yambda-discovery-ranking-v01 / yambda-opus-high-2026-09-24-00-34-15-04330cf-1 Recorded model: claude-opus-5 ## Step 1 · user · 2026-09-24T07:44:29.810Z # 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 · 2026-09-24T07:44:31.104Z I'll start by exploring the data and understanding the task setup. ### Bash · 2026-09-24T07:44:31.988Z { "command": "ls -la /task/ /task/data/ && cat /task/requirements.txt", "description": "List task files and requirements" } ### Tool output · 2026-09-24T07:44:32.046Z /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 07:37 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 07:37 . 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 07:37 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 07:37 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 ### Bash · 2026-09-24T07:44:32.851Z { "command": "cat /task/ranking_evaluation.py; echo \"=== EVAL ===\"; cat /task/evaluate_public.py", "description": "Read evaluation code" } ### Tool output · 2026-09-24T07:44:32.882Z """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), } === EVAL === #!/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": "14982081 tokens left" } ## Step 3 · agent · 2026-09-24T07:44:39.145Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:44:41.859Z { "command": "python -c \"\nimport pandas as pd, numpy as np, pyarrow.parquet as pq\nfor f in ['candidate_catalog','target_users','validation_preference_state','test_preference_state']:\n d=pd.read_parquet('/task/data/%s.parquet'%f); print(f, d.shape, list(d.columns)); print(d.head(3))\np=pq.ParquetFile('/task/data/train_listens.parquet'); print('train_listens rows', p.metadata.num_rows, p.schema_arrow)\nd=pd.read_parquet('/task/data/val_listens.parquet'); print('val listens', d.shape, d.timestamp.min(), d.timestamp.max())\nz=np.load('/task/data/validation_eligible_candidates.npz'); print({k:(v.shape,v.dtype) for k,v in z.items()})\n\"\n" } ### Tool output · 2026-09-24T07:44:42.827Z candidate_catalog (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 target_users (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 validation_preference_state (469124, 3) ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked test_preference_state (484776, 3) ['uid', 'item_id', 'state'] uid item_id state 0 100 410868 liked 1 100 732449 liked 2 100 982903 liked train_listens rows 43484159 uid: uint32 timestamp: uint32 item_id: uint32 is_organic: uint8 played_ratio_pct: uint16 track_length_seconds: uint32 -- schema metadata -- pandas: '{"index_columns": [], "column_indexes": [], "columns": [{"name":' + 797 val listens (1501697, 6) 24788600 25393395 {'uids': ((8456,), dtype('uint32')), 'item_ids': ((30000,), dtype('uint32')), 'packed_eligible': ((8456, 3750), dtype('uint8'))} ### Harness context { "type": "total_tokens_reminder", "text": "14974366 tokens left" } ## Step 4 · agent · 2026-09-24T07:44:58.327Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:44:58.799Z { "command": "free -g; nproc; mkdir -p /app/solution" } ### Tool output · 2026-09-24T07:44:58.819Z total used free shared buff/cache available Mem: 15 1 4 0 9 14 Swap: 0 0 0 8 ### Bash · 2026-09-24T07:45:01.466Z { "command": "cd /app/solution && python -c \"\nimport numpy as np, pandas as pd, time\nt=time.time()\nz=np.load('/task/data/validation_eligible_candidates.npz')\nuids=z['uids']; items=z['item_ids']; pk=z['packed_eligible']\nE=np.unpackbits(pk,axis=1,count=len(items),bitorder='little').astype(bool)\nprint('elig per user', E.sum(1).mean(), E.sum(1).min(), E.sum(1).max(), time.time()-t)\ntu=pd.read_parquet('/task/data/target_users.parquet')\nprint((uids==tu.uid.values).all() if len(uids)==len(tu) else 'diff')\n# check whether eligible items overlap user's train history\nimport pyarrow.parquet as pq\n\"\n" } ### Tool output · 2026-09-24T07:45:02.607Z elig per user 29067.306291390727 24657 30000 0.3459436893463135 True ### Harness context { "type": "total_tokens_reminder", "text": "14972372 tokens left" } ## Step 5 · agent · 2026-09-24T07:45:37.409Z ### Thinking unavailable — no text recorded Eligibility is near-full-catalog, so this is essentially full ranking. Let me write a safety submission first, then build the real model. ### Bash · 2026-09-24T07:45:41.223Z { "command": "cat > /app/solution/fallback.py <<'EOF'\nimport numpy as np, pandas as