_model(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=64) evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128) evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=256) evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True) evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, norm_rows=True) if __name__ == "__main__": main() ## Step 41 · agent · 2026-09-29T04:29:30.273Z ### run_shell_command · 2026-09-29T04:29:30.273Z { "description": "Run tuning experiments on validation set", "command": "python3 /app/solution/tune_weights.py" } ### Tool output · 2026-09-29T04:30:14.885Z Output: /opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py:1713: RuntimeWarning: divide by zero encountered in log np.log(X.data, X.data) Loading data... [0.7s] Filtered listens: 12099707 rows [10.7s] Pref state rows: 469124 [10.8s] --- Running parameter experiments --- HL=30.0d org=1.0 str=1.0 like=0.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03080 (4.9s) HL=30.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03359 (4.5s) HL=14.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03491 (4.5s) HL=7.0d org=2.0 str=2.0 like=10.0 k=64 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03527 (4.4s) HL=14.0d org=2.0 str=2.0 like=10.0 k=128 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03459 (5.2s) HL=14.0d org=2.0 str=2.0 like=10.0 k=256 tfidf=False norm=False pop=0.0 -> NDCG@10: 0.03370 (7.5s) Traceback (most recent call last): File "/app/solution/tune_weights.py", line 150, in main() File "/app/solution/tune_weights.py", line 146, in main evaluate_model(half_life_days=14.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, n_components=128, use_tfidf=True) File "/app/solution/tune_weights.py", line 104, in evaluate_model R_transformed = tfidf.fit_transform(R) ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_set_output.py", line 316, in wrapped data_to_wrap = f(self, X, *args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/base.py", line 894, in fit_transform return self.fit(X, **fit_params).transform(X) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/feature_extraction/text.py", line 1722, in transform X = normalize(X, norm=self.norm, copy=False) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/_param_validation.py", line 218, in wrapper return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/preprocessing/_data.py", line 1979, in normalize X = check_array( ^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 1012, in check_array array = _ensure_sparse_format( ^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 647, in _ensure_sparse_format _assert_all_finite( File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 120, in _assert_all_finite _assert_all_finite_element_wise( File "/opt/task-venv/lib/python3.12/site-packages/sklearn/utils/validation.py", line 169, in _assert_all_finite_element_wise raise ValueError(msg_err) ValueError: Input contains infinity or a value too large for dtype('float32'). Exit Code: 1 Process Group PGID: 2286 ## Step 42 · agent · 2026-09-29T04:30:36.093Z ### Recorded reasoning summary Analyzing Pre-TFIDF Data I'm examining performance metrics prior to addressing the TF-IDF issue. Initial runs with varying historical lengths and organization/stringency parameters show promising, though slightly lower, accuracy scores. I'm particularly noting the slight improvements seen with the 14-day historical window. ### Recorded reasoning summary Improving Scoring Model The model's score has significantly improved, exceeding a 3.3x baseline increase through cumulative adjustments like adding likes and weighting organic completion. I'm now investigating TF-IDF transformer errors, specifically the `log(0)` issue, and exploring alternative IDF calculation methods and normalization techniques. Further tuning of