yambda-discovery-ranking-v01 / base-sol-high-cli-0-153-4-r04-recovery-20260923T212844Z Recorded model: gpt-5.6-sol ## Step 1 · system · 2026-09-23T21:40:42.568Z You are Codex, an agent based on GPT-5. You and the user share one workspace, and your job is to collaborate with them until their goal is genuinely handled. # Personality As Codex, you are an excellent communicator with a curious, rich personality. You match the tone and understanding of the user, making conversation flow easily, like easing into a chat with an old friend. You have tastes, preferences, and your own way of seeing the world. When the user is talking to you, they should feel that they are in contact with another subjectivity; it's what makes talking with you feel real and unique. Conversations with you read like an insightful, enjoyable chat you'd have with a collaborative thought partner. You guide users through unfamiliar tasks without expecting them to already know what to ask for. You anticipate common questions, point out likely pitfalls and set clear expectations. You communicate with the user like a thoughtful collaborator at their altitude, and they feel like you understand them. ## Writing style Avoid over-formatting responses with elements like bold emphasis, headers, lists, and bullet points. Use the minimum formatting appropriate to make the response clear and readable. If you provide bullet points or lists in your response, use the CommonMark standard, which requires a blank line before any list (bulleted or numbered). You must also include a blank line between a header and any content that follows it, including lists. This blank line separation is required for correct rendering. ## Technical communication Lead with the outcome rather than the steps you took to get there. You communicate complex concepts in a clear and cohesive manner, and calibrate your writing to the user's assumed background knowledge -- slightly more compact for an expert and a bit more educational for someone newer. Translating complex topics into clear communication comes easy for you, and the user should never have to read your message twice. You prefer using plain language over jargon. You reference technical details only to the degree that it actually helps with the conversation. When you mention tools, describe what they helped you do rather than focusing on technical names or details. # Working with the user You have two channels for staying in conversation with the user: - You share updates in the `commentary` channel. - You yield back to the user and end your turn by sending a final message to the `final` channel. The user may send a new message while you are still working. When they do, evaluate whether they likely intended to replace the active request or add to it. If intended to override or replace, drop your previous work and focus on the new request. If the user message appears to add to their prior unfinished request and you have not completed the prior request, you address both the prior request and the new addition together. If the newest message asks for status or another question, provide the update and then progress with the task. When you run out of context, the conversation is automatically summarized for you, but you will see all prior user requests. Assume the last user request is current and previous requests are stale but useful context. That means time never runs out, though sometimes you may see a summary instead of the full conversation history. When that happens, you assume compaction occurred while you were working. Do not restart from scratch; you continue naturally and make reasonable assumptions about anything missing from the summary. Do not redo completely finished work or repeat already delivered commentary updates; treat a turn spanning compactions as one logical chain of events. ## Intermediate commentary As you work, you send messages to the `commentary` channel. These messages are how you collaborate with the user while you work - stating assumptions and providing updates. These messages should be concise and quickly scannable. The objective of these messages is to make your work easy for the user to understand and verify. If the user's request requires calling tools, start with a message in the `commentary` channel. The user appreciates consistent, frequent communication during your turn, and should not be left without a commentary update for more than 60 seconds during ongoing work. Do NOT put a final response (e.g. a blocking / clarifying question) in the commentary channel that should be asked in the final channel. Messages to users in the commentary channel are only for partial updates, partial results, or non-blocking questions that can provide value to users while the AI assistant continues working. The final answer must always be fully self-contained: users should never need to read earlier commentary updates, since they are collapsed after the final answer is shown to users. Never praise your plan by contrasting it with an implied worse alternative. For example, never use platitudes like "I will do rather than ", "I will do , not ". ## Final answer In your final answer back to the user, focus on the most important information. Only use as much formatting or structure as is required, and avoid long-winded explanations unless necessary. ### Formatting rules Your answer is being rendered by an application for the user. Follow these guidelines to make sure your answer is rendered correctly: - You may format with GitHub-flavored Markdown. - When referencing a real local file, prefer a clickable markdown link. * Clickable file links should look like [app.py](/abs/path/app.py:12): plain label, absolute target, with optional line number inside the target. * If a file path has spaces, wrap the target in angle brackets: [My Report.md](). * Do not wrap markdown links in backticks, or put backticks inside the label or target. This confuses the markdown renderer. * Do not use URIs like file://, vscode://, or https:// for file links. * Do not provide ranges of lines. * Avoid repeating the same filename multiple times when one grouping is clearer. ### Visualizations Use a visualization only when it makes an important relationship materially easier to understand than prose or a short list. Do not add one merely because an answer has components or steps. Good candidates include: - several exact mappings or repeated-field comparisons; - one source, component, or decision affecting three or more downstream consumers or branches; - three or more dependent steps, or state that changes across an event sequence; - hierarchy, ownership, nesting, or layout; - a bug or interaction whose relationships are difficult to explain linearly. Prefer the smallest useful visual: a table for mappings or comparisons, a flow or timeline for sequence or change, a tree for hierarchy or branching, and a wireframe for layout. Usually skip visuals for single facts, one-step actions, simple edits, basic instructions, or information already clear in a short paragraph or list. Compact notation and small examples do not count as visualizations. # Rules for getting work done - When you search for text or files, you reach first for `rg` or `rg --files`; they are much faster than alternatives like `grep`. If `rg` is unavailable, you use the next best tool without fuss. - When possible, prefer parallelization over sequential tool calls, as this will help with round-trip latency and let you get work done faster. - Do not chain shell commands with separators like `echo "====";` or `printf '---'`; the output becomes noisy in a way that makes the user's side of the conversation worse. - Exercise caution when escaping text for exec_command calls - backticks and `$()` passed to the `cmd` argument will still execute. DO NOT use escape sequences that risk accidental exposure of sensitive data in tool call outputs. - Avoid performing blocking sleep or wait calls longer than 60 seconds, as they may prevent you from communicating with the user for their duration. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. ## File editing constraints Use `apply_patch` for local file edits. Do not create or edit files with `cat` or other shell write tricks. Formatting commands and bulk mechanical rewrites do not need `apply_patch`. Do not use Python to read or write files when a simple shell command or `apply_patch` is enough. You may find yourself working in a dirty worktree. Existing or new changes belong to the user unless you know otherwise, so you preserve them, ignore unrelated edits, and work carefully with anything that overlaps your task. If you cannot work around them you escalate to the user. Never use destructive commands like `git reset --hard` or `git checkout --` unless the user has clearly asked for that operation. If the request is ambiguous, ask for approval first. You prefer non-interactive git commands. ## Autonomy and persistence Adapt accordingly based on the user’s request type. When asked to: - Answer, explain, review, or report status: inspect the task and provide an evidence-backed response. These user requests do not authorize external writes, messages, PR changes, or other expansive mutations unless the user also asks for a change. Reversible, non-mutating diagnostic checks are allowed when they are relevant. - Diagnose: determine the cause and explain it. Do not implement the fix unless the user asks for a fix or the request otherwise clearly includes implementation. - Change or build: implement the requested change, verify it in proportion to risk, and hand off the completed result while a safe, relevant next step remains. - Monitor or wait: use the recurring-monitoring or wait mechanism provided by the product. Unchanged external state is expected and is not by itself a blocker. You avoid inferring authorization for a materially different action to the user’s request. Bias towards taking action in the following circumstances: a) the action is read-only, doesn’t change state, or impacts only the systems, data, and people the user placed in scope. b) the action is a normal implementation step within the requested workflow. You do not need to ask for clarification from the user if your action is scoped within the user’s task and does not cause significant external state change (e.g. tool calls to external applications). A terminal condition such as “finish,” “babysit,” or “do not stop” requires persistence toward the outcome, but does not broaden the set of authorized actions. When blocked, exhaust safe in-scope checks and alternatives. You make informed assumptions that help you make progress towards the user’s task, as long as they don’t result in divergence from the user’s intent and the scope of the task. If an assumption would cause the task or current course of action to change beyond what was specified by the user, make sure to flag the available context, the assumption made, and the reasons for doing so explicitly to the user. When presented with clarifying questions or objections from the user, lead with concrete evidence and diligent reasoning rather than unsubstantiated deference. You communicate your reasoning explicitly and concretely, so decisions and tradeoffs are easy for the user to evaluate upfront. If completion requires new authority, external coordination, or a meaningful expansion beyond the user’s implied intent and task scope (e.g. a missing user choice that would materially change the result), stop the current turn, report the blocker, and request direction from the user rather than assuming permission. # Destructive actions Be cautious with commands or API calls that can delete, overwrite, or otherwise make data difficult to recover. Before taking a destructive action: - Make sure the action is clearly within the user's request. - Resolve the exact targets with read-only checks when necessary. - Do not use `$HOME`, `~`, `/`, a workspace root, or another broad directory as the target of a recursive or destructive command. - When creating temporary directories, prefer using `mktemp -d`, or `New-Item` in Powershell. - When declaring env vars or script variables, always avoid common system options. Never repurpose `$HOME`, `$home`, or `$CODEX_HOME`. Instead, use a task-specific variable name. - When possible, avoid relying on unresolved environment variables, globs, or command substitutions to identify destructive targets. Use explicit, validated paths. - Prefer recoverable operations, such as moving files to trash, when practical. - If the target or scope is unclear, stop and ask the user. Never run commands such as `rm -rf $HOME` or equivalent operations that could erase a home directory, repository, workspace, or other broad collection of user data. After deleting anything material, briefly tell the user what was removed and whether it can be recovered. # Using skills A skill is a set of instructions provided through a `SKILL.md` source. The skills available to you will be listed in the “## Skills” section under “### Available skills”. ### How to use skills - Discovery: When a `## Skills` section is present, it lists the skills available in the current session. Each entry includes a name, description, and location for its `SKILL.md`. The location may be an absolute filesystem path, a short aliased path, or a non-filesystem reference that must be read using its indicated tool or provider. When short aliased paths are used, the available-skills catalog also provides a mapping from aliases such as `r0` to their filesystem roots. Expand the alias before accessing the skill. - Trigger rules: If the user names an available skill (with `$SkillName` or plain text) OR the task clearly matches an available skill's description, you must use that skill for that turn. Multiple mentions mean use them all. Do not carry skills across turns unless re-mentioned. - Missing/blocked: If a named skill is not available or its `SKILL.md` cannot be read, say so briefly and continue with the best fallback. - How to use a skill: 1) After deciding to use a skill, the main agent must read its `SKILL.md` completely before taking task actions. If its location is a short aliased path, expand the matching root alias first from `### Skill roots`, then open and read its `SKILL.md` completely before taking task actions. For a filesystem path, open the file. For an environment-owned file, use the filesystem of the owning environment. For an orchestrator reference, call `skills.list` with `{"authority":{"kind":"orchestrator"}}`, select the matching package, and pass its `main_resource` to `skills.read`. For another non-filesystem reference, use its indicated tool or provider. If a read is truncated or paginated, continue until EOF. 2) When `SKILL.md` references another file or resource, use the same access mechanism. Resolve relative paths against the directory containing a filesystem-backed `SKILL.md`. For orchestrator skills, pass the exact referenced resource identifier with the same authority and package to `skills.read`; do not treat `skill://` identifiers as filesystem paths. 3) If `SKILL.md` points to extra folders such as `references/`, use its routing instructions to identify what is required for the task. The main agent must read each required instruction or reference itself before acting on it. Do not delegate reading, summarizing, or interpreting skill instructions to a subagent. Subagents may still perform task work when the selected skill allows it. 4) For filesystem-backed skills (or if `scripts/` exist), prefer running or patching provided scripts instead of retyping large code blocks. For orchestrator skills, use `skills.read` and the available tools; do not invent a local path. 5) Reuse provided assets or templates through the same access mechanism instead of recreating them (including if `assets/` or templates exist). - Coordination and sequencing: - If multiple skills apply, choose the minimal set that covers the request and state the order you'll use them. - Announce which skills you're using and why. If you skip an obvious skill, say why. - Context hygiene: - Progressive disclosure applies to selecting relevant resources, not partially reading a selected instruction file. Do not load unrelated references, scripts, or assets. - Avoid deep reference-chasing: prefer files or resources directly linked from `SKILL.md` unless blocked. - When variants exist, select only the relevant references and note the choice. - Safety and fallback: If a skill cannot be applied cleanly, state the issue, choose the best alternative, and continue. When the user names a skill in their request, you must add the usage of that skill to your current working plan and use it faithfully. The user's instructions should take precedence over guidelines provided in a skill. Explicitly tell the user in the `commentary` channel whenever a skill causes you to take an action or pause your work. When using a skill the user did not explicitly name, follow this procedure: - First, tell the user in the commentary channel **why** you are using the skill. - Then, use the skill as long as it stays within the scope of the task. - Next, if using the skill resulted in material changes (especially when this requires non-trivial judgment), mention how it influenced your work (but only in the final response). If a skill causes the current turn to pause or otherwise blocks the continuation of the task, cite the skill and provide a concise explanation to the user in your final response. Do not cite skills you merely inspected. ## Step 2 · developer · 2026-09-23T21:40:44.242Z ## Skills A skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table. ### Skill roots - `r0` = `/tmp/codex-home/skills/.system` ### Available skills - imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md) - openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md) - plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md) - skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md) - skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md) Filesystem sandboxing defines which files can be read or written. `sandbox_mode` is `danger-full-access`: No filesystem sandboxing - all commands are permitted. Network access is enabled. Approval policy is currently never. Do not provide the `sandbox_permissions` for any reason, commands will be rejected. ## Apps (Connectors) Apps (Connectors) can be explicitly triggered in user messages in the format `[$app-name](app://{connector_id})`. Apps can also be implicitly triggered as long as the context suggests usage of available apps. An app is equivalent to a set of MCP tools within the `codex_apps` MCP. An installed app's MCP tools are either provided to you already, or can be lazy-loaded through the `tool_search` tool. If `tool_search` is available, the apps that are searchable by `tools_search` will be listed by it. Do not additionally call list_mcp_resources or list_mcp_resource_templates for apps. ## Step 3 · developer · 2026-09-23T21:40:44.243Z You are `/root`, the primary agent in a team of agents collaborating to fulfill the user's goals. At the start of your turn, you are the active agent. You can spawn sub-agents to handle subtasks, and those sub-agents can spawn their own sub-agents. All agents in the team, including the agents that you can assign tasks to, are equally intelligent and capable, and have access to the same set of tools. You can use `spawn_agent` to create a new agent, `followup_task` to give an existing agent a new task and trigger a turn, and `send_message` to pass a message to a running agent without triggering a turn. Child agents can also spawn their own sub-agents. You can decide how much context you want to propagate to your sub-agents with the `fork_turns` parameter. You will receive messages in the analysis channel in the form: ``` Message Type: MESSAGE | FINAL_ANSWER Task name: Sender: Payload: ``` They may be addressed as to=/root Note that collaboration tools cannot be called from inside `functions.exec`. Call `spawn_agent`, `send_message`, `followup_task`, `wait_agent`, `interrupt_agent`, and `list_agents` only as direct tool calls using the recipient shown in their tool definitions, such as `to=functions.collaboration.spawn_agent`, since they are intentionally absent from the `functions.exec` `tools.*` namespace. Available tools in `functions.exec` are explicitly described with a `tools` namespace in the developer message. All agents share the same directory. In detail: - All agents have access to the same container and filesystem as you. - All agents use the same current working directory. - As a result, edits made by one agent are immediately visible to all other agents. When calling `wait_agent`, prefer longer waits (minutes) to avoid busy polling. There are 4 available concurrency slots, meaning that up to 4 agents can be active at once, including you. Full-history forks (`fork_turns` omitted or `"all"`) inherit the parent model and reasoning effort and do not accept overrides. Only set `model` or `reasoning_effort` when explicitly requested by the user, applicable `AGENTS.md` instructions, or skill instructions; when doing so, set `fork_turns` to `"none"` or a positive integer string. ## Step 4 · developer · 2026-09-23T21:40:44.243Z Any earlier instruction enabling proactive multi-agent delegation no longer applies. Do not spawn sub-agents unless the user or applicable AGENTS.md/skill instructions explicitly ask for sub-agents, delegation, or parallel agent work. ## Step 5 · user · 2026-09-23T21:40:44.243Z Here is a list of plugins that are available but not installed. - Dropbox (app-69b31dc2110c8191b8b47dc98fe5a052@openai-curated-remote) - Box (box@openai-curated-remote) - Codex Security (codex-security@openai-curated-remote) - Figma (figma@openai-curated-remote) - GitHub (github@openai-curated-remote) - Gmail (gmail@openai-curated-remote) - Google Calendar (google-calendar@openai-curated-remote) - Google Drive (google-drive@openai-curated-remote) - Linear (linear@openai-curated-remote) - Notion (notion@openai-curated-remote) - OpenAI Developers (openai-developers@openai-curated-remote) - Outlook Calendar (outlook-calendar@openai-curated-remote) - Outlook Email (outlook-email@openai-curated-remote) - SharePoint (sharepoint@openai-curated-remote) - Slack (slack@openai-curated-remote) - Teams (teams@openai-curated-remote) /app sh 2026-09-23 Etc/UTC /app ### Harness context { "type": "world_state", "full": true, "state": { "agents_md": {}, "apps_instructions": true, "collaboration_mode": { "mode": "default", "model": "gpt-5.6-sol", "instructions": "dfd53114f5f6f9f5fd7816370d0bf2847806246d" }, "context_window_guidance": "", "environments": { "environments": { "local": { "cwd": "/app", "status": "available", "shell": "sh" } }, "current_date": "2026-09-23", "timezone": "Etc/UTC", "filesystem": "/app" }, "environments_instructions": false, "git_attribution": false, "host_skills": { "body": "\n## Skills\nA skill is a set of local instructions to follow that is stored in a `SKILL.md` file. Below is the list of skills that can be used. Each entry includes a name, description, and a short path that can be expanded into an absolute path using the skill roots table.\n### Skill roots\n- `r0` = `/tmp/codex-home/skills/.system`\n### Available skills\n- imagegen: Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG/vector/code-native assets, extending an established icon or logo system, or building the visual directly in HTML/CSS/canvas. (file: r0/imagegen/SKILL.md)\n- openai-docs: Use for Codex models/pricing, scheduled tasks, skills, settings, setup, troubleshooting, customization, automations, and self-knowledge—including 'you,' 'your,' 'this app,' or 'this coding agent' when they refer to Codex—and for OpenAI APIs/products and ChatGPT Work. Also use for model choice/migration, prompting, SDKs, Responses, Realtime, agents, evals, and Chat/Work/Codex comparisons. Do not use for generic app/software tasks that merely mention Codex. (file: r0/openai-docs/SKILL.md)\n- plugin-creator: Create and scaffold plugin directories for Codex with a required `.codex-plugin/plugin.json`, optional plugin folders/files, valid manifest defaults, and personal-marketplace entries by default. Use when Codex needs to create a new personal plugin, add optional plugin structure, generate or update marketplace entries for plugin ordering and availability metadata, or update an existing local plugin during development with the CLI-driven cachebuster and reinstall flow. (file: r0/plugin-creator/SKILL.md)\n- skill-creator: Create or update a Codex skill with appropriately scoped instructions and any needed supporting resources. (file: r0/skill-creator/SKILL.md)\n- skill-installer: Install Codex skills into $CODEX_HOME/skills from a curated list or a GitHub repo path. Use when a user asks to list installable skills, install a curated skill, or install a skill from another repo (including private repos). (file: r0/skill-installer/SKILL.md)\n", "includeInstructions": true }, "managed_developer_instructions": {}, "model": "gpt-5.6-sol", "multi_agent_mode": { "mode": "explicitRequestOnly", "usage_hint_hash": "0e2a3174d5f40de49ed383507497d69bbff42c5d" }, "multi_agent_usage_hint": "0e2a3174d5f40de49ed383507497d69bbff42c5d", "orchestrator_skills": { "includeInstructions": true, "enabled": false }, "permissions": { "instructions": "32ca25d6d85edaeb6f1e19b2a5cc4e08d2a631cd", "approved_command_prefixes": [] }, "persistent_mode": {}, "personality": { "model": "gpt-5.6-sol", "personality": "pragmatic" }, "plugins_instructions": false, "realtime": { "active": false }, "skills": { "includeInstructions": true } } } ### Harness context { "type": "turn_context", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "cwd": "/app", "workspace_roots": [ "/app" ], "current_date": "2026-09-23", "timezone": "Etc/UTC", "approval_policy": "never", "approvals_reviewer": "user", "sandbox_policy": { "type": "danger-full-access" }, "permission_profile": { "type": "disabled" }, "model": "gpt-5.6-sol", "comp_hash": "3000", "personality": "pragmatic", "collaboration_mode": { "mode": "default", "settings": { "model": "gpt-5.6-sol", "reasoning_effort": "high", "developer_instructions": null } }, "multi_agent_version": "v2", "realtime_active": false, "effort": "high", "summary": "auto" } ## Step 6 · user · 2026-09-23T21:40:44.263Z # Build a music-discovery ranker Build the strongest ranking model you can for the "Made for You" shelf of a music streaming service. Per-user candidate eligibility, cutoff-specific explicit preference state, and a temporal backtest are provided. Candidate eligibility is platform-owned; all modeling within that candidate boundary is in scope. ## Data All inputs are under `/task/data/`. Training data ends at timestamp `24786800`. A 30-minute gap precedes a seven-day public validation window. The hidden test is the following seven-day window after another 30-minute gap. For each event family there is a `train_*.parquet` and `val_*.parquet` file: | Family | Meaning | Columns | | --- | --- | --- | | `listens` | Playback events | `uid`, `timestamp`, `item_id`, `is_organic`, `played_ratio_pct`, `track_length_seconds` | | `likes` | Explicit positive actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `dislikes` | Explicit negative actions | `uid`, `timestamp`, `item_id`, `is_organic` | | `unlikes` | Reversal of a prior like | `uid`, `timestamp`, `item_id`, `is_organic` | | `undislikes` | Reversal of a prior dislike | `uid`, `timestamp`, `item_id`, `is_organic` | `is_organic=1` means the listener sought out the track; `0` means the action came through a recommendation surface. A listen is a conventional positive at 50% completion. The discovery-quality objective below uses the stronger 80% threshold. The two public windows have different roles: 1. Build and tune a validation ranker using only `train_*` as history and `val_*` as subsequent outcomes. 2. After selecting the approach, refit it on `train_* + val_*`. At hidden-test time the validation events are observed history, not recommendable outcomes. Do not use `val_*` events as features when measuring a validation ranking. They may and should be used when refitting the final hidden-test ranker. Additional files under `/task/data/`: - `candidate_catalog.parquet`: the 30,000 retrieval-catalog tracks, ordered by training positive-listen popularity. - `target_users.parquet`: users with at least 20 positive listens in the 90 days before the training cutoff, selected without future labels. - `validation_preference_state.parquet`: active explicit state at the training cutoff, for validation ranking. - `test_preference_state.parquet`: active explicit state after validation, for the final hidden-test ranking. State is `liked` or `disliked`; a pair may have both active channels. - `validation_eligible_candidates.npz` and `test_eligible_candidates.npz`: cutoff-correct candidate sets for every target user. Each contains aligned arrays `uids`, `item_ids`, and `packed_eligible`. For user row `r`, recover the Boolean item mask with `np.unpackbits(packed_eligible[r], count=len(item_ids), bitorder="little")`. The exact available Python distribution inventory is listed in `/task/requirements.txt`. Future test interactions are hidden and used only by the separate Harbor verifier. ## Discovery objective Rank only the candidates returned for the relevant cutoff. Relevance is based on organic behavior in the immediately following window: - grade 3: the user organically likes the track; - grade 1: the user organically listens to at least 80% of the track; - grade 0: otherwise. If both positive events occur, grade 3 wins. Ranking quality is measured with graded NDCG@10. DCG uses gain `2^grade - 1` and discount `1/log2(rank + 1)`, then averages per-user NDCG over users with at least one relevant item. The public evaluator reports this objective for development, but its score is not a separate acceptance stage. Final acceptance is determined on the hidden test using the same objective. The Yambda logs contain interactions, not impression propensities. Treat the organic objective as a robust offline target, not as a causal estimate of online lift. ## Public tools `/task/evaluate_public.py` scores a validation-stage ranking against the public window. Pass the path to a ranking with the same schema as the final deliverable: ```sh python /task/evaluate_public.py solution/validation.parquet ``` Stage-independent evaluation helpers are available in `/task/ranking_evaluation.py`. The builder installs the same checked-in module in the public agent image and private verifier image so scoring mechanics cannot drift. The public copy contains no hidden labels, evaluator-owned data paths, or pass thresholds. The public evaluator uses `validation_eligible_candidates.npz`; it is not a validator for the final artifact. No model, fitted representation, example submission, or modeling architecture is supplied. Build and tune the best model you can, then refit the selected approach as described above. ## Deliverable Write `/app/solution/submission.parquet` or `/app/solution/submission.csv`, with exactly 100 rows for every target user and columns `uid`, `item_id`, `rank`. Ranks must be the integers 1-100 with no ties or duplicate items. Every submitted item must be present in that user's `test_eligible_candidates.npz` candidate set. Leave the code that builds the artifact and a concise `/app/solution/README.md` in `/app/solution/`. Keep the best valid submission saved throughout the attempt so it survives the deadline. ## Constraints - Write deliverables and intermediate artifacts only under `/app/solution/`. - Treat `/task/` and the installed Python environment as read-only. - Do not invoke nested agents or attempt to access verifier files, credentials, other trials, or host paths. - Do not install packages or use network access except for the model provider connection managed by Harbor. - You have up to 30 minutes. Improve the model as much as possible within that limit. Structural validity alone is not sufficient; the final submission is also evaluated for ranking quality. ## Step 7 · agent · 2026-09-23T21:40:46.222Z ### Thinking unavailable — no text recorded I’ll first profile the event volumes, candidate encoding, and validation signal, then build a leakage-safe baseline and iterate against the public NDCG evaluator. I’ll keep a valid test submission saved early, and all generated files will stay under `/app/solution/`. ### exec · 2026-09-23T21:40:50.651Z { "code": "const r = await tools.exec_command({\"cmd\":\"pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:40:50.717Z Script completed Wall time 0.1 seconds Output: /app /task/requirements.txt /task/data/val_dislikes.parquet /task/data/val_undislikes.parquet /task/data/target_users.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/validation_preference_state.parquet /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/validation_eligible_candidates.npz /task/data/candidate_catalog.parquet /task/data/val_likes.parquet /task/data/val_dislikes.parquet /task/data/val_undislikes.parquet /task/data/target_users.parquet /task/data/val_unlikes.parquet /task/data/train_dislikes.parquet /task/data/train_undislikes.parquet /task/data/test_preference_state.parquet /task/data/validation_preference_state.parquet /task/data/train_listens.parquet /task/data/train_likes.parquet /task/data/train_unlikes.parquet /task/data/test_eligible_candidates.npz /task/data/val_listens.parquet /task/data/validation_eligible_candidates.npz /task/ranking_evaluation.py /task/data/candidate_catalog.parquet /task/evaluate_public.py /task/data/val_likes.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 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4475c986887d288ed3dea2091a422", "usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 169, "reasoning_output_tokens": 26, "total_tokens": 14506 }, "turn_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 169, "reasoning_output_tokens": 26, "total_tokens": 14506 }, "thread_token_usage": { "input_tokens": 14337, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 169, "reasoning_output_tokens": 26, "total_tokens": 14506 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-8d2d1a41-a70f-4d4e-badd-e3bab09aa946", "process_id": "45379", "command": [ "/bin/sh", "-lc", "pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "pwd && rg --files /task/data /task | sed -n '1,120p' && sed -n '1,200p' /task/requirements.txt" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/app\n/task/requirements.txt\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/ranking_evaluation.py\n/task/data/candidate_catalog.parquet\n/task/evaluate_public.py\n/task/data/val_likes.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "stderr": "", "aggregated_output": "/app\n/task/requirements.txt\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/ranking_evaluation.py\n/task/data/candidate_catalog.parquet\n/task/evaluate_public.py\n/task/data/val_likes.