yambda-discovery-ranking-v01 / yambda-opus-high-2026-09-24-00-34-15-04330cf-5 Recorded model: claude-opus-5 ## Step 1 · user · 2026-09-24T07:44:50.857Z # 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. ### Harness context { "type": "deferred_tools_delta", "addedNames": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskOutput", "TaskStop", "WebFetch", "WebSearch" ], "addedLines": [ "CronCreate", "CronDelete", "CronList", "EnterWorktree", "ExitWorktree", "NotebookEdit", "RemoteTrigger", "SendMessage", "TaskOutput", "TaskStop", "WebFetch", "WebSearch" ], "removedNames": [], "wireHiddenNames": [], "readdedNames": [], "pendingMcpServers": [], "needsAuthMcpServers": [], "failedMcpServers": [] } ### Harness context { "type": "agent_listing_delta", "addedTypes": [ "claude", "Explore", "general-purpose", "Plan", "statusline-setup" ], "addedLines": [ "- claude: Catch-all for any task that doesn't fit a more specific agent. FleetView's default when no agent name is typed. (Tools: *)", "- Explore: Read-only search agent for broad fan-out searches — when answering means sweeping many files, directories, or naming conventions and you only need the conclusion, not the file dumps. It reads excerpts rather than whole files, so it locates code; it doesn't review or audit it. Specify search breadth: \"medium\" for moderate exploration, \"very thorough\" for multiple locations and naming conventions. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)", "- general-purpose: General-purpose agent for researching complex questions, searching for code, and executing multi-step tasks. When you are searching for a keyword or file and are not confident that you will find the right match in the first few tries use this agent to perform the search for you. (Tools: *)", "- Plan: Software architect agent for designing implementation plans. Use this when you need to plan the implementation strategy for a task. Returns step-by-step plans, identifies critical files, and considers architectural trade-offs. (Tools: All tools except Agent, Artifact, ExitPlanMode, Edit, Write, NotebookEdit)", "- statusline-setup: Use this agent to configure the user's Claude Code status line setting. (Tools: Read, Edit)" ], "removedTypes": [], "isInitial": true, "showConcurrencyNote": false } ### Harness context { "type": "skill_listing", "content": "- dataviz: Use this skill whenever you are about to create ANY chart, graph, plot, dashboard, or data visualization, in ANY output medium — an HTML or React artifact, inline SVG, plotting code in any library (matplotlib, plotly, d3, Recharts, …), an image/PNG you will render and upload, or a chart shared into Slack. Read it BEFORE writing the first line of chart code, choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. When the destination is a first-party document connector (host-designated, never self-described) that renders live charts, hand it the rows (inline, or as an uploaded data file the chart cites) rather than a rendered PNG/SVG — a picture of a chart loses hover, data inspection and per-value comments. Produces visualizations that read as one system — elegant, accessible, consistent in light and dark — using a brand-neutral placeholder palette you swap for your own. Teaches a design-system-agnostic method: a form heuristic, a color formula with a runnable validator, mark specs, and interaction rules. A validated default palette is documented in `references/palette.md` — swap that file's values for your brand's. Triggers on: \"chart\", \"graph\", \"plot\", \"data viz\", \"visualization\", \"dashboard\", \"analytics\", \"visualize data\", \"categorical colors\", \"sequential / diverging palette\", \"stat tile\", \"sparkline\", \"heatmap\", \"legend\", \"axis\", \"tooltip\", \"chart colors\", \"color by series\".\n- update-config: Use this skill to configure the Claude Code harness via settings.json. Automated behaviors (\"from now on when X\", \"each time X\", \"whenever X\", \"before/after X\") require hooks configured in settings.json - the harness executes these, not Claude, so memory/preferences cannot fulfill them. Also use for: permissions (\"allow X\", \"add permission\", \"move permission to\"), env vars (\"set X=Y\"), hook troubleshooting, or any changes to settings.json/settings.local.json files. Examples: \"allow npm commands\", \"add bq permission to global settings\", \"move permission to user settings\", \"set DEBUG=true\", \"when claude stops show X\". For simple settings like theme/model, suggest the /config command.\n- keybindings-help: Use when the user wants to customize keyboard shortcuts, rebind keys, add chord bindings, or modify ~/.claude/keybindings.json. Examples: \"rebind ctrl+s\", \"add a chord shortcut\", \"change the submit key\", \"customize keybindings\".\n- code-review: Review the current diff, or a PR number/branch/path target, for correctness bugs and reuse/simplification/efficiency cleanups at the given effort level (low/medium: fewer, high-confidence findings; high→max: broader coverage, may include uncertain findings); with no level given, it reuses the level you typed last. Pass --comment to post findings as inline PR comments, or --fix to apply the findings to the working tree after the review.\n- simplify: Review the changed code for reuse, simplification, efficiency, and altitude cleanups, then apply the fixes. Quality only — it does not hunt for bugs; use /code-review for that.\n- fewer-permission-prompts: Scan your transcripts for common read-only Bash and MCP tool calls, then add a prioritized allowlist to project .claude/settings.json to reduce permission prompts.\n- loop: Run a prompt or slash command on a recurring interval (e.g. /loop 5m /foo). Omit the interval to let the model self-pace. - When the user wants to set up a recurring task, poll for status, or run something repeatedly on an interval (e.g. \"check the deploy every 5 minutes\", \"keep running /babysit-prs\"). Do NOT invoke for one-off tasks.\n- schedule: Create, update, list, or run scheduled cloud agents (routines) that execute on a cron schedule. - When the user wants to schedule a recurring cloud agent, set up automated tasks, create a cron job for Claude Code, or manage their scheduled agents/routines. Also use when the user wants a one-time scheduled run (\"run this once at 3pm\", \"remind me to check X tomorrow\").\n- claude-api: Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration.\nTRIGGER — read BEFORE opening the target file; don't skip because it \"looks like a one-liner\" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens).\nSKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).\n- workflow-authoring: Reference for writing a Workflow tool script (script API and gotchas, resume, quality patterns, worked examples). Load before authoring a script for a workflow the user already opted into; it does not itself authorize running one.\n- run: Launch and drive this project's app to see a change working. Use when asked to run, start, or screenshot the app, or to confirm a change works in the real app (not just tests). First looks for a project skill that already covers launching the app; otherwise falls back to built-in patterns per project type (CLI, server, TUI, Electron, browser-driven, library).\n- init: Initialize a new CLAUDE.md file with codebase documentation\n- security-review: Complete a security review of the pending changes on the current branch", "skillCount": 13, "isInitial": true, "names": [ "dataviz", "update-config", "keybindings-help", "code-review", "simplify", "fewer-permission-prompts", "loop", "schedule", "claude-api", "workflow-authoring", "run", "init", "security-review" ] } ### Harness context { "type": "total_tokens_reminder", "text": "15000000 tokens left" } ## Step 2 · agent · 2026-09-24T07:44:52.416Z I'll start by exploring the data and environment. ### Bash · 2026-09-24T07:44:53.425Z { "command": "ls -la /task/ /task/data/ && cat /task/requirements.txt && nproc && free -g", "description": "Inspect task directory and data" } ### Tool output · 2026-09-24T07:44:53.492Z /task/: total 36 dr-xr-xr-x 1 root root 4096 Sep 24 07:41 . drwxr-xr-x 1 root root 4096 Sep 24 07:43 .. dr-xr-xr-x 1 root root 4096 Sep 24 07:38 data -r--r--r-- 1 root root 5774 Sep 24 07:37 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 24 07:37 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 24 07:37 requirements.txt /task/data/: total 347392 dr-xr-xr-x 1 root root 4096 Sep 24 07:38 . dr-xr-xr-x 1 root root 4096 Sep 24 07:41 .. -r--r--r-- 1 root root 349352 Sep 24 07:37 candidate_catalog.parquet -r--r--r-- 1 root root 84127 Sep 24 07:37 target_users.parquet -r--r--r-- 1 root root 7736341 Sep 24 07:37 test_eligible_candidates.npz -r--r--r-- 1 root root 1092329 Sep 24 07:38 test_preference_state.parquet -r--r--r-- 1 root root 857213 Sep 24 07:37 train_dislikes.parquet -r--r--r-- 1 root root 5374652 Sep 24 07:37 train_likes.parquet -r--r--r-- 1 root root 319183255 Sep 24 07:37 train_listens.parquet -r--r--r-- 1 root root 155988 Sep 24 07:37 train_undislikes.parquet -r--r--r-- 1 root root 1855885 Sep 24 07:37 train_unlikes.parquet -r--r--r-- 1 root root 36571 Sep 24 07:37 val_dislikes.parquet -r--r--r-- 1 root root 298484 Sep 24 07:37 val_likes.parquet -r--r--r-- 1 root root 9947609 Sep 24 07:37 val_listens.parquet -r--r--r-- 1 root root 7845 Sep 24 07:37 val_undislikes.parquet -r--r--r-- 1 root root 72536 Sep 24 07:37 val_unlikes.parquet -r--r--r-- 1 root root 7573821 Sep 24 07:37 validation_eligible_candidates.npz -r--r--r-- 1 root root 1061761 Sep 24 07:38 validation_preference_state.parquet # 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 8 total used free shared buff/cache available Mem: 15 1 4 0 9 14 Swap: 0 0 0 ### Bash · 2026-09-24T07:44:54.130Z { "command": "cat /task/ranking_evaluation.py; echo ---; cat /task/evaluate_public.py", "description": "Read evaluation code" } ### Tool output · 2026-09-24T07:44:54.153Z """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), } --- #!