half-life, multipliers, like weights, and the inclusion of dislikes is planned. ### write_file · 2026-09-29T04:30:36.093Z { "instruction": "Update tuning script to sweep half-life, organic/strong multipliers, like weight, components, idf, and normalization", "content": "import time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ndef main():\n t0 = time.time()\n targets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\n target_uids = targets_frame.uid.to_numpy()\n uid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\n catalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\n cat_items = catalog.item_id.to_numpy()\n item_to_col = {item: i for i, item in enumerate(cat_items)}\n\n val_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\n packed = val_npz[\"packed_eligible\"]\n eligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\n graded = validation_truth(eligibility, set(target_uids))\n\n cutoff = 24786800\n days90 = cutoff - 90 * 86400\n\n print(f\"Loading data... [{time.time()-t0:.1f}s]\")\n listens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n )\n mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\n listens = listens[mask].copy()\n print(f\"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]\")\n\n # Load preference state\n pref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\n pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\n listens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\n listens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\n pref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\n pref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\n likes_only = pref_targets[pref_targets.state == \"liked\"]\n dislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\n def build_matrix(\n half_life_days=7.0,\n organic_mult=2.0,\n strong_mult=2.0,\n like_weight=10.0,\n dislike_weight=0.0,\n use_idf=False,\n norm_type=\"none\"\n ):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n recency_weight = np.exp(-np.log(2) * delta_days / half_life_days)\n completion = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n strong_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, strong_mult, 1.0)\n organic_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, organic_mult, 1.0)\n event_weight = recency_weight * completion * strong_boost * organic_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n weights = [event_weight]\n\n if like_weight > 0 and len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32))\n\n if dislike_weight > 0 and len(dislikes_only) > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32))\n\n all_rows = np.concatenate(rows)\n all_cols = np.concatenate(cols)\n all_weights = np.concatenate(weights)\n\n R = sp.csr_matrix(\n (all_weights, (all_rows, all_cols)),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n # Apply signed log1p: sign(x) * log1p(|x|)\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n # Multiply columns by idf\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_type == \"l2_row\":\n R = normalize(R, norm=\"l2\", axis=1)\n elif norm_type == \"l2_col\":\n R = normalize(R, norm=\"l2\", axis=0)\n\n return R\n\n def evaluate_scores(scores, label=\"\"):\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"[{label}] NDCG@10: {score:.5f}\")\n return score\n\n print(\"\\n--- Tuning Half-Life ---\")\n for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]:\n R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"HL={hl}d\")\n\n print(\"\\n--- Tuning Organic and Strong Multipliers (HL=7d) ---\")\n for org in [1.5, 2.0, 3.0, 4.0]:\n for str_m in [1.5, 2.0, 3.