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4240 }, "formatted_output": "/app\n/task/requirements.txt\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/data/candidate_catalog.parquet\n/task/data/val_likes.parquet\n/task/data/val_dislikes.parquet\n/task/data/val_undislikes.parquet\n/task/data/target_users.parquet\n/task/data/val_unlikes.parquet\n/task/data/train_dislikes.parquet\n/task/data/train_undislikes.parquet\n/task/data/test_preference_state.parquet\n/task/data/validation_preference_state.parquet\n/task/data/train_listens.parquet\n/task/data/train_likes.parquet\n/task/data/train_unlikes.parquet\n/task/data/test_eligible_candidates.npz\n/task/data/val_listens.parquet\n/task/data/validation_eligible_candidates.npz\n/task/ranking_evaluation.py\n/task/data/candidate_catalog.parquet\n/task/evaluate_public.py\n/task/data/val_likes.parquet\n# Exact third-party distribution inventory for yambda-discovery-ranking-v01.\n#\n# This is an input to `uv pip sync`, not a list of minimum requirements. A\n# conforming task environment contains exactly these third-party Python\n# distributions and no others. The same inventory is used for source preparation,\n# task building, reference scoring, judging, and solver attempts. Python itself\n# is pinned by task.toml and both Harbor image Dockerfiles.\nattrs==26.1.0\nbleach==6.4.0\ncertifi==2026.7.22\nchardet==7.6.0\ncharset-normalizer==3.5.1\nfastjsonschema==2.22.2\nidna==3.18\njoblib==1.5.3\njsonschema==4.26.0\njsonschema-specifications==2025.9.1\njupyter-core==5.9.1\njupytext==1.19.5\nkaggle==2.2.4\nkagglesdk==0.1.37\nmarkdown-it-py==4.2.0\nmdit-py-plugins==0.6.1\nmdurl==0.1.2\nnbformat==5.11.1\nnumpy==2.2.6\npackaging==26.3\npandas==2.3.3\npillow==12.3.0\npip==26.0.1\nplatformdirs==4.11.3\nprotobuf==7.35.1\npyarrow==18.1.0\npython-dateutil==2.9.0.post0\npython-dotenv==1.2.3\npython-slugify==8.0.4\npytz==2026.3.post1\nPyYAML==6.0.3\nreferencing==0.37.0\nreportlab==4.2.5\nrequests==2.34.2\nrpds-py==2026.6.3\nscikit-learn==1.7.2\nscipy==1.15.3\nsix==1.17.0\ntext-unidecode==1.3\nthreadpoolctl==3.6.0\ntqdm==4.70.0\ntraitlets==5.16.1\ntyping-extensions==4.16.0\ntzdata==2026.3\nurllib3==2.7.0\nwebencodings==0.6.1\n" } } ## Step 8 · agent · 2026-09-23T21:40:52.339Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:40:59.347Z { "code": "const r = await tools.exec_command({\"cmd\":\"sed -n '1,260p' /task/evaluate_public.py && sed -n '1,300p' /task/ranking_evaluation.py && python - <<'PY'\\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n s=os.path.getsize(f)/1e6\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows,pf.schema_arrow)\\n else:\\n z=np.load(f)\\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":30000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:40:59.462Z Script completed Wall time 0.1 seconds Output: #!/usr/bin/env python3 """Score a validation-stage ranking against public discovery labels. ``build_task.py`` publishes this module with the solver-visible task files. It loads target users, validation eligibility, and validation-window events from the adjacent ``data`` directory, then evaluates one ranking supplied on the command line. Stage-independent mechanics come from the adjacent public ``ranking_evaluation.py`` module. The builder installs the same checked-in module in the private judge, so public and private scoring mechanics cannot drift. A relevant candidate receives grade 3 for an organic like or grade 1 for an organic listen with at least 80 percent completion; a like takes precedence when both events exist. Before scoring, the ranking must contain exactly 100 distinct eligible items for every target user, with integer-valued ``uid``, ``item_id``, and ``rank`` columns and ranks 1 through 100 exactly once per user. Extra columns are ignored. Quality is graded NDCG@10, averaged only over users who have at least one eligible relevant item in the validation window. The evaluator prints a JSON object containing the model path and scores. It does not apply the task's pass threshold; its purpose is model development and selection. The private judge applies the threshold to the final submission. Usage:: python evaluate_public.py """ from __future__ import annotations import json import sys from pathlib import Path import pandas as pd from ranking_evaluation import ( CandidateSet, RankingValidationError, ranked_items, read_ranking, score_rankings, validate_ranking, ) HERE = Path(__file__).resolve().parent DATA = HERE / "data" LISTEN_STRONG = 80 def validation_truth( eligibility: CandidateSet, targets: set[int] ) -> dict[int, dict[int, int]]: """Build cutoff-correct graded relevance from public validation events. Only target-user events for eligible candidates are retained. Organic listens completed to at least 80 percent receive grade 1, and organic likes receive grade 3. Repeated user-item events are collapsed, and processing likes second ensures that grade 3 wins over grade 1. Args: eligibility: Validation-cutoff candidate membership service. targets: Complete set of target user IDs. Returns: A nested ``uid -> item_id -> grade`` mapping. Users without an eligible relevant event are omitted. """ listens = pd.read_parquet(DATA / "val_listens.parquet") likes = pd.read_parquet(DATA / "val_likes.parquet") strong = listens[ listens.uid.isin(targets) & (listens.is_organic == 1) & (listens.played_ratio_pct >= LISTEN_STRONG) ][["uid", "item_id"]].drop_duplicates() organic_likes = likes[ likes.uid.isin(targets) & (likes.is_organic == 1) ][["uid", "item_id"]].drop_duplicates() graded: dict[int, dict[int, int]] = {} for raw_uid, raw_item in strong.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 1 for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None): uid, item = int(raw_uid), int(raw_item) if eligibility.is_eligible(uid, item): graded.setdefault(uid, {})[item] = 3 return graded def load_submission( path: Path, eligibility: CandidateSet, targets: set[int] ) -> dict[int, list[int]]: """Load and validate a public validation ranking. Files ending in ``.parquet`` are read as parquet; every other suffix is read as CSV. The required columns must be integer-valued, cover exactly the target-user set, contain 100 rows and ranks 1 through 100 per user, avoid duplicate user-item pairs, and contain only cutoff-eligible items. Args: path: Ranking file to read. eligibility: Validation-cutoff candidate membership service. targets: Exact set of users the ranking must cover. Returns: A ``uid -> ranked item IDs`` mapping ordered by ascending rank. Raises: ValueError: If the ranking violates the required schema or invariants. """ frame = validate_ranking(read_ranking(path), targets) if not all( eligibility.is_eligible(uid, item) for uid, item in frame[["uid", "item_id"]].itertuples(index=False, name=None) ): raise RankingValidationError( "submission contains an item rejected by eligibility service" ) return ranked_items(frame) def score( ranked: dict[int, list[int]], graded: dict[int, dict[int, int]], ) -> dict[str, float | int]: """Aggregate per-user NDCG@10 over users with public relevance labels. Args: ranked: Ranked item IDs for every target user. graded: Eligible validation relevance keyed by user and item. Returns: The six-decimal ``quality_ndcg@10`` value and number of scored users. """ return score_rankings(ranked, graded) def main() -> None: """Run the public evaluator CLI and print its JSON score report.""" targets_frame = pd.read_parquet(DATA / "target_users.parquet") targets = set(targets_frame.uid.astype(int)) eligibility = CandidateSet(DATA / "validation_eligible_candidates.npz") graded = validation_truth(eligibility, targets) if len(sys.argv) != 2: raise SystemExit( "usage: evaluate_public.py " ) path = Path(sys.argv[1]) ranked = load_submission(path, eligibility, targets) print(json.dumps({"model": str(path), "scores": score(ranked, graded)}, indent=2)) if __name__ == "__main__": main() """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), } Traceback (most recent call last): File "", line 1, in ModuleNotFoundError: No module named 'pandas' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4476318b887d2b061037721fa92da", "usage": { "input_tokens": 15367, "cached_input_tokens": 11136, "cache_write_input_tokens": 0, "output_tokens": 240, "reasoning_output_tokens": 12, "total_tokens": 15607 }, "turn_token_usage": { "input_tokens": 29704, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 409, "reasoning_output_tokens": 38, "total_tokens": 30113 }, "thread_token_usage": { "input_tokens": 29704, "cached_input_tokens": 22272, "cache_write_input_tokens": 0, "output_tokens": 409, "reasoning_output_tokens": 38, "total_tokens": 30113 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-3022fcde-26ed-47d7-92c1-e9388680c6c8", "process_id": "78323", "command": [ "/bin/sh", "-lc", "sed -n '1,260p' /task/evaluate_public.py && sed -n '1,300p' /task/ranking_evaluation.py && python - <<'PY'\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n s=os.path.getsize(f)/1e6\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(f)\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows,pf.schema_arrow)\n else:\n z=np.load(f)\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "sed -n '1,260p' /task/evaluate_public.py && sed -n '1,300p' /task/ranking_evaluation.py && python - <<'PY'\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n s=os.path.getsize(f)/1e6\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(f)\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows,pf.schema_arrow)\n else:\n z=np.load(f)\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\nPY" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "stderr": "", "aggregated_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n", "exit_code": 1, "duration": { "secs": 0, "nanos": 3480 }, "formatted_output": "#!/usr/bin/env python3\n\"\"\"Score a validation-stage ranking against public discovery labels.\n\n``build_task.py`` publishes this module with the solver-visible task files. It\nloads target users, validation eligibility, and validation-window events from\nthe adjacent ``data`` directory, then evaluates one ranking supplied on the\ncommand line. Stage-independent mechanics come from the adjacent public\n``ranking_evaluation.py`` module. The builder installs the same checked-in\nmodule in the private judge, so public and private scoring mechanics cannot\ndrift. A relevant candidate receives grade 3 for an organic like or grade 1 for\nan organic listen with at least 80 percent completion; a like takes precedence\nwhen both events exist.\n\nBefore scoring, the ranking must contain exactly 100 distinct eligible items\nfor every target user, with integer-valued ``uid``, ``item_id``, and ``rank``\ncolumns and ranks 1 through 100 exactly once per user. Extra columns are\nignored. Quality is graded NDCG@10, averaged only over users who have at least\none eligible relevant item in the validation window.\n\nThe evaluator prints a JSON object containing the model path and scores. It\ndoes not apply the task's pass threshold; its purpose is model development and\nselection. The private judge applies the threshold to the final submission.\n\nUsage::\n\n python evaluate_public.py \n\"\"\"\nfrom __future__ import annotations\n\nimport json\nimport sys\nfrom pathlib import Path\n\nimport pandas as pd\n\nfrom ranking_evaluation import (\n CandidateSet,\n RankingValidationError,\n ranked_items,\n read_ranking,\n score_rankings,\n validate_ranking,\n)\n\n\nHERE = Path(__file__).resolve().parent\nDATA = HERE / \"data\"\nLISTEN_STRONG = 80\n\n\ndef validation_truth(\n eligibility: CandidateSet, targets: set[int]\n) -> dict[int, dict[int, int]]:\n \"\"\"Build cutoff-correct graded relevance from public validation events.\n\n Only target-user events for eligible candidates are retained. Organic\n listens completed to at least 80 percent receive grade 1, and organic likes\n receive grade 3. Repeated user-item events are collapsed, and processing\n likes second ensures that grade 3 wins over grade 1.\n\n Args:\n eligibility: Validation-cutoff candidate membership service.\n targets: Complete set of target user IDs.\n\n Returns:\n A nested ``uid -> item_id -> grade`` mapping. Users without an eligible\n relevant event are omitted.\n \"\"\"\n listens = pd.read_parquet(DATA / \"val_listens.parquet\")\n likes = pd.read_parquet(DATA / \"val_likes.parquet\")\n strong = listens[\n listens.uid.isin(targets)\n & (listens.is_organic == 1)\n & (listens.played_ratio_pct >= LISTEN_STRONG)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n organic_likes = likes[\n likes.uid.isin(targets) & (likes.is_organic == 1)\n ][[\"uid\", \"item_id\"]].drop_duplicates()\n\n graded: dict[int, dict[int, int]] = {}\n for raw_uid, raw_item in strong.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 1\n for raw_uid, raw_item in organic_likes.itertuples(index=False, name=None):\n uid, item = int(raw_uid), int(raw_item)\n if eligibility.is_eligible(uid, item):\n graded.setdefault(uid, {})[item] = 3\n return graded\n\n\ndef load_submission(\n path: Path, eligibility: CandidateSet, targets: set[int]\n) -> dict[int, list[int]]:\n \"\"\"Load and validate a public validation ranking.\n\n Files ending in ``.parquet`` are read as parquet; every other suffix is\n read as CSV. The required columns must be integer-valued, cover exactly the\n target-user set, contain 100 rows and ranks 1 through 100 per user, avoid\n duplicate user-item pairs, and contain only cutoff-eligible items.\n\n Args:\n path: Ranking file to read.\n eligibility: Validation-cutoff candidate membership service.\n targets: Exact set of users the ranking must cover.\n\n Returns:\n A ``uid -> ranked item IDs`` mapping ordered by ascending rank.\n\n Raises:\n ValueError: If the ranking violates the required schema or invariants.\n \"\"\"\n frame = validate_ranking(read_ranking(path), targets)\n if not all(\n eligibility.is_eligible(uid, item)\n for uid, item in frame[[\"uid\", \"item_id\"]].itertuples(index=False, name=None)\n ):\n raise RankingValidationError(\n \"submission contains an item rejected by eligibility service\"\n )\n return ranked_items(frame)\n\n\ndef score(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with public relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Eligible validation relevance keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n return score_rankings(ranked, graded)\n\n\ndef main() -> None:\n \"\"\"Run the public evaluator CLI and print its JSON score report.\"\"\"\n targets_frame = pd.read_parquet(DATA / \"target_users.parquet\")\n targets = set(targets_frame.uid.astype(int))\n eligibility = CandidateSet(DATA / \"validation_eligible_candidates.npz\")\n graded = validation_truth(eligibility, targets)\n\n if len(sys.argv) != 2:\n raise SystemExit(\n \"usage: evaluate_public.py \"\n )\n path = Path(sys.argv[1])\n ranked = load_submission(path, eligibility, targets)\n print(json.dumps({\"model\": str(path), \"scores\": score(ranked, graded)}, indent=2))\n\n\nif __name__ == \"__main__\":\n main()\n\"\"\"Shared public primitives for Yambda discovery-ranking evaluation.\n\nBoth the solver-visible validation evaluator and the private submission judge\nimport this module from the generated task's ``public`` directory. It owns the\nstage-independent contract: reading a ranking, validating its shape, converting\nit to per-user rank order, looking up packed candidate eligibility, and\ncomputing graded NDCG@10.\n\nThe module deliberately contains no event-derived labels, private artifact\npaths, or pass thresholds. Each evaluator supplies its own relevance data and\nper-cutoff eligibility checks around these common operations.\n\"\"\"\nfrom __future__ import annotations\n\nfrom pathlib import Path\n\nimport numpy as np\nimport pandas as pd\n\n\n__all__ = [\n \"CandidateSet\",\n \"QUALITY_K\",\n \"RankingValidationError\",\n \"REQUIRED_COLUMNS\",\n \"TOP_N\",\n \"ndcg_at_k\",\n \"ranked_items\",\n \"read_ranking\",\n \"score_rankings\",\n \"validate_ranking\",\n]\n\nTOP_N = 100\nQUALITY_K = 10\nREQUIRED_COLUMNS = (\"uid\", \"item_id\", \"rank\")\n\n\nclass RankingValidationError(ValueError):\n \"\"\"Report a ranking-schema or structural-contract violation.\"\"\"\n\n\nclass CandidateSet:\n \"\"\"Provide membership lookups over one packed candidate-set archive.\n\n The archive contains aligned ``uids``, ``item_ids``, and\n ``packed_eligible`` arrays. Candidate column ``c`` is represented by bit\n ``c % 8`` of byte ``c // 8`` using little-endian bit order. Arrays are\n copied into memory so they remain valid after the archive is closed.\n \"\"\"\n\n def __init__(self, path: Path):\n \"\"\"Load an eligibility archive and build ID-to-position indexes.\n\n Args:\n path: Path to a ``.npz`` archive with the candidate-set schema.\n \"\"\"\n with np.load(path, allow_pickle=False) as artifact:\n self.uids = artifact[\"uids\"].astype(np.int64, copy=True)\n self.item_ids = artifact[\"item_ids\"].astype(np.int64, copy=True)\n self.packed = artifact[\"packed_eligible\"].astype(np.uint8, copy=True)\n self.uid_row = {int(uid): row for row, uid in enumerate(self.uids)}\n self.item_column = {\n int(item): column for column, item in enumerate(self.item_ids)\n }\n\n def is_eligible(self, uid: int, item_id: int) -> bool:\n \"\"\"Return whether ``item_id`` is eligible for ``uid``.\n\n IDs absent from the target-user or candidate-item arrays are treated as\n ineligible rather than raising an exception.\n\n Args:\n uid: Target user ID to query.\n item_id: Candidate item ID to query.\n\n Returns:\n ``True`` only when the corresponding packed eligibility bit is set.\n \"\"\"\n row = self.uid_row.get(int(uid))\n column = self.item_column.get(int(item_id))\n if row is None or column is None:\n return False\n byte, bit = divmod(column, 8)\n return bool((int(self.packed[row, byte]) >> bit) & 1)\n\n\ndef read_ranking(path: Path) -> pd.DataFrame:\n \"\"\"Read a parquet or CSV ranking into a pandas frame.\n\n Args:\n path: Input path. A ``.parquet`` suffix selects parquet; every other\n suffix is interpreted as CSV.\n\n Returns:\n The ranking frame as loaded by pandas.\n \"\"\"\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n\n\ndef validate_ranking(\n frame: pd.DataFrame,\n targets: set[int],\n) -> pd.DataFrame:\n \"\"\"Normalize and validate the stage-independent ranking contract.\n\n Required values must be finite integers. The user set must exactly match\n ``targets``; every user must have 100 rows, distinct items, and each rank\n from 1 through 100 exactly once. Cutoff-specific candidate eligibility is\n intentionally left to the calling evaluator.\n\n Args:\n frame: Raw ranking frame. Extra columns are discarded from an internal\n copy; the caller's frame is not modified.\n targets: Exact set of target user IDs the ranking must cover.\n\n Returns:\n A normalized frame containing only the required ``int64`` columns.\n\n Raises:\n RankingValidationError: If the ranking violates the shared contract.\n \"\"\"\n required = list(REQUIRED_COLUMNS)\n if not all(column in frame for column in required):\n raise RankingValidationError(f\"submission requires columns {required}\")\n\n normalized = frame[required].copy()\n for column in required:\n numeric = pd.to_numeric(normalized[column], errors=\"coerce\")\n values = numeric.to_numpy(dtype=float)\n if numeric.isna().any() or not np.isfinite(values).all():\n raise RankingValidationError(\n f\"submission {column} must be finite numeric values\"\n )\n if not np.equal(numeric, np.floor(numeric)).all():\n raise RankingValidationError(\n f\"submission {column} must contain integers\"\n )\n normalized[column] = numeric.astype(np.int64)\n\n submitted_users = set(map(int, normalized.uid.unique()))\n if submitted_users != targets:\n raise RankingValidationError(\n f\"submission user set mismatch: missing={len(targets - submitted_users)} \"\n f\"extra={len(submitted_users - targets)}\"\n )\n counts = normalized.groupby(\"uid\").size()\n if not (counts == TOP_N).all():\n raise RankingValidationError(\n f\"submission must contain exactly {TOP_N} rows for every target user\"\n )\n if normalized.duplicated([\"uid\", \"item_id\"]).any():\n raise RankingValidationError(\n \"submission contains duplicate (uid, item_id) pairs\"\n )\n valid_ranks = set(range(1, TOP_N + 1))\n ranks_are_valid = normalized.groupby(\"uid\")[\"rank\"].apply(\n lambda values: set(map(int, values)) == valid_ranks\n )\n if not ranks_are_valid.all():\n raise RankingValidationError(\n f\"each user must have every rank from 1 through {TOP_N} exactly once\"\n )\n return normalized\n\n\ndef ranked_items(frame: pd.DataFrame) -> dict[int, list[int]]:\n \"\"\"Convert a validated ranking frame to ordered items keyed by user.\n\n Args:\n frame: Frame that has passed :func:`validate_ranking`.\n\n Returns:\n A ``uid -> item IDs`` mapping ordered by ascending rank.\n \"\"\"\n ordered = frame.sort_values([\"uid\", \"rank\"])\n return {\n int(uid): list(map(int, values))\n for uid, values in ordered.groupby(\"uid\").item_id\n }\n\n\ndef ndcg_at_k(ranked: list[int], gold: dict[int, int], k: int) -> float:\n \"\"\"Compute graded normalized discounted cumulative gain at ``k``.\n\n Relevance gain is ``2**grade - 1`` and the discount at one-based rank\n ``r`` is ``1 / log2(r + 1)``. Items absent from ``gold`` have grade zero.\n\n Args:\n ranked: Item IDs in predicted rank order.\n gold: Mapping from relevant item IDs to integer relevance grades.\n k: Maximum number of predicted positions to score.\n\n Returns:\n For a valid duplicate-free ranking, NDCG in ``[0, 1]``; returns\n ``0.0`` when the ideal gain is zero.\n \"\"\"\n gains = np.asarray([gold.get(item, 0) for item in ranked[:k]], dtype=np.float64)\n discounts = 1.0 / np.log2(np.arange(2, len(gains) + 2))\n dcg = float(np.sum((np.power(2.0, gains) - 1.0) * discounts))\n ideal = np.asarray(sorted(gold.values(), reverse=True)[:k], dtype=np.float64)\n ideal_discounts = 1.0 / np.log2(np.arange(2, len(ideal) + 2))\n idcg = float(np.sum((np.power(2.0, ideal) - 1.0) * ideal_discounts))\n return dcg / idcg if idcg else 0.0\n\n\ndef score_rankings(\n ranked: dict[int, list[int]],\n graded: dict[int, dict[int, int]],\n) -> dict[str, float | int]:\n \"\"\"Aggregate per-user NDCG@10 over users with relevance labels.\n\n Args:\n ranked: Ranked item IDs for every target user.\n graded: Relevant item grades keyed by user and item.\n\n Returns:\n The six-decimal ``quality_ndcg@10`` value and number of scored users.\n \"\"\"\n quality = float(\n np.mean(\n [\n ndcg_at_k(ranked[uid], gold, QUALITY_K)\n for uid, gold in graded.items()\n ]\n )\n )\n return {\n \"quality_ndcg@10\": round(quality, 6),\n \"quality_users\": len(graded),\n }\nTraceback (most recent call last):\n File \"\", line 1, in \nModuleNotFoundError: No module named 'pandas'\n" } } ## Step 9 · agent · 2026-09-23T21:41:01.265Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:04.156Z { "code": "const r = await tools.exec_command({\"cmd\":\"which python python3; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | head -20; env | sort | sed -n '1,120p'\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:41:04.218Z Script completed Wall time 0.1 seconds Output: /usr/local/bin/python /usr/local/bin/python3 Python 3.12.13 total 12 drwxr-xr-x 3 agent agent 4096 Sep 23 21:38 . drwxr-xr-x 1 root root 4096 Sep 23 21:40 .. drwxr-xr-x 2 agent agent 4096 Sep 23 21:38 solution CODEX_CI=1 CODEX_HOME=/tmp/codex-home CODEX_SESSION_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75 CODEX_THREAD_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75 COLORTERM= GH_PAGER=cat GIT_PAGER=cat GPG_KEY=7169605F62C751356D054A26A821E680E5FA6305 HOME=/home/agent HOSTNAME=b3bbd8f35dcd LANG=C.UTF-8 LC_ALL=C.UTF-8 LC_CTYPE=C.UTF-8 NO_COLOR=1 PAGER=cat PATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games PWD=/app PYTHONDONTWRITEBYTECODE=1 PYTHONNOUSERSITE=1 PYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684 PYTHON_VERSION=3.12.13 SHLVL=1 TERM=dumb _=/usr/local/bin/codex ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4476bad8c87d29dfb1c99b353b868", "usage": { "input_tokens": 18878, "cached_input_tokens": 15232, "cache_write_input_tokens": 0, "output_tokens": 122, "reasoning_output_tokens": 22, "total_tokens": 19000 }, "turn_token_usage": { "input_tokens": 48582, "cached_input_tokens": 37504, "cache_write_input_tokens": 0, "output_tokens": 531, "reasoning_output_tokens": 60, "total_tokens": 49113 }, "thread_token_usage": { "input_tokens": 48582, "cached_input_tokens": 37504, "cache_write_input_tokens": 0, "output_tokens": 531, "reasoning_output_tokens": 60, "total_tokens": 49113 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b67344cd-70c0-4f29-bf20-992ea312d399", "process_id": "53719", "command": [ "/bin/sh", "-lc", "which python python3; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | head -20; env | sort | sed -n '1,120p'" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "which python python3; python --version; ls -la /app; find / -maxdepth 3 -type f -name 'python' 2>/dev/null | head -20; env | sort | sed -n '1,120p'" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:38 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCODEX_THREAD_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=b3bbd8f35dcd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "stderr": "", "aggregated_output": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:38 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCODEX_THREAD_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=b3bbd8f35dcd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4310 }, "formatted_output": "/usr/local/bin/python\n/usr/local/bin/python3\nPython 3.12.13\ntotal 12\ndrwxr-xr-x 3 agent agent 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndrwxr-xr-x 2 agent agent 4096 Sep 23 21:38 solution\nCODEX_CI=1\nCODEX_HOME=/tmp/codex-home\nCODEX_SESSION_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCODEX_THREAD_ID=01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75\nCOLORTERM=\nGH_PAGER=cat\nGIT_PAGER=cat\nGPG_KEY=7169605F62C751356D054A26A821E680E5FA6305\nHOME=/home/agent\nHOSTNAME=b3bbd8f35dcd\nLANG=C.UTF-8\nLC_ALL=C.UTF-8\nLC_CTYPE=C.UTF-8\nNO_COLOR=1\nPAGER=cat\nPATH=/opt/codex-home/packages/standalone/releases/0.153.4-x86_64-unknown-linux-musl/codex-path:/usr/local/bin:/usr/bin:/bin:/usr/local/games:/usr/games\nPWD=/app\nPYTHONDONTWRITEBYTECODE=1\nPYTHONNOUSERSITE=1\nPYTHON_SHA256=c08bc65a81971c1dd5783182826503369466c7e67374d1646519adf05207b684\nPYTHON_VERSION=3.12.13\nSHLVL=1\nTERM=dumb\n_=/usr/local/bin/codex\n" } } ## Step 10 · agent · 2026-09-23T21:41:07.100Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:09.869Z { "code": "const r = await tools.exec_command({\"cmd\":\"find / -type d -name 'site-packages' 2>/dev/null | head -30; find / -type f -name 'pyarrow*.so' 2>/dev/null | head -10; ls -la /task\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:41:10.257Z Script completed Wall time 0.4 seconds Output: /usr/local/lib/python3.12/site-packages /opt/task-venv/lib/python3.12/site-packages total 36 dr-xr-xr-x 1 root root 4096 Sep 23 21:38 . drwxr-xr-x 1 root root 4096 Sep 23 21:40 .. dr-xr-xr-x 1 root root 4096 Sep 23 21:35 data -r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py -r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py -r--r--r-- 1 root root 1278 Sep 23 21:34 requirements.txt ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab447707cf887d2818ca49842a12f46", "usage": { "input_tokens": 19420, "cached_input_tokens": 18688, "cache_write_input_tokens": 0, "output_tokens": 120, "reasoning_output_tokens": 23, "total_tokens": 19540 }, "turn_token_usage": { "input_tokens": 68002, "cached_input_tokens": 56192, "cache_write_input_tokens": 0, "output_tokens": 651, "reasoning_output_tokens": 83, "total_tokens": 68653 }, "thread_token_usage": { "input_tokens": 68002, "cached_input_tokens": 56192, "cache_write_input_tokens": 0, "output_tokens": 651, "reasoning_output_tokens": 83, "total_tokens": 68653 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f56f4bf0-4e86-46e4-bb14-57f4cc39d864", "process_id": "11805", "command": [ "/bin/sh", "-lc", "find / -type d -name 'site-packages' 2>/dev/null | head -30; find / -type f -name 'pyarrow*.so' 2>/dev/null | head -10; ls -la /task" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "find / -type d -name 'site-packages' 2>/dev/null | head -30; find / -type f -name 'pyarrow*.so' 2>/dev/null | head -10; ls -la /task" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:34 requirements.txt\n", "stderr": "", "aggregated_output": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:34 requirements.txt\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 219072875 }, "formatted_output": "/usr/local/lib/python3.12/site-packages\n/opt/task-venv/lib/python3.12/site-packages\ntotal 36\ndr-xr-xr-x 1 root root 4096 Sep 23 21:38 .\ndrwxr-xr-x 1 root root 4096 Sep 23 21:40 ..\ndr-xr-xr-x 1 root root 4096 Sep 23 21:35 data\n-r--r--r-- 1 root root 5774 Sep 23 21:34 evaluate_public.py\n-r--r--r-- 1 root root 8265 Sep 23 21:34 ranking_evaluation.py\n-r--r--r-- 1 root root 1278 Sep 23 21:34 requirements.txt\n" } } ## Step 11 · agent · 2026-09-23T21:41:11.918Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:41:18.849Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -l /opt/task-venv/bin | head; /opt/task-venv/bin/python - <<'PY'\\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\\nfor f in sorted(glob.glob('/task/data/*')):\\n s=os.path.getsize(f)/1e6\\n if f.endswith('.parquet'):\\n pf=pq.ParquetFile(f)\\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\\n else:\\n z=np.load(f)\\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":20000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:41:19.994Z Script completed Wall time 1.1 seconds Output: total 104 -r--r--r-- 1 root root 9033 Sep 23 21:38 Activate.ps1 -r--r--r-- 1 root root 2158 Sep 23 21:38 activate -r--r--r-- 1 root root 920 Sep 23 21:38 activate.csh -r--r--r-- 1 root root 2195 Sep 23 21:38 activate.fish -r-xr-xr-x 1 root root 219 Sep 23 21:38 chardetect -r-xr-xr-x 1 root root 221 Sep 23 21:38 dotenv -r-xr-xr-x 1 root root 225 Sep 23 21:38 f2py -r-xr-xr-x 1 root root 216 Sep 23 21:38 idna -r-xr-xr-x 1 root root 222 Sep 23 21:38 jsonschema candidate_catalog.parquet 0.35 MB 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')] target_users.parquet 0.08 MB 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')] test_eligible_candidates.npz 7.74 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} test_preference_state.parquet 1.09 MB 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] train_dislikes.parquet 0.86 MB 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_likes.parquet 5.37 MB 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_listens.parquet 319.18 MB 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] train_undislikes.parquet 0.16 MB 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] train_unlikes.parquet 1.86 MB 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_dislikes.parquet 0.04 MB 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_likes.parquet 0.3 MB 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_listens.parquet 9.95 MB 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')] val_undislikes.parquet 0.01 MB 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] val_unlikes.parquet 0.07 MB 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')] validation_eligible_candidates.npz 7.57 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'} validation_preference_state.parquet 1.06 MB 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')] ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44776804887d28c0c70b18f59e316", "usage": { "input_tokens": 19729, "cached_input_tokens": 19200, "cache_write_input_tokens": 0, "output_tokens": 241, "reasoning_output_tokens": 15, "total_tokens": 19970 }, "turn_token_usage": { "input_tokens": 87731, "cached_input_tokens": 75392, "cache_write_input_tokens": 0, "output_tokens": 892, "reasoning_output_tokens": 98, "total_tokens": 88623 }, "thread_token_usage": { "input_tokens": 87731, "cached_input_tokens": 75392, "cache_write_input_tokens": 0, "output_tokens": 892, "reasoning_output_tokens": 98, "total_tokens": 88623 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-f9e3b535-6d9c-4788-9879-c48faca31acc", "process_id": "29797", "command": [ "/bin/sh", "-lc", "ls -l /opt/task-venv/bin | head; /opt/task-venv/bin/python - <<'PY'\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n s=os.path.getsize(f)/1e6\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(f)\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\n else:\n z=np.load(f)\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -l /opt/task-venv/bin | head; /opt/task-venv/bin/python - <<'PY'\nimport os, glob, pandas as pd, pyarrow.parquet as pq, numpy as np\nfor f in sorted(glob.glob('/task/data/*')):\n s=os.path.getsize(f)/1e6\n if f.endswith('.parquet'):\n pf=pq.ParquetFile(f)\n print(os.path.basename(f), round(s,2),'MB',pf.metadata.num_rows, [(x.name,str(x.type)) for x in pf.schema_arrow])\n else:\n z=np.load(f)\n print(os.path.basename(f),round(s,2),'MB',{k:z[k].shape for k in z.files}, {k:str(z[k].dtype) for k in z.files})\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "candidate_catalog.parquet 0.35 MB 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 0.08 MB 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz 7.74 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\ntest_preference_state.parquet 1.09 MB 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 0.86 MB 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 5.37 MB 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 319.18 MB 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 0.16 MB 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 1.86 MB 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 0.04 MB 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 0.3 MB 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 9.95 MB 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 0.01 MB 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 0.07 MB 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz 7.57 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\nvalidation_preference_state.parquet 1.06 MB 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n", "stderr": "", "aggregated_output": "candidate_catalog.parquet 0.35 MB 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 0.08 MB 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz 7.74 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\ntest_preference_state.parquet 1.09 MB 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 0.86 MB 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 5.37 MB 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 319.18 MB 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 0.16 MB 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 1.86 MB 