/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() ### Harness context { "type": "total_tokens_reminder", "text": "14982084 tokens left" } ## Step 3 · agent · 2026-09-24T07:44:57.169Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:44:59.890Z { "command": "mkdir -p solution && python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\ntu=pd.read_parquet('/task/data/target_users.parquet'); print(tu.shape, tu.columns.tolist(), tu.head())\ncc=pd.read_parquet('/task/data/candidate_catalog.parquet'); print(cc.shape, cc.columns.tolist()); print(cc.head())\nps=pd.read_parquet('/task/data/validation_preference_state.parquet'); print(ps.shape, ps.columns.tolist()); print(ps.head())\nz=np.load('/task/data/validation_eligible_candidates.npz'); print({k:(v.shape,v.dtype) for k,v in z.items()})\nimport pyarrow.parquet as pq\nprint(pq.ParquetFile('/task/data/train_listens.parquet').metadata)\nprint(time.time()-t)\n\"" } ### Tool output · 2026-09-24T07:45:00.845Z (8456, 3) ['uid', 'recent_positive_listens', 'history_positive_listens'] 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 (30000, 4) ['item_id', 'positive_listens', 'organic_positive_listens', 'popularity_rank'] 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 (469124, 3) ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked 3 100 1730273 liked 4 100 2621343 liked {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} created_by: parquet-cpp-arrow version 18.1.0 num_columns: 6 num_rows: 43484159 num_row_groups: 42 format_version: 2.6 serialized_size: 25540 0.1710507869720459 ### Harness context { "type": "total_tokens_reminder", "text": "14974532 tokens left" } ## Step 4 · agent · 2026-09-24T07:45:25.602Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:45:27.271Z { "command": "cd /app/solution && python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\nfor f in ['train_likes','train_dislikes','train_unlikes','val_listens','val_likes']:\n d=pd.read_parquet(f'/task/data/{f}.parquet'); print(f, d.shape, d.dtypes.to_dict())\nprint(time.time()-t)\n\"" } ### Tool output · 2026-09-24T07:45:28.097Z train_likes (814831, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} train_dislikes (101075, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} train_unlikes (292665, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} val_listens (1501697, 6) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8'), 'played_ratio_pct': dtype('uint16'), 'track_length_seconds': dtype('uint32')} val_likes (37795, 4) {'uid': dtype('uint32'), 'timestamp': dtype('uint32'), 'item_id': dtype('uint32'), 'is_organic': dtype('uint8')} 0.09076857566833496 ### Bash · 2026-09-24T07:45:30.027Z { "command": "python -c \"\nimport numpy as np, pandas as pd, time\nt=time.time()\nz=np.load('/task/data/validation_eligible_candidates.npz')\np=z['packed_eligible']; items=z['item_ids']; uids=z['uids']\nm=np.unpackbits(p,axis=1,bitorder='little')[:,:len(items)]\nprint('elig per user', m.sum(1).mean(), m.sum(1).min(), m.sum(1).max())\nzt=np.load('/task/data/test_eligible_candidates.npz')\nprint('test uids equal', np.array_equal(zt['uids'],uids), np.array_equal(zt['item_ids'],items))\nmt=np.unpackbits(zt['packed_eligible'],axis=1,bitorder='little')[:,:len(items)]\nprint('test elig', mt.sum(1).mean())\nprint('overlap', (m&mt).sum()/m.sum())\nprint(time.time()-t)\n\"" } ### Tool output · 2026-09-24T07:45:31.398Z elig per user 29067.306291390727 24657 30000 test uids equal True True test elig 29041.3178807947 overlap 0.9991059229797388 0.6591532230377197 ### Harness context { "type": "total_tokens_reminder", "text": "14971756 tokens left" } ## Step 5 · agent · 2026-09-24T07:45:36.960Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:45:40.112Z { "command": "python -c \"\nimport pandas as pd, numpy as np, time\nt=time.time()\ntu=pd.read_parquet('/task/data/target_users.parquet').uid.astype(np.uint32).values\ntus=set(tu.tolist())\nvl=pd.read_parquet('/task/data/val_listens.parquet')\nprint('val users', vl.uid.nunique(), 'target in val', vl.uid.isin(tus).mean())\ntl=pd.read_parquet('/task/data/train_listens.parquet', columns=['uid','timestamp','item_id','is_organic','played_ratio_pct'])\nprint('train shape', tl.shape, 'users', tl.uid.nunique(), time.time()-t)\nprint('ts range', tl.timestamp.min(), tl.timestamp.max())\nprint('target frac', tl.uid.isin(tus).mean())\ntl.to_pickle('/tmp/none') if False else None\nprint(time.time()-t)\n\" 2>&1 | tail -20" } ### Tool output · 2026-09-24T07:45:44.542Z val users 8196 target in val 0.9872517558468852 train shape (43484159, 5) users 9238 0.7931411266326904 ts range 0 24786795 target frac 0.9939779909276847 3.5993707180023193 ### Harness context { "type": "total_tokens_reminder", "text": "14970502 tokens left" } ## Step 6 · agent · 2026-09-24T07:45:54.666Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:45:59.861Z { "command": "cat > prep.py <<'EOF'\nimport pandas as pd, numpy as np, time, pickle\nt=time.time()\nD='/task/data/'\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nitems=np.load(D+'validation_eligible_candidates.npz')['item_ids'].astype(np.int64)\nuids=np.load(D+'validation_eligible_candidates.npz')['uids'].astype(np.int64)\nimap=pd.Series(np.arange(len(items)),index=items)\numap=pd.Series(np.arange(len(uids)),index=uids)\ndef load(name, cols):\n d=pd.read_parquet(D+name+'.parquet', columns=cols)\n d['u']=umap.reindex(d.uid.values).values\n d['i']=imap.reindex(d.item_id.values).values\n d=d[d.u.notna()&d.i.notna()]\n d['u']=d.u.astype(np.int32); d['i']=d.i.astype(np.int32)\n return d.drop(columns=['uid','item_id'])\nout={}\nfor split in ['train','val']:\n out[split+'_listens']=load(split+'_listens',['uid','timestamp','item_id','is_organic','played_ratio_pct'])\n for f in ['likes','dislikes','unlikes','undislikes']:\n out[split+'_'+f]=load(split+'_'+f,['uid','timestamp','item_id','is_organic'])\n print(split, time.time()-t)\nwith open('/app/solution/cache.pkl','wb') as f: pickle.dump({'items':items,'uids':uids,'cat':cat,**out},f,protocol=4)\nprint('done',time.time()-t)\nEOF\npython prep.py" } ### Tool output · 2026-09-24T07:46:03.915Z train 2.601755142211914 val 2.6943094730377197 done 3.3041038513183594 ### Harness context { "type": "total_tokens_reminder", "text": "14969132 tokens left" } ## Step 7 · agent · 2026-09-24T07:46:08.246Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:46:12.310Z { "command": "python -c \"\nimport pickle,numpy as np,pandas as pd,time\nt=time.time()\nc=pickle.load(open('cache.pkl','rb'))\nprint({k:(v.shape if hasattr(v,'shape') else len(v)) for k,v in c.items()})\ntl=c['train_listens']; vl=c['val_listens']\nnU=len(c['uids']); nI=len(c['items'])\n# relevance\nvs=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['u','i']].drop_duplicates()\nvlk=c['val_likes']; vlk=vlk[vlk.is_organic==1][['u','i']].drop_duplicates()\nprint('rel strong',len(vs),'likes',len(vlk))\n# history pairs\nhist=tl[tl.played_ratio_pct>=50][['u','i']].drop_duplicates()\nhs=set(map(tuple,hist.values.tolist()))\nprint('repeat frac of strong rel', np.mean([tuple(x) in hs for x in vs.values.tolist()[:50000]]))\nprint('repeat frac of like rel', np.mean([tuple(x) in hs for x in vlk.values.tolist()[:50000]]))\nprint(time.time()-t)\n\"" } ### Tool output · 2026-09-24T07:46:20.365Z {'items': (30000,), 'uids': (8456,), 'cat': (30000, 4), 'train_listens': (31062540, 5), 'train_likes': (472889, 4), 'train_dislikes': (54901, 4), 'train_unlikes': (77990, 4), 'train_undislikes': (5792, 4), 'val_listens': (999062, 5), 'val_likes': (18307, 4), 'val_dislikes': (1532, 4), 'val_unlikes': (2062, 4), 'val_undislikes': (145, 4)} rel strong 170791 likes 8528 repeat frac of strong rel 0.75262 repeat frac of like rel 0.42295966228893056 5.024516582489014 ### Harness context { "type": "total_tokens_reminder", "text": "14968463 tokens left" } ## Step 8 · agent · 2026-09-24T07:46:50.397Z ### Thinking unavailable — no text recorded 75% of validation relevance is repeat-consumption of known tracks. Building the feature pipeline now. ### Bash · 2026-09-24T07:47:19.397Z { "command": "cat > feats.