pd\nz=np.load('/task/data/test_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\nE=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\ncat=pd.read_parquet('/task/data/candidate_catalog.parquet')\npop=cat.set_index('item_id').loc[items,'organic_positive_listens'].to_numpy(float)\norder=np.argsort(-pop)\nrows=[]\nfor r,u in enumerate(uids):\n sel=[c for c in order if E[r,c]][:100]\n rows.append(pd.DataFrame({'uid':u,'item_id':items[sel],'rank':np.arange(1,101)}))\npd.concat(rows).to_parquet('/app/solution/submission.parquet',index=False)\nprint('ok')\nEOF\ntimeout 600 python fallback.py" } ### Tool output · 2026-09-24T07:46:26.784Z ok ### Harness context { "type": "total_tokens_reminder", "text": "14969302 tokens left" } ## Step 6 · agent · 2026-09-24T07:47:05.567Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:47:40.578Z { "command": "cat > /app/solution/build.py <<'PYEOF'\n\"\"\"Discovery ranker: implicit-MF (randomized SVD) + repeat-affinity + popularity blend.\"\"\"\nimport sys, time, json\nimport numpy as np, pandas as pd\nimport scipy.sparse as sp\nfrom sklearn.utils.extmath import randomized_svd\n\nT0 = time.time()\ndef log(*a): print(f\"[{time.time()-T0:7.1f}s]\", *a, flush=True)\n\nDATA = \"/task/data/\"\nCAT = pd.read_parquet(DATA + \"candidate_catalog.parquet\")\nCAT_ITEMS = np.sort(CAT.item_id.to_numpy(np.int64))\nNI = len(CAT_ITEMS)\nPOPV = CAT.set_index(\"item_id\").loc[CAT_ITEMS, \"organic_positive_listens\"].to_numpy(np.float64)\nTARGETS = pd.read_parquet(DATA + \"target_users.parquet\").uid.to_numpy(np.int64)\n\ndef cat_idx(x):\n x = np.asarray(x, np.int64)\n pos = np.searchsorted(CAT_ITEMS, x)\n pos[pos >= NI] = 0\n return np.where(CAT_ITEMS[pos] == x, pos, -1)\n\ndef load_events(name, stage):\n fs = [DATA + f\"train_{name}.parquet\"] + ([DATA + f\"val_{name}.parquet\"] if stage == \"test\" else [])\n return pd.concat([pd.read_parquet(f) for f in fs], ignore_index=True)\n\ndef build_stage(stage):\n \"\"\"stage in {'val','test'}: history = train (+val for test).\"\"\"\n cols = [\"uid\", \"timestamp\", \"item_id\", \"is_organic\", \"played_ratio_pct\"]\n parts = [pd.read_parquet(DATA + \"train_listens.parquet\", columns=cols)]\n if stage == \"test\":\n parts.append(pd.read_parquet(DATA + \"val_listens.parquet\", columns=cols))\n L = pd.concat(parts, ignore_index=True) if len(parts) > 1 else parts[0]\n log(\"listens loaded\", L.shape)\n ii = cat_idx(L.item_id.to_numpy())\n keep = (ii >= 0) & (L.played_ratio_pct.to_numpy() >= 50)\n uid = L.uid.to_numpy(np.int64)[keep]\n ts = L.timestamp.to_numpy(np.int64)[keep]\n org = L.is_organic.to_numpy(np.uint8)[keep]\n pr = L.played_ratio_pct.to_numpy(np.int32)[keep]\n item = ii[keep]\n del L, ii, keep\n T_END = int(ts.max())\n log(\"positive listens in catalog\", len(uid), \"t_end\", T_END)\n\n # ---- global item-user matrix (all users) for implicit MF\n urow, uuniq = pd.factorize(uid, sort=False)\n NU = len(uuniq)\n w = np.ones(len(uid), np.float32)\n R = sp.csr_matrix((w, (urow, item)), shape=(NU, NI), dtype=np.float32)\n R.sum_duplicates()\n R.data = np.log1p(R.data).astype(np.float32) # count damping\n # bm25-ish: idf on items, l2 on users\n df = np.asarray((R > 0).sum(0)).ravel().astype(np.float64)\n idf = np.log1p(NU / np.maximum(df, 1)).astype(np.float32)\n R = R.multiply(idf[None, :]).tocsr().astype(np.float32)\n nr = np.sqrt(np.asarray(R.multiply(R).sum(1)).ravel()); nr[nr == 0] = 1\n R = sp.diags((1.0 / nr).astype(np.float32)) @ R\n R = R.astype(np.float32).tocsr()\n log(\"R built\", R.shape, R.nnz)\n\n K = 160\n U_, S_, Vt = randomized_svd(R, n_components=K, n_iter=4, random_state=0)\n V = (Vt.T * S_).astype(np.float32) # item factors 30000 x K\n Vn = V / np.maximum(np.linalg.norm(V, axis=1, keepdims=True), 1e-8)\n log(\"svd done\")\n del R, U_, Vt\n\n # ---- target-user histories\n tset = pd.Series(np.arange(len(TARGETS)), index=TARGETS)\n m = np.isin(uid, TARGETS)\n tu = tset.reindex(uid[m]).to_numpy()\n t_item, t_ts, t_org, t_pr = item[m], ts[m], org[m], pr[m]\n del uid, item, ts, org, pr\n\n HL = 14 * 86400.0\n age = (T_END - t_ts) / HL\n dec_long = np.exp2(-age).astype(np.float32)\n dec_short = np.exp2(-(T_END - t_ts) / (3 * 86400.0)).astype(np.float32)\n strong = ((t_pr >= 80) & (t_org == 1)).astype(np.float32)\n wgt = (0.4 + 0.6 * (t_pr >= 80)) * (0.5 + 0.5 * (t_org == 1))\n\n def spmat(vals):\n M = sp.csr_matrix((vals.astype(np.float32), (tu, t_item)),\n shape=(len(TARGETS), NI), dtype=np.float32)\n M.sum_duplicates()\n return M\n\n P_long = spmat(dec_long * wgt)\n P_short = spmat(dec_short * wgt)\n CNT = spmat(np.ones(len(tu), np.float32))\n STR = spmat(strong * dec_long)\n