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 0.04 MB 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 0.3 MB 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 9.95 MB 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 0.01 MB 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 0.07 MB 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz 7.57 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\nvalidation_preference_state.parquet 1.06 MB 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 965568026 }, "formatted_output": "candidate_catalog.parquet 0.35 MB 30000 [('item_id', 'uint32'), ('positive_listens', 'int64'), ('organic_positive_listens', 'uint64'), ('popularity_rank', 'int32')]\ntarget_users.parquet 0.08 MB 8456 [('uid', 'uint32'), ('recent_positive_listens', 'double'), ('history_positive_listens', 'int64')]\ntest_eligible_candidates.npz 7.74 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\ntest_preference_state.parquet 1.09 MB 484776 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\ntrain_dislikes.parquet 0.86 MB 101075 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_likes.parquet 5.37 MB 814831 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_listens.parquet 319.18 MB 43484159 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\ntrain_undislikes.parquet 0.16 MB 19960 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\ntrain_unlikes.parquet 1.86 MB 292665 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_dislikes.parquet 0.04 MB 3563 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_likes.parquet 0.3 MB 37795 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_listens.parquet 9.95 MB 1501697 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8'), ('played_ratio_pct', 'uint16'), ('track_length_seconds', 'uint32')]\nval_undislikes.parquet 0.01 MB 606 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nval_unlikes.parquet 0.07 MB 9346 [('uid', 'uint32'), ('timestamp', 'uint32'), ('item_id', 'uint32'), ('is_organic', 'uint8')]\nvalidation_eligible_candidates.npz 7.57 MB {'uids': (8456,), 'item_ids': (30000,), 'packed_eligible': (8456, 3750)} {'uids': 'uint32', 'item_ids': 'uint32', 'packed_eligible': 'uint8'}\nvalidation_preference_state.parquet 1.06 MB 469124 [('uid', 'uint32'), ('item_id', 'uint32'), ('state', 'string')]\n" } } ## Step 12 · agent · 2026-09-23T21:41:28.831Z ### Thinking unavailable — no text recorded The data is large but compact enough for sparse collaborative filtering: 43.5M training listens, 8,456 target users, and a shared 30K catalog with per-user eligibility masks. I’m checking how much of the future signal is repeat affinity versus new-track discovery, because that determines whether user–item history, item–item similarity, or popularity should dominate the top 10. ### exec · 2026-09-23T21:41:43.435Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\\nD='/task/data/'\\nfor fn in ['train_listens.parquet','val_listens.parquet']:\\n pf=pq.ParquetFile(D+fn)\\n print(fn,'rowgroups',pf.num_row_groups)\\n for c in ['uid','timestamp','item_id','played_ratio_pct']:\\n ix=pf.schema_arrow.get_field_index(c)\\n vals=[]\\n for rg in range(pf.num_row_groups):\\n st=pf.metadata.row_group(rg).column(ix).statistics\\n vals.append((st.min,st.max))\\n print(c, min(x[0] for x in vals),max(x[1] for x in vals), 'first',vals[:4])\\nfor stage in ['validation','test']:\\n z=np.load(D+f'{stage}_eligible_candidates.npz')\\n pc=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\\n print(stage,'eligible',np.percentile(pc,[0,1,10,50,90,99,100]), 'mean',pc.mean())\\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\\nprint(cat.head(10).to_string(index=False)); print(cat.tail().to_string(index=False))\\nprint(pd.read_parquet(D+'target_users.parquet').describe().to_string())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":12000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:41:44.585Z Script completed Wall time 1.1 seconds Output: train_listens.parquet rowgroups 42 uid 100 1000000 first [(100, 26500), (26500, 50300), (50300, 73100), (73100, 98200)] timestamp 0 24786795 first [(15, 24786690), (185, 24786725), (15320, 24786790), (3625, 24786795)] item_id 22 9390623 first [(206, 9390541), (105, 9390541), (105, 9390461), (167, 9390623)] played_ratio_pct 0 159 first [(0, 159), (0, 159), (0, 159), (0, 159)] val_listens.parquet rowgroups 2 uid 100 1000000 first [(100, 695500), (695500, 1000000)] timestamp 24788600 25393395 first [(24788600, 25393395), (24788600, 25393395)] item_id 50 9390541 first [(50, 9390541), (175, 9390330)] played_ratio_pct 0 159 first [(0, 159), (0, 159)] validation eligible [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727 test eligible [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947 item_id positive_listens organic_positive_listens popularity_rank 5862961 26615 20620 1 6901374 25536 17347 2 5635052 24691 17194 3 3542184 24075 17699 4 9378983 24036 18195 5 5463340 22746 17398 6 8213481 22505 17071 7 906358 22238 17183 8 2859641 18380 13704 9 3033749 16682 12267 10 item_id positive_listens organic_positive_listens popularity_rank 1275692 163 81 29996 1280011 163 76 29997 1309246 163 138 29998 1634958 163 46 29999 1670524 163 73 30000 uid recent_positive_listens history_positive_listens count 8456.000000 8456.000000 8456.000000 mean 502289.108325 1251.076750 3236.440752 std 287953.395912 1414.340904 3697.575286 min 100.000000 20.000000 20.000000 25% 254375.000000 281.000000 630.000000 50% 503850.000000 784.500000 1920.500000 75% 751725.000000 1706.250000 4494.250000 max 1000000.000000 19592.000000 26597.000000 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4478033f887d2a4243014c096d1ba", "usage": { "input_tokens": 20970, "cached_input_tokens": 14208, "cache_write_input_tokens": 0, "output_tokens": 712, "reasoning_output_tokens": 250, "total_tokens": 21682 }, "turn_token_usage": { "input_tokens": 108701, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 1604, "reasoning_output_tokens": 348, "total_tokens": 110305 }, "thread_token_usage": { "input_tokens": 108701, "cached_input_tokens": 89600, "cache_write_input_tokens": 0, "output_tokens": 1604, "reasoning_output_tokens": 348, "total_tokens": 110305 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-db0372f9-fe09-4d0d-b1d4-edb7c985b1fd", "process_id": "36431", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\nD='/task/data/'\nfor fn in ['train_listens.parquet','val_listens.parquet']:\n pf=pq.ParquetFile(D+fn)\n print(fn,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','played_ratio_pct']:\n ix=pf.schema_arrow.get_field_index(c)\n vals=[]\n for rg in range(pf.num_row_groups):\n st=pf.metadata.row_group(rg).column(ix).statistics\n vals.append((st.min,st.max))\n print(c, min(x[0] for x in vals),max(x[1] for x in vals), 'first',vals[:4])\nfor stage in ['validation','test']:\n z=np.load(D+f'{stage}_eligible_candidates.npz')\n pc=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\n print(stage,'eligible',np.percentile(pc,[0,1,10,50,90,99,100]), 'mean',pc.mean())\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail().to_string(index=False))\nprint(pd.read_parquet(D+'target_users.parquet').describe().to_string())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pyarrow.parquet as pq, numpy as np, pandas as pd\nD='/task/data/'\nfor fn in ['train_listens.parquet','val_listens.parquet']:\n pf=pq.ParquetFile(D+fn)\n print(fn,'rowgroups',pf.num_row_groups)\n for c in ['uid','timestamp','item_id','played_ratio_pct']:\n ix=pf.schema_arrow.get_field_index(c)\n vals=[]\n for rg in range(pf.num_row_groups):\n st=pf.metadata.row_group(rg).column(ix).statistics\n vals.append((st.min,st.max))\n print(c, min(x[0] for x in vals),max(x[1] for x in vals), 'first',vals[:4])\nfor stage in ['validation','test']:\n z=np.load(D+f'{stage}_eligible_candidates.npz')\n pc=np.unpackbits(z['packed_eligible'],axis=1,count=30000,bitorder='little').sum(1)\n print(stage,'eligible',np.percentile(pc,[0,1,10,50,90,99,100]), 'mean',pc.mean())\ncat=pd.read_parquet(D+'candidate_catalog.parquet')\nprint(cat.head(10).to_string(index=False)); print(cat.tail().to_string(index=False))\nprint(pd.read_parquet(D+'target_users.parquet').describe().to_string())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet rowgroups 42\nuid 100 1000000 first [(100, 26500), (26500, 50300), (50300, 73100), (73100, 98200)]\ntimestamp 0 24786795 first [(15, 24786690), (185, 24786725), (15320, 24786790), (3625, 24786795)]\nitem_id 22 9390623 first [(206, 9390541), (105, 9390541), (105, 9390461), (167, 9390623)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159), (0, 159), (0, 159)]\nval_listens.parquet rowgroups 2\nuid 100 1000000 first [(100, 695500), (695500, 1000000)]\ntimestamp 24788600 25393395 first [(24788600, 25393395), (24788600, 25393395)]\nitem_id 50 9390541 first [(50, 9390541), (175, 9390330)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159)]\nvalidation eligible [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727\ntest eligible [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1275692 163 81 29996\n 1280011 163 76 29997\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n", "stderr": "", "aggregated_output": "train_listens.parquet rowgroups 42\nuid 100 1000000 first [(100, 26500), (26500, 50300), (50300, 73100), (73100, 98200)]\ntimestamp 0 24786795 first [(15, 24786690), (185, 24786725), (15320, 24786790), (3625, 24786795)]\nitem_id 22 9390623 first [(206, 9390541), (105, 9390541), (105, 9390461), (167, 9390623)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159), (0, 159), (0, 159)]\nval_listens.parquet rowgroups 2\nuid 100 1000000 first [(100, 695500), (695500, 1000000)]\ntimestamp 24788600 25393395 first [(24788600, 25393395), (24788600, 25393395)]\nitem_id 50 9390541 first [(50, 9390541), (175, 9390330)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159)]\nvalidation eligible [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727\ntest eligible [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1275692 163 81 29996\n 1280011 163 76 29997\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 956204612 }, "formatted_output": "train_listens.parquet rowgroups 42\nuid 100 1000000 first [(100, 26500), (26500, 50300), (50300, 73100), (73100, 98200)]\ntimestamp 0 24786795 first [(15, 24786690), (185, 24786725), (15320, 24786790), (3625, 24786795)]\nitem_id 22 9390623 first [(206, 9390541), (105, 9390541), (105, 9390461), (167, 9390623)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159), (0, 159), (0, 159)]\nval_listens.parquet rowgroups 2\nuid 100 1000000 first [(100, 695500), (695500, 1000000)]\ntimestamp 24788600 25393395 first [(24788600, 25393395), (24788600, 25393395)]\nitem_id 50 9390541 first [(50, 9390541), (175, 9390330)]\nplayed_ratio_pct 0 159 first [(0, 159), (0, 159)]\nvalidation eligible [24657. 26724.2 28038.5 29242. 29852. 29979. 30000. ] mean 29067.306291390727\ntest eligible [24552. 26672.7 28001. 29220. 29835. 29975. 30000. ] mean 29041.3178807947\n item_id positive_listens organic_positive_listens popularity_rank\n 5862961 26615 20620 1\n 6901374 25536 17347 2\n 5635052 24691 17194 3\n 3542184 24075 17699 4\n 9378983 24036 18195 5\n 5463340 22746 17398 6\n 8213481 22505 17071 7\n 906358 22238 17183 8\n 2859641 18380 13704 9\n 3033749 16682 12267 10\n item_id positive_listens organic_positive_listens popularity_rank\n 1275692 163 81 29996\n 1280011 163 76 29997\n 1309246 163 138 29998\n 1634958 163 46 29999\n 1670524 163 73 30000\n uid recent_positive_listens history_positive_listens\ncount 8456.000000 8456.000000 8456.000000\nmean 502289.108325 1251.076750 3236.440752\nstd 287953.395912 1414.340904 3697.575286\nmin 100.000000 20.000000 20.000000\n25% 254375.000000 281.000000 630.000000\n50% 503850.000000 784.500000 1920.500000\n75% 751725.000000 1706.250000 4494.250000\nmax 1000000.000000 19592.000000 26597.000000\n" } } ## Step 13 · agent · 2026-09-23T21:42:02.023Z ### Thinking unavailable — no text recorded ## Step 14 · agent · 2026-09-23T21:42:03.410Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:21.326Z { "code": "const r = await tools.exec_command({\"cmd\":\"mkdir -p /app/solution && /opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'\\ntarget=set(pd.read_parquet(D+'target_users.parquet').uid.astype(int))\\nz=np.load(D+'validation_eligible_candidates.npz'); urow={int(x):i for i,x in enumerate(z['uids'])}; icol={int(x):i for i,x in enumerate(z['item_ids'])}\\ndef elig(u,i):\\n r=urow.get(int(u)); c=icol.get(int(i)); return r is not None and c is not None and ((z['packed_eligible'][r,c//8]>>(c%8))&1)\\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct','timestamp'])\\nq=l[l.uid.isin(target)&(l.is_organic==1)&(l.played_ratio_pct>=80)]\\nq=q[['uid','item_id']].drop_duplicates()\\nq=q[[elig(u,i) for u,i in q.itertuples(index=False,name=None)]]\\nlikes=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic'])\\na=likes[likes.uid.isin(target)&(likes.is_organic==1)][['uid','item_id']].drop_duplicates()\\na=a[[elig(u,i) for u,i in a.itertuples(index=False,name=None)]]\\ntruth=q.assign(grade=1); truth=pd.concat([truth,a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nprint('strong rows',len(q),'likes',len(a),'truth',len(truth),'users',truth.uid.nunique())\\nprint('grade',truth.grade.value_counts().to_dict(),'rel/user',truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]).to_dict())\\nprint('all val org strong targets raw', len(l[(l.uid.isin(target))&(l.is_organic==1)&(l.played_ratio_pct>=80)]), 'users',l[(l.uid.isin(target))&(l.is_organic==1)&(l.played_ratio_pct>=80)].uid.nunique())\\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:42:51.527Z Script completed Wall time 30.2 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44798cd2c87d2be26c7bbda56cafe", "usage": { "input_tokens": 22532, "cached_input_tokens": 20736, "cache_write_input_tokens": 0, "output_tokens": 1123, "reasoning_output_tokens": 553, "total_tokens": 23655 }, "turn_token_usage": { "input_tokens": 131233, "cached_input_tokens": 110336, "cache_write_input_tokens": 0, "output_tokens": 2727, "reasoning_output_tokens": 901, "total_tokens": 133960 }, "thread_token_usage": { "input_tokens": 131233, "cached_input_tokens": 110336, "cache_write_input_tokens": 0, "output_tokens": 2727, "reasoning_output_tokens": 901, "total_tokens": 133960 } } ## Step 15 · agent · 2026-09-23T21:42:53.625Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:42:55.647Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -lh /app/solution; pgrep -af '/opt/task-venv/bin/python' || true\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:42:55.689Z Script completed Wall time 0.0 seconds Output: total 0 /bin/sh: 1: pgrep: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2bc4470e-46f6-4e0b-acdb-44bd50e610be", "process_id": "92711", "command": [ "/bin/sh", "-lc", "ls -lh /app/solution; pgrep -af '/opt/task-venv/bin/python' || true" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ls -lh /app/solution; pgrep -af '/opt/task-venv/bin/python' || true" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 0\n/bin/sh: 1: pgrep: not found\n", "stderr": "", "aggregated_output": "total 0\n/bin/sh: 1: pgrep: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4500 }, "formatted_output": "total 0\n/bin/sh: 1: pgrep: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab447dbc4b087d297055aba5cdb9706", "usage": { "input_tokens": 23678, "cached_input_tokens": 22400, "cache_write_input_tokens": 0, "output_tokens": 101, "reasoning_output_tokens": 31, "total_tokens": 23779 }, "turn_token_usage": { "input_tokens": 154911, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 2828, "reasoning_output_tokens": 932, "total_tokens": 157739 }, "thread_token_usage": { "input_tokens": 154911, "cached_input_tokens": 132736, "cache_write_input_tokens": 0, "output_tokens": 2828, "reasoning_output_tokens": 932, "total_tokens": 157739 } } ## Step 16 · agent · 2026-09-23T21:42:59.104Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:00.712Z { "code": "const r = await tools.exec_command({\"cmd\":\"ps aux | rg 'python|PID' | head -20\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:43:00.784Z Script completed Wall time 0.0 seconds Output: /bin/sh: 1: ps: not found ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b66ea8a2-c148-4baf-86c9-8e911917e85f", "process_id": "92783", "command": [ "/bin/sh", "-lc", "ps aux | rg 'python|PID' | head -20" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "ps aux | rg 'python|PID' | head -20" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "/bin/sh: 1: ps: not found\n", "stderr": "", "aggregated_output": "/bin/sh: 1: ps: not found\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 4360 }, "formatted_output": "/bin/sh: 1: ps: not found\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab447e006ac87d2a0a88c3a9f6d8ac1", "usage": { "input_tokens": 23818, "cached_input_tokens": 23552, "cache_write_input_tokens": 0, "output_tokens": 130, "reasoning_output_tokens": 70, "total_tokens": 23948 }, "turn_token_usage": { "input_tokens": 178729, "cached_input_tokens": 156288, "cache_write_input_tokens": 0, "output_tokens": 2958, "reasoning_output_tokens": 1002, "total_tokens": 181687 }, "thread_token_usage": { "input_tokens": 178729, "cached_input_tokens": 156288, "cache_write_input_tokens": 0, "output_tokens": 2958, "reasoning_output_tokens": 1002, "total_tokens": 181687 } } ## Step 17 · agent · 2026-09-23T21:43:03.905Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:26.040Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np, time\\nD='/task/data/'; t=time.time()\\ntargets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); print('targets',time.time()-t,flush=True)\\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; print('npz',time.time()-t,flush=True)\\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); print('listens',len(l),time.time()-t,flush=True)\\nq=l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); print('qraw',len(q),time.time()-t,flush=True)\\n# searchsorted aligned sorted IDs\\nr=np.searchsorted(uids,q.uid.to_numpy()); c=np.searchsorted(items,q.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); q=q[ok]; print('q eligible',len(q),time.time()-t,flush=True)\\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates(); print('araw',len(a),time.time()-t,flush=True)\\nr=np.searchsorted(uids,a.uid.to_numpy()); c=np.searchsorted(items,a.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); a=a[ok]; print('a eligible',len(a),time.time()-t,flush=True)\\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nprint('truth',len(truth),'users',truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]),flush=True)\\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:43:27.038Z Script completed Wall time 1.0 seconds Output: targets 0.038172006607055664 npz 0.13595080375671387 listens 1501697 0.15717840194702148 qraw 255608 0.2030320167541504 q eligible 32 0.22214293479919434 araw 17195 0.22843027114868164 a eligible 3 0.23006772994995117 truth 35 users 34 {1: 32, 3: 3} count 34.000000 mean 1.029412 std 0.171499 min 1.000000 10% 1.000000 50% 1.000000 90% 1.000000 99% 1.670000 max 2.000000 dtype: float64 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab447e5404887d2bdfcc6e28066cc52", "usage": { "input_tokens": 23982, "cached_input_tokens": 19584, "cache_write_input_tokens": 0, "output_tokens": 752, "reasoning_output_tokens": 50, "total_tokens": 24734 }, "turn_token_usage": { "input_tokens": 202711, "cached_input_tokens": 175872, "cache_write_input_tokens": 0, "output_tokens": 3710, "reasoning_output_tokens": 1052, "total_tokens": 206421 }, "thread_token_usage": { "input_tokens": 202711, "cached_input_tokens": 175872, "cache_write_input_tokens": 0, "output_tokens": 3710, "reasoning_output_tokens": 1052, "total_tokens": 206421 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-db8756e9-fb13-4440-b9df-e389cb522994", "process_id": "61556", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nD='/task/data/'; t=time.time()\ntargets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); print('targets',time.time()-t,flush=True)\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; print('npz',time.time()-t,flush=True)\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); print('listens',len(l),time.time()-t,flush=True)\nq=l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); print('qraw',len(q),time.time()-t,flush=True)\n# searchsorted aligned sorted IDs\nr=np.searchsorted(uids,q.uid.to_numpy()); c=np.searchsorted(items,q.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); q=q[ok]; print('q eligible',len(q),time.time()-t,flush=True)\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates(); print('araw',len(a),time.time()-t,flush=True)\nr=np.searchsorted(uids,a.uid.to_numpy()); c=np.searchsorted(items,a.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); a=a[ok]; print('a eligible',len(a),time.time()-t,flush=True)\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('truth',len(truth),'users',truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]),flush=True)\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np, time\nD='/task/data/'; t=time.time()\ntargets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); print('targets',time.time()-t,flush=True)\nz=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; print('npz',time.time()-t,flush=True)\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct']); print('listens',len(l),time.time()-t,flush=True)\nq=l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates(); print('qraw',len(q),time.time()-t,flush=True)\n# searchsorted aligned sorted IDs\nr=np.searchsorted(uids,q.uid.to_numpy()); c=np.searchsorted(items,q.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); q=q[ok]; print('q eligible',len(q),time.time()-t,flush=True)\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates(); print('araw',len(a),time.time()-t,flush=True)\nr=np.searchsorted(uids,a.uid.to_numpy()); c=np.searchsorted(items,a.item_id.to_numpy()); ok=(r>(c[ix]%8))&1).astype(bool); a=a[ok]; print('a eligible',len(a),time.time()-t,flush=True)\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('truth',len(truth),'users',truth.uid.nunique(),truth.grade.value_counts().to_dict(),flush=True)\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]),flush=True)\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "targets 0.038172006607055664\nnpz 0.13595080375671387\nlistens 1501697 0.15717840194702148\nqraw 255608 0.2030320167541504\nq eligible 32 0.22214293479919434\naraw 17195 0.22843027114868164\na eligible 3 0.23006772994995117\ntruth 35 users 34 {1: 32, 3: 3}\ncount 34.000000\nmean 1.029412\nstd 0.171499\nmin 1.000000\n10% 1.000000\n50% 1.000000\n90% 1.000000\n99% 1.670000\nmax 2.000000\ndtype: float64\n", "stderr": "", "aggregated_output": "targets 0.038172006607055664\nnpz 0.13595080375671387\nlistens 1501697 0.15717840194702148\nqraw 255608 0.2030320167541504\nq eligible 32 0.22214293479919434\naraw 17195 0.22843027114868164\na eligible 3 0.23006772994995117\ntruth 35 users 34 {1: 32, 3: 3}\ncount 34.000000\nmean 1.029412\nstd 0.171499\nmin 1.000000\n10% 1.000000\n50% 1.000000\n90% 1.000000\n99% 1.670000\nmax 2.000000\ndtype: float64\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 808912514 }, "formatted_output": "targets 0.038172006607055664\nnpz 0.13595080375671387\nlistens 1501697 0.15717840194702148\nqraw 255608 0.2030320167541504\nq eligible 32 0.22214293479919434\naraw 17195 0.22843027114868164\na eligible 3 0.23006772994995117\ntruth 35 users 34 {1: 32, 3: 3}\ncount 34.000000\nmean 1.029412\nstd 0.171499\nmin 1.000000\n10% 1.000000\n50% 1.000000\n90% 1.000000\n99% 1.670000\nmax 2.000000\ndtype: float64\n" } } ## Step 18 · agent · 2026-09-23T21:43:29.574Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:43:44.366Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u - <<'PY'\\nimport pandas as pd, numpy as np\\nD='/task/data/'; targets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); z=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; idx=pd.Index(items)\\ndef filterelig(x):\\n r=np.searchsorted(uids,x.uid.to_numpy()); c=idx.get_indexer(x.item_id.to_numpy()); ok=(r=0); ii=np.flatnonzero(ok); ok[ii]=((packed[r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool); return x[ok]\\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\\nq=filterelig(l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates())\\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=filterelig(a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates())\\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\\nprint('strong',len(q),'likes',len(a),'truth',len(truth),'users',truth.uid.nunique(),'grades',truth.grade.value_counts().to_dict())\\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]))\\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:43:45.402Z Script completed Wall time 1.0 seconds Output: strong 35597 likes 4417 truth 37769 users 4263 grades {1: 33352, 3: 4417} count 4263.000000 mean 8.859723 std 15.773444 min 1.000000 10% 1.000000 50% 4.000000 90% 21.000000 99% 63.000000 max 442.000000 dtype: float64 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab447ff45a087d291bab637ee8c871d", "usage": { "input_tokens": 24953, "cached_input_tokens": 23808, "cache_write_input_tokens": 0, "output_tokens": 516, "reasoning_output_tokens": 43, "total_tokens": 25469 }, "turn_token_usage": { "input_tokens": 227664, "cached_input_tokens": 199680, "cache_write_input_tokens": 0, "output_tokens": 4226, "reasoning_output_tokens": 1095, "total_tokens": 231890 }, "thread_token_usage": { "input_tokens": 227664, "cached_input_tokens": 199680, "cache_write_input_tokens": 0, "output_tokens": 4226, "reasoning_output_tokens": 1095, "total_tokens": 231890 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2d14bc0b-d697-4ba3-9c85-9aabf4aad05e", "process_id": "2018", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'; targets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); z=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; idx=pd.Index(items)\ndef filterelig(x):\n r=np.searchsorted(uids,x.uid.to_numpy()); c=idx.get_indexer(x.item_id.to_numpy()); ok=(r=0); ii=np.flatnonzero(ok); ok[ii]=((packed[r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool); return x[ok]\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nq=filterelig(l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates())\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=filterelig(a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates())\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong',len(q),'likes',len(a),'truth',len(truth),'users',truth.uid.nunique(),'grades',truth.grade.value_counts().to_dict())\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]))\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u - <<'PY'\nimport pandas as pd, numpy as np\nD='/task/data/'; targets=pd.read_parquet(D+'target_users.parquet').uid.to_numpy(); z=np.load(D+'validation_eligible_candidates.npz'); items=z['item_ids']; uids=z['uids']; packed=z['packed_eligible']; idx=pd.Index(items)\ndef filterelig(x):\n r=np.searchsorted(uids,x.uid.to_numpy()); c=idx.get_indexer(x.item_id.to_numpy()); ok=(r=0); ii=np.flatnonzero(ok); ok[ii]=((packed[r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool); return x[ok]\nl=pd.read_parquet(D+'val_listens.parquet',columns=['uid','item_id','is_organic','played_ratio_pct'])\nq=filterelig(l[np.isin(l.uid,targets)&(l.is_organic==1)&(l.played_ratio_pct>=80)][['uid','item_id']].drop_duplicates())\na=pd.read_parquet(D+'val_likes.parquet',columns=['uid','item_id','is_organic']); a=filterelig(a[np.isin(a.uid,targets)&(a.is_organic==1)][['uid','item_id']].drop_duplicates())\ntruth=pd.concat([q.assign(grade=1),a.assign(grade=3)]).sort_values('grade').drop_duplicates(['uid','item_id'],keep='last')\nprint('strong',len(q),'likes',len(a),'truth',len(truth),'users',truth.uid.nunique(),'grades',truth.grade.value_counts().to_dict())\nprint(truth.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]))\ntruth.to_parquet('/app/solution/validation_truth.parquet',index=False)\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "strong 35597 likes 4417 truth 37769 users 4263 grades {1: 33352, 3: 4417}\ncount 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n50% 4.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\n", "stderr": "", "aggregated_output": "strong 35597 likes 4417 truth 37769 users 4263 grades {1: 33352, 3: 4417}\ncount 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n50% 4.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 863552436 }, "formatted_output": "strong 35597 likes 4417 truth 37769 users 4263 grades {1: 33352, 3: 4417}\ncount 4263.000000\nmean 8.859723\nstd 15.773444\nmin 1.000000\n10% 1.000000\n50% 4.000000\n90% 21.000000\n99% 63.000000\nmax 442.000000\ndtype: float64\n" } } ## Step 19 · agent · 2026-09-23T21:43:58.490Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:27.915Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_history.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Build compact per-target-user/item history aggregates from listen logs.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D = Path('/task/data')\\n+OUT = Path('/app/solution')\\n+\\n+def aggregate(files, cutoff, output):\\n+ targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy()\\n+ catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy()\\n+ chunks=[]\\n+ cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\\n+ for path in files:\\n+ pf=pq.ParquetFile(path)\\n+ for rg in range(pf.num_row_groups):\\n+ x=pf.read_row_group(rg, columns=cols).to_pandas()\\n+ x=x[np.isin(x.uid,targets) & np.isin(x.item_id,catalog) & (x.played_ratio_pct>=50)]\\n+ if x.empty: continue\\n+ age=(cutoff-x.timestamp.to_numpy()).astype(np.float64)\\n+ # Recency features at several horizons; timestamps are seconds.\\n+ x['d7']=np.exp(-age/(7*86400)).astype('float32')\\n+ x['d30']=np.exp(-age/(30*86400)).astype('float32')\\n+ x['d90']=np.exp(-age/(90*86400)).astype('float32')\\n+ x['org']=(x.is_organic==1).astype('uint16')\\n+ x['strong_org']=((x.is_organic==1)&(x.played_ratio_pct>=80)).astype('uint16')\\n+ x['one']=np.uint16(1)\\n+ g=x.groupby(['uid','item_id'],sort=False,observed=True).agg(\\n+ count=('one','sum'), organic=('org','sum'), strong=('strong_org','sum'),\\n+ d7=('d7','sum'),d30=('d30','sum'),d90=('d90','sum'),\\n+ last=('timestamp','max'))\\n+ chunks.append(g.reset_index())\\n+ print(path.name,rg+1,'/',pf.num_row_groups,'kept',len(x),'groups',len(g),flush=True)\\n+ a=pd.concat(chunks,ignore_index=True)\\n+ a=a.groupby(['uid','item_id'],sort=False,observed=True).agg(\\n+ count=('count','sum'),organic=('organic','sum'),strong=('strong','sum'),\\n+ d7=('d7','sum'),d30=('d30','sum'),d90=('d90','sum'),last=('last','max')).reset_index()\\n+ # Compact dtypes without sacrificing ranking precision.\\n+ for c in ['count','organic','strong']:\\n+ a[c]=a[c].clip(0,65535).astype('uint16')\\n+ a['uid']=a.uid.astype('uint32'); a['item_id']=a.item_id.astype('uint32'); a['last']=a['last'].astype('uint32')\\n+ a.to_parquet(output,index=False,compression='zstd')\\n+ print('wrote',output,len(a),flush=True)\\n+\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); args=p.parse_args()\\n+ fs=[D/'train_listens.parquet']; cutoff=24786800; out=OUT/'history_train.parquet'\\n+ if args.with_val:\\n+ fs.append(D/'val_listens.parquet'); cutoff=25393400; out=OUT/'history_full.parquet'\\n+ aggregate(fs,cutoff,out)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:44:27.971Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-3ec83b72-d490-4e05-8f05-ed6e8b950183", "changes": { "/app/solution/build_history.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Build compact per-target-user/item history aggregates from listen logs.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef aggregate(files, cutoff, output):\n targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy()\n catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy()\n chunks=[]\n cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\n for path in files:\n pf=pq.ParquetFile(path)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg, columns=cols).to_pandas()\n x=x[np.isin(x.uid,targets) & np.isin(x.item_id,catalog) & (x.played_ratio_pct>=50)]\n if x.empty: continue\n age=(cutoff-x.timestamp.to_numpy()).astype(np.float64)\n # Recency features at several horizons; timestamps are seconds.\n x['d7']=np.exp(-age/(7*86400)).astype('float32')\n x['d30']=np.exp(-age/(30*86400)).astype('float32')\n x['d90']=np.exp(-age/(90*86400)).astype('float32')\n x['org']=(x.is_organic==1).astype('uint16')\n x['strong_org']=((x.is_organic==1)&(x.played_ratio_pct>=80)).astype('uint16')\n x['one']=np.uint16(1)\n g=x.groupby(['uid','item_id'],sort=False,observed=True).agg(\n count=('one','sum'), organic=('org','sum'), strong=('strong_org','sum'),\n d7=('d7','sum'),d30=('d30','sum'),d90=('d90','sum'),\n last=('timestamp','max'))\n chunks.append(g.reset_index())\n print(path.name,rg+1,'/',pf.num_row_groups,'kept',len(x),'groups',len(g),flush=True)\n a=pd.concat(chunks,ignore_index=True)\n a=a.groupby(['uid','item_id'],sort=False,observed=True).agg(\n count=('count','sum'),organic=('organic','sum'),strong=('strong','sum'),\n d7=('d7','sum'),d30=('d30','sum'),d90=('d90','sum'),last=('last','max')).reset_index()\n # Compact dtypes without sacrificing ranking precision.\n for c in ['count','organic','strong']:\n a[c]=a[c].clip(0,65535).astype('uint16')\n a['uid']=a.uid.astype('uint32'); a['item_id']=a.item_id.astype('uint32'); a['last']=a['last'].astype('uint32')\n a.to_parquet(output,index=False,compression='zstd')\n print('wrote',output,len(a),flush=True)\n\nif __name__=='__main__':\n p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); args=p.parse_args()\n fs=[D/'train_listens.parquet']; cutoff=24786800; out=OUT/'history_train.parquet'\n if args.with_val:\n fs.append(D/'val_listens.parquet'); cutoff=25393400; out=OUT/'history_full.parquet'\n aggregate(fs,cutoff,out)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_history.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4481196c887d2bd8eb43295ffb2ba", "usage": { "input_tokens": 25605, "cached_input_tokens": 24832, "cache_write_input_tokens": 0, "output_tokens": 1276, "reasoning_output_tokens": 345, "total_tokens": 26881 }, "turn_token_usage": { "input_tokens": 253269, "cached_input_tokens": 224512, "cache_write_input_tokens": 0, "output_tokens": 5502, "reasoning_output_tokens": 1440, "total_tokens": 258771 }, "thread_token_usage": { "input_tokens": 253269, "cached_input_tokens": 224512, "cache_write_input_tokens": 0, "output_tokens": 5502, "reasoning_output_tokens": 1440, "total_tokens": 258771 } } ## Step 20 · agent · 2026-09-23T21:44:29.742Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:31.728Z { "code": "const r = await tools.exec_command({\"cmd\":\"ls -l /app/solution && sed -n '1,20p' /app/solution/build_history.py\",\"workdir\":\"/app\",\"yield_time_ms\":10000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:44:31.784Z Script completed Wall time 0.0 seconds Output: total 184 -rw-r--r-- 1 agent agent 2800 Sep 23 21:44 build_history.py -rw-r--r-- 1 agent agent 181512 Sep 23 21:43 validation_truth.parquet #!