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time\nfrom scipy import sparse\n\nNI=30000\nDAY=86400.0\n\ndef cat_events(c, splits, kind):\n return pd.concat([c[s+'_'+kind] for s in splits], ignore_index=True)\n\ndef active_state(likes, unlikes, dislikes, undislikes, cutoff):\n \"\"\"last-wins active like/dislike state per (u,i)\"\"\"\n def last(df, v):\n d=df[df.timestamp=50); strong=(pr>=80)\n F['n_all']=bc(None if False else np.ones(len(u)))\n F['n_pos']=bc(pos.astype(np.float64))\n F['n_strong']=bc(strong.astype(np.float64))\n F['n_org_strong']=bc((strong&(org>0)).astype(np.float64))\n F['n_org']=bc(org.astype(np.float64))\n F['sum_pr']=bc(pr.astype(np.float64))\n F['mean_pr']=F['sum_pr']/np.maximum(F['n_all'],1)\n for hl,nm in [(1.0,'d1'),(3.0,'d3'),(7.0,'d7'),(30.0,'d30')]:\n w=np.exp(-age/hl)\n F['dec_'+nm]=bc(w)\n F['decs_'+nm]=bc(w*strong)\n for wd in [1,3,7,14,30,90]:\n m=(age0,neg,-1e4)); F['last_org_age']=-reco\n first=np.full(P,1e4); np.minimum.at(first,code,age); F['first_age']=first\n # user aggregates\n ucnt=np.bincount(u,minlength=nU).astype(np.float64)\n ucnt7=np.bincount(u,weights=(age<7).astype(np.float64),minlength=nU)\n ucnt_s=np.bincount(u,weights=strong.astype(np.float64),minlength=nU)\n udist=np.bincount(pu,minlength=nU).astype(np.float64)\n uorg=np.bincount(u,weights=org.astype(np.float64),minlength=nU)/np.maximum(ucnt,1)\n # item aggregates\n icnt=np.bincount(i,minlength=NI).astype(np.float64)\n icnt7=np.bincount(i,weights=(age<7).astype(np.float64),minlength=NI)\n icnt_s=np.bincount(i,weights=strong.astype(np.float64),minlength=NI)\n icnt_os=np.bincount(i,weights=(strong&(org>0)).astype(np.float64),minlength=NI)\n iusers=np.bincount(pi,minlength=NI).astype(np.float64)\n irepeat=icnt/np.maximum(iusers,1)\n istrength=icnt_s/np.maximum(icnt,1)\n iorg=np.bincount(i,weights=org.astype(np.float64),minlength=NI)/np.maximum(icnt,1)\n # state\n st=active_state(likes,unlikes,dis,undis,cutoff)\n lk_k,lk_ts=st['like']; ds_k,ds_ts=st['dis']\n # CF: item-item cosine on strong-listen matrix, capped per user\n ord_=np.lexsort((-F['decs_d30'],pu))\n cap=np.zeros(P,dtype=bool)\n rk=np.arange(P)-np.searchsorted(pu[ord_],pu[ord_])\n cap[ord_[rk<300]]=True\n sel=cap&(F['n_strong']>0)\n val=np.sqrt(F['n_strong'][sel])\n X=sparse.csr_matrix((val,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32)\n norm=np.sqrt(np.asarray(X.multiply(X).sum(0)).ravel())+1e-6\n Xn=X.multiply(sparse.csr_matrix(1.0/norm)).tocsr().astype(np.float32)\n G=(Xn.T@Xn).tocsr()\n G.setdiag(0); G.eliminate_zeros()\n # recent-profile user vector\n valr=F['dec_d30'][sel].astype(np.float32)\n Xr=sparse.csr_matrix((valr,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32)\n Xr=sparse.diags(1.0/(np.asarray(Xr.sum(1)).ravel()+1e-6)).astype(np.float32)@Xr\n S=(Xr@G) # nU x NI dense-ish\n S=np.asarray(S.todense()) if sparse.issparse(S) else S\n print('cf done',time.time()-t0, S.shape)\n # likes-based CF\n lkh=likes[likes.timestamp14962839 tokens left" } ## Step 9 · agent · 2026-09-24T07:47:22.120Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:47:35.849Z { "command": "cat >> feats.py <<'PYEOF'\n\nFEATNAMES=None\n\ndef assemble(B, eligible_mask):\n global FEATNAMES\n t0=time.time()\n nU=B['nU']; F=B['F']; pu=B['pu']; pi=B['pi']; S=B['S']; Sl=B['Sl']\n P=len(pu)\n # discovery candidates: top n_extra by blended CF among non-history eligible\n blend=S+0.5*Sl\n blend=np.where(eligible_mask,blend,-1e9)\n bh=blend.copy()\n bh[pu,pi]=-1e9\n ne=B['n_extra']\n idx=np.argpartition(-bh,ne,axis=1)[:,:ne]\n eu=np.repeat(np.arange(nU,dtype=np.int32),ne); ei=idx.ravel().astype(np.int32)\n keep=bh[eu,ei]>-1e8\n eu=eu[keep]; ei=ei[keep]\n cu=np.concatenate([pu,eu]); ci=np.concatenate([pi,ei])\n ish=np.concatenate([np.ones(P,dtype=np.float32),np.zeros(len(eu),dtype=np.float32)])\n E=len(cu)\n def ext(a):\n return np.concatenate([a,np.zeros(E-P,dtype=a.dtype)])\n cols={}; \n for k,v in F.items(): cols[k]=ext(v.astype(np.float32))\n for k in ['last_age','last_strong_age','last_org_age']:\n cols[k]=np.where(ish>0,cols[k],999.0)\n cols[k]=np.minimum(cols[k],999.0)\n cols['first_age']=np.where(ish>0,np.minimum(cols['first_age'],999.0),999.0)\n cols['is_hist']=ish\n # user feats\n for nm,arr in [('u_cnt',B['ucnt']),('u_cnt7',B['ucnt7']),('u_cnt_s',B['ucnt_s']),('u_dist',B['udist']),('u_org',B['uorg'])]:\n cols[nm]=arr[cu].astype(np.float32)\n for nm,arr in [('i_cnt',B['icnt']),('i_cnt7',B['icnt7']),('i_cnt_s',B['icnt_s']),('i_cnt_os',B['icnt_os']),\n ('i_users',B['iusers']),('i_rep',B['irepeat']),('i_str',B['istrength']),('i_org',B['iorg'])]:\n cols[nm]=arr[ci].astype(np.float32)\n cols['cf']=S[cu,ci]; cols['cf_l']=Sl[cu,ci]\n # normalized shares\n cols['share']=cols['n_all']/np.maximum(cols['u_cnt'],1)\n cols['share_s']=cols['n_strong']/np.maximum(cols['u_cnt_s'],1)\n cols['share7']=cols['c7']/np.maximum(cols['u_cnt7'],1)\n cols['i_pop_rank']=np.log1p(ci.astype(np.float32))\n # state\n ck=cu.astype(np.int64)*NI+ci\n order=np.argsort(ck); cks=ck[order]\n def statefeat(k_arr,ts_arr):\n f=np.zeros(E,dtype=np.float32); fa=np.full(E,999.0,dtype=np.float32)\n if len(k_arr)==0: return f,fa\n p=np.searchsorted(cks,k_arr)\n ok=(p14961090 tokens left" } ## Step 10 · agent · 2026-09-24T07:47:45.504Z ### Bash · 2026-09-24T07:47:45.504Z { "command": "cat > run.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time, sys\nfrom feats import build_features, assemble, NI, DAY\nt0=time.time()\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nnU=len(c['uids'])\nez=np.load('/task/data/validation_eligible_candidates.npz')\nMval=np.unpackbits(ez['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nezt=np.load('/task/data/test_eligible_candidates.npz')\nMtest=np.unpackbits(ezt['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nTRAIN_CUT=24786800; WIN=604800; GAP=1800\n\ndef labels(splits, lo, hi):\n L=pd.concat([c[s+'_listens'] for s in splits],ignore_index=True)\n L=L[(L.timestamp>=lo)&(L.timestamp=80)]\n K=pd.concat([c[s+'_likes'] for s in splits],ignore_index=True)\n K=K[(K.timestamp>=lo)&(K.timestamp0).mean(),(y1==3).sum(),time.time()-t0,flush=True)\ndel B1\nnp.save('/tmp/x1.npy',X1)\npickle.dump((cu1,ci1,y1,names),open('/tmp/f1.pkl','wb'))\nPYEOF\npython run.py 2>&1 | tail -20" } ### Tool output · 2026-09-24T07:48:01.371Z building pseudo-train ### Harness context { "type": "total_tokens_reminder", "text": "14960061 tokens left" } ## Step 11 · agent · 2026-09-24T07:48:03.577Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:48:03.969Z { "command": "python run.py; echo \"EXIT=$?\"" } ### Tool output · 2026-09-24T07:48:22.556Z building pseudo-train /bin/bash: line 1: 8233 Killed python run.py EXIT=137 ### Harness context { "type": "total_tokens_reminder", "text": "14959885 tokens left" } ## Step 12 · agent · 2026-09-24T07:48:35.414Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:48:45.559Z { "command": "python - <<'EOF'\nimport re\ns=open('/app/solution/feats.py').read()\ns=s.replace(\"\"\" G=(Xn.T@Xn).tocsr()\n G.setdiag(0); G.eliminate_zeros()\n # recent-profile user vector\n valr=F['dec_d30'][sel].astype(np.float32)\n Xr=sparse.csr_matrix((valr,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32)\n Xr=sparse.diags(1.0/(np.asarray(Xr.sum(1)).ravel()+1e-6)).astype(np.float32)@Xr\n S=(Xr@G) # nU x NI dense-ish\n S=np.asarray(S.todense()) if sparse.issparse(S) else S\n print('cf done',time.time()-t0, S.shape)\"\"\",\n\"\"\" valr=F['dec_d30'][sel].astype(np.float32)\n Xr=sparse.csr_matrix((valr,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32)\n Xr=sparse.diags(1.0/(np.asarray(Xr.sum(1)).ravel()+1e-6)).astype(np.float32)@Xr\n UU=np.asarray((Xr@Xn.T).todense(),dtype=np.float32)\n np.fill_diagonal(UU,0.0)\n S=UU@np.asarray(Xn.todense(),dtype=np.float32)\n del UU\n print('cf done',time.time()-t0, S.shape)\"\"\")\ns=s.replace(\"\"\" Gl=(Xln.T@Xln).tocsr(); Gl.setdiag(0); Gl.eliminate_zeros()\n Xlu=sparse.diags(1.0/(np.asarray(Xl.sum(1)).ravel()+1e-6)).astype(np.float32)@Xl\n Sl=np.asarray((Xlu@Gl).todense())\"\"\",\n\"\"\" Xlu=sparse.diags(1.0/(np.asarray(Xl.sum(1)).ravel()+1e-6)).astype(np.float32)@Xl\n UUl=np.asarray((Xlu@Xln.T).todense(),dtype=np.float32)\n np.fill_diagonal(UUl,0.0)\n Sl=UUl@np.asarray(Xln.todense(),dtype=np.float32)\n del UUl\"\"\")\n# add capping of pairs at end of build_features\ns=s.replace(\"\"\" print('cf2 done',time.time()-t0)\n return dict(\"\"\",\n\"\"\" print('cf2 done',time.time()-t0)\n prio=F['dec_d30']+0.3*F['dec_d7']+0.05*F['n_strong']+0.002*F['n_all']\n o=np.lexsort((-prio,pu))\n rank=np.arange(len(pu))-np.searchsorted(pu[o],pu[o])\n keep=np.zeros(len(pu),dtype=bool); keep[o[rank14957680 tokens left" } ## Step 13 · agent · 2026-09-24T07:49:09.317Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:49:13.232Z { "command": "python - <<'EOF'\ns=open('feats.py').read()\ns=s.replace(\"import numpy as np, pandas as pd, pickle, time\\nfrom scipy import sparse\",\n\"import numpy as np, pandas as pd, pickle, time, os, gc\\nfrom scipy import sparse\\ndef rss(tag):\\n import resource; print('RSS',tag,round(resource.getrusage(resource.RUSAGE_SELF).ru_maxrss/1e6,2),'GB',flush=True)\")\ns=s.replace(\" upairs, code = np.unique(key, return_inverse=True)\",\" rss('pre-unique')\\n upairs, code = np.unique(key, return_inverse=True)\\n del key; gc.collect(); rss('post-unique')\")\ns=s.replace(\" # user aggregates\",\" rss('post-F')\\n # user aggregates\")\ns=s.replace(\" print('cf done',time.time()-t0, S.shape)\",\" rss('post-cf'); print('cf done',time.time()-t0, S.shape)\")\nopen('feats.py','w').write(s)\nEOF\ncd /app/solution && timeout 600 python run.py; echo \"EXIT=$?