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"org={org} str={str_m}\")\n\n print(\"\\n--- Tuning Like Weight ---\")\n for lw in [5.0, 10.0, 15.0, 25.0, 40.0]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"like_weight={lw}\")\n\n print(\"\\n--- Tuning SVD Components ---\")\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0)\n for k in [32, 48, 64, 80, 96, 128]:\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"k={k}\")\n\n print(\"\\n--- Testing IDF and Normalization ---\")\n for idf_val in [False, True]:\n for norm_val in [\"none\", \"l2_row\", \"l2_col\"]:\n R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val)\n svd = TruncatedSVD(n_components=64, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n evaluate_scores(scores, f\"idf={idf_val} norm={norm_val}\")\n\nif __name__ == \"__main__\":\n main()\n", "file_path": "/app/solution/tune_weights.py" } ### Tool output · 2026-09-29T04:30:36.118Z Successfully overwrote file: /app/solution/tune_weights.py. Here is the updated code: import time import numpy as np import pandas as pd import scipy.sparse as sp from sklearn.decomposition import TruncatedSVD from sklearn.preprocessing import normalize import sys sys.path.append("/task") from ranking_evaluation import score_rankings from evaluate_public import validation_truth, CandidateSet from pathlib import Path ... print(f"Loading data... [{time.time()-t0:.1f}s]") listens = pd.read_parquet( "/task/data/train_listens.parquet", columns=["uid", "item_id", "timestamp", "is_organic", "played_ratio_pct"] ) mask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row) listens = listens[mask].copy() print(f"Filtered listens: {len(listens)} rows [{time.time()-t0:.1f}s]") # Load preference state pref = pd.read_parquet("/task/data/validation_preference_state.parquet") pref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy() listens["row"] = listens.uid.map(uid_to_row).astype(np.int32) listens["col"] = listens.item_id.map(item_to_col).astype(np.int32) pref_targets["row"] = pref_targets.uid.map(uid_to_row).astype(np.int32) pref_targets["col"] = pref_targets.item_id.map(item_to_col).astype(np.int32) likes_only = pref_targets[pref_targets.state == "liked"] dislikes_only = pref_targets[pref_targets.state == "disliked"] def build_matrix( half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=10.0, dislike_weight=0.0, use_idf=False, norm_type="none" ): delta_days = (cutoff - listens["timestamp"].to_numpy()) / 86400.0 recency_weight = np.exp(-np.log(2) * delta_days / half_life_days) completion = np.clip(listens["played_ratio_pct"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5) strong_boost = np.where(listens["played_ratio_pct"].to_numpy() >= 80, strong_mult, 1.0) organic_boost = np.where(listens["is_organic"].to_numpy() == 1, organic_mult, 1.0) event_weight = recency_weight * completion * strong_boost * organic_boost rows = [listens["row"].to_numpy()] cols = [listens["col"].to_numpy()] weights = [event_weight] if like_weight > 0 and len(likes_only) > 0: rows.append(likes_only["row"].to_numpy()) cols.append(likes_only["col"].to_numpy()) weights.append(np.full(len(likes_only), fill_value=like_weight, dtype=np.float32)) if dislike_weight > 0 and len(dislikes_only) > 0: rows.append(dislikes_only["row"].to_numpy()) cols.append(dislikes_only["col"].to_numpy()) weights.append(np.full(len(dislikes_only), fill_value=-dislike_weight, dtype=np.float32)) all_rows = np.concatenate(rows) all_cols = np.concatenate(cols) all_weights = np.concatenate(weights) R = sp.csr_matrix( (all_weights, (all_rows, all_cols)), shape=(len(target_uids), len(cat_items)), dtype=np.float32 ) # Apply signed log1p: sign(x) * log1p(|x|) pos_mask = R.data > 0 neg_mask = R.data < 0 R.data[pos_mask] = np.log1p(R.data[pos_mask]) R.data[neg_mask] = -np.log1p(-R.data[neg_mask]) if use_idf: df = np.bincount(R.nonzero()[1], minlength=len(cat_items)) idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0 # Multiply columns by idf R = R.dot(sp.diags(idf.astype(np.float32))) if norm_type == "l2_row": R = normalize(R, norm="l2", axis=1) elif norm_type == "l2_col": R = normalize(R, norm="l2", axis=0) return R def evaluate_scores(scores, label=""): ranked = {} for r in range(len(target_uids)): uid = int(target_uids[r]) mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder="little") == 1 user_scores = scores[r].copy() ... top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])] ranked[uid] = list(cat_items[top100_idx]) res = score_rankings(ranked, graded) score = res["quality_ndcg@10"] print(f"[{label}] NDCG@10: {score:.5f}") return score print("\n--- Tuning Half-Life ---") for hl in [3.0, 5.0, 7.0, 10.0, 14.0, 21.0]: R = build_matrix(half_life_days=hl, organic_mult=2.0, strong_mult=2.0, like_weight=10.0) svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42) scores = svd.fit_transform(R) @ svd.components_ evaluate_scores(scores, f"HL={hl}d") print("\n--- Tuning Organic and Strong Multipliers (HL=7d) ---") for org in [1.5, 2.0, 3.0, 4.0]: for str_m in [1.5, 2.0, 3.0]: R = build_matrix(half_life_days=7.0, organic_mult=org, strong_mult=str_m, like_weight=10.0) svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42) scores = svd.fit_transform(R) @ svd.components_ evaluate_scores(scores, f"org={org} str={str_m}") print("\n--- Tuning Like Weight ---") for lw in [5.0, 10.0, 15.0, 25.0, 40.0]: R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=lw) svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42) scores = svd.fit_transform(R) @ svd.components_ evaluate_scores(scores, f"like_weight={lw}") print("\n--- Tuning SVD Components ---") R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0) for k in [32, 48, 64, 80, 96, 128]: svd = TruncatedSVD(n_components=k, algorithm="randomized", random_state=42) scores = svd.fit_transform(R) @ svd.components_ evaluate_scores(scores, f"k={k}") print("\n--- Testing IDF and Normalization ---") for idf_val in [False, True]: for norm_val in ["none", "l2_row", "l2_col"]: R = build_matrix(half_life_days=7.0, organic_mult=2.0, strong_mult=2.0, like_weight=15.0, use_idf=idf_val, norm_type=norm_val) svd = TruncatedSVD(n_components=64, algorithm="randomized", random_state=42) scores = svd.fit_transform(R) @ svd.components_ evaluate_scores(scores, f"idf={idf_val} norm={norm_val}") if __name__ == "__main__": main() ## Step 43 · agent · 2026-09-29T04:30:38.702Z ### run_shell_command · 2026-09-29T04:30:38.702Z { "description": "Run parameter tuning experiments", "command": "python3 /app/solution/tune_weights.py" } ### Tool output · 2026-09-29T04:33:24.643Z Output: Loading data... [0.7s] Filtered listens: 12099707 rows [10.7s] --- Tuning Half-Life --- [HL=3.0d] NDCG@10: 0.03181 [HL=5.0d] NDCG@10: 0.03448 [HL=7.0d] NDCG@10: 0.03527 [HL=10.0d] NDCG@10: 0.03608 [HL=14.0d] NDCG@10: 0.03491 [HL=21.0d] NDCG@10: 0.03424 --- Tuning Organic and Strong Multipliers (HL=7d) --- [org=1.5 str=1.5] NDCG@10: 0.03347 [org=1.5 str=2.0] NDCG@10: 0.03446 [org=1.5 str=3.0] NDCG@10: 0.03580 [org=2.0 str=1.5] NDCG@10: 0.03454 [org=2.0 str=2.0] NDCG@10: 0.03527 [org=2.0 str=3.0] NDCG@10: 0.03642 [org=3.0 str=1.5] NDCG@10: 0.03570 [org=3.0 str=2.0] NDCG@10: 0.03726 [org=3.0 str=3.0] NDCG@10: 0.03742 [org=4.0 str=1.5] NDCG@10: 0.03680 [org=4.0 str=2.0] NDCG@10: 0.03756 [org=4.0 str=3.0] NDCG@10: 0.03817 --- Tuning Like Weight --- [like_weight=5.0] NDCG@10: 0.03714 [like_weight=10.0] NDCG@10: 0.03527 [like_weight=15.0] NDCG@10: 0.03438 [like_weight=25.0] NDCG@10: 0.03322 [like_weight=40.0] NDCG@10: 0.03201 --- Tuning SVD Components --- [k=32] NDCG@10: 0.03309 [k=48] NDCG@10: 0.03291 [k=64] NDCG@10: 0.03438 [k=80] NDCG@10: 0.03337 [k=96] NDCG@10: 0.03426 [k=128] NDCG@10: 0.03362 --- Testing IDF and Normalization --- [idf=False norm=none] NDCG@10: 0.03438 [idf=False norm=l2_row] NDCG@10: 0.03260 [idf=False norm=l2_col] NDCG@10: 0.01218 [idf=True norm=none] NDCG@10: 0.03309 [idf=True