log(\"profiles built\")\n\n # explicit signals\n pref = pd.read_parquet(DATA + (\"validation_preference_state.parquet\" if stage == \"val\"\n else \"test_preference_state.parquet\"))\n pi = cat_idx(pref.item_id.to_numpy()); pu = tset.reindex(pref.uid.to_numpy()).to_numpy()\n okp = (pi >= 0) & ~np.isnan(pu)\n pu = pu[okp].astype(np.int64); pi = pi[okp]; pst = pref.state.to_numpy()[okp]\n LIKED = sp.csr_matrix((np.ones((pst == \"liked\").sum(), np.float32),\n (pu[pst == \"liked\"], pi[pst == \"liked\"])), shape=(len(TARGETS), NI))\n DISL = sp.csr_matrix((np.ones((pst == \"disliked\").sum(), np.float32),\n (pu[pst == \"disliked\"], pi[pst == \"disliked\"])), shape=(len(TARGETS), NI))\n LIKED.sum_duplicates(); DISL.sum_duplicates()\n\n likes = load_events(\"likes\", stage)\n li = cat_idx(likes.item_id.to_numpy()); lu = tset.reindex(likes.uid.to_numpy()).to_numpy()\n okl = (li >= 0) & ~np.isnan(lu)\n LIKE_T = sp.csr_matrix((np.exp2(-(T_END - likes.timestamp.to_numpy(np.int64)[okl]) / HL).astype(np.float32),\n (lu[okl].astype(np.int64), li[okl])), shape=(len(TARGETS), NI))\n LIKE_T.sum_duplicates()\n\n # profile embeddings (cosine-style user vectors)\n def emb(P):\n X = (P @ Vn).astype(np.float32)\n X /= np.maximum(np.linalg.norm(X, axis=1, keepdims=True), 1e-8)\n return X\n E_long, E_short = emb(P_long), emb(P_short)\n E_like = emb(LIKE_T + P_long * 0.0 + LIKE_T * 0.0) if LIKE_T.nnz else E_long\n log(\"embeddings built\")\n\n # popularity: global + recent trend\n pop_all = np.log1p(POPV).astype(np.float32)\n pop_all = (pop_all - pop_all.mean()) / pop_all.std()\n return dict(Vn=Vn, E_long=E_long, E_short=E_short, E_like=E_like,\n CNT=CNT, STR=STR, P_long=P_long, LIKED=LIKED, DISL=DISL,\n LIKE_T=LIKE_T, pop=pop_all, T_END=T_END)\n\ndef elig_mask(stage):\n z = np.load(DATA + (\"validation_eligible_candidates.npz\" if stage == \"val\"\n else \"test_eligible_candidates.npz\"))\n uids = z[\"uids\"].astype(np.int64); items = z[\"item_ids\"].astype(np.int64)\n E = np.unpackbits(z[\"packed_eligible\"], axis=1, count=len(items), bitorder=\"little\").astype(bool)\n assert (uids == TARGETS).all()\n col = cat_idx(items) # npz item order -> catalog order\n Ec = np.zeros((len(TARGETS), NI), bool)\n Ec[:, col] = E\n return Ec\n\nFEATS = [\"cf_long\", \"cf_short\", \"cf_like\", \"cnt\", \"str\", \"pop\", \"liked\", \"disl\"]\n\ndef feature_block(F, rows, cols):\n \"\"\"features for given (row, col) arrays -> dict of 1d arrays\"\"\"\n out = {}\n out[\"cf_long\"] = np.einsum(\"ij,ij->i\", F[\"E_long\"][rows], F[\"Vn\"][cols])\n out[\"cf_short\"] = np.einsum(\"ij,ij->i\", F[\"E_short\"][rows], F[\"Vn\"][cols])\n out[\"cf_like\"] = np.einsum(\"ij,ij->i\", F[\"E_like\"][rows], F[\"Vn\"][cols])\n for name, M in ((\"cnt\", F[\"CNT\"]), (\"str\", F[\"STR\"]), (\"liked\", F[\"LIKED\"]), (\"disl\", F[\"DISL\"])):\n v = np.asarray(M[rows, cols]).ravel()\n out[name] = np.log1p(v) if name in (\"cnt\", \"str\") else v\n out[\"pop\"] = F[\"pop\"][cols]\n return out\n\ndef full_score(F, w, E):\n \"\"\"dense score matrix (nusers x NI)\"\"\"\n S = (F[\"E_long\"] @ F[\"Vn\"].T) * np.float32(w[\"cf_long\"])\n S += (F[\"E_short\"] @ F[\"Vn\"].T) * np.float32(w[\"cf_short\"])\n S += (F[\"E_like\"] @ F[\"Vn\"].T) * np.float32(w[\"cf_like\"])\n S += F[\"pop\"][None, :] * np.float32(w[\"pop\"])\n for name, M in ((\"cnt\", F[\"CNT\"]), (\"str\", F[\"STR\"])):\n C = M.tocoo()\n S[C.row, C.col] += np.log1p(C.data) * np.float32(w[name])\n for name, M in ((\"liked\", F[\"LIKED\"]), (\"disl\", F[\"DISL\"])):\n C = M.tocoo()\n S[C.row, C.col] += C.data * np.float32(w[name])\n S[~E] = -1e9\n return S\nPYEOF\necho written" } ### Tool output · 2026-09-24T07:47:40.605Z written ### Harness context { "type": "total_tokens_reminder", "text": "14962599 tokens left" } ## Step 7 · agent · 2026-09-24T07:47:55.823Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:48:02.322Z { "command": "cat >> /app/solution/build.py <<'PYEOF'\n\n# ---------------- validation truth + fast NDCG ----------------\ndef val_gold(E):\n ln = pd.read_parquet(DATA + \"val_listens.parquet\")\n lk = pd.read_parquet(DATA + \"val_likes.parquet\")\n tset = pd.Series(np.arange(len(TARGETS)), index=TARGETS)\n G = np.zeros((len(TARGETS), NI), np.int8)\n def add(df, grade, cond):\n d = df[cond]\n r = tset.reindex(d.uid.to_numpy()).to_numpy()\n c = cat_idx(d.item_id.to_numpy())\n ok = (c >= 0) & ~np.isnan(r)\n r = r[ok].astype(np.int64); c = c[ok]\n ok2 = E[r, c]\n G[r[ok2], c[ok2]] = grade\n add(ln, 1, (ln.is_organic == 1) & (ln.played_ratio_pct >= 80))\n add(lk, 3, lk.is_organic == 1)\n return G\n\ndef ndcg_from_top(top, G, idcg, scored):\n g = G[np.arange(G.shape[0])[:, None], top].astype(np.float64)\n disc = 1.0 / np.log2(np.arange(2, g.shape[1] + 2))\n dcg = ((2.0 ** g - 1.0) * disc).sum(1)\n return float((dcg[scored] / idcg[scored]).mean())\n\ndef idcg_of(G):\n disc = 1.0 / np.log2(np.arange(2, 11))\n out = np.zeros(G.shape[0])\n for r in range(G.shape[0]):\n row = G[r]\n nz = row[row > 0]\n if nz.size:\n nz = np.sort(nz)[::-1][:10].astype(np.float64)\n out[r] = ((2.0 ** nz - 1.0) * disc[:len(nz)]).sum()\n return out\nPYEOF\necho ok" } ### Tool output · 2026-09-24T07:48:02.338Z ok ### Harness context { "type": "total_tokens_reminder", "text": "14960833 tokens left" } ## Step 8 · agent · 2026-09-24T07:48:15.932Z ### Bash · 2026-09-24T07:48:15.932Z { "command": "cat > /app/solution/run_val.py <<'PYEOF'\nimport numpy as np, pandas as pd, itertools, json, pickle, time\nimport sys; sys.path.insert(0,'/app/solution')\nfrom build import *\n\nE = elig_mask(\"val\")\nF = build_stage(\"val\")\nlog(\"stage built\")\nG = val_gold(E)\nidcg = idcg_of(G); scored = idcg > 0\nlog(\"gold users\", scored.sum())\n\nnu = len(TARGETS)\n# ---- candidate pool: union of tops from each component\ndef topk_cols(M, k):\n M = np.where(E, M, -1e9)\n return np.argpartition(-M, k, axis=1)[:, :k]\ncf_l = F[\"E_long\"] @ F[\"Vn\"].T\npools = [topk_cols(cf_l, 250)]\ndel cf_l\ncf_s = F[\"E_short\"] @ F[\"Vn\"].T; pools.append(topk_cols(cf_s, 120)); del cf_s\ncf_k = F[\"E_like\"] @ F[\"Vn\"].T; pools.append(topk_cols(cf_k, 120)); del cf_k\ncnt = np.zeros((nu, NI), np.float32); C = F[\"CNT\"].tocoo(); cnt[C.row, C.col] = np.log1p(C.data)\ncnt += F[\"pop\"][None,:]*1e-3\npools.append(topk_cols(cnt, 120)); del cnt\npop_top = np.argsort(-F[\"pop\"])[:400]\npools.append(np.where(E[:, pop_top], pop_top[None,:], pop_top[0])[:, :120])\nPOOL = np.concatenate(pools, axis=1)\nlog(\"pool\", POOL.shape)\nnp.save('/app/solution/pool_val.npy', POOL)\n\nrows = np.repeat(np.arange(nu), POOL.shape[1])\ncols = POOL.ravel()\nFB = feature_block(F, rows, cols)\nFB = {k: v.reshape(POOL.shape).astype(np.float32) for k, v in FB.items()}\nlog(\"features extracted\")\n\n# duplicate mask within pool\nsrt = np.argsort(POOL, axis=1)\nps = np.take_along_axis(POOL, srt, 1)\ndup = np.zeros(POOL.shape, bool)\nd = ps[:, 1:] == ps[:, :-1]\nnp.put_along_axis(dup, srt[:, 1:], d, 1)\nelig_pool = E[np.arange(nu)[:,None], POOL] & ~dup\nlog(\"dup masked\")\n\ndef evalw(w):\n S = np.zeros(POOL.shape, np.float32)\n for k, v in w.items(): S += FB[k] * np.float32(v)\n S[~elig_pool] = -1e9\n top = np.argpartition(-S, 10, axis=1)[:, :10]\n tv = np.take_along_axis(S, top, 1)\n order = np.argsort(-tv, axis=1)\n top = np.take_along_axis(top, order, 1)\n return ndcg_from_top(np.take_along_axis(POOL, top, 1), G, idcg, scored), S\n\nW0 = dict(cf_long=1.0, cf_short=0.3, cf_like=0.3, cnt=0.3, str=0.3, pop=0.05, liked=0.0, disl=-1.0)\nbase, _ = evalw(W0); log(\"base ndcg\", base)\npickle.dump(dict(FB=FB, elig_pool=elig_pool, POOL=POOL, G=G, idcg=idcg, scored=scored),\n open('/app/solution/valtune.pkl','wb'), protocol=4)\nlog(\"saved tune data\")\nPYEOF\ntimeout 900 python run_val.py 2>&1 | tail -30" } ### Tool output · 2026-09-24T07:48:33.929Z [ 2.1s] listens loaded (43484159, 5) [ 6.6s] positive listens in catalog 20056329 t_end 24786795 [ 7.8s] R built (9135, 30000) 5564926 [ 11.2s] svd done [ 15.7s] profiles built [ 16.3s] embeddings built [ 16.4s] stage built Traceback (most recent call last): File "/app/solution/run_val.py", line 9, in idcg = idcg_of(G); scored = idcg > 0 ^^^^^^^^^^ File "/app/solution/build.py", line 202, in idcg_of out[r] = ((2.0 ** nz - 1.0) * disc[:len(nz)]).sum() ~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~ ValueError: operands could not be broadcast together with shapes (10,) (9,) ### Harness context { "type": "total_tokens_reminder", "text": "14959462 tokens left" } ## Step 9 · agent · 2026-09-24T07:48:43.990Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:48:45.288Z { "command": "python - <<'EOF'\nimport re\ns=open('build.py').read()\ns=s.replace(\" disc = 1.0 / np.log2(np.arange(2, 11))\\n out\",\" disc = 1.0 / np.log2(np.arange(2, 12))\\n out\")\nopen('build.py','w').write(s)\nEOF\ntimeout 900 python run_val.py 2>&1 | tail -20" } ### Tool output · 2026-09-24T07:49:09.352Z [ 2.1s] listens loaded (43484159, 5) [ 6.6s] positive listens in catalog 20056329 t_end 24786795 [ 7.8s] R built (9135, 30000) 5564926 [ 12.1s] svd done [ 16.7s] profiles built [ 17.3s] embeddings built [ 17.3s] stage built [ 17.5s] gold users 4263 ### Harness context { "type": "total_tokens_reminder", "text": "14958243 tokens left" } ## Step 10 · agent · 2026-09-24T07:49:16.497Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:49:29.840Z { "command": "cat > /app/solution/run_val.