\"" } ### Tool output · 2026-09-24T07:49:29.720Z building pseudo-train RSS pre-unique 3.77 GB RSS post-unique 4.76 GB RSS post-F 6.29 GB /bin/bash: line 19: 8273 Killed timeout 600 python run.py EXIT=137 ### Harness context { "type": "total_tokens_reminder", "text": "14956735 tokens left" } ## Step 14 · agent · 2026-09-24T07:49:37.936Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:49:58.657Z { "command": "python - <<'EOF'\ns=open('feats.py').read()\nold_start=s.index(\" key=u*NI+i\")\nold_end=s.index(\" # user aggregates\")\nnew=\"\"\" key=u*NI+i\n rss('pre-unique')\n upairs, code = np.unique(key, return_inverse=True)\n del key; gc.collect()\n P=len(upairs); print('P0',P,flush=True)\n pu=(upairs//NI).astype(np.int32); pi=(upairs%NI).astype(np.int32)\n pos=(pr>=50); strong=(pr>=80)\n prio=np.bincount(code,weights=np.exp(-age/30.0),minlength=P)+0.3*np.bincount(code,weights=np.exp(-age/7.0),minlength=P)+0.05*np.bincount(code,weights=strong.astype(np.float64),minlength=P)\n o=np.lexsort((-prio,pu))\n rank=np.arange(P)-np.searchsorted(pu[o],pu[o])\n keep=np.zeros(P,dtype=bool); keep[o[rank0)).astype(np.float64),minlength=NI).astype(np.float32)\n iusers=np.bincount(pi,minlength=NI).astype(np.float32)\n irepeat=icnt/np.maximum(iusers,1); istrength=icnt_s/np.maximum(icnt,1)\n iorg=(np.bincount(i,weights=org.astype(np.float64),minlength=NI)/np.maximum(icnt,1)).astype(np.float32)\n # CF matrices built from kept pairs (need F-lite): use strong counts\n nstrong_all=np.bincount(code,weights=strong.astype(np.float64),minlength=P)\n dec30_all=np.bincount(code,weights=np.exp(-age/30.0),minlength=P)\n del u,i,ts; gc.collect()\n # filter events\n em=keep[code]\n code=newidx[code[em]]; age=age[em]; pr=pr[em]; org=org[em]\n pos=pos[em]; strong=strong[em]\n pu_k=pu[keep]; pi_k=pi[keep]\n nstrong_k=nstrong_all[keep]; dec30_k=dec30_all[keep]\n del em; gc.collect()\n P=keep.sum(); print('Pkept',P,flush=True)\n F={}\n def bc(w): return np.bincount(code,weights=w,minlength=P).astype(np.float32)\n F['n_all']=bc(np.ones(len(age)))\n F['n_pos']=bc(pos.astype(np.float64))\n F['n_strong']=bc(strong.astype(np.float64))\n F['n_org_strong']=bc((strong&(org>0)).astype(np.float64))\n F['n_org']=bc(org.astype(np.float64))\n F['mean_pr']=bc(pr.astype(np.float64))/np.maximum(F['n_all'],1)\n for hl,nm in [(1.0,'d1'),(3.0,'d3'),(7.0,'d7'),(30.0,'d30')]:\n w=np.exp(-age/hl); F['dec_'+nm]=bc(w); F['decs_'+nm]=bc(w*strong)\n for wd in [1,3,7,14,30,90]:\n m=(age0,neg,-1e4)); F['last_org_age']=(-reco).astype(np.float32)\n first=np.full(P,1e4); np.minimum.at(first,code,age); F['first_age']=first.astype(np.float32)\n del rec,recs,reco,first,age,pr,org,pos,strong,code; gc.collect()\n pu=pu_k; pi=pi_k\n rss('post-F')\n\"\"\"\ns=s[:old_start]+new+s[old_end:]\n# remove the old aggregate block (now duplicated) -> old block between '# user aggregates' and '# state'\na=s.index(\" # user aggregates\"); b=s.index(\" # state\\n\")\ns=s[:a]+s[b:]\n# fix CF section references\ns=s.replace(\"\"\" ord_=np.lexsort((-F['decs_d30'],pu))\n cap=np.zeros(P,dtype=bool)\n rk=np.arange(P)-np.searchsorted(pu[ord_],pu[ord_])\n cap[ord_[rk<300]]=True\n sel=cap&(F['n_strong']>0)\n val=np.sqrt(F['n_strong'][sel])\"\"\",\"\"\" sel=(nstrong_k>0)\n val=np.sqrt(nstrong_k[sel]).astype(np.float32)\"\"\")\ns=s.replace(\" valr=F['dec_d30'][sel].astype(np.float32)\",\" valr=dec30_k[sel].astype(np.float32)\")\ns=s.replace(\"\"\" prio=F['dec_d30']+0.3*F['dec_d7']+0.05*F['n_strong']+0.002*F['n_all']\n o=np.lexsort((-prio,pu))\n rank=np.arange(len(pu))-np.searchsorted(pu[o],pu[o])\n keep=np.zeros(len(pu),dtype=bool); keep[o[rank=50); strong=(pr>=80) prio=np.bincount(code,weights=np.exp(-age/30.0),minlength=P)+0.3*np.bincount(code,weights=np.exp(-age/7.0),minlength=P)+0.05*np.bincount(code,weights=strong.astype(np.float64),minlength=P) o=np.lexsort((-prio,pu)) rank=np.arange(P)-np.searchsorted(pu[o],pu[o]) keep=np.zeros(P,dtype=bool); keep[o[rank0)).astype(np.float64),minlength=NI).astype(np.float32) iusers=np.bincount(pi,minlength=NI).astype(np.float32) irepeat=icnt/np.maximum(iusers,1); istrength=icnt_s/np.maximum(icnt,1) iorg=(np.bincount(i,weights=org.astype(np.float64),minlength=NI)/np.maximum(icnt,1)).astype(np.float32) # CF matrices built from kept pairs (need F-lite): use strong counts nstrong_all=np.bincount(code,weights=strong.astype(np.float64),minlength=P) dec30_all=np.bincount(code,weights=np.exp(-age/30.0),minlength=P) del u,i,ts; gc.collect() # filter events em=keep[code] code=newidx[code[em]]; age=age[em]; pr=pr[em]; org=org[em] pos=pos[em]; strong=strong[em] pu_k=pu[keep]; pi_k=pi[keep] nstrong_k=nstrong_all[keep]; dec30_k=dec30_all[keep] del em; gc.collect() P=keep.sum(); print('Pkept',P,flush=True) F={} def bc(w): return np.bincount(code,weights=w,minlength=P).astype(np.float32) F['n_all']=bc(np.ones(len(age))) F['n_pos']=bc(pos.astype(np.float64)) F['n_strong']=bc(strong.astype(np.float64)) F['n_org_strong']=bc((strong&(org>0)).astype(np.float64)) F['n_org']=bc(org.astype(np.float64)) F['mean_pr']=bc(pr.astype(np.float64))/np.maximum(F['n_all'],1) for hl,nm in [(1.0,'d1'),(3.0,'d3'),(7.0,'d7'),(30.0,'d30')]: w=np.exp(-age/hl); F['dec_'+nm]=bc(w); F['decs_'+nm]=bc(w*strong) for wd in [1,3,7,14,30,90]: m=(age0,neg,-1e4)); F['last_org_age']=(-reco).astype(np.float32) first=np.full(P,1e4); np.minimum.at(first,code,age); F['first_age']=first.astype(np.float32) del rec,recs,reco,first,age,pr,org,pos,strong,code; gc.collect() pu=pu_k; pi=pi_k rss('post-F') # state st=active_state(likes,unlikes,dis,undis,cutoff) lk_k,lk_ts=st['like']; ds_k,ds_ts=st['dis'] # CF: item-item cosine on strong-listen matrix, capped per user sel=(nstrong_k>0) val=np.sqrt(nstrong_k[sel]).astype(np.float32) X=sparse.csr_matrix((val,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32) norm=np.sqrt(np.asarray(X.multiply(X).sum(0)).ravel())+1e-6 Xn=X.multiply(sparse.csr_matrix(1.0/norm)).tocsr().astype(np.float32) valr=dec30_k[sel].astype(np.float32) Xr=sparse.csr_matrix((valr,(pu[sel],pi[sel])),shape=(nU,NI),dtype=np.float32) Xr=sparse.diags(1.0/(np.asarray(Xr.sum(1)).ravel()+1e-6)).astype(np.float32)@Xr UU=np.asarray((Xr@Xn.T).todense(),dtype=np.float32) np.fill_diagonal(UU,0.0) S=UU@np.asarray(Xn.todense(),dtype=np.float32) del UU rss('post-cf'); print('cf done',time.time()-t0, S.shape) # likes-based CF lkh=likes[likes.timestamp14953550 tokens left" } ## Step 15 · agent · 2026-09-24T07:50:00.528Z ### Bash · 2026-09-24T07:50:00.528Z { "command": "timeout 900 python run.py; echo \"EXIT=$?\"" } ### Tool output · 2026-09-24T07:51:00.655Z building pseudo-train RSS pre-unique 3.77 GB P0 7627708 Pkept 3415858 RSS post-F 4.76 GB RSS post-cf 5.59 GB /bin/bash: line 1: 8296 Killed timeout 900 python run.py EXIT=137 ### Harness context { "type": "total_tokens_reminder", "text": "14950096 tokens left" } ## Step 16 · agent · 2026-09-24T07:51:12.031Z ### Thinking unavailable — no text recorded Time is tight — saving a safety-net submission first, then continuing with the model. ### Bash · 2026-09-24T07:51:20.836Z { "command": "cat > safety.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time\nNI=30000; DAY=86400.0\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nuids=c['uids']; items=c['items']; nU=len(uids)\nCUT=24786800+1800+604800 # test history includes train+val\nL=pd.concat([c['train_listens'],c['val_listens']],ignore_index=True)\nu=L.u.values.astype(np.int64); i=L.i.values.astype(np.int64)\nage=(CUT-L.timestamp.values.astype(np.float64))/DAY\nstrong=(L.played_ratio_pct.values>=80)\nkey=u*NI+i\nup,code=np.unique(key,return_inverse=True)\nP=len(up)\nsc=np.bincount(code,weights=np.exp(-age/30.0)*(1+strong),minlength=P)+0.5*np.bincount(code,weights=np.exp(-age/7.0)*(1+strong),minlength=P)\npu=(up//NI).astype(np.int32); pi=(up%NI).astype(np.int32)\nM=np.unpackbits(np.load('/task/data/test_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\npop=c['cat'].sort_values('popularity_rank').item_id.values\nimap=pd.Series(np.arange(NI),index=items); popcol=imap.reindex(pop).values\nok=M[pu,pi]\npu,pi,sc=pu[ok],pi[ok],sc[ok]\no=np.lexsort((-sc,pu)); pu,pi,sc=pu[o],pi[o],sc[o]\nstarts=np.searchsorted(pu,np.arange(nU)); ends=np.searchsorted(pu,np.arange(nU),side='right')\nrows=[]\nfor r in range(nU):\n sel=pi[starts[r]:ends[r]][:100]\n if len(sel)<100:\n extra=[x for x in popcol if M[r,x] and x not in set(sel.tolist())][:100-len(sel)]\n sel=np.concatenate([sel,np.array(extra,dtype=np.int32)])\n rows.append(sel)\nA=np.stack(rows)\nout=pd.DataFrame({'uid':np.repeat(uids,100).astype(np.int64),'item_id':items[A.ravel()].astype(np.int64),'rank':np.tile(np.arange(1,101),nU).astype(np.int64)})\nout.to_parquet('/app/solution/submission.parquet',index=False)\nprint('saved',out.shape)\nPYEOF\npython safety.py" } ### Tool output · 2026-09-24T07:53:11.185Z saved (845600, 3) ### Harness context { "type": "total_tokens_reminder", "text": "14948202 tokens left" } ## Step 17 · agent · 2026-09-24T07:53:17.467Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:53:38.932Z { "command": "python - <<'EOF'\ns=open('feats.py').read()\ns=s.replace(\"\"\" UU=np.asarray((Xr@Xn.T).todense(),dtype=np.float32)\n np.fill_diagonal(UU,0.0)\n S=UU@np.asarray(Xn.todense(),dtype=np.float32)\n del UU\n rss('post-cf'); print('cf done',time.time()-t0, S.shape)\"\"\",\"\"\" rss('post-cf')\"\"\")\ns=s.replace(\"\"\" Xlu=sparse.diags(1.0/(np.asarray(Xl.sum(1)).ravel()+1e-6)).astype(np.float32)@Xl\n UUl=np.asarray((Xlu@Xln.T).todense(),dtype=np.float32)\n np.fill_diagonal(UUl,0.0)\n Sl=UUl@np.asarray(Xln.todense(),dtype=np.float32)\n del UUl\n print('cf2 done',time.time()-t0)\"\"\",\"\"\" Xlu=sparse.diags(1.0/(np.asarray(Xl.sum(1)).ravel()+1e-6)).astype(np.float32)@Xl\n print('cf2 done',time.time()-t0)\"\"\")\ns=s.replace(\"S=S,Sl=Sl,\",\"Xn=Xn.tocsr(),Xr=Xr.tocsr(),Xln=Xln.tocsr(),Xlu=Xlu.tocsr(),\")\n# rewrite assemble\na=s.index(\"def assemble(\")\ns=s[:a]+'''def