norm=l2_row] NDCG@10: 0.03729 [idf=True norm=l2_col] NDCG@10: 0.01218 Process Group PGID: 2320 ## Step 44 · agent · 2026-09-29T04:33:44.650Z ### Recorded reasoning summary Analyzing Engagement Patterns I'm noticing a clear trend where a recency half-life of 10 days and higher organic/strong multipliers are driving better engagement metrics. It appears lower "like weight" values also contribute positively, suggesting overemphasis can dilute impact. ### Recorded reasoning summary Refining Interaction Weighting I'm now exploring the nuanced interaction of listen weights, specifically hypothesizing that over-weighting likes might be diluting their impact. I'm considering a low 'like_weight' and a significant disparity between organic and non-organic listen weights, potentially setting non-organic to 0.2 and organic to 1.0 for a 5.0x ratio. ### run_shell_command · 2026-09-29T04:33:44.650Z { "description": "Test combinations of weighting and normalization", "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.decomposition import TruncatedSVD\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\ndislikes_only = pref_targets[pref_targets.state == \"disliked\"]\n\ndef test_config(hl, org_mult, str_mult, non_org_mult, lw, dw, use_idf, norm_l2, k=64):\n delta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\n rec = np.exp(-np.log(2) * delta_days / hl)\n comp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\n s_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, str_mult, 1.0)\n o_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, org_mult, non_org_mult)\n weights = rec * comp * s_boost * o_boost\n\n rows = [listens[\"row\"].to_numpy()]\n cols = [listens[\"col\"].to_numpy()]\n vals = [weights]\n\n if lw > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=lw, dtype=np.float32))\n\n if dw > 0:\n rows.append(dislikes_only[\"row\"].to_numpy())\n cols.append(dislikes_only[\"col\"].to_numpy())\n vals.append(np.full(len(dislikes_only), fill_value=-dw, dtype=np.float32))\n\n R = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n )\n pos_mask = R.data > 0\n neg_mask = R.data < 0\n R.data[pos_mask] = np.log1p(R.data[pos_mask])\n R.data[neg_mask] = -np.log1p(-R.data[neg_mask])\n\n if use_idf:\n df = np.bincount(R.nonzero()[1], minlength=len(cat_items))\n idf = np.log((len(target_uids) + 1.0) / (df + 1.0)) + 1.0\n R = R.dot(sp.diags(idf.astype(np.float32)))\n\n if norm_l2:\n R = normalize(R, norm=\"l2\", axis=1)\n\n svd = TruncatedSVD(n_components=k, algorithm=\"randomized\", random_state=42)\n scores = svd.fit_transform(R) @ svd.components_\n\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"hl={hl} org={org_mult} non_org={non_org_mult} str={str_mult} lw={lw} dw={dw} idf={use_idf} norm={norm_l2} k={k} -> {score:.5f}\")\n return scores, score\n\nprint(\"--- Testing configurations ---\")\ntest_config(hl=10.0, org_mult=4.0, str_mult=3.0, non_org_mult=1.0, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.5, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=3.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=5.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=2.0, use_idf=False, norm_l2=False)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True)\ntest_config(hl=10.0, org_mult=5.0, str_mult=4.0, non_org_mult=0.2, lw=3.0, dw=0.0, use_idf=True, norm_l2=True, k=96)\n'" } ### Tool output · 2026-09-29T04:34:33.716Z Output: --- Testing configurations --- hl=10.0 org=4.0 non_org=1.0 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03817 hl=10.0 org=5.0 non_org=0.5 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03862 hl=10.0 org=5.0 non_org=0.2 str=3.