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, sys\nsys.path.insert(0,'/app/solution')\nfrom build import *\n\nE = elig_mask(\"val\")\nF = build_stage(\"val\")\nG = val_gold(E); idcg = idcg_of(G); scored = idcg > 0\nlog(\"gold users\", scored.sum())\nnu = len(TARGETS)\nCNTd = F[\"CNT\"].tocsr(); STRd = F[\"STR\"].tocsr()\npop_top = np.argsort(-F[\"pop\"])[:200]\nPOOLS = []\nCH = 1000\nfor s in range(0, nu, CH):\n e = min(s + CH, nu)\n sl = slice(s, e)\n Eb = E[sl]\n def tk(M, k):\n M = np.where(Eb, M, -1e9)\n return np.argpartition(-M, k, axis=1)[:, :k]\n parts = []\n for emb_, k in ((\"E_long\", 250), (\"E_short\", 120), (\"E_like\", 120)):\n parts.append(tk(F[emb_][sl] @ F[\"Vn\"].T, k))\n c = np.zeros((e - s, NI), np.float32)\n C = CNTd[sl].tocoo(); c[C.row, C.col] = np.log1p(C.data)\n C = STRd[sl].tocoo(); c[C.row, C.col] += np.log1p(C.data)\n c += F[\"pop\"][None, :] * 1e-3\n parts.append(tk(c, 120)); del c\n parts.append(np.broadcast_to(pop_top[None, :120], (e - s, 120)))\n POOLS.append(np.concatenate(parts, 1))\nPOOL = np.concatenate(POOLS, 0).astype(np.int32)\nlog(\"pool\", POOL.shape)\n\nrows = np.repeat(np.arange(nu), POOL.shape[1]); cols = POOL.ravel()\nFB = {k: v.reshape(POOL.shape).astype(np.float32) for k, v in feature_block(F, rows, cols).items()}\nlog(\"features\")\nsrt = np.argsort(POOL, 1); ps = np.take_along_axis(POOL, srt, 1)\ndup = np.zeros(POOL.shape, bool)\nnp.put_along_axis(dup, srt[:, 1:], ps[:, 1:] == ps[:, :-1], 1)\nelig_pool = E[np.arange(nu)[:, None], POOL] & ~dup\npickle.dump(dict(FB=FB, elig_pool=elig_pool, POOL=POOL, G=G, idcg=idcg, scored=scored),\n open('/app/solution/valtune.pkl','wb'), protocol=4)\nlog(\"saved\")\n\ndef evalw(w):\n S = np.zeros(POOL.shape, np.float32)\n for k, v in w.items(): S += FB[k] * np.float32(v)\n S[~elig_pool] = -1e9\n top = np.argpartition(-S, 10, 1)[:, :10]\n tv = np.take_along_axis(S, top, 1)\n top = np.take_along_axis(top, np.argsort(-tv, 1), 1)\n return ndcg_from_top(np.take_along_axis(POOL, top, 1), G, idcg, scored)\n\nW = dict(cf_long=1.0, cf_short=0.3, cf_like=0.3, cnt=0.3, str=0.3, pop=0.05, liked=0.0, disl=-1.0)\nbest = evalw(W); log(\"base\", round(best, 5), W)\ngrid = {\"cf_long\":[0,.5,1,2], \"cf_short\":[0,.1,.3,.6,1.2], \"cf_like\":[0,.1,.3,.6,1.2],\n \"cnt\":[0,.05,.15,.3,.6,1.2], \"str\":[0,.05,.15,.3,.6,1.2], \"pop\":[0,.02,.05,.1,.2],\n \"liked\":[-1,0,.5], \"disl\":[-2,0]}\nfor it in range(3):\n for k, vals in grid.items():\n cur = W[k]; bv = cur\n for v in vals:\n if v == cur: continue\n W[k] = v; s = evalw(W)\n if s > best: best, bv = s, v\n W[k] = bv\n log(\"iter\", it, round(best, 5), W)\njson.dump({\"w\": W, \"ndcg\": best}, open('/app/solution/best_weights.json','w'))\nlog(\"DONE\", best, W)\nPYEOF\ntimeout 900 python run_val.py 2>&1 | tail -20; echo \"EXIT=$?\"" } ### Tool output · 2026-09-24T07:49:58.501Z [ 2.1s] listens loaded (43484159, 5) [ 6.6s] positive listens in catalog 20056329 t_end 24786795 [ 7.8s] R built (9135, 30000) 5564926 [ 11.2s] svd done [ 15.7s] profiles built [ 16.3s] embeddings built [ 16.6s] gold users 4263 [ 26.0s] pool (8456, 730) EXIT=0 ### Harness context { "type": "total_tokens_reminder", "text": "14956073 tokens left" } ## Step 11 · agent · 2026-09-24T07:50:07.859Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:50:30.502Z { "command": "cat > /app/solution/pipeline.py <<'PYEOF'\nimport numpy as np, pandas as pd, pickle, sys, json\nsys.path.insert(0,'/app/solution')\nfrom build import *\n\ndef feats_for_pool(F, POOL, E):\n nu, P = POOL.shape\n out = {k: np.zeros((nu, P), np.float32) for k in FEATS}\n CH = 400\n for s in range(0, nu, CH):\n e = min(s + CH, nu); pl = POOL[s:e]\n for name, key in ((\"cf_long\",\"E_long\"), (\"cf_short\",\"E_short\"), (\"cf_like\",\"E_like\")):\n out[name][s:e] = np.einsum(\"ij,ikj->ik\", F[key][s:e], F[\"Vn\"][pl])\n for name, M in ((\"cnt\", F[\"CNT\"]), (\"str\", F[\"STR\"]), (\"liked\", F[\"LIKED\"]), (\"disl\", F[\"DISL\"])):\n d = np.asarray(M[s:e].todense())\n v = np.take_along_axis(d, pl, 1)\n out[name][s:e] = np.log1p(v) if name in (\"cnt\", \"str\") else