assemble(B, eligible_mask, chunk=800):\n global FEATNAMES\n t0=time.time()\n nU=B['nU']; F=B['F']; pu=B['pu']; pi=B['pi']\n Xn=B['Xn']; Xr=B['Xr']; Xln=B['Xln']; Xlu=B['Xlu']\n XnT=Xn.T.tocsr(); XlnT=Xln.T.tocsr()\n P=len(pu); ne=B['n_extra']\n starts=np.searchsorted(pu,np.arange(nU)); ends=np.searchsorted(pu,np.arange(nU),side='right')\n cf_h=np.zeros(P,dtype=np.float32); cfl_h=np.zeros(P,dtype=np.float32)\n eu_l=[]; ei_l=[]; ecf_l=[]; ecfl_l=[]\n for s0 in range(0,nU,chunk):\n s1=min(s0+chunk,nU); n=s1-s0\n UU=np.asarray((Xr[s0:s1]@XnT).todense(),dtype=np.float32)\n UU[np.arange(n),np.arange(s0,s1)]=0.0\n Sc=UU@Xn\n Sc=np.asarray(Sc.todense() if sparse.issparse(Sc) else Sc,dtype=np.float32)\n UL=np.asarray((Xlu[s0:s1]@XlnT).todense(),dtype=np.float32)\n UL[np.arange(n),np.arange(s0,s1)]=0.0\n SL=UL@Xln\n SL=np.asarray(SL.todense() if sparse.issparse(SL) else SL,dtype=np.float32)\n del UU,UL\n for r in range(s0,s1):\n sl=slice(starts[r],ends[r])\n cf_h[sl]=Sc[r-s0,pi[sl]]; cfl_h[sl]=SL[r-s0,pi[sl]]\n bl=Sc+0.5*SL\n bl[~eligible_mask[s0:s1]]=-1e9\n for r in range(s0,s1):\n bl[r-s0,pi[starts[r]:ends[r]]]=-1e9\n idx=np.argpartition(-bl,ne,axis=1)[:,:ne]\n rr=np.repeat(np.arange(n),ne); cc=idx.ravel()\n good=bl[rr,cc]>-1e8\n eu_l.append((rr[good]+s0).astype(np.int32)); ei_l.append(cc[good].astype(np.int32))\n ecf_l.append(Sc[rr[good],cc[good]]); ecfl_l.append(SL[rr[good],cc[good]])\n del Sc,SL,bl\n eu=np.concatenate(eu_l); ei=np.concatenate(ei_l)\n ecf=np.concatenate(ecf_l); ecfl=np.concatenate(ecfl_l)\n del eu_l,ei_l,ecf_l,ecfl_l; gc.collect()\n cu=np.concatenate([pu,eu]); ci=np.concatenate([pi,ei])\n ish=np.concatenate([np.ones(P,dtype=np.float32),np.zeros(len(eu),dtype=np.float32)])\n E=len(cu)\n cols={}\n for k,v in F.items(): cols[k]=np.concatenate([v.astype(np.float32),np.zeros(E-P,dtype=np.float32)])\n for k in ['last_age','last_strong_age','last_org_age','first_age']:\n cols[k]=np.where(ish>0,np.minimum(cols[k],999.0),999.0).astype(np.float32)\n cols['is_hist']=ish\n cols['cf']=np.concatenate([cf_h,ecf]); cols['cf_l']=np.concatenate([cfl_h,ecfl])\n for nm,arr in [('u_cnt',B['ucnt']),('u_cnt7',B['ucnt7']),('u_cnt_s',B['ucnt_s']),('u_dist',B['udist']),('u_org',B['uorg'])]:\n cols[nm]=arr[cu].astype(np.float32)\n for nm,arr in [('i_cnt',B['icnt']),('i_cnt7',B['icnt7']),('i_cnt_s',B['icnt_s']),('i_cnt_os',B['icnt_os']),\n ('i_users',B['iusers']),('i_rep',B['irepeat']),('i_str',B['istrength']),('i_org',B['iorg'])]:\n cols[nm]=arr[ci].astype(np.float32)\n cols['share']=cols['n_all']/np.maximum(cols['u_cnt'],1)\n cols['share_s']=cols['n_strong']/np.maximum(cols['u_cnt_s'],1)\n cols['share7']=cols['c7']/np.maximum(cols['u_cnt7'],1)\n cols['i_pop_rank']=np.log1p(ci.astype(np.float32))\n ck=cu.astype(np.int64)*NI+ci\n order=np.argsort(ck); cks=ck[order]\n def statefeat(k_arr,ts_arr):\n f=np.zeros(E,dtype=np.float32); fa=np.full(E,999.0,dtype=np.float32)\n if len(k_arr)==0: return f,fa\n p=np.searchsorted(cks,k_arr)\n ok=(p14945002 tokens left" } ## Step 18 · agent · 2026-09-24T07:53:50.266Z ### Bash · 2026-09-24T07:53:50.266Z { "command": "cat > pipeline.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time, gc, sys\nfrom feats import build_features, assemble, NI, DAY\nt0=time.time()\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nuids=c['uids']; items=c['items']; nU=len(uids)\nMval=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nMtest=np.unpackbits(np.load('/task/data/test_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nTC=24786800; WIN=604800; GAP=1800\n\ndef labels(splits, lo, hi):\n L=pd.concat([c[s+'_listens'] for s in splits],ignore_index=True)\n L=L[(L.timestamp>=lo)&(L.timestamp=80)]\n K=pd.concat([c[s+'_likes'] for s in splits],ignore_index=True)\n K=K[(K.timestamp>=lo)&(K.timestamp0).mean(),(y==3).sum(),'recall-proxy',(y>0).sum(),time.time()-t0)\nelif stage=='fold2':\n B=build_features(c,['train'],TC); cu,ci,X,names=assemble(B,Mval); del B; gc.collect()\n k1,k3=labels(['val'],TC+GAP,TC+GAP+WIN)\n y=grades(cu,ci,k1,k3)\n np.savez('/app/solution/fold2.npz',cu=cu,ci=ci,X=X,y=y)\n print('fold2',X.shape,(y>0).sum(),(y==3).sum(),time.time()-t0)\nelif stage=='test':\n B=build_features(c,['train','val'],TC+GAP+WIN); cu,ci,X,names=assemble(B,Mtest); del B; gc.collect()\n np.savez('/app/solution/foldt.npz',cu=cu,ci=ci,X=X)\n print('test',X.shape,time.time()-t0)\nPYEOF\n(timeout 400 python pipeline.py fold1; echo \"E1=$?\") 2>&1 | tail -8" } ### Tool output · 2026-09-24T07:54:18.575Z P0 7627708 Pkept 3415858 RSS post-F 4.76 GB RSS post-cf 4.76 GB cf2 done 9.416386365890503 assemble 15.927395343780518 (5529858, 54) fold1 (5529858, 54) 0.021703993122427375 1732 recall-proxy 120020 27.2522451877594 E1=0 ### Harness context { "type": "total_tokens_reminder", "text": "14943683 tokens left" } ## Step 19 · agent · 2026-09-24T07:54:20.488Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:54:21.114Z { "command": "timeout 400 python pipeline.py fold2 2>&1|tail -3 && timeout 400 python pipeline.py test 2>&1|tail -3" } ### Tool output · 2026-09-24T07:55:18.445Z cf2 done 9.365339994430542 assemble 16.21085238456726 (5606783, 54) fold2 (5606783, 54) 128872 3179 27.442497730255127 cf2 done 9.878832340240479 assemble 16.558231592178345 (5663347, 54) test (5663347, 54) 27.786338567733765 ### Harness context { "type": "total_tokens_reminder", "text": "14943361 tokens left" } ## Step 20 · agent · 2026-09-24T07:55:27.799Z ### Bash · 2026-09-24T07:55:27.799Z { "command": "cat > train_rank.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time\nfrom sklearn.ensemble import HistGradientBoostingRegressor\nNI=30000\nt0=time.time()\nc=pickle.load(open('/app/solution/cache.pkl','rb')); uids=c['uids']; items=c['items']; nU=len(uids)\nnames=pickle.load(open('/app/solution/names.pkl','rb'))\nf1=np.load('/app/solution/fold1.npz'); f2=np.load('/app/solution/fold2.npz')\ndef gain(y): return (2.0**y-1.0).astype(np.float32)\nm=HistGradientBoostingRegressor(max_iter=300,learning_rate=0.08,max_leaf_nodes=63,min_samples_leaf=50,l2_regularization=1.0,early_stopping=False,random_state=0)\nm.fit(f1['X'],gain(f1['y'])); print('fit',time.time()-t0,flush=True)\np=m.predict(f2['X'])\ndef topk(cu,ci,sc,k=100):\n o=np.lexsort((-sc,cu)); cu,ci,sc=cu[o],ci[o],sc[o]\n st=np.searchsorted(cu,np.arange(nU)); en=np.searchsorted(cu,np.arange(nU),side='right')\n return [ci[st[r]:en[r]][:k] for r in range(nU)]\ndef write(rows,path,M):\n out=[]\n for r,sel in enumerate(rows):\n sel=list(dict.fromkeys(int(x) for x in sel))[:100]\n if len(sel)<100:\n extra=[x for x in range(NI) if M[r,x] and x not in set(sel)][:100-len(sel)]\n sel=sel+extra\n out.append(sel)\n A=np.array(out,dtype=np.int64)\n pd.DataFrame({'uid':np.repeat(uids,100).astype(np.int64),'item_id':items[A.ravel()].astype(np.int64),\n 'rank':np.tile(np.arange(1,101),nU).astype(np.int64)}).to_parquet(path,index=False)\nMval=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nwrite(topk(f2['cu'],f2['ci'],p),'/app/solution/validation.parquet',Mval)\nprint('val written',time.time()-t0,flush=True)\nPYEOF\ntimeout 500 python train_rank.py && python /task/evaluate_public.py /app/solution/validation.parquet" } ### Tool error · 2026-09-24T07:57:27.847Z Exit code 143 Command timed out after 2m 0s fit 99.66689658164978 ### Harness context { "type": "total_tokens_reminder", "text": "14942243 tokens left" } ## Step 21 · agent · 2026-09-24T07:57:31.743Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:57:35.224Z { "command": "cat >> train_rank.py <<'PYEOF'\n# refit on both folds for the hidden test\nX=np.vstack([f1['X'],f2['X']]); Y=np.concatenate([gain(f1['y']),gain(f2['y'])])\nm2=HistGradientBoostingRegressor(max_iter=300,learning_rate=0.08,max_leaf_nodes=63,min_samples_leaf=50,l2_regularization=1.0,early_stopping=False,random_state=0)\nm2.fit(X,Y); print('fit2',time.time()-t0,flush=True)\nft=np.load('/app/solution/foldt.npz')\npt=m2.predict(ft['X'])\nMtest=np.unpackbits(np.load('/task/data/test_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nwrite(topk(ft['cu'],ft['ci'],pt),'/app/solution/submission_model.parquet',Mtest)\nprint('test written',time.time()-t0,flush=True)\nPYEOF\nnohup python train_rank.py > train.log 2>&1 &\necho started" } ### Tool output · 2026-09-24T07:57:35.244Z started ### Harness context { "type": "total_tokens_reminder", "text": "14941544 tokens left" } ## Step 22 · agent · 2026-09-24T07:57:36.974Z ### Bash · 2026-09-24T07:57:36.974Z { "command": "sleep 115; cat /app/solution/train.log" } ### Tool output · 2026-09-24T07:59:31.989Z fit 100.362065076828 ### Harness context { "type": "total_tokens_reminder", "text": "14941451 tokens left" } ## Step 23 · agent · 2026-09-24T07:59:33.879Z ### Bash · 2026-09-24T07:59:33.879Z { "command": "sleep 60; cat /app/solution/train.log; ls -la /app/solution/*.parquet" } ### Tool output · 2026-09-24T08:00:33.972Z fit 100.362065076828 val written 147.34349632263184 -rw-r--r-- 1 agent agent 735840 Sep 24 07:53 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 1854827 Sep 24 08:00 /app/solution/validation.