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03698 hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=5.0 dw=0.0 idf=False norm=False k=64 -> 0.03689 hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=False norm=False k=64 -> 0.03758 hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=2.0 idf=False norm=False k=64 -> 0.03701 hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=64 -> 0.03714 hl=10.0 org=5.0 non_org=0.2 str=4.0 lw=3.0 dw=0.0 idf=True norm=True k=96 -> 0.03660 Process Group PGID: 2366 ## Step 45 · agent · 2026-09-29T04:34:45.049Z ### Recorded reasoning summary Testing New Parameters I've achieved a score of 0.03862 with the current configuration. My next steps involve exploring variations around `non_org` at 0.5 and 0.7, and `org` at 6.0 and 8.0. I'll also be adjusting the half-life to 8, 10, and 12 days to see how these impact performance. ### Recorded reasoning summary Incorporating Recency Decay I'm now considering incorporating recency decay for `train_likes`, as their timestamps offer valuable temporal information. I'm also exploring item frequency calibration to address potential biases in SVD scores and investigating ensembling various SVD models with different half-lives, alongside item-item similarity. ### run_shell_command · 2026-09-29T04:34:45.049Z { "description": "Benchmark Cholesky factorization for 5000x5000", "command": "python3 -c '\nimport time\nimport numpy as np\n\n# test 5000 x 5000\nA = np.random.randn(5000, 5000).astype(np.float32)\nG = A.T @ A + 10.0 * np.eye(5000, dtype=np.float32)\nt0 = time.time()\nnp.linalg.cholesky(G)\nprint(f\"5000x5000 Cholesky time: {time.time()-t0:.2f}s\")\n'" } ### Tool output · 2026-09-29T04:34:47.164Z Output: 5000x5000 Cholesky time: 0.72s Process Group PGID: 2400 ## Step 46 · agent · 2026-09-29T04:34:53.153Z ### Recorded reasoning summary Accelerating Cholesky Factorization I'm thrilled by the speed of the 5000x5000 Cholesky, clocking in at just 0.72s. I'm now contemplating scaling this up to 15,000x15,000 and even 30,000x30,000. Based on the $O(N^3)$ complexity, I estimate the larger matrices will take around 2.5 minutes. However, a question arises: is full EASE necessary? ### run_shell_command · 2026-09-29T04:34:53.153Z { "description": "Benchmark 8456x8456 Cholesky time", "command": "python3 -c '\nimport time\nimport numpy as np\n\nN = 8456\nA = np.random.randn(N, 64).astype(np.float32)\nG = A @ A.T + 10.0 * np.eye(N, dtype=np.float32)\n\nt0 = time.time()\nL = np.linalg.cholesky(G)\nprint(f\"8456x8456 Cholesky time: {time.time()-t0:.2f}s\")\n'" } ### Tool output · 2026-09-29T04:34:58.276Z Output: 8456x8456 Cholesky time: 2.97s Process Group PGID: 2410 ## Step 47 · agent · 2026-09-29T04:35:11.299Z ### Recorded reasoning summary Calculating Cholesky Efficiency My recent computations show an $8456 \times 8456$ Cholesky decomposition completing in just 2.97 seconds, which is quite promising. My next step is to delve into the specifics of user-user EASE, particularly how to predict a user's interactions by modeling them based on other users' behaviors. ### run_shell_command · 2026-09-29T04:35:11.299Z { "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nt0 = time.time()\nprint(\"Computing G = R @ R.T...\")\nG = (R @ R.T).toarray()\nprint(f\"G computed in {time.time()-t0:.2f}s\")\n\nfor lam in [100.0, 500.0, 1000.0, 2000.0, 5000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # dense x sparse\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'", "description": "Test User-EASE collaborative filtering" } ### Tool output · 2026-09-29T04:37:43.952Z Output: Computing G = R @ R.T... G computed in 4.57s User-EASE lam=100.0 -> NDCG@10: 0.02989 (27.08s) User-EASE lam=500.0 -> NDCG@10: 0.03617 (26.94s) User-EASE lam=1000.0 -> NDCG@10: 0.03871 (27.14s) User-EASE lam=2000.0 -> NDCG@10: 0.04170 (27.07s) User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.89s) Process Group PGID: 2420 ## Step 48 · agent · 2026-09-29T04:37:57.982Z ### Recorded reasoning summary Refining User-EASE I've successfully boosted the User-EASE metric to 0.04417 with a lambda of 5000, a significant leap from previous methods like Naive and Tuned SVD. The convergence was remarkably swift. Now, I'm exploring the impact of even higher lambda values, such as 10,000 and 20,000, to further optimize performance. ### run_shell_command · 2026-09-29T04:37:57.982Z { "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nG = (R @ R.T).toarray()\n\nfor lam in [5000.0, 10000.0, 20000.0, 40000.0, 100000.0]:\n t1 = time.time()\n G_reg = G.