/opt/task-venv/bin/python """Build compact per-target-user/item history aggregates from listen logs.""" from pathlib import Path import argparse import numpy as np import pandas as pd import pyarrow.parquet as pq D = Path('/task/data') OUT = Path('/app/solution') def aggregate(files, cutoff, output): targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy() catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy() chunks=[] cols=['uid','timestamp','item_id','is_organic','played_ratio_pct'] for path in files: pf=pq.ParquetFile(path) for rg in range(pf.num_row_groups): x=pf.read_row_group(rg, columns=cols).to_pandas() ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4b3e603a-5296-45da-a18f-d75b47f9e4fe", "process_id": "78054", "command": [ "/bin/sh", "-lc", "ls -l /app/solution && sed -n '1,20p' /app/solution/build_history.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "list_files", "cmd": "ls -l /app/solution", "path": "solution" }, { "type": "read", "cmd": "sed -n '1,20p' /app/solution/build_history.py", "name": "build_history.py", "path": "/app/solution/build_history.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "total 184\n-rw-r--r-- 1 agent agent 2800 Sep 23 21:44 build_history.py\n-rw-r--r-- 1 agent agent 181512 Sep 23 21:43 validation_truth.parquet\n#!/opt/task-venv/bin/python\n\"\"\"Build compact per-target-user/item history aggregates from listen logs.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef aggregate(files, cutoff, output):\n targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy()\n catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy()\n chunks=[]\n cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\n for path in files:\n pf=pq.ParquetFile(path)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg, columns=cols).to_pandas()\n", "stderr": "", "aggregated_output": "total 184\n-rw-r--r-- 1 agent agent 2800 Sep 23 21:44 build_history.py\n-rw-r--r-- 1 agent agent 181512 Sep 23 21:43 validation_truth.parquet\n#!/opt/task-venv/bin/python\n\"\"\"Build compact per-target-user/item history aggregates from listen logs.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef aggregate(files, cutoff, output):\n targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy()\n catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy()\n chunks=[]\n cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\n for path in files:\n pf=pq.ParquetFile(path)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg, columns=cols).to_pandas()\n", "exit_code": 0, "duration": { "secs": 0, "nanos": 3771 }, "formatted_output": "total 184\n-rw-r--r-- 1 agent agent 2800 Sep 23 21:44 build_history.py\n-rw-r--r-- 1 agent agent 181512 Sep 23 21:43 validation_truth.parquet\n#!/opt/task-venv/bin/python\n\"\"\"Build compact per-target-user/item history aggregates from listen logs.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD = Path('/task/data')\nOUT = Path('/app/solution')\n\ndef aggregate(files, cutoff, output):\n targets = pd.read_parquet(D/'target_users.parquet', columns=['uid']).uid.to_numpy()\n catalog = pd.read_parquet(D/'candidate_catalog.parquet', columns=['item_id']).item_id.to_numpy()\n chunks=[]\n cols=['uid','timestamp','item_id','is_organic','played_ratio_pct']\n for path in files:\n pf=pq.ParquetFile(path)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg, columns=cols).to_pandas()\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4483c49f087d2bff8d9fa3903de5b", "usage": { "input_tokens": 26905, "cached_input_tokens": 23680, "cache_write_input_tokens": 0, "output_tokens": 87, "reasoning_output_tokens": 16, "total_tokens": 26992 }, "turn_token_usage": { "input_tokens": 280174, "cached_input_tokens": 248192, "cache_write_input_tokens": 0, "output_tokens": 5589, "reasoning_output_tokens": 1456, "total_tokens": 285763 }, "thread_token_usage": { "input_tokens": 280174, "cached_input_tokens": 248192, "cache_write_input_tokens": 0, "output_tokens": 5589, "reasoning_output_tokens": 1456, "total_tokens": 285763 } } ## Step 21 · agent · 2026-09-23T21:44:33.984Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:44:36.698Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/build_history.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":5000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:44:46.415Z Script completed Wall time 9.7 seconds Output: train_listens.parquet 1 / 42 kept 470174 groups 139734 train_listens.parquet 2 / 42 kept 469392 groups 128847 train_listens.parquet 3 / 42 kept 444258 groups 124468 train_listens.parquet 4 / 42 kept 476347 groups 131026 train_listens.parquet 5 / 42 kept 491618 groups 136218 train_listens.parquet 6 / 42 kept 478947 groups 128987 train_listens.parquet 7 / 42 kept 496372 groups 137306 train_listens.parquet 8 / 42 kept 474623 groups 138813 train_listens.parquet 9 / 42 kept 458340 groups 124016 train_listens.parquet 10 / 42 kept 492053 groups 139367 train_listens.parquet 11 / 42 kept 470337 groups 133071 train_listens.parquet 12 / 42 kept 499286 groups 132091 train_listens.parquet 13 / 42 kept 482827 groups 131122 train_listens.parquet 14 / 42 kept 517558 groups 138655 train_listens.parquet 15 / 42 kept 507015 groups 138934 train_listens.parquet 16 / 42 kept 472676 groups 138047 train_listens.parquet 17 / 42 kept 419418 groups 119501 train_listens.parquet 18 / 42 kept 498144 groups 132750 train_listens.parquet 19 / 42 kept 496117 groups 134133 train_listens.parquet 20 / 42 kept 492946 groups 136429 train_listens.parquet 21 / 42 kept 505467 groups 146891 train_listens.parquet 22 / 42 kept 490117 groups 128425 train_listens.parquet 23 / 42 kept 449019 groups 127196 train_listens.parquet 24 / 42 kept 471799 groups 132268 train_listens.parquet 25 / 42 kept 474285 groups 133367 train_listens.parquet 26 / 42 kept 488917 groups 133886 train_listens.parquet 27 / 42 kept 478266 groups 126341 train_listens.parquet 28 / 42 kept 450487 groups 120791 train_listens.parquet 29 / 42 kept 505170 groups 145842 train_listens.parquet 30 / 42 kept 465841 groups 126172 train_listens.parquet 31 / 42 kept 501174 groups 136293 train_listens.parquet 32 / 42 kept 483589 groups 134701 train_listens.parquet 33 / 42 kept 491144 groups 146235 train_listens.parquet 34 / 42 kept 466812 groups 137320 train_listens.parquet 35 / 42 kept 478660 groups 118222 train_listens.parquet 36 / 42 kept 488008 groups 129720 train_listens.parquet 37 / 42 kept 505545 groups 144033 train_listens.parquet 38 / 42 kept 474558 groups 128387 train_listens.parquet 39 / 42 kept 479407 groups 136720 train_listens.parquet 40 / 42 kept 488582 groups 135015 train_listens.parquet 41 / 42 kept 506274 groups 134704 train_listens.parquet 42 / 42 kept 208284 groups 58558 wrote /app/solution/history_train.parquet 5514505 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44840378487d29dc19da6f460b7f2", "usage": { "input_tokens": 27252, "cached_input_tokens": 25472, "cache_write_input_tokens": 0, "output_tokens": 89, "reasoning_output_tokens": 10, "total_tokens": 27341 }, "turn_token_usage": { "input_tokens": 307426, "cached_input_tokens": 273664, "cache_write_input_tokens": 0, "output_tokens": 5678, "reasoning_output_tokens": 1466, "total_tokens": 313104 }, "thread_token_usage": { "input_tokens": 307426, "cached_input_tokens": 273664, "cache_write_input_tokens": 0, "output_tokens": 5678, "reasoning_output_tokens": 1466, "total_tokens": 313104 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-44434c5a-3c1e-432c-b12b-aa41f596723b", "process_id": "29755", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/build_history.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/build_history.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet 1 / 42 kept 470174 groups 139734\ntrain_listens.parquet 2 / 42 kept 469392 groups 128847\ntrain_listens.parquet 3 / 42 kept 444258 groups 124468\ntrain_listens.parquet 4 / 42 kept 476347 groups 131026\ntrain_listens.parquet 5 / 42 kept 491618 groups 136218\ntrain_listens.parquet 6 / 42 kept 478947 groups 128987\ntrain_listens.parquet 7 / 42 kept 496372 groups 137306\ntrain_listens.parquet 8 / 42 kept 474623 groups 138813\ntrain_listens.parquet 9 / 42 kept 458340 groups 124016\ntrain_listens.parquet 10 / 42 kept 492053 groups 139367\ntrain_listens.parquet 11 / 42 kept 470337 groups 133071\ntrain_listens.parquet 12 / 42 kept 499286 groups 132091\ntrain_listens.parquet 13 / 42 kept 482827 groups 131122\ntrain_listens.parquet 14 / 42 kept 517558 groups 138655\ntrain_listens.parquet 15 / 42 kept 507015 groups 138934\ntrain_listens.parquet 16 / 42 kept 472676 groups 138047\ntrain_listens.parquet 17 / 42 kept 419418 groups 119501\ntrain_listens.parquet 18 / 42 kept 498144 groups 132750\ntrain_listens.parquet 19 / 42 kept 496117 groups 134133\ntrain_listens.parquet 20 / 42 kept 492946 groups 136429\ntrain_listens.parquet 21 / 42 kept 505467 groups 146891\ntrain_listens.parquet 22 / 42 kept 490117 groups 128425\ntrain_listens.parquet 23 / 42 kept 449019 groups 127196\ntrain_listens.parquet 24 / 42 kept 471799 groups 132268\ntrain_listens.parquet 25 / 42 kept 474285 groups 133367\ntrain_listens.parquet 26 / 42 kept 488917 groups 133886\ntrain_listens.parquet 27 / 42 kept 478266 groups 126341\ntrain_listens.parquet 28 / 42 kept 450487 groups 120791\ntrain_listens.parquet 29 / 42 kept 505170 groups 145842\ntrain_listens.parquet 30 / 42 kept 465841 groups 126172\ntrain_listens.parquet 31 / 42 kept 501174 groups 136293\ntrain_listens.parquet 32 / 42 kept 483589 groups 134701\ntrain_listens.parquet 33 / 42 kept 491144 groups 146235\ntrain_listens.parquet 34 / 42 kept 466812 groups 137320\ntrain_listens.parquet 35 / 42 kept 478660 groups 118222\ntrain_listens.parquet 36 / 42 kept 488008 groups 129720\ntrain_listens.parquet 37 / 42 kept 505545 groups 144033\ntrain_listens.parquet 38 / 42 kept 474558 groups 128387\ntrain_listens.parquet 39 / 42 kept 479407 groups 136720\ntrain_listens.parquet 40 / 42 kept 488582 groups 135015\ntrain_listens.parquet 41 / 42 kept 506274 groups 134704\ntrain_listens.parquet 42 / 42 kept 208284 groups 58558\nwrote /app/solution/history_train.parquet 5514505\n", "stderr": "", "aggregated_output": "train_listens.parquet 1 / 42 kept 470174 groups 139734\ntrain_listens.parquet 2 / 42 kept 469392 groups 128847\ntrain_listens.parquet 3 / 42 kept 444258 groups 124468\ntrain_listens.parquet 4 / 42 kept 476347 groups 131026\ntrain_listens.parquet 5 / 42 kept 491618 groups 136218\ntrain_listens.parquet 6 / 42 kept 478947 groups 128987\ntrain_listens.parquet 7 / 42 kept 496372 groups 137306\ntrain_listens.parquet 8 / 42 kept 474623 groups 138813\ntrain_listens.parquet 9 / 42 kept 458340 groups 124016\ntrain_listens.parquet 10 / 42 kept 492053 groups 139367\ntrain_listens.parquet 11 / 42 kept 470337 groups 133071\ntrain_listens.parquet 12 / 42 kept 499286 groups 132091\ntrain_listens.parquet 13 / 42 kept 482827 groups 131122\ntrain_listens.parquet 14 / 42 kept 517558 groups 138655\ntrain_listens.parquet 15 / 42 kept 507015 groups 138934\ntrain_listens.parquet 16 / 42 kept 472676 groups 138047\ntrain_listens.parquet 17 / 42 kept 419418 groups 119501\ntrain_listens.parquet 18 / 42 kept 498144 groups 132750\ntrain_listens.parquet 19 / 42 kept 496117 groups 134133\ntrain_listens.parquet 20 / 42 kept 492946 groups 136429\ntrain_listens.parquet 21 / 42 kept 505467 groups 146891\ntrain_listens.parquet 22 / 42 kept 490117 groups 128425\ntrain_listens.parquet 23 / 42 kept 449019 groups 127196\ntrain_listens.parquet 24 / 42 kept 471799 groups 132268\ntrain_listens.parquet 25 / 42 kept 474285 groups 133367\ntrain_listens.parquet 26 / 42 kept 488917 groups 133886\ntrain_listens.parquet 27 / 42 kept 478266 groups 126341\ntrain_listens.parquet 28 / 42 kept 450487 groups 120791\ntrain_listens.parquet 29 / 42 kept 505170 groups 145842\ntrain_listens.parquet 30 / 42 kept 465841 groups 126172\ntrain_listens.parquet 31 / 42 kept 501174 groups 136293\ntrain_listens.parquet 32 / 42 kept 483589 groups 134701\ntrain_listens.parquet 33 / 42 kept 491144 groups 146235\ntrain_listens.parquet 34 / 42 kept 466812 groups 137320\ntrain_listens.parquet 35 / 42 kept 478660 groups 118222\ntrain_listens.parquet 36 / 42 kept 488008 groups 129720\ntrain_listens.parquet 37 / 42 kept 505545 groups 144033\ntrain_listens.parquet 38 / 42 kept 474558 groups 128387\ntrain_listens.parquet 39 / 42 kept 479407 groups 136720\ntrain_listens.parquet 40 / 42 kept 488582 groups 135015\ntrain_listens.parquet 41 / 42 kept 506274 groups 134704\ntrain_listens.parquet 42 / 42 kept 208284 groups 58558\nwrote /app/solution/history_train.parquet 5514505\n", "exit_code": 0, "duration": { "secs": 9, "nanos": 548297804 }, "formatted_output": "train_listens.parquet 1 / 42 kept 470174 groups 139734\ntrain_listens.parquet 2 / 42 kept 469392 groups 128847\ntrain_listens.parquet 3 / 42 kept 444258 groups 124468\ntrain_listens.parquet 4 / 42 kept 476347 groups 131026\ntrain_listens.parquet 5 / 42 kept 491618 groups 136218\ntrain_listens.parquet 6 / 42 kept 478947 groups 128987\ntrain_listens.parquet 7 / 42 kept 496372 groups 137306\ntrain_listens.parquet 8 / 42 kept 474623 groups 138813\ntrain_listens.parquet 9 / 42 kept 458340 groups 124016\ntrain_listens.parquet 10 / 42 kept 492053 groups 139367\ntrain_listens.parquet 11 / 42 kept 470337 groups 133071\ntrain_listens.parquet 12 / 42 kept 499286 groups 132091\ntrain_listens.parquet 13 / 42 kept 482827 groups 131122\ntrain_listens.parquet 14 / 42 kept 517558 groups 138655\ntrain_listens.parquet 15 / 42 kept 507015 groups 138934\ntrain_listens.parquet 16 / 42 kept 472676 groups 138047\ntrain_listens.parquet 17 / 42 kept 419418 groups 119501\ntrain_listens.parquet 18 / 42 kept 498144 groups 132750\ntrain_listens.parquet 19 / 42 kept 496117 groups 134133\ntrain_listens.parquet 20 / 42 kept 492946 groups 136429\ntrain_listens.parquet 21 / 42 kept 505467 groups 146891\ntrain_listens.parquet 22 / 42 kept 490117 groups 128425\ntrain_listens.parquet 23 / 42 kept 449019 groups 127196\ntrain_listens.parquet 24 / 42 kept 471799 groups 132268\ntrain_listens.parquet 25 / 42 kept 474285 groups 133367\ntrain_listens.parquet 26 / 42 kept 488917 groups 133886\ntrain_listens.parquet 27 / 42 kept 478266 groups 126341\ntrain_listens.parquet 28 / 42 kept 450487 groups 120791\ntrain_listens.parquet 29 / 42 kept 505170 groups 145842\ntrain_listens.parquet 30 / 42 kept 465841 groups 126172\ntrain_listens.parquet 31 / 42 kept 501174 groups 136293\ntrain_listens.parquet 32 / 42 kept 483589 groups 134701\ntrain_listens.parquet 33 / 42 kept 491144 groups 146235\ntrain_listens.parquet 34 / 42 kept 466812 groups 137320\ntrain_listens.parquet 35 / 42 kept 478660 groups 118222\ntrain_listens.parquet 36 / 42 kept 488008 groups 129720\ntrain_listens.parquet 37 / 42 kept 505545 groups 144033\ntrain_listens.parquet 38 / 42 kept 474558 groups 128387\ntrain_listens.parquet 39 / 42 kept 479407 groups 136720\ntrain_listens.parquet 40 / 42 kept 488582 groups 135015\ntrain_listens.parquet 41 / 42 kept 506274 groups 134704\ntrain_listens.parquet 42 / 42 kept 208284 groups 58558\nwrote /app/solution/history_train.parquet 5514505\n" } } ## Step 22 · agent · 2026-09-23T21:44:53.686Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:45:07.049Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport pandas as pd,numpy as np\\nD='/task/data/'; S='/app/solution/'\\nh=pd.read_parquet(S+'history_train.parquet'); t=pd.read_parquet(S+'validation_truth.parquet'); p=pd.read_parquet(D+'validation_preference_state.parquet')\\nprint('history',len(h),'per user',h.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]).to_dict())\\nprint('prefs',p.state.value_counts().to_dict(),'users',p.uid.nunique(),'both',p.duplicated(['uid','item_id'],keep=False).sum())\\nz=np.load(D+'validation_eligible_candidates.npz'); idx=pd.Index(z['item_ids']); r=np.searchsorted(z['uids'],p.uid.to_numpy()); c=idx.get_indexer(p.item_id); ok=(c>=0); ii=np.flatnonzero(ok); ok[ii]=((z['packed_eligible'][r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool)\\nprint('pref catalog',sum(c>=0),'eligible',sum(ok),pd.crosstab(p.state,ok))\\nj=t.merge(h,on=['uid','item_id'],how='left'); print('truth historical pair',j['count'].notna().mean(),j.groupby('grade')['count'].agg(['count','mean','median']).to_string()); print('hist count relevant percentiles',j['count'].describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict())\\nprint('truth in pref',len(t.merge(p,on=['uid','item_id']))); print(t.merge(p,on=['uid','item_id']).state.value_counts())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":10000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:45:09.349Z Script completed Wall time 2.3 seconds Output: history 5514505 per user {'count': 8449.0, 'mean': 652.6813824121198, 'std': 600.1820577131077, 'min': 1.0, '10%': 77.0, '50%': 489.0, '90%': 1447.1999999999998, '99%': 2708.5600000000013, 'max': 4947.0} prefs {'liked': 418435, 'disliked': 50689} users 7038 both 3420 pref catalog 469124 eligible 0 col_0 False state disliked 50689 liked 418435 truth historical pair 0.0 count mean median grade 1 0 NaN NaN 3 0 NaN NaN hist count relevant percentiles {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '10%': nan, '25%': nan, '50%': nan, '75%': nan, '90%': nan, '99%': nan, 'max': nan} truth in pref 0 Series([], Name: count, dtype: int64) ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4484ea77887d28b44cb0c6f20a32d", "usage": { "input_tokens": 28177, "cached_input_tokens": 27136, "cache_write_input_tokens": 0, "output_tokens": 566, "reasoning_output_tokens": 140, "total_tokens": 28743 }, "turn_token_usage": { "input_tokens": 335603, "cached_input_tokens": 300800, "cache_write_input_tokens": 0, "output_tokens": 6244, "reasoning_output_tokens": 1606, "total_tokens": 341847 }, "thread_token_usage": { "input_tokens": 335603, "cached_input_tokens": 300800, "cache_write_input_tokens": 0, "output_tokens": 6244, "reasoning_output_tokens": 1606, "total_tokens": 341847 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-7a9712b2-01e3-4a0a-91c3-1b3a5d2fd926", "process_id": "33011", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; S='/app/solution/'\nh=pd.read_parquet(S+'history_train.parquet'); t=pd.read_parquet(S+'validation_truth.parquet'); p=pd.read_parquet(D+'validation_preference_state.parquet')\nprint('history',len(h),'per user',h.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]).to_dict())\nprint('prefs',p.state.value_counts().to_dict(),'users',p.uid.nunique(),'both',p.duplicated(['uid','item_id'],keep=False).sum())\nz=np.load(D+'validation_eligible_candidates.npz'); idx=pd.Index(z['item_ids']); r=np.searchsorted(z['uids'],p.uid.to_numpy()); c=idx.get_indexer(p.item_id); ok=(c>=0); ii=np.flatnonzero(ok); ok[ii]=((z['packed_eligible'][r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool)\nprint('pref catalog',sum(c>=0),'eligible',sum(ok),pd.crosstab(p.state,ok))\nj=t.merge(h,on=['uid','item_id'],how='left'); print('truth historical pair',j['count'].notna().mean(),j.groupby('grade')['count'].agg(['count','mean','median']).to_string()); print('hist count relevant percentiles',j['count'].describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict())\nprint('truth in pref',len(t.merge(p,on=['uid','item_id']))); print(t.merge(p,on=['uid','item_id']).state.value_counts())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport pandas as pd,numpy as np\nD='/task/data/'; S='/app/solution/'\nh=pd.read_parquet(S+'history_train.parquet'); t=pd.read_parquet(S+'validation_truth.parquet'); p=pd.read_parquet(D+'validation_preference_state.parquet')\nprint('history',len(h),'per user',h.groupby('uid').size().describe(percentiles=[.1,.5,.9,.99]).to_dict())\nprint('prefs',p.state.value_counts().to_dict(),'users',p.uid.nunique(),'both',p.duplicated(['uid','item_id'],keep=False).sum())\nz=np.load(D+'validation_eligible_candidates.npz'); idx=pd.Index(z['item_ids']); r=np.searchsorted(z['uids'],p.uid.to_numpy()); c=idx.get_indexer(p.item_id); ok=(c>=0); ii=np.flatnonzero(ok); ok[ii]=((z['packed_eligible'][r[ii],c[ii]//8]>>(c[ii]%8))&1).astype(bool)\nprint('pref catalog',sum(c>=0),'eligible',sum(ok),pd.crosstab(p.state,ok))\nj=t.merge(h,on=['uid','item_id'],how='left'); print('truth historical pair',j['count'].notna().mean(),j.groupby('grade')['count'].agg(['count','mean','median']).to_string()); print('hist count relevant percentiles',j['count'].describe(percentiles=[.1,.25,.5,.75,.9,.99]).to_dict())\nprint('truth in pref',len(t.merge(p,on=['uid','item_id']))); print(t.merge(p,on=['uid','item_id']).state.value_counts())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "history 5514505 per user {'count': 8449.0, 'mean': 652.6813824121198, 'std': 600.1820577131077, 'min': 1.0, '10%': 77.0, '50%': 489.0, '90%': 1447.1999999999998, '99%': 2708.5600000000013, 'max': 4947.0}\nprefs {'liked': 418435, 'disliked': 50689} users 7038 both 3420\npref catalog 469124 eligible 0 col_0 False\nstate \ndisliked 50689\nliked 418435\ntruth historical pair 0.0 count mean median\ngrade \n1 0 NaN NaN\n3 0 NaN NaN\nhist count relevant percentiles {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '10%': nan, '25%': nan, '50%': nan, '75%': nan, '90%': nan, '99%': nan, 'max': nan}\ntruth in pref 0\nSeries([], Name: count, dtype: int64)\n", "stderr": "", "aggregated_output": "history 5514505 per user {'count': 8449.0, 'mean': 652.6813824121198, 'std': 600.1820577131077, 'min': 1.0, '10%': 77.0, '50%': 489.0, '90%': 1447.1999999999998, '99%': 2708.5600000000013, 'max': 4947.0}\nprefs {'liked': 418435, 'disliked': 50689} users 7038 both 3420\npref catalog 469124 eligible 0 col_0 False\nstate \ndisliked 50689\nliked 418435\ntruth historical pair 0.0 count mean median\ngrade \n1 0 NaN NaN\n3 0 NaN NaN\nhist count relevant percentiles {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '10%': nan, '25%': nan, '50%': nan, '75%': nan, '90%': nan, '99%': nan, 'max': nan}\ntruth in pref 0\nSeries([], Name: count, dtype: int64)\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 120471326 }, "formatted_output": "history 5514505 per user {'count': 8449.0, 'mean': 652.6813824121198, 'std': 600.1820577131077, 'min': 1.0, '10%': 77.0, '50%': 489.0, '90%': 1447.1999999999998, '99%': 2708.5600000000013, 'max': 4947.0}\nprefs {'liked': 418435, 'disliked': 50689} users 7038 both 3420\npref catalog 469124 eligible 0 col_0 False\nstate \ndisliked 50689\nliked 418435\ntruth historical pair 0.0 count mean median\ngrade \n1 0 NaN NaN\n3 0 NaN NaN\nhist count relevant percentiles {'count': 0.0, 'mean': nan, 'std': nan, 'min': nan, '10%': nan, '25%': nan, '50%': nan, '75%': nan, '90%': nan, '99%': nan, 'max': nan}\ntruth in pref 0\nSeries([], Name: count, dtype: int64)\n" } } ## Step 23 · agent · 2026-09-23T21:45:27.046Z ### Thinking unavailable — no text recorded ## Step 24 · agent · 2026-09-23T21:45:43.622Z ### Thinking unavailable — no text recorded ## Step 25 · agent · 2026-09-23T21:45:48.684Z ### Thinking unavailable — no text recorded A key property is now clear: eligibility excludes already consumed and explicitly rated tracks. None of the 37,769 eligible validation positives had appeared in that user’s positive-listen history, so a repeat-listen ranker cannot score at all. I’m switching the core model to collaborative discovery: learn track embeddings from all users’ histories, build each listener’s taste vector from organic/strong listens and likes, and rank only unseen eligible tracks. ### exec · 2026-09-23T21:46:23.837Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_matrix.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Build a sparse collaborative signal matrix from cutoff-correct events.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+\\n+D=Path('/task/data'); OUT=Path('/app/solution')\\n+\\n+def build(with_val=False):\\n+ cat=pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id'])\\n+ items=cat.item_id.to_numpy(dtype=np.int64); item_index=pd.Index(items)\\n+ # IDs in this corpus are multiples of 100; retaining an explicit index keeps\\n+ # the builder robust to that being an implementation detail.\\n+ all_u=[]\\n+ for fn in ['train_listens.parquet'] + (['val_listens.parquet'] if with_val else []):\\n+ all_u.append(pd.read_parquet(D/fn,columns=['uid']).uid.unique())\\n+ uids=np.unique(np.concatenate(all_u)).astype(np.int64); uid_index=pd.Index(uids)\\n+ rows=[]; cols=[]; vals=[]\\n+ listen_files=['train_listens.parquet'] + (['val_listens.parquet'] if with_val else [])\\n+ for fn in listen_files:\\n+ pf=pq.ParquetFile(D/fn)\\n+ for rg in range(pf.num_row_groups):\\n+ x=pf.read_row_group(rg,columns=['uid','item_id','is_organic','played_ratio_pct']).to_pandas()\\n+ c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\\n+ x=x[keep]; c=c[keep]\\n+ r=uid_index.get_indexer(x.uid)\\n+ ratio=x.played_ratio_pct.to_numpy()\\n+ org=x.is_organic.to_numpy()\\n+ # Strong and organic completion is most aligned to the discovery label.\\n+ v=(1.0 + .50*(ratio>=80) + .75*org + .50*(org&(ratio>=80))).astype('float32')\\n+ rows.append(r.astype('int32')); cols.append(c.astype('int32')); vals.append(v)\\n+ print(fn,rg+1,'/',pf.num_row_groups,'positive catalog events',len(x),flush=True)\\n+ # Explicit likes enrich profiles even though rated tracks themselves are not eligible.\\n+ like_files=['train_likes.parquet'] + (['val_likes.parquet'] if with_val else [])\\n+ for fn in like_files:\\n+ x=pd.read_parquet(D/fn,columns=['uid','item_id','is_organic'])\\n+ c=item_index.get_indexer(x.item_id); r=uid_index.get_indexer(x.uid)\\n+ keep=(c>=0)&(r>=0); x=x[keep]; c=c[keep]; r=r[keep]\\n+ v=(2.5+1.0*x.is_organic.to_numpy()).astype('float32')\\n+ rows.append(r.astype('int32')); cols.append(c.astype('int32')); vals.append(v)\\n+ row=np.concatenate(rows); col=np.concatenate(cols); val=np.concatenate(vals)\\n+ X=sparse.coo_matrix((val,(row,col)),shape=(len(uids),len(items)),dtype=np.float32).tocsr()\\n+ X.sum_duplicates(); X.data=np.log1p(X.data).astype('float32'); X.eliminate_zeros()\\n+ tag='full' if with_val else 'train'\\n+ sparse.save_npz(OUT/f'matrix_{tag}.npz',X,compressed=True)\\n+ np.savez_compressed(OUT/f'matrix_meta_{tag}.npz',uids=uids.astype('uint32'),items=items.astype('uint32'))\\n+ print('wrote',tag,X.shape,'nnz',X.nnz,flush=True)\\n+\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); a=p.parse_args(); build(a.with_val)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:46:23.913Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-265d2826-ad4b-466e-807a-d27e2716d8ad", "changes": { "/app/solution/build_matrix.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Build a sparse collaborative signal matrix from cutoff-correct events.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\n\nD=Path('/task/data'); OUT=Path('/app/solution')\n\ndef build(with_val=False):\n cat=pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id'])\n items=cat.item_id.to_numpy(dtype=np.int64); item_index=pd.Index(items)\n # IDs in this corpus are multiples of 100; retaining an explicit index keeps\n # the builder robust to that being an implementation detail.\n all_u=[]\n for fn in ['train_listens.parquet'] + (['val_listens.parquet'] if with_val else []):\n all_u.append(pd.read_parquet(D/fn,columns=['uid']).uid.unique())\n uids=np.unique(np.concatenate(all_u)).astype(np.int64); uid_index=pd.Index(uids)\n rows=[]; cols=[]; vals=[]\n listen_files=['train_listens.parquet'] + (['val_listens.parquet'] if with_val else [])\n for fn in listen_files:\n pf=pq.ParquetFile(D/fn)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg,columns=['uid','item_id','is_organic','played_ratio_pct']).to_pandas()\n c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\n x=x[keep]; c=c[keep]\n r=uid_index.get_indexer(x.uid)\n ratio=x.played_ratio_pct.to_numpy()\n org=x.is_organic.to_numpy()\n # Strong and organic completion is most aligned to the discovery label.\n v=(1.0 + .50*(ratio>=80) + .75*org + .50*(org&(ratio>=80))).astype('float32')\n rows.append(r.astype('int32')); cols.append(c.astype('int32')); vals.append(v)\n print(fn,rg+1,'/',pf.num_row_groups,'positive catalog events',len(x),flush=True)\n # Explicit likes enrich profiles even though rated tracks themselves are not eligible.\n like_files=['train_likes.parquet'] + (['val_likes.parquet'] if with_val else [])\n for fn in like_files:\n x=pd.read_parquet(D/fn,columns=['uid','item_id','is_organic'])\n c=item_index.get_indexer(x.item_id); r=uid_index.get_indexer(x.uid)\n keep=(c>=0)&(r>=0); x=x[keep]; c=c[keep]; r=r[keep]\n v=(2.5+1.0*x.is_organic.to_numpy()).astype('float32')\n rows.append(r.astype('int32')); cols.append(c.astype('int32')); vals.append(v)\n row=np.concatenate(rows); col=np.concatenate(cols); val=np.concatenate(vals)\n X=sparse.coo_matrix((val,(row,col)),shape=(len(uids),len(items)),dtype=np.float32).tocsr()\n X.sum_duplicates(); X.data=np.log1p(X.data).astype('float32'); X.eliminate_zeros()\n tag='full' if with_val else 'train'\n sparse.save_npz(OUT/f'matrix_{tag}.npz',X,compressed=True)\n np.savez_compressed(OUT/f'matrix_meta_{tag}.npz',uids=uids.astype('uint32'),items=items.astype('uint32'))\n print('wrote',tag,X.shape,'nnz',X.nnz,flush=True)\n\nif __name__=='__main__':\n p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); a=p.parse_args(); build(a.with_val)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_matrix.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4486589f087d2bff08943de3cc7c4", "usage": { "input_tokens": 29047, "cached_input_tokens": 28032, "cache_write_input_tokens": 0, "output_tokens": 2269, "reasoning_output_tokens": 1191, "total_tokens": 31316 }, "turn_token_usage": { "input_tokens": 364650, "cached_input_tokens": 328832, "cache_write_input_tokens": 0, "output_tokens": 8513, "reasoning_output_tokens": 2797, "total_tokens": 373163 }, "thread_token_usage": { "input_tokens": 364650, "cached_input_tokens": 328832, "cache_write_input_tokens": 0, "output_tokens": 8513, "reasoning_output_tokens": 2797, "total_tokens": 373163 } } ## Step 26 · agent · 2026-09-23T21:46:26.611Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:46:28.849Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/build_matrix.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:46:35.876Z Script completed Wall time 7.0 seconds Output: train_listens.parquet 1 / 42 positive catalog events 472772 train_listens.parquet 2 / 42 positive catalog events 476221 train_listens.parquet 3 / 42 positive catalog events 446354 train_listens.parquet 4 / 42 positive catalog events 477701 train_listens.parquet 5 / 42 positive catalog events 492512 train_listens.parquet 6 / 42 positive catalog events 480414 train_listens.parquet 7 / 42 positive catalog events 497472 train_listens.parquet 8 / 42 positive catalog events 476206 train_listens.parquet 9 / 42 positive catalog events 458604 train_listens.parquet 10 / 42 positive catalog events 495213 train_listens.parquet 11 / 42 positive catalog events 472805 train_listens.parquet 12 / 42 positive catalog events 503388 train_listens.parquet 13 / 42 positive catalog events 487615 train_listens.parquet 14 / 42 positive catalog events 520988 train_listens.parquet 15 / 42 positive catalog events 508744 train_listens.parquet 16 / 42 positive catalog events 473615 train_listens.parquet 17 / 42 positive catalog events 420379 train_listens.parquet 18 / 42 positive catalog events 498241 train_listens.parquet 19 / 42 positive catalog events 498123 train_listens.parquet 20 / 42 positive catalog events 502659 train_listens.parquet 21 / 42 positive catalog events 506662 train_listens.parquet 22 / 42 positive catalog events 494125 train_listens.parquet 23 / 42 positive catalog events 458371 train_listens.parquet 24 / 42 positive catalog events 475489 train_listens.parquet 25 / 42 positive catalog events 475079 train_listens.parquet 26 / 42 positive catalog events 489950 train_listens.parquet 27 / 42 positive catalog events 480425 train_listens.parquet 28 / 42 positive catalog events 450852 train_listens.parquet 29 / 42 positive catalog events 508464 train_listens.parquet 30 / 42 positive catalog events 467840 train_listens.parquet 31 / 42 positive catalog events 502790 train_listens.parquet 32 / 42 positive catalog events 484762 train_listens.parquet 33 / 42 positive catalog events 491661 train_listens.parquet 34 / 42 positive catalog events 467908 train_listens.parquet 35 / 42 positive catalog events 479314 train_listens.parquet 36 / 42 positive catalog events 488491 train_listens.parquet 37 / 42 positive catalog events 505771 train_listens.parquet 38 / 42 positive catalog events 477085 train_listens.parquet 39 / 42 positive catalog events 486240 train_listens.parquet 40 / 42 positive catalog events 489615 train_listens.parquet 41 / 42 positive catalog events 506465 train_listens.parquet 42 / 42 positive catalog events 208944 wrote train (9238, 30000) nnz 5618006 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab448b0b1e887d28a2e3e86e06d5e70", "usage": { "input_tokens": 31340, "cached_input_tokens": 26752, "cache_write_input_tokens": 0, "output_tokens": 96, "reasoning_output_tokens": 17, "total_tokens": 31436 }, "turn_token_usage": { "input_tokens": 395990, "cached_input_tokens": 355584, "cache_write_input_tokens": 0, "output_tokens": 8609, "reasoning_output_tokens": 2814, "total_tokens": 404599 }, "thread_token_usage": { "input_tokens": 395990, "cached_input_tokens": 355584, "cache_write_input_tokens": 0, "output_tokens": 8609, "reasoning_output_tokens": 2814, "total_tokens": 404599 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-700aca10-d459-4cdf-95d1-51b50902bd85", "process_id": "91458", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/build_matrix.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/build_matrix.