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14941334 tokens left" } ## Step 24 · agent · 2026-09-24T08:00:35.921Z ### Bash · 2026-09-24T08:00:35.921Z { "command": "python /task/evaluate_public.py /app/solution/validation.parquet" } ### Tool error · 2026-09-24T08:00:37.546Z Exit code 1 Traceback (most recent call last): File "/task/evaluate_public.py", line 157, in main() File "/task/evaluate_public.py", line 152, in main ranked = load_submission(path, eligibility, targets) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/task/evaluate_public.py", line 118, in load_submission raise RankingValidationError( ranking_evaluation.RankingValidationError: submission contains an item rejected by eligibility service ### Harness context { "type": "total_tokens_reminder", "text": "14941128 tokens left" } ## Step 25 · agent · 2026-09-24T08:00:47.521Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:00:51.378Z { "command": "pkill -f train_rank.py; python - <<'EOF'\ns=open('/app/solution/train_rank.py').read()\ns=s.replace(\"def topk(cu,ci,sc,k=100):\\n o=\",\"def topk(cu,ci,sc,M,k=100):\\n ok=M[cu,ci]; cu,ci,sc=cu[ok],ci[ok],sc[ok]\\n o=\")\ns=s.replace(\"write(topk(f2['cu'],f2['ci'],p),'/app/solution/validation.parquet',Mval)\",\"np.save('/app/solution/pval.npy',p)\\nwrite(topk(f2['cu'],f2['ci'],p,Mval),'/app/solution/validation.parquet',Mval)\")\ns=s.replace(\"write(topk(ft['cu'],ft['ci'],pt),'/app/solution/submission_model.parquet',Mtest)\",\"np.save('/app/solution/ptest.npy',pt)\\nwrite(topk(ft['cu'],ft['ci'],pt,Mtest),'/app/solution/submission.parquet',Mtest)\")\ns=s.replace(\"Mval=np.unpackbits\",\"import os\\nMval=np.unpackbits\")\nopen('/app/solution/train_rank.py','w').write(s)\nEOF\nnohup python train_rank.py > train.log 2>&1 & echo started" } ### Tool error · 2026-09-24T08:00:51.412Z Exit code 144 ### Harness context { "type": "total_tokens_reminder", "text": "14939773 tokens left" } ## Step 26 · agent · 2026-09-24T08:00:53.191Z ### Bash · 2026-09-24T08:00:53.191Z { "command": "sleep 5 && (pgrep -af train_rank.py || echo \"not running\"); cat train.log" } ### Tool output · 2026-09-24T08:00:58.213Z 8489 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790235893457-brxzyq.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 5 && (pgrep -af train_rank.py || echo "not running"); cat train.log' < /dev/null && pwd -P >| /tmp/claude-6a7d-cwd 8492 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790235893457-brxzyq.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'sleep 5 && (pgrep -af train_rank.py || echo "not running"); cat train.log' < /dev/null && pwd -P >| /tmp/claude-6a7d-cwd fit 100.362065076828 val written 147.34349632263184 ### Harness context { "type": "total_tokens_reminder", "text": "14939653 tokens left" } ## Step 27 · agent · 2026-09-24T08:01:01.650Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:01:02.674Z { "command": "grep -n \"topk(\" train_rank.py | head; nohup python train_rank.py > train.log 2>&1 < /dev/null & sleep 3; echo \"pid check:\"; pgrep -f \"python train_rank.py\" | head -1" } ### Tool output · 2026-09-24T08:01:05.730Z 12:def topk(cu,ci,sc,k=100): 28:write(topk(f2['cu'],f2['ci'],p),'/app/solution/validation.parquet',Mval) 37:write(topk(ft['cu'],ft['ci'],pt),'/app/solution/submission_model.parquet',Mtest) pid check: 8495 ### Harness context { "type": "total_tokens_reminder", "text": "14938967 tokens left" } ## Step 28 · agent · 2026-09-24T08:01:07.779Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:01:11.430Z { "command": "kill 8495 2>/dev/null; sleep 1; python - <<'EOF'\np='/app/solution/train_rank.py'\ns=open(p).read()\ns=s.replace(\"def topk(cu,ci,sc,k=100):\\n o=\",\"def topk(cu,ci,sc,M,k=100):\\n ok=M[cu,ci]; cu,ci,sc=cu[ok],ci[ok],sc[ok]\\n o=\")\ns=s.replace(\"write(topk(f2['cu'],f2['ci'],p),'/app/solution/validation.parquet',Mval)\",\"np.save('/app/solution/pval.npy',p)\\nwrite(topk(f2['cu'],f2['ci'],p,Mval),'/app/solution/validation.parquet',Mval)\")\ns=s.replace(\"write(topk(ft['cu'],ft['ci'],pt),'/app/solution/submission_model.parquet',Mtest)\",\"np.save('/app/solution/ptest.npy',pt)\\nwrite(topk(ft['cu'],ft['ci'],pt,Mtest),'/app/solution/submission_model.parquet',Mtest)\")\nopen(p,'w').write(s)\nprint(s[s.index('def topk'):s.index('def write')])\nprint([l for l in s.split('\\n') if 'write(topk' in l or 'np.save' in l])\nEOF" } ### Tool output · 2026-09-24T08:01:12.463Z def topk(cu,ci,sc,M,k=100): ok=M[cu,ci]; cu,ci,sc=cu[ok],ci[ok],sc[ok] o=np.lexsort((-sc,cu)); cu,ci,sc=cu[o],ci[o],sc[o] st=np.searchsorted(cu,np.arange(nU)); en=np.searchsorted(cu,np.arange(nU),side='right') return [ci[st[r]:en[r]][:k] for r in range(nU)] ["np.save('/app/solution/pval.npy',p)", "write(topk(f2['cu'],f2['ci'],p,Mval),'/app/solution/validation.parquet',Mval)", "np.save('/app/solution/ptest.npy',pt)", "write(topk(ft['cu'],ft['ci'],pt,Mtest),'/app/solution/submission_model.parquet',Mtest)"] ### Harness context { "type": "total_tokens_reminder", "text": "14938267 tokens left" } ## Step 29 · agent · 2026-09-24T08:01:14.263Z ### Bash · 2026-09-24T08:01:14.263Z { "command": "nohup python train_rank.py > train.log 2>&1 < /dev/null & sleep 2; pgrep -f \"python train_rank.py\" | head -1; echo launched" } ### Tool output · 2026-09-24T08:01:16.286Z 8500 launched ### Harness context { "type": "total_tokens_reminder", "text": "14937807 tokens left" } ## Step 30 · agent · 2026-09-24T08:01:17.860Z ### Bash · 2026-09-24T08:01:17.860Z { "command": "sleep 170; cat /app/solution/train.log" } ### Tool error · 2026-09-24T08:03:17.924Z Exit code 143 Command timed out after 2m 0s ### Harness context { "type": "total_tokens_reminder", "text": "14937718 tokens left" } ## Step 31 · agent · 2026-09-24T08:03:19.926Z ### Bash · 2026-09-24T08:03:19.926Z { "command": "sleep 60; cat /app/solution/train.log; ls -la /app/solution/*.parquet" } ### Tool output · 2026-09-24T08:04:19.967Z -rw-r--r-- 1 agent agent 735840 Sep 24 07:53 /app/solution/submission.parquet -rw-r--r-- 1 agent agent 1854827 Sep 24 08:00 /app/solution/validation.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14937585 tokens left" } ## Step 32 · agent · 2026-09-24T08:04:21.524Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:04:22.232Z { "command": "pgrep -af \"python train_rank\" || echo NOTRUNNING; wc -c /app/solution/train.log; free -g | head -2" } ### Tool output · 2026-09-24T08:04:22.258Z 8560 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790235893457-brxzyq.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'pgrep -af "python train_rank" || echo NOTRUNNING; wc -c /app/solution/train.log; free -g | head -2' < /dev/null && pwd -P >| /tmp/claude-05e7-cwd 0 /app/solution/train.log total used free shared buff/cache available Mem: 15 1 8 0 5 14 ### Harness context { "type": "total_tokens_reminder", "text": "14937356 tokens left" } ## Step 33 · agent · 2026-09-24T08:04:28.400Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:04:29.054Z { "command": "setsid nohup python train_rank.py > train.log 2>&1 < /dev/null & disown; sleep 3; pgrep -f \"python train_rank\" | head -1" } ### Tool output · 2026-09-24T08:04:32.072Z 8566 ### Harness context { "type": "total_tokens_reminder", "text": "14936643 tokens left" } ## Step 34 · agent · 2026-09-24T08:04:33.998Z ### Bash · 2026-09-24T08:04:33.998Z { "command": "sleep 110; cat /app/solution/train.log; pgrep -f \"python train_rank\" >/dev/null && echo RUNNING" } ### Tool output · 2026-09-24T08:06:24.028Z fit 106.7129921913147 RUNNING ### Harness context { "type": "total_tokens_reminder", "text": "14936529 tokens left" } ## Step 35 · agent · 2026-09-24T08:06:26.114Z ### Bash · 2026-09-24T08:06:26.114Z { "command": "sleep 60; cat /app/solution/train.log; python /task/evaluate_public.py /app/solution/validation.parquet 2>&1 | tail -6" } ### Tool output · 2026-09-24T08:07:28.883Z fit 106.7129921913147 val written 154.74133920669556 "model": "/app/solution/validation.parquet", "scores": { "quality_ndcg@10": 0.029465, "quality_users": 4263 } } ### Harness context { "type": "total_tokens_reminder", "text": "14936385 tokens left" } ## Step 36 · agent · 2026-09-24T08:07:34.128Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:07:38.624Z { "command": "python - <<'EOF'\nimport numpy as np, pandas as pd, pickle\nNI=30000\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nf2=np.load('/app/solution/fold2.npz')\nM=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\ncu,ci,y,X=f2['cu'],f2['ci'],f2['y'],f2['X']\nok=M[cu,ci]\nprint('cands eligible frac',ok.mean())\nprint('rel total',(y>0).sum(),'rel & eligible',((y>0)&ok).sum())\nnames=pickle.load(open('/app/solution/names.pkl','rb'))\nih=X[:,names.index('is_hist')]\nprint('eligible rel that are hist:', ih[(y>0)&ok].mean())\nprint('hist cand eligible frac', ok[ih>0].mean())\n# how many eligible relevant per user\nimport collections\nuu=cu[(y>0)&ok]\nprint('users with >=1 eligible rel in candidates', len(np.unique(uu)))\nEOF" } ### Tool output · 2026-09-24T08:07:53.872Z cands eligible frac 0.3770433062952499 rel total 128872 rel & eligible 6799 eligible rel that are hist: 0.0 hist cand eligible frac 0.0 users with >=1 eligible rel in candidates 2095 ### Harness context { "type": "total_tokens_reminder", "text": "14935513 tokens left" } ## Step 37 · agent · 2026-09-24T08:08:06.478Z ### Thinking unavailable — no text recorded Critical finding: eligibility excludes **all** of a user's listening history — this is pure cold-item discovery, so my repeat features were dead weight. Rebuilding as a CF-blend over the full eligible grid. ### Bash · 2026-09-24T08:08:29.182Z { "command": "cat > disc.