v\n out[\"pop\"][s:e] = F[\"pop\"][pl]\n return out\n\ndef make_pool(F, E, sizes=(250,120,120,120,120)):\n nu = len(TARGETS); pop_top = np.argsort(-F[\"pop\"])[:200]\n CNTd, STRd = F[\"CNT\"].tocsr(), F[\"STR\"].tocsr()\n POOLS = []\n for s in range(0, nu, 1000):\n e = min(s + 1000, nu); sl = slice(s, e); Eb = E[sl]\n def tk(M, k):\n M = np.where(Eb, M, -1e9)\n return np.argpartition(-M, k, 1)[:, :k]\n parts = [tk(F[k_][sl] @ F[\"Vn\"].T, n) for k_, n in\n ((\"E_long\", sizes[0]), (\"E_short\", sizes[1]), (\"E_like\", sizes[2]))]\n c = np.zeros((e - s, NI), np.float32)\n C = CNTd[sl].tocoo(); c[C.row, C.col] = np.log1p(C.data)\n C = STRd[sl].tocoo(); c[C.row, C.col] += np.log1p(C.data)\n c += F[\"pop\"][None, :] * 1e-3\n parts.append(tk(c, sizes[3])); del c\n parts.append(np.broadcast_to(pop_top[None, :sizes[4]], (e - s, sizes[4])))\n POOLS.append(np.concatenate(parts, 1))\n return np.concatenate(POOLS, 0).astype(np.int32)\n\ndef dedup_mask(POOL, E):\n nu = POOL.shape[0]\n srt = np.argsort(POOL, 1); ps = np.take_along_axis(POOL, srt, 1)\n dup = np.zeros(POOL.shape, bool)\n np.put_along_axis(dup, srt[:, 1:], ps[:, 1:] == ps[:, :-1], 1)\n return E[np.arange(nu)[:, None], POOL] & ~dup\n\n# ---------- validation: tune ----------\nEv = elig_mask(\"val\"); Fv = build_stage(\"val\")\nG = val_gold(Ev); idcg = idcg_of(G); scored = idcg > 0\nPOOL = make_pool(Fv, Ev); log(\"pool\", POOL.shape)\nFB = feats_for_pool(Fv, POOL, Ev); log(\"feats\")\nep = dedup_mask(POOL, Ev)\n\ndef evalw(w, K=10):\n S = np.zeros(POOL.shape, np.float32)\n for k, v in w.items(): S += FB[k] * np.float32(v)\n S[~ep] = -1e9\n top = np.argpartition(-S, K, 1)[:, :K]\n top = np.take_along_axis(top, np.argsort(-np.take_along_axis(S, top, 1), 1), 1)\n return ndcg_from_top(np.take_along_axis(POOL, top, 1), G, idcg, scored)\n\nW = dict(cf_long=1.0, cf_short=0.3, cf_like=0.3, cnt=0.3, str=0.3, pop=0.05, liked=0.0, disl=-1.0)\nbest = evalw(W); log(\"base\", round(best, 5))\ngrid = {\"cf_long\":[0,.5,1,2,4], \"cf_short\":[0,.1,.3,.6,1.2], \"cf_like\":[0,.1,.3,.6,1.2],\n \"cnt\":[0,.05,.15,.3,.6,1.2,2.5], \"str\":[0,.05,.15,.3,.6,1.2,2.5],\n \"pop\":[0,.02,.05,.1,.25,.5], \"liked\":[-1,0,.5,1.5], \"disl\":[-2,0,.5]}\nfor it in range(3):\n 改 = False\n for k, vals in grid.items():\n cur = W[k]; bv = cur\n for v in vals:\n if v == cur: continue\n W[k] = v; s = evalw(W)\n if s > best + 1e-6: best, bv, 改 = s, v, True\n W[k] = bv\n log(\"iter\", it, round(best, 5), W)\n if not 改: break\njson.dump({\"w\": W, \"val_ndcg\": best}, open('/app/solution/best_weights.json','w'), indent=1)\nlog(\"TUNED\", best, W)\n\n# validation artifact for the public evaluator\ndef write_ranking(F, E, W, path):\n nu = len(TARGETS); rows = []\n CNTc, STRc, LKc, DSc = F[\"CNT\"].tocsr(), F[\"STR\"].tocsr(), F[\"LIKED\"].tocsr(), F[\"DISL\"].tocsr()\n for s in range(0, nu, 1000):\n e = min(s + 1000, nu); sl = slice(s, e)\n S = (F[\"E_long\"][sl] @ F[\"Vn\"].T) * np.float32(W[\"cf_long\"])\n S += (F[\"E_short\"][sl] @ F[\"Vn\"].T) * np.float32(W[\"cf_short\"])\n S += (F[\"E_like\"][sl] @ F[\"Vn\"].T) * np.float32(W[\"cf_like\"])\n S += F[\"pop\"][None, :] * np.float32(W[\"pop\"])\n for name, M in ((\"cnt\", CNTc), (\"str\", STRc)):\n C = M[sl].tocoo(); S[C.row, C.col] += np.log1p(C.data) * np.float32(W[name])\n for name, M in ((\"liked\", LKc), (\"disl\", DSc)):\n C = M[sl].tocoo(); S[C.row, C.col] += C.data.astype(np.float32) * np.float32(W[name])\n S[~E[sl]] = -1e9\n top = np.argpartition(-S, 100, 1)[:, :100]\n top = np.take_along_axis(top, np.argsort(-np.take_along_axis(S, top, 1), 1), 1)\n rows.append(pd.DataFrame({\n \"uid\": np.repeat(TARGETS[sl], 100),\n \"item_id\": CAT_ITEMS[top.ravel()],\n \"rank\": np.tile(np.arange(1, 101), e - s)}))\n pd.concat(rows, ignore_index=True).to_parquet(path, index=False)\n log(\"wrote\", path)\n\nwrite_ranking(Fv, Ev, W, '/app/solution/validation.parquet')\ndel Fv, FB, POOL, ep, G, Ev\n\n# ---------- test: refit on train+val ----------\nEt = elig_mask(\"test\"); Ft = build_stage(\"test\")\nwrite_ranking(Ft, Et, W, '/app/solution/submission_new.parquet')\nPYEOF\nnohup timeout 1500 python /app/solution/pipeline.py > /app/solution/pipeline.log 2>&1 &\nsleep 60; tail -5 /app/solution/pipeline.log" } ### Tool output · 2026-09-24T07:51:30.577Z [ 43.8s] wrote /app/solution/validation.parquet [ 46.0s] listens loaded (44985856, 5) [ 50.7s] positive listens in catalog 20712971 t_end 25393395 [ 51.9s] R built (9153, 30000) 5724357 [ 55.0s] svd done ### Harness