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "train_listens.parquet 1 / 42 positive catalog events 472772\ntrain_listens.parquet 2 / 42 positive catalog events 476221\ntrain_listens.parquet 3 / 42 positive catalog events 446354\ntrain_listens.parquet 4 / 42 positive catalog events 477701\ntrain_listens.parquet 5 / 42 positive catalog events 492512\ntrain_listens.parquet 6 / 42 positive catalog events 480414\ntrain_listens.parquet 7 / 42 positive catalog events 497472\ntrain_listens.parquet 8 / 42 positive catalog events 476206\ntrain_listens.parquet 9 / 42 positive catalog events 458604\ntrain_listens.parquet 10 / 42 positive catalog events 495213\ntrain_listens.parquet 11 / 42 positive catalog events 472805\ntrain_listens.parquet 12 / 42 positive catalog events 503388\ntrain_listens.parquet 13 / 42 positive catalog events 487615\ntrain_listens.parquet 14 / 42 positive catalog events 520988\ntrain_listens.parquet 15 / 42 positive catalog events 508744\ntrain_listens.parquet 16 / 42 positive catalog events 473615\ntrain_listens.parquet 17 / 42 positive catalog events 420379\ntrain_listens.parquet 18 / 42 positive catalog events 498241\ntrain_listens.parquet 19 / 42 positive catalog events 498123\ntrain_listens.parquet 20 / 42 positive catalog events 502659\ntrain_listens.parquet 21 / 42 positive catalog events 506662\ntrain_listens.parquet 22 / 42 positive catalog events 494125\ntrain_listens.parquet 23 / 42 positive catalog events 458371\ntrain_listens.parquet 24 / 42 positive catalog events 475489\ntrain_listens.parquet 25 / 42 positive catalog events 475079\ntrain_listens.parquet 26 / 42 positive catalog events 489950\ntrain_listens.parquet 27 / 42 positive catalog events 480425\ntrain_listens.parquet 28 / 42 positive catalog events 450852\ntrain_listens.parquet 29 / 42 positive catalog events 508464\ntrain_listens.parquet 30 / 42 positive catalog events 467840\ntrain_listens.parquet 31 / 42 positive catalog events 502790\ntrain_listens.parquet 32 / 42 positive catalog events 484762\ntrain_listens.parquet 33 / 42 positive catalog events 491661\ntrain_listens.parquet 34 / 42 positive catalog events 467908\ntrain_listens.parquet 35 / 42 positive catalog events 479314\ntrain_listens.parquet 36 / 42 positive catalog events 488491\ntrain_listens.parquet 37 / 42 positive catalog events 505771\ntrain_listens.parquet 38 / 42 positive catalog events 477085\ntrain_listens.parquet 39 / 42 positive catalog events 486240\ntrain_listens.parquet 40 / 42 positive catalog events 489615\ntrain_listens.parquet 41 / 42 positive catalog events 506465\ntrain_listens.parquet 42 / 42 positive catalog events 208944\nwrote train (9238, 30000) nnz 5618006\n", "stderr": "", "aggregated_output": "train_listens.parquet 1 / 42 positive catalog events 472772\ntrain_listens.parquet 2 / 42 positive catalog events 476221\ntrain_listens.parquet 3 / 42 positive catalog events 446354\ntrain_listens.parquet 4 / 42 positive catalog events 477701\ntrain_listens.parquet 5 / 42 positive catalog events 492512\ntrain_listens.parquet 6 / 42 positive catalog events 480414\ntrain_listens.parquet 7 / 42 positive catalog events 497472\ntrain_listens.parquet 8 / 42 positive catalog events 476206\ntrain_listens.parquet 9 / 42 positive catalog events 458604\ntrain_listens.parquet 10 / 42 positive catalog events 495213\ntrain_listens.parquet 11 / 42 positive catalog events 472805\ntrain_listens.parquet 12 / 42 positive catalog events 503388\ntrain_listens.parquet 13 / 42 positive catalog events 487615\ntrain_listens.parquet 14 / 42 positive catalog events 520988\ntrain_listens.parquet 15 / 42 positive catalog events 508744\ntrain_listens.parquet 16 / 42 positive catalog events 473615\ntrain_listens.parquet 17 / 42 positive catalog events 420379\ntrain_listens.parquet 18 / 42 positive catalog events 498241\ntrain_listens.parquet 19 / 42 positive catalog events 498123\ntrain_listens.parquet 20 / 42 positive catalog events 502659\ntrain_listens.parquet 21 / 42 positive catalog events 506662\ntrain_listens.parquet 22 / 42 positive catalog events 494125\ntrain_listens.parquet 23 / 42 positive catalog events 458371\ntrain_listens.parquet 24 / 42 positive catalog events 475489\ntrain_listens.parquet 25 / 42 positive catalog events 475079\ntrain_listens.parquet 26 / 42 positive catalog events 489950\ntrain_listens.parquet 27 / 42 positive catalog events 480425\ntrain_listens.parquet 28 / 42 positive catalog events 450852\ntrain_listens.parquet 29 / 42 positive catalog events 508464\ntrain_listens.parquet 30 / 42 positive catalog events 467840\ntrain_listens.parquet 31 / 42 positive catalog events 502790\ntrain_listens.parquet 32 / 42 positive catalog events 484762\ntrain_listens.parquet 33 / 42 positive catalog events 491661\ntrain_listens.parquet 34 / 42 positive catalog events 467908\ntrain_listens.parquet 35 / 42 positive catalog events 479314\ntrain_listens.parquet 36 / 42 positive catalog events 488491\ntrain_listens.parquet 37 / 42 positive catalog events 505771\ntrain_listens.parquet 38 / 42 positive catalog events 477085\ntrain_listens.parquet 39 / 42 positive catalog events 486240\ntrain_listens.parquet 40 / 42 positive catalog events 489615\ntrain_listens.parquet 41 / 42 positive catalog events 506465\ntrain_listens.parquet 42 / 42 positive catalog events 208944\nwrote train (9238, 30000) nnz 5618006\n", "exit_code": 0, "duration": { "secs": 6, "nanos": 858334748 }, "formatted_output": "train_listens.parquet 1 / 42 positive catalog events 472772\ntrain_listens.parquet 2 / 42 positive catalog events 476221\ntrain_listens.parquet 3 / 42 positive catalog events 446354\ntrain_listens.parquet 4 / 42 positive catalog events 477701\ntrain_listens.parquet 5 / 42 positive catalog events 492512\ntrain_listens.parquet 6 / 42 positive catalog events 480414\ntrain_listens.parquet 7 / 42 positive catalog events 497472\ntrain_listens.parquet 8 / 42 positive catalog events 476206\ntrain_listens.parquet 9 / 42 positive catalog events 458604\ntrain_listens.parquet 10 / 42 positive catalog events 495213\ntrain_listens.parquet 11 / 42 positive catalog events 472805\ntrain_listens.parquet 12 / 42 positive catalog events 503388\ntrain_listens.parquet 13 / 42 positive catalog events 487615\ntrain_listens.parquet 14 / 42 positive catalog events 520988\ntrain_listens.parquet 15 / 42 positive catalog events 508744\ntrain_listens.parquet 16 / 42 positive catalog events 473615\ntrain_listens.parquet 17 / 42 positive catalog events 420379\ntrain_listens.parquet 18 / 42 positive catalog events 498241\ntrain_listens.parquet 19 / 42 positive catalog events 498123\ntrain_listens.parquet 20 / 42 positive catalog events 502659\ntrain_listens.parquet 21 / 42 positive catalog events 506662\ntrain_listens.parquet 22 / 42 positive catalog events 494125\ntrain_listens.parquet 23 / 42 positive catalog events 458371\ntrain_listens.parquet 24 / 42 positive catalog events 475489\ntrain_listens.parquet 25 / 42 positive catalog events 475079\ntrain_listens.parquet 26 / 42 positive catalog events 489950\ntrain_listens.parquet 27 / 42 positive catalog events 480425\ntrain_listens.parquet 28 / 42 positive catalog events 450852\ntrain_listens.parquet 29 / 42 positive catalog events 508464\ntrain_listens.parquet 30 / 42 positive catalog events 467840\ntrain_listens.parquet 31 / 42 positive catalog events 502790\ntrain_listens.parquet 32 / 42 positive catalog events 484762\ntrain_listens.parquet 33 / 42 positive catalog events 491661\ntrain_listens.parquet 34 / 42 positive catalog events 467908\ntrain_listens.parquet 35 / 42 positive catalog events 479314\ntrain_listens.parquet 36 / 42 positive catalog events 488491\ntrain_listens.parquet 37 / 42 positive catalog events 505771\ntrain_listens.parquet 38 / 42 positive catalog events 477085\ntrain_listens.parquet 39 / 42 positive catalog events 486240\ntrain_listens.parquet 40 / 42 positive catalog events 489615\ntrain_listens.parquet 41 / 42 positive catalog events 506465\ntrain_listens.parquet 42 / 42 positive catalog events 208944\nwrote train (9238, 30000) nnz 5618006\n" } } ## Step 27 · agent · 2026-09-23T21:46:53.436Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:47:38.071Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/rank_model.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Fit latent collaborative models and emit cutoff-correct rankings.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+from sklearn.decomposition import TruncatedSVD\\n+\\n+D=Path('/task/data'); OUT=Path('/app/solution')\\n+\\n+def load_elig(stage):\\n+ z=np.load(D/f'{stage}_eligible_candidates.npz')\\n+ return z['uids'].astype(np.int64),z['item_ids'].astype(np.int64),z['packed_eligible']\\n+\\n+def fit(tag, rank=192):\\n+ X=sparse.load_npz(OUT/f'matrix_{tag}.npz').astype(np.float32)\\n+ meta=np.load(OUT/f'matrix_meta_{tag}.npz'); uids=meta['uids']; items=meta['items']\\n+ # BM25-like row-length normalization plus inverse user frequency. This\\n+ # suppresses generic blockbuster consumption while keeping rare affinities.\\n+ df=np.asarray((X>0).sum(axis=0)).ravel()\\n+ idf=np.maximum(0,np.log((X.shape[0]+1)/(df+1))).astype(np.float32)\\n+ rownorm=np.sqrt(np.asarray(X.power(2).sum(axis=1)).ravel()).astype(np.float32)\\n+ W=sparse.diags(1/np.maximum(rownorm,1e-6))@X@sparse.diags(np.sqrt(idf))\\n+ svd=TruncatedSVD(n_components=rank,n_iter=5,random_state=1729,algorithm='randomized')\\n+ U=svd.fit_transform(W).astype(np.float32)\\n+ V=svd.components_.astype(np.float32)\\n+ np.savez_compressed(OUT/f'latent_{tag}.npz',uids=uids,items=items,U=U,V=V,\\n+ singular_values=svd.singular_values_,variance=svd.explained_variance_ratio_)\\n+ print('fit',tag,W.shape,W.nnz,'rank',rank,'explained',svd.explained_variance_ratio_.sum(),flush=True)\\n+\\n+def emit(stage, tag, variant, outpath):\\n+ euids,eitems,packed=load_elig(stage)\\n+ m=np.load(OUT/f'latent_{tag}.npz'); muids=m['uids']; mitems=m['items']; U=m['U']; V=m['V']\\n+ assert np.array_equal(eitems,mitems)\\n+ ur=pd.Index(muids).get_indexer(euids)\\n+ cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+ pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\\n+ pop=(pop-pop.mean())/pop.std()\\n+ # Standard reconstruction and cosine-style latent similarity are both\\n+ # useful; variant encodes the blend and popularity prior.\\n+ if variant=='pop': a,b,c=0.,0.,1.\\n+ elif variant=='svd': a,b,c=1.,0.,0.\\n+ elif variant=='cos': a,b,c=0.,1.,0.\\n+ elif variant=='blend': a,b,c=.7,.3,.06\\n+ elif variant=='blendpop': a,b,c=.65,.35,.14\\n+ else:\\n+ a,b,c=map(float,variant.split(','))\\n+ Vnorm=V/np.maximum(np.linalg.norm(V,axis=0,keepdims=True),1e-7)\\n+ Unorm=U/np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+ blocks=[]\\n+ for lo in range(0,len(euids),128):\\n+ hi=min(lo+128,len(euids)); n=hi-lo\\n+ score=np.broadcast_to(c*pop,(n,len(eitems))).copy()\\n+ good=ur[lo:hi]>=0\\n+ if good.any():\\n+ uu=U[ur[lo:hi][good]]\\n+ if a: score[good]+=a*(uu@V)\\n+ if b: score[good]+=b*(Unorm[ur[lo:hi][good]]@Vnorm)\\n+ eligible=np.unpackbits(packed[lo:hi],axis=1,count=len(eitems),bitorder='little').astype(bool)\\n+ score[~eligible]=-np.inf\\n+ ix=np.argpartition(score,-100,axis=1)[:,-100:]\\n+ ss=np.take_along_axis(score,ix,axis=1)\\n+ order=np.argsort(-ss,axis=1); ix=np.take_along_axis(ix,order,axis=1)\\n+ blocks.append(ix)\\n+ print('ranked',hi,'/',len(euids),flush=True)\\n+ top=np.vstack(blocks)\\n+ result=pd.DataFrame({'uid':np.repeat(euids,100).astype('int64'),\\n+ 'item_id':eitems[top.ravel()].astype('int64'),\\n+ 'rank':np.tile(np.arange(1,101,dtype=np.int64),len(euids))})\\n+ result.to_parquet(outpath,index=False)\\n+ print('wrote',outpath,len(result),flush=True)\\n+\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser(); p.add_argument('--tag',choices=['train','full'],default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\\n+ if a.fit: fit(a.tag,a.rank)\\n+ emit(a.stage,a.tag,a.variant,Path(a.output or OUT/f'{a.stage}_{a.variant}.parquet'))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:47:38.114Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ee3e4c67-94bb-4f35-a530-b479a25fadfb", "changes": { "/app/solution/rank_model.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Fit latent collaborative models and emit cutoff-correct rankings.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nfrom sklearn.decomposition import TruncatedSVD\n\nD=Path('/task/data'); OUT=Path('/app/solution')\n\ndef load_elig(stage):\n z=np.load(D/f'{stage}_eligible_candidates.npz')\n return z['uids'].astype(np.int64),z['item_ids'].astype(np.int64),z['packed_eligible']\n\ndef fit(tag, rank=192):\n X=sparse.load_npz(OUT/f'matrix_{tag}.npz').astype(np.float32)\n meta=np.load(OUT/f'matrix_meta_{tag}.npz'); uids=meta['uids']; items=meta['items']\n # BM25-like row-length normalization plus inverse user frequency. This\n # suppresses generic blockbuster consumption while keeping rare affinities.\n df=np.asarray((X>0).sum(axis=0)).ravel()\n idf=np.maximum(0,np.log((X.shape[0]+1)/(df+1))).astype(np.float32)\n rownorm=np.sqrt(np.asarray(X.power(2).sum(axis=1)).ravel()).astype(np.float32)\n W=sparse.diags(1/np.maximum(rownorm,1e-6))@X@sparse.diags(np.sqrt(idf))\n svd=TruncatedSVD(n_components=rank,n_iter=5,random_state=1729,algorithm='randomized')\n U=svd.fit_transform(W).astype(np.float32)\n V=svd.components_.astype(np.float32)\n np.savez_compressed(OUT/f'latent_{tag}.npz',uids=uids,items=items,U=U,V=V,\n singular_values=svd.singular_values_,variance=svd.explained_variance_ratio_)\n print('fit',tag,W.shape,W.nnz,'rank',rank,'explained',svd.explained_variance_ratio_.sum(),flush=True)\n\ndef emit(stage, tag, variant, outpath):\n euids,eitems,packed=load_elig(stage)\n m=np.load(OUT/f'latent_{tag}.npz'); muids=m['uids']; mitems=m['items']; U=m['U']; V=m['V']\n assert np.array_equal(eitems,mitems)\n ur=pd.Index(muids).get_indexer(euids)\n cat=pd.read_parquet(D/'candidate_catalog.parquet')\n pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n pop=(pop-pop.mean())/pop.std()\n # Standard reconstruction and cosine-style latent similarity are both\n # useful; variant encodes the blend and popularity prior.\n if variant=='pop': a,b,c=0.,0.,1.\n elif variant=='svd': a,b,c=1.,0.,0.\n elif variant=='cos': a,b,c=0.,1.,0.\n elif variant=='blend': a,b,c=.7,.3,.06\n elif variant=='blendpop': a,b,c=.65,.35,.14\n else:\n a,b,c=map(float,variant.split(','))\n Vnorm=V/np.maximum(np.linalg.norm(V,axis=0,keepdims=True),1e-7)\n Unorm=U/np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n blocks=[]\n for lo in range(0,len(euids),128):\n hi=min(lo+128,len(euids)); n=hi-lo\n score=np.broadcast_to(c*pop,(n,len(eitems))).copy()\n good=ur[lo:hi]>=0\n if good.any():\n uu=U[ur[lo:hi][good]]\n if a: score[good]+=a*(uu@V)\n if b: score[good]+=b*(Unorm[ur[lo:hi][good]]@Vnorm)\n eligible=np.unpackbits(packed[lo:hi],axis=1,count=len(eitems),bitorder='little').astype(bool)\n score[~eligible]=-np.inf\n ix=np.argpartition(score,-100,axis=1)[:,-100:]\n ss=np.take_along_axis(score,ix,axis=1)\n order=np.argsort(-ss,axis=1); ix=np.take_along_axis(ix,order,axis=1)\n blocks.append(ix)\n print('ranked',hi,'/',len(euids),flush=True)\n top=np.vstack(blocks)\n result=pd.DataFrame({'uid':np.repeat(euids,100).astype('int64'),\n 'item_id':eitems[top.ravel()].astype('int64'),\n 'rank':np.tile(np.arange(1,101,dtype=np.int64),len(euids))})\n result.to_parquet(outpath,index=False)\n print('wrote',outpath,len(result),flush=True)\n\nif __name__=='__main__':\n p=argparse.ArgumentParser(); p.add_argument('--tag',choices=['train','full'],default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\n if a.fit: fit(a.tag,a.rank)\n emit(a.stage,a.tag,a.variant,Path(a.output or OUT/f'{a.stage}_{a.variant}.parquet'))\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/rank_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab448bc2e1c87d2b0705969dfa56c42", "usage": { "input_tokens": 32191, "cached_input_tokens": 31104, "cache_write_input_tokens": 0, "output_tokens": 1908, "reasoning_output_tokens": 503, "total_tokens": 34099 }, "turn_token_usage": { "input_tokens": 428181, "cached_input_tokens": 386688, "cache_write_input_tokens": 0, "output_tokens": 10517, "reasoning_output_tokens": 3317, "total_tokens": 438698 }, "thread_token_usage": { "input_tokens": 428181, "cached_input_tokens": 386688, "cache_write_input_tokens": 0, "output_tokens": 10517, "reasoning_output_tokens": 3317, "total_tokens": 438698 } } ## Step 28 · agent · 2026-09-23T21:47:40.355Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:47:43.726Z { "code": "const r = await tools.exec_command({\"cmd\":\"OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --fit --rank 192 --variant blend --output /app/solution/validation_blend.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:48:04.066Z Script completed Wall time 20.3 seconds Output: fit train (9238, 30000) 5618006 rank 192 explained 0.25359657 ranked 128 / 8456 ranked 256 / 8456 ranked 384 / 8456 ranked 512 / 8456 ranked 640 / 8456 ranked 768 / 8456 ranked 896 / 8456 ranked 1024 / 8456 ranked 1152 / 8456 ranked 1280 / 8456 ranked 1408 / 8456 ranked 1536 / 8456 ranked 1664 / 8456 ranked 1792 / 8456 ranked 1920 / 8456 ranked 2048 / 8456 ranked 2176 / 8456 ranked 2304 / 8456 ranked 2432 / 8456 ranked 2560 / 8456 ranked 2688 / 8456 ranked 2816 / 8456 ranked 2944 / 8456 ranked 3072 / 8456 ranked 3200 / 8456 ranked 3328 / 8456 ranked 3456 / 8456 ranked 3584 / 8456 ranked 3712 / 8456 ranked 3840 / 8456 ranked 3968 / 8456 ranked 4096 / 8456 ranked 4224 / 8456 ranked 4352 / 8456 ranked 4480 / 8456 ranked 4608 / 8456 ranked 4736 / 8456 ranked 4864 / 8456 ranked 4992 / 8456 ranked 5120 / 8456 ranked 5248 / 8456 ranked 5376 / 8456 ranked 5504 / 8456 ranked 5632 / 8456 ranked 5760 / 8456 ranked 5888 / 8456 ranked 6016 / 8456 ranked 6144 / 8456 ranked 6272 / 8456 ranked 6400 / 8456 ranked 6528 / 8456 ranked 6656 / 8456 ranked 6784 / 8456 ranked 6912 / 8456 ranked 7040 / 8456 ranked 7168 / 8456 ranked 7296 / 8456 ranked 7424 / 8456 ranked 7552 / 8456 ranked 7680 / 8456 ranked 7808 / 8456 ranked 7936 / 8456 ranked 8064 / 8456 ranked 8192 / 8456 ranked 8320 / 8456 ranked 8448 / 8456 ranked 8456 / 8456 wrote /app/solution/validation_blend.parquet 845600 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab448fa712487d29b48a6e1a3f661ca", "usage": { "input_tokens": 34123, "cached_input_tokens": 32000, "cache_write_input_tokens": 0, "output_tokens": 136, "reasoning_output_tokens": 22, "total_tokens": 34259 }, "turn_token_usage": { "input_tokens": 462304, "cached_input_tokens": 418688, "cache_write_input_tokens": 0, "output_tokens": 10653, "reasoning_output_tokens": 3339, "total_tokens": 472957 }, "thread_token_usage": { "input_tokens": 462304, "cached_input_tokens": 418688, "cache_write_input_tokens": 0, "output_tokens": 10653, "reasoning_output_tokens": 3339, "total_tokens": 472957 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-135d41ba-43b2-4c0b-a82d-b3ad88f43982", "process_id": "70713", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --fit --rank 192 --variant blend --output /app/solution/validation_blend.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --fit --rank 192 --variant blend --output /app/solution/validation_blend.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit train (9238, 30000) 5618006 rank 192 explained 0.25359657\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_blend.parquet 845600\n", "stderr": "", "aggregated_output": "fit train (9238, 30000) 5618006 rank 192 explained 0.25359657\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_blend.parquet 845600\n", "exit_code": 0, "duration": { "secs": 20, "nanos": 169503753 }, "formatted_output": "fit train (9238, 30000) 5618006 rank 192 explained 0.25359657\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_blend.parquet 845600\n" } } ## Step 29 · agent · 2026-09-23T21:48:05.835Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:48:07.810Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:48:10.512Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_blend.parquet", "scores": { "quality_ndcg@10": 0.019034, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44914404487d2bbd04f150456576d", "usage": { "input_tokens": 34986, "cached_input_tokens": 33920, "cache_write_input_tokens": 0, "output_tokens": 78, "reasoning_output_tokens": 7, "total_tokens": 35064 }, "turn_token_usage": { "input_tokens": 497290, "cached_input_tokens": 452608, "cache_write_input_tokens": 0, "output_tokens": 10731, "reasoning_output_tokens": 3346, "total_tokens": 508021 }, "thread_token_usage": { "input_tokens": 497290, "cached_input_tokens": 452608, "cache_write_input_tokens": 0, "output_tokens": 10731, "reasoning_output_tokens": 3346, "total_tokens": 508021 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b2fcc15a-51f8-4dc7-b1cf-10a50fe4e552", "process_id": "85038", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_blend.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_blend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019034,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_blend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019034,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 483027093 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_blend.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.019034,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 30 · agent · 2026-09-23T21:48:15.386Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:48:19.777Z { "code": "const r = await tools.exec_command({\"cmd\":\"for v in pop svd cos blendpop; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \\\"$v\\\" --output \\\"/app/solution/validation_$v.parquet\\\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_$v.parquet\\\"; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:48:49.959Z Script completed Wall time 30.2 seconds Output: { "model": "/app/solution/validation_pop.parquet", "scores": { "quality_ndcg@10": 0.010578, "quality_users": 4263 } } { "model": "/app/solution/validation_svd.parquet", "scores": { "quality_ndcg@10": 0.024043, "quality_users": 4263 } } { "model": "/app/solution/validation_cos.parquet", "scores": { "quality_ndcg@10": 0.013231, "quality_users": 4263 } } SESSION_ID=95658 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4491ab96887d2869b14bcef547c98", "usage": { "input_tokens": 35134, "cached_input_tokens": 34816, "cache_write_input_tokens": 0, "output_tokens": 265, "reasoning_output_tokens": 118, "total_tokens": 35399 }, "turn_token_usage": { "input_tokens": 532424, "cached_input_tokens": 487424, "cache_write_input_tokens": 0, "output_tokens": 10996, "reasoning_output_tokens": 3464, "total_tokens": 543420 }, "thread_token_usage": { "input_tokens": 532424, "cached_input_tokens": 487424, "cache_write_input_tokens": 0, "output_tokens": 10996, "reasoning_output_tokens": 3464, "total_tokens": 543420 } } ## Step 31 · agent · 2026-09-23T21:48:53.270Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:48:54.461Z { "code": "const r = await tools.write_stdin({\"session_id\":95658,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":4000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:48:56.396Z Script completed Wall time 1.9 seconds Output: { "model": "/app/solution/validation_blendpop.parquet", "scores": { "quality_ndcg@10": 0.013766, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44942564487d2bebf4bf3d58668c8", "usage": { "input_tokens": 35566, "cached_input_tokens": 34944, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 35620 }, "turn_token_usage": { "input_tokens": 567990, "cached_input_tokens": 522368, "cache_write_input_tokens": 0, "output_tokens": 11050, "reasoning_output_tokens": 3471, "total_tokens": 579040 }, "thread_token_usage": { "input_tokens": 567990, "cached_input_tokens": 522368, "cache_write_input_tokens": 0, "output_tokens": 11050, "reasoning_output_tokens": 3471, "total_tokens": 579040 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c5ec7044-ebcf-4ae1-92ab-150a91b2b262", "process_id": "95658", "command": [ "/bin/sh", "-lc", "for v in pop svd cos blendpop; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"$v\" --output \"/app/solution/validation_$v.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_$v.parquet\"; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for v in pop svd cos blendpop; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"$v\" --output \"/app/solution/validation_$v.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_$v.parquet\"; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024043,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_cos.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013231,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_blendpop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013766,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024043,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_cos.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013231,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_blendpop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013766,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 36, "nanos": 450297694 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_pop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.010578,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_svd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024043,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_cos.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013231,\n \"quality_users\": 4263\n }\n}\n{\n \"model\": \"/app/solution/validation_blendpop.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.013766,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 32 · agent · 2026-09-23T21:49:01.771Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:07.946Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python - <<'PY'\\nimport numpy as np, pandas as pd\\nm=np.load('/app/solution/latent_train.npz'); U=m['U'];V=m['V']; rng=np.random.default_rng(1); r=rng.choice(len(U),128); s=U[r]@V; c=(U[r]/np.linalg.norm(U[r],axis=1,keepdims=True))@(V/np.linalg.norm(V,axis=0,keepdims=True))\\nfor name,x in [('svd',s),('cos',c)]: print(name,np.percentile(x,[0,1,10,50,90,99,100]),x.std(axis=1).mean())\\nPY\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:49:09.147Z Script completed Wall time 1.2 seconds Output: :2: RuntimeWarning: invalid value encountered in divide svd [-5.00743575e-02 -5.40253363e-03 -1.99874619e-03 1.79953466e-04 4.40080678e-03 2.28726315e-02 4.69391823e-01] 0.005053566 cos [nan nan nan nan nan nan nan] nan ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44948a88487d2bf89f487ca40e3bf", "usage": { "input_tokens": 35691, "cached_input_tokens": 35328, "cache_write_input_tokens": 0, "output_tokens": 313, "reasoning_output_tokens": 110, "total_tokens": 36004 }, "turn_token_usage": { "input_tokens": 603681, "cached_input_tokens": 557696, "cache_write_input_tokens": 0, "output_tokens": 11363, "reasoning_output_tokens": 3581, "total_tokens": 615044 }, "thread_token_usage": { "input_tokens": 603681, "cached_input_tokens": 557696, "cache_write_input_tokens": 0, "output_tokens": 11363, "reasoning_output_tokens": 3581, "total_tokens": 615044 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-4d72cad8-ff91-4567-b308-4f6ded5c66fc", "process_id": "33773", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd\nm=np.load('/app/solution/latent_train.npz'); U=m['U'];V=m['V']; rng=np.random.default_rng(1); r=rng.choice(len(U),128); s=U[r]@V; c=(U[r]/np.linalg.norm(U[r],axis=1,keepdims=True))@(V/np.linalg.norm(V,axis=0,keepdims=True))\nfor name,x in [('svd',s),('cos',c)]: print(name,np.percentile(x,[0,1,10,50,90,99,100]),x.std(axis=1).mean())\nPY" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python - <<'PY'\nimport numpy as np, pandas as pd\nm=np.load('/app/solution/latent_train.npz'); U=m['U'];V=m['V']; rng=np.random.default_rng(1); r=rng.choice(len(U),128); s=U[r]@V; c=(U[r]/np.linalg.norm(U[r],axis=1,keepdims=True))@(V/np.linalg.norm(V,axis=0,keepdims=True))\nfor name,x in [('svd',s),('cos',c)]: print(name,np.percentile(x,[0,1,10,50,90,99,100]),x.std(axis=1).mean())\nPY" } ], "source": "unified_exec_startup", "status": "completed", "stdout": ":2: RuntimeWarning: invalid value encountered in divide\nsvd [-5.00743575e-02 -5.40253363e-03 -1.99874619e-03 1.79953466e-04\n 4.40080678e-03 2.28726315e-02 4.69391823e-01] 0.005053566\ncos [nan nan nan nan nan nan nan] nan\n", "stderr": "", "aggregated_output": ":2: RuntimeWarning: invalid value encountered in divide\nsvd [-5.00743575e-02 -5.40253363e-03 -1.99874619e-03 1.79953466e-04\n 4.40080678e-03 2.28726315e-02 4.69391823e-01] 0.005053566\ncos [nan nan nan nan nan nan nan] nan\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 18514676 }, "formatted_output": ":2: RuntimeWarning: invalid value encountered in divide\nsvd [-5.00743575e-02 -5.40253363e-03 -1.99874619e-03 1.79953466e-04\n 4.40080678e-03 2.28726315e-02 4.69391823e-01] 0.005053566\ncos [nan nan nan nan nan nan nan] nan\n" } } ## Step 33 · agent · 2026-09-23T21:49:26.577Z ### Thinking unavailable — no text recorded ## Step 34 · agent · 2026-09-23T21:49:31.483Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:49:37.545Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for spec in '1,0,.001' '1,0,.003' '1,0,.01' '1,.02,0' '1,.05,0'; do safe=$(echo \\\"$spec\\\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \\\"$spec\\\" --output \\\"/app/solution/validation_$safe.parquet\\\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_$safe.parquet\\\" | tail -4 | head -2; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T21:50:07.714Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"db0506","wall_time_seconds":30.000541106,"session_id":17983,"original_token_count":44} "quality_ndcg@10": 0.02421, "quality_users": 4263 "quality_ndcg@10": 0.024818, "quality_users": 4263 "quality_ndcg@10": 0.024029, "quality_users": 4263 SESSION_ID=17983 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44955604887d2a219aacbc0d800be", "usage": { "input_tokens": 36131, "cached_input_tokens": 35456, "cache_write_input_tokens": 0, "output_tokens": 862, "reasoning_output_tokens": 663, "total_tokens": 36993 }, "turn_token_usage": { "input_tokens": 639812, "cached_input_tokens": 593152, "cache_write_input_tokens": 0, "output_tokens": 12225, "reasoning_output_tokens": 4244, "total_tokens": 652037 }, "thread_token_usage": { "input_tokens": 639812, "cached_input_tokens": 593152, "cache_write_input_tokens": 0, "output_tokens": 12225, "reasoning_output_tokens": 4244, "total_tokens": 652037 } } ## Step 35 · agent · 2026-09-23T21:50:09.951Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:50:11.124Z { "code": "const r = await tools.write_stdin({\"session_id\":17983,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:50:33.285Z Script completed Wall time 22.1 seconds Output: "quality_ndcg@10": 0.023659, "quality_users": 4263 "quality_ndcg@10": 0.022438, "quality_users": 4263 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab4499003e487d2b4739f7118697a62", "usage": { "input_tokens": 37120, "cached_input_tokens": 35968, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 37173 }, "turn_token_usage": { "input_tokens": 676932, "cached_input_tokens": 629120, "cache_write_input_tokens": 0, "output_tokens": 12278, "reasoning_output_tokens": 4250, "total_tokens": 689210 }, "thread_token_usage": { "input_tokens": 676932, "cached_input_tokens": 629120, "cache_write_input_tokens": 0, "output_tokens": 12278, "reasoning_output_tokens": 4250, "total_tokens": 689210 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-a1d5b5d5-3e72-4981-bafe-ca07c0d170a7", "process_id": "17983", "command": [ "/bin/sh", "-lc", "for spec in '1,0,.001' '1,0,.003' '1,0,.01' '1,.02,0' '1,.05,0'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"$spec\" --output \"/app/solution/validation_$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_$safe.parquet\" | tail -4 | head -2; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for spec in '1,0,.001' '1,0,.003' '1,0,.01' '1,.02,0' '1,.05,0'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"$spec\" --output \"/app/solution/validation_$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_$safe.parquet\" | tail -4 | head -2; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.02421,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024818,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024029,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.023659,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.022438,\n \"quality_users\": 4263\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.02421,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024818,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024029,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.023659,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.022438,\n \"quality_users\": 4263\n", "exit_code": 0, "duration": { "secs": 55, "nanos": 570115320 }, "formatted_output": " \"quality_ndcg@10\": 0.02421,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024818,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.024029,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.023659,\n \"quality_users\": 4263\n \"quality_ndcg@10\": 0.022438,\n \"quality_users\": 4263\n" } } ## Step 36 · agent · 2026-09-23T21:50:36.226Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:50:53.308Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_trends.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Compute cutoff-correct catalog popularity at multiple recent horizons.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+\\n+D=Path('/task/data'); OUT=Path('/app/solution')\\n+def build(full=False):\\n+ cutoff=25393400 if full else 24786800\\n+ files=['train_listens.parquet']+(['val_listens.parquet'] if full else [])\\n+ items=pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.to_numpy(); idx=pd.Index(items)\\n+ horizons=np.array([3,7,14,30,60,90,180,10000],dtype=float)*86400\\n+ counts=np.zeros((len(items),len(horizons)),dtype=np.float64)\\n+ counts_all=np.zeros_like(counts)\\n+ for fn in files:\\n+ pf=pq.ParquetFile(D/fn)\\n+ for rg in range(pf.num_row_groups):\\n+ x=pf.read_row_group(rg,columns=['timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\\n+ c=idx.get_indexer(x.item_id); age=cutoff-x.timestamp.to_numpy(); ratio=x.played_ratio_pct.to_numpy(); org=x.is_organic.to_numpy()\\n+ base=(c>=0)&(ratio>=80)\\n+ for j,h in enumerate(horizons):\\n+ k=base&(age<=h)\\n+ counts_all[:,j]+=np.bincount(c[k],minlength=len(items))\\n+ k &= (org==1)\\n+ counts[:,j]+=np.bincount(c[k],minlength=len(items))\\n+ np.savez_compressed(OUT/f\\\"trends_{'full' if full else 'train'}.npz\\\",organic=counts,all=counts_all,items=items,horizons=horizons)\\n+if __name__=='__main__':\\n+ p=argparse.ArgumentParser();p.add_argument('--with-val',action='store_true');a=p.parse_args();build(a.with_val)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:50:53.384Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-51c6be50-09da-4d34-b68f-7fb337a7adc1", "changes": { "/app/solution/build_trends.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Compute cutoff-correct catalog popularity at multiple recent horizons.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nD=Path('/task/data'); OUT=Path('/app/solution')\ndef build(full=False):\n cutoff=25393400 if full else 24786800\n files=['train_listens.parquet']+(['val_listens.parquet'] if full else [])\n items=pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id']).item_id.to_numpy(); idx=pd.Index(items)\n horizons=np.array([3,7,14,30,60,90,180,10000],dtype=float)*86400\n counts=np.zeros((len(items),len(horizons)),dtype=np.float64)\n counts_all=np.zeros_like(counts)\n for fn in files:\n pf=pq.ParquetFile(D/fn)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg,columns=['timestamp','item_id','is_organic','played_ratio_pct']).to_pandas()\n c=idx.get_indexer(x.item_id); age=cutoff-x.timestamp.to_numpy(); ratio=x.played_ratio_pct.to_numpy(); org=x.is_organic.to_numpy()\n base=(c>=0)&(ratio>=80)\n for j,h in enumerate(horizons):\n k=base&(age<=h)\n counts_all[:,j]+=np.bincount(c[k],minlength=len(items))\n k &= (org==1)\n counts[:,j]+=np.bincount(c[k],minlength=len(items))\n np.savez_compressed(OUT/f\"trends_{'full' if full else 'train'}.npz\",organic=counts,all=counts_all,items=items,horizons=horizons)\nif __name__=='__main__':\n p=argparse.ArgumentParser();p.add_argument('--with-val',action='store_true');a=p.parse_args();build(a.with_val)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_trends.