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time, gc, sys\nfrom scipy import sparse\nNI=30000; DAY=86400.0\nt0=time.time()\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nuids=c['uids']; items=c['items']; nU=len(uids)\n\ndef signals(splits, cutoff):\n L=pd.concat([c[s+'_listens'] for s in splits],ignore_index=True); L=L[L.timestamp=80); org=(L.is_organic.values==1)\n key=u.astype(np.int64)*NI+i\n up,code=np.unique(key,return_inverse=True); P=len(up)\n pu=(up//NI).astype(np.int32); pi=(up%NI).astype(np.int32)\n ns=np.bincount(code,weights=strong.astype(np.float64),minlength=P).astype(np.float32)\n d30=np.bincount(code,weights=np.exp(-age/30.0),minlength=P).astype(np.float32)\n d7=np.bincount(code,weights=np.exp(-age/7.0),minlength=P).astype(np.float32)\n ic=np.bincount(i,weights=(strong&org).astype(np.float64),minlength=NI).astype(np.float32)\n ic7=np.bincount(i,weights=((age<7)&strong).astype(np.float64),minlength=NI).astype(np.float32)\n ic30=np.bincount(i,weights=((age<30)&strong).astype(np.float64),minlength=NI).astype(np.float32)\n iu=np.bincount(pi,minlength=NI).astype(np.float32)\n LK=pd.concat([c[s+'_likes'] for s in splits],ignore_index=True); LK=LK[LK.timestamp0\n # binary/implicit matrices\n def mk(vals,rows,cols):\n X=sparse.csr_matrix((vals.astype(np.float32),(rows,cols)),shape=(nU,NI),dtype=np.float32)\n return X\n Xb=mk(np.ones(sel.sum(),np.float32),pu[sel],pi[sel]) # binary interaction\n Xr=mk(d30[sel]+0.5*d7[sel],pu[sel],pi[sel]) # recency profile\n Xl=mk(np.ones(len(LK),np.float32),LK.u.values.astype(np.int32),LK.i.values.astype(np.int32)); Xl.sum_duplicates(); Xl.data[:]=1.0\n return dict(Xb=Xb,Xr=Xr,Xl=Xl,ic=ic,ic7=ic7,ic30=ic30,iu=iu,ilike=ilike,idis=idis,\n pu=pu[sel],pi=pi[sel],Xh=mk(np.ones(sel.sum(),np.float32),pu[sel],pi[sel]))\n\ndef normcols(X, alpha=0.5):\n n=np.asarray(X.multiply(X).sum(0)).ravel()\n w=1.0/np.maximum(n,1e-6)**alpha\n return X.multiply(sparse.csr_matrix(w)).tocsr().astype(np.float32)\n\ndef build_scorers(S):\n Xb=S['Xb']; Xr=S['Xr']; Xl=S['Xl']\n Bn=normcols(Xb,0.5); Ln=normcols(Xl,0.5)\n Rn=sparse.diags(1.0/(np.asarray(Xr.sum(1)).ravel()+1e-6)).astype(np.float32)@Xr\n Bu=sparse.diags(1.0/np.sqrt(np.asarray(Xb.sum(1)).ravel()+1e-6)).astype(np.float32)@Bn\n Lu=sparse.diags(1.0/np.sqrt(np.asarray(Xl.sum(1)).ravel()+1e-6)).astype(np.float32)@Ln\n return dict(Bn=Bn.tocsr(),Ln=Ln.tocsr(),Rn=Rn.tocsr(),Bu=Bu.tocsr(),Lu=Lu.tocsr(),\n BnT=Bn.T.tocsr(),LnT=Ln.T.tocsr(),Xh=S['Xh'].tocsr())\n\ndef chunk_scores(SC,S,s0,s1,topn_users=300):\n n=s1-s0\n # user-user CF on listens\n UU=np.asarray((SC['Bu'][s0:s1]@SC['BnT']).todense(),dtype=np.float32)\n UU[np.arange(n),np.arange(s0,s1)]=0.0\n if topn_users=th,UU,0.0)\n A=np.asarray((sparse.csr_matrix(UU)@SC['Bn']).todense(),dtype=np.float32)\n del UU\n UL=np.asarray((SC['Lu'][s0:s1]@SC['LnT']).todense(),dtype=np.float32)\n UL[np.arange(n),np.arange(s0,s1)]=0.0\n if topn_users=th,UL,0.0)\n Bm=np.asarray((sparse.csr_matrix(UL)@SC['Ln']).todense(),dtype=np.float32)\n del UL\n # item-item: recency profile x item cooc (Rn @ (Bn^T Bn)) computed as (Rn@Bn^T)@Bn\n RU=np.asarray((SC['Rn'][s0:s1]@SC['BnT']).todense(),dtype=np.float32)\n RU[np.arange(n),np.arange(s0,s1)]=0.0\n C=np.asarray((sparse.csr_matrix(RU)@SC['Bn']).todense(),dtype=np.float32)\n del RU\n # cross: listen-profile -> likes (what similar-listening users LIKE)\n D=np.asarray((sparse.csr_matrix(np.asarray((SC['Bu'][s0:s1]@SC['BnT']).todense(),dtype=np.float32))@SC['Ln']).todense(),dtype=np.float32)\n return A,Bm,C,D\nPYEOF\necho ok" } ### Tool output · 2026-09-24T08:08:29.201Z ok ### Harness context { "type": "total_tokens_reminder", "text": "14931965 tokens left" } ## Step 38 · agent · 2026-09-24T08:08:35.022Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:08:44.946Z { "command": "cat > gen.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time, gc, sys\nfrom scipy import sparse\nfrom disc import *\nstage=sys.argv[1]\nTC=24786800; WIN=604800; GAP=1800\nif stage=='val':\n S=signals(['train'],TC); M=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nelse:\n S=signals(['train','val'],TC+GAP+WIN); M=np.unpackbits(np.load('/task/data/test_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nSC=build_scorers(S); print('scorers',time.time()-t0,flush=True)\nlp=np.log1p(S['ic']); lp7=np.log1p(S['ic7']); lp30=np.log1p(S['ic30'])\nllike=np.log1p(S['ilike']); ldis=np.log1p(S['idis']); liu=np.log1p(S['iu'])\nKEEP=400\nUS=[];IS=[];FS=[]\nCH=800\nfor s0 in range(0,nU,CH):\n s1=min(s0+CH,nU); n=s1-s0\n A,B,C,D=chunk_scores(SC,S,s0,s1)\n em=M[s0:s1]\n for X in (A,B,C,D): X[~em]=0.0\n def nz(X):\n m=X.max(1,keepdims=True); return X/np.maximum(m,1e-9)\n An,Bn_,Cn,Dn=nz(A),nz(B),nz(C),nz(D)\n base=An+0.6*Bn_+0.8*Cn+0.6*Dn+0.15*(lp/max(lp.max(),1e-9))[None,:]\n base[~em]=-1e9\n idx=np.argpartition(-base,KEEP,axis=1)[:,:KEEP]\n rr=np.repeat(np.arange(n),KEEP); cc=idx.ravel()\n good=base[rr,cc]>-1e8\n rr,cc=rr[good],cc[good]\n US.append((rr+s0).astype(np.int32)); IS.append(cc.astype(np.int32))\n FS.append(np.column_stack([An[rr,cc],Bn_[rr,cc],Cn[rr,cc],Dn[rr,cc],\n A[rr,cc],B[rr,cc],C[rr,cc],D[rr,cc],\n lp[cc],lp7[cc],lp30[cc],llike[cc],ldis[cc],liu[cc]]).astype(np.float32))\n del A,B,C,D,An,Bn_,Cn,Dn,base\n if s0%3200==0: print(s0,time.time()-t0,flush=True)\nnp.savez('/app/solution/cand_%s.npz'%stage,u=np.concatenate(US),i=np.concatenate(IS),F=np.vstack(FS))\nprint('done',stage,time.time()-t0)\nPYEOF\nsetsid nohup python gen.py val > gen_val.log 2>&1 < /dev/null & disown; sleep 45; cat gen_val.log" } ### Tool output · 2026-09-24T08:09:29.970Z scorers 3.4694154262542725 0 16.03297710418701 ### Harness context { "type": "total_tokens_reminder", "text": "14930449 tokens left" } ## Step 39 · agent · 2026-09-24T08:09:32.219Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:09:44.409Z { "command": "cat > tune.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time\nNI=30000\nc=pickle.load(open('/app/solution/cache.pkl','rb'))\nuids=c['uids']; items=c['items']; nU=len(uids)\nM=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\nvl=c['val_listens']; vk=c['val_likes']\ns=vl[(vl.is_organic==1)&(vl.played_ratio_pct>=80)][['u','i']].drop_duplicates()\nk=vk[vk.is_organic==1][['u','i']].drop_duplicates()\nG={}\nfor uu,ii in s.values.tolist():\n if M[uu,ii]: G.setdefault(uu,{})[ii]=1\nfor uu,ii in k.values.tolist():\n if M[uu,ii]: G.setdefault(uu,{})[ii]=3\nprint('users with labels',len(G))\nd=np.load('/app/solution/cand_val.npz'); U,I,F=d['u'],d['i'],d['F']\norder=np.argsort(U,kind='stable'); U,I,F=U[order],I[order],F[order]\nst=np.searchsorted(U,np.arange(nU)); en=np.searchsorted(U,np.arange(nU),side='right')\ndisc=1.0/np.log2(np.arange(2,12))\nidcg={u:float(np.sum((2.0**np.array(sorted(g.values(),reverse=True)[:10])-1)*disc[:len(list(g)[:10])])) for u,g in G.items()}\ndef evalw(w):\n tot=0.0\n for u,g in G.items():\n a,b=st[u],en[u]\n if a==b: continue\n sc=F[a:b]@w\n kk=min(10,b-a)\n top=np.argpartition(-sc,kk-1)[:kk]\n top=top[np.argsort(-sc[top])]\n its=I[a:b][top]\n gains=np.array([g.get(int(x),0) for x in its],dtype=np.float64)\n tot+=float(np.sum((2.0**gains-1)*disc[:len(gains)]))/idcg[u]\n return tot/len(G)\nnames=['An','Bn','Cn','Dn','A','B','C','D','lp','lp7','lp30','llike','ldis','liu']\nbase=np.zeros(14); \nfor nm,v in [('An',1.0),('Bn',0.6),('Cn',0.8),('Dn',0.6),('lp',0.05)]: base[names.index(nm)]=v\nprint('base',round(evalw(base),5),flush=True)\nbest=base.copy(); bs=evalw(best)\nimport itertools, random\nrng=np.random.default_rng(0)\ngrid={'An':[0,0.5,1,1.5],'Bn':[0,0.3,0.6,1.0],'Cn':[0,0.4,0.8,1.5],'Dn':[0,0.3,0.6,1.0],'lp':[0,0.05,0.15,0.3],'lp7':[0,0.05,0.15],'llike':[0,0.05,0.15],'ldis':[0,-0.05,-0.15],'liu':[0,-0.05,0.05]}\nfor rounds in range(3):\n for nm,vals in grid.items():\n j=names.index(nm); cur=best[j]; bv=cur\n for v in vals:\n best[j]=v; sc=evalw(best)\n if sc>bs: bs=sc; bv=v\n best[j]=bv\n print('round',rounds,round(bs,5),dict(zip(names,np.round(best,3))),flush=True)\nnp.save('/app/solution/wbest.npy',best)\nprint('BEST',bs)\nPYEOF\nsleep 100; cat gen_val.log | tail -3" } ### Tool output · 2026-09-24T08:11:24.429Z 3200 66.31668305397034 6400 116.0188148021698 done val 135.8141942024231 ### Harness context { "type": "total_tokens_reminder", "text": "14928928 tokens left" } ## Step 40 · agent · 2026-09-24T08:11:26.765Z ### Bash · 2026-09-24T08:11:26.765Z { "command": "setsid nohup python gen.py test > gen_test.log 2>&1 < /dev/null & disown; sleep 2; timeout 115 python tune.py 2>&1 | tail -20" } ### Tool output · 2026-09-24T08:11:44.347Z users with labels 4263 base 0.01899 round 0 0.02321 {'An': np.float64(1.0), 'Bn': np.float64(1.0), 'Cn': np.float64(1.5), 'Dn': np.float64(0.6), 'A': np.float64(0.0), 'B': np.float64(0.0), 'C': np.float64(0.0), 'D': np.float64(0.0), 'lp': np.float64(0.05), 'lp7': np.float64(0.15), 'lp30': np.float64(0.0), 'llike': np.float64(0.0), 'ldis': np.float64(-0.15), 'liu': np.float64(0.0)} round 1 0.03037 {'An': np.float64(0.0), 'Bn': np.float64(0.3), 'Cn': np.float64(0.0), 'Dn': np.float64(0.3), 'A': np.float64(0.0), 'B': np.float64(0.0), 'C': np.float64(0.0), 'D': np.float64(0.0), 'lp': np.float64(0.05), 'lp7': np.float64(0.15), 'lp30': np.float64(0.0), 'llike': np.float64(0.0), 'ldis': np.float64(-0.15), 'liu': np.float64(0.05)} round 2 0.03037 {'An': np.float64(0.0), 'Bn': np.float64(0.3), 'Cn': np.float64(0.0), 'Dn': np.float64(0.3), 'A': np.float64(0.0), 'B': np.float64(0.0), 'C': np.float64(0.0), 'D': np.float64(0.0), 'lp': np.float64(0.05), 'lp7': np.float64(0.15), 'lp30': np.float64(0.0), 