context { "type": "total_tokens_reminder", "text": "14952487 tokens left" } ## Step 12 · agent · 2026-09-24T07:51:32.525Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:51:32.867Z { "command": "cat /app/solution/pipeline.log; ls -la /app/solution/" } ### Tool output · 2026-09-24T07:51:33.046Z [ 2.2s] listens loaded (43484159, 5) [ 6.6s] positive listens in catalog 20056329 t_end 24786795 [ 7.8s] R built (9135, 30000) 5564926 [ 11.2s] svd done [ 15.8s] profiles built [ 16.4s] embeddings built [ 26.0s] pool (8456, 730) [ 29.1s] feats [ 29.4s] base 0.0203 [ 32.8s] iter 0 0.02263 {'cf_long': 0.5, 'cf_short': 1.2, 'cf_like': 0, 'cnt': 0.3, 'str': 0.3, 'pop': 0.05, 'liked': 0.0, 'disl': -1.0} [ 36.1s] iter 1 0.02305 {'cf_long': 0, 'cf_short': 1.2, 'cf_like': 0.1, 'cnt': 0.3, 'str': 0.3, 'pop': 0.05, 'liked': 0.0, 'disl': -1.0} [ 39.3s] iter 2 0.02305 {'cf_long': 0, 'cf_short': 1.2, 'cf_like': 0.1, 'cnt': 0.3, 'str': 0.3, 'pop': 0.05, 'liked': 0.0, 'disl': -1.0} [ 39.3s] TUNED 0.023054987356453685 {'cf_long': 0, 'cf_short': 1.2, 'cf_like': 0.1, 'cnt': 0.3, 'str': 0.3, 'pop': 0.05, 'liked': 0.0, 'disl': -1.0} [ 43.8s] wrote /app/solution/validation.parquet [ 46.0s] listens loaded (44985856, 5) [ 50.7s] positive listens in catalog 20712971 t_end 25393395 [ 51.9s] R built (9153, 30000) 5724357 [ 55.0s] svd done [ 59.7s] profiles built [ 60.3s] embeddings built total 2540 drwxr-xr-x 1 agent agent 4096 Sep 24 07:51 . drwxr-xr-x 1 agent agent 4096 Sep 24 07:41 .. -rw-r--r-- 1 agent agent 176 Sep 24 07:51 best_weights.json -rw-r--r-- 1 agent agent 8925 Sep 24 07:48 build.py -rw-r--r-- 1 agent agent 667 Sep 24 07:45 fallback.py -rw-r--r-- 1 agent agent 1122 Sep 24 07:51 pipeline.log -rw-r--r-- 1 agent agent 5045 Sep 24 07:50 pipeline.py -rw-r--r-- 1 agent agent 2765 Sep 24 07:49 run_val.py -rw-r--r-- 1 agent agent 704183 Sep 24 07:46 submission.parquet -rw-r--r-- 1 agent agent 1845788 Sep 24 07:51 validation.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14952241 tokens left" } ## Step 13 · agent · 2026-09-24T07:51:37.278Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:51:37.815Z { "command": "timeout 400 python /task/evaluate_public.py validation.parquet; tail -3 pipeline.log" } ### Tool output · 2026-09-24T07:51:40.469Z { "model": "validation.parquet", "scores": { "quality_ndcg@10": 0.023055, "quality_users": 4263 } } [ 59.7s] profiles built [ 60.3s] embeddings built [ 64.7s] wrote /app/solution/submission_new.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14950833 tokens left" } ## Step 14 · agent · 2026-09-24T07:51:45.334Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:51:48.997Z { "command": "timeout 300 python -c \"\nimport numpy as np, pandas as pd, sys\nsys.path.insert(0,'/task')\nfrom ranking_evaluation import validate_ranking\nd=pd.read_parquet('submission_new.parquet')\nt=set(pd.read_parquet('/task/data/target_users.parquet').uid.astype(int))\nn=validate_ranking(d,t); print('schema ok',n.shape)\nz=np.load('/task/data/test_eligible_candidates.npz')\nuids=z['uids'].astype(np.int64); items=z['item_ids'].astype(np.int64)\nE=np.unpackbits(z['packed_eligible'],axis=1,count=len(items),bitorder='little').astype(bool)\nur={int(u):i for i,u in enumerate(uids)}; ic={int(i):c for c,i in enumerate(items)}\nr=n.uid.map(ur).to_numpy(); c=n.item_id.map(ic).to_numpy()\nassert not pd.isna(c).any(); print('all eligible:', E[r,c.astype(int)].all())\n\" && cp submission_new.parquet submission.parquet && echo SWAPPED" } ### Tool output · 2026-09-24T07:51:50.374Z schema ok (845600, 3) all eligible: True SWAPPED ### Harness context { "type": "total_tokens_reminder", "text": "14949988 tokens left" } ## Step 15 · agent · 2026-09-24T07:51:53.290Z ### Thinking unavailable — no text recorded ### Bash · 2026-09-24T07:52:07.596Z { "command": "cat > /app/solution/README.md <<'EOF'\n# Made-for-You discovery ranker\n\nFinal artifact: `submission.parquet` (8,456 users x 100 ranked eligible items).\nValidation-stage ranking: `validation.parquet` — public evaluator score\n**quality_ndcg@10 = 0.023055** over 4,263 scored users.\n\n## Approach\n\nA linear blend of collaborative-filtering and behavioural features, with blend\nweights tuned directly on the public validation window.\n\n1. **History construction** (`build.py:build_stage`). Positive listens\n (`played_ratio_pct >= 50`) restricted to the 30k candidate catalog. For the\n validation stage history = `train_*` only; for the hidden-test stage the\n whole pipeline is refit with history = `train_* + val_*` (the same code path,\n `stage=\"test\"`), so validation events are used as observed history only.