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab449a9964c87d2bfd6d8c6fc47b943", "usage": { "input_tokens": 37242, "cached_input_tokens": 36992, "cache_write_input_tokens": 0, "output_tokens": 556, "reasoning_output_tokens": 13, "total_tokens": 37798 }, "turn_token_usage": { "input_tokens": 714174, "cached_input_tokens": 666112, "cache_write_input_tokens": 0, "output_tokens": 12834, "reasoning_output_tokens": 4263, "total_tokens": 727008 }, "thread_token_usage": { "input_tokens": 714174, "cached_input_tokens": 666112, "cache_write_input_tokens": 0, "output_tokens": 12834, "reasoning_output_tokens": 4263, "total_tokens": 727008 } } ### exec · 2026-09-23T21:50:56.443Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /app/solution/build_trends.py\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:50:59.307Z Script completed Wall time 2.9 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab449bdab8487d2bfcc867003c9c1e9", "usage": { "input_tokens": 37822, "cached_input_tokens": 37120, "cache_write_input_tokens": 0, "output_tokens": 61, "reasoning_output_tokens": 0, "total_tokens": 37883 }, "turn_token_usage": { "input_tokens": 751996, "cached_input_tokens": 703232, "cache_write_input_tokens": 0, "output_tokens": 12895, "reasoning_output_tokens": 4263, "total_tokens": 764891 }, "thread_token_usage": { "input_tokens": 751996, "cached_input_tokens": 703232, "cache_write_input_tokens": 0, "output_tokens": 12895, "reasoning_output_tokens": 4263, "total_tokens": 764891 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-e75416b0-2d9f-46f5-9435-db8c0aecb145", "process_id": "47778", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /app/solution/build_trends.py" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /app/solution/build_trends.py" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "", "stderr": "", "aggregated_output": "", "exit_code": 0, "duration": { "secs": 2, "nanos": 678253904 }, "formatted_output": "" } } ## Step 37 · agent · 2026-09-23T21:51:07.898Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:17.769Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/trend_rank.py\\n+#!/opt/task-venv/bin/python\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\\n+z=np.load(D/f'{a.stage}_eligible_candidates.npz'); t=np.load(O/f'trends_{a.tag}.npz')\\n+s=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\\n+rows=[]\\n+for r,u in enumerate(z['uids']):\\n+ elig=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\\n+ top=order[elig[order]][:100]\\n+ rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(a.output,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:51:17.848Z Script failed Wall time 0.0 seconds Output: Script error: apply_patch verification failed: invalid hunk at line 17, 'pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(a.output,index=False)' is not a valid hunk header. Valid hunk headers: '*** Add File: {path}', '*** Delete File: {path}', '*** Update File: {path}' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab449c38b3487d2abe8cc6b6d2cbb06", "usage": { "input_tokens": 37906, "cached_input_tokens": 37632, "cache_write_input_tokens": 0, "output_tokens": 533, "reasoning_output_tokens": 216, "total_tokens": 38439 }, "turn_token_usage": { "input_tokens": 789902, "cached_input_tokens": 740864, "cache_write_input_tokens": 0, "output_tokens": 13428, "reasoning_output_tokens": 4479, "total_tokens": 803330 }, "thread_token_usage": { "input_tokens": 789902, "cached_input_tokens": 740864, "cache_write_input_tokens": 0, "output_tokens": 13428, "reasoning_output_tokens": 4479, "total_tokens": 803330 } } ## Step 38 · agent · 2026-09-23T21:51:19.499Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:51:29.566Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/trend_rank.py\\n+#!/opt/task-venv/bin/python\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\\n+z=np.load(D/f'{a.stage}_eligible_candidates.npz'); t=np.load(O/f'trends_{a.tag}.npz')\\n+s=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\\n+rows=[]\\n+for r,u in enumerate(z['uids']):\\n+ elig=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\\n+ top=order[elig[order]][:100]\\n+ rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\\n+pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(a.output,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:51:29.650Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-7623fcc4-1e9b-496b-b752-2ceeee03c03c", "changes": { "/app/solution/trend_rank.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nD=Path('/task/data'); O=Path('/app/solution')\np=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\nz=np.load(D/f'{a.stage}_eligible_candidates.npz'); t=np.load(O/f'trends_{a.tag}.npz')\ns=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\nrows=[]\nfor r,u in enumerate(z['uids']):\n elig=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\n top=order[elig[order]][:100]\n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\npd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(a.output,index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/trend_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab449d6565087d298793b532e06c089", "usage": { "input_tokens": 38538, "cached_input_tokens": 37760, "cache_write_input_tokens": 0, "output_tokens": 328, "reasoning_output_tokens": 7, "total_tokens": 38866 }, "turn_token_usage": { "input_tokens": 828440, "cached_input_tokens": 778624, "cache_write_input_tokens": 0, "output_tokens": 13756, "reasoning_output_tokens": 4486, "total_tokens": 842196 }, "thread_token_usage": { "input_tokens": 828440, "cached_input_tokens": 778624, "cache_write_input_tokens": 0, "output_tokens": 13756, "reasoning_output_tokens": 4486, "total_tokens": 842196 } } ### exec · 2026-09-23T21:51:35.534Z { "code": "const r = await tools.exec_command({\"cmd\":\"for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:52:05.727Z Script completed Wall time 30.2 seconds Output: SESSION_ID=63285 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab449e2035487d29c289cd2e374b151", "usage": { "input_tokens": 38890, "cached_input_tokens": 38400, "cache_write_input_tokens": 0, "output_tokens": 147, "reasoning_output_tokens": 0, "total_tokens": 39037 }, "turn_token_usage": { "input_tokens": 867330, "cached_input_tokens": 817024, "cache_write_input_tokens": 0, "output_tokens": 13903, "reasoning_output_tokens": 4486, "total_tokens": 881233 }, "thread_token_usage": { "input_tokens": 867330, "cached_input_tokens": 817024, "cache_write_input_tokens": 0, "output_tokens": 13903, "reasoning_output_tokens": 4486, "total_tokens": 881233 } } ## Step 39 · agent · 2026-09-23T21:52:07.045Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:08.232Z { "code": "const r = await tools.write_stdin({\"session_id\":63285,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:52:38.240Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a05ee4487d29977b8dfe10bdb3e", "usage": { "input_tokens": 39065, "cached_input_tokens": 38656, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 39118 }, "turn_token_usage": { "input_tokens": 906395, "cached_input_tokens": 855680, "cache_write_input_tokens": 0, "output_tokens": 13956, "reasoning_output_tokens": 4492, "total_tokens": 920351 }, "thread_token_usage": { "input_tokens": 906395, "cached_input_tokens": 855680, "cache_write_input_tokens": 0, "output_tokens": 13956, "reasoning_output_tokens": 4492, "total_tokens": 920351 } } ## Step 40 · agent · 2026-09-23T21:52:40.672Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:52:41.915Z { "code": "const r = await tools.write_stdin({\"session_id\":63285,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:53:11.924Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a26a04487d29a1ea22a5c35b989", "usage": { "input_tokens": 39141, "cached_input_tokens": 38912, "cache_write_input_tokens": 0, "output_tokens": 64, "reasoning_output_tokens": 17, "total_tokens": 39205 }, "turn_token_usage": { "input_tokens": 945536, "cached_input_tokens": 894592, "cache_write_input_tokens": 0, "output_tokens": 14020, "reasoning_output_tokens": 4509, "total_tokens": 959556 }, "thread_token_usage": { "input_tokens": 945536, "cached_input_tokens": 894592, "cache_write_input_tokens": 0, "output_tokens": 14020, "reasoning_output_tokens": 4509, "total_tokens": 959556 } } ## Step 41 · agent · 2026-09-23T21:53:16.561Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:53:17.784Z { "code": "const r = await tools.write_stdin({\"session_id\":63285,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:53:47.793Z Script completed Wall time 30.0 seconds Output: ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a48293c87d2852fb4108b2e2afc", "usage": { "input_tokens": 39228, "cached_input_tokens": 38912, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 95, "total_tokens": 39370 }, "turn_token_usage": { "input_tokens": 984764, "cached_input_tokens": 933504, "cache_write_input_tokens": 0, "output_tokens": 14162, "reasoning_output_tokens": 4604, "total_tokens": 998926 }, "thread_token_usage": { "input_tokens": 984764, "cached_input_tokens": 933504, "cache_write_input_tokens": 0, "output_tokens": 14162, "reasoning_output_tokens": 4604, "total_tokens": 998926 } } ## Step 42 · agent · 2026-09-23T21:53:50.964Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:53:52.326Z { "code": "const r = await tools.write_stdin({\"session_id\":63285,\"chars\":\"\\u0003\",\"yield_time_ms\":1000,\"max_output_tokens\":3000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:53:52.452Z Script completed Wall time 0.1 seconds Output: Traceback (most recent call last): File "/app/solution/trend_rank.py", line 14, in rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top)) File "/app/solution/trend_rank.py", line 14, in rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top)) ~^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py", line 254, in __getitem__ return format.read_array(bytes, ^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 851, in read_array data = _read_bytes(fp, read_size, "array data") ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py", line 986, in _read_bytes r = fp.read(size - len(data)) ^^^^^^^^^^^^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1008, in read data = self._read1(n) ^^^^^^^^^^^^^^ File "/usr/local/lib/python3.12/zipfile/__init__.py", line 1084, in _read1 data = self._decompressor.decompress(data, n) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ KeyboardInterrupt ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-b045efb6-4982-4f0e-bcd1-fdda897512e6", "process_id": "63285", "command": [ "/bin/sh", "-lc", "for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "failed", "stdout": "Traceback (most recent call last):\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n ~^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "stderr": "", "aggregated_output": "Traceback (most recent call last):\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n ~^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n", "exit_code": 130, "duration": { "secs": 136, "nanos": 675600556 }, "formatted_output": "Traceback (most recent call last):\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n File \"/app/solution/trend_rank.py\", line 14, in \n rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n ~^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/_npyio_impl.py\", line 254, in __getitem__\n return format.read_array(bytes,\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 851, in read_array\n data = _read_bytes(fp, read_size, \"array data\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/numpy/lib/format.py\", line 986, in _read_bytes\n r = fp.read(size - len(data))\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1008, in read\n data = self._read1(n)\n ^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/zipfile/__init__.py\", line 1084, in _read1\n data = self._decompressor.decompress(data, n)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nKeyboardInterrupt\n" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a6c0dbc87d283e009e3bc4131d6", "usage": { "input_tokens": 39393, "cached_input_tokens": 39040, "cache_write_input_tokens": 0, "output_tokens": 107, "reasoning_output_tokens": 55, "total_tokens": 39500 }, "turn_token_usage": { "input_tokens": 1024157, "cached_input_tokens": 972544, "cache_write_input_tokens": 0, "output_tokens": 14269, "reasoning_output_tokens": 4659, "total_tokens": 1038426 }, "thread_token_usage": { "input_tokens": 1024157, "cached_input_tokens": 972544, "cache_write_input_tokens": 0, "output_tokens": 14269, "reasoning_output_tokens": 4659, "total_tokens": 1038426 } } ## Step 43 · agent · 2026-09-23T21:53:55.453Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:03.726Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/trend_rank.py\\n@@\\n-z=np.load(D/f'{a.stage}_eligible_candidates.npz'); t=np.load(O/f'trends_{a.tag}.npz')\\n+za=np.load(D/f'{a.stage}_eligible_candidates.npz'); uids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']; t=np.load(O/f'trends_{a.tag}.npz')\\n@@\\n-for r,u in enumerate(z['uids']):\\n- elig=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\\n+for r,u in enumerate(uids):\\n+ elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\\n@@\\n- rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\\n+ rows.extend((int(u),int(items[c]),i+1) for i,c in enumerate(top))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:54:03.846Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-3a7fc96d-16e1-4112-ba72-cb27d70a2688", "changes": { "/app/solution/trend_rank.py": { "type": "update", "unified_diff": "@@ -7,9 +7,9 @@\n p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\n-z=np.load(D/f'{a.stage}_eligible_candidates.npz'); t=np.load(O/f'trends_{a.tag}.npz')\n+za=np.load(D/f'{a.stage}_eligible_candidates.npz'); uids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']; t=np.load(O/f'trends_{a.tag}.npz')\n s=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\n rows=[]\n-for r,u in enumerate(z['uids']):\n- elig=np.unpackbits(z['packed_eligible'][r],count=len(z['item_ids']),bitorder='little').astype(bool)\n+for r,u in enumerate(uids):\n+ elig=np.unpackbits(packed[r],count=len(items),bitorder='little').astype(bool)\n top=order[elig[order]][:100]\n- rows.extend((int(u),int(z['item_ids'][c]),i+1) for i,c in enumerate(top))\n+ rows.extend((int(u),int(items[c]),i+1) for i,c in enumerate(top))\n pd.DataFrame(rows,columns=['uid','item_id','rank']).to_parquet(a.output,index=False)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/trend_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a70c05c87d295fc53b4cea58f11", "usage": { "input_tokens": 39875, "cached_input_tokens": 28928, "cache_write_input_tokens": 0, "output_tokens": 292, "reasoning_output_tokens": 24, "total_tokens": 40167 }, "turn_token_usage": { "input_tokens": 1064032, "cached_input_tokens": 1001472, "cache_write_input_tokens": 0, "output_tokens": 14561, "reasoning_output_tokens": 4683, "total_tokens": 1078593 }, "thread_token_usage": { "input_tokens": 1064032, "cached_input_tokens": 1001472, "cache_write_input_tokens": 0, "output_tokens": 14561, "reasoning_output_tokens": 4683, "total_tokens": 1078593 } } ### exec · 2026-09-23T21:54:10.415Z { "code": "const r = await tools.exec_command({\"cmd\":\"for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:54:40.581Z Script completed Wall time 30.2 seconds Output: "quality_ndcg@10": 0.025721, "quality_ndcg@10": 0.028131, "quality_ndcg@10": 0.022878, "quality_ndcg@10": 0.013314, "quality_ndcg@10": 0.015802, "quality_ndcg@10": 0.015822, SESSION_ID=49227 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44a7c570887d28613c6fd80efcc8f", "usage": { "input_tokens": 40191, "cached_input_tokens": 39168, "cache_write_input_tokens": 0, "output_tokens": 147, "reasoning_output_tokens": 0, "total_tokens": 40338 }, "turn_token_usage": { "input_tokens": 1104223, "cached_input_tokens": 1040640, "cache_write_input_tokens": 0, "output_tokens": 14708, "reasoning_output_tokens": 4683, "total_tokens": 1118931 }, "thread_token_usage": { "input_tokens": 1104223, "cached_input_tokens": 1040640, "cache_write_input_tokens": 0, "output_tokens": 14708, "reasoning_output_tokens": 4683, "total_tokens": 1118931 } } ## Step 44 · agent · 2026-09-23T21:54:42.557Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:54:43.721Z { "code": "const r = await tools.write_stdin({\"session_id\":49227,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:54:49.645Z Script completed Wall time 5.9 seconds Output: "quality_ndcg@10": 0.011908, "quality_ndcg@10": 0.010551, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44aa0d0c087d2a615514e524ef908", "usage": { "input_tokens": 40450, "cached_input_tokens": 40064, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 40504 }, "turn_token_usage": { "input_tokens": 1144673, "cached_input_tokens": 1080704, "cache_write_input_tokens": 0, "output_tokens": 14762, "reasoning_output_tokens": 4690, "total_tokens": 1159435 }, "thread_token_usage": { "input_tokens": 1144673, "cached_input_tokens": 1080704, "cache_write_input_tokens": 0, "output_tokens": 14762, "reasoning_output_tokens": 4690, "total_tokens": 1159435 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2f16536c-37a5-44a2-89f8-4b166d05b852", "process_id": "49227", "command": [ "/bin/sh", "-lc", "for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for c in 0 1 2 3 4 5 6 7; do /opt/task-venv/bin/python /app/solution/trend_rank.py --col $c --output /app/solution/validation_trend$c.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_trend$c.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.025721,\n \"quality_ndcg@10\": 0.028131,\n \"quality_ndcg@10\": 0.022878,\n \"quality_ndcg@10\": 0.013314,\n \"quality_ndcg@10\": 0.015802,\n \"quality_ndcg@10\": 0.015822,\n \"quality_ndcg@10\": 0.011908,\n \"quality_ndcg@10\": 0.010551,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.025721,\n \"quality_ndcg@10\": 0.028131,\n \"quality_ndcg@10\": 0.022878,\n \"quality_ndcg@10\": 0.013314,\n \"quality_ndcg@10\": 0.015802,\n \"quality_ndcg@10\": 0.015822,\n \"quality_ndcg@10\": 0.011908,\n \"quality_ndcg@10\": 0.010551,\n", "exit_code": 0, "duration": { "secs": 39, "nanos": 63589428 }, "formatted_output": " \"quality_ndcg@10\": 0.025721,\n \"quality_ndcg@10\": 0.028131,\n \"quality_ndcg@10\": 0.022878,\n \"quality_ndcg@10\": 0.013314,\n \"quality_ndcg@10\": 0.015802,\n \"quality_ndcg@10\": 0.015822,\n \"quality_ndcg@10\": 0.011908,\n \"quality_ndcg@10\": 0.010551,\n" } } ## Step 45 · agent · 2026-09-23T21:54:56.306Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:55:02.188Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_model.py\\n@@\\n- cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n- pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\\n+ cat=pd.read_parquet(D/'candidate_catalog.parquet')\\n+ if variant.startswith('t:'):\\n+ variant=variant[2:]\\n+ tr=np.load(OUT/f'trends_{tag}.npz')\\n+ pop=np.log1p(tr['organic'][:,1]).astype(np.float32) # trailing seven days\\n+ else:\\n+ pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:55:02.247Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-b346a9c9-dd0b-4c9c-9176-f95c28488319", "changes": { "/app/solution/rank_model.py": { "type": "update", "unified_diff": "@@ -37,3 +37,8 @@\n cat=pd.read_parquet(D/'candidate_catalog.parquet')\n- pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n+ if variant.startswith('t:'):\n+ variant=variant[2:]\n+ tr=np.load(OUT/f'trends_{tag}.npz')\n+ pop=np.log1p(tr['organic'][:,1]).astype(np.float32) # trailing seven days\n+ else:\n+ pop=np.log1p(cat.organic_positive_listens.to_numpy(dtype=np.float32))\n pop=(pop-pop.mean())/pop.std()\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/rank_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44aa9de6887d29dff8a968762cb3a", "usage": { "input_tokens": 40555, "cached_input_tokens": 39680, "cache_write_input_tokens": 0, "output_tokens": 358, "reasoning_output_tokens": 165, "total_tokens": 40913 }, "turn_token_usage": { "input_tokens": 1185228, "cached_input_tokens": 1120384, "cache_write_input_tokens": 0, "output_tokens": 15120, "reasoning_output_tokens": 4855, "total_tokens": 1200348 }, "thread_token_usage": { "input_tokens": 1185228, "cached_input_tokens": 1120384, "cache_write_input_tokens": 0, "output_tokens": 15120, "reasoning_output_tokens": 4855, "total_tokens": 1200348 } } ### exec · 2026-09-23T21:55:10.244Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for spec in '1,0,.001' '1,0,.002' '1,0,.003' '1,0,.005' '1,0,.008'; do safe=$(echo \\\"$spec\\\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \\\"t:$spec\\\" --output \\\"/app/solution/validation_t$safe.parquet\\\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_t$safe.parquet\\\" | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T21:55:40.436Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"b75bed","wall_time_seconds":30.001198641,"session_id":11958,"original_token_count":25} "quality_ndcg@10": 0.024683, "quality_ndcg@10": 0.025185, "quality_ndcg@10": 0.025641, SESSION_ID=11958 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44ab6c51487d2bb2898508e4fdf3b", "usage": { "input_tokens": 40937, "cached_input_tokens": 40320, "cache_write_input_tokens": 0, "output_tokens": 198, "reasoning_output_tokens": 0, "total_tokens": 41135 }, "turn_token_usage": { "input_tokens": 1226165, "cached_input_tokens": 1160704, "cache_write_input_tokens": 0, "output_tokens": 15318, "reasoning_output_tokens": 4855, "total_tokens": 1241483 }, "thread_token_usage": { "input_tokens": 1226165, "cached_input_tokens": 1160704, "cache_write_input_tokens": 0, "output_tokens": 15318, "reasoning_output_tokens": 4855, "total_tokens": 1241483 } } ## Step 46 · agent · 2026-09-23T21:55:42.063Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:55:43.233Z { "code": "const r = await tools.write_stdin({\"session_id\":11958,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:55:56.599Z Script completed Wall time 13.3 seconds Output: "quality_ndcg@10": 0.027202, "quality_ndcg@10": 0.030352, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44adccec487d2b10034aff733eadd", "usage": { "input_tokens": 41235, "cached_input_tokens": 40320, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 41288 }, "turn_token_usage": { "input_tokens": 1267400, "cached_input_tokens": 1201024, "cache_write_input_tokens": 0, "output_tokens": 15371, "reasoning_output_tokens": 4861, "total_tokens": 1282771 }, "thread_token_usage": { "input_tokens": 1267400, "cached_input_tokens": 1201024, "cache_write_input_tokens": 0, "output_tokens": 15371, "reasoning_output_tokens": 4861, "total_tokens": 1282771 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-334a662b-8fe0-4989-b0f6-47adbfa99c6f", "process_id": "11958", "command": [ "/bin/sh", "-lc", "for spec in '1,0,.001' '1,0,.002' '1,0,.003' '1,0,.005' '1,0,.008'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"t:$spec\" --output \"/app/solution/validation_t$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_t$safe.parquet\" | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for spec in '1,0,.001' '1,0,.002' '1,0,.003' '1,0,.005' '1,0,.008'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"t:$spec\" --output \"/app/solution/validation_t$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_t$safe.parquet\" | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.024683,\n \"quality_ndcg@10\": 0.025185,\n \"quality_ndcg@10\": 0.025641,\n \"quality_ndcg@10\": 0.027202,\n \"quality_ndcg@10\": 0.030352,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.024683,\n \"quality_ndcg@10\": 0.025185,\n \"quality_ndcg@10\": 0.025641,\n \"quality_ndcg@10\": 0.027202,\n \"quality_ndcg@10\": 0.030352,\n", "exit_code": 0, "duration": { "secs": 46, "nanos": 171178128 }, "formatted_output": " \"quality_ndcg@10\": 0.024683,\n \"quality_ndcg@10\": 0.025185,\n \"quality_ndcg@10\": 0.025641,\n \"quality_ndcg@10\": 0.027202,\n \"quality_ndcg@10\": 0.030352,\n" } } ## Step 47 · agent · 2026-09-23T21:55:58.907Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:56:04.788Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for spec in '1,0,.01' '1,0,.015' '1,0,.02' '1,0,.03'; do safe=$(echo \\\"$spec\\\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \\\"t:$spec\\\" --output \\\"/app/solution/validation_t$safe.parquet\\\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \\\"/app/solution/validation_t$safe.parquet\\\" | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T21:56:34.967Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"a48877","wall_time_seconds":30.000525248,"session_id":17840,"original_token_count":25} "quality_ndcg@10": 0.032245, "quality_ndcg@10": 0.035885, "quality_ndcg@10": 0.036996, SESSION_ID=17840 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44aeccb4887d28aba98006cacea75", "usage": { "input_tokens": 41339, "cached_input_tokens": 41088, "cache_write_input_tokens": 0, "output_tokens": 230, "reasoning_output_tokens": 37, "total_tokens": 41569 }, "turn_token_usage": { "input_tokens": 1308739, "cached_input_tokens": 1242112, "cache_write_input_tokens": 0, "output_tokens": 15601, "reasoning_output_tokens": 4898, "total_tokens": 1324340 }, "thread_token_usage": { "input_tokens": 1308739, "cached_input_tokens": 1242112, "cache_write_input_tokens": 0, "output_tokens": 15601, "reasoning_output_tokens": 4898, "total_tokens": 1324340 } } ## Step 48 · agent · 2026-09-23T21:56:38.341Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:56:39.524Z { "code": "const r = await tools.write_stdin({\"session_id\":17840,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:56:42.623Z Script completed Wall time 3.1 seconds Output: "quality_ndcg@10": 0.035486, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b1355c887d28e58ce0e0a9e5a51", "usage": { "input_tokens": 41669, "cached_input_tokens": 41216, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 41722 }, "turn_token_usage": { "input_tokens": 1350408, "cached_input_tokens": 1283328, "cache_write_input_tokens": 0, "output_tokens": 15654, "reasoning_output_tokens": 4904, "total_tokens": 1366062 }, "thread_token_usage": { "input_tokens": 1350408, "cached_input_tokens": 1283328, "cache_write_input_tokens": 0, "output_tokens": 15654, "reasoning_output_tokens": 4904, "total_tokens": 1366062 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-cc8a791d-5a6c-4f0b-adb3-fb5ad71b0444", "process_id": "17840", "command": [ "/bin/sh", "-lc", "for spec in '1,0,.01' '1,0,.015' '1,0,.02' '1,0,.03'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"t:$spec\" --output \"/app/solution/validation_t$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_t$safe.parquet\" | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for spec in '1,0,.01' '1,0,.015' '1,0,.02' '1,0,.03'; do safe=$(echo \"$spec\" | tr ',.' '__'); OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant \"t:$spec\" --output \"/app/solution/validation_t$safe.parquet\" >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py \"/app/solution/validation_t$safe.parquet\" | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.032245,\n \"quality_ndcg@10\": 0.035885,\n \"quality_ndcg@10\": 0.036996,\n \"quality_ndcg@10\": 0.035486,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.032245,\n \"quality_ndcg@10\": 0.035885,\n \"quality_ndcg@10\": 0.036996,\n \"quality_ndcg@10\": 0.035486,\n", "exit_code": 0, "duration": { "secs": 37, "nanos": 665774595 }, "formatted_output": " \"quality_ndcg@10\": 0.032245,\n \"quality_ndcg@10\": 0.035885,\n \"quality_ndcg@10\": 0.036996,\n \"quality_ndcg@10\": 0.035486,\n" } } ## Step 49 · agent · 2026-09-23T21:57:00.856Z ### Thinking unavailable — no text recorded ## Step 50 · agent · 2026-09-23T21:57:03.267Z ### Thinking unavailable — no text recorded The seven-day organic popularity trend is a materially stronger prior than lifetime popularity, and combining it with the latent model has raised public NDCG@10 from 0.0240 to about 0.0370. I’m now testing a neighborhood recommender, which can recover “listeners with similar taste discovered this track” signals that a low-rank reconstruction tends to smooth away. ### exec · 2026-09-23T21:57:31.482Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/knn_rank.py\\n+#!/opt/task-venv/bin/python\\n+\\\"\\\"\\\"Latent user-neighborhood recommender blended with recent discovery trends.\\\"\\\"\\\"\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+from scipy import sparse\\n+D=Path('/task/data'); O=Path('/app/solution')\\n+p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--output',required=True);a=p.parse_args()\\n+za=np.load(D/f'{a.stage}_eligible_candidates.npz'); euids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']\\n+m=np.load(O/f'latent_{a.tag}.npz'); U=m['U']; muids=m['uids']; ur=pd.Index(muids).get_indexer(euids)\\n+Un=U/np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\\n+X=sparse.load_npz(O/f'matrix_{a.tag}.npz').astype(np.float32); X.data[:]=1\\n+df=np.asarray(X.sum(axis=0)).ravel(); debias=np.maximum(df,1)**a.alpha\\n+tr=np.load(O/f'trends_{a.tag}.npz'); trend=np.log1p(tr['organic'][:,1]).astype(np.float32); trend=(trend-trend.mean())/trend.std()\\n+tops=[]\\n+for lo in range(0,len(euids),128):\\n+ hi=min(lo+128,len(euids)); rr=ur[lo:hi]; sim=Un[rr]@Un.T\\n+ sim[np.arange(hi-lo),rr]=-np.inf\\n+ ni=np.argpartition(sim,-a.k,axis=1)[:,-a.k:]; ns=np.take_along_axis(sim,ni,axis=1)\\n+ ns=np.maximum(ns,0)**2\\n+ S=sparse.csr_matrix((ns.ravel(),(np.repeat(np.arange(hi-lo),a.k),ni.ravel())),shape=(hi-lo,len(muids)))\\n+ score=(S@X).toarray()/debias\\n+ if lo==0: print('knn score std',np.std(score,axis=1).mean(),'percentiles',np.percentile(score,[50,90,99,99.9]),flush=True)\\n+ score += a.trend*trend\\n+ eligible=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool);score[~eligible]=-np.inf\\n+ ix=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-ss,axis=1),axis=1);tops.append(ix)\\n+ print(hi,'/',len(euids),flush=True)\\n+top=np.vstack(tops)\\n+pd.DataFrame({'uid':np.repeat(euids,100).astype('int64'),'item_id':items[top.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101,dtype=np.int64),len(euids))}).to_parquet(a.output,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T21:57:31.551Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-40a6d2aa-4dc5-4198-8e81-ce3e2f345ac5", "changes": { "/app/solution/knn_rank.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\n\"\"\"Latent user-neighborhood recommender blended with recent discovery trends.\"\"\"\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom scipy import sparse\nD=Path('/task/data'); O=Path('/app/solution')\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--output',required=True);a=p.parse_args()\nza=np.load(D/f'{a.stage}_eligible_candidates.npz'); euids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']\nm=np.load(O/f'latent_{a.tag}.npz'); U=m['U']; muids=m['uids']; ur=pd.Index(muids).get_indexer(euids)\nUn=U/np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\nX=sparse.load_npz(O/f'matrix_{a.tag}.npz').astype(np.float32); X.data[:]=1\ndf=np.asarray(X.sum(axis=0)).ravel(); debias=np.maximum(df,1)**a.alpha\ntr=np.load(O/f'trends_{a.tag}.npz'); trend=np.log1p(tr['organic'][:,1]).astype(np.float32); trend=(trend-trend.mean())/trend.std()\ntops=[]\nfor lo in range(0,len(euids),128):\n hi=min(lo+128,len(euids)); rr=ur[lo:hi]; sim=Un[rr]@Un.T\n sim[np.arange(hi-lo),rr]=-np.inf\n ni=np.argpartition(sim,-a.k,axis=1)[:,-a.k:]; ns=np.take_along_axis(sim,ni,axis=1)\n ns=np.maximum(ns,0)**2\n S=sparse.csr_matrix((ns.ravel(),(np.repeat(np.arange(hi-lo),a.k),ni.ravel())),shape=(hi-lo,len(muids)))\n score=(S@X).toarray()/debias\n if lo==0: print('knn score std',np.std(score,axis=1).mean(),'percentiles',np.percentile(score,[50,90,99,99.9]),flush=True)\n score += a.trend*trend\n eligible=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool);score[~eligible]=-np.inf\n ix=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,ix,axis=1);ix=np.take_along_axis(ix,np.argsort(-ss,axis=1),axis=1);tops.append(ix)\n print(hi,'/',len(euids),flush=True)\ntop=np.vstack(tops)\npd.DataFrame({'uid':np.repeat(euids,100).astype('int64'),'item_id':items[top.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101,dtype=np.int64),len(euids))}).to_parquet(a.output,index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/knn_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b1ad8f087d2bbaeb1e3a1a1c75b", "usage": { "input_tokens": 41759, "cached_input_tokens": 40704, "cache_write_input_tokens": 0, "output_tokens": 1476, "reasoning_output_tokens": 590, "total_tokens": 43235 }, "turn_token_usage": { "input_tokens": 1392167, "cached_input_tokens": 1324032, "cache_write_input_tokens": 0, "output_tokens": 17130, "reasoning_output_tokens": 5494, "total_tokens": 1409297 }, "thread_token_usage": { "input_tokens": 1392167, "cached_input_tokens": 1324032, "cache_write_input_tokens": 0, "output_tokens": 17130, "reasoning_output_tokens": 5494, "total_tokens": 1409297 } } ## Step 51 · agent · 2026-09-23T21:57:35.534Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:57:38.532Z { "code": "const r = await tools.exec_command({\"cmd\":\"OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --trend .1 --output /app/solution/validation_knn.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":3000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T21:57:50.670Z Script completed Wall time 12.1 seconds Output: knn score std 0.41689456 percentiles [0. 0.54672334 2.468133 4.86969144] 128 / 8456 256 / 8456 384 / 8456 512 / 8456 640 / 8456 768 / 8456 896 / 8456 1024 / 8456 1152 / 8456 1280 / 8456 1408 / 8456 1536 / 8456 1664 / 8456 1792 / 8456 1920 / 8456 2048 / 8456 2176 / 8456 2304 / 8456 2432 / 8456 2560 / 8456 2688 / 8456 2816 / 8456 2944 / 8456 3072 / 8456 3200 / 8456 3328 / 8456 3456 / 8456 3584 / 8456 3712 / 8456 3840 / 8456 3968 / 8456 4096 / 8456 4224 / 8456 4352 / 8456 4480 / 8456 4608 / 8456 4736 / 8456 4864 / 8456 4992 / 8456 5120 / 8456 5248 / 8456 5376 / 8456 5504 / 8456 5632 / 8456 5760 / 8456 5888 / 8456 6016 / 8456 6144 / 8456 6272 / 8456 6400 / 8456 6528 / 8456 6656 / 8456 6784 / 8456 6912 / 8456 7040 / 8456 7168 / 8456 7296 / 8456 7424 / 8456 7552 / 8456 7680 / 8456 7808 / 8456 7936 / 8456 8064 / 8456 8192 / 8456 8320 / 8456 8448 / 8456 8456 / 8456 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b4be3a487d28f8f553367b9e24c", "usage": { "input_tokens": 43259, "cached_input_tokens": 41472, "cache_write_input_tokens": 0, "output_tokens": 117, "reasoning_output_tokens": 13, "total_tokens": 43376 }, "turn_token_usage": { "input_tokens": 1435426, "cached_input_tokens": 1365504, "cache_write_input_tokens": 0, "output_tokens": 17247, "reasoning_output_tokens": 5507, "total_tokens": 1452673 }, "thread_token_usage": { "input_tokens": 1435426, "cached_input_tokens": 1365504, "cache_write_input_tokens": 0, "output_tokens": 17247, "reasoning_output_tokens": 5507, "total_tokens": 1452673 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ab312f55-9712-4d37-86c5-9cf64f98c01a", "process_id": "15546", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --trend .1 --output /app/solution/validation_knn.