'llike': np.float64(0.0), 'ldis': np.float64(-0.15), 'liu': np.float64(0.05)} BEST 0.03036780028946922 ### Harness context { "type": "total_tokens_reminder", "text": "14928758 tokens left" } ## Step 41 · agent · 2026-09-24T08:11:53.294Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:11:58.504Z { "command": "timeout 110 python - <<'EOF' 2>&1 | tail -12\nexec(open('/app/solution/tune.py').read().split(\"names=['An'\")[0])\nnames=['An','Bn','Cn','Dn','A','B','C','D','lp','lp7','lp30','llike','ldis','liu']\ndef W(d):\n w=np.zeros(14)\n for k,v in d.items(): w[names.index(k)]=v\n return w\n# pool recall\npool={}\nfor u,g in G.items():\n a,b=st[u],en[u]; s_=set(I[a:b].tolist())\n pool[u]=sum(1 for x in g if x in s_)/len(g)\nprint('pool recall',np.mean(list(pool.values())))\nfor nm,d in [('pop',{'lp':1.0}),('pop7',{'lp7':1.0}),('cfA',{'An':1.0}),('cfB',{'Bn':1.0}),('cfC',{'Cn':1.0}),('cfD',{'Dn':1.0}),\n ('best',{'Bn':0.3,'Dn':0.3,'lp':0.05,'lp7':0.15,'ldis':-0.15,'liu':0.05}),\n ('b2',{'Bn':0.3,'Dn':0.3,'lp':0.1,'lp7':0.3,'ldis':-0.15,'liu':0.05}),\n ('b3',{'An':0.2,'Bn':0.3,'Cn':0.2,'Dn':0.3,'lp':0.05,'lp7':0.15,'ldis':-0.15,'liu':0.05})]:\n print(nm, round(evalw(W(d)),5), flush=True)\nEOF\ntail -2 gen_test.log" } ### Tool output · 2026-09-24T08:12:01.482Z users with labels 4263 pool recall 0.23037955977863755 pop 0.01332 pop7 0.02928 cfA 0.01015 cfB 0.00877 cfC 0.01741 cfD 0.01586 best 0.03037 b2 0.02951 b3 0.0295 scorers 3.796710968017578 0 16.69308614730835 ### Harness context { "type": "total_tokens_reminder", "text": "14926854 tokens left" } ## Step 42 · agent · 2026-09-24T08:12:10.796Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T08:12:19.852Z { "command": "pkill -f \"gen.py test\"; cat > final.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, time, sys\nfrom scipy import sparse\nfrom disc import *\nTC=24786800; WIN=604800; GAP=1800\nW=dict(An=0.0,Bn=0.3,Cn=0.0,Dn=0.3,lp=0.05,lp7=0.15,ldis=-0.15,liu=0.05)\ndef run(stage,out):\n if stage=='val':\n S=signals(['train'],TC); M=np.unpackbits(np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\n else:\n S=signals(['train','val'],TC+GAP+WIN); M=np.unpackbits(np.load('/task/data/test_eligible_candidates.npz')['packed_eligible'],axis=1,bitorder='little')[:,:NI].astype(bool)\n SC=build_scorers(S)\n lp=np.log1p(S['ic']); lp7=np.log1p(S['ic7']); ldis=np.log1p(S['idis']); liu=np.log1p(S['iu'])\n ipart=(W['lp']*lp+W['lp7']*lp7+W['ldis']*ldis+W['liu']*liu).astype(np.float32)\n top=np.zeros((nU,100),dtype=np.int32)\n CH=800\n for s0 in range(0,nU,CH):\n s1=min(s0+CH,nU); n=s1-s0\n A,B,C,D=chunk_scores(SC,S,s0,s1)\n em=M[s0:s1]\n for X in (A,B,C,D): X[~em]=0.0\n def nz(X):\n m=X.max(1,keepdims=True); return X/np.maximum(m,1e-9)\n sc=W['An']*nz(A)+W['Bn']*nz(B)+W['Cn']*nz(C)+W['Dn']*nz(D)+ipart[None,:]\n sc[~em]=-1e9\n idx=np.argpartition(-sc,100,axis=1)[:,:100]\n vals=np.take_along_axis(sc,idx,1)\n o=np.argsort(-vals,axis=1)\n top[s0:s1]=np.take_along_axis(idx,o,1)\n del A,B,C,D,sc\n print(stage,s0,time.time()-t0,flush=True)\n assert M[np.repeat(np.arange(nU),100),top.ravel()].all()\n pd.DataFrame({'uid':np.repeat(uids,100).astype(np.int64),'item_id':items[top.ravel()].astype(np.int64),\n 'rank':np.tile(np.arange(1,101),nU).astype(np.int64)}).to_parquet(out,index=False)\n print('WROTE',out,time.time()-t0,flush=True)\nrun('val','/app/solution/validation.parquet')\nrun('test','/app/solution/submission_new.parquet')\nPYEOF\nsetsid nohup python final.py > final.log 2>&1 < /dev/null & disown; sleep 3; echo started" } ### Tool error · 2026-09-24T08:12:19.880Z Exit code 144 ### Harness context { "type": "total_tokens_reminder", "text": "14924889 tokens left" } ## Step 43 · agent · 2026-09-24T08:12:22.665Z ### Bash · 2026-09-24T08:12:22.665Z { "command": "pgrep -f \"python final.py\" >/dev/null && echo RUNNING || (setsid nohup python final.py > final.log 2>&1 < /dev/null & disown; sleep 3; echo relaunched); tail -2 final.log 2>/dev/null" } ### Tool error · 2026-09-24T08:12:22.684Z Exit code 1 RUNNING ### Harness context { "type": "total_tokens_reminder", "text": "14924715 tokens left" } ## Step 44 · agent · 2026-09-24T08:12:24.436Z ### Bash · 2026-09-24T08:12:24.436Z { "command": "sleep 115; tail -3 /app/solution/final.log" } ### Tool error · 2026-09-24T08:14:19.451Z Exit code 1 tail: cannot open '/app/solution/final.log' for reading: No such file or directory ### Harness context { "type": "total_tokens_reminder", "text": "14924602 tokens left" } ## Step 45 · agent · 2026-09-24T08:14:21.524Z ### Bash · 2026-09-24T08:14:21.524Z { "command": "ls -la /app/solution/ | tail -20; pgrep -af \"python final.py\" | head -2" } ### Tool output · 2026-09-24T08:14:21.548Z -rw-r--r-- 1 agent agent 4359 Sep 24 08:08 disc.py -rw-r--r-- 1 agent agent 10616 Sep 24 07:53 feats.py -rw-r--r-- 1 agent agent 1244219012 Sep 24 07:54 fold1.npz -rw-r--r-- 1 agent agent 1261527137 Sep 24 07:54 fold2.npz -rw-r--r-- 1 agent agent 1268590456 Sep 24 07:55 foldt.npz -rw-r--r-- 1 agent agent 1815 Sep 24 08:08 gen.py -rw-r--r-- 1 agent agent 46 Sep 24 08:11 gen_test.log -rw-r--r-- 1 agent agent 120 Sep 24 08:11 gen_val.log -rw-r--r-- 1 agent agent 506 Sep 24 07:54 names.pkl -rw-r--r-- 1 agent agent 2077 Sep 24 07:53 pipeline.py -rw-r--r-- 1 agent agent 1167 Sep 24 07:45 prep.py -rw-r--r-- 1 agent agent 44854392 Sep 24 08:07 pval.npy -rw-r--r-- 1 agent agent 1578 Sep 24 07:47 run.py -rw-r--r-- 1 agent agent 1690 Sep 24 07:51 safety.py -rw-r--r-- 1 agent agent 735840 Sep 24 07:53 submission.parquet -rw-r--r-- 1 agent agent 53 Sep 24 08:07 train.log -rw-r--r-- 1 agent agent 2483 Sep 24 08:01 train_rank.py -rw-r--r-- 1 agent agent 2335 Sep 24 08:09 tune.py -rw-r--r-- 1 agent agent 1825745 Sep 24 08:07 validation.parquet -rw-r--r-- 1 agent agent 240 Sep 24 08:11 wbest.npy 8748 /bin/bash -c source /logs/agent/sessions/shell-snapshots/snapshot-bash-1790235893457-brxzyq.sh 2>/dev/null || true && shopt -u extglob 2>/dev/null || true && { \builtin unalias -- 'unsetenv'; \builtin unset -f -- 'unsetenv'; } >/dev/null 2>&1 || true && eval 'ls -la /app/solution/ | tail -20; pgrep -af "python final.py" | head -2' < /dev/null && pwd -P >| /tmp/claude-ba0a-cwd ### Harness context { "type": "total_tokens_reminder", "text": "14924453 tokens left" } ## Step 46 · agent · 2026-09-24T08:14:26.322Z ### Thinking unavailable — no text recorded ### Write · 2026-09-24T08:14:35.833Z { "file_path": "/app/solution/final.py", "content": "\"\"\"Score every eligible candidate with the tuned discovery blend and write rankings.\"\"\"\nimport numpy as np, pandas as pd, time\nfrom disc import * # noqa: F401,F403 (signals, build_scorers, chunk_scores, NI, nU, uids, items, t0)\n\nTC = 24786800\nWIN = 604800\nGAP = 1800\nW = dict(An=0.0, Bn=0.3, Cn=0.0, Dn=0.3, lp=0.05, lp7=0.15, ldis=-0.15, liu=0.05)\n\n\ndef run(stage, out):\n if stage == 'val':\n S = signals(['train'], TC)\n pack = np.load('/task/data/validation_eligible_candidates.npz')['packed_eligible']\n else:\n S = signals(['train', 'val'], TC + GAP + WIN)\n pack = np.load('/task/data/test_eligible_candidates.npz')['packed_eligible']\n M = np.unpackbits(pack, axis=1, bitorder='little')[:, :NI].astype(bool)\n SC = build_scorers(S)\n lp = np.log1p(S['ic']); lp7 = np.log1p(S['ic7'])\n ldis = np.log1p(S['idis']); liu = np.log1p(S['iu'])\n ipart = (W['lp'] * lp + W['lp7'] * lp7 + W['ldis'] * ldis + W['liu'] * liu).astype(np.float32)\n top = np.zeros((nU, 100), dtype=np.int32)\n CH = 800\n for s0 in range(0, nU, CH):\n s1 = min(s0 + CH, nU)\n A, B, C, D = chunk_scores(SC, S, s0, s1)\n em = M[s0:s1]\n for X in (A, B, C, D):\n X[~em] = 0.0\n\n def nz(X):\n return X / np.maximum(X.max(1, keepdims=True), 1e-9)\n\n sc = W['An'] * nz(A) + W['Bn'] * nz(B) + W['Cn'] * nz(C) + W['Dn'] * nz(D) + ipart[None, :]\n sc[~em] = -1e9\n idx = np.argpartition(-sc, 100, axis=1)[:, :100]\n vals = np.take_along_axis(sc, idx, 1)\n top[s0:s1] = np.take_along_axis(idx, np.argsort(-vals, axis=1), 1)\n del A, B, C, D, sc\n print(stage, s0, round(time.time() - t0, 1), flush=True)\n assert M[np.repeat(np.arange(nU), 100), top.ravel()].all()\n pd.DataFrame({\n 'uid': np.repeat(uids, 100).astype(np.int64),\n 'item_id': items[top.ravel()].astype(np.int64),\n 'rank': np.tile(np.arange(1, 101), nU).astype(np.int64),\n }).to_parquet(out, index=False)\n print('WROTE', out, round(time.time() - t0, 1), flush=True)\n\n\nif __name__ == '__main__':\n run('val', '/app/solution/validation.parquet')\n run('test', '/app/solution/submission_new.parquet')\n" } ### Tool output · 2026-09-24T08:14:35.885Z File created successfully at: /app/solution/final.py (file state is current in your context — no need to Read it back) ### Harness context { "type": "total_tokens_reminder", "text": "14922116 tokens left" } ## Step 47 · agent · 2026-09-24T08:14:38.387Z ### Bash · 2026-09-24T08:14:38.387Z { "command": "rm -f /app/solution/fold1.npz /app/solution/fold2.npz /app/solution/foldt.npz /app/solution/pval.npy; setsid nohup python /app/solution/final.py > /app/solution/final.log 2>&1 < /dev/null & disown; sleep 20; tail -3 /app/solution/final.log" } ### Tool output · 2026-09-24T08:14:58.443Z val 0 16.0 ### Harness context { "type": "total_tokens_reminder", "text": "14921894 tokens left" } ## Step 48 · agent · 2026-09-24T08:15:00.071Z ### Bash · 2026-09-24T08:15:00.071Z { "command": "sleep 115; tail -2 /app/solution/final.log" } ### Tool output · Timestamp not recorded No response recorded.