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'", "description": "Test higher lambda values for User-EASE" } ### Tool output · 2026-09-29T04:40:31.588Z Output: User-EASE lam=5000.0 -> NDCG@10: 0.04417 (26.98s) User-EASE lam=10000.0 -> NDCG@10: 0.04491 (26.89s) User-EASE lam=20000.0 -> NDCG@10: 0.04296 (27.27s) User-EASE lam=40000.0 -> NDCG@10: 0.04076 (27.10s) User-EASE lam=100000.0 -> NDCG@10: 0.03593 (27.20s) Process Group PGID: 2460 ## Step 49 · agent · 2026-09-29T04:42:21.395Z ### Recorded reasoning summary Testing Lambda Values I'm exploring lambda values around 10000, specifically testing 7500, 10000, 12500, and 15000. I've also started investigating the impact of row-wise normalization of users prior to calculating G. ### run_shell_command · 2026-09-29T04:42:21.395Z { "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [10.0, 50.0, 100.0, 200.0, 500.0, 1000.0]:\n t1 = time.time()\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n scores = B @ R # user weights applied to original R\n \n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"Cosine User-EASE lam={lam} -> NDCG@10: {score:.5f} ({time.time()-t1:.2f}s)\")\n'", "description": "Test Cosine User-EASE" } ### Tool output · 2026-09-29T04:45:22.060Z Output: Cosine User-EASE lam=10.0 -> NDCG@10: 0.04518 (27.08s) Cosine User-EASE lam=50.0 -> NDCG@10: 0.03998 (27.08s) Cosine User-EASE lam=100.0 -> NDCG@10: 0.03626 (27.21s) Cosine User-EASE lam=200.0 -> NDCG@10: 0.03315 (27.14s) Cosine User-EASE lam=500.0 -> NDCG@10: 0.02964 (27.01s) Cosine User-EASE lam=1000.0 -> NDCG@10: 0.02779 (27.10s) Process Group PGID: 2506 ## Step 50 · agent · 2026-09-29T04:45:32.921Z ### run_shell_command · 2026-09-29T04:45:32.921Z { "description": "Test fine lambda grid and R vs R_norm for Cosine User-EASE", "command": "python3 -c '\nimport time\nimport numpy as np\nimport pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.preprocessing import normalize\nimport sys\nsys.path.append(\"/task\")\nfrom ranking_evaluation import score_rankings\nfrom evaluate_public import validation_truth, CandidateSet\nfrom pathlib import Path\n\ntargets_frame = pd.read_parquet(\"/task/data/target_users.parquet\")\ntarget_uids = targets_frame.uid.to_numpy()\nuid_to_row = {uid: i for i, uid in enumerate(target_uids)}\n\ncatalog = pd.read_parquet(\"/task/data/candidate_catalog.parquet\")\ncat_items = catalog.item_id.to_numpy()\nitem_to_col = {item: i for i, item in enumerate(cat_items)}\n\nval_npz = np.load(\"/task/data/validation_eligible_candidates.npz\")\npacked = val_npz[\"packed_eligible\"]\neligibility = CandidateSet(Path(\"/task/data/validation_eligible_candidates.npz\"))\ngraded = validation_truth(eligibility, set(target_uids))\n\ncutoff = 24786800\ndays90 = cutoff - 90 * 86400\n\nlistens = pd.read_parquet(\n \"/task/data/train_listens.parquet\",\n columns=[\"uid\", \"item_id\", \"timestamp\", \"is_organic\", \"played_ratio_pct\"]\n)\nmask = (listens.timestamp >= days90) & listens.item_id.isin(item_to_col) & listens.uid.isin(uid_to_row)\nlistens = listens[mask].copy()\n\npref = pd.read_parquet(\"/task/data/validation_preference_state.parquet\")\npref_targets = pref[pref.uid.isin(uid_to_row) & pref.item_id.isin(item_to_col)].copy()\n\nlistens[\"row\"] = listens.uid.map(uid_to_row).astype(np.int32)\nlistens[\"col\"] = listens.item_id.map(item_to_col).astype(np.int32)\n\npref_targets[\"row\"] = pref_targets.uid.map(uid_to_row).astype(np.int32)\npref_targets[\"col\"] = pref_targets.item_id.map(item_to_col).astype(np.int32)\nlikes_only = pref_targets[pref_targets.state == \"liked\"]\n\nhl = 10.0\ndelta_days = (cutoff - listens[\"timestamp\"].to_numpy()) / 86400.0\nrec = np.exp(-np.log(2) * delta_days / hl)\ncomp = np.clip(listens[\"played_ratio_pct\"].to_numpy(dtype=np.float32) / 100.0, 0.0, 1.5)\ns_boost = np.where(listens[\"played_ratio_pct\"].to_numpy() >= 80, 3.0, 1.0)\no_boost = np.where(listens[\"is_organic\"].to_numpy() == 1, 5.0, 0.5)\nweights = rec * comp * s_boost * o_boost\n\nrows = [listens[\"row\"].to_numpy()]\ncols = [listens[\"col\"].to_numpy()]\nvals = [weights]\nif len(likes_only) > 0:\n rows.append(likes_only[\"row\"].to_numpy())\n cols.append(likes_only[\"col\"].to_numpy())\n vals.append(np.full(len(likes_only), fill_value=5.0, dtype=np.float32))\n\nR = sp.csr_matrix(\n (np.concatenate(vals), (np.concatenate(rows), np.concatenate(cols))),\n shape=(len(target_uids), len(cat_items)),\n dtype=np.float32\n)\nR.data = np.log1p(R.data)\n\nR_norm = normalize(R, norm=\"l2\", axis=1)\nG_cos = (R_norm @ R_norm.T).toarray()\n\nfor lam in [1.0, 2.0, 5.0, 10.0, 15.0]:\n G_reg = G_cos.copy()\n np.fill_diagonal(G_reg, G_reg.diagonal() + lam)\n P = np.linalg.inv(G_reg)\n B = -P / np.diag(P)[:, None]\n np.fill_diagonal(B, 0.0)\n \n # Try B @ R vs B @ R_norm\n for use_r_norm in [False, True]:\n scores = B @ (R_norm if use_r_norm else R)\n ranked = {}\n for r in range(len(target_uids)):\n uid = int(target_uids[r])\n mask_eligible = np.unpackbits(packed[r], count=len(cat_items), bitorder=\"little\") == 1\n user_scores = scores[r].copy()\n user_scores[~mask_eligible] = -1e9\n top100_idx = np.argpartition(-user_scores, 100)[:100]\n top100_idx = top100_idx[np.argsort(-user_scores[top100_idx])]\n ranked[uid] = list(cat_items[top100_idx])\n \n res = score_rankings(ranked, graded)\n score = res[\"quality_ndcg@10\"]\n print(f\"lam={lam} use_r_norm={use_r_norm} -> NDCG@10: {score:.5f}\")\n'" } ### Tool output · 2026-09-29T04:48:58.715Z Output: lam=1.0 use_r_norm=False -> NDCG@10: 0.03891 lam=1.0 use_r_norm=True -> NDCG@10: 0.03356 lam=2.0 use_r_norm=False -> NDCG@10: 0.04149 lam=2.0 use_r_norm=True -> NDCG@10: 0.03741 lam=5.0 use_r_norm=False -> NDCG@10: 0.04439 lam=5.0 use_r_norm=True -> NDCG@10: 0.04081 lam=10.0 use_r_norm=False -> NDCG@10: 0.04518 lam=10.0 use_r_norm=True -> NDCG@10: 0.04312 lam=15.0 use_r_norm=False -> NDCG@10: 0.04467 lam=15.0 use_r_norm=True -> NDCG@10: 0.04337 Process Group PGID: 2552 ## Step 51 · agent · 2026-09-29T04:49:31.517Z ### Recorded reasoning summary Exploring Ensemble Methods I'm exploring how ensembling Cosine User-EASE with TruncatedSVD might improve performance. My current focus is understanding how these distinct structural representations, User-EASE and SVD, might complement each other when combined. ## Recorded session usage [ { "messageId": "1a928ebe-17be-4020-9ce3-bfe94ee72dd4", "timestamp": "2026-09-29T04:19:55.761Z", "model": "gemini-3.8-flash", "tokens": { "input": 9890, "output": 203, "cached": 7644, "thoughts": 318, "tool": 0, "total": 10411 } }, { "messageId": "e64463e2-fa14-414a-b334-d023bfcd221d", "timestamp": "2026-09-29T04:20:06.201Z", "model": "gemini-3.8-flash", "tokens": { "input": 15374, "output": 34, "cached": 7776, "thoughts": 39, "tool": 0, "total": 15447 } }, { "messageId": "5289e7c0-611b-4730-a98c-a5b80caa4587", "timestamp": "2026-09-29T04:20:09.351Z", "model": "gemini-3.8-flash", "tokens": { "input": 15500, "output": 46, "cached": 11651, "thoughts": 110, "tool": 0, "total": 15656 } }, { "messageId": "08a57e44-ef75-4357-9051-78ece69627fb", "timestamp": "2026-09-29T04:20:14.462Z", "model": "gemini-3.8-flash", "tokens": { 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