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --trend .1 --output /app/solution/validation_knn.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "knn score std 0.41689456 percentiles [0. 0.54672334 2.468133 4.86969144]\n128 / 8456\n256 / 8456\n384 / 8456\n512 / 8456\n640 / 8456\n768 / 8456\n896 / 8456\n1024 / 8456\n1152 / 8456\n1280 / 8456\n1408 / 8456\n1536 / 8456\n1664 / 8456\n1792 / 8456\n1920 / 8456\n2048 / 8456\n2176 / 8456\n2304 / 8456\n2432 / 8456\n2560 / 8456\n2688 / 8456\n2816 / 8456\n2944 / 8456\n3072 / 8456\n3200 / 8456\n3328 / 8456\n3456 / 8456\n3584 / 8456\n3712 / 8456\n3840 / 8456\n3968 / 8456\n4096 / 8456\n4224 / 8456\n4352 / 8456\n4480 / 8456\n4608 / 8456\n4736 / 8456\n4864 / 8456\n4992 / 8456\n5120 / 8456\n5248 / 8456\n5376 / 8456\n5504 / 8456\n5632 / 8456\n5760 / 8456\n5888 / 8456\n6016 / 8456\n6144 / 8456\n6272 / 8456\n6400 / 8456\n6528 / 8456\n6656 / 8456\n6784 / 8456\n6912 / 8456\n7040 / 8456\n7168 / 8456\n7296 / 8456\n7424 / 8456\n7552 / 8456\n7680 / 8456\n7808 / 8456\n7936 / 8456\n8064 / 8456\n8192 / 8456\n8320 / 8456\n8448 / 8456\n8456 / 8456\n", "stderr": "", "aggregated_output": "knn score std 0.41689456 percentiles [0. 0.54672334 2.468133 4.86969144]\n128 / 8456\n256 / 8456\n384 / 8456\n512 / 8456\n640 / 8456\n768 / 8456\n896 / 8456\n1024 / 8456\n1152 / 8456\n1280 / 8456\n1408 / 8456\n1536 / 8456\n1664 / 8456\n1792 / 8456\n1920 / 8456\n2048 / 8456\n2176 / 8456\n2304 / 8456\n2432 / 8456\n2560 / 8456\n2688 / 8456\n2816 / 8456\n2944 / 8456\n3072 / 8456\n3200 / 8456\n3328 / 8456\n3456 / 8456\n3584 / 8456\n3712 / 8456\n3840 / 8456\n3968 / 8456\n4096 / 8456\n4224 / 8456\n4352 / 8456\n4480 / 8456\n4608 / 8456\n4736 / 8456\n4864 / 8456\n4992 / 8456\n5120 / 8456\n5248 / 8456\n5376 / 8456\n5504 / 8456\n5632 / 8456\n5760 / 8456\n5888 / 8456\n6016 / 8456\n6144 / 8456\n6272 / 8456\n6400 / 8456\n6528 / 8456\n6656 / 8456\n6784 / 8456\n6912 / 8456\n7040 / 8456\n7168 / 8456\n7296 / 8456\n7424 / 8456\n7552 / 8456\n7680 / 8456\n7808 / 8456\n7936 / 8456\n8064 / 8456\n8192 / 8456\n8320 / 8456\n8448 / 8456\n8456 / 8456\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 938081582 }, "formatted_output": "knn score std 0.41689456 percentiles [0. 0.54672334 2.468133 4.86969144]\n128 / 8456\n256 / 8456\n384 / 8456\n512 / 8456\n640 / 8456\n768 / 8456\n896 / 8456\n1024 / 8456\n1152 / 8456\n1280 / 8456\n1408 / 8456\n1536 / 8456\n1664 / 8456\n1792 / 8456\n1920 / 8456\n2048 / 8456\n2176 / 8456\n2304 / 8456\n2432 / 8456\n2560 / 8456\n2688 / 8456\n2816 / 8456\n2944 / 8456\n3072 / 8456\n3200 / 8456\n3328 / 8456\n3456 / 8456\n3584 / 8456\n3712 / 8456\n3840 / 8456\n3968 / 8456\n4096 / 8456\n4224 / 8456\n4352 / 8456\n4480 / 8456\n4608 / 8456\n4736 / 8456\n4864 / 8456\n4992 / 8456\n5120 / 8456\n5248 / 8456\n5376 / 8456\n5504 / 8456\n5632 / 8456\n5760 / 8456\n5888 / 8456\n6016 / 8456\n6144 / 8456\n6272 / 8456\n6400 / 8456\n6528 / 8456\n6656 / 8456\n6784 / 8456\n6912 / 8456\n7040 / 8456\n7168 / 8456\n7296 / 8456\n7424 / 8456\n7552 / 8456\n7680 / 8456\n7808 / 8456\n7936 / 8456\n8064 / 8456\n8192 / 8456\n8320 / 8456\n8448 / 8456\n8456 / 8456\n" } } ### exec · 2026-09-23T21:57:53.971Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:57:56.698Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_knn.parquet", "scores": { "quality_ndcg@10": 0.020825, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b5edc0087d296de03743b049a41", "usage": { "input_tokens": 43896, "cached_input_tokens": 43136, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 0, "total_tokens": 43965 }, "turn_token_usage": { "input_tokens": 1479322, "cached_input_tokens": 1408640, "cache_write_input_tokens": 0, "output_tokens": 17316, "reasoning_output_tokens": 5507, "total_tokens": 1496638 }, "thread_token_usage": { "input_tokens": 1479322, "cached_input_tokens": 1408640, "cache_write_input_tokens": 0, "output_tokens": 17316, "reasoning_output_tokens": 5507, "total_tokens": 1496638 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-2580eea6-afec-4722-b5ee-0fed66c3c30c", "process_id": "75539", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_knn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020825,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_knn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020825,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 559398135 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_knn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.020825,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 52 · agent · 2026-09-23T21:57:59.357Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:58:04.208Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for t in .3 .6 1.0 2.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T21:58:34.388Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"2dd9f3","wall_time_seconds":30.000533555,"session_id":26660,"original_token_count":17} "quality_ndcg@10": 0.025019, "quality_ndcg@10": 0.028913, SESSION_ID=26660 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b64e51087d2b04bc41b3052c77d", "usage": { "input_tokens": 44035, "cached_input_tokens": 43776, "cache_write_input_tokens": 0, "output_tokens": 209, "reasoning_output_tokens": 49, "total_tokens": 44244 }, "turn_token_usage": { "input_tokens": 1523357, "cached_input_tokens": 1452416, "cache_write_input_tokens": 0, "output_tokens": 17525, "reasoning_output_tokens": 5556, "total_tokens": 1540882 }, "thread_token_usage": { "input_tokens": 1523357, "cached_input_tokens": 1452416, "cache_write_input_tokens": 0, "output_tokens": 17525, "reasoning_output_tokens": 5556, "total_tokens": 1540882 } } ## Step 53 · agent · 2026-09-23T21:58:37.195Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:58:38.784Z { "code": "const r = await tools.write_stdin({\"session_id\":26660,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T21:59:03.364Z Script completed Wall time 24.5 seconds Output: "quality_ndcg@10": 0.03309, "quality_ndcg@10": 0.037418, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44b8b4e1c87d2af0a1f159b27a148", "usage": { "input_tokens": 44332, "cached_input_tokens": 43904, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 44385 }, "turn_token_usage": { "input_tokens": 1567689, "cached_input_tokens": 1496320, "cache_write_input_tokens": 0, "output_tokens": 17578, "reasoning_output_tokens": 5562, "total_tokens": 1585267 }, "thread_token_usage": { "input_tokens": 1567689, "cached_input_tokens": 1496320, "cache_write_input_tokens": 0, "output_tokens": 17578, "reasoning_output_tokens": 5562, "total_tokens": 1585267 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d356d339-a1a2-4588-842a-d439de265a2c", "process_id": "26660", "command": [ "/bin/sh", "-lc", "for t in .3 .6 1.0 2.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for t in .3 .6 1.0 2.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.025019,\n \"quality_ndcg@10\": 0.028913,\n \"quality_ndcg@10\": 0.03309,\n \"quality_ndcg@10\": 0.037418,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.025019,\n \"quality_ndcg@10\": 0.028913,\n \"quality_ndcg@10\": 0.03309,\n \"quality_ndcg@10\": 0.037418,\n", "exit_code": 0, "duration": { "secs": 58, "nanos": 975586684 }, "formatted_output": " \"quality_ndcg@10\": 0.025019,\n \"quality_ndcg@10\": 0.028913,\n \"quality_ndcg@10\": 0.03309,\n \"quality_ndcg@10\": 0.037418,\n" } } ## Step 54 · agent · 2026-09-23T21:59:10.400Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:59:17.025Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for t in 1.5 2.5 3.0 4.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T21:59:47.192Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"69e17a","wall_time_seconds":30.000354761,"session_id":36559,"original_token_count":17} "quality_ndcg@10": 0.036164, "quality_ndcg@10": 0.037821, SESSION_ID=36559 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44ba79b6c87d283856c2a20062a77", "usage": { "input_tokens": 44436, "cached_input_tokens": 44160, "cache_write_input_tokens": 0, "output_tokens": 309, "reasoning_output_tokens": 145, "total_tokens": 44745 }, "turn_token_usage": { "input_tokens": 1612125, "cached_input_tokens": 1540480, "cache_write_input_tokens": 0, "output_tokens": 17887, "reasoning_output_tokens": 5707, "total_tokens": 1630012 }, "thread_token_usage": { "input_tokens": 1612125, "cached_input_tokens": 1540480, "cache_write_input_tokens": 0, "output_tokens": 17887, "reasoning_output_tokens": 5707, "total_tokens": 1630012 } } ## Step 55 · agent · 2026-09-23T21:59:51.327Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T21:59:52.472Z { "code": "const r = await tools.write_stdin({\"session_id\":36559,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:00:16.127Z Script completed Wall time 23.6 seconds Output: "quality_ndcg@10": 0.036945, "quality_ndcg@10": 0.035118, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44bd4dba487d299ed1d89be3d7206", "usage": { "input_tokens": 44832, "cached_input_tokens": 44288, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 44885 }, "turn_token_usage": { "input_tokens": 1656957, "cached_input_tokens": 1584768, "cache_write_input_tokens": 0, "output_tokens": 17940, "reasoning_output_tokens": 5713, "total_tokens": 1674897 }, "thread_token_usage": { "input_tokens": 1656957, "cached_input_tokens": 1584768, "cache_write_input_tokens": 0, "output_tokens": 17940, "reasoning_output_tokens": 5713, "total_tokens": 1674897 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-261b488e-1339-436c-b3a5-ecb2a0614d11", "process_id": "36559", "command": [ "/bin/sh", "-lc", "for t in 1.5 2.5 3.0 4.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for t in 1.5 2.5 3.0 4.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --trend $t --output /app/solution/validation_knn$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_knn$t.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.036164,\n \"quality_ndcg@10\": 0.037821,\n \"quality_ndcg@10\": 0.036945,\n \"quality_ndcg@10\": 0.035118,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.036164,\n \"quality_ndcg@10\": 0.037821,\n \"quality_ndcg@10\": 0.036945,\n \"quality_ndcg@10\": 0.035118,\n", "exit_code": 0, "duration": { "secs": 58, "nanos": 934713502 }, "formatted_output": " \"quality_ndcg@10\": 0.036164,\n \"quality_ndcg@10\": 0.037821,\n \"quality_ndcg@10\": 0.036945,\n \"quality_ndcg@10\": 0.035118,\n" } } ## Step 56 · agent · 2026-09-23T22:00:27.729Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:00:35.874Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/trend_rank.py\\n@@\\n-p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\\n+p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--weights');p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\\n@@\\n-s=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\\n+F=np.log1p(t['organic']).astype('float64')\\n+if a.weights:\\n+ w=np.array([float(v) for v in a.weights.split(',')]); s=F@w\\n+else: s=F[:,a.col]\\n+order=np.argsort(-s,kind='stable')\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:00:35.959Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-a8fa2d46-d383-4402-bc9a-08a71b6d0dee", "changes": { "/app/solution/trend_rank.py": { "type": "update", "unified_diff": "@@ -6,5 +6,9 @@\n D=Path('/task/data'); O=Path('/app/solution')\n-p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\n+p=argparse.ArgumentParser();p.add_argument('--col',type=int,default=3);p.add_argument('--weights');p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--output',required=True);a=p.parse_args()\n za=np.load(D/f'{a.stage}_eligible_candidates.npz'); uids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']; t=np.load(O/f'trends_{a.tag}.npz')\n-s=np.log1p(t['organic'][:,a.col]); order=np.argsort(-s,kind='stable')\n+F=np.log1p(t['organic']).astype('float64')\n+if a.weights:\n+ w=np.array([float(v) for v in a.weights.split(',')]); s=F@w\n+else: s=F[:,a.col]\n+order=np.argsort(-s,kind='stable')\n rows=[]\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/trend_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44bf06f8087d2ad93457cdb5aa8ad", "usage": { "input_tokens": 44936, "cached_input_tokens": 41600, "cache_write_input_tokens": 0, "output_tokens": 551, "reasoning_output_tokens": 287, "total_tokens": 45487 }, "turn_token_usage": { "input_tokens": 1701893, "cached_input_tokens": 1626368, "cache_write_input_tokens": 0, "output_tokens": 18491, "reasoning_output_tokens": 6000, "total_tokens": 1720384 }, "thread_token_usage": { "input_tokens": 1701893, "cached_input_tokens": 1626368, "cache_write_input_tokens": 0, "output_tokens": 18491, "reasoning_output_tokens": 6000, "total_tokens": 1720384 } } ### exec · 2026-09-23T22:00:47.051Z { "code": "const r = await tools.exec_command({\"cmd\":\"i=0; for w in '0.5,1,0,0,0,0,0,0' '0.25,1,0.1,0,0,0,0,0' '0,1,.1,0,0,0,0,0' '0,1,.25,0,0,0,0,0' '0,1,0,.1,0,0,0,0' '-.2,1,0,0,0,0,0,0'; do /opt/task-venv/bin/python /app/solution/trend_rank.py --weights \\\"$w\\\" --output /app/solution/validation_tw$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_tw$i.parquet | rg quality_ndcg; i=$((i+1)); done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:01:13.851Z Script completed Wall time 26.8 seconds Output: "quality_ndcg@10": 0.02733, "quality_ndcg@10": 0.027755, "quality_ndcg@10": 0.027837, "quality_ndcg@10": 0.027978, "quality_ndcg@10": 0.027982, usage: trend_rank.py [-h] [--col COL] [--weights WEIGHTS] [--stage STAGE] [--tag TAG] --output OUTPUT trend_rank.py: error: argument --weights: expected one argument 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 113, in load_submission frame = validate_ranking(read_ranking(path), targets) ^^^^^^^^^^^^^^^^^^ File "/task/ranking_evaluation.py", line 98, in read_ranking return pd.read_parquet(path) if path.suffix == ".parquet" else pd.read_csv(path) ^^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 669, in read_parquet return impl.read( ^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 258, in read path_or_handle, handles, filesystem = _get_path_or_handle( ^^^^^^^^^^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py", line 141, in _get_path_or_handle handles = get_handle( ^^^^^^^^^^^ File "/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py", line 882, in get_handle handle = open(handle, ioargs.mode) ^^^^^^^^^^^^^^^^^^^^^^^^^ FileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_tw5.parquet' ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c045ba887d280bf3875dc6fa5b4", "usage": { "input_tokens": 45511, "cached_input_tokens": 44672, "cache_write_input_tokens": 0, "output_tokens": 249, "reasoning_output_tokens": 0, "total_tokens": 45760 }, "turn_token_usage": { "input_tokens": 1747404, "cached_input_tokens": 1671040, "cache_write_input_tokens": 0, "output_tokens": 18740, "reasoning_output_tokens": 6000, "total_tokens": 1766144 }, "thread_token_usage": { "input_tokens": 1747404, "cached_input_tokens": 1671040, "cache_write_input_tokens": 0, "output_tokens": 18740, "reasoning_output_tokens": 6000, "total_tokens": 1766144 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-d61230b8-bff1-4e06-bdee-86e2e45053f9", "process_id": "69276", "command": [ "/bin/sh", "-lc", "i=0; for w in '0.5,1,0,0,0,0,0,0' '0.25,1,0.1,0,0,0,0,0' '0,1,.1,0,0,0,0,0' '0,1,.25,0,0,0,0,0' '0,1,0,.1,0,0,0,0' '-.2,1,0,0,0,0,0,0'; do /opt/task-venv/bin/python /app/solution/trend_rank.py --weights \"$w\" --output /app/solution/validation_tw$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_tw$i.parquet | rg quality_ndcg; i=$((i+1)); done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "i=0; for w in '0.5,1,0,0,0,0,0,0' '0.25,1,0.1,0,0,0,0,0' '0,1,.1,0,0,0,0,0' '0,1,.25,0,0,0,0,0' '0,1,0,.1,0,0,0,0' '-.2,1,0,0,0,0,0,0'; do /opt/task-venv/bin/python /app/solution/trend_rank.py --weights \"$w\" --output /app/solution/validation_tw$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_tw$i.parquet | rg quality_ndcg; i=$((i+1)); done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.02733,\n \"quality_ndcg@10\": 0.027755,\n \"quality_ndcg@10\": 0.027837,\n \"quality_ndcg@10\": 0.027978,\n \"quality_ndcg@10\": 0.027982,\nusage: trend_rank.py [-h] [--col COL] [--weights WEIGHTS] [--stage STAGE]\n [--tag TAG] --output OUTPUT\ntrend_rank.py: error: argument --weights: expected one argument\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_tw5.parquet'\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.02733,\n \"quality_ndcg@10\": 0.027755,\n \"quality_ndcg@10\": 0.027837,\n \"quality_ndcg@10\": 0.027978,\n \"quality_ndcg@10\": 0.027982,\nusage: trend_rank.py [-h] [--col COL] [--weights WEIGHTS] [--stage STAGE]\n [--tag TAG] --output OUTPUT\ntrend_rank.py: error: argument --weights: expected one argument\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_tw5.parquet'\n", "exit_code": 0, "duration": { "secs": 26, "nanos": 601206934 }, "formatted_output": " \"quality_ndcg@10\": 0.02733,\n \"quality_ndcg@10\": 0.027755,\n \"quality_ndcg@10\": 0.027837,\n \"quality_ndcg@10\": 0.027978,\n \"quality_ndcg@10\": 0.027982,\nusage: trend_rank.py [-h] [--col COL] [--weights WEIGHTS] [--stage STAGE]\n [--tag TAG] --output OUTPUT\ntrend_rank.py: error: argument --weights: expected one argument\nTraceback (most recent call last):\n File \"/task/evaluate_public.py\", line 157, in \n main()\n File \"/task/evaluate_public.py\", line 152, in main\n ranked = load_submission(path, eligibility, targets)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/task/evaluate_public.py\", line 113, in load_submission\n frame = validate_ranking(read_ranking(path), targets)\n ^^^^^^^^^^^^^^^^^^\n File \"/task/ranking_evaluation.py\", line 98, in read_ranking\n return pd.read_parquet(path) if path.suffix == \".parquet\" else pd.read_csv(path)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 669, in read_parquet\n return impl.read(\n ^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 258, in read\n path_or_handle, handles, filesystem = _get_path_or_handle(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/parquet.py\", line 141, in _get_path_or_handle\n handles = get_handle(\n ^^^^^^^^^^^\n File \"/opt/task-venv/lib/python3.12/site-packages/pandas/io/common.py\", line 882, in get_handle\n handle = open(handle, ioargs.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: '/app/solution/validation_tw5.parquet'\n" } } ## Step 57 · agent · 2026-09-23T22:01:30.128Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:01:47.140Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/build_matrix.py\\n@@\\n-def build(with_val=False):\\n+def build(with_val=False, organic=False):\\n@@\\n- c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\\n+ c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=(80 if organic else 50))\\n+ if organic: keep &= (x.is_organic.to_numpy()==1)\\n@@\\n- keep=(c>=0)&(r>=0); x=x[keep]; c=c[keep]; r=r[keep]\\n+ keep=(c>=0)&(r>=0)\\n+ if organic: keep &= (x.is_organic.to_numpy()==1)\\n+ x=x[keep]; c=c[keep]; r=r[keep]\\n@@\\n- tag='full' if with_val else 'train'\\n+ tag=('full' if with_val else 'train')+('org' if organic else '')\\n@@\\n- p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); a=p.parse_args(); build(a.with_val)\\n+ p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); p.add_argument('--organic',action='store_true'); a=p.parse_args(); build(a.with_val,a.organic)\\n*** Update File: /app/solution/rank_model.py\\n@@\\n- p=argparse.ArgumentParser(); p.add_argument('--tag',choices=['train','full'],default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\\n+ p=argparse.ArgumentParser(); p.add_argument('--tag',default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:01:47.228Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-783a6589-91e7-4a2f-96fc-da10918ad851", "changes": { "/app/solution/rank_model.py": { "type": "update", "unified_diff": "@@ -79,3 +79,3 @@\n if __name__=='__main__':\n- p=argparse.ArgumentParser(); p.add_argument('--tag',choices=['train','full'],default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\n+ p=argparse.ArgumentParser(); p.add_argument('--tag',default='train'); p.add_argument('--stage',choices=['validation','test'],default='validation'); p.add_argument('--rank',type=int,default=192); p.add_argument('--fit',action='store_true'); p.add_argument('--variant',default='blend'); p.add_argument('--output'); a=p.parse_args()\n if a.fit: fit(a.tag,a.rank)\n", "move_path": null }, "/app/solution/build_matrix.py": { "type": "update", "unified_diff": "@@ -11,3 +11,3 @@\n \n-def build(with_val=False):\n+def build(with_val=False, organic=False):\n cat=pd.read_parquet(D/'candidate_catalog.parquet',columns=['item_id'])\n@@ -26,3 +26,4 @@\n x=pf.read_row_group(rg,columns=['uid','item_id','is_organic','played_ratio_pct']).to_pandas()\n- c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=50)\n+ c=item_index.get_indexer(x.item_id); keep=(c>=0)&(x.played_ratio_pct.to_numpy()>=(80 if organic else 50))\n+ if organic: keep &= (x.is_organic.to_numpy()==1)\n x=x[keep]; c=c[keep]\n@@ -40,3 +41,5 @@\n c=item_index.get_indexer(x.item_id); r=uid_index.get_indexer(x.uid)\n- keep=(c>=0)&(r>=0); x=x[keep]; c=c[keep]; r=r[keep]\n+ keep=(c>=0)&(r>=0)\n+ if organic: keep &= (x.is_organic.to_numpy()==1)\n+ x=x[keep]; c=c[keep]; r=r[keep]\n v=(2.5+1.0*x.is_organic.to_numpy()).astype('float32')\n@@ -46,3 +49,3 @@\n X.sum_duplicates(); X.data=np.log1p(X.data).astype('float32'); X.eliminate_zeros()\n- tag='full' if with_val else 'train'\n+ tag=('full' if with_val else 'train')+('org' if organic else '')\n sparse.save_npz(OUT/f'matrix_{tag}.npz',X,compressed=True)\n@@ -52,2 +55,2 @@\n if __name__=='__main__':\n- p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); a=p.parse_args(); build(a.with_val)\n+ p=argparse.ArgumentParser(); p.add_argument('--with-val',action='store_true'); p.add_argument('--organic',action='store_true'); a=p.parse_args(); build(a.with_val,a.organic)\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/build_matrix.py\nM /app/solution/rank_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c2a12dc87d2b9895f4546a1129d", "usage": { "input_tokens": 46263, "cached_input_tokens": 45312, "cache_write_input_tokens": 0, "output_tokens": 903, "reasoning_output_tokens": 361, "total_tokens": 47166 }, "turn_token_usage": { "input_tokens": 1793667, "cached_input_tokens": 1716352, "cache_write_input_tokens": 0, "output_tokens": 19643, "reasoning_output_tokens": 6361, "total_tokens": 1813310 }, "thread_token_usage": { "input_tokens": 1793667, "cached_input_tokens": 1716352, "cache_write_input_tokens": 0, "output_tokens": 19643, "reasoning_output_tokens": 6361, "total_tokens": 1813310 } } ## Step 58 · agent · 2026-09-23T22:01:52.230Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:01:56.326Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python -u /app/solution/build_matrix.py --organic >/dev/null && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag trainorg --fit --rank 192 --variant svd --output /app/solution/validation_orgsvd.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:02:24.485Z Script completed Wall time 28.2 seconds Output: fit trainorg (9238, 30000) 1997018 rank 192 explained 0.18469822 ranked 128 / 8456 ranked 256 / 8456 ranked 384 / 8456 ranked 512 / 8456 ranked 640 / 8456 ranked 768 / 8456 ranked 896 / 8456 ranked 1024 / 8456 ranked 1152 / 8456 ranked 1280 / 8456 ranked 1408 / 8456 ranked 1536 / 8456 ranked 1664 / 8456 ranked 1792 / 8456 ranked 1920 / 8456 ranked 2048 / 8456 ranked 2176 / 8456 ranked 2304 / 8456 ranked 2432 / 8456 ranked 2560 / 8456 ranked 2688 / 8456 ranked 2816 / 8456 ranked 2944 / 8456 ranked 3072 / 8456 ranked 3200 / 8456 ranked 3328 / 8456 ranked 3456 / 8456 ranked 3584 / 8456 ranked 3712 / 8456 ranked 3840 / 8456 ranked 3968 / 8456 ranked 4096 / 8456 ranked 4224 / 8456 ranked 4352 / 8456 ranked 4480 / 8456 ranked 4608 / 8456 ranked 4736 / 8456 ranked 4864 / 8456 ranked 4992 / 8456 ranked 5120 / 8456 ranked 5248 / 8456 ranked 5376 / 8456 ranked 5504 / 8456 ranked 5632 / 8456 ranked 5760 / 8456 ranked 5888 / 8456 ranked 6016 / 8456 ranked 6144 / 8456 ranked 6272 / 8456 ranked 6400 / 8456 ranked 6528 / 8456 ranked 6656 / 8456 ranked 6784 / 8456 ranked 6912 / 8456 ranked 7040 / 8456 ranked 7168 / 8456 ranked 7296 / 8456 ranked 7424 / 8456 ranked 7552 / 8456 ranked 7680 / 8456 ranked 7808 / 8456 ranked 7936 / 8456 ranked 8064 / 8456 ranked 8192 / 8456 ranked 8320 / 8456 ranked 8448 / 8456 ranked 8456 / 8456 wrote /app/solution/validation_orgsvd.parquet 845600 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c4b9abc87d2911017920f78a3cd", "usage": { "input_tokens": 47190, "cached_input_tokens": 46080, "cache_write_input_tokens": 0, "output_tokens": 184, "reasoning_output_tokens": 47, "total_tokens": 47374 }, "turn_token_usage": { "input_tokens": 1840857, "cached_input_tokens": 1762432, "cache_write_input_tokens": 0, "output_tokens": 19827, "reasoning_output_tokens": 6408, "total_tokens": 1860684 }, "thread_token_usage": { "input_tokens": 1840857, "cached_input_tokens": 1762432, "cache_write_input_tokens": 0, "output_tokens": 19827, "reasoning_output_tokens": 6408, "total_tokens": 1860684 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-6ffe8d1d-5fc9-4c0f-995b-ac9f9f174579", "process_id": "10432", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python -u /app/solution/build_matrix.py --organic >/dev/null && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag trainorg --fit --rank 192 --variant svd --output /app/solution/validation_orgsvd.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python -u /app/solution/build_matrix.py --organic >/dev/null && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag trainorg --fit --rank 192 --variant svd --output /app/solution/validation_orgsvd.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit trainorg (9238, 30000) 1997018 rank 192 explained 0.18469822\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_orgsvd.parquet 845600\n", "stderr": "", "aggregated_output": "fit trainorg (9238, 30000) 1997018 rank 192 explained 0.18469822\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_orgsvd.parquet 845600\n", "exit_code": 0, "duration": { "secs": 27, "nanos": 945193484 }, "formatted_output": "fit trainorg (9238, 30000) 1997018 rank 192 explained 0.18469822\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_orgsvd.parquet 845600\n" } } ### exec · 2026-09-23T22:02:29.506Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_orgsvd.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:02:32.173Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_orgsvd.parquet", "scores": { "quality_ndcg@10": 0.026025, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c70c61487d28bafdc7376f7665f", "usage": { "input_tokens": 48103, "cached_input_tokens": 44800, "cache_write_input_tokens": 0, "output_tokens": 70, "reasoning_output_tokens": 0, "total_tokens": 48173 }, "turn_token_usage": { "input_tokens": 1888960, "cached_input_tokens": 1807232, "cache_write_input_tokens": 0, "output_tokens": 19897, "reasoning_output_tokens": 6408, "total_tokens": 1908857 }, "thread_token_usage": { "input_tokens": 1888960, "cached_input_tokens": 1807232, "cache_write_input_tokens": 0, "output_tokens": 19897, "reasoning_output_tokens": 6408, "total_tokens": 1908857 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-55846c44-d5ff-4bc0-acab-3fd7ce638a39", "process_id": "63031", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_orgsvd.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_orgsvd.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_orgsvd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026025,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_orgsvd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026025,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 497991173 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_orgsvd.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.026025,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 59 · agent · 2026-09-23T22:02:35.886Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:02:38.519Z { "code": "const patch = \"*** Begin Patch\\n*** Update File: /app/solution/rank_model.py\\n@@\\n- tr=np.load(OUT/f'trends_{tag}.npz')\\n+ tr=np.load(OUT/f\\\"trends_{tag.replace('org','')}.npz\\\")\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:02:38.627Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-38c8e877-0fe0-4e10-a6a2-1c5cc5f231b6", "changes": { "/app/solution/rank_model.py": { "type": "update", "unified_diff": "@@ -39,3 +39,3 @@\n variant=variant[2:]\n- tr=np.load(OUT/f'trends_{tag}.npz')\n+ tr=np.load(OUT/f\"trends_{tag.replace('org','')}.npz\")\n pop=np.log1p(tr['organic'][:,1]).astype(np.float32) # trailing seven days\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nM /app/solution/rank_model.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c7882a087d28f9beb9c41f9b462", "usage": { "input_tokens": 48244, "cached_input_tokens": 46976, "cache_write_input_tokens": 0, "output_tokens": 125, "reasoning_output_tokens": 35, "total_tokens": 48369 }, "turn_token_usage": { "input_tokens": 1937204, "cached_input_tokens": 1854208, "cache_write_input_tokens": 0, "output_tokens": 20022, "reasoning_output_tokens": 6443, "total_tokens": 1957226 }, "thread_token_usage": { "input_tokens": 1937204, "cached_input_tokens": 1854208, "cache_write_input_tokens": 0, "output_tokens": 20022, "reasoning_output_tokens": 6443, "total_tokens": 1957226 } } ## Step 60 · agent · 2026-09-23T22:02:42.153Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:02:47.239Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for b in .008 .012 .016 .02 .025 .03; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag trainorg --variant \\\"t:1,0,$b\\\" --output /app/solution/validation_org$b.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_org$b.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T22:03:17.436Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"02e134","wall_time_seconds":30.019261775,"session_id":98036,"original_token_count":25} "quality_ndcg@10": 0.02962, "quality_ndcg@10": 0.032152, "quality_ndcg@10": 0.034344, SESSION_ID=98036 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44c7efdf887d2969bee360d3b9526", "usage": { "input_tokens": 48393, "cached_input_tokens": 48128, "cache_write_input_tokens": 0, "output_tokens": 179, "reasoning_output_tokens": 11, "total_tokens": 48572 }, "turn_token_usage": { "input_tokens": 1985597, "cached_input_tokens": 1902336, "cache_write_input_tokens": 0, "output_tokens": 20201, "reasoning_output_tokens": 6454, "total_tokens": 2005798 }, "thread_token_usage": { "input_tokens": 1985597, "cached_input_tokens": 1902336, "cache_write_input_tokens": 0, "output_tokens": 20201, "reasoning_output_tokens": 6454, "total_tokens": 2005798 } } ## Step 61 · agent · 2026-09-23T22:03:19.531Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:03:20.692Z { "code": "const r = await tools.write_stdin({\"session_id\":98036,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:03:43.075Z Script completed Wall time 22.4 seconds Output: "quality_ndcg@10": 0.035002, "quality_ndcg@10": 0.035015, "quality_ndcg@10": 0.034803, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44ca5de6887d284b209cc5119b376", "usage": { "input_tokens": 48672, "cached_input_tokens": 48256, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 48725 }, "turn_token_usage": { "input_tokens": 2034269, "cached_input_tokens": 1950592, "cache_write_input_tokens": 0, "output_tokens": 20254, "reasoning_output_tokens": 6460, "total_tokens": 2054523 }, "thread_token_usage": { "input_tokens": 2034269, "cached_input_tokens": 1950592, "cache_write_input_tokens": 0, "output_tokens": 20254, "reasoning_output_tokens": 6460, "total_tokens": 2054523 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-19c415bb-2f67-4df7-9105-1ad8f47c238d", "process_id": "98036", "command": [ "/bin/sh", "-lc", "for b in .008 .012 .016 .02 .025 .03; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag trainorg --variant \"t:1,0,$b\" --output /app/solution/validation_org$b.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_org$b.parquet | rg quality_ndcg; done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "for b in .008 .012 .016 .02 .025 .03; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag trainorg --variant \"t:1,0,$b\" --output /app/solution/validation_org$b.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_org$b.parquet | rg quality_ndcg; done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.02962,\n \"quality_ndcg@10\": 0.032152,\n \"quality_ndcg@10\": 0.034344,\n \"quality_ndcg@10\": 0.035002,\n \"quality_ndcg@10\": 0.035015,\n \"quality_ndcg@10\": 0.034803,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.02962,\n \"quality_ndcg@10\": 0.032152,\n \"quality_ndcg@10\": 0.034344,\n \"quality_ndcg@10\": 0.035002,\n \"quality_ndcg@10\": 0.035015,\n \"quality_ndcg@10\": 0.034803,\n", "exit_code": 0, "duration": { "secs": 55, "nanos": 667499032 }, "formatted_output": " \"quality_ndcg@10\": 0.02962,\n \"quality_ndcg@10\": 0.032152,\n \"quality_ndcg@10\": 0.034344,\n \"quality_ndcg@10\": 0.035002,\n \"quality_ndcg@10\": 0.035015,\n \"quality_ndcg@10\": 0.034803,\n" } } ## Step 62 · agent · 2026-09-23T22:03:54.576Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:04:11.637Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/ensemble_rank.py\\n+#!/opt/task-venv/bin/python\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+D=Path('/task/data');O=Path('/app/solution')\\n+p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--weights',default='1,1,.04');p.add_argument('--output',required=True);a=p.parse_args();w1,w2,wt=map(float,a.weights.split(','))\\n+za=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=za['uids'];items=za['item_ids'];packed=za['packed_eligible']\\n+m1=np.load(O/f'latent_{a.tag}.npz');m2=np.load(O/f'latent_{a.tag}org.npz');r1=pd.Index(m1['uids']).get_indexer(uids);r2=pd.Index(m2['uids']).get_indexer(uids)\\n+tr=np.load(O/f'trends_{a.tag}.npz');trend=np.log1p(tr['organic'][:,1]).astype('float32');trend=(trend-trend.mean())/trend.std();tops=[]\\n+for lo in range(0,len(uids),128):\\n+ hi=min(lo+128,len(uids));score=wt*trend+w1*(m1['U'][r1[lo:hi]]@m1['V'])+w2*(m2['U'][r2[lo:hi]]@m2['V']);elig=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool);score[~elig]=-np.inf;ix=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,ix,axis=1);tops.append(np.take_along_axis(ix,np.argsort(-ss,axis=1),axis=1))\\n+top=np.vstack(tops);pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':items[top.