\n2. **Implicit MF item factors.** User x item matrix with `log1p` count damping,\n item IDF weighting and L2 user normalisation, factorised by randomized SVD\n (k=160). Item factors are L2-normalised so CF scores are cosine-like and do\n not simply re-rank by popularity.\n3. **User profiles / features** scored per (user, candidate):\n - `cf_long`, `cf_short`: fold-in profile embeddings over the user's positive\n listens with 14-day and 3-day half-life decay, weighted by completion\n (>=80%) and organic flag, dotted with item factors.\n - `cf_like`: same fold-in over the user's decayed explicit likes.\n - `cnt`, `str`: log repeat-affinity — the user's own past listen count and\n recency-decayed strong-organic-listen count for that exact track.\n - `pop`: standardised `log1p(organic_positive_listens)` catalog prior.\n - `liked`, `disl`: active explicit preference state at the correct cutoff\n (`validation_preference_state` / `test_preference_state`).\n4. **Weight tuning.** A generous per-user candidate pool (730 = union of top-N\n by each individual signal) is materialised; coordinate-ascent over a weight\n grid maximises graded NDCG@10 directly on the validation window. The internal\n scorer reproduces `evaluate_public.py` exactly (0.023055 both ways).\n Selected weights are in `best_weights.json`; the short-horizon CF term plus\n repeat-affinity dominate, dislikes are strongly penalised.\n5. **Final ranking.** Scores are recomputed densely in user chunks, ineligible\n candidates masked to `-inf`, top-100 emitted per user. Every submitted item\n was verified present in `test_eligible_candidates.npz`.\n\n## Files\n- `build.py` — data loading, feature/profile construction, eligibility, NDCG.\n- `pipeline.py` — end-to-end: tune on validation, refit on train+val, write both\n rankings. Run: `python pipeline.py`.\n- `fallback.py` — popularity-only safety submission (superseded).\n\n## Notes / limits\nOnly the checked-in environment (numpy/scipy/sklearn/pandas) was available, so\nno GBDT ranker; with more time the natural next steps are a full item-item\ncosine KNN term (feasible here — only ~9.1k distinct users) and a learned\npointwise ranker trained on an in-train temporal split instead of a hand-tuned\nlinear blend.\nEOF\nrm -f /app/solution/run_val.py /app/solution/pool_val.npy; ls /app/solution" } ### Tool output · 2026-09-24T07:52:07.636Z README.md best_weights.json build.py fallback.py pipeline.log pipeline.py submission.parquet submission_new.parquet validation.parquet ### Harness context { "type": "total_tokens_reminder", "text": "14948560 tokens left" } ## Step 16 · agent · 2026-09-24T07:52:16.400Z Done. Final state in `/app/solution/`: **Deliverable:** `submission.parquet` — 845,600 rows (8,456 users × 100 ranks), schema-validated with `ranking_evaluation.validate_ranking`, and every item confirmed present in that user's `test_eligible_candidates.npz` mask. **Model:** linear blend of - randomized-SVD implicit-MF item factors (k=160, IDF-weighted, L2-normalised) scored against two recency-decayed user fold-in profiles (14-day and 3-day half-life) and a likes-based profile, - log repeat-affinity (past listen count, recency-weighted strong-organic listens on the same track), - catalog popularity prior, - cutoff-correct explicit like/dislike state. Weights were fit by coordinate ascent on graded NDCG@10 over the validation window using a 730-candidate per-user pool; the final ranker was then refit end-to-end with `train_* + val_*` as history for the test cutoff. **Measured quality:** `python /task/evaluate_public.py validation.parquet` → `quality_ndcg@10 = 0.023055` over 4,263 scored users (up from 0.0203 at the untuned starting weights). My internal scorer reproduces the public evaluator exactly. The hidden-test ranking uses the identical code path with the later cutoff, so it is unscored here. Two things worth flagging: the tuned weights zeroed the long-horizon CF term in favour of the 3-day one, which is a fairly aggressive recency preference fit on a single 7-day window — some overfitting risk to the public window. And the environment has no GBDT library, so the ranker is a hand-tuned linear blend rather than a learned one; a full item-item KNN term is cheap here (only ~9.1k distinct users in the logs) and is the first thing I'd add with more time. Both are noted in the README.