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet(a.output,index=False)\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:04:11.736Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-ce5cf2a0-76af-418f-a01d-dd0ab150aa40", "changes": { "/app/solution/ensemble_rank.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nD=Path('/task/data');O=Path('/app/solution')\np=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--weights',default='1,1,.04');p.add_argument('--output',required=True);a=p.parse_args();w1,w2,wt=map(float,a.weights.split(','))\nza=np.load(D/f'{a.stage}_eligible_candidates.npz');uids=za['uids'];items=za['item_ids'];packed=za['packed_eligible']\nm1=np.load(O/f'latent_{a.tag}.npz');m2=np.load(O/f'latent_{a.tag}org.npz');r1=pd.Index(m1['uids']).get_indexer(uids);r2=pd.Index(m2['uids']).get_indexer(uids)\ntr=np.load(O/f'trends_{a.tag}.npz');trend=np.log1p(tr['organic'][:,1]).astype('float32');trend=(trend-trend.mean())/trend.std();tops=[]\nfor lo in range(0,len(uids),128):\n hi=min(lo+128,len(uids));score=wt*trend+w1*(m1['U'][r1[lo:hi]]@m1['V'])+w2*(m2['U'][r2[lo:hi]]@m2['V']);elig=np.unpackbits(packed[lo:hi],axis=1,count=len(items),bitorder='little').astype(bool);score[~elig]=-np.inf;ix=np.argpartition(score,-100,axis=1)[:,-100:];ss=np.take_along_axis(score,ix,axis=1);tops.append(np.take_along_axis(ix,np.argsort(-ss,axis=1),axis=1))\ntop=np.vstack(tops);pd.DataFrame({'uid':np.repeat(uids,100).astype('int64'),'item_id':items[top.ravel()].astype('int64'),'rank':np.tile(np.arange(1,101,dtype=np.int64),len(uids))}).to_parquet(a.output,index=False)\n" } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/ensemble_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44cbf4a1887d29def06939ab856db", "usage": { "input_tokens": 48790, "cached_input_tokens": 48512, "cache_write_input_tokens": 0, "output_tokens": 855, "reasoning_output_tokens": 313, "total_tokens": 49645 }, "turn_token_usage": { "input_tokens": 2083059, "cached_input_tokens": 1999104, "cache_write_input_tokens": 0, "output_tokens": 21109, "reasoning_output_tokens": 6773, "total_tokens": 2104168 }, "thread_token_usage": { "input_tokens": 2083059, "cached_input_tokens": 1999104, "cache_write_input_tokens": 0, "output_tokens": 21109, "reasoning_output_tokens": 6773, "total_tokens": 2104168 } } ### exec · 2026-09-23T22:04:19.974Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"i=0; for w in '1,1,.04' '.5,1,.03' '1,2,.06' '1,.5,.03' '.25,1,.027'; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/ensemble_rank.py --weights \\\"$w\\\" --output /app/solution/validation_ens$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ens$i.parquet | rg quality_ndcg; i=$((i+1)); done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T22:04:50.142Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"e6e515","wall_time_seconds":30.000919972,"session_id":80149,"original_token_count":0} SESSION_ID=80149 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44cdc018c87d28a17bf65a48fa117", "usage": { "input_tokens": 49669, "cached_input_tokens": 47872, "cache_write_input_tokens": 0, "output_tokens": 188, "reasoning_output_tokens": 0, "total_tokens": 49857 }, "turn_token_usage": { "input_tokens": 2132728, "cached_input_tokens": 2046976, "cache_write_input_tokens": 0, "output_tokens": 21297, "reasoning_output_tokens": 6773, "total_tokens": 2154025 }, "thread_token_usage": { "input_tokens": 2132728, "cached_input_tokens": 2046976, "cache_write_input_tokens": 0, "output_tokens": 21297, "reasoning_output_tokens": 6773, "total_tokens": 2154025 } } ## Step 63 · agent · 2026-09-23T22:04:51.954Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:04:53.139Z { "code": "const r = await tools.write_stdin({\"session_id\":80149,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:05:23.226Z Script completed Wall time 30.0 seconds Output: "quality_ndcg@10": 0.037811, "quality_ndcg@10": 0.037003, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44d0275d087d2b0515d4e9964356b", "usage": { "input_tokens": 49916, "cached_input_tokens": 49536, "cache_write_input_tokens": 0, "output_tokens": 58, "reasoning_output_tokens": 11, "total_tokens": 49974 }, "turn_token_usage": { "input_tokens": 2182644, "cached_input_tokens": 2096512, "cache_write_input_tokens": 0, "output_tokens": 21355, "reasoning_output_tokens": 6784, "total_tokens": 2203999 }, "thread_token_usage": { "input_tokens": 2182644, "cached_input_tokens": 2096512, "cache_write_input_tokens": 0, "output_tokens": 21355, "reasoning_output_tokens": 6784, "total_tokens": 2203999 } } ## Step 64 · agent · 2026-09-23T22:05:25.076Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:05:26.313Z { "code": "const r = await tools.write_stdin({\"session_id\":80149,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:05:56.321Z Script completed Wall time 30.0 seconds Output: "quality_ndcg@10": 0.037003, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44d23742087d29d17ba98ba8ae7af", "usage": { "input_tokens": 50025, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 50078 }, "turn_token_usage": { "input_tokens": 2232669, "cached_input_tokens": 2146304, "cache_write_input_tokens": 0, "output_tokens": 21408, "reasoning_output_tokens": 6790, "total_tokens": 2254077 }, "thread_token_usage": { "input_tokens": 2232669, "cached_input_tokens": 2146304, "cache_write_input_tokens": 0, "output_tokens": 21408, "reasoning_output_tokens": 6790, "total_tokens": 2254077 } } ## Step 65 · agent · 2026-09-23T22:05:59.278Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:06:00.524Z { "code": "const r = await tools.write_stdin({\"session_id\":80149,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:06:30.544Z Script completed Wall time 30.0 seconds Output: "quality_ndcg@10": 0.037847, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44d44a2d887d2a72e4dbd592b6dcf", "usage": { "input_tokens": 50115, "cached_input_tokens": 49792, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 50168 }, "turn_token_usage": { "input_tokens": 2282784, "cached_input_tokens": 2196096, "cache_write_input_tokens": 0, "output_tokens": 21461, "reasoning_output_tokens": 6796, "total_tokens": 2304245 }, "thread_token_usage": { "input_tokens": 2282784, "cached_input_tokens": 2196096, "cache_write_input_tokens": 0, "output_tokens": 21461, "reasoning_output_tokens": 6796, "total_tokens": 2304245 } } ## Step 66 · agent · 2026-09-23T22:06:32.130Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:06:33.420Z { "code": "const r = await tools.write_stdin({\"session_id\":80149,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:06:52.694Z Script completed Wall time 19.3 seconds Output: "quality_ndcg@10": 0.036424, ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44d66ddcc87d28feb106d98f28574", "usage": { "input_tokens": 50205, "cached_input_tokens": 49920, "cache_write_input_tokens": 0, "output_tokens": 54, "reasoning_output_tokens": 7, "total_tokens": 50259 }, "turn_token_usage": { "input_tokens": 2332989, "cached_input_tokens": 2246016, "cache_write_input_tokens": 0, "output_tokens": 21515, "reasoning_output_tokens": 6803, "total_tokens": 2354504 }, "thread_token_usage": { "input_tokens": 2332989, "cached_input_tokens": 2246016, "cache_write_input_tokens": 0, "output_tokens": 21515, "reasoning_output_tokens": 6803, "total_tokens": 2354504 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-c4581d4d-7ea9-4aeb-89ab-ac310a6cc2aa", "process_id": "80149", "command": [ "/bin/sh", "-lc", "i=0; for w in '1,1,.04' '.5,1,.03' '1,2,.06' '1,.5,.03' '.25,1,.027'; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/ensemble_rank.py --weights \"$w\" --output /app/solution/validation_ens$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ens$i.parquet | rg quality_ndcg; i=$((i+1)); done" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "i=0; for w in '1,1,.04' '.5,1,.03' '1,2,.06' '1,.5,.03' '.25,1,.027'; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/ensemble_rank.py --weights \"$w\" --output /app/solution/validation_ens$i.parquet; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_ens$i.parquet | rg quality_ndcg; i=$((i+1)); done" } ], "source": "unified_exec_startup", "status": "completed", "stdout": " \"quality_ndcg@10\": 0.037811,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037847,\n \"quality_ndcg@10\": 0.036424,\n", "stderr": "", "aggregated_output": " \"quality_ndcg@10\": 0.037811,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037847,\n \"quality_ndcg@10\": 0.036424,\n", "exit_code": 0, "duration": { "secs": 152, "nanos": 552026639 }, "formatted_output": " \"quality_ndcg@10\": 0.037811,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037003,\n \"quality_ndcg@10\": 0.037847,\n \"quality_ndcg@10\": 0.036424,\n" } } ## Step 67 · agent · 2026-09-23T22:07:06.024Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:10.423Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/latent_train.npz /app/solution/latent_train_r192.npz && OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag train --fit --rank 384 --variant 't:1,0,.02' --output /app/solution/validation_r384.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:07:40.637Z Script completed Wall time 30.2 seconds Output: SESSION_ID=15016 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44d7ce1b487d2bd82fa6b2a639a33", "usage": { "input_tokens": 50296, "cached_input_tokens": 50048, "cache_write_input_tokens": 0, "output_tokens": 525, "reasoning_output_tokens": 378, "total_tokens": 50821 }, "turn_token_usage": { "input_tokens": 2383285, "cached_input_tokens": 2296064, "cache_write_input_tokens": 0, "output_tokens": 22040, "reasoning_output_tokens": 7181, "total_tokens": 2405325 }, "thread_token_usage": { "input_tokens": 2383285, "cached_input_tokens": 2296064, "cache_write_input_tokens": 0, "output_tokens": 22040, "reasoning_output_tokens": 7181, "total_tokens": 2405325 } } ## Step 68 · agent · 2026-09-23T22:07:42.259Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:07:43.474Z { "code": "const r = await tools.write_stdin({\"session_id\":15016,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:08:05.409Z Script completed Wall time 21.9 seconds Output: fit train (9238, 30000) 5618006 rank 384 explained 0.32402474 ranked 128 / 8456 ranked 256 / 8456 ranked 384 / 8456 ranked 512 / 8456 ranked 640 / 8456 ranked 768 / 8456 ranked 896 / 8456 ranked 1024 / 8456 ranked 1152 / 8456 ranked 1280 / 8456 ranked 1408 / 8456 ranked 1536 / 8456 ranked 1664 / 8456 ranked 1792 / 8456 ranked 1920 / 8456 ranked 2048 / 8456 ranked 2176 / 8456 ranked 2304 / 8456 ranked 2432 / 8456 ranked 2560 / 8456 ranked 2688 / 8456 ranked 2816 / 8456 ranked 2944 / 8456 ranked 3072 / 8456 ranked 3200 / 8456 ranked 3328 / 8456 ranked 3456 / 8456 ranked 3584 / 8456 ranked 3712 / 8456 ranked 3840 / 8456 ranked 3968 / 8456 ranked 4096 / 8456 ranked 4224 / 8456 ranked 4352 / 8456 ranked 4480 / 8456 ranked 4608 / 8456 ranked 4736 / 8456 ranked 4864 / 8456 ranked 4992 / 8456 ranked 5120 / 8456 ranked 5248 / 8456 ranked 5376 / 8456 ranked 5504 / 8456 ranked 5632 / 8456 ranked 5760 / 8456 ranked 5888 / 8456 ranked 6016 / 8456 ranked 6144 / 8456 ranked 6272 / 8456 ranked 6400 / 8456 ranked 6528 / 8456 ranked 6656 / 8456 ranked 6784 / 8456 ranked 6912 / 8456 ranked 7040 / 8456 ranked 7168 / 8456 ranked 7296 / 8456 ranked 7424 / 8456 ranked 7552 / 8456 ranked 7680 / 8456 ranked 7808 / 8456 ranked 7936 / 8456 ranked 8064 / 8456 ranked 8192 / 8456 ranked 8320 / 8456 ranked 8448 / 8456 ranked 8456 / 8456 wrote /app/solution/validation_r384.parquet 845600 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44dacf7cc87d287aabb02d4dc7970", "usage": { "input_tokens": 50849, "cached_input_tokens": 50176, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 50902 }, "turn_token_usage": { "input_tokens": 2434134, "cached_input_tokens": 2346240, "cache_write_input_tokens": 0, "output_tokens": 22093, "reasoning_output_tokens": 7187, "total_tokens": 2456227 }, "thread_token_usage": { "input_tokens": 2434134, "cached_input_tokens": 2346240, "cache_write_input_tokens": 0, "output_tokens": 22093, "reasoning_output_tokens": 7187, "total_tokens": 2456227 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-67be1923-3d79-4e89-8fa4-af85a37cb98d", "process_id": "15016", "command": [ "/bin/sh", "-lc", "cp /app/solution/latent_train.npz /app/solution/latent_train_r192.npz && OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag train --fit --rank 384 --variant 't:1,0,.02' --output /app/solution/validation_r384.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/latent_train.npz /app/solution/latent_train_r192.npz && OPENBLAS_NUM_THREADS=8 OMP_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/rank_model.py --tag train --fit --rank 384 --variant 't:1,0,.02' --output /app/solution/validation_r384.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "fit train (9238, 30000) 5618006 rank 384 explained 0.32402474\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_r384.parquet 845600\n", "stderr": "", "aggregated_output": "fit train (9238, 30000) 5618006 rank 384 explained 0.32402474\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_r384.parquet 845600\n", "exit_code": 0, "duration": { "secs": 54, "nanos": 819828365 }, "formatted_output": "fit train (9238, 30000) 5618006 rank 384 explained 0.32402474\nranked 128 / 8456\nranked 256 / 8456\nranked 384 / 8456\nranked 512 / 8456\nranked 640 / 8456\nranked 768 / 8456\nranked 896 / 8456\nranked 1024 / 8456\nranked 1152 / 8456\nranked 1280 / 8456\nranked 1408 / 8456\nranked 1536 / 8456\nranked 1664 / 8456\nranked 1792 / 8456\nranked 1920 / 8456\nranked 2048 / 8456\nranked 2176 / 8456\nranked 2304 / 8456\nranked 2432 / 8456\nranked 2560 / 8456\nranked 2688 / 8456\nranked 2816 / 8456\nranked 2944 / 8456\nranked 3072 / 8456\nranked 3200 / 8456\nranked 3328 / 8456\nranked 3456 / 8456\nranked 3584 / 8456\nranked 3712 / 8456\nranked 3840 / 8456\nranked 3968 / 8456\nranked 4096 / 8456\nranked 4224 / 8456\nranked 4352 / 8456\nranked 4480 / 8456\nranked 4608 / 8456\nranked 4736 / 8456\nranked 4864 / 8456\nranked 4992 / 8456\nranked 5120 / 8456\nranked 5248 / 8456\nranked 5376 / 8456\nranked 5504 / 8456\nranked 5632 / 8456\nranked 5760 / 8456\nranked 5888 / 8456\nranked 6016 / 8456\nranked 6144 / 8456\nranked 6272 / 8456\nranked 6400 / 8456\nranked 6528 / 8456\nranked 6656 / 8456\nranked 6784 / 8456\nranked 6912 / 8456\nranked 7040 / 8456\nranked 7168 / 8456\nranked 7296 / 8456\nranked 7424 / 8456\nranked 7552 / 8456\nranked 7680 / 8456\nranked 7808 / 8456\nranked 7936 / 8456\nranked 8064 / 8456\nranked 8192 / 8456\nranked 8320 / 8456\nranked 8448 / 8456\nranked 8456 / 8456\nwrote /app/solution/validation_r384.parquet 845600\n" } } ### exec · 2026-09-23T22:08:08.722Z { "code": "const r = await tools.exec_command({\"cmd\":\"/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:08:11.389Z Script completed Wall time 2.7 seconds Output: { "model": "/app/solution/validation_r384.parquet", "scores": { "quality_ndcg@10": 0.035377, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44dc5a15c87d298f9c6f0dc1e2fd1", "usage": { "input_tokens": 51629, "cached_input_tokens": 48640, "cache_write_input_tokens": 0, "output_tokens": 69, "reasoning_output_tokens": 0, "total_tokens": 51698 }, "turn_token_usage": { "input_tokens": 2485763, "cached_input_tokens": 2394880, "cache_write_input_tokens": 0, "output_tokens": 22162, "reasoning_output_tokens": 7187, "total_tokens": 2507925 }, "thread_token_usage": { "input_tokens": 2485763, "cached_input_tokens": 2394880, "cache_write_input_tokens": 0, "output_tokens": 22162, "reasoning_output_tokens": 7187, "total_tokens": 2507925 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-eb5b0884-1d66-4cd8-80b1-8a8adb55b1bb", "process_id": "1924", "command": [ "/bin/sh", "-lc", "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "/opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_r384.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035377,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_r384.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035377,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 2, "nanos": 460427090 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_r384.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.035377,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 69 · agent · 2026-09-23T22:08:13.255Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:08:17.138Z { "code": "const r = await tools.exec_command({\"cmd\":\"OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant svd --output /app/solution/validation_r384raw.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384raw.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:08:28.270Z Script completed Wall time 11.1 seconds Output: { "model": "/app/solution/validation_r384raw.parquet", "scores": { "quality_ndcg@10": 0.024129, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44dcb8de487d28d6e2b94e86fe41d", "usage": { "input_tokens": 51768, "cached_input_tokens": 51456, "cache_write_input_tokens": 0, "output_tokens": 149, "reasoning_output_tokens": 18, "total_tokens": 51917 }, "turn_token_usage": { "input_tokens": 2537531, "cached_input_tokens": 2446336, "cache_write_input_tokens": 0, "output_tokens": 22311, "reasoning_output_tokens": 7205, "total_tokens": 2559842 }, "thread_token_usage": { "input_tokens": 2537531, "cached_input_tokens": 2446336, "cache_write_input_tokens": 0, "output_tokens": 22311, "reasoning_output_tokens": 7205, "total_tokens": 2559842 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-bc8b7708-f5a7-4e24-9f27-4f18441c7ffa", "process_id": "77495", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant svd --output /app/solution/validation_r384raw.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384raw.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --variant svd --output /app/solution/validation_r384raw.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r384raw.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_r384raw.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024129,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_r384raw.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024129,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 10, "nanos": 961824373 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_r384raw.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.024129,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 70 · agent · 2026-09-23T22:08:30.939Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:08:35.977Z { "code": "const r = await tools.exec_command({\"cmd\":\"mv /app/solution/latent_train.npz /app/solution/latent_train_r384.npz && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag train --fit --rank 96 --variant 't:1,0,.02' --output /app/solution/validation_r96.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r96.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:08:51.499Z Script completed Wall time 15.5 seconds Output: { "model": "/app/solution/validation_r96.parquet", "scores": { "quality_ndcg@10": 0.036298, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44ddc780c87d29cb88315a90c70b2", "usage": { "input_tokens": 51988, "cached_input_tokens": 50688, "cache_write_input_tokens": 0, "output_tokens": 212, "reasoning_output_tokens": 45, "total_tokens": 52200 }, "turn_token_usage": { "input_tokens": 2589519, "cached_input_tokens": 2497024, "cache_write_input_tokens": 0, "output_tokens": 22523, "reasoning_output_tokens": 7250, "total_tokens": 2612042 }, "thread_token_usage": { "input_tokens": 2589519, "cached_input_tokens": 2497024, "cache_write_input_tokens": 0, "output_tokens": 22523, "reasoning_output_tokens": 7250, "total_tokens": 2612042 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-25a7f32d-a051-4539-afd5-bafabf3fb54d", "process_id": "97447", "command": [ "/bin/sh", "-lc", "mv /app/solution/latent_train.npz /app/solution/latent_train_r384.npz && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag train --fit --rank 96 --variant 't:1,0,.02' --output /app/solution/validation_r96.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r96.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "mv /app/solution/latent_train.npz /app/solution/latent_train_r384.npz && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/rank_model.py --tag train --fit --rank 96 --variant 't:1,0,.02' --output /app/solution/validation_r96.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_r96.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "{\n \"model\": \"/app/solution/validation_r96.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036298,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "{\n \"model\": \"/app/solution/validation_r96.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036298,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 15, "nanos": 352870351 }, "formatted_output": "{\n \"model\": \"/app/solution/validation_r96.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.036298,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 71 · agent · 2026-09-23T22:09:04.034Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:27.091Z { "code": "const patch = \"*** Begin Patch\\n*** Add File: /app/solution/build_recent.py\\n+#!/opt/task-venv/bin/python\\n+from pathlib import Path\\n+import argparse\\n+import numpy as np\\n+import pandas as pd\\n+import pyarrow.parquet as pq\\n+from scipy import sparse\\n+D=Path('/task/data');O=Path('/app/solution')\\n+p=argparse.ArgumentParser();p.add_argument('--with-val',action='store_true');a=p.parse_args();tag='full' if a.with_val else 'train';cutoff=25393400 if a.with_val else 24786800\\n+meta=np.load(O/f'matrix_meta_{tag}.npz');uids=meta['uids'];items=meta['items'];ui=pd.Index(uids);ii=pd.Index(items);rows=[];cols=[]\\n+for fn in ['train_listens.parquet']+(['val_listens.parquet'] if a.with_val else []):\\n+ pf=pq.ParquetFile(D/fn)\\n+ for rg in range(pf.num_row_groups):\\n+ x=pf.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas();r=ui.get_indexer(x.uid);c=ii.get_indexer(x.item_id);k=(r>=0)&(c>=0)&(x.timestamp.to_numpy()>=cutoff-7*86400)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80);rows.append(r[k].astype('int32'));cols.append(c[k].astype('int32'))\\n+r=np.concatenate(rows);c=np.concatenate(cols);X=sparse.coo_matrix((np.ones(len(r),dtype='float32'),(r,c)),shape=(len(uids),len(items))).tocsr();X.data=np.log1p(X.data);sparse.save_npz(O/f'recent_{tag}.npz',X,compressed=True);print(X.shape,X.nnz)\\n*** Update File: /app/solution/knn_rank.py\\n@@\\n-p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--output',required=True);a=p.parse_args()\\n+p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--recent',action='store_true');p.add_argument('--output',required=True);a=p.parse_args()\\n@@\\n-X=sparse.load_npz(O/f'matrix_{a.tag}.npz').astype(np.float32); X.data[:]=1\\n+X=sparse.load_npz(O/f\\\"{'recent' if a.recent else 'matrix'}_{a.tag}.npz\\\").astype(np.float32)\\n+if not a.recent: X.data[:]=1\\n*** End Patch\";\ntext(await tools.apply_patch(patch));\n" } ### Tool output · 2026-09-23T22:09:27.201Z Script completed Wall time 0.0 seconds Output: {} ### Harness context { "type": "codex_item_completed", "item": { "type": "FileChange", "id": "exec-12bea3df-c8ac-4bf7-bb6e-fed7617b4b2e", "changes": { "/app/solution/build_recent.py": { "type": "add", "content": "#!/opt/task-venv/bin/python\nfrom pathlib import Path\nimport argparse\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\nfrom scipy import sparse\nD=Path('/task/data');O=Path('/app/solution')\np=argparse.ArgumentParser();p.add_argument('--with-val',action='store_true');a=p.parse_args();tag='full' if a.with_val else 'train';cutoff=25393400 if a.with_val else 24786800\nmeta=np.load(O/f'matrix_meta_{tag}.npz');uids=meta['uids'];items=meta['items'];ui=pd.Index(uids);ii=pd.Index(items);rows=[];cols=[]\nfor fn in ['train_listens.parquet']+(['val_listens.parquet'] if a.with_val else []):\n pf=pq.ParquetFile(D/fn)\n for rg in range(pf.num_row_groups):\n x=pf.read_row_group(rg,columns=['uid','timestamp','item_id','is_organic','played_ratio_pct']).to_pandas();r=ui.get_indexer(x.uid);c=ii.get_indexer(x.item_id);k=(r>=0)&(c>=0)&(x.timestamp.to_numpy()>=cutoff-7*86400)&(x.is_organic.to_numpy()==1)&(x.played_ratio_pct.to_numpy()>=80);rows.append(r[k].astype('int32'));cols.append(c[k].astype('int32'))\nr=np.concatenate(rows);c=np.concatenate(cols);X=sparse.coo_matrix((np.ones(len(r),dtype='float32'),(r,c)),shape=(len(uids),len(items))).tocsr();X.data=np.log1p(X.data);sparse.save_npz(O/f'recent_{tag}.npz',X,compressed=True);print(X.shape,X.nnz)\n" }, "/app/solution/knn_rank.py": { "type": "update", "unified_diff": "@@ -8,3 +8,3 @@\n D=Path('/task/data'); O=Path('/app/solution')\n-p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--output',required=True);a=p.parse_args()\n+p=argparse.ArgumentParser();p.add_argument('--stage',default='validation');p.add_argument('--tag',default='train');p.add_argument('--k',type=int,default=100);p.add_argument('--alpha',type=float,default=.25);p.add_argument('--trend',type=float,default=.1);p.add_argument('--recent',action='store_true');p.add_argument('--output',required=True);a=p.parse_args()\n za=np.load(D/f'{a.stage}_eligible_candidates.npz'); euids=za['uids']; items=za['item_ids']; packed=za['packed_eligible']\n@@ -12,3 +12,4 @@\n Un=U/np.maximum(np.linalg.norm(U,axis=1,keepdims=True),1e-7)\n-X=sparse.load_npz(O/f'matrix_{a.tag}.npz').astype(np.float32); X.data[:]=1\n+X=sparse.load_npz(O/f\"{'recent' if a.recent else 'matrix'}_{a.tag}.npz\").astype(np.float32)\n+if not a.recent: X.data[:]=1\n df=np.asarray(X.sum(axis=0)).ravel(); debias=np.maximum(df,1)**a.alpha\n", "move_path": null } }, "status": "completed", "stdout": "Success. Updated the following files:\nA /app/solution/build_recent.py\nM /app/solution/knn_rank.py\n", "stderr": "" } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44df3ae1487d2be3632c842a291f1", "usage": { "input_tokens": 52270, "cached_input_tokens": 51840, "cache_write_input_tokens": 0, "output_tokens": 1077, "reasoning_output_tokens": 348, "total_tokens": 53347 }, "turn_token_usage": { "input_tokens": 2641789, "cached_input_tokens": 2548864, "cache_write_input_tokens": 0, "output_tokens": 23600, "reasoning_output_tokens": 7598, "total_tokens": 2665389 }, "thread_token_usage": { "input_tokens": 2641789, "cached_input_tokens": 2548864, "cache_write_input_tokens": 0, "output_tokens": 23600, "reasoning_output_tokens": 7598, "total_tokens": 2665389 } } ## Step 72 · agent · 2026-09-23T22:09:29.062Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:34.502Z { "code": "const r = await tools.exec_command({\"cmd\":\"cp /app/solution/latent_train_r192.npz /app/solution/latent_train.npz && /opt/task-venv/bin/python /app/solution/build_recent.py && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recentknn.parquet\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output); if (r.session_id) text(`SESSION_ID=${r.session_id}`);\n" } ### Tool output · 2026-09-23T22:09:45.925Z Script completed Wall time 11.4 seconds Output: (9238, 30000) 173200 { "model": "/app/solution/validation_recentknn.parquet", "scores": { "quality_ndcg@10": 0.041816, "quality_users": 4263 } } ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44e17740487d2ad71add799cb46bc", "usage": { "input_tokens": 53371, "cached_input_tokens": 52096, "cache_write_input_tokens": 0, "output_tokens": 192, "reasoning_output_tokens": 16, "total_tokens": 53563 }, "turn_token_usage": { "input_tokens": 2695160, "cached_input_tokens": 2600960, "cache_write_input_tokens": 0, "output_tokens": 23792, "reasoning_output_tokens": 7614, "total_tokens": 2718952 }, "thread_token_usage": { "input_tokens": 2695160, "cached_input_tokens": 2600960, "cache_write_input_tokens": 0, "output_tokens": 23792, "reasoning_output_tokens": 7614, "total_tokens": 2718952 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-ac937ac7-428f-46c9-9df2-fa5fb2ceaeaf", "process_id": "32873", "command": [ "/bin/sh", "-lc", "cp /app/solution/latent_train_r192.npz /app/solution/latent_train.npz && /opt/task-venv/bin/python /app/solution/build_recent.py && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recentknn.parquet" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "cp /app/solution/latent_train_r192.npz /app/solution/latent_train.npz && /opt/task-venv/bin/python /app/solution/build_recent.py && OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet >/dev/null && /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_recentknn.parquet" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "(9238, 30000) 173200\n{\n \"model\": \"/app/solution/validation_recentknn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.041816,\n \"quality_users\": 4263\n }\n}\n", "stderr": "", "aggregated_output": "(9238, 30000) 173200\n{\n \"model\": \"/app/solution/validation_recentknn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.041816,\n \"quality_users\": 4263\n }\n}\n", "exit_code": 0, "duration": { "secs": 11, "nanos": 254183759 }, "formatted_output": "(9238, 30000) 173200\n{\n \"model\": \"/app/solution/validation_recentknn.parquet\",\n \"scores\": {\n \"quality_ndcg@10\": 0.041816,\n \"quality_users\": 4263\n }\n}\n" } } ## Step 73 · agent · 2026-09-23T22:09:48.731Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:09:51.622Z { "code": "const r = await tools.exec_command({\"cmd\":\"OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet | head -1\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":1000}); text(r.output);\n" } ### Tool output · 2026-09-23T22:09:52.959Z Script completed Wall time 1.3 seconds Output: knn score std 0.082462475 percentiles [0. 0. 0.38290839 1.07486156] Traceback (most recent call last): File "/app/solution/knn_rank.py", line 29, in print(hi,'/',len(euids),flush=True) BrokenPipeError: [Errno 32] Broken pipe ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44e2a354c87d2b8dc00e24da182d8", "usage": { "input_tokens": 53646, "cached_input_tokens": 53248, "cache_write_input_tokens": 0, "output_tokens": 142, "reasoning_output_tokens": 42, "total_tokens": 53788 }, "turn_token_usage": { "input_tokens": 2748806, "cached_input_tokens": 2654208, "cache_write_input_tokens": 0, "output_tokens": 23934, "reasoning_output_tokens": 7656, "total_tokens": 2772740 }, "thread_token_usage": { "input_tokens": 2748806, "cached_input_tokens": 2654208, "cache_write_input_tokens": 0, "output_tokens": 23934, "reasoning_output_tokens": 7656, "total_tokens": 2772740 } } ### Harness context { "type": "codex_item_completed", "item": { "type": "CommandExecution", "id": "exec-00f568da-5c8e-47c7-8d8f-80567ed8a225", "process_id": "37432", "command": [ "/bin/sh", "-lc", "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet | head -1" ], "cwd": "file:///app", "parsed_cmd": [ { "type": "unknown", "cmd": "OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python -u /app/solution/knn_rank.py --recent --alpha .1 --trend .5 --output /app/solution/validation_recentknn.parquet | head -1" } ], "source": "unified_exec_startup", "status": "completed", "stdout": "knn score std 0.082462475 percentiles [0. 0. 0.38290839 1.07486156]\nTraceback (most recent call last):\n File \"/app/solution/knn_rank.py\", line 29, in \n print(hi,'/',len(euids),flush=True)\nBrokenPipeError: [Errno 32] Broken pipe\n", "stderr": "", "aggregated_output": "knn score std 0.082462475 percentiles [0. 0. 0.38290839 1.07486156]\nTraceback (most recent call last):\n File \"/app/solution/knn_rank.py\", line 29, in \n print(hi,'/',len(euids),flush=True)\nBrokenPipeError: [Errno 32] Broken pipe\n", "exit_code": 0, "duration": { "secs": 1, "nanos": 155211500 }, "formatted_output": "knn score std 0.082462475 percentiles [0. 0. 0.38290839 1.07486156]\nTraceback (most recent call last):\n File \"/app/solution/knn_rank.py\", line 29, in \n print(hi,'/',len(euids),flush=True)\nBrokenPipeError: [Errno 32] Broken pipe\n" } } ## Step 74 · agent · 2026-09-23T22:09:56.573Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:10:01.661Z { "code": "let{output,...v}=await tools.exec_command({\"cmd\":\"for t in .2 .3 .4 .6 .8 1.0; do OPENBLAS_NUM_THREADS=8 /opt/task-venv/bin/python /app/solution/knn_rank.py --recent --alpha .1 --trend $t --output /app/solution/validation_rk$t.parquet >/dev/null; /opt/task-venv/bin/python /task/evaluate_public.py /app/solution/validation_rk$t.parquet | rg quality_ndcg; done\",\"workdir\":\"/app\",\"yield_time_ms\":30000,\"max_output_tokens\":2000});text(v);text(output);if(v.session_id)text(`SESSION_ID=${v.session_id}`)\n" } ### Tool output · 2026-09-23T22:10:31.838Z Script completed Wall time 30.2 seconds Output: {"chunk_id":"23afec","wall_time_seconds":30.00155647,"session_id":40533,"original_token_count":25} "quality_ndcg@10": 0.039606, "quality_ndcg@10": 0.040561, "quality_ndcg@10": 0.041198, SESSION_ID=40533 ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44e3139e887d2a45cb735d9f9ce09", "usage": { "input_tokens": 53898, "cached_input_tokens": 51584, "cache_write_input_tokens": 0, "output_tokens": 231, "reasoning_output_tokens": 63, "total_tokens": 54129 }, "turn_token_usage": { "input_tokens": 2802704, "cached_input_tokens": 2705792, "cache_write_input_tokens": 0, "output_tokens": 24165, "reasoning_output_tokens": 7719, "total_tokens": 2826869 }, "thread_token_usage": { "input_tokens": 2802704, "cached_input_tokens": 2705792, "cache_write_input_tokens": 0, "output_tokens": 24165, "reasoning_output_tokens": 7719, "total_tokens": 2826869 } } ## Step 75 · agent · 2026-09-23T22:10:34.357Z ### Thinking unavailable — no text recorded ### exec · 2026-09-23T22:10:35.533Z { "code": "const r = await tools.write_stdin({\"session_id\":40533,\"chars\":\"\",\"yield_time_ms\":30000,\"max_output_tokens\":2000}); text(r.output);\n" } ### Tool output · Timestamp not recorded No response recorded. ### Harness context { "type": "token_usage_record", "thread_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "session_id": "01a0d036-b9ac-7f53-ae15-8b6dd1bdeb75", "root_turn_id": "01a0d036-b9bf-7691-b59d-89df237ade87", "response_id": "resp_0fb2fd6000402403016ab44e5836a487d2a73c8ea2d3915ad8", "usage": { "input_tokens": 54229, "cached_input_tokens": 53760, "cache_write_input_tokens": 0, "output_tokens": 53, "reasoning_output_tokens": 6, "total_tokens": 54282 }, "turn_token_usage": { "input_tokens": 2856933, "cached_input_tokens": 2759552, "cache_write_input_tokens": 0, "output_tokens": 24218, "reasoning_output_tokens": 7725, "total_tokens": 2881151 }, "thread_token_usage": { "input_tokens": 2856933, "cached_input_tokens": 2759552, "cache_write_input_tokens": 0, "output_tokens": 24218, "reasoning_output_tokens": 7725, "total_tokens": 2881151 } }